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

Science.gov (United States)

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.

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

Science.gov (United States)

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.

Personalized Age Progression with Bi-Level Aging Dictionary Learning.

Science.gov (United States)

Shu, Xiangbo; Tang, Jinhui; Li, Zechao; Lai, Hanjiang; Zhang, Liyan; Yan, Shuicheng

2018-04-01

Age progression is defined as aesthetically re-rendering the aging face at any future age for an individual face. In this work, we aim to automatically render aging faces in a personalized way. Basically, for each age group, we learn an aging dictionary to reveal its aging characteristics (e.g., wrinkles), where the dictionary bases corresponding to the same index yet from two neighboring aging dictionaries form a particular aging pattern cross these two age groups, and a linear combination of all these patterns expresses a particular personalized aging process. Moreover, two factors are taken into consideration in the dictionary learning process. First, beyond the aging dictionaries, each person may have extra personalized facial characteristics, e.g., mole, which are invariant in the aging process. Second, it is challenging or even impossible to collect faces of all age groups for a particular person, yet much easier and more practical to get face pairs from neighboring age groups. To this end, we propose a novel Bi-level Dictionary Learning based Personalized Age Progression (BDL-PAP) method. Here, bi-level dictionary learning is formulated to learn the aging dictionaries based on face pairs from neighboring age groups. Extensive experiments well demonstrate the advantages of the proposed BDL-PAP over other state-of-the-arts in term of personalized age progression, as well as the performance gain for cross-age face verification by synthesizing aging faces.

TWRS privatization support project waste characterization resource dictionary

International Nuclear Information System (INIS)

Patello, G.K.; Wiemers, K.D.

1996-09-01

A single estimate of waste characteristics for each underground storage tanks at the Hanford Site is not available. The information that is available was developed for specific programmatic objectives and varies in format and level of descriptive detail, depending on the intended application. This dictionary reflects an attempt to define what waste characterization information is available. It shows the relationship between the identified resource and the original data source and the inter-relationships among the resources; it also provides a brief description of each resource. Developed as a general dictionary for waste characterization information, this document is intended to make the user aware of potenially useful resources

Adaptive structured dictionary learning for image fusion based on group-sparse-representation

Science.gov (United States)

Yang, Jiajie; Sun, Bin; Luo, Chengwei; Wu, Yuzhong; Xu, Limei

2018-04-01

Dictionary learning is the key process of sparse representation which is one of the most widely used image representation theories in image fusion. The existing dictionary learning method does not use the group structure information and the sparse coefficients well. In this paper, we propose a new adaptive structured dictionary learning algorithm and a l1-norm maximum fusion rule that innovatively utilizes grouped sparse coefficients to merge the images. In the dictionary learning algorithm, we do not need prior knowledge about any group structure of the dictionary. By using the characteristics of the dictionary in expressing the signal, our algorithm can automatically find the desired potential structure information that hidden in the dictionary. The fusion rule takes the physical meaning of the group structure dictionary, and makes activity-level judgement on the structure information when the images are being merged. Therefore, the fused image can retain more significant information. Comparisons have been made with several state-of-the-art dictionary learning methods and fusion rules. The experimental results demonstrate that, the dictionary learning algorithm and the fusion rule both outperform others in terms of several objective evaluation metrics.

Learning Dictionaries of Discriminative Image Patches

DEFF Research Database (Denmark)

Dahl, Anders Lindbjerg; Larsen, Rasmus

2011-01-01

using dictionaries of image patches with associated label data. The approach is based on ideas from sparse generative image models and texton based texture modeling. The intensity and label dictionaries are learned from training images with associated label information of (a subset) of the pixels based...... on a modified vector quantization approach. For new images the intensity dictionary is used to encode the image data and the label dictionary is used to build a segmentation of the image. We demonstrate the algorithm on composite and real texture images and show how successful training is possible even...

Accurate classification of brain gliomas by discriminate dictionary learning based on projective dictionary pair learning of proton magnetic resonance spectra.

Science.gov (United States)

Adebileje, Sikiru Afolabi; Ghasemi, Keyvan; Aiyelabegan, Hammed Tanimowo; Saligheh Rad, Hamidreza

2017-04-01

Proton magnetic resonance spectroscopy is a powerful noninvasive technique that complements the structural images of cMRI, which aids biomedical and clinical researches, by identifying and visualizing the compositions of various metabolites within the tissues of interest. However, accurate classification of proton magnetic resonance spectroscopy is still a challenging issue in clinics due to low signal-to-noise ratio, overlapping peaks of metabolites, and the presence of background macromolecules. This paper evaluates the performance of a discriminate dictionary learning classifiers based on projective dictionary pair learning method for brain gliomas proton magnetic resonance spectroscopy spectra classification task, and the result were compared with the sub-dictionary learning methods. The proton magnetic resonance spectroscopy data contain a total of 150 spectra (74 healthy, 23 grade II, 23 grade III, and 30 grade IV) from two databases. The datasets from both databases were first coupled together, followed by column normalization. The Kennard-Stone algorithm was used to split the datasets into its training and test sets. Performance comparison based on the overall accuracy, sensitivity, specificity, and precision was conducted. Based on the overall accuracy of our classification scheme, the dictionary pair learning method was found to outperform the sub-dictionary learning methods 97.78% compared with 68.89%, respectively. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

On A Nonlinear Generalization of Sparse Coding and Dictionary Learning.

Science.gov (United States)

Xie, Yuchen; Ho, Jeffrey; Vemuri, Baba

2013-01-01

Existing dictionary learning algorithms are based on the assumption that the data are vectors in an Euclidean vector space ℝ d , and the dictionary is learned from the training data using the vector space structure of ℝ d and its Euclidean L 2 -metric. However, in many applications, features and data often originated from a Riemannian manifold that does not support a global linear (vector space) structure. Furthermore, the extrinsic viewpoint of existing dictionary learning algorithms becomes inappropriate for modeling and incorporating the intrinsic geometry of the manifold that is potentially important and critical to the application. This paper proposes a novel framework for sparse coding and dictionary learning for data on a Riemannian manifold, and it shows that the existing sparse coding and dictionary learning methods can be considered as special (Euclidean) cases of the more general framework proposed here. We show that both the dictionary and sparse coding can be effectively computed for several important classes of Riemannian manifolds, and we validate the proposed method using two well-known classification problems in computer vision and medical imaging analysis.

Nonparametric, Coupled ,Bayesian ,Dictionary ,and Classifier Learning for Hyperspectral Classification.

Science.gov (United States)

Akhtar, Naveed; Mian, Ajmal

2017-10-03

We present a principled approach to learn a discriminative dictionary along a linear classifier for hyperspectral classification. Our approach places Gaussian Process priors over the dictionary to account for the relative smoothness of the natural spectra, whereas the classifier parameters are sampled from multivariate Gaussians. We employ two Beta-Bernoulli processes to jointly infer the dictionary and the classifier. These processes are coupled under the same sets of Bernoulli distributions. In our approach, these distributions signify the frequency of the dictionary atom usage in representing class-specific training spectra, which also makes the dictionary discriminative. Due to the coupling between the dictionary and the classifier, the popularity of the atoms for representing different classes gets encoded into the classifier. This helps in predicting the class labels of test spectra that are first represented over the dictionary by solving a simultaneous sparse optimization problem. The labels of the spectra are predicted by feeding the resulting representations to the classifier. Our approach exploits the nonparametric Bayesian framework to automatically infer the dictionary size--the key parameter in discriminative dictionary learning. Moreover, it also has the desirable property of adaptively learning the association between the dictionary atoms and the class labels by itself. We use Gibbs sampling to infer the posterior probability distributions over the dictionary and the classifier under the proposed model, for which, we derive analytical expressions. To establish the effectiveness of our approach, we test it on benchmark hyperspectral images. The classification performance is compared with the state-of-the-art dictionary learning-based classification methods.

Robust Visual Tracking via Online Discriminative and Low-Rank Dictionary Learning.

Science.gov (United States)

Zhou, Tao; Liu, Fanghui; Bhaskar, Harish; Yang, Jie

2017-09-12

In this paper, we propose a novel and robust tracking framework based on online discriminative and low-rank dictionary learning. The primary aim of this paper is to obtain compact and low-rank dictionaries that can provide good discriminative representations of both target and background. We accomplish this by exploiting the recovery ability of low-rank matrices. That is if we assume that the data from the same class are linearly correlated, then the corresponding basis vectors learned from the training set of each class shall render the dictionary to become approximately low-rank. The proposed dictionary learning technique incorporates a reconstruction error that improves the reliability of classification. Also, a multiconstraint objective function is designed to enable active learning of a discriminative and robust dictionary. Further, an optimal solution is obtained by iteratively computing the dictionary, coefficients, and by simultaneously learning the classifier parameters. Finally, a simple yet effective likelihood function is implemented to estimate the optimal state of the target during tracking. Moreover, to make the dictionary adaptive to the variations of the target and background during tracking, an online update criterion is employed while learning the new dictionary. Experimental results on a publicly available benchmark dataset have demonstrated that the proposed tracking algorithm performs better than other state-of-the-art trackers.

Pronunciation dictionary development in resource-scarce environments

CSIR Research Space (South Africa)

Davel, M

2009-09-01

Full Text Available The deployment of speech technology systems in the developing world is often hampered by the lack of appropriate linguistic resources. A suitable pronunciation dictionary is one such resource that can be difficult to obtain for lesser...

Customized Dictionary Learning for Subdatasets with Fine Granularity

Directory of Open Access Journals (Sweden)

Lei Ye

2016-01-01

Full Text Available Sparse models have a wide range of applications in machine learning and computer vision. Using a learned dictionary instead of an “off-the-shelf” one can dramatically improve performance on a particular dataset. However, learning a new one for each subdataset (subject with fine granularity may be unwarranted or impractical, due to restricted availability subdataset samples and tremendous numbers of subjects. To remedy this, we consider the dictionary customization problem, that is, specializing an existing global dictionary corresponding to the total dataset, with the aid of auxiliary samples obtained from the target subdataset. Inspired by observation and then deduced from theoretical analysis, a regularizer is employed penalizing the difference between the global and the customized dictionary. By minimizing the sum of reconstruction errors of the above regularizer under sparsity constraints, we exploit the characteristics of the target subdataset contained in the auxiliary samples while maintaining the basic sketches stored in the global dictionary. An efficient algorithm is presented and validated with experiments on real-world data.

Supervised dictionary learning for inferring concurrent brain networks.

Science.gov (United States)

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.

Comparing dictionary-induced vocabulary learning and inferencing ...

African Journals Online (AJOL)

This research examines dictionary-induced vocabulary learning and inferencing in the context of reading. One hundred and four intermediate English learners completed one of two word-focused tasks: reading comprehension and dictionary consultation, and reading comprehen-sion and inferencing. In addition to ...

Regularized spherical polar fourier diffusion MRI with optimal dictionary learning.

Science.gov (United States)

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

Dictionary Networking in an LSP Learning Context

DEFF Research Database (Denmark)

Nielsen, Sandro

2007-01-01

text production, but discusses an individual dictionary for a particular function. It is shown that in a general context of learning accounting and its relevant LSP with a view to writing or translating financial reporting texts, the modern theory of dictionary functions provides a good theoretical...... and usage of a subject-field, particularly when they have to read, write or translate domain-specific texts. The modern theory of dictionary functions presented in Bergenholtz and Tarp (2002) opens up exciting new possibilities for theoretical and practical lexicography and encourages lexicographers......-lexicographic environment, i.e. what happens outside the dictionary when users write or translate texts, and relate these findings to the lexicographic environment represented by the theoretical basis and the dictionary itself. Nielsen (2006) gives a preliminary discussion of monolingual accounting dictionaries for EFL...

Weighted Discriminative Dictionary Learning based on Low-rank Representation

International Nuclear Information System (INIS)

Chang, Heyou; Zheng, Hao

2017-01-01

Low-rank representation has been widely used in the field of pattern classification, especially when both training and testing images are corrupted with large noise. Dictionary plays an important role in low-rank representation. With respect to the semantic dictionary, the optimal representation matrix should be block-diagonal. However, traditional low-rank representation based dictionary learning methods cannot effectively exploit the discriminative information between data and dictionary. To address this problem, this paper proposed weighted discriminative dictionary learning based on low-rank representation, where a weighted representation regularization term is constructed. The regularization associates label information of both training samples and dictionary atoms, and encourages to generate a discriminative representation with class-wise block-diagonal structure, which can further improve the classification performance where both training and testing images are corrupted with large noise. Experimental results demonstrate advantages of the proposed method over the state-of-the-art methods. (paper)

SDL: Saliency-Based Dictionary Learning Framework for Image Similarity.

Science.gov (United States)

Sarkar, Rituparna; Acton, Scott T

2018-02-01

In image classification, obtaining adequate data to learn a robust classifier has often proven to be difficult in several scenarios. Classification of histological tissue images for health care analysis is a notable application in this context due to the necessity of surgery, biopsy or autopsy. To adequately exploit limited training data in classification, we propose a saliency guided dictionary learning method and subsequently an image similarity technique for histo-pathological image classification. Salient object detection from images aids in the identification of discriminative image features. We leverage the saliency values for the local image regions to learn a dictionary and respective sparse codes for an image, such that the more salient features are reconstructed with smaller error. The dictionary learned from an image gives a compact representation of the image itself and is capable of representing images with similar content, with comparable sparse codes. We employ this idea to design a similarity measure between a pair of images, where local image features of one image, are encoded with the dictionary learned from the other and vice versa. To effectively utilize the learned dictionary, we take into account the contribution of each dictionary atom in the sparse codes to generate a global image representation for image comparison. The efficacy of the proposed method was evaluated using three tissue data sets that consist of mammalian kidney, lung and spleen tissue, breast cancer, and colon cancer tissue images. From the experiments, we observe that our methods outperform the state of the art with an increase of 14.2% in the average classification accuracy over all data sets.

Tensor-Dictionary Learning with Deep Kruskal-Factor Analysis

Energy Technology Data Exchange (ETDEWEB)

Stevens, Andrew J.; Pu, Yunchen; Sun, Yannan; Spell, Gregory; Carin, Lawrence

2017-04-20

We introduce new dictionary learning methods for tensor-variate data of any order. We represent each data item as a sum of Kruskal decomposed dictionary atoms within the framework of beta-process factor analysis (BPFA). Our model is nonparametric and can infer the tensor-rank of each dictionary atom. This Kruskal-Factor Analysis (KFA) is a natural generalization of BPFA. We also extend KFA to a deep convolutional setting and develop online learning methods. We test our approach on image processing and classification tasks achieving state of the art results for 2D & 3D inpainting and Caltech 101. The experiments also show that atom-rank impacts both overcompleteness and sparsity.

Classification of Polarimetric SAR Data Using Dictionary Learning

DEFF Research Database (Denmark)

Vestergaard, Jacob Schack; Nielsen, Allan Aasbjerg; Dahl, Anders Lindbjerg

2012-01-01

This contribution deals with classification of multilook fully polarimetric synthetic aperture radar (SAR) data by learning a dictionary of crop types present in the Foulum test site. The Foulum test site contains a large number of agricultural fields, as well as lakes, forests, natural vegetation......, grasslands and urban areas, which make it ideally suited for evaluation of classification algorithms. Dictionary learning centers around building a collection of image patches typical for the classification problem at hand. This requires initial manual labeling of the classes present in the data and is thus...... a method for supervised classification. Sparse coding of these image patches aims to maintain a proficient number of typical patches and associated labels. Data is consecutively classified by a nearest neighbor search of the dictionary elements and labeled with probabilities of each class. Each dictionary...

DOLPHIn—Dictionary Learning for Phase Retrieval

Science.gov (United States)

Tillmann, Andreas M.; Eldar, Yonina C.; Mairal, Julien

2016-12-01

We propose a new algorithm to learn a dictionary for reconstructing and sparsely encoding signals from measurements without phase. Specifically, we consider the task of estimating a two-dimensional image from squared-magnitude measurements of a complex-valued linear transformation of the original image. Several recent phase retrieval algorithms exploit underlying sparsity of the unknown signal in order to improve recovery performance. In this work, we consider such a sparse signal prior in the context of phase retrieval, when the sparsifying dictionary is not known in advance. Our algorithm jointly reconstructs the unknown signal - possibly corrupted by noise - and learns a dictionary such that each patch of the estimated image can be sparsely represented. Numerical experiments demonstrate that our approach can obtain significantly better reconstructions for phase retrieval problems with noise than methods that cannot exploit such "hidden" sparsity. Moreover, on the theoretical side, we provide a convergence result for our method.

Image fusion via nonlocal sparse K-SVD dictionary learning.

Science.gov (United States)

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.

Unsupervised behaviour-specific dictionary learning for abnormal event detection

DEFF Research Database (Denmark)

Ren, Huamin; Liu, Weifeng; Olsen, Søren Ingvor

2015-01-01

the training data is only a small proportion of the surveillance data. Therefore, we propose behavior-specific dictionaries (BSD) through unsupervised learning, pursuing atoms from the same type of behavior to represent one behavior dictionary. To further improve the dictionary by introducing information from...... potential infrequent normal patterns, we refine the dictionary by searching ‘missed atoms’ that have compact coefficients. Experimental results show that our BSD algorithm outperforms state-of-the-art dictionaries in abnormal event detection on the public UCSD dataset. Moreover, BSD has less false alarms...

Dictionary Learning Based on Nonnegative Matrix Factorization Using Parallel Coordinate Descent

Directory of Open Access Journals (Sweden)

Zunyi Tang

2013-01-01

Full Text Available Sparse representation of signals via an overcomplete dictionary has recently received much attention as it has produced promising results in various applications. Since the nonnegativities of the signals and the dictionary are required in some applications, for example, multispectral data analysis, the conventional dictionary learning methods imposed simply with nonnegativity may become inapplicable. In this paper, we propose a novel method for learning a nonnegative, overcomplete dictionary for such a case. This is accomplished by posing the sparse representation of nonnegative signals as a problem of nonnegative matrix factorization (NMF with a sparsity constraint. By employing the coordinate descent strategy for optimization and extending it to multivariable case for processing in parallel, we develop a so-called parallel coordinate descent dictionary learning (PCDDL algorithm, which is structured by iteratively solving the two optimal problems, the learning process of the dictionary and the estimating process of the coefficients for constructing the signals. Numerical experiments demonstrate that the proposed algorithm performs better than the conventional nonnegative K-SVD (NN-KSVD algorithm and several other algorithms for comparison. What is more, its computational consumption is remarkably lower than that of the compared algorithms.

Joint seismic data denoising and interpolation with double-sparsity dictionary learning

Science.gov (United States)

Zhu, Lingchen; Liu, Entao; McClellan, James H.

2017-08-01

Seismic data quality is vital to geophysical applications, so that methods of data recovery, including denoising and interpolation, are common initial steps in the seismic data processing flow. We present a method to perform simultaneous interpolation and denoising, which is based on double-sparsity dictionary learning. This extends previous work that was for denoising only. The original double-sparsity dictionary learning algorithm is modified to track the traces with missing data by defining a masking operator that is integrated into the sparse representation of the dictionary. A weighted low-rank approximation algorithm is adopted to handle the dictionary updating as a sparse recovery optimization problem constrained by the masking operator. Compared to traditional sparse transforms with fixed dictionaries that lack the ability to adapt to complex data structures, the double-sparsity dictionary learning method learns the signal adaptively from selected patches of the corrupted seismic data, while preserving compact forward and inverse transform operators. Numerical experiments on synthetic seismic data indicate that this new method preserves more subtle features in the data set without introducing pseudo-Gibbs artifacts when compared to other directional multi-scale transform methods such as curvelets.

Concept dictionary creation and maintenance under resource constraints: lessons from the AMPATH Medical Record System.

Science.gov (United States)

Were, Martin C; Mamlin, Burke W; Tierney, William M; Wolfe, Ben; Biondich, Paul G

2007-10-11

The challenges of creating and maintaining concept dictionaries are compounded in resource-limited settings. Approaches to alleviate this burden need to be based on information derived in these settings. We created a concept dictionary and evaluated new concept proposals for an open source EMR in a resource-limited setting. Overall, 87% of the concepts in the initial dictionary were used. There were 5137 new concepts proposed, with 77% of these proposed only once. Further characterization of new concept proposals revealed that 41% were due to deficiency in the existing dictionary, and 19% were synonyms to existing concepts. 25% of the requests contained misspellings, 41% were complex terms, and 17% were ambiguous. Given the resource-intensive nature of dictionary creation and maintenance, there should be considerations for centralizing the concept dictionary service, using standards, prioritizing concept proposals, and redesigning the user-interface to reduce this burden in settings with limited resources.

A dictionary learning approach for human sperm heads classification.

Science.gov (United States)

Shaker, Fariba; Monadjemi, S Amirhassan; Alirezaie, Javad; Naghsh-Nilchi, Ahmad Reza

2017-12-01

To diagnose infertility in men, semen analysis is conducted in which sperm morphology is one of the factors that are evaluated. Since manual assessment of sperm morphology is time-consuming and subjective, automatic classification methods are being developed. Automatic classification of sperm heads is a complicated task due to the intra-class differences and inter-class similarities of class objects. In this research, a Dictionary Learning (DL) technique is utilized to construct a dictionary of sperm head shapes. This dictionary is used to classify the sperm heads into four different classes. Square patches are extracted from the sperm head images. Columnized patches from each class of sperm are used to learn class-specific dictionaries. The patches from a test image are reconstructed using each class-specific dictionary and the overall reconstruction error for each class is used to select the best matching class. Average accuracy, precision, recall, and F-score are used to evaluate the classification method. The method is evaluated using two publicly available datasets of human sperm head shapes. The proposed DL based method achieved an average accuracy of 92.2% on the HuSHeM dataset, and an average recall of 62% on the SCIAN-MorphoSpermGS dataset. The results show a significant improvement compared to a previously published shape-feature-based method. We have achieved high-performance results. In addition, our proposed approach offers a more balanced classifier in which all four classes are recognized with high precision and recall. In this paper, we use a Dictionary Learning approach in classifying human sperm heads. It is shown that the Dictionary Learning method is far more effective in classifying human sperm heads than classifiers using shape-based features. Also, a dataset of human sperm head shapes is introduced to facilitate future research. Copyright © 2017 Elsevier Ltd. All rights reserved.

Automatic Microaneurysms Detection Based on Multifeature Fusion Dictionary Learning

Directory of Open Access Journals (Sweden)

Wei Zhou

2017-01-01

Full Text Available Recently, microaneurysm (MA detection has attracted a lot of attention in the medical image processing community. Since MAs can be seen as the earliest lesions in diabetic retinopathy, their detection plays a critical role in diabetic retinopathy diagnosis. In this paper, we propose a novel MA detection approach named multifeature fusion dictionary learning (MFFDL. The proposed method consists of four steps: preprocessing, candidate extraction, multifeature dictionary learning, and classification. The novelty of our proposed approach lies in incorporating the semantic relationships among multifeatures and dictionary learning into a unified framework for automatic detection of MAs. We evaluate the proposed algorithm by comparing it with the state-of-the-art approaches and the experimental results validate the effectiveness of our algorithm.

A tensor-based dictionary learning approach to tomographic image reconstruction

DEFF Research Database (Denmark)

Soltani, Sara; Kilmer, Misha E.; Hansen, Per Christian

2016-01-01

We consider tomographic reconstruction using priors in the form of a dictionary learned from training images. The reconstruction has two stages: first we construct a tensor dictionary prior from our training data, and then we pose the reconstruction problem in terms of recovering the expansion...... coefficients in that dictionary. Our approach differs from past approaches in that (a) we use a third-order tensor representation for our images and (b) we recast the reconstruction problem using the tensor formulation. The dictionary learning problem is presented as a non-negative tensor factorization problem...... with sparsity constraints. The reconstruction problem is formulated in a convex optimization framework by looking for a solution with a sparse representation in the tensor dictionary. Numerical results show that our tensor formulation leads to very sparse representations of both the training images...

Reconstruction of magnetic resonance imaging by three-dimensional dual-dictionary learning.

Science.gov (United States)

Song, Ying; Zhu, Zhen; Lu, Yang; Liu, Qiegen; Zhao, Jun

2014-03-01

To improve the magnetic resonance imaging (MRI) data acquisition speed while maintaining the reconstruction quality, a novel method is proposed for multislice MRI reconstruction from undersampled k-space data based on compressed-sensing theory using dictionary learning. There are two aspects to improve the reconstruction quality. One is that spatial correlation among slices is used by extending the atoms in dictionary learning from patches to blocks. The other is that the dictionary-learning scheme is used at two resolution levels; i.e., a low-resolution dictionary is used for sparse coding and a high-resolution dictionary is used for image updating. Numerical experiments are carried out on in vivo 3D MR images of brains and abdomens with a variety of undersampling schemes and ratios. The proposed method (dual-DLMRI) achieves better reconstruction quality than conventional reconstruction methods, with the peak signal-to-noise ratio being 7 dB higher. The advantages of the dual dictionaries are obvious compared with the single dictionary. Parameter variations ranging from 50% to 200% only bias the image quality within 15% in terms of the peak signal-to-noise ratio. Dual-DLMRI effectively uses the a priori information in the dual-dictionary scheme and provides dramatically improved reconstruction quality. Copyright © 2013 Wiley Periodicals, Inc.

Scene recognition based on integrating active learning with dictionary learning

Science.gov (United States)

Wang, Chengxi; Yin, Xueyan; Yang, Lin; Gong, Chengrong; Zheng, Caixia; Yi, Yugen

2018-04-01

Scene recognition is a significant topic in the field of computer vision. Most of the existing scene recognition models require a large amount of labeled training samples to achieve a good performance. However, labeling image manually is a time consuming task and often unrealistic in practice. In order to gain satisfying recognition results when labeled samples are insufficient, this paper proposed a scene recognition algorithm named Integrating Active Learning and Dictionary Leaning (IALDL). IALDL adopts projective dictionary pair learning (DPL) as classifier and introduces active learning mechanism into DPL for improving its performance. When constructing sampling criterion in active learning, IALDL considers both the uncertainty and representativeness as the sampling criteria to effectively select the useful unlabeled samples from a given sample set for expanding the training dataset. Experiment results on three standard databases demonstrate the feasibility and validity of the proposed IALDL.

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

Science.gov (United States)

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.

Dynamic Textures Modeling via Joint Video Dictionary Learning.

Science.gov (United States)

Wei, Xian; Li, Yuanxiang; Shen, Hao; Chen, Fang; Kleinsteuber, Martin; Wang, Zhongfeng

2017-04-06

Video representation is an important and challenging task in the computer vision community. In this paper, we consider the problem of modeling and classifying video sequences of dynamic scenes which could be modeled in a dynamic textures (DT) framework. At first, we assume that image frames of a moving scene can be modeled as a Markov random process. We propose a sparse coding framework, named joint video dictionary learning (JVDL), to model a video adaptively. By treating the sparse coefficients of image frames over a learned dictionary as the underlying "states", we learn an efficient and robust linear transition matrix between two adjacent frames of sparse events in time series. Hence, a dynamic scene sequence is represented by an appropriate transition matrix associated with a dictionary. In order to ensure the stability of JVDL, we impose several constraints on such transition matrix and dictionary. The developed framework is able to capture the dynamics of a moving scene by exploring both sparse properties and the temporal correlations of consecutive video frames. Moreover, such learned JVDL parameters can be used for various DT applications, such as DT synthesis and recognition. Experimental results demonstrate the strong competitiveness of the proposed JVDL approach in comparison with state-of-the-art video representation methods. Especially, it performs significantly better in dealing with DT synthesis and recognition on heavily corrupted data.

Using Dictionary Pair Learning for Seizure Detection.

Science.gov (United States)

Ma, Xin; Yu, Nana; Zhou, Weidong

2018-02-13

Automatic seizure detection is extremely important in the monitoring and diagnosis of epilepsy. The paper presents a novel method based on dictionary pair learning (DPL) for seizure detection in the long-term intracranial electroencephalogram (EEG) recordings. First, for the EEG data, wavelet filtering and differential filtering are applied, and the kernel function is performed to make the signal linearly separable. In DPL, the synthesis dictionary and analysis dictionary are learned jointly from original training samples with alternating minimization method, and sparse coefficients are obtained by using of linear projection instead of costly [Formula: see text]-norm or [Formula: see text]-norm optimization. At last, the reconstructed residuals associated with seizure and nonseizure sub-dictionary pairs are calculated as the decision values, and the postprocessing is performed for improving the recognition rate and reducing the false detection rate of the system. A total of 530[Formula: see text]h from 20 patients with 81 seizures were used to evaluate the system. Our proposed method has achieved an average segment-based sensitivity of 93.39%, specificity of 98.51%, and event-based sensitivity of 96.36% with false detection rate of 0.236/h.

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

Science.gov (United States)

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.

An Analysis Dictionary Learning Algorithm under a Noisy Data Model with Orthogonality Constraint

Directory of Open Access Journals (Sweden)

Ye Zhang

2014-01-01

Full Text Available 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.

Exploiting Attribute Correlations: A Novel Trace Lasso-Based Weakly Supervised Dictionary Learning Method.

Science.gov (United States)

Wu, Lin; Wang, Yang; Pan, Shirui

2017-12-01

It is now well established that sparse representation models are working effectively for many visual recognition tasks, and have pushed forward the success of dictionary learning therein. Recent studies over dictionary learning focus on learning discriminative atoms instead of purely reconstructive ones. However, the existence of intraclass diversities (i.e., data objects within the same category but exhibit large visual dissimilarities), and interclass similarities (i.e., data objects from distinct classes but share much visual similarities), makes it challenging to learn effective recognition models. To this end, a large number of labeled data objects are required to learn models which can effectively characterize these subtle differences. However, labeled data objects are always limited to access, committing it difficult to learn a monolithic dictionary that can be discriminative enough. To address the above limitations, in this paper, we propose a weakly-supervised dictionary learning method to automatically learn a discriminative dictionary by fully exploiting visual attribute correlations rather than label priors. In particular, the intrinsic attribute correlations are deployed as a critical cue to guide the process of object categorization, and then a set of subdictionaries are jointly learned with respect to each category. The resulting dictionary is highly discriminative and leads to intraclass diversity aware sparse representations. Extensive experiments on image classification and object recognition are conducted to show the effectiveness of our approach.

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

Science.gov (United States)

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.

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

Science.gov (United States)

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.

Discriminative object tracking via sparse representation and online dictionary learning.

Science.gov (United States)

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.

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

Directory of Open Access Journals (Sweden)

Donghao Wang

2016-09-01

Full Text Available 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.

Structured Kernel Dictionary Learning with Correlation Constraint for Object Recognition.

Science.gov (United States)

Wang, Zhengjue; Wang, Yinghua; Liu, Hongwei; Zhang, Hao

2017-06-21

In this paper, we propose a new discriminative non-linear dictionary learning approach, called correlation constrained structured kernel KSVD, for object recognition. The objective function for dictionary learning contains a reconstructive term and a discriminative term. In the reconstructive term, signals are implicitly non-linearly mapped into a space, where a structured kernel dictionary, each sub-dictionary of which lies in the span of the mapped signals from the corresponding class, is established. In the discriminative term, by analyzing the classification mechanism, the correlation constraint is proposed in kernel form, constraining the correlations between different discriminative codes, and restricting the coefficient vectors to be transformed into a feature space, where the features are highly correlated inner-class and nearly independent between-classes. The objective function is optimized by the proposed structured kernel KSVD. During the classification stage, the specific form of the discriminative feature is needless to be known, while the inner product of the discriminative feature with kernel matrix embedded is available, and is suitable for a linear SVM classifier. Experimental results demonstrate that the proposed approach outperforms many state-of-the-art dictionary learning approaches for face, scene and synthetic aperture radar (SAR) vehicle target recognition.

Magnetic Resonance Super-resolution Imaging Measurement with Dictionary-optimized Sparse Learning

Directory of Open Access Journals (Sweden)

Li Jun-Bao

2017-06-01

Full Text Available Magnetic Resonance Super-resolution Imaging Measurement (MRIM is an effective way of measuring materials. MRIM has wide applications in physics, chemistry, biology, geology, medical and material science, especially in medical diagnosis. It is feasible to improve the resolution of MR imaging through increasing radiation intensity, but the high radiation intensity and the longtime of magnetic field harm the human body. Thus, in the practical applications the resolution of hardware imaging reaches the limitation of resolution. Software-based super-resolution technology is effective to improve the resolution of image. This work proposes a framework of dictionary-optimized sparse learning based MR super-resolution method. The framework is to solve the problem of sample selection for dictionary learning of sparse reconstruction. The textural complexity-based image quality representation is proposed to choose the optimal samples for dictionary learning. Comprehensive experiments show that the dictionary-optimized sparse learning improves the performance of sparse representation.

Creating a medical dictionary using word alignment: The influence of sources and resources

Directory of Open Access Journals (Sweden)

Åhlfeldt Hans

2007-11-01

Full Text Available Abstract Background Automatic word alignment of parallel texts with the same content in different languages is among other things used to generate dictionaries for new translations. The quality of the generated word alignment depends on the quality of the input resources. In this paper we report on automatic word alignment of the English and Swedish versions of the medical terminology systems ICD-10, ICF, NCSP, KSH97-P and parts of MeSH and how the terminology systems and type of resources influence the quality. Methods We automatically word aligned the terminology systems using static resources, like dictionaries, statistical resources, like statistically derived dictionaries, and training resources, which were generated from manual word alignment. We varied which part of the terminology systems that we used to generate the resources, which parts that we word aligned and which types of resources we used in the alignment process to explore the influence the different terminology systems and resources have on the recall and precision. After the analysis, we used the best configuration of the automatic word alignment for generation of candidate term pairs. We then manually verified the candidate term pairs and included the correct pairs in an English-Swedish dictionary. Results The results indicate that more resources and resource types give better results but the size of the parts used to generate the resources only partly affects the quality. The most generally useful resources were generated from ICD-10 and resources generated from MeSH were not as general as other resources. Systematic inter-language differences in the structure of the terminology system rubrics make the rubrics harder to align. Manually created training resources give nearly as good results as a union of static resources, statistical resources and training resources and noticeably better results than a union of static resources and statistical resources. The verified English

Creating a medical dictionary using word alignment: the influence of sources and resources.

Science.gov (United States)

Nyström, Mikael; Merkel, Magnus; Petersson, Håkan; Ahlfeldt, Hans

2007-11-23

Automatic word alignment of parallel texts with the same content in different languages is among other things used to generate dictionaries for new translations. The quality of the generated word alignment depends on the quality of the input resources. In this paper we report on automatic word alignment of the English and Swedish versions of the medical terminology systems ICD-10, ICF, NCSP, KSH97-P and parts of MeSH and how the terminology systems and type of resources influence the quality. We automatically word aligned the terminology systems using static resources, like dictionaries, statistical resources, like statistically derived dictionaries, and training resources, which were generated from manual word alignment. We varied which part of the terminology systems that we used to generate the resources, which parts that we word aligned and which types of resources we used in the alignment process to explore the influence the different terminology systems and resources have on the recall and precision. After the analysis, we used the best configuration of the automatic word alignment for generation of candidate term pairs. We then manually verified the candidate term pairs and included the correct pairs in an English-Swedish dictionary. The results indicate that more resources and resource types give better results but the size of the parts used to generate the resources only partly affects the quality. The most generally useful resources were generated from ICD-10 and resources generated from MeSH were not as general as other resources. Systematic inter-language differences in the structure of the terminology system rubrics make the rubrics harder to align. Manually created training resources give nearly as good results as a union of static resources, statistical resources and training resources and noticeably better results than a union of static resources and statistical resources. The verified English-Swedish dictionary contains 24,000 term pairs in base

SU-F-I-12: Region-Specific Dictionary Learning for Low-Dose X-Ray CT Reconstruction

International Nuclear Information System (INIS)

Xu, Q; Han, H; Xing, L

2016-01-01

Purpose: Dictionary learning based method has attracted more and more attentions in low-dose CT due to the superior performance on suppressing noise and preserving structural details. Considering the structures and noise vary from region to region in one imaging object, we propose a region-specific dictionary learning method to improve the low-dose CT reconstruction. Methods: A set of normal-dose images was used for dictionary learning. Segmentations were performed on these images, so that the training patch sets corresponding to different regions can be extracted out. After that, region-specific dictionaries were learned from these training sets. For the low-dose CT reconstruction, a conventional reconstruction, such as filtered back-projection (FBP), was performed firstly, and then segmentation was followed to segment the image into different regions. Sparsity constraints of each region based on its dictionary were used as regularization terms. The regularization parameters were selected adaptively according to different regions. A low-dose human thorax dataset was used to evaluate the proposed method. The single dictionary based method was performed for comparison. Results: Since the lung region is very different from the other part of thorax, two dictionaries corresponding to lung region and the rest part of thorax respectively were learned to better express the structural details and avoid artifacts. With only one dictionary some artifact appeared in the body region caused by the spot atoms corresponding to the structures in the lung region. And also some structure in the lung regions cannot be recovered well by only one dictionary. The quantitative indices of the result by the proposed method were also improved a little compared to the single dictionary based method. Conclusion: Region-specific dictionary can make the dictionary more adaptive to different region characteristics, which is much desirable for enhancing the performance of dictionary learning

SU-F-I-12: Region-Specific Dictionary Learning for Low-Dose X-Ray CT Reconstruction

Energy Technology Data Exchange (ETDEWEB)

Xu, Q; Han, H; Xing, L [Stanford University School of Medicine, Stanford, CA (United States)

2016-06-15

Purpose: Dictionary learning based method has attracted more and more attentions in low-dose CT due to the superior performance on suppressing noise and preserving structural details. Considering the structures and noise vary from region to region in one imaging object, we propose a region-specific dictionary learning method to improve the low-dose CT reconstruction. Methods: A set of normal-dose images was used for dictionary learning. Segmentations were performed on these images, so that the training patch sets corresponding to different regions can be extracted out. After that, region-specific dictionaries were learned from these training sets. For the low-dose CT reconstruction, a conventional reconstruction, such as filtered back-projection (FBP), was performed firstly, and then segmentation was followed to segment the image into different regions. Sparsity constraints of each region based on its dictionary were used as regularization terms. The regularization parameters were selected adaptively according to different regions. A low-dose human thorax dataset was used to evaluate the proposed method. The single dictionary based method was performed for comparison. Results: Since the lung region is very different from the other part of thorax, two dictionaries corresponding to lung region and the rest part of thorax respectively were learned to better express the structural details and avoid artifacts. With only one dictionary some artifact appeared in the body region caused by the spot atoms corresponding to the structures in the lung region. And also some structure in the lung regions cannot be recovered well by only one dictionary. The quantitative indices of the result by the proposed method were also improved a little compared to the single dictionary based method. Conclusion: Region-specific dictionary can make the dictionary more adaptive to different region characteristics, which is much desirable for enhancing the performance of dictionary learning

Manifold optimization-based analysis dictionary learning with an ℓ1∕2-norm regularizer.

Science.gov (United States)

Li, Zhenni; Ding, Shuxue; Li, Yujie; Yang, Zuyuan; Xie, Shengli; Chen, Wuhui

2018-02-01

Recently there has been increasing attention towards analysis dictionary learning. In analysis dictionary learning, it is an open problem to obtain the strong sparsity-promoting solutions efficiently while simultaneously avoiding the trivial solutions of the dictionary. In this paper, to obtain the strong sparsity-promoting solutions, we employ the ℓ 1∕2 norm as a regularizer. The very recent study on ℓ 1∕2 norm regularization theory in compressive sensing shows that its solutions can give sparser results than using the ℓ 1 norm. We transform a complex nonconvex optimization into a number of one-dimensional minimization problems. Then the closed-form solutions can be obtained efficiently. To avoid trivial solutions, we apply manifold optimization to update the dictionary directly on the manifold satisfying the orthonormality constraint, so that the dictionary can avoid the trivial solutions well while simultaneously capturing the intrinsic properties of the dictionary. The experiments with synthetic and real-world data verify that the proposed algorithm for analysis dictionary learning can not only obtain strong sparsity-promoting solutions efficiently, but also learn more accurate dictionary in terms of dictionary recovery and image processing than the state-of-the-art algorithms. Copyright © 2017 Elsevier Ltd. All rights reserved.

Electronic Dictionary as a Tool for Integration of Additional Learning Content

Directory of Open Access Journals (Sweden)

Stefka Kovacheva

2015-12-01

Full Text Available Electronic Dictionary as a Tool for Integration of Additional Learning Content This article discusses electronic dictionary as an element of the „Bulgarian cultural and historical heritage under the protection of UNESCO” database developed in IMI (BAS, that will be used to integrate additional learning content. The electronic dictionary is described as an easily accessible book of reference, offering information to the shape, meaning, usage and the origin of words in connection to the cultural-historical heritage sites in Bulgaria, protected by UNESCO. The dictionary targets 9–11 year old students from Bulgarian schools, who study the subjects “Man and Society” in 4th grade and “History and Civilization” in 5th grade.

The Effectiveness of Using Contextual Clues, Dictionary Strategy and Computer Assisted Language Learning (Call In Learning Vocabulary

Directory of Open Access Journals (Sweden)

Zuraina Ali

2013-07-01

Full Text Available This study investigates the effectiveness of three vocabulary learning methods that are Contextual Clues, Dictionary Strategy, and Computer Assisted Language Learning (CALL in learning vocabulary among ESL learners. First, it aims at finding which of the vocabulary learning methods namely Dictionary Strategy, Contextual Clues, and CALL that may result in the highest number of words learnt in the immediate and delayed recall tests. Second, it compares the results of the Pre-test and the Delayed Recall Post-test to determine the differences of learning vocabulary using the methods. A quasi-experiment that tested the effectiveness of learning vocabulary using Dictionary Strategy, Contextual clues, and CALL involved 123 first year university students. Qualitative procedures included the collection of data from interviews which were conducted to triangulate the data obtain from the quantitative inquiries. Findings from the study using ANOVA revealed that there were significant differences when students were exposed to Dictionary Strategy, Contextual Clues and CALL in the immediate recall tests but not in the Delayed Recall Post-test. Also, there were significant differences when t test was used to compare the scores between the Pre-test and the Delayed Recall Post-test in using the three methods of vocabulary learning. Although many researchers have advocated the relative effectiveness of Dictionary Strategy, Contextual Clues, and CALL in learning vocabulary, the study however, is still paramount since there is no study has ever empirically investigated the relative efficacy of these three methods in a single study.

Classification of multiple sclerosis lesions using adaptive dictionary learning.

Science.gov (United States)

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. Copyright © 2015 Elsevier Ltd. All rights reserved.

Hyperspectral Image Classification Using Discriminative Dictionary Learning

International Nuclear Information System (INIS)

Zongze, Y; Hao, S; Kefeng, J; Huanxin, Z

2014-01-01

The hyperspectral image (HSI) processing community has witnessed a surge of papers focusing on the utilization of sparse prior for effective HSI classification. In sparse representation based HSI classification, there are two phases: sparse coding with an over-complete dictionary and classification. In this paper, we first apply a novel fisher discriminative dictionary learning method, which capture the relative difference in different classes. The competitive selection strategy ensures that atoms in the resulting over-complete dictionary are the most discriminative. Secondly, motivated by the assumption that spatially adjacent samples are statistically related and even belong to the same materials (same class), we propose a majority voting scheme incorporating contextual information to predict the category label. Experiment results show that the proposed method can effectively strengthen relative discrimination of the constructed dictionary, and incorporating with the majority voting scheme achieve generally an improved prediction performance

Stochastic learning of multi-instance dictionary for earth mover’s distance-based histogram comparison

KAUST Repository

Fan, Jihong

2016-09-17

Dictionary plays an important role in multi-instance data representation. It maps bags of instances to histograms. Earth mover’s distance (EMD) is the most effective histogram distance metric for the application of multi-instance retrieval. However, up to now, there is no existing multi-instance dictionary learning methods designed for EMD-based histogram comparison. To fill this gap, we develop the first EMD-optimal dictionary learning method using stochastic optimization method. In the stochastic learning framework, we have one triplet of bags, including one basic bag, one positive bag, and one negative bag. These bags are mapped to histograms using a multi-instance dictionary. We argue that the EMD between the basic histogram and the positive histogram should be smaller than that between the basic histogram and the negative histogram. Base on this condition, we design a hinge loss. By minimizing this hinge loss and some regularization terms of the dictionary, we update the dictionary instances. The experiments over multi-instance retrieval applications shows its effectiveness when compared to other dictionary learning methods over the problems of medical image retrieval and natural language relation classification. © 2016 The Natural Computing Applications Forum

3D dictionary learning based iterative cone beam CT reconstruction

Directory of Open Access Journals (Sweden)

Ti Bai

2014-03-01

Full Text Available Purpose: This work is to develop a 3D dictionary learning based cone beam CT (CBCT reconstruction algorithm on graphic processing units (GPU to improve the quality of sparse-view CBCT reconstruction with high efficiency. Methods: A 3D dictionary containing 256 small volumes (atoms of 3 × 3 × 3 was trained from a large number of blocks extracted from a high quality volume image. On the basis, we utilized cholesky decomposition based orthogonal matching pursuit algorithm to find the sparse representation of each block. To accelerate the time-consuming sparse coding in the 3D case, we implemented the sparse coding in a parallel fashion by taking advantage of the tremendous computational power of GPU. Conjugate gradient least square algorithm was adopted to minimize the data fidelity term. 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 tight frame (TF by performing reconstructions on a subset data of 121 projections. Results: Compared to TF based CBCT reconstruction that shows good overall performance, our experiments indicated that 3D dictionary learning based CBCT reconstruction is able to recover finer structures, remove more streaking artifacts and also induce less blocky artifacts. Conclusion: 3D dictionary learning based CBCT reconstruction algorithm is able to sense the structural information while suppress the noise, and hence to achieve high quality reconstruction under the case of sparse view. The GPU realization of the whole algorithm offers a significant efficiency enhancement, making this algorithm more feasible for potential clinical application.-------------------------------Cite this article as: Bai T, Yan H, Shi F, Jia X, Lou Y, Xu Q, Jiang S, Mou X. 3D dictionary learning based iterative cone beam CT reconstruction. Int J Cancer Ther Oncol 2014; 2(2:020240. DOI: 10

Incoherent dictionary learning for reducing crosstalk noise in least-squares reverse time migration

Science.gov (United States)

Wu, Juan; Bai, Min

2018-05-01

We propose to apply a novel incoherent dictionary learning (IDL) algorithm for regularizing the least-squares inversion in seismic imaging. The IDL is proposed to overcome the drawback of traditional dictionary learning algorithm in losing partial texture information. Firstly, the noisy image is divided into overlapped image patches, and some random patches are extracted for dictionary learning. Then, we apply the IDL technology to minimize the coherency between atoms during dictionary learning. Finally, the sparse representation problem is solved by a sparse coding algorithm, and image is restored by those sparse coefficients. By reducing the correlation among atoms, it is possible to preserve most of the small-scale features in the image while removing much of the long-wavelength noise. The application of the IDL method to regularization of seismic images from least-squares reverse time migration shows successful performance.

Denoising of gravitational wave signals via dictionary learning algorithms

Science.gov (United States)

Torres-Forné, Alejandro; Marquina, Antonio; Font, José A.; Ibáñez, José M.

2016-12-01

Gravitational wave astronomy has become a reality after the historical detections accomplished during the first observing run of the two advanced LIGO detectors. In the following years, the number of detections is expected to increase significantly with the full commissioning of the advanced LIGO, advanced Virgo and KAGRA detectors. The development of sophisticated data analysis techniques to improve the opportunities of detection for low signal-to-noise-ratio events is, hence, a most crucial effort. In this paper, we present one such technique, dictionary-learning algorithms, which have been extensively developed in the last few years and successfully applied mostly in the context of image processing. However, to the best of our knowledge, such algorithms have not yet been employed to denoise gravitational wave signals. By building dictionaries from numerical relativity templates of both binary black holes mergers and bursts of rotational core collapse, we show how machine-learning algorithms based on dictionaries can also be successfully applied for gravitational wave denoising. We use a subset of signals from both catalogs, embedded in nonwhite Gaussian noise, to assess our techniques with a large sample of tests and to find the best model parameters. The application of our method to the actual signal GW150914 shows promising results. Dictionary-learning algorithms could be a complementary addition to the gravitational wave data analysis toolkit. They may be used to extract signals from noise and to infer physical parameters if the data are in good enough agreement with the morphology of the dictionary atoms.

Natural-Annotation-based Unsupervised Construction of Korean-Chinese Domain Dictionary

Science.gov (United States)

Liu, Wuying; Wang, Lin

2018-03-01

The large-scale bilingual parallel resource is significant to statistical learning and deep learning in natural language processing. This paper addresses the automatic construction issue of the Korean-Chinese domain dictionary, and presents a novel unsupervised construction method based on the natural annotation in the raw corpus. We firstly extract all Korean-Chinese word pairs from Korean texts according to natural annotations, secondly transform the traditional Chinese characters into the simplified ones, and finally distill out a bilingual domain dictionary after retrieving the simplified Chinese words in an extra Chinese domain dictionary. The experimental results show that our method can automatically build multiple Korean-Chinese domain dictionaries efficiently.

Using Different Types of Dictionaries for Improving EFL Reading Comprehension and Vocabulary Learning

Science.gov (United States)

Alharbi, Majed A.

2016-01-01

This study investigated the effects of monolingual book dictionaries, popup dictionaries, and type-in dictionaries on improving reading comprehension and vocabulary learning in an EFL program. An experimental design involving four groups and a post-test was chosen for the experiment: (1) pop-up dictionary (experimental group 1); (2) type-in…

The Role of Consulting a Dictionary in Reading and Vocabulary Learning

Directory of Open Access Journals (Sweden)

Carol A. Fraser

1999-12-01

Full Text Available Abstract This article reviews recent research on consulting a dictionary in L2 reading and vocabulary learning. From the perspective of cognitive learning theory, the author re-evaluates the limited role that has often been accorded to dictionary consulting. It is noted that, among the three available lexical processing strategies (inferencing, consulting and ignoring, learners tend to use consulting infrequently and selectively and also to differ among each other in their strategy use. Consulting in combination with inferencing is shown to have the greatest positive effect on performance in L2 reading and vocabulary learning, although consulting is found to slow down task completion. Excerpts from think-aloud protocols illustrate the potential contribution of strategic dictionary use to the cognitive processes required for vocabulary acquisition: attention to form-meaning connections, rehearsal of words for storage in long-term memory and elaboration of associations with other knowledge. Among the pedagogical implications of these findings is the need for training in lexical processing strategies in order to help learners use the dictionary effectively and accurately in L2 reading comprehension and vocabulary learning.

Stochastic Learning of Multi-Instance Dictionary for Earth Mover's Distance based Histogram Comparison

OpenAIRE

Fan, Jihong; Liang, Ru-Ze

2016-01-01

Dictionary plays an important role in multi-instance data representation. It maps bags of instances to histograms. Earth mover's distance (EMD) is the most effective histogram distance metric for the application of multi-instance retrieval. However, up to now, there is no existing multi-instance dictionary learning methods designed for EMD based histogram comparison. To fill this gap, we develop the first EMD-optimal dictionary learning method using stochastic optimization method. In the stoc...

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

Science.gov (United States)

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.

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

Science.gov (United States)

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.

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

Science.gov (United States)

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.

Dictionary Pair Learning on Grassmann Manifolds for Image Denoising.

Science.gov (United States)

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.

Paper, Electronic or Online? Different Dictionaries for Different Activities

Science.gov (United States)

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…

A Dictionary Learning Method with Total Generalized Variation for MRI Reconstruction

Directory of Open Access Journals (Sweden)

Hongyang Lu

2016-01-01

Full Text Available 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.

Incremental Structured Dictionary Learning for Video Sensor-Based Object Tracking

Science.gov (United States)

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

Multi-Source Multi-Target Dictionary Learning for Prediction of Cognitive Decline.

Science.gov (United States)

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

2017-06-01

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

Abnormality detection of mammograms by discriminative dictionary learning on DSIFT descriptors.

Science.gov (United States)

Tavakoli, Nasrin; Karimi, Maryam; Nejati, Mansour; Karimi, Nader; Reza Soroushmehr, S M; Samavi, Shadrokh; Najarian, Kayvan

2017-07-01

Detection and classification of breast lesions using mammographic images are one of the most difficult studies in medical image processing. A number of learning and non-learning methods have been proposed for detecting and classifying these lesions. However, the accuracy of the detection/classification still needs improvement. In this paper we propose a powerful classification method based on sparse learning to diagnose breast cancer in mammograms. For this purpose, a supervised discriminative dictionary learning approach is applied on dense scale invariant feature transform (DSIFT) features. A linear classifier is also simultaneously learned with the dictionary which can effectively classify the sparse representations. Our experimental results show the superior performance of our method compared to existing approaches.

Active Discriminative Dictionary Learning for Weather Recognition

Directory of Open Access Journals (Sweden)

Caixia Zheng

2016-01-01

Full Text Available Weather recognition based on outdoor images is a brand-new and challenging subject, which is widely required in many fields. This paper presents a novel framework for recognizing different weather conditions. Compared with other algorithms, the proposed method possesses the following advantages. Firstly, our method extracts both visual appearance features of the sky region and physical characteristics features of the nonsky region in images. Thus, the extracted features are more comprehensive than some of the existing methods in which only the features of sky region are considered. Secondly, unlike other methods which used the traditional classifiers (e.g., SVM and K-NN, we use discriminative dictionary learning as the classification model for weather, which could address the limitations of previous works. Moreover, the active learning procedure is introduced into dictionary learning to avoid requiring a large number of labeled samples to train the classification model for achieving good performance of weather recognition. Experiments and comparisons are performed on two datasets to verify the effectiveness of the proposed method.

Low-Dose X-ray CT Reconstruction via Dictionary Learning

Science.gov (United States)

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

Image Denoising Algorithm Combined with SGK Dictionary Learning and Principal Component Analysis Noise Estimation

Directory of Open Access Journals (Sweden)

Wenjing Zhao

2018-01-01

Full Text Available SGK (sequential generalization of K-means dictionary learning denoising algorithm has the characteristics of fast denoising speed and excellent denoising performance. However, the noise standard deviation must be known in advance when using SGK algorithm to process the image. This paper presents a denoising algorithm combined with SGK dictionary learning and the principal component analysis (PCA noise estimation. At first, the noise standard deviation of the image is estimated by using the PCA noise estimation algorithm. And then it is used for SGK dictionary learning algorithm. Experimental results show the following: (1 The SGK algorithm has the best denoising performance compared with the other three dictionary learning algorithms. (2 The SGK algorithm combined with PCA is superior to the SGK algorithm combined with other noise estimation algorithms. (3 Compared with the original SGK algorithm, the proposed algorithm has higher PSNR and better denoising performance.

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

Science.gov (United States)

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.

Incremental Structured Dictionary Learning for Video Sensor-Based Object Tracking

Directory of Open Access Journals (Sweden)

Ming Xue

2014-02-01

Full Text Available 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.

Anomaly-sensitive dictionary learning for structural diagnostics from ultrasonic wavefields.

Science.gov (United States)

Druce, Jeffrey M; Haupt, Jarvis D; Gonella, Stefano

2015-07-01

This paper proposes a strategy for the detection and triangulation of localized anomalies, such as defects, inclusions, or damage zones, in solid and structural media. The method revolves around the construction of sparse representations of the structure's ultrasonic wavefield response, which are obtained by learning instructive dictionaries that form a suitable basis for the response data. The resulting sparse coding problem is cast as a modified dictionary learning task with additional spatial sparsity constraints enforced on the atoms of the learned dictionaries, which provide them with the ability to unveil anomalous regions in the physical domain. The proposed methodology is model-agnostic, i.e., it forsakes the need for a physical model and requires virtually no a priori knowledge of the material properties. This characteristic makes the approach especially powerful for anomaly identification in systems with unknown or highly heterogeneous property distribution, for which a material model is unsuitable or unreliable. The method is tested against synthetically generated data as well as experimental data acquired using a scanning laser Doppler vibrometer.

Efficient Sum of Outer Products Dictionary Learning (SOUP-DIL) and Its Application to Inverse Problems.

Science.gov (United States)

Ravishankar, Saiprasad; Nadakuditi, Raj Rao; Fessler, Jeffrey A

2017-12-01

The sparsity of signals in a transform domain or dictionary has been exploited in applications such as compression, denoising and inverse problems. More recently, data-driven adaptation of synthesis dictionaries has shown promise compared to analytical dictionary models. However, dictionary learning problems are typically non-convex and NP-hard, and the usual alternating minimization approaches for these problems are often computationally expensive, with the computations dominated by the NP-hard synthesis sparse coding step. This paper exploits the ideas that drive algorithms such as K-SVD, and investigates in detail efficient methods for aggregate sparsity penalized dictionary learning by first approximating the data with a sum of sparse rank-one matrices (outer products) and then using a block coordinate descent approach to estimate the unknowns. The resulting block coordinate descent algorithms involve efficient closed-form solutions. Furthermore, we consider the problem of dictionary-blind image reconstruction, and propose novel and efficient algorithms for adaptive image reconstruction using block coordinate descent and sum of outer products methodologies. We provide a convergence study of the algorithms for dictionary learning and dictionary-blind image reconstruction. Our numerical experiments show the promising performance and speedups provided by the proposed methods over previous schemes in sparse data representation and compressed sensing-based image reconstruction.

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

Science.gov (United States)

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.

Machinery vibration signal denoising based on learned dictionary and sparse representation

International Nuclear Information System (INIS)

Guo, Liang; Gao, Hongli; Li, Jun; Huang, Haifeng; Zhang, Xiaochen

2015-01-01

Mechanical vibration signal denoising has been an import problem for machine damage assessment and health monitoring. Wavelet transfer and sparse reconstruction are the powerful and practical methods. However, those methods are based on the fixed basis functions or atoms. In this paper, a novel method is presented. The atoms used to represent signals are learned from the raw signal. And in order to satisfy the requirements of real-time signal processing, an online dictionary learning algorithm is adopted. Orthogonal matching pursuit is applied to extract the most pursuit column in the dictionary. At last, denoised signal is calculated with the sparse vector and learned dictionary. A simulation signal and real bearing fault signal are utilized to evaluate the improved performance of the proposed method through the comparison with kinds of denoising algorithms. Then Its computing efficiency is demonstrated by an illustrative runtime example. The results show that the proposed method outperforms current algorithms with efficiency calculation. (paper)

LeadMine: a grammar and dictionary driven approach to entity recognition

Science.gov (United States)

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

Dictionary Learning on the Manifold of Square Root Densities and Application to Reconstruction of Diffusion Propagator Fields*

Science.gov (United States)

Sun, Jiaqi; Xie, Yuchen; Ye, Wenxing; Ho, Jeffrey; Entezari, Alireza; Blackband, Stephen J.

2013-01-01

In this paper, we present a novel dictionary learning framework for data lying on the manifold of square root densities and apply it to the reconstruction of diffusion propagator (DP) fields given a multi-shell diffusion MRI data set. Unlike most of the existing dictionary learning algorithms which rely on the assumption that the data points are vectors in some Euclidean space, our dictionary learning algorithm is designed to incorporate the intrinsic geometric structure of manifolds and performs better than traditional dictionary learning approaches when applied to data lying on the manifold of square root densities. Non-negativity as well as smoothness across the whole field of the reconstructed DPs is guaranteed in our approach. We demonstrate the advantage of our approach by comparing it with an existing dictionary based reconstruction method on synthetic and real multi-shell MRI data. PMID:24684004

Basis Expansion Approaches for Regularized Sequential Dictionary Learning Algorithms With Enforced Sparsity for fMRI Data Analysis.

Science.gov (United States)

Seghouane, Abd-Krim; Iqbal, Asif

2017-09-01

Sequential dictionary learning algorithms have been successfully applied to functional magnetic resonance imaging (fMRI) data analysis. fMRI data sets are, however, structured data matrices with the notions of temporal smoothness in the column direction. This prior information, which can be converted into a constraint of smoothness on the learned dictionary atoms, has seldomly been included in classical dictionary learning algorithms when applied to fMRI data analysis. In this paper, we tackle this problem by proposing two new sequential dictionary learning algorithms dedicated to fMRI data analysis by accounting for this prior information. These algorithms differ from the existing ones in their dictionary update stage. The steps of this stage are derived as a variant of the power method for computing the SVD. The proposed algorithms generate regularized dictionary atoms via the solution of a left regularized rank-one matrix approximation problem where temporal smoothness is enforced via regularization through basis expansion and sparse basis expansion in the dictionary update stage. Applications on synthetic data experiments and real fMRI data sets illustrating the performance of the proposed algorithms are provided.

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

Science.gov (United States)

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

2014-05-27

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.

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

Directory of Open Access Journals (Sweden)

Zheng-Zhou Li

2014-05-01

Full Text Available 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.

Travel time tomography with local image regularization by sparsity constrained dictionary learning

Science.gov (United States)

Bianco, M.; Gerstoft, P.

2017-12-01

We propose a regularization approach for 2D seismic travel time tomography which models small rectangular groups of slowness pixels, within an overall or `global' slowness image, as sparse linear combinations of atoms from a dictionary. The groups of slowness pixels are referred to as patches and a dictionary corresponds to a collection of functions or `atoms' describing the slowness in each patch. These functions could for example be wavelets.The patch regularization is incorporated into the global slowness image. The global image models the broad features, while the local patch images incorporate prior information from the dictionary. Further, high resolution slowness within patches is permitted if the travel times from the global estimates support it. The proposed approach is formulated as an algorithm, which is repeated until convergence is achieved: 1) From travel times, find the global slowness image with a minimum energy constraint on the pixel variance relative to a reference. 2) Find the patch level solutions to fit the global estimate as a sparse linear combination of dictionary atoms.3) Update the reference as the weighted average of the patch level solutions.This approach relies on the redundancy of the patches in the seismic image. Redundancy means that the patches are repetitions of a finite number of patterns, which are described by the dictionary atoms. Redundancy in the earth's structure was demonstrated in previous works in seismics where dictionaries of wavelet functions regularized inversion. We further exploit redundancy of the patches by using dictionary learning algorithms, a form of unsupervised machine learning, to estimate optimal dictionaries from the data in parallel with the inversion. We demonstrate our approach on densely, but irregularly sampled synthetic seismic images.

An Integrated Dictionary-Learning Entropy-Based Medical Image Fusion Framework

Directory of Open Access Journals (Sweden)

Guanqiu Qi

2017-10-01

Full Text Available Image fusion is widely used in different areas and can integrate complementary and relevant information of source images captured by multiple sensors into a unitary synthetic image. Medical image fusion, as an important image fusion application, can extract the details of multiple images from different imaging modalities and combine them into an image that contains complete and non-redundant information for increasing the accuracy of medical diagnosis and assessment. The quality of the fused image directly affects medical diagnosis and assessment. However, existing solutions have some drawbacks in contrast, sharpness, brightness, blur and details. This paper proposes an integrated dictionary-learning and entropy-based medical image-fusion framework that consists of three steps. First, the input image information is decomposed into low-frequency and high-frequency components by using a Gaussian filter. Second, low-frequency components are fused by weighted average algorithm and high-frequency components are fused by the dictionary-learning based algorithm. In the dictionary-learning process of high-frequency components, an entropy-based algorithm is used for informative blocks selection. Third, the fused low-frequency and high-frequency components are combined to obtain the final fusion results. The results and analyses of comparative experiments demonstrate that the proposed medical image fusion framework has better performance than existing solutions.

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

Science.gov (United States)

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

2014-07-01

Automatic prostate segmentation from MR images is an important task in various clinical applications such as prostate cancer staging and MR-guided radiotherapy planning. However, the large appearance and shape variations of the prostate in MR images make the segmentation problem difficult to solve. Traditional Active Shape/Appearance Model (ASM/AAM) has limited accuracy on this problem, since its basic assumption, i.e., both shape and appearance of the targeted organ follow Gaussian distributions, is invalid in prostate MR images. To this end, the authors propose a sparse dictionary learning method to model the image appearance in a nonparametric fashion and further integrate the appearance model into a deformable segmentation framework for prostate MR segmentation. To drive the deformable model for prostate segmentation, the authors propose nonparametric appearance and shape models. The nonparametric appearance model is based on a novel dictionary learning method, namely distributed discriminative dictionary (DDD) learning, which is able to capture fine distinctions in image appearance. To increase the differential power of traditional dictionary-based classification methods, the authors' DDD learning approach takes three strategies. First, two dictionaries for prostate and nonprostate tissues are built, respectively, using the discriminative features obtained from minimum redundancy maximum relevance feature selection. Second, linear discriminant analysis is employed as a linear classifier to boost the optimal separation between prostate and nonprostate tissues, based on the representation residuals from sparse representation. Third, to enhance the robustness of the authors' classification method, multiple local dictionaries are learned for local regions along the prostate boundary (each with small appearance variations), instead of learning one global classifier for the entire prostate. These discriminative dictionaries are located on different patches of the

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

Science.gov (United States)

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

2014-01-01

Purpose: Automatic prostate segmentation from MR images is an important task in various clinical applications such as prostate cancer staging and MR-guided radiotherapy planning. However, the large appearance and shape variations of the prostate in MR images make the segmentation problem difficult to solve. Traditional Active Shape/Appearance Model (ASM/AAM) has limited accuracy on this problem, since its basic assumption, i.e., both shape and appearance of the targeted organ follow Gaussian distributions, is invalid in prostate MR images. To this end, the authors propose a sparse dictionary learning method to model the image appearance in a nonparametric fashion and further integrate the appearance model into a deformable segmentation framework for prostate MR segmentation. Methods: To drive the deformable model for prostate segmentation, the authors propose nonparametric appearance and shape models. The nonparametric appearance model is based on a novel dictionary learning method, namely distributed discriminative dictionary (DDD) learning, which is able to capture fine distinctions in image appearance. To increase the differential power of traditional dictionary-based classification methods, the authors' DDD learning approach takes three strategies. First, two dictionaries for prostate and nonprostate tissues are built, respectively, using the discriminative features obtained from minimum redundancy maximum relevance feature selection. Second, linear discriminant analysis is employed as a linear classifier to boost the optimal separation between prostate and nonprostate tissues, based on the representation residuals from sparse representation. Third, to enhance the robustness of the authors' classification method, multiple local dictionaries are learned for local regions along the prostate boundary (each with small appearance variations), instead of learning one global classifier for the entire prostate. These discriminative dictionaries are located on different

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

International Nuclear Information System (INIS)

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

2014-01-01

Purpose: Automatic prostate segmentation from MR images is an important task in various clinical applications such as prostate cancer staging and MR-guided radiotherapy planning. However, the large appearance and shape variations of the prostate in MR images make the segmentation problem difficult to solve. Traditional Active Shape/Appearance Model (ASM/AAM) has limited accuracy on this problem, since its basic assumption, i.e., both shape and appearance of the targeted organ follow Gaussian distributions, is invalid in prostate MR images. To this end, the authors propose a sparse dictionary learning method to model the image appearance in a nonparametric fashion and further integrate the appearance model into a deformable segmentation framework for prostate MR segmentation. Methods: To drive the deformable model for prostate segmentation, the authors propose nonparametric appearance and shape models. The nonparametric appearance model is based on a novel dictionary learning method, namely distributed discriminative dictionary (DDD) learning, which is able to capture fine distinctions in image appearance. To increase the differential power of traditional dictionary-based classification methods, the authors' DDD learning approach takes three strategies. First, two dictionaries for prostate and nonprostate tissues are built, respectively, using the discriminative features obtained from minimum redundancy maximum relevance feature selection. Second, linear discriminant analysis is employed as a linear classifier to boost the optimal separation between prostate and nonprostate tissues, based on the representation residuals from sparse representation. Third, to enhance the robustness of the authors' classification method, multiple local dictionaries are learned for local regions along the prostate boundary (each with small appearance variations), instead of learning one global classifier for the entire prostate. These discriminative dictionaries are located on

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

Energy Technology Data Exchange (ETDEWEB)

Guo, Yanrong; Shao, Yeqin [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina 27599 (United States); Gao, Yaozong; Price, True [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina 27599 and Department of Computer Science, University of North Carolina at Chapel Hill, North Carolina 27599 (United States); Oto, Aytekin [Department of Radiology, Section of Urology, University of Chicago, Illinois 60637 (United States); Shen, Dinggang, E-mail: dgshen@med.unc.edu [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina 27599 and Department of Brain and Cognitive Engineering, Korea University, Seoul 136-713 (Korea, Republic of)

2014-07-15

Purpose: Automatic prostate segmentation from MR images is an important task in various clinical applications such as prostate cancer staging and MR-guided radiotherapy planning. However, the large appearance and shape variations of the prostate in MR images make the segmentation problem difficult to solve. Traditional Active Shape/Appearance Model (ASM/AAM) has limited accuracy on this problem, since its basic assumption, i.e., both shape and appearance of the targeted organ follow Gaussian distributions, is invalid in prostate MR images. To this end, the authors propose a sparse dictionary learning method to model the image appearance in a nonparametric fashion and further integrate the appearance model into a deformable segmentation framework for prostate MR segmentation. Methods: To drive the deformable model for prostate segmentation, the authors propose nonparametric appearance and shape models. The nonparametric appearance model is based on a novel dictionary learning method, namely distributed discriminative dictionary (DDD) learning, which is able to capture fine distinctions in image appearance. To increase the differential power of traditional dictionary-based classification methods, the authors' DDD learning approach takes three strategies. First, two dictionaries for prostate and nonprostate tissues are built, respectively, using the discriminative features obtained from minimum redundancy maximum relevance feature selection. Second, linear discriminant analysis is employed as a linear classifier to boost the optimal separation between prostate and nonprostate tissues, based on the representation residuals from sparse representation. Third, to enhance the robustness of the authors' classification method, multiple local dictionaries are learned for local regions along the prostate boundary (each with small appearance variations), instead of learning one global classifier for the entire prostate. These discriminative dictionaries are located on

Self-expressive Dictionary Learning for Dynamic 3D Reconstruction.

Science.gov (United States)

Zheng, Enliang; Ji, Dinghuang; Dunn, Enrique; Frahm, Jan-Michael

2017-08-22

We target the problem of sparse 3D reconstruction of dynamic objects observed by multiple unsynchronized video cameras with unknown temporal overlap. To this end, we develop a framework to recover the unknown structure without sequencing information across video sequences. Our proposed compressed sensing framework poses the estimation of 3D structure as the problem of dictionary learning, where the dictionary is defined as an aggregation of the temporally varying 3D structures. Given the smooth motion of dynamic objects, we observe any element in the dictionary can be well approximated by a sparse linear combination of other elements in the same dictionary (i.e. self-expression). Our formulation optimizes a biconvex cost function that leverages a compressed sensing formulation and enforces both structural dependency coherence across video streams, as well as motion smoothness across estimates from common video sources. We further analyze the reconstructability of our approach under different capture scenarios, and its comparison and relation to existing methods. Experimental results on large amounts of synthetic data as well as real imagery demonstrate the effectiveness of our approach.

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

Science.gov (United States)

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

2016-02-27

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.

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

Science.gov (United States)

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.

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

International Nuclear Information System (INIS)

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

2015-01-01

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

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

Energy Technology Data Exchange (ETDEWEB)

Xu, Q [School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi 710049 (China); Department of Radiation Oncology, Stanford University, Stanford, CA 94305 (United States); Xing, L [Department of Radiation Oncology, Stanford University, Stanford, CA 94305 (United States); Xiong, G; Elmore, K; Min, J [Dalio Institute of Cardiovascular Imaging, New York-Presbyterian Hospital and Weill Cornell Medical College, New York, NY (United States)

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.

Tensor-based Dictionary Learning for Spectral CT Reconstruction

Science.gov (United States)

Zhang, Yanbo; Wang, Ge

2016-01-01

Spectral computed tomography (CT) produces an energy-discriminative attenuation map of an object, extending a conventional image volume with a spectral dimension. In spectral CT, an image can be sparsely represented in each of multiple energy channels, and are highly correlated among energy channels. According to this characteristics, we propose a tensor-based dictionary learning method for spectral CT reconstruction. In our method, tensor patches are extracted from an image tensor, which is reconstructed using the filtered backprojection (FBP), to form a training dataset. With the Candecomp/Parafac decomposition, a tensor-based dictionary is trained, in which each atom is a rank-one tensor. Then, the trained dictionary is used to sparsely represent image tensor patches during an iterative reconstruction process, and the alternating minimization scheme is adapted for optimization. The effectiveness of our proposed method is validated with both numerically simulated and real preclinical mouse datasets. The results demonstrate that the proposed tensor-based method generally produces superior image quality, and leads to more accurate material decomposition than the currently popular popular methods. PMID:27541628

Tensor-Based Dictionary Learning for Spectral CT Reconstruction.

Science.gov (United States)

Zhang, Yanbo; Mou, Xuanqin; Wang, Ge; Yu, Hengyong

2017-01-01

Spectral computed tomography (CT) produces an energy-discriminative attenuation map of an object, extending a conventional image volume with a spectral dimension. In spectral CT, an image can be sparsely represented in each of multiple energy channels, and are highly correlated among energy channels. According to this characteristics, we propose a tensor-based dictionary learning method for spectral CT reconstruction. In our method, tensor patches are extracted from an image tensor, which is reconstructed using the filtered backprojection (FBP), to form a training dataset. With the Candecomp/Parafac decomposition, a tensor-based dictionary is trained, in which each atom is a rank-one tensor. Then, the trained dictionary is used to sparsely represent image tensor patches during an iterative reconstruction process, and the alternating minimization scheme is adapted for optimization. The effectiveness of our proposed method is validated with both numerically simulated and real preclinical mouse datasets. The results demonstrate that the proposed tensor-based method generally produces superior image quality, and leads to more accurate material decomposition than the currently popular popular methods.

Multi-View Multi-Instance Learning Based on Joint Sparse Representation and Multi-View Dictionary Learning.

Science.gov (United States)

Li, Bing; Yuan, Chunfeng; Xiong, Weihua; Hu, Weiming; Peng, Houwen; Ding, Xinmiao; Maybank, Steve

2017-12-01

In multi-instance learning (MIL), the relations among instances in a bag convey important contextual information in many applications. Previous studies on MIL either ignore such relations or simply model them with a fixed graph structure so that the overall performance inevitably degrades in complex environments. To address this problem, this paper proposes a novel multi-view multi-instance learning algorithm (MIL) that combines multiple context structures in a bag into a unified framework. The novel aspects are: (i) we propose a sparse -graph model that can generate different graphs with different parameters to represent various context relations in a bag, (ii) we propose a multi-view joint sparse representation that integrates these graphs into a unified framework for bag classification, and (iii) we propose a multi-view dictionary learning algorithm to obtain a multi-view graph dictionary that considers cues from all views simultaneously to improve the discrimination of the MIL. Experiments and analyses in many practical applications prove the effectiveness of the M IL.

Three-dimensional dictionary-learning reconstruction of (23)Na MRI data.

Science.gov (United States)

Behl, Nicolas G R; Gnahm, Christine; Bachert, Peter; Ladd, Mark E; Nagel, Armin M

2016-04-01

To reduce noise and artifacts in (23)Na MRI with a Compressed Sensing reconstruction and a learned dictionary as sparsifying transform. A three-dimensional dictionary-learning compressed sensing reconstruction algorithm (3D-DLCS) for the reconstruction of undersampled 3D radial (23)Na data is presented. The dictionary used as the sparsifying transform is learned with a K-singular-value-decomposition (K-SVD) algorithm. The reconstruction parameters are optimized on simulated data, and the quality of the reconstructions is assessed with peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). The performance of the algorithm is evaluated in phantom and in vivo (23)Na MRI data of seven volunteers and compared with nonuniform fast Fourier transform (NUFFT) and other Compressed Sensing reconstructions. The reconstructions of simulated data have maximal PSNR and SSIM for an undersampling factor (USF) of 10 with numbers of averages equal to the USF. For 10-fold undersampling, the PSNR is increased by 5.1 dB compared with the NUFFT reconstruction, and the SSIM by 24%. These results are confirmed by phantom and in vivo (23)Na measurements in the volunteers that show markedly reduced noise and undersampling artifacts in the case of 3D-DLCS reconstructions. The 3D-DLCS algorithm enables precise reconstruction of undersampled (23)Na MRI data with markedly reduced noise and artifact levels compared with NUFFT reconstruction. Small structures are well preserved. © 2015 Wiley Periodicals, Inc.

Learning multimodal dictionaries.

Science.gov (United States)

Monaci, Gianluca; Jost, Philippe; Vandergheynst, Pierre; Mailhé, Boris; Lesage, Sylvain; Gribonval, Rémi

2007-09-01

Real-world phenomena involve complex interactions between multiple signal modalities. As a consequence, humans are used to integrate at each instant perceptions from all their senses in order to enrich their understanding of the surrounding world. This paradigm can be also extremely useful in many signal processing and computer vision problems involving mutually related signals. The simultaneous processing of multimodal data can, in fact, reveal information that is otherwise hidden when considering the signals independently. However, in natural multimodal signals, the statistical dependencies between modalities are in general not obvious. Learning fundamental multimodal patterns could offer deep insight into the structure of such signals. In this paper, we present a novel model of multimodal signals based on their sparse decomposition over a dictionary of multimodal structures. An algorithm for iteratively learning multimodal generating functions that can be shifted at all positions in the signal is proposed, as well. The learning is defined in such a way that it can be accomplished by iteratively solving a generalized eigenvector problem, which makes the algorithm fast, flexible, and free of user-defined parameters. The proposed algorithm is applied to audiovisual sequences and it is able to discover underlying structures in the data. The detection of such audio-video patterns in audiovisual clips allows to effectively localize the sound source on the video in presence of substantial acoustic and visual distractors, outperforming state-of-the-art audiovisual localization algorithms.

Learning Words from Context and Dictionaries: An Experimental Comparison.

Science.gov (United States)

Fischer, Ute

1994-01-01

Investigated the independent and interactive effects of contextual and definitional information on vocabulary learning. German students of English received either a text with unfamiliar English words or their monolingual English dictionary entries. A third group received both. Information about word context is crucial to understanding meaning. (44…

Segmentation of MR images via discriminative dictionary learning and sparse coding: Application to hippocampus labeling

OpenAIRE

Tong, Tong; Wolz, Robin; Coupe, Pierrick; Hajnal, Joseph V.; Rueckert, Daniel

2013-01-01

International audience; 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 labe...

Super resolution reconstruction of infrared images based on classified dictionary learning

Science.gov (United States)

Liu, Fei; Han, Pingli; Wang, Yi; Li, Xuan; Bai, Lu; Shao, Xiaopeng

2018-05-01

Infrared images always suffer from low-resolution problems resulting from limitations of imaging devices. An economical approach to combat this problem involves reconstructing high-resolution images by reasonable methods without updating devices. Inspired by compressed sensing theory, this study presents and demonstrates a Classified Dictionary Learning method to reconstruct high-resolution infrared images. It classifies features of the samples into several reasonable clusters and trained a dictionary pair for each cluster. The optimal pair of dictionaries is chosen for each image reconstruction and therefore, more satisfactory results is achieved without the increase in computational complexity and time cost. Experiments and results demonstrated that it is a viable method for infrared images reconstruction since it improves image resolution and recovers detailed information of targets.

An efficient dictionary learning algorithm and its application to 3-D medical image denoising.

Science.gov (United States)

Li, Shutao; Fang, Leyuan; Yin, Haitao

2012-02-01

In this paper, we propose an efficient dictionary learning algorithm for sparse representation of given data and suggest a way to apply this algorithm to 3-D medical image denoising. Our learning approach is composed of two main parts: sparse coding and dictionary updating. On the sparse coding stage, an efficient algorithm named multiple clusters pursuit (MCP) is proposed. The MCP first applies a dictionary structuring strategy to cluster the atoms with high coherence together, and then employs a multiple-selection strategy to select several competitive atoms at each iteration. These two strategies can greatly reduce the computation complexity of the MCP and assist it to obtain better sparse solution. On the dictionary updating stage, the alternating optimization that efficiently approximates the singular value decomposition is introduced. Furthermore, in the 3-D medical image denoising application, a joint 3-D operation is proposed for taking the learning capabilities of the presented algorithm to simultaneously capture the correlations within each slice and correlations across the nearby slices, thereby obtaining better denoising results. The experiments on both synthetically generated data and real 3-D medical images demonstrate that the proposed approach has superior performance compared to some well-known methods. © 2011 IEEE

Sinogram denoising via simultaneous sparse representation in learned dictionaries

International Nuclear Information System (INIS)

Karimi, Davood; Ward, Rabab K

2016-01-01

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. (paper)

The Role of Dictionaries in Language Learning.

Science.gov (United States)

White, Philip A.

1997-01-01

Examines assumptions about dictionaries, especially the bilingual dictionary, and suggests ways of integrating the monolingual dictionary into the second-language instructional process. Findings indicate that the monolingual dictionary can coexist with bilingual dictionaries within a foreign-language course if the latter are appropriately used as…

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

Science.gov (United States)

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.…

Online Dictionary Learning Aided Target Recognition In Cognitive GPR

OpenAIRE

Giovanneschi, Fabio; Mishra, Kumar Vijay; Gonzalez-Huici, Maria Antonia; Eldar, Yonina C.; Ender, Joachim H. G.

2017-01-01

Sparse decomposition of ground penetration radar (GPR) signals facilitates the use of compressed sensing techniques for faster data acquisition and enhanced feature extraction for target classification. In this paper, we investigate the application of an online dictionary learning (ODL) technique in the context of GPR to bring down the learning time as well as improve identification of abandoned anti-personnel landmines. Our experimental results using real data from an L-band GPR for PMN/PMA2...

Generating a Spanish Affective Dictionary with Supervised Learning Techniques

Science.gov (United States)

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…

3D Reconstruction of human bones based on dictionary learning.

Science.gov (United States)

Zhang, Binkai; Wang, Xiang; Liang, Xiao; Zheng, Jinjin

2017-11-01

An effective method for reconstructing a 3D model of human bones from computed tomography (CT) image data based on dictionary learning is proposed. In this study, the dictionary comprises the vertices of triangular meshes, and the sparse coefficient matrix indicates the connectivity information. For better reconstruction performance, we proposed a balance coefficient between the approximation and regularisation terms and a method for optimisation. Moreover, we applied a local updating strategy and a mesh-optimisation method to update the dictionary and the sparse matrix, respectively. The two updating steps are iterated alternately until the objective function converges. Thus, a reconstructed mesh could be obtained with high accuracy and regularisation. The experimental results show that the proposed method has the potential to obtain high precision and high-quality triangular meshes for rapid prototyping, medical diagnosis, and tissue engineering. Copyright © 2017 IPEM. Published by Elsevier Ltd. All rights reserved.

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

International Nuclear Information System (INIS)

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. (paper)

Dictionary learning for data recovery in positron emission tomography

International Nuclear Information System (INIS)

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

2015-01-01

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. (paper)

Teaching Dictionary Skills through a Slang Dictionary.

Science.gov (United States)

Steed, Stanley M.

A unit for teaching dictionary skills through the compilation of a slang dictionary was written with the purpose of providing an inductive learning situation. The students are to begin by defining slang usage and bringing in slang words and definitions on cards. Small groups are to be formed to evaluate the definitions and make additions. In…

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

Science.gov (United States)

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…

Building a RAPPOR with the Unknown: Privacy-Preserving Learning of Associations and Data Dictionaries

Directory of Open Access Journals (Sweden)

Fanti Giulia

2016-07-01

Full Text Available Techniques based on randomized response enable the collection of potentially sensitive data from clients in a privacy-preserving manner with strong local differential privacy guarantees. A recent such technology, RAPPOR [12], enables estimation of the marginal frequencies of a set of strings via privacy-preserving crowdsourcing. However, this original estimation process relies on a known dictionary of possible strings; in practice, this dictionary can be extremely large and/or unknown. In this paper, we propose a novel decoding algorithm for the RAPPOR mechanism that enables the estimation of “unknown unknowns,” i.e., strings we do not know we should be estimating. To enable learning without explicit dictionary knowledge, we develop methodology for estimating the joint distribution of multiple variables collected with RAPPOR. Our contributions are not RAPPOR-specific, and can be generalized to other local differential privacy mechanisms for learning distributions of string-valued random variables.

Noise-aware dictionary-learning-based sparse representation framework for detection and removal of single and combined noises from ECG signal.

Science.gov (United States)

Satija, Udit; Ramkumar, Barathram; Sabarimalai Manikandan, M

2017-02-01

Automatic electrocardiogram (ECG) signal enhancement has become a crucial pre-processing step in most ECG signal analysis applications. In this Letter, the authors propose an automated noise-aware dictionary learning-based generalised ECG signal enhancement framework which can automatically learn the dictionaries based on the ECG noise type for effective representation of ECG signal and noises, and can reduce the computational load of sparse representation-based ECG enhancement system. The proposed framework consists of noise detection and identification, noise-aware dictionary learning, sparse signal decomposition and reconstruction. The noise detection and identification is performed based on the moving average filter, first-order difference, and temporal features such as number of turning points, maximum absolute amplitude, zerocrossings, and autocorrelation features. The representation dictionary is learned based on the type of noise identified in the previous stage. The proposed framework is evaluated using noise-free and noisy ECG signals. Results demonstrate that the proposed method can significantly reduce computational load as compared with conventional dictionary learning-based ECG denoising approaches. Further, comparative results show that the method outperforms existing methods in automatically removing noises such as baseline wanders, power-line interference, muscle artefacts and their combinations without distorting the morphological content of local waves of ECG signal.

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

International Nuclear Information System (INIS)

Valiollahzadeh, S; Clark, J; Mawlawi, O

2014-01-01

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

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

Energy Technology Data Exchange (ETDEWEB)

Valiollahzadeh, S [RICE University, Houston, Tx (United States); Clark, J [MD Anderson Cancer Ctr., Houston, TX (United States); 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.

Sparsity and Nullity: Paradigm for Analysis Dictionary Learning

Science.gov (United States)

2016-08-09

applied mathematics , on account of its theoretical complexity, and its high relevance to big data problems. Dictionary learning has been one of the key...and hence rank(Ni ⊕ ni) = rank(Ni) + 1. There are two inherent difficulties in this formulation. First, ‖ · ‖0 is of combinatorial nature, hence the...from incomplete and inaccurate measurements, Communications on pure and applied mathematics , 59 (2006), pp. 1207–1223. [8] Thomas F Coleman and Alex

Shape prior modeling using sparse representation and online dictionary learning.

Science.gov (United States)

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.

Dictionary of Microscopy

Science.gov (United States)

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.

Pocket Electronic Dictionaries for Second Language Learning: Help or Hindrance?

Science.gov (United States)

Tang, Gloria M.

1997-01-01

Reports on the concerns of English-as-a-Second-Language (ESL) teachers in Canada regarding their students' use of pocket bilingual electronic dictionaries (EDs). The article highlights the ED's features, uses, and effectiveness as a tool for learning ESL at the secondary level and ESL students' perceptions of the ED's usefulness. (nine references)…

Discriminative Structured Dictionary Learning on Grassmann Manifolds and Its Application on Image Restoration.

Science.gov (United States)

Pan, Han; Jing, Zhongliang; Qiao, Lingfeng; Li, Minzhe

2017-09-25

Image restoration is a difficult and challenging problem in various imaging applications. However, despite of the benefits of a single overcomplete dictionary, there are still several challenges for capturing the geometric structure of image of interest. To more accurately represent the local structures of the underlying signals, we propose a new problem formulation for sparse representation with block-orthogonal constraint. There are three contributions. First, a framework for discriminative structured dictionary learning is proposed, which leads to a smooth manifold structure and quotient search spaces. Second, an alternating minimization scheme is proposed after taking both the cost function and the constraints into account. This is achieved by iteratively alternating between updating the block structure of the dictionary defined on Grassmann manifold and sparsifying the dictionary atoms automatically. Third, Riemannian conjugate gradient is considered to track local subspaces efficiently with a convergence guarantee. Extensive experiments on various datasets demonstrate that the proposed method outperforms the state-of-the-art methods on the removal of mixed Gaussian-impulse noise.

Graded Lexicons: New Resources for Educational Purposes and Much More

Science.gov (United States)

Gala, Núria; Billami, Mokhtar B.; François, Thomas; Bernhard, Delphine

2015-01-01

Computational tools and resources play an important role for vocabulary acquisition. Although a large variety of dictionaries and learning games are available, few resources provide information about the complexity of a word, either for learning or for comprehension. The idea here is to use frequency counts combined with intralexical variables to…

Overcoming complexities: Damage detection using dictionary learning framework

Science.gov (United States)

Alguri, K. Supreet; Melville, Joseph; Deemer, Chris; Harley, Joel B.

2018-04-01

For in situ damage detection, guided wave structural health monitoring systems have been widely researched due to their ability to evaluate large areas and their ability detect many types of damage. These systems often evaluate structural health by recording initial baseline measurements from a pristine (i.e., undamaged) test structure and then comparing later measurements with that baseline. Yet, it is not always feasible to have a pristine baseline. As an alternative, substituting the baseline with data from a surrogate (nearly identical and pristine) structure is a logical option. While effective in some circumstance, surrogate data is often still a poor substitute for pristine baseline measurements due to minor differences between the structures. To overcome this challenge, we present a dictionary learning framework to adapt surrogate baseline data to better represent an undamaged test structure. We compare the performance of our framework with two other surrogate-based damage detection strategies: (1) using raw surrogate data for comparison and (2) using sparse wavenumber analysis, a precursor to our framework for improving the surrogate data. We apply our framework to guided wave data from two 108 mm by 108 mm aluminum plates. With 20 measurements, we show that our dictionary learning framework achieves a 98% accuracy, raw surrogate data achieves a 92% accuracy, and sparse wavenumber analysis achieves a 57% accuracy.

Web Resources and Tools for Slovenian with a Focus on the Slovenian-English Language Infrastructure: Dictionaries in the Digital Age

Directory of Open Access Journals (Sweden)

Mojca Šorli

2017-12-01

Full Text Available The article begins with a presentation of a selection of electronic monolingual and bi/multilingual lexicographic resources and corpora available today to contemporary users of Slovene. The focus is on works combined with English and designed for translation purposes which provide information on the meaning of words and wider lexical units, i.e., e-dictionaries, lexical databases, web translation tools and various corpora. In a separate sub-section the most common translation technologies are presented, together with an evaluation of their role in the modern translation process. Sections 2 and 3 provide a brief outline of the changes that have affected classical dictionary planning, compilation and use in the new digital environment, as well as of the relationship between dictionaries and related resources, such as lexical databases. Some stereotypes regarding dictionary use are identified and, in conclusion, the existing corpus-based databases for the Slovenian-English pair are presented, with a view to determining priorities for the future interlingual infrastructure action plans in Slovenia.

MO-DE-207A-05: Dictionary Learning Based Reconstruction with Low-Rank Constraint for Low-Dose Spectral CT

International Nuclear Information System (INIS)

Xu, Q; Liu, H; Xing, L; Yu, H; Wang, G

2016-01-01

Purpose: Spectral CT enabled by an energy-resolved photon-counting detector outperforms conventional CT in terms of material discrimination, contrast resolution, etc. One reconstruction method for spectral CT is to generate a color image from a reconstructed component in each energy channel. However, given the radiation dose, the number of photons in each channel is limited, which will result in strong noise in each channel and affect the final color reconstruction. Here we propose a novel dictionary learning method for spectral CT that combines dictionary-based sparse representation method and the patch based low-rank constraint to simultaneously improve the reconstruction in each channel and to address the inter-channel correlations to further improve the reconstruction. Methods: The proposed method has two important features: (1) guarantee of the patch based sparsity in each energy channel, which is the result of the dictionary based sparse representation constraint; (2) the explicit consideration of the correlations among different energy channels, which is realized by patch-by-patch nuclear norm-based low-rank constraint. For each channel, the dictionary consists of two sub-dictionaries. One is learned from the average of the images in all energy channels, and the other is learned from the average of the images in all energy channels except the current channel. With average operation to reduce noise, these two dictionaries can effectively preserve the structural details and get rid of artifacts caused by noise. Combining them together can express all structural information in current channel. Results: Dictionary learning based methods can obtain better results than FBP and the TV-based method. With low-rank constraint, the image quality can be further improved in the channel with more noise. The final color result by the proposed method has the best visual quality. Conclusion: The proposed method can effectively improve the image quality of low-dose spectral

MO-DE-207A-05: Dictionary Learning Based Reconstruction with Low-Rank Constraint for Low-Dose Spectral CT

Energy Technology Data Exchange (ETDEWEB)

Xu, Q [Xi’an Jiaotong University, Xi’an (China); Stanford University School of Medicine, Stanford, CA (United States); Liu, H; Xing, L [Stanford University School of Medicine, Stanford, CA (United States); Yu, H [University of Massachusetts Lowell, Lowell, MA (United States); Wang, G [Rensselaer Polytechnic Instute., Troy, NY (United States)

2016-06-15

Purpose: Spectral CT enabled by an energy-resolved photon-counting detector outperforms conventional CT in terms of material discrimination, contrast resolution, etc. One reconstruction method for spectral CT is to generate a color image from a reconstructed component in each energy channel. However, given the radiation dose, the number of photons in each channel is limited, which will result in strong noise in each channel and affect the final color reconstruction. Here we propose a novel dictionary learning method for spectral CT that combines dictionary-based sparse representation method and the patch based low-rank constraint to simultaneously improve the reconstruction in each channel and to address the inter-channel correlations to further improve the reconstruction. Methods: The proposed method has two important features: (1) guarantee of the patch based sparsity in each energy channel, which is the result of the dictionary based sparse representation constraint; (2) the explicit consideration of the correlations among different energy channels, which is realized by patch-by-patch nuclear norm-based low-rank constraint. For each channel, the dictionary consists of two sub-dictionaries. One is learned from the average of the images in all energy channels, and the other is learned from the average of the images in all energy channels except the current channel. With average operation to reduce noise, these two dictionaries can effectively preserve the structural details and get rid of artifacts caused by noise. Combining them together can express all structural information in current channel. Results: Dictionary learning based methods can obtain better results than FBP and the TV-based method. With low-rank constraint, the image quality can be further improved in the channel with more noise. The final color result by the proposed method has the best visual quality. Conclusion: The proposed method can effectively improve the image quality of low-dose spectral

Recent research in data description of the measurement property resource on common data dictionary

Science.gov (United States)

Lu, Tielin; Fan, Zitian; Wang, Chunxi; Liu, Xiaojing; Wang, Shuo; Zhao, Hua

2018-03-01

A method for measurement equipment data description has been proposed based on the property resource analysis. The applications of common data dictionary (CDD) to devices and equipment is mainly used in digital factory to advance the management not only in the enterprise, also to the different enterprise in the same data environment. In this paper, we can make a brief of the data flow in the whole manufacture enterprise and the automatic trigger the process of the data exchange. Furthermore,the application of the data dictionary is available for the measurement and control equipment, which can also be used in other different industry in smart manufacture.

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

Science.gov (United States)

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.

Direct user guidance in e-dictionaries for text production and text ...

African Journals Online (AJOL)

This article introduces a prototype of a writing (and learning) assistant for verbal relative clauses of the African language Sepedi, accessible from within a dictionary or from a word processor. It is an example of how a user support tool for complicated grammatical structures in a scarcely resourced language can be compiled.

Could a multimodal dictionary serve as a learning tool? An examination of the impact of technologically enhanced visual glosses on L2 text comprehension

Directory of Open Access Journals (Sweden)

Takeshi Sato

2016-09-01

Full Text Available This study examines the efficacy of a multimodal online bilingual dictionary based on cognitive linguistics in order to explore the advantages and limitations of explicit multimodal L2 vocabulary learning. Previous studies have examined the efficacy of the verbal and visual representation of words while reading L2 texts, concluding that it facilitates incidental word retention. This study explores other potentials of multimodal L2 vocabulary learning: explicit learning with a multimodal dictionary could enhance not only word retention, but also text comprehension; the dictionary could serve not only as a reference tool, but also as a learning tool; and technology-enhanced visual glosses could facilitate deeper text comprehension. To verify these claims, this study investigates the multimodal representations’ effects on Japanese students learning L2 locative prepositions by developing two online dictionaries, one with static pictures and one with animations. The findings show the advantage of such dictionaries in explicit learning; however, no significant differences are found between the two types of visual glosses, either in the vocabulary or in the listening tests. This study confirms the effectiveness of multimodal L2 materials, but also emphasizes the need for further research into making the technologically enhanced materials more effective.

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

Science.gov (United States)

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.

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

Science.gov (United States)

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

Classification of multispectral or hyperspectral satellite imagery using clustering of sparse approximations on sparse representations in learned dictionaries obtained using efficient convolutional sparse coding

Science.gov (United States)

Moody, Daniela; Wohlberg, Brendt

2018-01-02

An approach for land cover classification, seasonal and yearly change detection and monitoring, and identification of changes in man-made features may use a clustering of sparse approximations (CoSA) on sparse representations in learned dictionaries. The learned dictionaries may be derived using efficient convolutional sparse coding to build multispectral or hyperspectral, multiresolution dictionaries that are adapted to regional satellite image data. Sparse image representations of images over the learned dictionaries may be used to perform unsupervised k-means clustering into land cover categories. The clustering process behaves as a classifier in detecting real variability. This approach may combine spectral and spatial textural characteristics to detect geologic, vegetative, hydrologic, and man-made features, as well as changes in these features over time.

Implementation of dictionary pair learning algorithm for image quality improvement

Science.gov (United States)

Vimala, C.; Aruna Priya, P.

2018-04-01

This paper proposes an image denoising on dictionary pair learning algorithm. Visual information is transmitted in the form of digital images is becoming a major method of communication in the modern age, but the image obtained after transmissions is often corrupted with noise. The received image needs processing before it can be used in applications. Image denoising involves the manipulation of the image data to produce a visually high quality image.

Use of Monolingual and Bilingual Dictionaries among Students of English

Directory of Open Access Journals (Sweden)

Monika Kavalir

2010-12-01

Full Text Available The study of dictionary use in 32 firstyear students of English at the University of Ljubljana in the academic year 2009/2010 shows that students use a variety of dictionaries with a slight preponderance of monolingual dictionaries over bilingual ones. The bilingual dictionaries listed do not include some of the most recent and most comprehensive dictionaries while some of the most frequently used resources are quite modest sized. The students are already predominantly users of electronic and online dictionaries with a lower frequency of printed resources – a trend which is only likely to accelerate with the advent of new bilingual online dictionaries. These results have practical relevance for teachers in all sectors, from primary and secondary schools to universities, as they point towards a need for additional training in the use of bilingual dictionaries. The transition from printed to electronic and online resources can also be expected to induce changes in EFL methodology at all levels.

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

Science.gov (United States)

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…

Online English-English Learner Dictionaries Boost Word Learning

Science.gov (United States)

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…

JaSlo: Integration of a Japanese-Slovene Bilingual Dictionary with a Corpus Search System

Directory of Open Access Journals (Sweden)

Kristina HMELJAK SANGAWA

2012-12-01

Full Text Available The paper presents a set of integrated on-line language resources targeted at Japanese language learners, primarily those whose mother tongue is Slovene. The resources consist of the on-line Japanese-Slovene learners’ dictionary jaSlo and two corpora, a 1 million word Japanese-Slovene parallel corpus and a 300 million word corpus of web pages, where each word and sentence is marked by its difficulty level; this corpus is furthermore available as a set of five distinct corpora, each one containing sentences of the particular level. The corpora are available for exploration through NoSketch Engine, the open source version of the commercial state-of-the-art corpus analysis software Sketch Engine. The dictionary is available for Web searching, and dictionary entries have direct links to examples from the corpora, thus offering a wider picture of a possible translations in concrete contextualised examples, and b monolingual Japanese usage examples of different difficulty levels to support language learning.

JST Thesaurus Headwords and Synonyms: e‐learning [MeCab user dictionary for science technology term[Archive

Lifescience Database Archive (English)

Full Text Available MeCab user dictionary for science technology term e‐learning 名詞 一般 * * * * ｅラーニング ｅ...ラーニング イーラーニング Thesaurus2015 200906043727726486 C EG01 UNKNOWN_2 e ‐ learning

A Spectral Reconstruction Algorithm of Miniature Spectrometer Based on Sparse Optimization and Dictionary Learning.

Science.gov (United States)

Zhang, Shang; Dong, Yuhan; Fu, Hongyan; Huang, Shao-Lun; Zhang, Lin

2018-02-22

The miniaturization of spectrometer can broaden the application area of spectrometry, which has huge academic and industrial value. Among various miniaturization approaches, filter-based miniaturization is a promising implementation by utilizing broadband filters with distinct transmission functions. Mathematically, filter-based spectral reconstruction can be modeled as solving a system of linear equations. In this paper, we propose an algorithm of spectral reconstruction based on sparse optimization and dictionary learning. To verify the feasibility of the reconstruction algorithm, we design and implement a simple prototype of a filter-based miniature spectrometer. The experimental results demonstrate that sparse optimization is well applicable to spectral reconstruction whether the spectra are directly sparse or not. As for the non-directly sparse spectra, their sparsity can be enhanced by dictionary learning. In conclusion, the proposed approach has a bright application prospect in fabricating a practical miniature spectrometer.

Parametric dictionary learning for modeling EAP and ODF in diffusion MRI.

Science.gov (United States)

Merlet, Sylvain; Caruyer, Emmanuel; Deriche, Rachid

2012-01-01

In this work, we propose an original and efficient approach to exploit the ability of Compressed Sensing (CS) to recover diffusion MRI (dMRI) signals from a limited number of samples while efficiently recovering important diffusion features such as the ensemble average propagator (EAP) and the orientation distribution function (ODF). Some attempts to sparsely represent the diffusion signal have already been performed. However and contrarly to what has been presented in CS dMRI, in this work we propose and advocate the use of a well adapted learned dictionary and show that it leads to a sparser signal estimation as well as to an efficient reconstruction of very important diffusion features. We first propose to learn and design a sparse and parametric dictionary from a set of training diffusion data. Then, we propose a framework to analytically estimate in closed form two important diffusion features: the EAP and the ODF. Various experiments on synthetic, phantom and human brain data have been carried out and promising results with reduced number of atoms have been obtained on diffusion signal reconstruction, thus illustrating the added value of our method over state-of-the-art SHORE and SPF based approaches.

Deep supervised dictionary learning for no-reference image quality assessment

Science.gov (United States)

Huang, Yuge; Liu, Xuesong; Tian, Xiang; Zhou, Fan; Chen, Yaowu; Jiang, Rongxin

2018-03-01

We propose a deep convolutional neural network (CNN) for general no-reference image quality assessment (NR-IQA), i.e., accurate prediction of image quality without a reference image. The proposed model consists of three components such as a local feature extractor that is a fully CNN, an encoding module with an inherent dictionary that aggregates local features to output a fixed-length global quality-aware image representation, and a regression module that maps the representation to an image quality score. Our model can be trained in an end-to-end manner, and all of the parameters, including the weights of the convolutional layers, the dictionary, and the regression weights, are simultaneously learned from the loss function. In addition, the model can predict quality scores for input images of arbitrary sizes in a single step. We tested our method on commonly used image quality databases and showed that its performance is comparable with that of state-of-the-art general-purpose NR-IQA algorithms.

Effective Look-up Techniques to Approach a Monolingual Dictionary

Directory of Open Access Journals (Sweden)

Nauman Al Amin Ali El Sayed

2013-05-01

Full Text Available A dictionary is (a learning tool that can help the language learner in acquiring great knowledge of and about a foreign language. Almost all language learners buy or at least possess, at one time, a monolingual or bilingual dictionary, to which the learner may refer to look up the meaning of words. Unfortunately, using dictionary to look up the meaning of words seems to be the most important service, which a dictionary is expected to provide to language learners. In fact, a dictionary provides much data about language to its readers such as telling them about: the word spelling, phonology, phonetics, etymology, stylistics and definitions among other aspects. This paper sheds light on how the dictionary can teach its readers with special focus on monolingual dictionary. Hence, the discussion of this paper will centre on how dictionaries can teach students rather than on how students can learn from them.

Den Engelske Regnskabsordbog/English Dictionary of Accounting

DEFF Research Database (Denmark)

Nielsen, Sandro; Mourier, Lise; Bergenholtz, Henning

The English Dictionary of Accounting contains about 5.600 accounting terms, both British, American and international (IFRS). The terms are defined and the dictionary gives language information about the terms. The dictionary can be used when writing and reading English accounting texts and when y...... want to learn more about accounting and financial reporting. The dictionary is designed for accountants, auditors, translators, students communication officers and others interested in financial reporting....

A Spectral Reconstruction Algorithm of Miniature Spectrometer Based on Sparse Optimization and Dictionary Learning

Science.gov (United States)

Zhang, Shang; Fu, Hongyan; Huang, Shao-Lun; Zhang, Lin

2018-01-01

The miniaturization of spectrometer can broaden the application area of spectrometry, which has huge academic and industrial value. Among various miniaturization approaches, filter-based miniaturization is a promising implementation by utilizing broadband filters with distinct transmission functions. Mathematically, filter-based spectral reconstruction can be modeled as solving a system of linear equations. In this paper, we propose an algorithm of spectral reconstruction based on sparse optimization and dictionary learning. To verify the feasibility of the reconstruction algorithm, we design and implement a simple prototype of a filter-based miniature spectrometer. The experimental results demonstrate that sparse optimization is well applicable to spectral reconstruction whether the spectra are directly sparse or not. As for the non-directly sparse spectra, their sparsity can be enhanced by dictionary learning. In conclusion, the proposed approach has a bright application prospect in fabricating a practical miniature spectrometer. PMID:29470406

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

Science.gov (United States)

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…

Resources and Resourcefulness in Language Teaching and Learning

African Journals Online (AJOL)

Attempts will be made in this paper to examine what we mean by language, language teaching and learning, resources and resourcefulness in language teaching and learning and the benefit of teachers being resourceful in language teaching and learning to both the learners, the teachers, the society and the nation at ...

Image fusion using sparse overcomplete feature dictionaries

Science.gov (United States)

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.

Evaluating Online Bilingual Dictionaries: The Case of Popular Free English-Polish Dictionaries

Science.gov (United States)

Lew, Robert; Szarowska, Agnieszka

2017-01-01

Language learners today exhibit a strong preference for free online resources. One problem with such resources is that their quality can vary dramatically. Building on related work on monolingual resources for English, we propose an evaluation framework for online bilingual dictionaries, designed to assess lexicographic quality in four major…

Towards a supervised rescoring system for unstructured data bases used to build specialized dictionaries

Directory of Open Access Journals (Sweden)

Antonio Rico-Sulayes

2014-12-01

Full Text Available This article proposes the architecture for a system that uses previously learned weights to sort query results from unstructured data bases when building specialized dictionaries. A common resource in the construction of dictionaries, unstructured data bases have been especially useful in providing information about lexical items frequencies and examples in use. However, when building specialized dictionaries, whose selection of lexical items does not rely on frequency, the use of these data bases gets restricted to a simple provider of examples. Even in this task, the information unstructured data bases provide may not be very useful when looking for specialized uses of lexical items with various meanings and very long lists of results. In the face of this problem, long lists of hits can be rescored based on a supervised learning model that relies on previously helpful results. The allocation of a vast set of high quality training data for this rescoring system is reported here. Finally, the architecture of sucha system,an unprecedented tool in specialized lexicography, is proposed.

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

International Nuclear Information System (INIS)

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

2015-01-01

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. (paper)

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

Science.gov (United States)

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.

Speckle Reduction on Ultrasound Liver Images Based on a Sparse Representation over a Learned Dictionary

Directory of Open Access Journals (Sweden)

Mohamed Yaseen Jabarulla

2018-05-01

Full Text Available Ultrasound images are corrupted with multiplicative noise known as speckle, which reduces the effectiveness of image processing and hampers interpretation. This paper proposes a multiplicative speckle suppression technique for ultrasound liver images, based on a new signal reconstruction model known as sparse representation (SR over dictionary learning. In the proposed technique, the non-uniform multiplicative signal is first converted into additive noise using an enhanced homomorphic filter. This is followed by pixel-based total variation (TV regularization and patch-based SR over a dictionary trained using K-singular value decomposition (KSVD. Finally, the split Bregman algorithm is used to solve the optimization problem and estimate the de-speckled image. The simulations performed on both synthetic and clinical ultrasound images for speckle reduction, the proposed technique achieved peak signal-to-noise ratios of 35.537 dB for the dictionary trained on noisy image patches and 35.033 dB for the dictionary trained using a set of reference ultrasound image patches. Further, the evaluation results show that the proposed method performs better than other state-of-the-art denoising algorithms in terms of both peak signal-to-noise ratio and subjective visual quality assessment.

Dictionary-Based Image Denoising by Fused-Lasso Atom Selection

Directory of Open Access Journals (Sweden)

Ao Li

2014-01-01

Full Text Available We proposed an efficient image denoising scheme by fused lasso with dictionary learning. The scheme has two important contributions. The first one is that we learned the patch-based adaptive dictionary by principal component analysis (PCA with clustering the image into many subsets, which can better preserve the local geometric structure. The second one is that we coded the patches in each subset by fused lasso with the clustering learned dictionary and proposed an iterative Split Bregman to solve it rapidly. We present the capabilities with several experiments. The results show that the proposed scheme is competitive to some excellent denoising algorithms.

Understanding Cognitive Load Using On-line Dictionaries

OpenAIRE

Robert F. , Dilenschneider

2017-01-01

Cognitive Load Theory may useful for language instructors to understand how the look up conditions ofusing an on-line dictionary might influence learning. This paper first reviews previous studies that haveinvestigated dictionary use for vocabulary acquisition and reading comprehension Second, it explainsthe various elements of Cognitive Load Theory. Third, it describes how Cognitive Load Theory appliesto language learners' to learn unknown words and comprehend texts Last, it discusses the pe...

Dictionaries and distributions: Combining expert knowledge and large scale textual data content analysis : Distributed dictionary representation.

Science.gov (United States)

Garten, Justin; Hoover, Joe; Johnson, Kate M; Boghrati, Reihane; Iskiwitch, Carol; Dehghani, Morteza

2018-02-01

Theory-driven text analysis has made extensive use of psychological concept dictionaries, leading to a wide range of important results. These dictionaries have generally been applied through word count methods which have proven to be both simple and effective. In this paper, we introduce Distributed Dictionary Representations (DDR), a method that applies psychological dictionaries using semantic similarity rather than word counts. This allows for the measurement of the similarity between dictionaries and spans of text ranging from complete documents to individual words. We show how DDR enables dictionary authors to place greater emphasis on construct validity without sacrificing linguistic coverage. We further demonstrate the benefits of DDR on two real-world tasks and finally conduct an extensive study of the interaction between dictionary size and task performance. These studies allow us to examine how DDR and word count methods complement one another as tools for applying concept dictionaries and where each is best applied. Finally, we provide references to tools and resources to make this method both available and accessible to a broad psychological audience.

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

International Nuclear Information System (INIS)

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

2014-01-01

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

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

Energy Technology Data Exchange (ETDEWEB)

Bai, T [Xi' an Jiaotong University, Xi' an (China); UT Southwestern Medical Center, Dallas, TX (United States); Yan, H; Shi, F; Jia, X; Jiang, Steve B. [UT Southwestern Medical Center, Dallas, TX (United States); Lou, Y [University of California Irvine, Irvine, CA (United States); Xu, Q; Mou, X [Xi' an Jiaotong University, Xi' an (China)

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

Z-Index Parameterization for Volumetric CT Image Reconstruction via 3-D Dictionary Learning.

Science.gov (United States)

Bai, Ti; Yan, Hao; Jia, Xun; Jiang, Steve; Wang, Ge; Mou, Xuanqin

2017-12-01

Despite the rapid developments of X-ray cone-beam CT (CBCT), image noise still remains a major issue for the low dose CBCT. To suppress the noise effectively while retain the structures well for low dose CBCT image, in this paper, a sparse constraint based on the 3-D dictionary is incorporated into a regularized iterative reconstruction framework, defining the 3-D dictionary learning (3-DDL) method. In addition, by analyzing the sparsity level curve associated with different regularization parameters, a new adaptive parameter selection strategy is proposed to facilitate our 3-DDL method. To justify the proposed method, we first analyze the distributions of the representation coefficients associated with the 3-D dictionary and the conventional 2-D dictionary to compare their efficiencies in representing volumetric images. Then, multiple real data experiments are conducted for performance validation. Based on these results, we found: 1) the 3-D dictionary-based sparse coefficients have three orders narrower Laplacian distribution compared with the 2-D dictionary, suggesting the higher representation efficiencies of the 3-D dictionary; 2) the sparsity level curve demonstrates a clear Z-shape, and hence referred to as Z-curve, in this paper; 3) the parameter associated with the maximum curvature point of the Z-curve suggests a nice parameter choice, which could be adaptively located with the proposed Z-index parameterization (ZIP) method; 4) the proposed 3-DDL algorithm equipped with the ZIP method could deliver reconstructions with the lowest root mean squared errors and the highest structural similarity index compared with the competing methods; 5) similar noise performance as the regular dose FDK reconstruction regarding the standard deviation metric could be achieved with the proposed method using (1/2)/(1/4)/(1/8) dose level projections. The contrast-noise ratio is improved by ~2.5/3.5 times with respect to two different cases under the (1/8) dose level compared

An Electronic Dictionary and Translation System for Murrinh-Patha

Science.gov (United States)

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…

Convolutional Dictionary Learning: Acceleration and Convergence

Science.gov (United States)

Chun, Il Yong; Fessler, Jeffrey A.

2018-04-01

Convolutional dictionary learning (CDL or sparsifying CDL) has many applications in image processing and computer vision. There has been growing interest in developing efficient algorithms for CDL, mostly relying on the augmented Lagrangian (AL) method or the variant alternating direction method of multipliers (ADMM). When their parameters are properly tuned, AL methods have shown fast convergence in CDL. However, the parameter tuning process is not trivial due to its data dependence and, in practice, the convergence of AL methods depends on the AL parameters for nonconvex CDL problems. To moderate these problems, this paper proposes a new practically feasible and convergent Block Proximal Gradient method using a Majorizer (BPG-M) for CDL. The BPG-M-based CDL is investigated with different block updating schemes and majorization matrix designs, and further accelerated by incorporating some momentum coefficient formulas and restarting techniques. All of the methods investigated incorporate a boundary artifacts removal (or, more generally, sampling) operator in the learning model. Numerical experiments show that, without needing any parameter tuning process, the proposed BPG-M approach converges more stably to desirable solutions of lower objective values than the existing state-of-the-art ADMM algorithm and its memory-efficient variant do. Compared to the ADMM approaches, the BPG-M method using a multi-block updating scheme is particularly useful in single-threaded CDL algorithm handling large datasets, due to its lower memory requirement and no polynomial computational complexity. Image denoising experiments show that, for relatively strong additive white Gaussian noise, the filters learned by BPG-M-based CDL outperform those trained by the ADMM approach.

Hand Depth Image Denoising and Superresolution via Noise-Aware Dictionaries

Directory of Open Access Journals (Sweden)

Huayang Li

2016-01-01

Full Text Available This paper proposes a two-stage method for hand depth image denoising and superresolution, using bilateral filters and learned dictionaries via noise-aware orthogonal matching pursuit (NAOMP based K-SVD. The bilateral filtering phase recovers singular points and removes artifacts on silhouettes by averaging depth data using neighborhood pixels on which both depth difference and RGB similarity restrictions are imposed. The dictionary learning phase uses NAOMP for training dictionaries which separates faithful depth from noisy data. Compared with traditional OMP, NAOMP adds a residual reduction step which effectively weakens the noise term within the residual during the residual decomposition in terms of atoms. Experimental results demonstrate that the bilateral phase and the NAOMP-based learning dictionaries phase corporately denoise both virtual and real depth images effectively.

"It's Just Reflex Now": German Language Learners' Use of Online Resources

Science.gov (United States)

Larson-Guenette, Julie

2013-01-01

This study examined how often and to what extent university learners of German use online resources (e.g., online dictionaries and translators) in relation to German coursework, their motivations for use, and their beliefs about online resources and language learning. Data for this study consisted of open-ended surveys ("n" = 71) and face-to-face…

Adaptive Greedy Dictionary Selection for Web Media Summarization.

Science.gov (United States)

Cong, Yang; Liu, Ji; Sun, Gan; You, Quanzeng; Li, Yuncheng; Luo, Jiebo

2017-01-01

Initializing an effective dictionary is an indispensable step for sparse representation. In this paper, we focus on the dictionary selection problem with the objective to select a compact subset of basis from original training data instead of learning a new dictionary matrix as dictionary learning models do. We first design a new dictionary selection model via l 2,0 norm. For model optimization, we propose two methods: one is the standard forward-backward greedy algorithm, which is not suitable for large-scale problems; the other is based on the gradient cues at each forward iteration and speeds up the process dramatically. In comparison with the state-of-the-art dictionary selection models, our model is not only more effective and efficient, but also can control the sparsity. To evaluate the performance of our new model, we select two practical web media summarization problems: 1) we build a new data set consisting of around 500 users, 3000 albums, and 1 million images, and achieve effective assisted albuming based on our model and 2) by formulating the video summarization problem as a dictionary selection issue, we employ our model to extract keyframes from a video sequence in a more flexible way. Generally, our model outperforms the state-of-the-art methods in both these two tasks.

Effective Look-up Techniques to Approach a Monolingual Dictionary

OpenAIRE

Nauman Al Amin Ali El Sayed; Ahmed Gumaa Siddiek

2013-01-01

A dictionary is (a) learning tool that can help the language learner in acquiring great knowledge of and about a foreign language. Almost all language learners buy or at least possess, at one time, a monolingual or bilingual dictionary, to which the learner may refer to look up the meaning of words. Unfortunately, using dictionary to look up the meaning of words seems to be the most important service, which a dictionary is expected to provide to language learners. In fact, a dictionary provid...

Evaluating Bilingual and Monolingual Dictionaries for L2 Learners.

Science.gov (United States)

Hunt, Alan

1997-01-01

A discussion of dictionaries and their use for second language (L2) learning suggests that lack of computerized modern language corpora can adversely affect bilingual dictionaries, commonly used by L2 learners, and shows how use of such corpora has benefitted two contemporary monolingual L2 learner dictionaries (1995 editions of the Longman…

A Latin Functionalist Dictionary as a Self-Learning Language Device: Previous Experiences to Digitalization

Science.gov (United States)

Márquez, Manuel; Chaves, Beatriz

2016-01-01

The application of a methodology based on S.C. Dik's Functionalist Grammar linguistic principles, which is addressed to the teaching of Latin to secondary students, has resulted in a quantitative improvement in students' acquisition process of knowledge. To do so, we have used a self-learning tool, an ad hoc dictionary, of which the use in…

On development of “smart” dictionaries

Directory of Open Access Journals (Sweden)

Mark Kit

2015-11-01

Full Text Available On development of “smart” dictionaries The paper discusses the need for development of intelligent dictionaries that allow for two-way interaction with its users. Theoretical ground for such development is suggested. Practical implementation as LexSite lexical resource is shown, concepts for further improvement of the efficiency are proposed.

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

Science.gov (United States)

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. Copyright © 2015 Elsevier Inc. All rights reserved.

Learning Essential Terms and Concepts in Statistics and Accounting

Science.gov (United States)

Peters, Pam; Smith, Adam; Middledorp, Jenny; Karpin, Anne; Sin, Samantha; Kilgore, Alan

2014-01-01

This paper describes a terminological approach to the teaching and learning of fundamental concepts in foundation tertiary units in Statistics and Accounting, using an online dictionary-style resource (TermFinder) with customised "termbanks" for each discipline. Designed for independent learning, the termbanks support inquiring students…

Low dose CT reconstruction via L1 norm dictionary learning using alternating minimization algorithm and balancing principle.

Science.gov (United States)

Wu, Junfeng; Dai, Fang; Hu, Gang; Mou, Xuanqin

2018-04-18

Excessive radiation exposure in computed tomography (CT) scans increases the chance of developing cancer and has become a major clinical concern. Recently, statistical iterative reconstruction (SIR) with l0-norm dictionary learning regularization has been developed to reconstruct CT images from the low dose and few-view dataset in order to reduce radiation dose. Nonetheless, the sparse regularization term adopted in this approach is l0-norm, which cannot guarantee the global convergence of the proposed algorithm. To address this problem, in this study we introduced the l1-norm dictionary learning penalty into SIR framework for low dose CT image reconstruction, and developed an alternating minimization algorithm to minimize the associated objective function, which transforms CT image reconstruction problem into a sparse coding subproblem and an image updating subproblem. During the image updating process, an efficient model function approach based on balancing principle is applied to choose the regularization parameters. The proposed alternating minimization algorithm was evaluated first using real projection data of a sheep lung CT perfusion and then using numerical simulation based on sheep lung CT image and chest image. Both visual assessment and quantitative comparison using terms of root mean square error (RMSE) and structural similarity (SSIM) index demonstrated that the new image reconstruction algorithm yielded similar performance with l0-norm dictionary learning penalty and outperformed the conventional filtered backprojection (FBP) and total variation (TV) minimization algorithms.

Developing a hybrid dictionary-based bio-entity recognition technique

Science.gov (United States)

2015-01-01

Background Bio-entity extraction is a pivotal component for information extraction from biomedical literature. The dictionary-based bio-entity extraction is the first generation of Named Entity Recognition (NER) techniques. Methods This paper presents a hybrid dictionary-based bio-entity extraction technique. The approach expands the bio-entity dictionary by combining different data sources and improves the recall rate through the shortest path edit distance algorithm. In addition, the proposed technique adopts text mining techniques in the merging stage of similar entities such as Part of Speech (POS) expansion, stemming, and the exploitation of the contextual cues to further improve the performance. Results The experimental results show that the proposed technique achieves the best or at least equivalent performance among compared techniques, GENIA, MESH, UMLS, and combinations of these three resources in F-measure. Conclusions The results imply that the performance of dictionary-based extraction techniques is largely influenced by information resources used to build the dictionary. In addition, the edit distance algorithm shows steady performance with three different dictionaries in precision whereas the context-only technique achieves a high-end performance with three difference dictionaries in recall. PMID:26043907

Developing a hybrid dictionary-based bio-entity recognition technique.

Science.gov (United States)

Song, Min; Yu, Hwanjo; Han, Wook-Shin

2015-01-01

Bio-entity extraction is a pivotal component for information extraction from biomedical literature. The dictionary-based bio-entity extraction is the first generation of Named Entity Recognition (NER) techniques. This paper presents a hybrid dictionary-based bio-entity extraction technique. The approach expands the bio-entity dictionary by combining different data sources and improves the recall rate through the shortest path edit distance algorithm. In addition, the proposed technique adopts text mining techniques in the merging stage of similar entities such as Part of Speech (POS) expansion, stemming, and the exploitation of the contextual cues to further improve the performance. The experimental results show that the proposed technique achieves the best or at least equivalent performance among compared techniques, GENIA, MESH, UMLS, and combinations of these three resources in F-measure. The results imply that the performance of dictionary-based extraction techniques is largely influenced by information resources used to build the dictionary. In addition, the edit distance algorithm shows steady performance with three different dictionaries in precision whereas the context-only technique achieves a high-end performance with three difference dictionaries in recall.

Examining the Conditions of Using an On-Line Dictionary to Learn Words and Comprehend Texts

Science.gov (United States)

Dilenschneider, Robert Francis

2018-01-01

This study investigated three look-up conditions for language learners to learn unknown target words and comprehend a reading passage when their attention is transferred away to an on-line dictionary. The research questions focused on how each look-up condition impacted the recall and recognition of word forms, word meanings, and passage…

The Development of an Android-Based Anggah-Ungguhing Balinese Language Dictionary

Directory of Open Access Journals (Sweden)

I Made Agus Wirawan

2018-01-01

Full Text Available Indonesia is an archipelago country with a variety of local languages; one of which is Balinese (mother tongue, used by the Balinese people in daily life and in certain ritual ceremonies. In Universitas Pendidikan Ganesha, Department of Balinese Language Education, students have been given Anggah-Ungguhing in speaking subjects where they are taught to understand the use of Balinese language based on social strata.  But in the process of learning the Anggah-Ungguhing, there are some problems, including: 1 There is no media that supports learning of Anggah-Ungguhing vocabulary. 2 The motivation of students when learning Anggah-Ungguhing by using books is low. Based upon the analysis on the problems and previous researches, this study aims to: 1 Development of mobile dictionary to support the learning process Angggah - Ungguhing anywhere and anytime. 2 Measuring the level of student’s motivation are using mobile dictionary while learning vocabulary Anggah - Ungguhing. The method used in this research is Software Development Life Cycle (SDLC with Waterfall based model. Based on the results of tests that have been done, mobile dictionaries can be declared successfully developed based on user needs. In this research has distributed about 60 questionnaires to measure the level of student’s motivation who use mobile dictionaries on learning Anggah - Ungguhing Balinese language. The result of the student’s motivation measurement shows that the motivation of the students that the learns Anggah - Ungguhing Balinese languange using mobile dictionary is in the positive category.

The Latent Structure of Dictionaries.

Science.gov (United States)

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. Copyright © 2016 Cognitive Science Society, Inc.

Sparse dictionary learning of resting state fMRI networks.

Science.gov (United States)

Eavani, Harini; Filipovych, Roman; Davatzikos, Christos; Satterthwaite, Theodore D; Gur, Raquel E; Gur, Ruben C

2012-07-02

Research in resting state fMRI (rsfMRI) has revealed the presence of stable, anti-correlated functional subnetworks in the brain. Task-positive networks are active during a cognitive process and are anti-correlated with task-negative networks, which are active during rest. In this paper, based on the assumption that the structure of the resting state functional brain connectivity is sparse, we utilize sparse dictionary modeling to identify distinct functional sub-networks. We propose two ways of formulating the sparse functional network learning problem that characterize the underlying functional connectivity from different perspectives. Our results show that the whole-brain functional connectivity can be concisely represented with highly modular, overlapping task-positive/negative pairs of sub-networks.

A sense inventory for clinical abbreviations and acronyms created using clinical notes and medical dictionary resources.

Science.gov (United States)

Moon, Sungrim; Pakhomov, Serguei; Liu, Nathan; Ryan, James O; Melton, Genevieve B

2014-01-01

To create a sense inventory of abbreviations and acronyms from clinical texts. The most frequently occurring abbreviations and acronyms from 352,267 dictated clinical notes were used to create a clinical sense inventory. Senses of each abbreviation and acronym were manually annotated from 500 random instances and lexically matched with long forms within the Unified Medical Language System (UMLS V.2011AB), Another Database of Abbreviations in Medline (ADAM), and Stedman's Dictionary, Medical Abbreviations, Acronyms & Symbols, 4th edition (Stedman's). Redundant long forms were merged after they were lexically normalized using Lexical Variant Generation (LVG). The clinical sense inventory was found to have skewed sense distributions, practice-specific senses, and incorrect uses. Of 440 abbreviations and acronyms analyzed in this study, 949 long forms were identified in clinical notes. This set was mapped to 17,359, 5233, and 4879 long forms in UMLS, ADAM, and Stedman's, respectively. After merging long forms, only 2.3% matched across all medical resources. The UMLS, ADAM, and Stedman's covered 5.7%, 8.4%, and 11% of the merged clinical long forms, respectively. The sense inventory of clinical abbreviations and acronyms and anonymized datasets generated from this study are available for public use at http://www.bmhi.umn.edu/ihi/research/nlpie/resources/index.htm ('Sense Inventories', website). Clinical sense inventories of abbreviations and acronyms created using clinical notes and medical dictionary resources demonstrate challenges with term coverage and resource integration. Further work is needed to help with standardizing abbreviations and acronyms in clinical care and biomedicine to facilitate automated processes such as text-mining and information extraction.

Cross-Modality Image Synthesis via Weakly Coupled and Geometry Co-Regularized Joint Dictionary Learning.

Science.gov (United States)

Huang, Yawen; Shao, Ling; Frangi, Alejandro F

2018-03-01

Multi-modality medical imaging is increasingly used for comprehensive assessment of complex diseases in either diagnostic examinations or as part of medical research trials. Different imaging modalities provide complementary information about living tissues. However, multi-modal examinations are not always possible due to adversary factors, such as patient discomfort, increased cost, prolonged scanning time, and scanner unavailability. In additionally, in large imaging studies, incomplete records are not uncommon owing to image artifacts, data corruption or data loss, which compromise the potential of multi-modal acquisitions. In this paper, we propose a weakly coupled and geometry co-regularized joint dictionary learning method to address the problem of cross-modality synthesis while considering the fact that collecting the large amounts of training data is often impractical. Our learning stage requires only a few registered multi-modality image pairs as training data. To employ both paired images and a large set of unpaired data, a cross-modality image matching criterion is proposed. Then, we propose a unified model by integrating such a criterion into the joint dictionary learning and the observed common feature space for associating cross-modality data for the purpose of synthesis. Furthermore, two regularization terms are added to construct robust sparse representations. Our experimental results demonstrate superior performance of the proposed model over state-of-the-art methods.

e-Learning Resource Brokers

NARCIS (Netherlands)

Retalis, Symeon; Papasalouros, Andreas; Avgeriou, Paris; Siassiakos, Kostas

2004-01-01

There is an exponentially increasing demand for provisioning of high-quality learning resources, which is not satisfied by current web technologies and systems. E-Learning Resource Brokers are a potential solution to this problem, as they represent the state-of-the-art in facilitating the exchange

From Learning Object to Learning Cell: A Resource Organization Model for Ubiquitous Learning

Science.gov (United States)

Yu, Shengquan; Yang, Xianmin; Cheng, Gang; Wang, Minjuan

2015-01-01

This paper presents a new model for organizing learning resources: Learning Cell. This model is open, evolving, cohesive, social, and context-aware. By introducing a time dimension into the organization of learning resources, Learning Cell supports the dynamic evolution of learning resources while they are being used. In addition, by introducing a…

Extended dynamic mode decomposition with dictionary learning: A data-driven adaptive spectral decomposition of the Koopman operator.

Science.gov (United States)

Li, Qianxiao; Dietrich, Felix; Bollt, Erik M; Kevrekidis, Ioannis G

2017-10-01

Numerical approximation methods for the Koopman operator have advanced considerably in the last few years. In particular, data-driven approaches such as dynamic mode decomposition (DMD) 51 and its generalization, the extended-DMD (EDMD), are becoming increasingly popular in practical applications. The EDMD improves upon the classical DMD by the inclusion of a flexible choice of dictionary of observables which spans a finite dimensional subspace on which the Koopman operator can be approximated. This enhances the accuracy of the solution reconstruction and broadens the applicability of the Koopman formalism. Although the convergence of the EDMD has been established, applying the method in practice requires a careful choice of the observables to improve convergence with just a finite number of terms. This is especially difficult for high dimensional and highly nonlinear systems. In this paper, we employ ideas from machine learning to improve upon the EDMD method. We develop an iterative approximation algorithm which couples the EDMD with a trainable dictionary represented by an artificial neural network. Using the Duffing oscillator and the Kuramoto Sivashinsky partical differential equation as examples, we show that our algorithm can effectively and efficiently adapt the trainable dictionary to the problem at hand to achieve good reconstruction accuracy without the need to choose a fixed dictionary a priori. Furthermore, to obtain a given accuracy, we require fewer dictionary terms than EDMD with fixed dictionaries. This alleviates an important shortcoming of the EDMD algorithm and enhances the applicability of the Koopman framework to practical problems.

Few-view image reconstruction with dual dictionaries

International Nuclear Information System (INIS)

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. (paper)

The effects of dictionary training on Turkish EFL students' reading comprehension and vocabulary learning

OpenAIRE

Altun, Arif

1995-01-01

Ankara : The Institute of Economic and Social Sciences of Bilkent Univ., 1995. Thesis (Master's) -- Bilkent University, 1995. Includes bibliographical references leaves 55-59 The present study investigated the effects of monolingual dictionary training on Turkish EFL students' reading comprehension and vocabulary learning. Thirty-seven intermediate-level Turkish EFL preparatory students in the Department of English Language Teaching at Mustafa Kemal University participated in this st...

Linguistic and Cultural Strategies in ELT Dictionaries

Science.gov (United States)

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…

Dictionary Based Machine Translation from Kannada to Telugu

Science.gov (United States)

Sindhu, D. V.; Sagar, B. M.

2017-08-01

Machine Translation is a task of translating from one language to another language. For the languages with less linguistic resources like Kannada and Telugu Dictionary based approach is the best approach. This paper mainly focuses on Dictionary based machine translation for Kannada to Telugu. The proposed methodology uses dictionary for translating word by word without much correlation of semantics between them. The dictionary based machine translation process has the following sub process: Morph analyzer, dictionary, transliteration, transfer grammar and the morph generator. As a part of this work bilingual dictionary with 8000 entries is developed and the suffix mapping table at the tag level is built. This system is tested for the children stories. In near future this system can be further improved by defining transfer grammar rules.

A sense inventory for clinical abbreviations and acronyms created using clinical notes and medical dictionary resources

Science.gov (United States)

Moon, Sungrim; Pakhomov, Serguei; Liu, Nathan; Ryan, James O; Melton, Genevieve B

2014-01-01

Objective To create a sense inventory of abbreviations and acronyms from clinical texts. Methods The most frequently occurring abbreviations and acronyms from 352 267 dictated clinical notes were used to create a clinical sense inventory. Senses of each abbreviation and acronym were manually annotated from 500 random instances and lexically matched with long forms within the Unified Medical Language System (UMLS V.2011AB), Another Database of Abbreviations in Medline (ADAM), and Stedman's Dictionary, Medical Abbreviations, Acronyms & Symbols, 4th edition (Stedman's). Redundant long forms were merged after they were lexically normalized using Lexical Variant Generation (LVG). Results The clinical sense inventory was found to have skewed sense distributions, practice-specific senses, and incorrect uses. Of 440 abbreviations and acronyms analyzed in this study, 949 long forms were identified in clinical notes. This set was mapped to 17 359, 5233, and 4879 long forms in UMLS, ADAM, and Stedman's, respectively. After merging long forms, only 2.3% matched across all medical resources. The UMLS, ADAM, and Stedman's covered 5.7%, 8.4%, and 11% of the merged clinical long forms, respectively. The sense inventory of clinical abbreviations and acronyms and anonymized datasets generated from this study are available for public use at http://www.bmhi.umn.edu/ihi/research/nlpie/resources/index.htm (‘Sense Inventories’, website). Conclusions Clinical sense inventories of abbreviations and acronyms created using clinical notes and medical dictionary resources demonstrate challenges with term coverage and resource integration. Further work is needed to help with standardizing abbreviations and acronyms in clinical care and biomedicine to facilitate automated processes such as text-mining and information extraction. PMID:23813539

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

Science.gov (United States)

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

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

Science.gov (United States)

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

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

Directory of Open Access Journals (Sweden)

Xin Tang

Full Text Available 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

Ursula Wingate. The Effectiveness of Different Learner Dictionaries ...

African Journals Online (AJOL)

rbr

Investigation into the Use of Dictionaries for Reading Comprehension by Inter- mediate Learners of German. 2002, X + 301 pp. ... empirical research conducted in dictionary use. Ursula Wingate is responsible for the ... Her expertise in applied linguistics and in learning strategies has been a particular asset in this research.

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

Science.gov (United States)

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…

Sparse representations via learned dictionaries for x-ray angiogram image denoising

Science.gov (United States)

Shang, Jingfan; Huang, Zhenghua; Li, Qian; Zhang, Tianxu

2018-03-01

X-ray angiogram image denoising is always an active research topic in the field of computer vision. In particular, the denoising performance of many existing methods had been greatly improved by the widely use of nonlocal similar patches. However, the only nonlocal self-similar (NSS) patch-based methods can be still be improved and extended. In this paper, we propose an image denoising model based on the sparsity of the NSS patches to obtain high denoising performance and high-quality image. In order to represent the sparsely NSS patches in every location of the image well and solve the image denoising model more efficiently, we obtain dictionaries as a global image prior by the K-SVD algorithm over the processing image; Then the single and effectively alternating directions method of multipliers (ADMM) method is used to solve the image denoising model. The results of widely synthetic experiments demonstrate that, owing to learned dictionaries by K-SVD algorithm, a sparsely augmented lagrangian image denoising (SALID) model, which perform effectively, obtains a state-of-the-art denoising performance and better high-quality images. Moreover, we also give some denoising results of clinical X-ray angiogram images.

Medical student use of digital learning resources.

Science.gov (United States)

Scott, Karen; Morris, Anne; Marais, Ben

2018-02-01

University students expect to use technology as part of their studies, yet health professional teachers can struggle with the change in student learning habits fuelled by technology. Our research aimed to document the learning habits of contemporary medical students during a clinical rotation by exploring the use of locally and externally developed digital and print self-directed learning resources, and study groups. We investigated the learning habits of final-stage medical students during their clinical paediatric rotation using mixed methods, involving learning analytics and a student questionnaire. Learning analytics tracked aggregate student usage statistics of locally produced e-learning resources on two learning management systems and mobile learning resources. The questionnaire recorded student-reported use of digital and print learning resources and study groups. The students made extensive use of digital self-directed learning resources, especially in the 2 weeks before the examination, which peaked the day before the written examination. All students used locally produced digital formative assessment, and most (74/98; 76%) also used digital resources developed by other institutions. Most reported finding locally produced e-learning resources beneficial for learning. In terms of traditional forms of self-directed learning, one-third (28/94; 30%) indicated that they never read the course textbook, and few students used face-to-face 39/98 (40%) or online 6/98 (6%) study groups. Learning analytics and student questionnaire data confirmed the extensive use of digital resources for self-directed learning. Through clarification of learning habits and experiences, we think teachers can help students to optimise effective learning strategies; however, the impact of contemporary learning habits on learning efficacy requires further evaluation. Health professional teachers can struggle with the change in student learning habits fuelled by technology. © 2017 John

Designing Learning Resources in Synchronous Learning Environments

DEFF Research Database (Denmark)

Christiansen, Rene B

2015-01-01

Computer-mediated Communication (CMC) and synchronous learning environments offer new solutions for teachers and students that transcend the singular one-way transmission of content knowledge from teacher to student. CMC makes it possible not only to teach computer mediated but also to design...... and create new learning resources targeted to a specific group of learners. This paper addresses the possibilities of designing learning resources within synchronous learning environments. The empirical basis is a cross-country study involving students and teachers in primary schools in three Nordic...... Countries (Denmark, Sweden and Norway). On the basis of these empirical studies a set of design examples is drawn with the purpose of showing how the design fulfills the dual purpose of functioning as a remote, synchronous learning environment and - using the learning materials used and recordings...

The First Steps to a New Comprehensive Slovenian-Hungarian Dictionary: The Analysis of Relevant Bilingual Resources

Directory of Open Access Journals (Sweden)

Júlia Bálint Čeh

2018-01-01

Full Text Available The paper presents the analysis of existing bilingual Slovenian-Hungarian dictionaries, which was made as part of the project aiming to design a concept for a new comprehensive Slovenian-Hungarian dictionary. First, a short historical overview of Slovenian-Hungarian lexicography is provided, including first collections of dialect vocabulary, glossaries, and collections and dictionaries of idioms. Then, an overview of Slovenian-Hungarian and Hungarian-Slovenian dictionaries is made, the first one being published in 1961. The paper then focuses on a comparison on three Slovenian-Hungarian dictionaries, which are currently used by majority of users, namely Slovenian-Hungarian part of the dictionary by Elizabeta Bernjak (1995, Slovenian-Hungarian dictionary by Jože Hradil (1996, and Slovenian-Hungarian part of the Hradil’s bidirectional dictionary. The dictionaries are compared in terms of size, headword list, coverage, headword presentation, grammar information, as well as in terms of other elements of dictionary microstructure such as translations and examples. The discussion section includes an analysis of the coverage offered by the dictionaries of the vocabulary compilled by teachers at bilingual schools in Prekmurje. The results indicate that the coverage of various levels of vocabulary, frequent or rare, is rather poor; as dictionaries are medium-sized and outdated, this is to be expected, however as the analysis shows, some basic concepts are also often not covered (e.g. research, death, allergy. The second part of the discussion is dedicated to the presentation of selected examples of good practice in bilingual lexicography, such as Comprehensive English-Slovenian dictionary Oxford-DZS as the first bilingual dictionary in Slovenia to use the corpus-based approach, as well as offer much more contextual information on the headwords. Also presented are English-Spanish online dictionaries by Oxford University Press and Collins, the focus

Seismic detection method for small-scale discontinuities based on dictionary learning and sparse representation

Science.gov (United States)

Yu, Caixia; Zhao, Jingtao; Wang, Yanfei

2017-02-01

Studying small-scale geologic discontinuities, such as faults, cavities and fractures, plays a vital role in analyzing the inner conditions of reservoirs, as these geologic structures and elements can provide storage spaces and migration pathways for petroleum. However, these geologic discontinuities have weak energy and are easily contaminated with noises, and therefore effectively extracting them from seismic data becomes a challenging problem. In this paper, a method for detecting small-scale discontinuities using dictionary learning and sparse representation is proposed that can dig up high-resolution information by sparse coding. A K-SVD (K-means clustering via Singular Value Decomposition) sparse representation model that contains two stage of iteration procedure: sparse coding and dictionary updating, is suggested for mathematically expressing these seismic small-scale discontinuities. Generally, the orthogonal matching pursuit (OMP) algorithm is employed for sparse coding. However, the method can only update one dictionary atom at one time. In order to improve calculation efficiency, a regularized version of OMP algorithm is presented for simultaneously updating a number of atoms at one time. Two numerical experiments demonstrate the validity of the developed method for clarifying and enhancing small-scale discontinuities. The field example of carbonate reservoirs further demonstrates its effectiveness in revealing masked tiny faults and small-scale cavities.

The Efficacy of Dictionary Use while Reading for Learning New Words

Science.gov (United States)

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…

Change detection of medical images using dictionary learning techniques and PCA

Science.gov (United States)

Nika, Varvara; Babyn, Paul; Zhu, Hongmei

2014-03-01

Automatic change detection methods for identifying the changes of serial MR images taken at different times are of great interest to radiologists. The majority of existing change detection methods in medical imaging, and those of brain images in particular, include many preprocessing steps and rely mostly on statistical analysis of MRI scans. Although most methods utilize registration software, tissue classification remains a difficult and overwhelming task. Recently, dictionary learning techniques are used in many areas of image processing, such as image surveillance, face recognition, remote sensing, and medical imaging. In this paper we present the Eigen-Block Change Detection algorithm (EigenBlockCD). It performs local registration and identifies the changes between consecutive MR images of the brain. Blocks of pixels from baseline scan are used to train local dictionaries that are then used to detect changes in the follow-up scan. We use PCA to reduce the dimensionality of the local dictionaries and the redundancy of data. Choosing the appropriate distance measure significantly affects the performance of our algorithm. We examine the differences between L1 and L2 norms as two possible similarity measures in the EigenBlockCD. We show the advantages of L2 norm over L1 norm theoretically and numerically. We also demonstrate the performance of the EigenBlockCD algorithm for detecting changes of MR images and compare our results with those provided in recent literature. Experimental results with both simulated and real MRI scans show that the EigenBlockCD outperforms the previous methods. It detects clinical changes while ignoring the changes due to patient's position and other acquisition artifacts.

A critical analysis of multilingual dictionaries | Prinsloo | Lexikos

African Journals Online (AJOL)

This article evaluates the lexicographic value of multilingual dictionaries. Dictionaries covering three or more languages spoken in South Africa are taken as a case in point. An attempt will be made to reflect on their merits and shortcomings as reference works and learning tools but the focus will be on presumed ...

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

Science.gov (United States)

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…

ANALYTICAL REVIEW OF ELECTRONIC RESOURCES FOR THE STUDY OF LATIN

Directory of Open Access Journals (Sweden)

Olena Yu. Balalaieva

2014-04-01

Full Text Available The article investigates the current state of development of e-learning content in the Latin language. It is noted that the introduction of ICT in the educational space has expanded the possibility of studying Latin, opened access to digital libraries resources, made it possible to use scientific and educational potential and teaching Latin best practices of world's leading universities. A review of foreign and Ukrainian information resources and electronic editions for the study of Latin is given. Much attention was paid to the didactic potential of local and online multimedia courses of Latin, electronic textbooks, workbooks of interactive tests and exercises, various dictionaries and software translators, databases and digital libraries. Based on analysis of the world market of educational services and products the main trends in the development of information resources and electronic books are examined. It was found that multimedia courses with interactive exercises or workbooks with interactive tests, online dictionaries and translators are the most widely represented and demanded. The noticeable lagging of Ukrainian education and computer linguistics in quantitative and qualitative measures in this industry is established. The obvious drawback of existing Ukrainian resources and electronic editions for the study of Latin is their noninteractive nature. The prospects of e-learning content in Latin in Ukraine are outlined.

NCI Dictionary of Genetics Terms

Science.gov (United States)

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

Managing Human Resource Learning for Innovation

DEFF Research Database (Denmark)

Nielsen, Peter

Managing human resource learning for innovation develops a systemic understanding of building innovative capabilities. Building innovative capabilities require active creation, coordination and absorption of useful knowledge and thus a cohesive management approach to learning. Often learning...... in organizations and work is approached without considerations on how to integrate it in the management of human resources. The book investigates the empirical conditions for managing human resources learning for innovation. With focus on innovative performance the importance of modes of innovation, clues...

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

Science.gov (United States)

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.

The Ideology of the Perfect Dictionary: How Efficient Can a Dictionary Be?

Directory of Open Access Journals (Sweden)

Michaël Abecassis

2011-10-01

Full Text Available

Abstract: Dictionaries have become essential tools of the modern world. Not only have dictionary sales dramatically increased, but the variety of dictionaries and the competition between editors are also very much on the rise. Monolingual dictionaries attract native speakers for several reasons. Some wish to capture the subtleties of their own language, others to speak the 'standard' language, and an 'ideologically' politically correct variety devoid of colloquialisms, hence the crucial role played by style labels. Furthermore, a large number of word enthusiasts enjoy linguistic curiosities, archaisms and other vestiges from the past conserved in dictionaries. Is the concept of a perfect dictionary a reality or an ideal? There is no perfect student. Language learners, for whom dictionaries are of great importance, seek user-friendly material which will improve both their fluency in and understanding of the target language, and embed acquired lexis in their long-term memory. Lexicographers, in their search for perfection and in compliance with users' wishes, are constantly innovating, and every dictionary hopes to become a landmark in lexicography and in second language acquisition. This article aims to look at the way dictionaries have evolved and assess the latest generation of computer-based dictionaries, as well as consider possible developments which will contribute to the compilation of future dictionaries.

Keywords: LEXICOGRAPHY, LEXICAL ACQUISITION, VOCABULARY, STYLE LABELS,CORPUS/CORPORA, DICTIONARIES, IDEOLOGY, STANDARD, LANGUAGE LEARNING,FRENCH MONOLINGUAL DICTIONARIES, ELECTRONIC DICTIONARIES, CD-ROMS

Opsomming: Die ideologie van die volmaakte woordeboek: Hoe doeltreffendkan 'n woordeboek wees? Woordeboeke het noodsaaklike werktuie van die modernewêreld geword. Nie alleen het woordeboekverkope dramaties vermeerder nie, maar die verskeidenheidwoordeboeke en die wedywering tussen redakteurs is ook aansienlik aan die

The effectiveness of using a bilingualized dictionary for determining ...

African Journals Online (AJOL)

This article discusses the use of a bilingualized dictionary, namely Oxford Advanced Learner's English–Chinese Dictionary 8 (OALECD8), by advanced Hong Kong Cantonese ESL learn-ers in the determination of noun countability and associated article use. A homogenous group of 30 English majors in a local university ...

Parsing and Tagging of Binlingual Dictionary

National Research Council Canada - National Science Library

Ma, Huanfeng; Karagol-Ayan, Burcu; Doermann, David S; Oard, Doug; Wang, Jianqiang

2003-01-01

Bilingual dictionaries hold great potential as a source of lexical resources for training and testing automated systems for optical character recognition, machine translation, and cross-language information retrieval...

Parsing and Tagging of Bilingual Dictionary

National Research Council Canada - National Science Library

Ma, Huanfeng; Karagol-Ayan, Burcu; Doermann, David S; Oard, Doug; Wang, Jianqiang

2003-01-01

Bilingual dictionaries hold great potential as a source of lexical resources for training and testing automated systems for optical character recognition, machine translation, and cross-language information retrieval...

Patient-Specific Seizure Detection in Long-Term EEG Using Signal-Derived Empirical Mode Decomposition (EMD)-based Dictionary Approach.

Science.gov (United States)

Kaleem, Muhammad; Gurve, Dharmendra; Guergachi, Aziz; Krishnan, Sridhar

2018-06-25

The objective of the work described in this paper is development of a computationally efficient methodology for patient-specific automatic seizure detection in long-term multi-channel EEG recordings. Approach: A novel patient-specific seizure detection approach based on signal-derived Empirical Mode Decomposition (EMD)-based dictionary approach is proposed. For this purpose, we use an empirical framework for EMD-based dictionary creation and learning, inspired by traditional dictionary learning methods, in which the EMD-based dictionary is learned from the multi-channel EEG data being analyzed for automatic seizure detection. We present the algorithm for dictionary creation and learning, whose purpose is to learn dictionaries with a small number of atoms. Using training signals belonging to seizure and non-seizure classes, an initial dictionary, termed as the raw dictionary, is formed. The atoms of the raw dictionary are composed of intrinsic mode functions obtained after decomposition of the training signals using the empirical mode decomposition algorithm. The raw dictionary is then trained using a learning algorithm, resulting in a substantial decrease in the number of atoms in the trained dictionary. The trained dictionary is then used for automatic seizure detection, such that coefficients of orthogonal projections of test signals against the trained dictionary form the features used for classification of test signals into seizure and non-seizure classes. Thus no hand-engineered features have to be extracted from the data as in traditional seizure detection approaches. Main results: The performance of the proposed approach is validated using the CHB-MIT benchmark database, and averaged accuracy, sensitivity and specificity values of 92.9%, 94.3% and 91.5%, respectively, are obtained using support vector machine classifier and five-fold cross-validation method. These results are compared with other approaches using the same database, and the suitability

Sign Inference for Dynamic Signed Networks via Dictionary Learning

Directory of Open Access Journals (Sweden)

Yi Cen

2013-01-01

Full Text Available Mobile online social network (mOSN is a burgeoning research area. However, most existing works referring to mOSNs deal with static network structures and simply encode whether relationships among entities exist or not. In contrast, relationships in signed mOSNs can be positive or negative and may be changed with time and locations. Applying certain global characteristics of social balance, in this paper, we aim to infer the unknown relationships in dynamic signed mOSNs and formulate this sign inference problem as a low-rank matrix estimation problem. Specifically, motivated by the Singular Value Thresholding (SVT algorithm, a compact dictionary is selected from the observed dataset. Based on this compact dictionary, the relationships in the dynamic signed mOSNs are estimated via solving the formulated problem. Furthermore, the estimation accuracy is improved by employing a dictionary self-updating mechanism.

WordEdge® A Career Mobility Guide to High Speed Dictionary-Based Electronic Learning and Testing

Directory of Open Access Journals (Sweden)

Robert Oliphant

2009-01-01

Full Text Available As Thomas Kuhn taught us, misery loves innovation even more than company. Small wonder our recession worriers — and who isn’t one these days, directly or indirectly? — are desperately looking for new and practical ways to increase their job mobility. Statistically considered, since most unskilled jobs are already filled, jobseekers from shrinking fields of employment are being advised to broaden their search to include entry level jobs in new high tech fields that are either stable or expanding, e.g., health care.Let’s grant that each high tech field has its own hands-on skills. But it’s also true that each field, e.g., plumbing, has its own high tech vocabulary which each candidate for employment is expected to know or learn, including correct pronunciation, very much like an aspiring restaurant server learning the complete menu by heart. Hence the desirability of acquiring preliminary mastery of an employment field’s high tech vocabulary well in ADVANCE of the first interview, not in a panicky last minute cram session. Until recently, the only way we could acquire a preliminary mastery of, say, health care terms was to take a course (inconvenient and expensive or to study a specific-field booklet (usually limited inscope. Today, however, our current partnership between print dictionaries and their electronic versions gives any job candidate quick access to an amazingly efficient learning tool for masteringa wide range of high tech vocabularies in current use. Here’s the why and how of our dictionary-based learning and testing route.

MANDARIN CHINESE DICTIONARY.

Science.gov (United States)

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…

High-recall protein entity recognition using a dictionary

Science.gov (United States)

Kou, Zhenzhen; Cohen, William W.; Murphy, Robert F.

2010-01-01

Protein name extraction is an important step in mining biological literature. We describe two new methods for this task: semiCRFs and dictionary HMMs. SemiCRFs are a recently-proposed extension to conditional random fields that enables more effective use of dictionary information as features. Dictionary HMMs are a technique in which a dictionary is converted to a large HMM that recognizes phrases from the dictionary, as well as variations of these phrases. Standard training methods for HMMs can be used to learn which variants should be recognized. We compared the performance of our new approaches to that of Maximum Entropy (Max-Ent) and normal CRFs on three datasets, and improvement was obtained for all four methods over the best published results for two of the datasets. CRFs and semiCRFs achieved the highest overall performance according to the widely-used F-measure, while the dictionary HMMs performed the best at finding entities that actually appear in the dictionary—the measure of most interest in our intended application. PMID:15961466

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

Science.gov (United States)

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.

A Study of Comparatively Low Achievement Students' Bilingualized Dictionary Use and Their English Learning

Science.gov (United States)

Chen, Szu-An

2016-01-01

This study investigates bilingualized dictionary use of Taiwanese university students. It aims to examine EFL learners' overall dictionary use behavior and their perspectives on book dictionary as well as the necessity of advance guidance in using dictionaries. Data was collected through questionnaires and analyzed by SPSS 15.0. Findings indicate…

Topological structure of dictionary graphs

International Nuclear Information System (INIS)

Fuks, Henryk; Krzeminski, Mark

2009-01-01

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 10 3 and 10 4 . 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.

INTEGRATING CORPUS-BASED RESOURCES AND NATURAL LANGUAGE PROCESSING TOOLS INTO CALL

Directory of Open Access Journals (Sweden)

Pascual Cantos Gomez

2002-06-01

Full Text Available This paper ainis at presenting a survey of computational linguistic tools presently available but whose potential has been neither fully considered not exploited to its full in modern CALL. It starts with a discussion on the rationale of DDL to language learning, presenting typical DDL-activities. DDL-software and potential extensions of non-typical DDL-software (electronic dictionaries and electronic dictionary facilities to DDL . An extended section is devoted to describe NLP-technology and how it can be integrated into CALL, within already existing software or as stand alone resources. A range of NLP-tools is presentcd (MT programs, taggers, lemn~atizersp, arsers and speech technologies with special emphasis on tagged concordancing. The paper finishes with a number of reflections and ideas on how language technologies can be used efficiently within the language learning context and how extensive exploration and integration of these technologies might change and extend both modern CAI,I, and the present language learning paradigiii..

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

Science.gov (United States)

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

Mobile-Based Dictionary of Information and Communication Technology

Science.gov (United States)

Liando, O. E. S.; Mewengkang, A.; Kaseger, D.; Sangkop, F. I.; Rantung, V. P.; Rorimpandey, G. C.

2018-02-01

This study aims to design and build mobile-based dictionary of information and communication technology applications to provide access to information in the form of glossary of terms in the context of information and communication technologies. Applications built in this study using the Android platform, with SQLite database model. This research uses prototype model development method which covers the stages of communication, Quick Plan, Quick Design Modeling, Construction of Prototype, Deployment Delivery & Feedback, and Full System Transformation. The design of this application is designed in such a way as to facilitate the user in the process of learning and understanding the new terms or vocabularies encountered in the world of information and communication technology. Mobile-based dictionary of Information And Communication Technology applications that have been built can be an alternative to learning literature. In its simplest form, this application is able to meet the need for a comprehensive and accurate dictionary of Information And Communication Technology function.

A Dictionary Learning Approach for Signal Sampling in Task-Based fMRI for Reduction of Big Data

Science.gov (United States)

Ge, Bao; Li, Xiang; Jiang, Xi; Sun, Yifei; Liu, Tianming

2018-01-01

The exponential growth of fMRI big data offers researchers an unprecedented opportunity to explore functional brain networks. However, this opportunity has not been fully explored yet due to the lack of effective and efficient tools for handling such fMRI big data. One major challenge is that computing capabilities still lag behind the growth of large-scale fMRI databases, e.g., it takes many days to perform dictionary learning and sparse coding of whole-brain fMRI data for an fMRI database of average size. Therefore, how to reduce the data size but without losing important information becomes a more and more pressing issue. To address this problem, we propose a signal sampling approach for significant fMRI data reduction before performing structurally-guided dictionary learning and sparse coding of whole brain's fMRI data. We compared the proposed structurally guided sampling method with no sampling, random sampling and uniform sampling schemes, and experiments on the Human Connectome Project (HCP) task fMRI data demonstrated that the proposed method can achieve more than 15 times speed-up without sacrificing the accuracy in identifying task-evoked functional brain networks. PMID:29706880

A Dictionary Learning Approach for Signal Sampling in Task-Based fMRI for Reduction of Big Data.

Science.gov (United States)

Ge, Bao; Li, Xiang; Jiang, Xi; Sun, Yifei; Liu, Tianming

2018-01-01

The exponential growth of fMRI big data offers researchers an unprecedented opportunity to explore functional brain networks. However, this opportunity has not been fully explored yet due to the lack of effective and efficient tools for handling such fMRI big data. One major challenge is that computing capabilities still lag behind the growth of large-scale fMRI databases, e.g., it takes many days to perform dictionary learning and sparse coding of whole-brain fMRI data for an fMRI database of average size. Therefore, how to reduce the data size but without losing important information becomes a more and more pressing issue. To address this problem, we propose a signal sampling approach for significant fMRI data reduction before performing structurally-guided dictionary learning and sparse coding of whole brain's fMRI data. We compared the proposed structurally guided sampling method with no sampling, random sampling and uniform sampling schemes, and experiments on the Human Connectome Project (HCP) task fMRI data demonstrated that the proposed method can achieve more than 15 times speed-up without sacrificing the accuracy in identifying task-evoked functional brain networks.

Pocket dictionary of laboratory equipment

International Nuclear Information System (INIS)

Junge, H.D.

1987-01-01

This pocket dictionary contains the 2500 most common terms for scientific and technical equipment in chemical laboratories. It is a useful tool for those who are used to communicating in German and English, but have to learn the special terminology in this field. (orig.) [de

Integrative learning for practicing adaptive resource management

Directory of Open Access Journals (Sweden)

Craig A. McLoughlin

2015-03-01

Full Text Available Adaptive resource management is a learning-by-doing approach to natural resource management. Its effective practice involves the activation, completion, and regeneration of the "adaptive management cycle" while working toward achieving a flexible set of collaboratively identified objectives. This iterative process requires application of single-, double-, and triple-loop learning, to strategically modify inputs, outputs, assumptions, and hypotheses linked to improving policies, management strategies, and actions, along with transforming governance. Obtaining an appropriate balance between these three modes of learning has been difficult to achieve in practice and building capacity in this area can be achieved through an emphasis on reflexive learning, by employing adaptive feedback systems. A heuristic reflexive learning framework for adaptive resource management is presented in this manuscript. It is built on the conceptual pillars of the following: stakeholder driven adaptive feedback systems; strategic adaptive management (SAM; and hierarchy theory. The SAM Reflexive Learning Framework (SRLF emphasizes the types, roles, and transfer of information within a reflexive learning context. Its adaptive feedback systems enhance the facilitation of single-, double-, and triple-loop learning. Focus on the reflexive learning process is further fostered by streamlining objectives within and across all governance levels; incorporating multiple interlinked adaptive management cycles; having learning as an ongoing, nested process; recognizing when and where to employ the three-modes of learning; distinguishing initiating conditions for this learning; and contemplating practitioner mandates for this learning across governance levels. The SRLF is a key enabler for implementing the "adaptive management cycle," and thereby translating the theory of adaptive resource management into practice. It promotes the heuristics of adaptive management within a cohesive

Monolingual and Bilingual Learners' Dictionaries*

Directory of Open Access Journals (Sweden)

Rufus H. Gouws

2011-10-01

Full Text Available

Abstract: When deciding on the best learners' dictionary for a specific user and a specificsituation of usage one often has to make a choice between a monolingual and a bilingual learners'dictionary. This article discusses some aspects of the user-driven approach so prevalent in moderndaylexicographic thought, focuses broadly on dictionary typology and takes a closer look at monolingualand bilingual learners' dictionaries. Some problems users experience when learning a newlanguage, e.g. language distortion and problems related to the phenomenon of false friends, especiallyin closely related languages, are mentioned. It is indicated that a typological hybrid dictionarycould assist certain users. The importance of an unambiguous identification of the relevantlexicographic functions is emphasised and the notions of function condensation and function mergingare introduced. It is shown that the typological choice should be determined by a function-basedapproach to dictionary usage.

Keywords: BILINGUAL DICTIONARY, FALSE FRIENDS, FUNCTION CONDENSATION,FUNCTION MERGING, GENUINE PURPOSE, LEARNERS' DICTIONARY, LEXICOGRAPHICFUNCTIONS, MONOLINGUAL DICTIONARY, TEXT PRODUCTION, TEXT RECEPTION,TYPOLOGICAL HYBRID, TYPOLOGY.

Opsomming: Eentalige en tweetalige aanleerderwoordeboeke. Wanneerbesluit moet word oor die beste aanleerderwoordeboek vir 'n spesifieke gebruiker en 'n spesifiekegebruiksituasie moet daar dikwels gekies word tussen 'n eentalige en 'n tweetalige aanleerderwoordeboek.Hierdie artikel bespreek bepaalde aspekte van die gebruikersgedrewe benaderingwat kenmerkend is van die moderne leksikografiese denke, fokus breedweg op woordeboektipologieen gee in meer besonderhede aandag aan sekere aspekte van eentalige en tweetalige aanleerderwoordeboeke.Bepaalde probleme wat gebruikers ervaar by die aanleer van 'n vreemde taal,bv. taalversteuring en probleme verwant aan die verskynsel van valse vriende, veral in nou verwantetale, kry aandag

Constrained dictionary learning and probabilistic hypergraph ranking for person re-identification

Science.gov (United States)

He, You; Wu, Song; Pu, Nan; Qian, Li; Xiao, Guoqiang

2018-04-01

Person re-identification is a fundamental and inevitable task in public security. In this paper, we propose a novel framework to improve the performance of this task. First, two different types of descriptors are extracted to represent a pedestrian: (1) appearance-based superpixel features, which are constituted mainly by conventional color features and extracted from the supepixel rather than a whole picture and (2) due to the limitation of discrimination of appearance features, the deep features extracted by feature fusion Network are also used. Second, a view invariant subspace is learned by dictionary learning constrained by the minimum negative sample (termed as DL-cMN) to reduce the noise in appearance-based superpixel feature domain. Then, we use deep features and sparse codes transformed by appearancebased features to establish the hyperedges respectively by k-nearest neighbor, rather than jointing different features simply. Finally, a final ranking is performed by probabilistic hypergraph ranking algorithm. Extensive experiments on three challenging datasets (VIPeR, PRID450S and CUHK01) demonstrate the advantages and effectiveness of our proposed algorithm.

Deformable segmentation via sparse representation and dictionary learning.

Science.gov (United States)

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. Copyright © 2012 Elsevier B.V. All rights reserved.

Spatio-Temporal Series Remote Sensing Image Prediction Based on Multi-Dictionary Bayesian Fusion

Directory of Open Access Journals (Sweden)

Chu He

2017-11-01

Full Text Available Contradictions in spatial resolution and temporal coverage emerge from earth observation remote sensing images due to limitations in technology and cost. Therefore, how to combine remote sensing images with low spatial yet high temporal resolution as well as those with high spatial yet low temporal resolution to construct images with both high spatial resolution and high temporal coverage has become an important problem called spatio-temporal fusion problem in both research and practice. A Multi-Dictionary Bayesian Spatio-Temporal Reflectance Fusion Model (MDBFM has been proposed in this paper. First, multiple dictionaries from regions of different classes are trained. Second, a Bayesian framework is constructed to solve the dictionary selection problem. A pixel-dictionary likehood function and a dictionary-dictionary prior function are constructed under the Bayesian framework. Third, remote sensing images before and after the middle moment are combined to predict images at the middle moment. Diverse shapes and textures information is learned from different landscapes in multi-dictionary learning to help dictionaries capture the distinctions between regions. The Bayesian framework makes full use of the priori information while the input image is classified. The experiments with one simulated dataset and two satellite datasets validate that the MDBFM is highly effective in both subjective and objective evaluation indexes. The results of MDBFM show more precise details and have a higher similarity with real images when dealing with both type changes and phenology changes.

The Efficacy of Dictionary Use while Reading for Learning New Words

Science.gov (United States)

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…

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

Science.gov (United States)

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…

eLearning resources to supplement postgraduate neurosurgery training.

Science.gov (United States)

Stienen, Martin N; Schaller, Karl; Cock, Hannah; Lisnic, Vitalie; Regli, Luca; Thomson, Simon

2017-02-01

In an increasingly complex and competitive professional environment, improving methods to educate neurosurgical residents is key to ensure high-quality patient care. Electronic (e)Learning resources promise interactive knowledge acquisition. We set out to give a comprehensive overview on available eLearning resources that aim to improve postgraduate neurosurgical training and review the available literature. A MEDLINE query was performed, using the search term "electronic AND learning AND neurosurgery". Only peer-reviewed English-language articles on the use of any means of eLearning to improve theoretical knowledge in postgraduate neurosurgical training were included. Reference lists were crosschecked for further relevant articles. Captured parameters were the year, country of origin, method of eLearning reported, and type of article, as well as its conclusion. eLearning resources were additionally searched for using Google. Of n = 301 identified articles by the MEDLINE search, n = 43 articles were analysed in detail. Applying defined criteria, n = 28 articles were excluded and n = 15 included. Most articles were generated within this decade, with groups from the USA, the UK and India having a leadership role. The majority of articles reviewed existing eLearning resources, others reported on the concept, development and use of generated eLearning resources. There was no article that scientifically assessed the effectiveness of eLearning resources (against traditional learning methods) in terms of efficacy or costs. Only one article reported on satisfaction rates with an eLearning tool. All authors of articles dealing with eLearning and the use of new media in neurosurgery uniformly agreed on its great potential and increasing future use, but most also highlighted some weaknesses and possible dangers. This review found only a few articles dealing with the modern aspects of eLearning as an adjunct to postgraduate neurosurgery training. Comprehensive

Dictionaries of Mexican Sexual Slang for NLP

Directory of Open Access Journals (Sweden)

Roberto Villarejo-Martínez

2018-04-01

Full Text Available Abstract: In this paper the creation of two relevant resources for the double entendre and humour recognition problem in Mexican Spanish is described: a morphological dictionary and a semantic dictionary. These were created from two sources: a corpus of albures (drawn from “Antología del albur” book and a Mexican slang dictionary (“El chilangonario”. The morphological dictionary consists of 410 forms of words that corresponds to 350 lemmas. The semantic dictionary consists of 27 synsets that are associated to lemmas of morphological dictionary. Since both resources are based on Freeling library, they are easy to implement for tasks in Natural Language Processing. The motivation for this work comes from the need to address problems such as double entendre and computational humour. The usefulness of these disciplines has been discussed many times and it has been shown that they have a direct impact on user interfaces and, mainly, in human-computer interaction. This work aims to promote that the scientific community generates more resources about informal language in Spanish and other languages.  Spanish Abstract: En este artículo se describe la creación de dos recursos relevantes para el reconocimiento del doble sentido y el humor en el español mexicano: un diccionario morfológico y un diccionario semántico. Éstos fueron creados a partir de dos fuentes: un corpus de albures (extraídos del libro "Antología del albur" y un diccionario de argot mexicano ("El chilangonario". El diccionario morfológico consiste en 410 formas de palabras que corresponden a 350 lemas. El diccionario semántico consiste en 27 synsets que están asociados a lemas del diccionario morfológico. Puesto que ambos recursos están basados en la biblioteca Freeling, son fáciles de implementar en tareas de Procesamiento del Lenguaje Natural. La motivación de este trabajo proviene de la necesidad de abordar problemas como el doble sentido y el humor

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

Science.gov (United States)

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.

Change detection of medical images using dictionary learning techniques and principal component analysis.

Science.gov (United States)

Nika, Varvara; Babyn, Paul; Zhu, Hongmei

2014-07-01

Automatic change detection methods for identifying the changes of serial MR images taken at different times are of great interest to radiologists. The majority of existing change detection methods in medical imaging, and those of brain images in particular, include many preprocessing steps and rely mostly on statistical analysis of magnetic resonance imaging (MRI) scans. Although most methods utilize registration software, tissue classification remains a difficult and overwhelming task. Recently, dictionary learning techniques are being used in many areas of image processing, such as image surveillance, face recognition, remote sensing, and medical imaging. We present an improved version of the EigenBlockCD algorithm, named the EigenBlockCD-2. The EigenBlockCD-2 algorithm performs an initial global registration and identifies the changes between serial MR images of the brain. Blocks of pixels from a baseline scan are used to train local dictionaries to detect changes in the follow-up scan. We use PCA to reduce the dimensionality of the local dictionaries and the redundancy of data. Choosing the appropriate distance measure significantly affects the performance of our algorithm. We examine the differences between [Formula: see text] and [Formula: see text] norms as two possible similarity measures in the improved EigenBlockCD-2 algorithm. We show the advantages of the [Formula: see text] norm over the [Formula: see text] norm both theoretically and numerically. We also demonstrate the performance of the new EigenBlockCD-2 algorithm for detecting changes of MR images and compare our results with those provided in the recent literature. Experimental results with both simulated and real MRI scans show that our improved EigenBlockCD-2 algorithm outperforms the previous methods. It detects clinical changes while ignoring the changes due to the patient's position and other acquisition artifacts.

SAR Target Recognition via Supervised Discriminative Dictionary Learning and Sparse Representation of the SAR-HOG Feature

Directory of Open Access Journals (Sweden)

Shengli Song

2016-08-01

Full Text Available Automatic target recognition (ATR in synthetic aperture radar (SAR images plays an important role in both national defense and civil applications. Although many methods have been proposed, SAR ATR is still very challenging due to the complex application environment. Feature extraction and classification are key points in SAR ATR. In this paper, we first design a novel feature, which is a histogram of oriented gradients (HOG-like feature for SAR ATR (called SAR-HOG. Then, we propose a supervised discriminative dictionary learning (SDDL method to learn a discriminative dictionary for SAR ATR and propose a strategy to simplify the optimization problem. Finally, we propose a SAR ATR classifier based on SDDL and sparse representation (called SDDLSR, in which both the reconstruction error and the classification error are considered. Extensive experiments are performed on the MSTAR database under standard operating conditions and extended operating conditions. The experimental results show that SAR-HOG can reliably capture the structures of targets in SAR images, and SDDL can further capture subtle differences among the different classes. By virtue of the SAR-HOG feature and SDDLSR, the proposed method achieves the state-of-the-art performance on MSTAR database. Especially for the extended operating conditions (EOC scenario “Training 17 ∘ —Testing 45 ∘ ”, the proposed method improves remarkably with respect to the previous works.

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

Science.gov (United States)

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;…

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

Science.gov (United States)

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…

THE MIGHT OF RUSSIAN LANGUAGE ACCORDING TO SYNONYMIC DICTIONARY BY COMPUTER EVALUATION SYSTEM ASIS®

Directory of Open Access Journals (Sweden)

Vitaly N. Trishin

2013-01-01

Full Text Available The article describes electronic dictionary of synonyms in Russian language by ASIS® system (more than 500 000 words and collocations, 190 000 synonymic connections.The program can be used not just as a dictionary of synonyms and close meaning words, but also as spelling dictionary and definition dictionary of Russian language in order to check the orthography and define the meaning of unknown words. The dictionary is also designed to be an instrument of philological surveys and studies of the language trough the extensive query system on different characteristic of words (definition, composition, synonymy, etc.. Program’s lexical base includes words from dictionaries and guides in all subject areas - from astronomy to Japanese painting. Over compilation of dictionary developer used published dictionaries: spelling, synonymic, definition dictionaries, dictionary of collocations, dictionary of foreign words and etc. of 19-21 cc. Newspapers, magazines and web-resources were active used as well for appending the dictionary. This dictionary practically shows, that by the amount of words Russian language belongs with the most developed languages in the world, and by the scale and density of synonymic space, in the author’s opinion, it has no equal.

A Geometric Dictionary Learning Based Approach for Fluorescence Spectroscopy Image Fusion

Directory of Open Access Journals (Sweden)

Zhiqin Zhu

2017-02-01

Full Text Available In recent years, sparse representation approaches have been integrated into multi-focus image fusion methods. The fused images of sparse-representation-based image fusion methods show great performance. Constructing an informative dictionary is a key step for sparsity-based image fusion method. In order to ensure sufficient number of useful bases for sparse representation in the process of informative dictionary construction, image patches from all source images are classified into different groups based on geometric similarities. The key information of each image-patch group is extracted by principle component analysis (PCA to build dictionary. According to the constructed dictionary, image patches are converted to sparse coefficients by simultaneous orthogonal matching pursuit (SOMP algorithm for representing the source multi-focus images. At last the sparse coefficients are fused by Max-L1 fusion rule and inverted to fused image. Due to the limitation of microscope, the fluorescence image cannot be fully focused. The proposed multi-focus image fusion solution is applied to fluorescence imaging area for generating all-in-focus images. The comparison experimentation results confirm the feasibility and effectiveness of the proposed multi-focus image fusion solution.

Internet accounting dictionaries

DEFF Research Database (Denmark)

Nielsen, Sandro; Mourier, Lise

2005-01-01

An examination of existing accounting dictionaries on the Internet reveals a general need for a new type of dictionary. In contrast to the dictionaries now accessible, the future accounting dictionaries should be designed as proper Internet dictionaries based on a functional approach so they can...

A dictionary to identify small molecules and drugs in free text.

Science.gov (United States)

Hettne, Kristina M; Stierum, Rob H; Schuemie, Martijn J; Hendriksen, Peter J M; Schijvenaars, Bob J A; Mulligen, Erik M van; Kleinjans, Jos; Kors, Jan A

2009-11-15

From the scientific community, a lot of effort has been spent on the correct identification of gene and protein names in text, while less effort has been spent on the correct identification of chemical names. Dictionary-based term identification has the power to recognize the diverse representation of chemical information in the literature and map the chemicals to their database identifiers. We developed a dictionary for the identification of small molecules and drugs in text, combining information from UMLS, MeSH, ChEBI, DrugBank, KEGG, HMDB and ChemIDplus. Rule-based term filtering, manual check of highly frequent terms and disambiguation rules were applied. We tested the combined dictionary and the dictionaries derived from the individual resources on an annotated corpus, and conclude the following: (i) each of the different processing steps increase precision with a minor loss of recall; (ii) the overall performance of the combined dictionary is acceptable (precision 0.67, recall 0.40 (0.80 for trivial names); (iii) the combined dictionary performed better than the dictionary in the chemical recognizer OSCAR3; (iv) the performance of a dictionary based on ChemIDplus alone is comparable to the performance of the combined dictionary. The combined dictionary is freely available as an XML file in Simple Knowledge Organization System format on the web site http://www.biosemantics.org/chemlist.

Empirical research on dictionary use in foreign-language learning: survey and discussion

NARCIS (Netherlands)

Hulstijn, J.H.; Atkins, B.T.S.; Atkins, B.T.S.

1998-01-01

This paper begins with a brief survey, in the form of a classified bibliography of research into dictionary use. A discussion follows of the type of research required in order to increase one's insight into the cognitive processes involved in using a dictionary; the principal factors which affect

TH-CD-206-09: Learning-Based MRI-CT Prostate Registration Using Spare Patch-Deformation Dictionary

International Nuclear Information System (INIS)

Yang, X; Jani, A; Rossi, P; Mao, H; Curran, W; Liu, T

2016-01-01

Purpose: To enable MRI-guided prostate radiotherapy, MRI-CT deformable registration is required to map the MRI-defined tumor and key organ contours onto the CT images. Due to the intrinsic differences in grey-level intensity characteristics between MRI and CT images, the integration of MRI into CT-based radiotherapy is very challenging. We are developing a learning-based registration approach to address this technical challenge. Methods: We propose to estimate the deformation between MRI and CT images in a patch-wise fashion by using the sparse representation technique. Specifically, we assume that two image patches should follow the same deformation if their patch-wise appearance patterns are similar. We first extract a set of key points in the new CT image. Then, for each key point, we adaptively construct a coupled dictionary from the training MRI-CT images, where each coupled element includes both appearance and deformation of the same image patch. After calculating the sparse coefficients in representing the patch appearance of each key point based on the constructed dictionary, we can predict the deformation for this point by applying the same sparse coefficients to the respective deformations in the dictionary. Results: This registration technique was validated with 10 prostate-cancer patients’ data and its performance was compared with the commonly used free-form-deformation-based registration. Several landmarks in both images were identified to evaluate the accuracy of our approach. Overall, the averaged target registration error of the intensity-based registration and the proposed method was 3.8±0.4 mm and 1.9±0.3 mm, respectively. Conclusion: We have developed a novel prostate MR-CT registration approach based on patch-deformation dictionary, demonstrated its clinical feasibility, and validated its accuracy. This technique will either reduce or compensate for the effect of patient-specific treatment variation measured during the course of

TH-CD-206-09: Learning-Based MRI-CT Prostate Registration Using Spare Patch-Deformation Dictionary

Energy Technology Data Exchange (ETDEWEB)

Yang, X; Jani, A; Rossi, P; Mao, H; Curran, W; Liu, T [Emory University, Atlanta, GA (United States)

2016-06-15

Purpose: To enable MRI-guided prostate radiotherapy, MRI-CT deformable registration is required to map the MRI-defined tumor and key organ contours onto the CT images. Due to the intrinsic differences in grey-level intensity characteristics between MRI and CT images, the integration of MRI into CT-based radiotherapy is very challenging. We are developing a learning-based registration approach to address this technical challenge. Methods: We propose to estimate the deformation between MRI and CT images in a patch-wise fashion by using the sparse representation technique. Specifically, we assume that two image patches should follow the same deformation if their patch-wise appearance patterns are similar. We first extract a set of key points in the new CT image. Then, for each key point, we adaptively construct a coupled dictionary from the training MRI-CT images, where each coupled element includes both appearance and deformation of the same image patch. After calculating the sparse coefficients in representing the patch appearance of each key point based on the constructed dictionary, we can predict the deformation for this point by applying the same sparse coefficients to the respective deformations in the dictionary. Results: This registration technique was validated with 10 prostate-cancer patients’ data and its performance was compared with the commonly used free-form-deformation-based registration. Several landmarks in both images were identified to evaluate the accuracy of our approach. Overall, the averaged target registration error of the intensity-based registration and the proposed method was 3.8±0.4 mm and 1.9±0.3 mm, respectively. Conclusion: We have developed a novel prostate MR-CT registration approach based on patch-deformation dictionary, demonstrated its clinical feasibility, and validated its accuracy. This technique will either reduce or compensate for the effect of patient-specific treatment variation measured during the course of

Tensor-based dictionary learning for dynamic tomographic reconstruction

International Nuclear Information System (INIS)

Tan, Shengqi; Wu, Zhifang; Zhang, Yanbo; Mou, Xuanqin; Wang, Ge; Cao, Guohua; 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. (paper)

Tensor-based Dictionary Learning for Dynamic Tomographic Reconstruction

Science.gov (United States)

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

Pupil Science Learning in Resource-Based e-Learning Environments

Science.gov (United States)

So, Wing-mui Winnie; Ching, Ngai-ying Fiona

2011-01-01

With the rapid expansion of broadband Internet connection and availability of high performance yet low priced computers, many countries around the world are advocating the adoption of e-learning, the use of computer technology to improve learning and teaching. The trend of e-learning has urged many teachers to incorporate online resources in their…

A Geometric Dictionary Learning Based Approach for Fluorescence Spectroscopy Image Fusion

OpenAIRE

Zhiqin Zhu; Guanqiu Qi; Yi Chai; Penghua Li

2017-01-01

In recent years, sparse representation approaches have been integrated into multi-focus image fusion methods. The fused images of sparse-representation-based image fusion methods show great performance. Constructing an informative dictionary is a key step for sparsity-based image fusion method. In order to ensure sufficient number of useful bases for sparse representation in the process of informative dictionary construction, image patches from all source images are classified into different ...

Learning Method, Facilities And Infrastructure, And Learning Resources In Basic Networking For Vocational School

OpenAIRE

Pamungkas, Bian Dwi

2017-01-01

This study aims to examine the contribution of learning methods on learning output, the contribution of facilities and infrastructure on output learning, the contribution of learning resources on learning output, and the contribution of learning methods, the facilities and infrastructure, and learning resources on learning output. The research design is descriptive causative, using a goal-oriented assessment approach in which the assessment focuses on assessing the achievement of a goal. The ...

Extending an Afrikaans pronunciation dictionary using Dutch resources and P2P/GP2P

CSIR Research Space (South Africa)

Loots, L

2010-11-01

Full Text Available . This is compared to the more common approach of extending the Afrikaans dictionary by means of graphemeto-phoneme (G2P) conversion. The results indicate that the Afrikaans pronunciations obtained by P2P and GP2P from the Dutch dictionary are more accurate than...

Dictionaries for text production

DEFF Research Database (Denmark)

Fuertes-Olivera, Pedro; Bergenholtz, Henning

2018-01-01

Dictionaries for Text Production are information tools that are designed and constructed for helping users to produce (i.e. encode) texts, both oral and written texts. These can be broadly divided into two groups: (a) specialized text production dictionaries, i.e., dictionaries that only offer...... a small amount of lexicographic data, most or all of which are typically used in a production situation, e.g. synonym dictionaries, grammar and spelling dictionaries, collocation dictionaries, concept dictionaries such as the Longman Language Activator, which is advertised as the World’s First Production...... Dictionary; (b) general text production dictionaries, i.e., dictionaries that offer all or most of the lexicographic data that are typically used in a production situation. A review of existing production dictionaries reveals that there are many specialized text production dictionaries but only a few general...

Dictionary quality and dictionary design: a methodology for ...

African Journals Online (AJOL)

Although recent dictionaries for the ESL market have been praised for their innovative design features, the prime concern of users, lexicographers and metalexicographers is the functional quality of the dictionary products provided for the market. The functional quality of dictionaries and the scientific assessment thereof ...

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

International Nuclear Information System (INIS)

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

2013-01-01

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. (paper)

Translation Dictionaries and Bilingual Dictionaries. Two Different Concepts

DEFF Research Database (Denmark)

Tarp, Sven

2002-01-01

of dictionaries - and in some cases even not the best ones - to assist the translator who runs into problems in the translation process. In my paper, I will argue that monolingual dictionaries - together with bilingual dictionaries «the other way around«, e.g. L2-L1 dictionaries when translating from L1 into L2...... in relation to translation and what types of problems pop up during the translation process in order to clarify up to which point lexicography can assist translator. Finally, I will discuss in which types of dictionary (monolingual or bilingual) the assistance to the translator should be provided and......The starting point in any scientific process is always the formulation of the problem and then the search for a solution. In my opinion the question on the relaton between lexicography and translation should be put in this way: How can dictionaries assist translators in finding solutions...

Cognitive aspects of problem solving using dictionaries in L2 writing

Directory of Open Access Journals (Sweden)

Inna Kozlova

2015-11-01

Full Text Available This article reports on the use of dictionaries for L2 text production purposes by first-year ESP students. Research into dictionary use and cognitive studies of L2 writing are combined in this paper to outline the cognitive dimension of a dictionary consultation. Our objective is to focus on the situation in which an information need occurs, with a freshman ESP student as a specific user in mind. In an attempt to guarantee the relevance of consulting a dictionary, for the purposes of our study we separated the draft stage of a composition from that of its revision. In the latter stage, external resources like dictionaries were made available. Our data suggest that our students were able to detect problems in their writing and reported having improved their compositions after having had the chance to consult dictionaries. The corrections were nonetheless implemented only in one-third of all the problems detected. It was also found that the tentative solution in L2 allowed for monolingual dictionary consultation but students often opted for generating another access key in their native language.

Parenting styles and learned resourcefulness of Turkish adolescents.

Science.gov (United States)

Türkel, Yeşim Deniz; Tezer, Esin

2008-01-01

This study investigated the differences among 834 high school students regarding learned resourcefulness in terms of perceived parenting style and gender. The data were gathered by administering the Parenting Style Inventory (PSI) and Rosenbaum's Self-Control Schedule (SCS). The results of ANOVA pertaining to the scores of learned resourcefulness yielded a significant main effect for parenting style groups. Neither the main effect for gender nor the gender and parenting style interaction effect was significant. The findings suggest that those who perceived their parents as authoritative had a relatively high level of learned resourcefulness as compared to those who perceived their parents as neglectful and authoritarian. Findings also indicated that those who perceived their parents as indulgent had a higher level of learned resourcefulness than those who perceived their parents as neglectful and authoritarian.

Towards the Sigma Online Learning Model for crowdsourced recommendations of good web-based learning resources

OpenAIRE

Aaberg, Robin Garen

2016-01-01

The web based learning resources is believed to be playing an active role in the learning environment of higher education today. This qualitative study is exploring how students at Bergen University College incorporate web-based learning resources in their learning activities. At the core of this research is the problem of retrieving good web-resources after their first discovery. Usefull and knowledge granting web-resources are discovered within a context of topics, objectives. It is here ar...

Dictionary Use of Undergraduate Students in Foreign Language Departments in Turkey at Present

Science.gov (United States)

Tulgar, Aysegül Takkaç

2017-01-01

Foreign language learning has always been a process carried out with the help of dictionaries which are both in target language and from native language to target language/from target language to native language. Dictionary use is an especially delicate issue for students in foreign language departments because students in those departments are…

Dictionary criticism

DEFF Research Database (Denmark)

Nielsen, Sandro

2018-01-01

Dictionary criticism is part of the lexicographical universe and reviewing of electronic and printed dictionaries is not an exercise in linguistics or in subject fields but an exercise in lexicography. It does not follow from this that dictionary reviews should not be based on a linguistic approach......, but that the linguistic approach is only one of several approaches to dictionary reviewing. Similarly, the linguistic and factual competences of reviewers should not be relegated to an insignificant position in the review process. Moreover, reviewers should define the object of their reviews, the dictionary, as a complex...... information tool with several components and in terms of significant lexicographical features: lexicographical functions, data and structures. This emphasises the fact that dictionaries are much more than mere vessels of linguistic categories, namely lexicographical tools that have been developed to fulfil...

Knot detection in X-ray images of wood planks using dictionary learning

DEFF Research Database (Denmark)

Hansson, Nils Mattias; Enescu, Alexandru; Brandt, Sami Sebastian

2015-01-01

This paper considers a novel application of x-ray imaging of planks, for the purpose of detecting knots in high quality furniture wood. X-ray imaging allows the detection of knots invisible from the surface to conventional cameras. Our approach is based on texture analysis, or more specifically, ......, discriminative dictionary learning. Experiments show that the knot detection and segmentation can be accurately performed by our approach. This is a promising result and can be directly applied in industrial processing of furniture wood.......This paper considers a novel application of x-ray imaging of planks, for the purpose of detecting knots in high quality furniture wood. X-ray imaging allows the detection of knots invisible from the surface to conventional cameras. Our approach is based on texture analysis, or more specifically...

INFORMATION PROVISION OF DISTANCE LEARNING SYSTEM

Directory of Open Access Journals (Sweden)

Viacheslav M. Oleksenko

2010-09-01

Full Text Available The article deals with the results of the research concerning the relevant information resources elaborated and introduced into the pedagogical process by the author. The peculiarities of the first in Ukraine dictionary on theory and practice of distance learning, distance course “Linear Algebra” and the course-book “Linear Algebra and Analytical Geometry”, which promote the raising in quality of education and training of specialists, are revealed.

Sparse time-frequency decomposition based on dictionary adaptation.

Science.gov (United States)

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. © 2016 The Author(s).

Use of Language Resources by Teachers at Bilingual Schools in Prekmurje

Directory of Open Access Journals (Sweden)

Iztok Kosem

2018-01-01

Full Text Available The paper presents the results of a survey on the use of different language resources (dictionaries, orthographies, thesauri, etc. by teachers at bilingual schools in Prekmurje. The survey was conducted as part of the project focussed on developing a concept of a new comprehensive Slovenian-Hungarian dictionary. The dictionary aims to meet the needs of a wider community, as well as needs specific to bilingual education. The main aim of the survey was thus to establish how well teachers know language resources available to them, how often they use them during their work, and which types of dictionary information do they find useful. Furthermore, the survey also tried to find out which communication activities in the Hungarian language pose most problems to teachers. The analysis of the survey has shown that majority of teachers know available language resources, and also used them at their work. Due to various problems with communication in Hungarian, teachers need to use a wide variety of language resources, both bilingual and monolingual. The fact that many of the existing resources, especially bilingual ones, are not available in digital form, is definitely a major obstacle. Teachers consider all types of dictionary information to be important/useful, but especially translation equivalents, indication of the correct spelling, explanations of word meanings, and dictionary examples. Importantly, the types of information not available in existing resources, such as audio pronunciation and whole-sentence examples, are considered to be very useful. The survey findings will be considered in the preparation of a new comprehensive Slovenian-Hungarian dictionary, from headword selection to selecting the parts of dictionary microstructure. However, even more important is the fact that the findings have made us consider a more substantial inclusion of contents relevant for language production, gradual publication of the dictionary, prioritizing the

CLOUD EDUCATIONAL RESOURCES FOR PHYSICS LEARNING RESEARCHES SUPPORT

Directory of Open Access Journals (Sweden)

Oleksandr V. Merzlykin

2015-10-01

Full Text Available The definition of cloud educational resource is given in paper. Its program and information components are characterized. The virtualization as the technological ground of transforming from traditional electronic educational resources to cloud ones is reviewed. Such levels of virtualization are described: data storage device virtualization (Data as Service, hardware virtualization (Hardware as Service, computer virtualization (Infrastructure as Service, software system virtualization (Platform as Service, «desktop» virtualization (Desktop as Service, software user interface virtualization (Software as Service. Possibilities of designing the cloud educational resources system for physics learning researches support taking into account standards of learning objects metadata (accessing via OAI-PMH protocol and standards of learning tools interoperability (LTI are shown. The example of integration cloud educational resources into Moodle learning management system with use of OAI-PMH and LTI is given.

Health professional learner attitudes and use of digital learning resources.

Science.gov (United States)

Maloney, Stephen; Chamberlain, Michael; Morrison, Shane; Kotsanas, George; Keating, Jennifer L; Ilic, Dragan

2013-01-16

Web-based digital repositories allow educational resources to be accessed efficiently and conveniently from diverse geographic locations, hold a variety of resource formats, enable interactive learning, and facilitate targeted access for the user. Unlike some other learning management systems (LMS), resources can be retrieved through search engines and meta-tagged labels, and content can be streamed, which is particularly useful for multimedia resources. The aim of this study was to examine usage and user experiences of an online learning repository (Physeek) in a population of physiotherapy students. The secondary aim of this project was to examine how students prefer to access resources and which resources they find most helpful. The following data were examined using an audit of the repository server: (1) number of online resources accessed per day in 2010, (2) number of each type of resource accessed, (3) number of resources accessed during business hours (9 am to 5 pm) and outside business hours (years 1-4), (4) session length of each log-on (years 1-4), and (5) video quality (bit rate) of each video accessed. An online questionnaire and 3 focus groups assessed student feedback and self-reported experiences of Physeek. Students preferred the support provided by Physeek to other sources of educational material primarily because of its efficiency. Peak usage commonly occurred at times of increased academic need (ie, examination times). Students perceived online repositories as a potential tool to support lifelong learning and health care delivery. The results of this study indicate that today's health professional students welcome the benefits of online learning resources because of their convenience and usability. This represents a transition away from traditional learning styles and toward technological learning support and may indicate a growing link between social immersions in Internet-based connections and learning styles. The true potential for Web

Culinary Arts Dictionary 1. Project HIRE.

Science.gov (United States)

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…

Culinary Arts Dictionary 3. Project HIRE.

Science.gov (United States)

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 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…

Culinary Arts Dictionary 2. Project HIRE.

Science.gov (United States)

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…

Learning about water resource sharing through game play

Directory of Open Access Journals (Sweden)

T. Ewen

2016-10-01

Full Text Available Games are an optimal way to teach about water resource sharing, as they allow real-world scenarios to be enacted. Both students and professionals learning about water resource management can benefit from playing games, through the process of understanding both the complexity of sharing of resources between different groups and decision outcomes. Here we address how games can be used to teach about water resource sharing, through both playing and developing water games. An evaluation of using the web-based game Irrigania in the classroom setting, supported by feedback from several educators who have used Irrigania to teach about the sustainable use of water resources, and decision making, at university and high school levels, finds Irrigania to be an effective and easy tool to incorporate into a curriculum. The development of two water games in a course for masters students in geography is also presented as a way to teach and communicate about water resource sharing. Through game development, students learned soft skills, including critical thinking, problem solving, team work, and time management, and overall the process was found to be an effective way to learn about water resource decision outcomes. This paper concludes with a discussion of learning outcomes from both playing and developing water games.

Learning about water resource sharing through game play

Science.gov (United States)

Ewen, Tracy; Seibert, Jan

2016-10-01

Games are an optimal way to teach about water resource sharing, as they allow real-world scenarios to be enacted. Both students and professionals learning about water resource management can benefit from playing games, through the process of understanding both the complexity of sharing of resources between different groups and decision outcomes. Here we address how games can be used to teach about water resource sharing, through both playing and developing water games. An evaluation of using the web-based game Irrigania in the classroom setting, supported by feedback from several educators who have used Irrigania to teach about the sustainable use of water resources, and decision making, at university and high school levels, finds Irrigania to be an effective and easy tool to incorporate into a curriculum. The development of two water games in a course for masters students in geography is also presented as a way to teach and communicate about water resource sharing. Through game development, students learned soft skills, including critical thinking, problem solving, team work, and time management, and overall the process was found to be an effective way to learn about water resource decision outcomes. This paper concludes with a discussion of learning outcomes from both playing and developing water games.

Motivational Factors in Self-Directed Informal Learning from Online Learning Resources

Science.gov (United States)

Song, Donggil; Bonk, Curtis J.

2016-01-01

Learning is becoming more self-directed and informal with the support of emerging technologies. A variety of online resources have promoted informal learning by allowing people to learn on demand and just when needed. It is significant to understand self-directed informal learners' motivational aspects, their learning goals, obstacles, and…

Metadata and Ontologies in Learning Resources Design

Science.gov (United States)

Vidal C., Christian; Segura Navarrete, Alejandra; Menéndez D., Víctor; Zapata Gonzalez, Alfredo; Prieto M., Manuel

Resource design and development requires knowledge about educational goals, instructional context and information about learner's characteristics among other. An important information source about this knowledge are metadata. However, metadata by themselves do not foresee all necessary information related to resource design. Here we argue the need to use different data and knowledge models to improve understanding the complex processes related to e-learning resources and their management. This paper presents the use of semantic web technologies, as ontologies, supporting the search and selection of resources used in design. Classification is done, based on instructional criteria derived from a knowledge acquisition process, using information provided by IEEE-LOM metadata standard. The knowledge obtained is represented in an ontology using OWL and SWRL. In this work we give evidence of the implementation of a Learning Object Classifier based on ontology. We demonstrate that the use of ontologies can support the design activities in e-learning.

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

Directory of Open Access Journals (Sweden)

Yuan Xu

2015-01-01

Full Text Available 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.

Macmillan English Dictionary: The End of Print?

Directory of Open Access Journals (Sweden)

Michael Rundell

2014-12-01

Full Text Available This paper reports on the Macmillan English Dictionary (MED and its transition from printed book to digital-only resource. The background to this decision is explained in terms of changes both in technology and in dictionary-users’ behaviour: was this move inevitable, and will other dictionary publishers follow (sooner or later? The possible downsides of abandoning print are discussed, alongside the advantages of digital media. As well as offering great opportunities (many still unexplored, being online also creates new demands. With easy access to numerous free reference sites, users searching for lexical information have a huge variety of options. Consequently, publishers are under pressure to continually broaden the range of content they supply, to improve the quality of the design and “user experience”, and above all to stay abreast of language change. And, it will be shown, there is much more to keeping a dictionary up to date than simply adding new words as they emerge. The imperative of moving to digital has generated a good deal of turbulence in the world of dictionary publishing (especially for commercial publishers who cannot run at a loss, and there is considerable uncertainty around the long-term survival of “the dictionary” as the autonomous object we are all familiar with. But humans’ communicative needs should ensure a continued demand for high-quality lexical data – even if this data is delivered and accessed in new and different ways.

Dictionary Visions, Research and Practice

DEFF Research Database (Denmark)

This book is about dictionaries and dictionary making. In six thematic sections it presents nineteen contributions covering a wide field within lexicography: Online Lexicography, Dictionary Structure, Phraseology in Dictionaries, LSP Lexicography, Dictionaries and the User, plus Etymology, History...... and Culture in Lexicography. Some chapters focus on theoretical aspects, others report on dictionary work in the making, and still others compare and analyze existing dictionaries. Common to all authors, however, is the concern for the dictionary user. Trivial as it may seem, the fact that dictionaries...

Dictionary Culture of University Students Learning English as a Foreign Language in Turkey

Science.gov (United States)

Baskin, Sami; Mumcu, Muhsin

2018-01-01

Dictionaries, one of the oldest tools of language education, have continued to be a part of education although information technologies and concept of education has changed over time. Until today, with the help of the developments in technology both types of dictionaries have increased, and usage areas have expanded. Therefore, it is possible to…

Neologisms in bilingual digital dictionaries (on the example of Bulgarian-Polish dictionary

Directory of Open Access Journals (Sweden)

Ludmila Dimitrova

2015-11-01

Full Text Available Neologisms in bilingual digital dictionaries (on the example of Bulgarian-Polish dictionary The paper discusses the presentation of neologisms in the recent version of the Bulgarian-Polish digital dictionary. We also continue the discussion of important problems related to the classifiers of the verbs as headwords of the digital dictionary entries. We analyze some examples from ongoing experimental version of the Bulgarian-Polish digital dictionary.

Understanding Behaviors in Videos through Behavior-Specific Dictionaries

DEFF Research Database (Denmark)

Ren, Huamin; Liu, Weifeng; Olsen, Søren Ingvor

2018-01-01

Understanding behaviors is the core of video content analysis, which is highly related to two important applications: abnormal event detection and action recognition. Dictionary learning, as one of the mid-level representations, is an important step to process a video. It has achieved state...

A Sparse Dictionary Learning-Based Adaptive Patch Inpainting Method for Thick Clouds Removal from High-Spatial Resolution Remote Sensing Imagery.

Science.gov (United States)

Meng, Fan; Yang, Xiaomei; Zhou, Chenghu; Li, Zhi

2017-09-15

Cloud cover is inevitable in optical remote sensing (RS) imagery on account of the influence of observation conditions, which limits the availability of RS data. Therefore, it is of great significance to be able to reconstruct the cloud-contaminated ground information. This paper presents a sparse dictionary learning-based image inpainting method for adaptively recovering the missing information corrupted by thick clouds patch-by-patch. A feature dictionary was learned from exemplars in the cloud-free regions, which was later utilized to infer the missing patches via sparse representation. To maintain the coherence of structures, structure sparsity was brought in to encourage first filling-in of missing patches on image structures. The optimization model of patch inpainting was formulated under the adaptive neighborhood-consistency constraint, which was solved by a modified orthogonal matching pursuit (OMP) algorithm. In light of these ideas, the thick-cloud removal scheme was designed and applied to images with simulated and true clouds. Comparisons and experiments show that our method can not only keep structures and textures consistent with the surrounding ground information, but also yield rare smoothing effect and block effect, which is more suitable for the removal of clouds from high-spatial resolution RS imagery with salient structures and abundant textured features.

The fluidities of digital learning environments and resources

DEFF Research Database (Denmark)

Hansbøl, Mikala

2012-01-01

The research project “Educational cultures and serious games on a global market place” (2009-2011) dealt with the challenge of the digital learning environment and hence it’s educational development space always existing outside the present space and hence scope of activities. With a reference...... and establishments of the virtual universe called Mingoville.com, the research shows a need to include in researchers’ conceptualizations of digital learning environments and resources, their shifting materialities and platformations and hence emerging (often unpredictable) agencies and educational development...... spaces. Keywords: Fluidity, digital learning environment, digital learning resource, educational development space...

Dictionary of distances

CERN Document Server

Deza, Michel-Marie

2006-01-01

This book comes out of need and urgency (expressed especially in areas of Information Retrieval with respect to Image, Audio, Internet and Biology) to have a working tool to compare data.The book will provide powerful resource for all researchers using Mathematics as well as for mathematicians themselves. In the time when over-specialization and terminology fences isolate researchers, this Dictionary try to be ""centripedal"" and ""oikoumeni"", providing some access and altitude of vision but without taking the route of scientific vulgarisation. This attempted balance is the main philosophy

The SMAP Dictionary Management System

Science.gov (United States)

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.

EMITEL: E-Encyclopaedia and E-Dictionary of Medical Imaging Technologies

International Nuclear Information System (INIS)

Medvedec, M.; Kovacevic, N.; Magjarevic, R.

2011-01-01

EMITEL (European Medical Imaging Technology e-Encyclopaedia for Lifelong Learning) is an electronic encyclopaedia and multilingual dictionary related to medical imaging technologies. It is a result of the multi-annual international project which involved more than 250 contributors from 35 countries, aiming to foster development of medical physics and biomedical/clinical engineering by a lifelong e-learning web tool for all interested individuals or groups. Currently, the encyclopaedia is equivalent to about 2100 hard copy pages and includes about 3300 terms with an explanatory article for each term. The dictionary provides bidirectional cross-translation of terms between any two among 28 languages from its current database. Dictionary entries are divided into seven groups: diagnostic radiology, nuclear medicine, radiotherapy, magnetic resonance imaging, ultrasound imaging, radiation protection and general terms. Croatian language was implemented in EMITEL dictionary in April 2010. There were 17 Croatian translators and reviewers from 8 institutions and 3 cities, ranging from medical physics experts to linguist. The basic terminological principles of translation were final intelligibility of terms, desirable Croatian origin and linguistic appropriateness. Croatian contribution in the actual phase of EMITEL project attempted to improve the quality and efficiency of the specific professional, scientific and teaching terminology. A sort of novel, consistent and verified pool of terms of emerging medical imaging technologies was built up, as a one small part of the process of developing information technologies and socio-cultural transition from the industrial society into the society of knowledge. (author)

Efficient generation of pronunciation dictionaries: human factors factors during bootstrapping

CSIR Research Space (South Africa)

Davel, MH

2004-10-01

Full Text Available Bootstrapping techniques have significant potential for the efficient generation of linguistic resources such as electronic pronunciation dictionaries. The authors describe a system and an approach to bootstrapping for the development...

Self-Similarity Superresolution for Resource-Constrained Image Sensor Node in Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Yuehai Wang

2014-01-01

Full Text Available Wireless sensor networks, in combination with image sensors, open up a grand sensing application field. It is a challenging problem to recover a high resolution (HR image from its low resolution (LR counterpart, especially for low-cost resource-constrained image sensors with limited resolution. Sparse representation-based techniques have been developed recently and increasingly to solve this ill-posed inverse problem. Most of these solutions are based on an external dictionary learned from huge image gallery, consequently needing tremendous iteration and long time to match. In this paper, we explore the self-similarity inside the image itself, and propose a new combined self-similarity superresolution (SR solution, with low computation cost and high recover performance. In the self-similarity image super resolution model (SSIR, a small size sparse dictionary is learned from the image itself by the methods such as KSVD. The most similar patch is searched and specially combined during the sparse regulation iteration. Detailed information, such as edge sharpness, is preserved more faithfully and clearly. Experiment results confirm the effectiveness and efficiency of this double self-learning method in the image super resolution.

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

International Nuclear Information System (INIS)

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

2014-01-01

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

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

Energy Technology Data Exchange (ETDEWEB)

Gou, S; Rapacchi, S; Hu, P; Sheng, K [UCLA School of Medicine, Los Angeles, CA (United States)

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.

From Data to Dictionary

DEFF Research Database (Denmark)

Nielsen, Sandro; Almind, Richard

2011-01-01

definitions, thereby creating both monolingual and bilingual dictionaries. Users access the data through online dictionaries that allow them to make structured searches. The dictionaries mainly provide help in communicative situations such as understanding, producing and translating accounting texts, but also......Most online dictionaries are based on printed dictionaries or specially developed databases. However, these dictionaries do not fully satisfy the needs for help and knowledge users have, so a re-assessment of the practical and theoretical foundation is necessary. Based on the work on the Accounting...... help users acquire knowledge about general or specific accounting matters in cognitive user situations. This theoretical foundation allows lexicographers to develop dictionaries that search in structured data sets and then present data types selected because they provide help in specific situations...

Could a Multimodal Dictionary Serve as a Learning Tool? An Examination of the Impact of Technologically Enhanced Visual Glosses on L2 Text Comprehension

Science.gov (United States)

Sato, Takeshi

2016-01-01

This study examines the efficacy of a multimodal online bilingual dictionary based on cognitive linguistics in order to explore the advantages and limitations of explicit multimodal L2 vocabulary learning. Previous studies have examined the efficacy of the verbal and visual representation of words while reading L2 texts, concluding that it…

Insurance dictionary

International Nuclear Information System (INIS)

Mueller-Lutz, H.L.

1984-01-01

Special technical terms used in the world of insurance can hardly be found in general dictionaries. This is a gap which the 'Insurance dictionary' now presented is designed to fill. In view of its supplementary function, the number of terms covered is limited to 1200. To make this dictionary especially convenient for ready reference, only the most commonly used translations are given for each key word in any of the four languages. This dictionary is subdivided into four parts, each containing the translation of the selected terms in the three other languages. To further facilitate the use of the booklet, paper of different colours was used for the printing of the German, English, French and Greek sections. The present volume was developed from a Swedish insurance dictionary (Fickordbok Foersaekring), published in 1967, which - with Swedish as the key language- offers English, French and German translations of the basic insurance terms. (orig./HP) [de

Data Presentation Structures in Specialised Dictionaries: Law Dictionaries with Communicative Functions

DEFF Research Database (Denmark)

Nielsen, Sandro

2015-01-01

data stand out, lexicographers should prioritize functional data that are directly related to and support the function(s) of dictionaries on a need-to-have/nice-to have basis, because data presentation structures with functional focus may better help users achieve their intended goals, i.e. finding......Theoretical lexicographers have developed a range of elaborate structures to describe the arrangement of data inside dictionaries, in particular in dictionary articles. However, most of these structures have been developed on the basis of detailed analyses of print dictionaries and relatively...... little has been said about the arrangement of data in e-dictionaries. The relevant data types are lexicographical data providing help concerning the function(s) and use of dictionaries on search results pages. In order to create a visual hierarchy on screen that makes the most important search result...

The gap between medical faculty's perceptions and use of e-learning resources.

Science.gov (United States)

Kim, Kyong-Jee; Kang, Youngjoon; Kim, Giwoon

2017-01-01

e-Learning resources have become increasingly popular in medical education; however, there has been scant research on faculty perceptions and use of these resources. To investigate medical faculty's use of e-learning resources and to draw on practical implications for fostering their use of such resources. Approximately 500 full-time faculty members in 35 medical schools across the nation in South Korea were invited to participate in a 30-item questionnaire on their perceptions and use of e-learning resources in medical education. The questionnaires were distributed in both online and paper formats. Descriptive analysis and reliability analysis were conducted of the data. Eighty faculty members from 28 medical schools returned the questionnaires. Twenty-two percent of respondents were female and 78% were male, and their rank, disciplines, and years of teaching experience all varied. Participants had positive perceptions of e-learning resources in terms of usefulness for student learning and usability; still, only 39% of them incorporated those resources in their teaching. The most frequently selected reasons for not using e-learning resources in their teaching were 'lack of resources relevant to my lectures,' 'lack of time to use them during lectures,' and 'was not aware of their availability.' Our study indicates a gap between medical faculty's positive perceptions of e-learning resources and their low use of such resources. Our findings highlight the needs for further study of individual and institutional barriers to faculty adoption of e-learning resources to bridge this gap.

Werner Hüllen. English Dictionaries 800–1700: The Topical ...

African Journals Online (AJOL)

rbr

Monographs attempting to treat the history of English dictionaries have to restrict their .... learning, but contemporary learners have printed books and note-paper to ..... His 'scientific' principle was to include all the synonyms in the language.

Low-dose CT image reconstruction using gain intervention-based dictionary learning

Science.gov (United States)

Pathak, Yadunath; Arya, K. V.; Tiwari, Shailendra

2018-05-01

Computed tomography (CT) approach is extensively utilized in clinical diagnoses. However, X-ray residue in human body may introduce somatic damage such as cancer. Owing to radiation risk, research has focused on the radiation exposure distributed to patients through CT investigations. Therefore, low-dose CT has become a significant research area. Many researchers have proposed different low-dose CT reconstruction techniques. But, these techniques suffer from various issues such as over smoothing, artifacts, noise, etc. Therefore, in this paper, we have proposed a novel integrated low-dose CT reconstruction technique. The proposed technique utilizes global dictionary-based statistical iterative reconstruction (GDSIR) and adaptive dictionary-based statistical iterative reconstruction (ADSIR)-based reconstruction techniques. In case the dictionary (D) is predetermined, then GDSIR can be used and if D is adaptively defined then ADSIR is appropriate choice. The gain intervention-based filter is also used as a post-processing technique for removing the artifacts from low-dose CT reconstructed images. Experiments have been done by considering the proposed and other low-dose CT reconstruction techniques on well-known benchmark CT images. Extensive experiments have shown that the proposed technique outperforms the available approaches.

Discovery and Use of Online Learning Resources: Case Study Findings

OpenAIRE

Laurie Miller Nelson; James Dorward; Mimi M. Recker

2004-01-01

Much recent research and funding have focused on building Internet-based repositories that contain collections of high-quality learning resources, often called learning objects. Yet little is known about how non-specialist users, in particular teachers, find, access, and use digital learning resources. To address this gap, this article describes a case study of mathematics and science teachers practices and desires surrounding the discovery, selection, and use of digital library resources for...

Data-Dictionary-Editing Program

Science.gov (United States)

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.

The English Monolingual Dictionary: Its Use among Second Year Students of University Technology of Malaysia, International Campus, Kuala Lumpur

Directory of Open Access Journals (Sweden)

Amerrudin Abd. Manan

2011-07-01

Full Text Available This research was conducted to seek information on English Monolingual Dictionary (EMD use among 2nd year students of Universiti Teknologi Malaysia, International Campus, Kuala Lumpur (UTMKL. Specifically, the researchers wish to discover, firstly the students’ habit and attitude in EMD use; secondly, to discover their knowledge with regard to the language learning resources available in EMD; thirdly, to discover their skill in using EMD, and finally, to discover whether there were formal instructions in EMD use when they were studying in their former schools and tertiary education. One hundred and ninety-six students took part in the survey by answering a questionnaire. The results of the study reveal that the respondents were poor users of EMD. They rarely consulted the EMD; their knowledge of the language learning resources in the EMD was limited; most perceived their EMD skill as average, and there was no instruction in EMD when they were at tertiary education and previously when they were at school.

Sentiment analysis of political communication: combining a dictionary approach with crowdcoding.

Science.gov (United States)

Haselmayer, Martin; Jenny, Marcelo

2017-01-01

Sentiment is important in studies of news values, public opinion, negative campaigning or political polarization and an explosive expansion of digital textual data and fast progress in automated text analysis provide vast opportunities for innovative social science research. Unfortunately, tools currently available for automated sentiment analysis are mostly restricted to English texts and require considerable contextual adaption to produce valid results. We present a procedure for collecting fine-grained sentiment scores through crowdcoding to build a negative sentiment dictionary in a language and for a domain of choice. The dictionary enables the analysis of large text corpora that resource-intensive hand-coding struggles to cope with. We calculate the tonality of sentences from dictionary words and we validate these estimates with results from manual coding. The results show that the crowdbased dictionary provides efficient and valid measurement of sentiment. Empirical examples illustrate its use by analyzing the tonality of party statements and media reports.

Discovery and Use of Online Learning Resources: Case Study Findings

Science.gov (United States)

Recker, Mimi M.; Dorward, James; Nelson, Laurie Miller

2004-01-01

Much recent research and funding have focused on building Internet-based repositories that contain collections of high-quality learning resources, often called "learning objects." Yet little is known about how non-specialist users, in particular teachers, find, access, and use digital learning resources. To address this gap, this article…

Incorporating High-Frequency Physiologic Data Using Computational Dictionary Learning Improves Prediction of Delayed Cerebral Ischemia Compared to Existing Methods.

Science.gov (United States)

Megjhani, Murad; Terilli, Kalijah; Frey, Hans-Peter; Velazquez, Angela G; Doyle, Kevin William; Connolly, Edward Sander; Roh, David Jinou; Agarwal, Sachin; Claassen, Jan; Elhadad, Noemie; Park, Soojin

2018-01-01

Accurate prediction of delayed cerebral ischemia (DCI) after subarachnoid hemorrhage (SAH) can be critical for planning interventions to prevent poor neurological outcome. This paper presents a model using convolution dictionary learning to extract features from physiological data available from bedside monitors. We develop and validate a prediction model for DCI after SAH, demonstrating improved precision over standard methods alone. 488 consecutive SAH admissions from 2006 to 2014 to a tertiary care hospital were included. Models were trained on 80%, while 20% were set aside for validation testing. Modified Fisher Scale was considered the standard grading scale in clinical use; baseline features also analyzed included age, sex, Hunt-Hess, and Glasgow Coma Scales. An unsupervised approach using convolution dictionary learning was used to extract features from physiological time series (systolic blood pressure and diastolic blood pressure, heart rate, respiratory rate, and oxygen saturation). Classifiers (partial least squares and linear and kernel support vector machines) were trained on feature subsets of the derivation dataset. Models were applied to the validation dataset. The performances of the best classifiers on the validation dataset are reported by feature subset. Standard grading scale (mFS): AUC 0.54. Combined demographics and grading scales (baseline features): AUC 0.63. Kernel derived physiologic features: AUC 0.66. Combined baseline and physiologic features with redundant feature reduction: AUC 0.71 on derivation dataset and 0.78 on validation dataset. Current DCI prediction tools rely on admission imaging and are advantageously simple to employ. However, using an agnostic and computationally inexpensive learning approach for high-frequency physiologic time series data, we demonstrated that we could incorporate individual physiologic data to achieve higher classification accuracy.

French Dictionaries. Series: Specialised Bibliographies.

Science.gov (United States)

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,…

Lexicographic Approaches to Sense Disambiguation in Monolingual Dictionaries and Equivalent Differentiation in Bilingual Dictionaries

Directory of Open Access Journals (Sweden)

Marjeta Vrbinc

2011-05-01

Full Text Available The article discusses methods of sense disambiguation in monolingual dictionaries and equivalent differentiation in bilingual dictionaries. In current dictionaries, sense disambiguation and equivalent differentiation is presented in the form of specifiers or glosses, collocators or indications of context, (domain labels, metalinguistic and encyclopaedic information. Each method is presented and illustrated by actual samples of dictionary articles taken from mono and bilingual dictionaries. The last part of the article is devoted to equivalent differentiation in bilingual decoding dictionaries. In bilingual dictionaries, equivalent differentiation is often needed to describe the lack of agreement between the source language (SL and target language (TL. The article concludes by stating that equivalent differentiation should be written in the native language of the target audience and sense indicators in a monolingual learner’s dictionary should be words that the users are most familiar with.

Learning dictionaries of sparse codes of 3D movements of body joints for real-time human activity understanding.

Science.gov (United States)

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.

Learning dictionaries of sparse codes of 3D movements of body joints for real-time human activity understanding.

Directory of Open Access Journals (Sweden)

Jin Qi

Full Text Available 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.

Dr.Johnson's Dictionary in Miniature

OpenAIRE

Imazato, Chiaki

1988-01-01

More than hundred 'Johnson's' dictionaries have so far been published not only in English but in other countries, and there are numerous books and articles on Johnson's Dictionary. But few have referred to Johnson's Dictionary in Miniature; nor were there any books or articles on it. Fortunately, however, I've got one copy of Johnson's Dictionary in Miniature, which was published in 1806. Johnson's Dictionary (1755) has 41,677 entries, whereas Johnson's Dictionary in Miniature 23,439 entr...

The Dictionary Unit for South African English. South African Concise Oxford Dictionary

Directory of Open Access Journals (Sweden)

Rajend Mesthrie

2011-10-01

Full Text Available The South African Concise Oxford Dictionary (henceforth SACOD is a South Af-rican version of the Concise Oxford Dictionary, the first time that this particular hybrid has been prepared. It is testimony to the enduring success of the work of the Dictionary Unit for South African English at Rhodes University, headed by teams that included Jean and William Branford in the 1970s, Penny Silva in the 1990s and now, Kathryn Kavanagh. The lexicographical work from the unit saw the publication of four editions of the Dictionary of Southern African English (1978, 1980, 1987, 1991, a South African Pocket Oxford Dictionary (SAPOD and the Dictionary of South African English on Historical Principles (DOSAEHP (1995. SACOD differs from the rest in several ways. It is larger in scope than SAPOD, smaller than DOSAEHP, and unlike DOSAE and DOSAEHP, does not deal with South African words alone. Based on the 10th edition of the Concise Oxford Dictionary SACOD has excised some words from the parent, whilst adding many new words of general English as well as of South Africa.

Classic Classroom Activities: The Oxford Picture Dictionary Program.

Science.gov (United States)

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…

eLearning resources to supplement postgraduate neurosurgery training.

OpenAIRE

Stienen, MN; Schaller, K; Cock, H; Lisnic, V; Regli, L; Thomson, S

2017-01-01

BACKGROUND: In an increasingly complex and competitive professional environment, improving methods to educate neurosurgical residents is key to ensure high-quality patient care. Electronic (e)Learning resources promise interactive knowledge acquisition. We set out to give a comprehensive overview on available eLearning resources that aim to improve postgraduate neurosurgical training and review the available literature. MATERIAL AND METHODS: A MEDLINE query was performed, using the search ter...

The Effectiveness of Using a Bilingualized Dictionary for ...

African Journals Online (AJOL)

user

Learner's English–Chinese Dictionary 8 (OALECD8), by advanced Hong Kong Cantonese ESL learn- ers in the ... Whether the perceptual system of noun countability that native ...... the use of the word with an (e.g. an awareness of the importance of eating a healthy diet ..... Language, Culture and Curriculum 24(1): 1-21.

SYNONYMS IN GERMAN ONLINE MONOLINGUAL DICTIONARIES

Directory of Open Access Journals (Sweden)

Paloma Sánchez Hernández

2017-03-01

possibilities at their disposal with which to express themselves. From these possibilities, the user can choose the one that best suits his or her purpose based on a variety of requisites, such as the type of text, stylistic recourses and so on, allowing the most fitting linguistic element to be inserted into the text. Another related objective is learning the ways in which paradigmatic information is reflected in these dictionaries. Thus, the differences between general monolingual dictionaries presenting paradigmatic information and paradigmatic dictionaries are revealed

MEAT: An Authoring Tool for Generating Adaptable Learning Resources

Science.gov (United States)

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…

Measuring learning gain: Comparing anatomy drawing screencasts and paper-based resources.

Science.gov (United States)

Pickering, James D

2017-07-01

The use of technology-enhanced learning (TEL) resources is now a common tool across a variety of healthcare programs. Despite this popular approach to curriculum delivery there remains a paucity in empirical evidence that quantifies the change in learning gain. The aim of the study was to measure the changes in learning gain observed with anatomy drawing screencasts in comparison to a traditional paper-based resource. Learning gain is a widely used term to describe the tangible changes in learning outcomes that have been achieved after a specific intervention. In regard to this study, a cohort of Year 2 medical students voluntarily participated and were randomly assigned to either a screencast or textbook group to compare changes in learning gain across resource type. Using a pre-test/post-test protocol, and a range of statistical analyses, the learning gain was calculated at three test points: immediate post-test, 1-week post-test and 4-week post-test. Results at all test points revealed a significant increase in learning gain and large effect sizes for the screencast group compared to the textbook group. Possible reasons behind the difference in learning gain are explored by comparing the instructional design of both resources. Strengths and weaknesses of the study design are also considered. This work adds to the growing area of research that supports the effective design of TEL resources which are complimentary to the cognitive theory of multimedia learning to achieve both an effective and efficient learning resource for anatomical education. Anat Sci Educ 10: 307-316. © 2016 American Association of Anatomists. © 2016 American Association of Anatomists.

Dictionary Snakes

DEFF Research Database (Denmark)

Dahl, Anders Bjorholm; Dahl, Vedrana Andersen

2014-01-01

for image segmentation that operates without training data. Our method is based on a probabilistic dictionary of image patches coupled with a deformable model inspired by snakes and active contours without edges. We separate the image into two classes based on the information provided by the evolving curve......, which moves according to the probabilistic information obtained from the dictionary. Initially, the image patches are assigned to the nearest dictionary element, where the image is sampled at each pixel such that patches overlap. The curve divides the image into an inside and an outside region allowing...... us to estimate the pixel-wise probability of the dictionary elements. In each iteration we evolve the curve and update the probabilities, which merges similar texture patterns and pulls dissimilar patterns apart. We experimentally evaluate our approach, and show how textured objects are precisely...

Multilingualism and Dictionaries

Directory of Open Access Journals (Sweden)

Wojciech Paweł Sosnowski

2015-12-01

Full Text Available Multilingualism and Dictionaries The Russian-Bulgarian-Polish dictionary that we (Wojciech Sosnowski, Violetta Koseska-Toszewa and Anna Kisiel are currently developing has no precedent as far as its theoretical foundations and its structure are concerned. The dictionary offers a unique combination of three Slavic languages that belong to three different groups: a West Slavic language (Polish, a South Slavic language (Bulgarian and an East Slavic language (Russian. The dictionary describes semantic and syntactic equivalents of words between the languages. When completed, the dictionary will contain around 30,000 entries. The principle we build the dictionary on is that every language should be given equal status. Many of our data come from the Parallel Polish-Bulgarian-Russian corpus developed by us as part of the CLARIN-PL initiative. In the print version, the entries come in the order of the Cyrillic alphabet and they are not numbered (except for homonyms, which are disambiguated with Roman numbers. We selected the lemmas for the dictionary on the basis of their frequency in the corpus. Our dictionary is the first dictionary to include forms of address and most recent neologisms in the three languages. Faithful to the recent developments in contrastive linguistics, we begin with a form from the dictionary’s primary language and we define it in Polish. Subsequently, based on this definition, we try to find an equivalent in the second and the third language. Therefore, the meaning comes first and only then we look for the form (i.e. the equivalent that corresponds to this meaning. This principle, outlined in Gramatyka konfrontatywna języków polskiego i bułgarskiego (GKBP, allows us to treat data from multiple languages as equal. In the dictionary, we draw attention to the correct choice of equivalents in translation; we also provide categorisers that indicate the meaning of verbal tenses and aspects. The definitions of states, events and

Dictionary Writing System (DWS) + Corpus Query Package (CQP ...

African Journals Online (AJOL)

In this article the integrated corpus query functionality of the dictionary compilation software TshwaneLex is analysed. Attention is given to the handling of both raw corpus data and annotated corpus data. With regard to the latter it is shown how, with a minimum of human effort, machine learning techniques can be employed ...

Big Data X-Learning Resources Integration and Processing in Cloud Environments

Directory of Open Access Journals (Sweden)

Kong Xiangsheng

2014-09-01

Full Text Available The cloud computing platform has good flexibility characteristics, more and more learning systems are migrated to the cloud platform. Firstly, this paper describes different types of educational environments and the data they provide. Then, it proposes a kind of heterogeneous learning resources mining, integration and processing architecture. In order to integrate and process the different types of learning resources in different educational environments, this paper specifically proposes a novel solution and massive storage integration algorithm and conversion algorithm to the heterogeneous learning resources storage and management cloud environments.

Discovery and Use of Online Learning Resources: Case Study Findings

Directory of Open Access Journals (Sweden)

Laurie Miller Nelson

2004-04-01

Full Text Available Much recent research and funding have focused on building Internet-based repositories that contain collections of high-quality learning resources, often called ‘learning objects.’ Yet little is known about how non-specialist users, in particular teachers, find, access, and use digital learning resources. To address this gap, this article describes a case study of mathematics and science teachers’ practices and desires surrounding the discovery, selection, and use of digital library resources for instructional purposes. Findings suggest that the teacher participants used a broad range of search strategies in order to find resources that they deemed were age-appropriate, current, and accurate. They intended to include these resources with little modifications into planned instructional activities. The article concludes with a discussion of the implications of the findings for improving the design of educational digital library systems, including tools supporting resource reuse.

ONE OF THE TURKISH-ARABIC VERSE DICTIONARIES: SÜBHA-İ SIBYÁN -1- (INVESTIGATION / TÜRKÇE-ARAPÇA MANZUM SÖZLÜKLERDEN SÜBHA-İ SIBYÁN -1- (İNCELEME

Directory of Open Access Journals (Sweden)

Assoc. Prof. Dr. Atabey KILIÇ

2006-12-01

Full Text Available We learn from sourches that many dictionaries had been written for Turkish, Arabic and Persian. But we must make it clear that there is a tradition of writing verse dictionary in the Classical Turkish Poem which is not seen in the other literatures. According to many sources we understand these dictionaries are not known and they are far from being significant and persuasive sources in science. These dictionaries have importance for children to learn “aruz/prosody” and also Arabic, Persian words and their meanings in Turkish. Children learn these words easily with the help of these dictionaries. So they take attention from this point. İt is an important point that in many verse dictionaries’ foreword part it is said that “lugat ilmi/dictionary science” make people clever and poets say that even they memorized at least one verse dictionary in their childhood. When we published Mustafa b. Osman Keskin’s “Manzûme-i Keskin” called Turkish-Arabic-Persian verse dictionary in 2001, we saw that there are verse dictionaries more than 30 in Classical Turkish Literature. We believe in it is possible that there are many unknown verse dictionaries.In this article we aim to give knowledge about Sübha-i Sıbyân Turkish-Arabic verse dictionary which has at least 50-60 copies in libraries and which has been published 30 times between 1801-1900. This dictionary has about 460 couplets and had been used as a text-book in Sıbyân Schools.

Compiling the First Monolingual Lusoga Dictionary

Directory of Open Access Journals (Sweden)

Minah Nabirye

2011-10-01

Full Text Available

Abstract: In this research article a study is made of the approach followed to compile the first-ever monolingual dictionary for Lusoga. Lusoga is a Bantu language spoken in Uganda by slightly over two mil-lion people. Being an under-resourced language, the Lusoga orthography had to be designed, a grammar written, and a corpus built, before embarking on the compilation of the dictionary. This compilation was aimed at attaining an academic degree, hence requiring a rigorous research methodology. Firstly, the prevail-ing methods for compiling dictionaries were mainly practical and insufficient in explaining the theoretical linguistic basis for dictionary compilation. Since dictionaries are based on meaning, the theory of meaning was used to account for all linguistic data considered in dictionaries. However, meaning is considered at a very abstract level, far removed from the process of compiling dictionaries. Another theory, the theory of modularity, was used to bridge the gap between the theory of meaning and the compilation process. The modular theory explains how the different modules of a language contribute information to the different parts of the dictionary article or dictionary information in general. Secondly, the research also had to contend with the different approaches for analysing Bantu languages for Bantu and European audiences. A descrip-tion of the Bantu- and European-centred approaches to Bantu studies was undertaken in respect of (a the classification of Lusoga words, and (b the specification of their citations. As a result, Lusoga lexicography deviates from the prevailing Bantu classification and citation of nouns, adjectives and verbs in particular. The dictionary was tested on two separate occasions and all the feedback was considered in the compilation pro-cess. This article, then, gives an overall summary of all the steps involved in the compilation of the Eiwanika ly'Olusoga, i.e. the Monolingual Lusoga Dictionary

Resource Guide for Persons with Learning Impairments.

Science.gov (United States)

IBM, Atlanta, GA. National Support Center for Persons with Disabilities.

The resource guide identifies products which assist learning disabled and mentally retarded individuals in accessing IBM (International Business Machine) Personal Computers or the IBM Personal System/2 family of products. An introduction provides a general overview of ways computers can help learning disabled or retarded persons. The document then…

Incorporating High-Frequency Physiologic Data Using Computational Dictionary Learning Improves Prediction of Delayed Cerebral Ischemia Compared to Existing Methods

Directory of Open Access Journals (Sweden)

Murad Megjhani

2018-03-01

Full Text Available PurposeAccurate prediction of delayed cerebral ischemia (DCI after subarachnoid hemorrhage (SAH can be critical for planning interventions to prevent poor neurological outcome. This paper presents a model using convolution dictionary learning to extract features from physiological data available from bedside monitors. We develop and validate a prediction model for DCI after SAH, demonstrating improved precision over standard methods alone.Methods488 consecutive SAH admissions from 2006 to 2014 to a tertiary care hospital were included. Models were trained on 80%, while 20% were set aside for validation testing. Modified Fisher Scale was considered the standard grading scale in clinical use; baseline features also analyzed included age, sex, Hunt–Hess, and Glasgow Coma Scales. An unsupervised approach using convolution dictionary learning was used to extract features from physiological time series (systolic blood pressure and diastolic blood pressure, heart rate, respiratory rate, and oxygen saturation. Classifiers (partial least squares and linear and kernel support vector machines were trained on feature subsets of the derivation dataset. Models were applied to the validation dataset.ResultsThe performances of the best classifiers on the validation dataset are reported by feature subset. Standard grading scale (mFS: AUC 0.54. Combined demographics and grading scales (baseline features: AUC 0.63. Kernel derived physiologic features: AUC 0.66. Combined baseline and physiologic features with redundant feature reduction: AUC 0.71 on derivation dataset and 0.78 on validation dataset.ConclusionCurrent DCI prediction tools rely on admission imaging and are advantageously simple to employ. However, using an agnostic and computationally inexpensive learning approach for high-frequency physiologic time series data, we demonstrated that we could incorporate individual physiologic data to achieve higher classification accuracy.

Learning foreign languages in teletandem: Resources and strategies

Directory of Open Access Journals (Sweden)

João A. TELLES

2015-12-01

Full Text Available ABSTRACT Teletandem is a virtual, collaborative, and autonomous context in which two speakers of different languages use the text, voice, and webcam image resources of VOIP technology (Skype to help each other learn their native language (or language of proficiency. This paper focuses on learners' studying processes and their responses to teletandem. We collected quantitative and qualitative data from 134 university students through an online questionnaire. Results show the content of students' learning processes, resources, activities, and strategies. We conclude with a critical discussion of the results and raise pedagogical implications for the use o-f teletandem as a mode of online intercultural contact to learn foreign languages.

Dictionaries of Canadian English | Considine | Lexikos

African Journals Online (AJOL)

... its best, reached a high degree of sophistication, there are still major opportunities waiting to be taken. keywords: dictionary, lexicography, canadian english, canadianisms, national dictionaries, canadian french, canadian first nations lan-guages, bilingual dictionaries, regional dictionaries, unfinished diction-ary projects ...

Learning the Greek Language via Greeklish

Directory of Open Access Journals (Sweden)

Alexandros Karakos

2013-02-01

Full Text Available Learning Greek as a second or foreign language has drawn the attention of many researchers throughout time. A dictionary is amongst the first things a foreign language student uses. Reading comprehension is significantly improved by the use of a dictionary, especially when this includes the way words are pronounced. We developed a assistance software for learning the Greek Language via Greeklish. Since, the basic vocabulary of a language is the basis of understanding the language itself, the dictionary proposed aims to make the basic Greek words easier to pronounce as well as to give the explanation of the word in English. The aim of this software is to provide a useful tool to learn the Greek language individually. Moreover, it aims to be involved, as an assistance tool for learning Greek as a second or foreign language.

Learning Networks: connecting people, organizations, autonomous agents and learning resources to establish the emergence of effective lifelong learning

NARCIS (Netherlands)

Koper, Rob; Sloep, Peter

2003-01-01

Koper, E.J.R., Sloep, P.B. (2002) Learning Networks connecting people, organizations, autonomous agents and learning resources to establish the emergence of effective lifelong learning. RTD Programma into Learning Technologies 2003-2008. More is different… Heerlen, Nederland: Open Universiteit

Emo, love and god: making sense of Urban Dictionary, a crowd-sourced online dictionary.

Science.gov (United States)

Nguyen, Dong; McGillivray, Barbara; Yasseri, Taha

2018-05-01

The Internet facilitates large-scale collaborative projects and the emergence of Web 2.0 platforms, where producers and consumers of content unify, has drastically changed the information market. On the one hand, the promise of the 'wisdom of the crowd' has inspired successful projects such as Wikipedia, which has become the primary source of crowd-based information in many languages. On the other hand, the decentralized and often unmonitored environment of such projects may make them susceptible to low-quality content. In this work, we focus on Urban Dictionary, a crowd-sourced online dictionary. We combine computational methods with qualitative annotation and shed light on the overall features of Urban Dictionary in terms of growth, coverage and types of content. We measure a high presence of opinion-focused entries, as opposed to the meaning-focused entries that we expect from traditional dictionaries. Furthermore, Urban Dictionary covers many informal, unfamiliar words as well as proper nouns. Urban Dictionary also contains offensive content, but highly offensive content tends to receive lower scores through the dictionary's voting system. The low threshold to include new material in Urban Dictionary enables quick recording of new words and new meanings, but the resulting heterogeneous content can pose challenges in using Urban Dictionary as a source to study language innovation.

Kirkeby's English–Swahili Dictionary

African Journals Online (AJOL)

rbr

largest Swahili dictionary is the Swahili–French dictionary of Sacleux (1939) with 1 112 pages. Kirkeby ... An entry in this dictionary could be a basic form, a derived or inflectional form of the ...... Cf. cook, boil, fry, roast, bake, etc. (cookery); ugali ...

Using Bilingual Dictionaries.

Science.gov (United States)

Thompson, Geoff

1987-01-01

Monolingual dictionaries have serious disadvantages in many language teaching situations; bilingual dictionaries are potentially more efficient and more motivating sources of information for language learners. (Author/CB)

Learn-and-Adapt Stochastic Dual Gradients for Network Resource Allocation

OpenAIRE

Chen, Tianyi; Ling, Qing; Giannakis, Georgios B.

2017-01-01

Network resource allocation shows revived popularity in the era of data deluge and information explosion. Existing stochastic optimization approaches fall short in attaining a desirable cost-delay tradeoff. Recognizing the central role of Lagrange multipliers in network resource allocation, a novel learn-and-adapt stochastic dual gradient (LA-SDG) method is developed in this paper to learn the sample-optimal Lagrange multiplier from historical data, and accordingly adapt the upcoming resource...

Building a protein name dictionary from full text: a machine learning term extraction approach

Directory of Open Access Journals (Sweden)

Campagne Fabien

2005-04-01

Full Text Available Abstract Background The majority of information in the biological literature resides in full text articles, instead of abstracts. Yet, abstracts remain the focus of many publicly available literature data mining tools. Most literature mining tools rely on pre-existing lexicons of biological names, often extracted from curated gene or protein databases. This is a limitation, because such databases have low coverage of the many name variants which are used to refer to biological entities in the literature. Results We present an approach to recognize named entities in full text. The approach collects high frequency terms in an article, and uses support vector machines (SVM to identify biological entity names. It is also computationally efficient and robust to noise commonly found in full text material. We use the method to create a protein name dictionary from a set of 80,528 full text articles. Only 8.3% of the names in this dictionary match SwissProt description lines. We assess the quality of the dictionary by studying its protein name recognition performance in full text. Conclusion This dictionary term lookup method compares favourably to other published methods, supporting the significance of our direct extraction approach. The method is strong in recognizing name variants not found in SwissProt.

Do Dictionaries Help Students Write?

Science.gov (United States)

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'…

How Dictionary Users Choose Senses in Bilingual Dictionary Entries ...

African Journals Online (AJOL)

advanced Polish learners of English, consulted 26 Polish-to-English dictionary pages prompted with a sentence translation task. ... structural involvedness of dictionaries themselves, the quality of the data returned is questionable. In contrast ...... scans patterned differently. They tended to be more rapid and the landing.

Syntactic and Semantic Specifications in Online English Learners' Dictionaries

Science.gov (United States)

Rizo-Rodriguez, Alfonso

2009-01-01

Among the multifarious linguistic resources currently available on the Internet, learners of English as a foreign language, as well as teachers and translators, can effortlessly access a vast variety of electronic dictionaries well suited to a multiplicity of lookup operations. A particular kind of lexicographical work on the Web is the…

Learning Resources and MOOCs

DEFF Research Database (Denmark)

Christiansen, René Boyer

MOOCs (Massive Open Online Courses) have become a serious player within the field of education and learning in the past few years. MOOC research is thus a new field but within the last 2-3 years, it has developed rapidly (Liyanagunawardena et al., 2013, Bayne & Ross, 2014). Much of this research...... has had an emphasis on learners and outcome as well as suitable business models. And even though the internet merely flows over with lists of MOOCs to attend (such as the list from “Top 5 onlinecolleges” which features a list of 99 MOOC environments) not much emphasis has been brought on the actual...... construction of learning resources within all these MOOCs – and what demands they lay on teachers competences and teachers skills....

Specialised Translation Dictionaries for Learners

DEFF Research Database (Denmark)

Nielsen, Sandro

2010-01-01

Specialised translation dictionaries for learners are reference tools that can help users with domain discourse in a foreign language in connection with translation. The most common type is the business dictionary covering several more or less related subject fields. However, business dictionaries...... the needs of learners, it is proposed that specialised translation dictionaries should be designed as augmented reference tools. It is argued that electronic and printed dictionaries should include sections or CD-ROMs with syntactic, translation etc. data as well as exercises and illustrative documents...

Selection and Use of Online Learning Resources by First-Year Medical Students: Cross-Sectional Study.

Science.gov (United States)

Judd, Terry; Elliott, Kristine

2017-10-02

Medical students have access to a wide range of learning resources, many of which have been specifically developed for or identified and recommended to them by curriculum developers or teaching staff. There is an expectation that students will access and use these resources to support their self-directed learning. However, medical educators lack detailed and reliable data about which of these resources students use to support their learning and how this use relates to key learning events or activities. The purpose of this study was to comprehensively document first-year medical student selection and use of online learning resources to support their bioscience learning within a case-based curriculum and assess these data in relation to our expectations of student learning resource requirements and use. Study data were drawn from 2 sources: a survey of student learning resource selection and use (2013 cohort; n=326) and access logs from the medical school learning platform (2012 cohort; n=337). The paper-based survey, which was distributed to all first-year students, was designed to assess the frequency and types of online learning resources accessed by students and included items about their perceptions of the usefulness, quality, and reliability of various resource types and sources. Of 237 surveys returned, 118 complete responses were analyzed (36.2% response rate). Usage logs from the learning platform for an entire semester were processed to provide estimates of first-year student resource use on an individual and cohort-wide basis according to method of access, resource type, and learning event. According to the survey data, students accessed learning resources via the learning platform several times per week on average, slightly more often than they did for resources from other online sources. Google and Wikipedia were the most frequently used nonuniversity sites, while scholarly information sites (eg, online journals and scholarly databases) were accessed

Automatic Dictionary Expansion Using Non-parallel Corpora

Science.gov (United States)

Rapp, Reinhard; Zock, Michael

Automatically generating bilingual dictionaries from parallel, manually translated texts is a well established technique that works well in practice. However, parallel texts are a scarce resource. Therefore, it is desirable also to be able to generate dictionaries from pairs of comparable monolingual corpora. For most languages, such corpora are much easier to acquire, and often in considerably larger quantities. In this paper we present the implementation of an algorithm which exploits such corpora with good success. Based on the assumption that the co-occurrence patterns between different languages are related, it expands a small base lexicon. For improved performance, it also realizes a novel interlingua approach. That is, if corpora of more than two languages are available, the translations from one language to another can be determined not only directly, but also indirectly via a pivot language.

An assessment of student experiences and learning based on a novel undergraduate e-learning resource.

Science.gov (United States)

Mehta, S; Clarke, F; Fleming, P S

2016-08-12

Purpose/objectives The aims of this study were to describe the development of a novel e-learning resource and to assess its impact on student learning experiences and orthodontic knowledge.Methods Thirty-two 4th year dental undergraduate students at Queen Mary University of London were randomly allocated to receive electronic access to e-learning material covering various undergraduate orthodontic topics over a 6-week period. Thirty-one control students were not given access during the study period. All students were asked to complete electronic quizzes both before (T0) and after (T1) the study period and a general questionnaire concerning familiarity with e-learning. The test group also completed a user satisfaction questionnaire at T1. Two focus groups were also undertaken to explore learners' experiences and suggestions in relation to the resource.Results The mean quiz result improved by 3.9% and 4.5% in the control and test groups, respectively. An independent t-test, however, demonstrated a lack of statistical significance in knowledge gain between control and test groups (P = 0.941). The qualitative feedback indicated that students believed that use of the resource enhanced knowledge and basic understanding with students expressing a wish to ingrain similar resources in other areas of undergraduate teaching.Conclusions Use of the novel orthodontic e-resource by 4th year undergraduate students over a 6-week period did not result in a significant improvement in subject knowledge. However, the e-learning has proven popular among undergraduates and the resources will continue to be refined.

Stressors, academic performance, and learned resourcefulness in baccalaureate nursing students.

Science.gov (United States)

Goff, Anne-Marie

2011-01-01

High stress levels in nursing students may affect memory, concentration, and problem-solving ability, and may lead to decreased learning, coping, academic performance, and retention. College students with higher levels of learned resourcefulness develop greater self-confidence, motivation, and academic persistence, and are less likely to become anxious, depressed, and frustrated, but no studies specifically involve nursing students. This explanatory correlational study used Gadzella's Student-life Stress Inventory (SSI) and Rosenbaum's Self Control Scale (SCS) to explore learned resourcefulness, stressors, and academic performance in 53 baccalaureate nursing students. High levels of personal and academic stressors were evident, but not significant predictors of academic performance (p = .90). Age was a significant predictor of academic performance (p = learned resourcefulness scores than females and Caucasians. Studies in larger, more diverse samples are necessary to validate these findings.

Pocket dictionary of laboratory equipment. English/German. Taschenwoerterbuch Laborausruestung. Deutsch/Englisch

Energy Technology Data Exchange (ETDEWEB)

Junge, H D

1987-01-01

This pocket dictionary contains the 2500 most common terms for scientific and technical equipment in chemical laboratories. It is a useful tool for those who are used to communicating in German and English, but have to learn the special terminology in this field.

Kirkeby's English?Swahili Dictionary

Directory of Open Access Journals (Sweden)

James S. Mdee

2011-10-01

Full Text Available

Abstract: Kirkeby's English–Swahili Dictionary is a bilingual dictionary of more than 50 000entries. The most laudable feature of the dictionary is its attempt to be user-friendly especially inthe way the entry words have been arranged and the amount of information given. However, aclear objective for the compilation of the ditionary is lacking. The compilers do not seem to knowthe lexicographical gap they want to fill, the users they are targeting, and their dictionary-usingskills. In discussing the strong and weak points of the dictionary, the article will refer to theories ofdictionary criticism. Three criteria set by McMillan (1949 will guide this review article: (1 thequantity of the information in the dictionary; (2 the quality of the information presented; and (3the effectiveness of the presentation of the information. Questions posed in the course of this articlewill include: Does the dictionary give the information required by the user? Is the informationtransparently accessible? How is the information presented?

Keywords: DICTIONARY EVALUATION, USER-FRIENDLY, DICTIONARY-USINGSKILLS, LEXICOGRAPHICAL ENTRIES, GRAMMATICAL CATEGORIES, SUBGRAMMATICALCATEGORIES, WORD COMBINATIONS, COLLOCATIONS, TRANSLATION EQUIVALENTS

Opsomming: Kirkeby se English–Swahili Dictionary. Kirkeby se English–SwahiliDictionary is 'n tweetalige woordeboek met meer as 50 000 inskrywings. Die mees prysenswaardigekenmerk van die woordeboek is sy poging om gebruikersvriendelik te wees, veral deur die manierwaarop die trefwoorde gerangskik is en die hoeveelheid inligting wat verskaf word. 'n Duidelikedoelwit vir die samestelling van die woordeboek ontbreek egter. Die samestellers is skynbaaronseker oor die leksikografiese leemte wat hulle wil vul, en die gebruikers vir wie dit bedoel is enhul woordeboekgebruikersvaardighede. In die bespreking van die sterk en die swak eienskappevan die woordeboek sal die artikel verwys na teorieë van

Studying the Quality of Colloquial Infinitives in Moin Persian Dictionary

Directory of Open Access Journals (Sweden)

Parisa Shekoohi

2017-04-01

Full Text Available Mohammad Moin has been considered as one of the most committed literary men of the present time who recorded a considerable amount of Persian words, expressions, and declarations in his own 6 volumes Persian dictionary according to scientific research methods and in a different way in comparison to the previous dictionaries. This article argues the quality of colloquial infinitives which have been recorded in Moin Persian Dictionary. The most important obstacles in all researches related to literature and colloquial language is the recognition criterion of "being colloquial". In this article, the recognition criterion is that of Moin's criterion who was a great master in this field. In the other words, any infinitives in front of which he put the abbreviation "Ɂam", have been extracted and at the next stage, according to the syntactic resources, have been divided into 8 categories. Finally, the examples of each category have been presented through tables.

Dictionaries: British and American. The Language Library.

Science.gov (United States)

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…

The ABCs of Data Dictionaries

Science.gov (United States)

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…

LZ-Compressed String Dictionaries

OpenAIRE

Arz, Julian; Fischer, Johannes

2013-01-01

We show how to compress string dictionaries using the Lempel-Ziv (LZ78) data compression algorithm. Our approach is validated experimentally on dictionaries of up to 1.5 GB of uncompressed text. We achieve compression ratios often outperforming the existing alternatives, especially on dictionaries containing many repeated substrings. Our query times remain competitive.

A Remote Sensing Image Fusion Method based on adaptive dictionary learning

Science.gov (United States)

He, Tongdi; Che, Zongxi

2018-01-01

This paper discusses using a remote sensing fusion method, based on' adaptive sparse representation (ASP)', to provide improved spectral information, reduce data redundancy and decrease system complexity. First, the training sample set is formed by taking random blocks from the images to be fused, the dictionary is then constructed using the training samples, and the remaining terms are clustered to obtain the complete dictionary by iterated processing at each step. Second, the self-adaptive weighted coefficient rule of regional energy is used to select the feature fusion coefficients and complete the reconstruction of the image blocks. Finally, the reconstructed image blocks are rearranged and an average is taken to obtain the final fused images. Experimental results show that the proposed method is superior to other traditional remote sensing image fusion methods in both spectral information preservation and spatial resolution.

Personalized Resource Recommendations using Learning from Positive and Unlabeled Examples

Directory of Open Access Journals (Sweden)

Priyank Thakkar

2016-08-01

Full Text Available This paper proposes a novel approach for recommending social resources using learning from positive and unlabeled examples. Bookmarks submitted on social bookmarking system delicious1 and artists on online music system last.fm2 are considered as social resources. The foremost feature of this problem is that there are no labeled negative resources/examples available for learning a recommender/classifier. The memory based collaborative filtering has served as the most widely used algorithm for social resource recommendation. However, its predictions are based on some ad hoc heuristic rules and its success depends on the availability of a critical mass of users. This paper proposes model based two-step techniques to learn a classifier using positive and unlabeled examples to address personalized resource recommendations. In the first step of these techniques, naïve Bayes classifier is employed to identify reliable negative resources. In the second step, to generate effective resource recommender, classification and regression tree and least square support vector machine (LS-SVM are exercised. A direct method based on LS-SVM is also put forward to realize the recommendation task. LS-SVM is customized for learning from positive and unlabeled data. Furthermore, the impact of feature selection on our proposed techniques is also studied. Memory based collaborative filtering as well as our proposed techniques exploit usage data to generate personalized recommendations. Experimental results show that the proposed techniques outperform existing method appreciably.

The concept of a bilingual dictionary

DEFF Research Database (Denmark)

Tarp, Sven

2005-01-01

The term bilingual dictionary is widely used, not only by librarians and dictionary users en general but also by professional lexicographers dedicated to the theory and practice of dictionary making. For this reason it should be expected that there were a common and well-established definition...... of the concept of a bilingual dictionary. It is evident that most people has an intuitive idea of what is meant by «bilingual dictionary». But science-based lexicographic theory - at least if it wants to be considered as such - must go beyond intuition and furnish precise definitions of the concepts used...... chapters, various definitions will be discussed and related to dictionary practice and, subsequently, the very concept of a bilingual dictionary will be examined in the light of a dictionary typology based upon the modern theory of lexicographic functions....

Making a dictionary without words

DEFF Research Database (Denmark)

Kristoffersen, Jette Hedegaard; Troelsgård, Thomas

2010-01-01

This paper addresses some of the particular problems connected with lemma representation and lemmatization in a sign language dictionary. The paper is mainly based on the authors' work experience from the Danish Sign Language Dictionary project. In a sign language dictionary sign representation...... constitutes a problem. as there is - at least for Danish Sign Language - no conventional notation used by native signers and the various other sign user groups. We look into the different possibilities of representing signs and present the solution that we chose for the Danish Sign Language Dictionary....... Defining the criteria for lernmatization is another area where sign language dictionaries differ from written language dictionaries. The criteria should obviously include the manual expression of the signs, but a sign's manual expression has features from several categories (e.g. handshape, place...

Sparse Coding and Dictionary Learning Based on the MDL Principle

Science.gov (United States)

2010-10-01

average bits per pixel obtained was 4.08 bits per pixel ( bpp ), with p = 250 atoms in the final dictionary. We repeated this using `2 instead of Huber...loss, obtaining 4.12 bpp and p = 245. We now show example results obtained with our framework in two very different applications. In both cases we

Learning Resource Centre in a Company

Directory of Open Access Journals (Sweden)

Aleksander Pokovec

2001-12-01

Full Text Available In the conditions of growing competition people are becoming the essential competitive advantage. Because of too much work, stress, too many responsibilities and other factors, employees are often unmotivated. Everyday self-study is the best way that leads to excellence. In order to enable self-study for all employees, the organisation should organise their own learning resource centre that includes: educational videoprogrammes, audio tapes, books and e-learning programmes. All educational programmes should cover business and personal topics.

Unsupervised method for automatic construction of a disease dictionary from a large free text collection.

Science.gov (United States)

Xu, Rong; Supekar, Kaustubh; Morgan, Alex; Das, Amar; Garber, Alan

2008-11-06

Concept specific lexicons (e.g. diseases, drugs, anatomy) are a critical source of background knowledge for many medical language-processing systems. However, the rapid pace of biomedical research and the lack of constraints on usage ensure that such dictionaries are incomplete. Focusing on disease terminology, we have developed an automated, unsupervised, iterative pattern learning approach for constructing a comprehensive medical dictionary of disease terms from randomized clinical trial (RCT) abstracts, and we compared different ranking methods for automatically extracting con-textual patterns and concept terms. When used to identify disease concepts from 100 randomly chosen, manually annotated clinical abstracts, our disease dictionary shows significant performance improvement (F1 increased by 35-88%) over available, manually created disease terminologies.

Emo, love and god: making sense of Urban Dictionary, a crowd-sourced online dictionary

Science.gov (United States)

McGillivray, Barbara

2018-01-01

The Internet facilitates large-scale collaborative projects and the emergence of Web 2.0 platforms, where producers and consumers of content unify, has drastically changed the information market. On the one hand, the promise of the ‘wisdom of the crowd’ has inspired successful projects such as Wikipedia, which has become the primary source of crowd-based information in many languages. On the other hand, the decentralized and often unmonitored environment of such projects may make them susceptible to low-quality content. In this work, we focus on Urban Dictionary, a crowd-sourced online dictionary. We combine computational methods with qualitative annotation and shed light on the overall features of Urban Dictionary in terms of growth, coverage and types of content. We measure a high presence of opinion-focused entries, as opposed to the meaning-focused entries that we expect from traditional dictionaries. Furthermore, Urban Dictionary covers many informal, unfamiliar words as well as proper nouns. Urban Dictionary also contains offensive content, but highly offensive content tends to receive lower scores through the dictionary’s voting system. The low threshold to include new material in Urban Dictionary enables quick recording of new words and new meanings, but the resulting heterogeneous content can pose challenges in using Urban Dictionary as a source to study language innovation. PMID:29892417

The concept of 'dictionary usage'

DEFF Research Database (Denmark)

Bergenholtz, Henning; Tarp, Sven

2004-01-01

that users that might have a bad dictionary culture feel that the dictionaries meet their needs. In doing so, you generate inbreeding and block the necessary innovation. This is the unavoidable result of a practice that pays excessive attention to the study of existing dictionaries and doesn't endeavour...... to produce new concepts and to introduce a new dictionary culture. It is, in other words, a poor lexicography....

Sentiment Polarity Analysis based multi-dictionary

Science.gov (United States)

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.

Seismic classification through sparse filter dictionaries

Energy Technology Data Exchange (ETDEWEB)

Hickmann, Kyle Scott [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Srinivasan, Gowri [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

2017-09-13

We tackle a multi-label classi cation problem involving the relation between acoustic- pro le features and the measured seismogram. To isolate components of the seismo- grams unique to each class of acoustic pro le we build dictionaries of convolutional lters. The convolutional- lter dictionaries for the individual classes are then combined into a large dictionary for the entire seismogram set. A given seismogram is classi ed by computing its representation in the large dictionary and then comparing reconstruction accuracy with this representation using each of the sub-dictionaries. The sub-dictionary with the minimal reconstruction error identi es the seismogram class.

Using a Metro Map Metaphor for organizing Web-based learning resources

DEFF Research Database (Denmark)

Grønbæk, Kaj; Bang, Tove; Hansen, Per Steen

2002-01-01

This paper briefly describes the WebNize system and how it applies a Metro Map metaphor for organizing guided tours in Web based resources. Then, experiences in using the Metro Map based tours in a Knowledge Sharing project at the library at Aarhus School of Business (ASB) in Denmark, are discussed...... is to create models for Intelligent Knowledge Solutions that can contribute to form the learning environments of the School in the 21st century. The WebNize system is used for sharing of knowledge through metro maps for specific subject areas made available in the Learning Resource Centre at ASB. The metro....... The Library has been involved in establishing a Learning Resource Center (LRC). The LRC serves as an exploratorium for the development and the testing of new forms of communication and learning, at the same time as it integrates the information resources of the electronic research library. The objective...

Bilingual Dictionaries for Communication in the Domain of Economics: Function-Based Translation Dictionaries

DEFF Research Database (Denmark)

Nielsen, Sandro

2015-01-01

With their focus on terms, bilingual dictionaries are important tools for translating texts on economics. The most common type is the multi-fi eld dictionary covering several related subject fi elds; however, multi-fi eld dictionaries treat one or few fi elds extensively thereby neglecting other fi...... elds in contrast to single-fi eld and sub-fi eld dictionaries. Furthermore, recent research shows that economic translation is not limited to terms so lexicographers who identify and analyse the needs of translators, usage situations and stages in translating economic texts will have a sound basis...... for designing their lexicographic tools. The function theory allows lexicographers to study these basics so that they can offer translation tools to the domain of economics. Dictionaries should include data about terms, their grammatical properties, and their combinatorial potential as well as language...

Early Portuguese lexicographic tradition: equivalents and loans in Caetano de Lima’s dictionary manuscripts

Directory of Open Access Journals (Sweden)

J. P. Silvestre

2013-10-01

Full Text Available The exhaustive comparison between Portuguese and the main modern languages is a late phenomenon. Only at the end of seventeenth century, did Portugal feel the need for a methodical learning of foreign languages and for didactic tools with a marked lexical component, in which we include the essential bilingual dictionaries. Nevertheless, it is in the first half of the eighteenth century that the first glossaries are published, while the first bilingual dictionaries with alphabetical order and an extensive nomenclature will appear only in the second half of the century. In order to overcome this lag, Vernacular-Latin dictionaries were imported and handwritten dictionaries were compiled and adapted according to the needs of each user. Research on the origins of Portuguese-Italian lexicography has provided important information on the development of the techniques used to compile dictionaries and the contrastive analysis of languages. The first printed dictionary is an addition to Caetano de Lima’s Grammatica Italiana (1734 and it contains an appendix of hard words (where semantic peculiarities and idiomatic expressions are explained and an onomasiologic dictionary of common words. It has been described as a basic albeit functional lexicographic tool, suitable for beginners’ needs. Recently, a manuscript version of this dictionary has been identified (Diccionario de Nomes Portuguezes e Italianos Dispostos por Materias, Biblioteca Pública de Évora, CXIII-1-33, as well as the manuscript of a larger general dictionary (Diccionario Italiano-Portuguez, Biblioteca Nacional de Portugal, cod. 3342. This impressive lexicographic corpus is compiled about fifty years before the first printed Italian-Portuguese dictionary. The analysis demonstrates that the author tried to compile a much wider dictionary than the version that was actually published. In the manuscript, we can identify problems in some areas, such as the definition of lexical domains (due

Online Law Dictionaries

DEFF Research Database (Denmark)

Nielsen, Sandro

2012-01-01

Online dictionaries that assist users in writing legal texts in English as a foreign language are important lexicographic tools. They can help law students bridge the factual and linguistic gaps between the two legal universes involved. However, existing online law dictionaries with English...... as the target language primarily focus on terms, but students also need to write the remainder of the texts in factually and linguistically correct English. It is therefore important to have a sound theoretical foundation before embarking on a dictionary project that aims to help law students communicate...... in English as a foreign language. The function theory of lexicography offers an appropriate basis as it focuses on three key concepts: user needs, user competences, and user situations. It is proposed that online dictionaries should be designed to satisfy the lexicographically relevant user needs...

Parenting Styles and Learned Resourcefulness of Turkish Adolescents

Science.gov (United States)

Turkel, Yesim Deniz; Tezer, Esin

2008-01-01

This study investigated the differences among 834 high school students regarding learned resourcefulness in terms of perceived parenting style and gender. The data were gathered by administering the Parenting Style Inventory (PSI) and Rosenbaum's Self-Control Schedule (SCS). The results of ANOVA pertaining to the scores of learned resourcefulness…

Resource-based learning strategies: implications for students and institutions

Directory of Open Access Journals (Sweden)

Malcolm Ryan

1996-12-01

Full Text Available In its strategic plan, the University of Greenwich envisages a significant shift to resource-based learning (RBL. Enterprise in Higher Education (EHE has funded five pilot RBL projects during the past year, including one in introductory economics. The project was managed by three lecturers in the School of Social Sciences, supported by an Academic Development Officer. Learning outcomes were completely revised, and a range of assessment strategies, including computer-based tests, was identified. A resources guide was produced which identified the materials and activities that would enable students to achieve the learning outcomes. A number of innovations were adopted, including: • computer-based curriculum delivery, assessment, and student evaluation of the course; • an open approach to assessment; • abolishing lectures in favour of a diverse range of teaching and learning activities.

PHRASEOLOGY IN CHAKAVIAN DIALECTOLOGICAL DICTIONARIES

Directory of Open Access Journals (Sweden)

Sanja Bogović

1997-01-01

Full Text Available The paper analyses the presentation and processing of idioms in eight dictionaries of chakavian organic language systems. According to the systematicality of their presentation and elaboration, the dictionaries are divided into three groups: systematic, partially systematic and non-systematic. Their analysis has shown the prevalence of dictionaries with partially systematic presentation and processing of idioms. Based on the results of the analysis, the paper presents procedures for a systematic presentation of idioms in organic language dictionaries.

Adoption of Technology and Augmentation of Resources for Teaching-Learning in Higher Education

OpenAIRE

P. M. Suresh Kumar

2017-01-01

Learner centred education through appropriate methodologies facilitates effective learning as teaching-learning modalities of higher education are considered to be relevant to the learner group. Curriculum delivery and pedagogy should incorporate multitude of learning experiences and innovative learning methodologies through adoption of technology. Plenty of resources external to the curriculum come into use, which offer valuable learning experiences. Augmentation of resources for teaching...

Dictionary criticism and lexicographical function theory

DEFF Research Database (Denmark)

Tarp, Sven

2017-01-01

This contribution discusses dictionary criticism in the light of the function theory. It starts analyzing the objective of dictionary criticism and lists eight of the most important purposes with which criticism has been made by supporters of the function theory. It then discusses the two main...... types of dictionary criticism, namely criticism of other authors’ dictionaries and self-criticism of one’s own dictionaries. Based on this discussion, it proceeds to a definition of the concept of dictionary criticism which is above all considered a theory-based activity, the outcome of which may...... by the supporters of the function theory, and the way it could be presented in order to create debate. Finally, the contribution indicates the important role dictionary criticism has had in the development of the function theory and endorses an open and critical discussion culture within lexicography....

A Review of Smart Phone Dictionaries%智能手机词典评介

Institute of Scientific and Technical Information of China (English)

肖志清

2015-01-01

With the popularity of smart phones, a number of mobile phone dictionary applications have come into view, advancing the mobile learning.The three archetypal mobile phone dictionaries have their own functions and fea-tures, meanwhile have their problems and shortcomings.For further development, mobile phone dictionaries should ac-knowledge their problems and take corresponding development strategies to win the user market and welcome the arrival of mobile learning era.%随着智能手机的普及，大量的手机词典应用软件应运而生，成为移动学习时代的有力推手。三大类手机词典软件各有自己的功能特色，但同时也存在诸多问题和不足。手机词典要获得进一步的发展，须正视问题，并采取相应发展策略，以赢得用户市场，迎接移动学习时代的到来。

An evaluation of learning resources in the teaching of formal philosophical methods

Directory of Open Access Journals (Sweden)

Susan A.J. Stuart

2003-12-01

Full Text Available In any discipline, across a wide variety of subjects, there are numerous learning resources available to students. For many students the resources that will be most beneficial to them are quickly apparent but, because of the nature of philosophy and the philosophical method, it is not immediately clear which resources will be most valuable to students for whom the development of critical thinking skills is crucial. If we are to support these students effectively in their learning we must establish what these resources are how we can continue to maintain and improve them, and how we can encourage students to make good use of them. In this paper we describe and assess our evaluation of the use made by students of learning resources in the context of learning logic and in developing their critical thinking skills. We also assess the use of a new resource, electronic handsets, the purpose of which is to encourage students to respond to questions in lectures and to gain feedback about how they are progressing with the material.

Dictionary criticism in Scandinavian lexicographic journals

DEFF Research Database (Denmark)

Nielsen, Sandro

2017-01-01

Dictionary criticism has a long tradition in the Nordic countries in for instance academic journals and newspapers. In order to limit the scope of the paper, the examination of dictionary reviews in the Nordic countries is restricted to the Nordic lexicographic journal LexicoNordica. The examinat......Dictionary criticism has a long tradition in the Nordic countries in for instance academic journals and newspapers. In order to limit the scope of the paper, the examination of dictionary reviews in the Nordic countries is restricted to the Nordic lexicographic journal Lexico......Nordica. The examination shows that there is great variation in the way reviewers write their critiques, and reviewers address many relevant topics and do not focus on one or two topics. Another characteristic of dictionary criticism in LexicoNordica is that reviewers often compare the dictionary under review with other...... dictionaries and previous editions and where relevant compare print and online editions. Furthermore, many reviewers address outside matter and meta-texts, thereby treating dictionaries as complex lexicographic products that consist of several distinct but interrelated components. A final characteristic...

Strategic Resource Use for Learning: A Self-Administered Intervention That Guides Self-Reflection on Effective Resource Use Enhances Academic Performance.

Science.gov (United States)

Chen, Patricia; Chavez, Omar; Ong, Desmond C; Gunderson, Brenda

2017-06-01

Many educational policies provide learners with more resources (e.g., new learning activities, study materials, or technologies), but less often do they address whether students are using these resources effectively. We hypothesized that making students more self-reflective about how they should approach their learning with the resources available to them would improve their class performance. We designed a novel Strategic Resource Use intervention that students could self-administer online and tested its effects in two cohorts of a college-level introductory statistics class. Before each exam, students randomly assigned to the treatment condition strategized about which academic resources they would use for studying, why each resource would be useful, and how they would use their resources. Students randomly assigned to the treatment condition reported being more self-reflective about their learning throughout the class, used their resources more effectively, and outperformed students in the control condition by an average of one third of a letter grade in the class.

Development of Human Resources Using New Technologies in Long-Life Learning

Directory of Open Access Journals (Sweden)

Micu Bogdan Ghilic

2011-01-01

Full Text Available Information and communication technologies (ICT offer new opportunities to reinvent the education and to make people and makes learning more fun and contemporary but poses many problems to educational institutions. Implementation of ICT determines major structural changes in the organizations and mental switch from bureaucratic mentality to customer-oriented one. In this paper I try to evaluate methods of developing the lifelong learning programs, impact to human resources training and development and the impact of this process on educational institutions. E-learning usage in training the human resources can make a new step in development of the education institutions, human resources and companies.

A Video Game for Learning Brain Evolution: A Resource or a Strategy?

Science.gov (United States)

Barbosa Gomez, Luisa Fernanda; Bohorquez Sotelo, Maria Cristina; Roja Higuera, Naydu Shirley; Rodriguez Mendoza, Brigitte Julieth

2016-01-01

Learning resources are part of the educational process of students. However, how video games act as learning resources in a population that has not selected the virtual formation as their main methodology? The aim of this study was to identify the influence of a video game in the learning process of brain evolution. For this purpose, the opinions…

Improving Dictionary Skills in Ndebele

African Journals Online (AJOL)

rbr

Abstract: This article proposes ways of improving dictionary skills amongst the Ndebele. One way of accomplishing this is incorporating the teaching of dictionary skills into teacher training syllabi. Teachers can impart their knowledge to students and a dictionary culture can develop for enhancing effective use of current ...

Dictionaries of Canadian English

Directory of Open Access Journals (Sweden)

John Considine

2011-10-01

Full Text Available

Abstract: The lexicographical record of English in Canada began with wordlists of the late eighteenth, nineteenth, and early twentieth centuries. From the beginning of the twentieth century onwards, the general vocabulary of English in Canada has been represented in bilingual and monolingual dictionaries, often adapted from American or British dictionaries. In the 1950s, several important projects were initiated, resulting in the publication of general dictionaries of English in Canada, and of dictionaries of Canadianisms and of the vocabulary of particular regions of Can-ada. This article gives an overview of these dictionaries and of their reception, contextualizing them in the larger picture of the lexicography of Canada's other official language, French, and of a number of its non-official languages. It concludes by looking at the future of English-language lexicography in Canada, and by observing that although it has, at its best, reached a high degree of sophistication, there are still major opportunities waiting to be taken.

Keywords: DICTIONARY, LEXICOGRAPHY, CANADIAN ENGLISH, CANADIANISMS, NATIONAL DICTIONARIES, CANADIAN FRENCH, CANADIAN FIRST NATIONS LAN-GUAGES, BILINGUAL DICTIONARIES, REGIONAL DICTIONARIES, UNFINISHED DICTIONARY PROJECTS

Opsomming: Woordeboeke van Kanadese Engels. Die leksikografiese optekening van Engels in Kanada begin met woordelyste van die laat agtiende, neëntiende en vroeë twintigste eeue. Van die begin van die twintigste eeu af en verder, is die algemene woordeskat van Engels weergegee in tweetalige en eentalige woordeboeke, dikwels met wysiginge ontleen aan Ameri-kaanse en Britse woordeboeke. In die 1950's is verskeie belangrike projekte onderneem wat gelei het tot die publikasie van algemene woordeboeke van Engels in Kanada, en van woordeboeke van Kanadeïsmes en van die woordeskat van bepaalde streke van Kanada. Hierdie artikel gee 'n oorsig van dié woordeboeke, en van hul ontvangs, deur

Dictionary as Database.

Science.gov (United States)

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)

On the timelessness of music dictionaries

DEFF Research Database (Denmark)

Bergenholtz, Henning; Bergenholtz, Inger

2007-01-01

A music dictionary for the Internet serves the same functions as printed music dictionaries. An old music dictionary is as useful as a new one if its information is correct. But the fact that an Internet dictionary can at any time be corrected according to modern practices makes it, if not timeless...... reception rather than translation or text production. It is described what was the starting point of the dictionary and in what way the possibilities of the Internet has influenced the concept and the content of the articles and the outer texts....

Big Rock Candy Mountain. Resources for Our Education. A Learning to Learn Catalog. Winter 1970.

Science.gov (United States)

Portola Inst., Inc., Menlo Park, CA.

Imaginative learning resources of various types are reported in this catalog under the subject headings of process learning, education environments, classroom materials and methods, home learning, and self discovery. Books reviewed are on the subjects of superstition, Eastern religions, fairy tales, philosophy, creativity, poetry, child care,…

Adaptation of mathematical educational content in e-learning resources

Directory of Open Access Journals (Sweden)

Yuliya V. Vainshtein

2017-01-01

Full Text Available Modern trends in the world electronic educational system development determine the necessity of adaptive learning intellectual environments and resources’ development and implementation. An upcoming trend in improvement the quality of studying mathematical disciplines is the development and application of adaptive electronic educational resources. However, the development and application experience of adaptive technologies in higher education is currently extremely limited and does not imply the usage flexibility. Adaptive educational resources in the electronic environment are electronic educational resources that provide the student with a personal educational space, filled with educational content that “adapts” to the individual characteristics of the students and provides them with the necessary information.This article focuses on the mathematical educational content adaptation algorithms development and their implementation in the e-learning system. The peculiarity of the proposed algorithms is the possibility of their application and distribution for adaptive e-learning resources construction. The novelty of the proposed approach is the three-step content organization of the adaptive algorithms for the educational content: “introductory adaptation of content”, “the current adaptation of content”, “estimative and a corrective adaptation”. For each stage of the proposed system, mathematical algorithms for educational content adaptation in adaptive e-learning resources are presented.Due to the high level of abstraction and complexity perception of mathematical disciplines, educational content is represented in the various editions of presentation that correspond to the levels of assimilation of the course material. Adaptation consists in the selection of the optimal edition of the material that best matches the individual characteristics of the student. The introduction of a three-step content organization of the adaptive

The Dictionary Unit for South African English. South African Concise Oxford Dictionary

OpenAIRE

Rajend Mesthrie

2011-01-01

The South African Concise Oxford Dictionary (henceforth SACOD) is a South Af-rican version of the Concise Oxford Dictionary, the first time that this particular hybrid has been prepared. It is testimony to the enduring success of the work of the Dictionary Unit for South African English at Rhodes University, headed by teams that included Jean and William Branford in the 1970s, Penny Silva in the 1990s and now, Kathryn Kavanagh. The lexicographical work from the unit saw the publication of fou...

Model of e-learning with electronic educational resources of new generation

Directory of Open Access Journals (Sweden)

A. V. Loban

2017-01-01

Full Text Available Purpose of the article: improving of scientific and methodical base of the theory of the е-learning of variability. Methods used: conceptual and logical modeling of the е-learning of variability process with electronic educational resource of new generation and system analysis of the interconnection of the studied subject area, methods, didactics approaches and information and communication technologies means. Results: the formalization complex model of the е-learning of variability with electronic educational resource of new generation is developed, conditionally decomposed into three basic components: the formalization model of the course in the form of the thesaurusclassifier (“Author of e-resource”, the model of learning as management (“Coordination. Consultation. Control”, the learning model with the thesaurus-classifier (“Student”. Model “Author of e-resource” allows the student to achieve completeness, high degree of didactic elaboration and structuring of the studied material in triples of variants: modules of education information, practical task and control tasks; the result of the student’s (author’s of e-resource activity is the thesaurus-classifier. Model of learning as management is based on the principle of personal orientation of learning in computer environment and determines the logic of interaction between the lecturer and the student when determining the triple of variants individually for each student; organization of a dialogue between the lecturer and the student for consulting purposes; personal control of the student’s success (report generation and iterative search for the concept of the class assignment in the thesaurus-classifier before acquiring the required level of training. Model “Student” makes it possible to concretize the learning tasks in relation to the personality of the student and to the training level achieved; the assumption of the lecturer about the level of training of a

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

Science.gov (United States)

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.

Mobile authoring of open educational resources for authentic learning scenarios

NARCIS (Netherlands)

Tabuenca, Bernardo; Kalz, Marco; Ternier, Stefaan; Specht, Marcus

2014-01-01

The proliferation of smartphones in the last decade and the number of publications in the field of authoring systems for computer-assisted learning depict a scenario that needs to be explored in order to facilitate the scaffolding of learning activities across contexts. Learning resources are

Sustainability Learning in Natural Resource Use and Management

Directory of Open Access Journals (Sweden)

J. David Tàbara

2007-12-01

Full Text Available We contribute to the normative discussion on sustainability learning and provide a theoretical integrative framework intended to underlie the main components and interrelations of what learning is required for social learning to become sustainability learning. We demonstrate how this framework has been operationalized in a participatory modeling interface to support processes of natural resource integrated assessment and management. The key modeling components of our view are: structure (S, energy and resources (E, information and knowledge (I, social-ecological change (C, and the size, thresholds, and connections of different social-ecological systems. Our approach attempts to overcome many of the cultural dualisms that exist in the way social and ecological systems are perceived and affect many of the most common definitions of sustainability. Our approach also emphasizes the issue of limits within a total social-ecological system and takes a multiscale, agent-based perspective. Sustainability learning is different from social learning insofar as not all of the outcomes of social learning processes necessarily improve what we consider as essential for the long-term sustainability of social-ecological systems, namely, the co-adaptive systemic capacity of agents to anticipate and deal with the unintended, undesired, and irreversible negative effects of development. Hence, the main difference of sustainability learning from social learning is the content of what is learned and the criteria used to assess such content; these are necessarily related to increasing the capacity of agents to manage, in an integrative and organic way, the total social-ecological system of which they form a part. The concept of sustainability learning and the SEIC social-ecological framework can be useful to assess and communicate the effectiveness of multiple agents to halt or reverse the destructive trends affecting the life-support systems upon which all humans

Impact of e-resources on learning in biochemistry: first-year medical students' perceptions.

Science.gov (United States)

Varghese, Joe; Faith, Minnie; Jacob, Molly

2012-05-16

E-learning resources (e-resources) have been widely used to facilitate self-directed learning among medical students. The Department of Biochemistry at Christian Medical College (CMC), Vellore, India, has made available e-resources to first-year medical students to supplement conventional lecture-based teaching in the subject. This study was designed to assess students' perceptions of the impact of these e-resources on various aspects of their learning in biochemistry. Sixty first-year medical students were the subjects of this study. At the end of the one-year course in biochemistry, the students were administered a questionnaire that asked them to assess the impact of the e-resources on various aspects of their learning in biochemistry. Ninety-eight percent of students had used the e-resources provided to varying extents. Most of them found the e-resources provided useful and of a high quality. The majority of them used these resources to prepare for periodic formative and final summative assessments in the course. The use of these resources increased steadily as the academic year progressed. Students said that the extent to which they understood the subject (83%) and their ability to answer questions in assessments (86%) had improved as a result of using these resources. They also said that they found biochemistry interesting (73%) and felt motivated to study the subject (59%). We found that first-year medical students extensively used the e-resources in biochemistry that were provided. They perceived that these resources had made a positive impact on various aspects of their learning in biochemistry. We conclude that e-resources are a useful supplement to conventional lecture-based teaching in the medical curriculum.