Integrating conventional and inverse representation for face recognition.

PubMed

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

2014-10-01

Representation-based classification methods are all constructed on the basis of the conventional representation, which first expresses the test sample as a linear combination of the training samples and then exploits the deviation between the test sample and the expression result of every class to perform classification. However, this deviation does not always well reflect the difference between the test sample and each class. With this paper, we propose a novel representation-based classification method for face recognition. This method integrates conventional and the inverse representation-based classification for better recognizing the face. It first produces conventional representation of the test sample, i.e., uses a linear combination of the training samples to represent the test sample. Then it obtains the inverse representation, i.e., provides an approximation representation of each training sample of a subject by exploiting the test sample and training samples of the other subjects. Finally, the proposed method exploits the conventional and inverse representation to generate two kinds of scores of the test sample with respect to each class and combines them to recognize the face. The paper shows the theoretical foundation and rationale of the proposed method. Moreover, this paper for the first time shows that a basic nature of the human face, i.e., the symmetry of the face can be exploited to generate new training and test samples. As these new samples really reflect some possible appearance of the face, the use of them will enable us to obtain higher accuracy. The experiments show that the proposed conventional and inverse representation-based linear regression classification (CIRLRC), an improvement to linear regression classification (LRC), can obtain very high accuracy and greatly outperforms the naive LRC and other state-of-the-art conventional representation based face recognition methods. The accuracy of CIRLRC can be 10% greater than that of LRC.

Fuzziness-based active learning framework to enhance hyperspectral image classification performance for discriminative and generative classifiers

PubMed Central

2018-01-01

Hyperspectral image classification with a limited number of training samples without loss of accuracy is desirable, as collecting such data is often expensive and time-consuming. However, classifiers trained with limited samples usually end up with a large generalization error. To overcome the said problem, we propose a fuzziness-based active learning framework (FALF), in which we implement the idea of selecting optimal training samples to enhance generalization performance for two different kinds of classifiers, discriminative and generative (e.g. SVM and KNN). The optimal samples are selected by first estimating the boundary of each class and then calculating the fuzziness-based distance between each sample and the estimated class boundaries. Those samples that are at smaller distances from the boundaries and have higher fuzziness are chosen as target candidates for the training set. Through detailed experimentation on three publically available datasets, we showed that when trained with the proposed sample selection framework, both classifiers achieved higher classification accuracy and lower processing time with the small amount of training data as opposed to the case where the training samples were selected randomly. Our experiments demonstrate the effectiveness of our proposed method, which equates favorably with the state-of-the-art methods. PMID:29304512

Efficient method of image edge detection based on FSVM

NASA Astrophysics Data System (ADS)

Cai, Aiping; Xiong, Xiaomei

2013-07-01

For efficient object cover edge detection in digital images, this paper studied traditional methods and algorithm based on SVM. It analyzed Canny edge detection algorithm existed some pseudo-edge and poor anti-noise capability. In order to provide a reliable edge extraction method, propose a new detection algorithm based on FSVM. Which contains several steps: first, trains classify sample and gives the different membership function to different samples. Then, a new training sample is formed by increase the punishment some wrong sub-sample, and use the new FSVM classification model for train and test them. Finally the edges are extracted of the object image by using the model. Experimental result shows that good edge detection image will be obtained and adding noise experiments results show that this method has good anti-noise.

Nonlinear inversion of electrical resistivity imaging using pruning Bayesian neural networks

NASA Astrophysics Data System (ADS)

Jiang, Fei-Bo; Dai, Qian-Wei; Dong, Li

2016-06-01

Conventional artificial neural networks used to solve electrical resistivity imaging (ERI) inversion problem suffer from overfitting and local minima. To solve these problems, we propose to use a pruning Bayesian neural network (PBNN) nonlinear inversion method and a sample design method based on the K-medoids clustering algorithm. In the sample design method, the training samples of the neural network are designed according to the prior information provided by the K-medoids clustering results; thus, the training process of the neural network is well guided. The proposed PBNN, based on Bayesian regularization, is used to select the hidden layer structure by assessing the effect of each hidden neuron to the inversion results. Then, the hyperparameter α k , which is based on the generalized mean, is chosen to guide the pruning process according to the prior distribution of the training samples under the small-sample condition. The proposed algorithm is more efficient than other common adaptive regularization methods in geophysics. The inversion of synthetic data and field data suggests that the proposed method suppresses the noise in the neural network training stage and enhances the generalization. The inversion results with the proposed method are better than those of the BPNN, RBFNN, and RRBFNN inversion methods as well as the conventional least squares inversion.

Convolutional neural networks based on augmented training samples for synthetic aperture radar target recognition

NASA Astrophysics Data System (ADS)

Yan, Yue

2018-03-01

A synthetic aperture radar (SAR) automatic target recognition (ATR) method based on the convolutional neural networks (CNN) trained by augmented training samples is proposed. To enhance the robustness of CNN to various extended operating conditions (EOCs), the original training images are used to generate the noisy samples at different signal-to-noise ratios (SNRs), multiresolution representations, and partially occluded images. Then, the generated images together with the original ones are used to train a designed CNN for target recognition. The augmented training samples can contrapuntally improve the robustness of the trained CNN to the covered EOCs, i.e., the noise corruption, resolution variance, and partial occlusion. Moreover, the significantly larger training set effectively enhances the representation capability for other conditions, e.g., the standard operating condition (SOC), as well as the stability of the network. Therefore, better performance can be achieved by the proposed method for SAR ATR. For experimental evaluation, extensive experiments are conducted on the Moving and Stationary Target Acquisition and Recognition dataset under SOC and several typical EOCs.

Research and development of a field-ready protocol for sampling of phosgene from stationary source emissions: Diethylamine reagent studies. Research report, 11 July 1995--30 September 1996

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

Steger, J.L.; Bursey, J.T.; Merrill, R.G.

1999-03-01

This report presents the results of laboratory studies to develop and evaluate a method for the sampling and analysis of phosgene from stationary sources of air emissions using diethylamine (DEA) in toluene as the collection media. The method extracts stack gas from emission sources and stabilizes the reactive gas for subsequent analysis. DEA was evaluated both in a benchtop study and in a laboratory train spiking study. This report includes results for both the benchtop study and the train spiking study. Benchtop studies to evaluate the suitability of DEA for collecting and analyzing phosgene investigated five variables: storage time, DEAmore » concentration, moisture/pH, phosgene concentration, and sample storage temperature. Prototype sampling train studies were performed to determine if the benchtop chemical studies were transferable to a Modified Method 5 sampling train collecting phosgene in the presence of clean air mixed with typical stack gas components. Four conditions, which varied the moisture and phosgene spike were evaluated in triplicate. In addition to research results, the report includes a detailed draft method for sampling and analysis of phosgene from stationary source emissions.« less

Active learning based segmentation of Crohns disease from abdominal MRI.

PubMed

Mahapatra, Dwarikanath; Vos, Franciscus M; Buhmann, Joachim M

2016-05-01

This paper proposes a novel active learning (AL) framework, and combines it with semi supervised learning (SSL) for segmenting Crohns disease (CD) tissues from abdominal magnetic resonance (MR) images. Robust fully supervised learning (FSL) based classifiers require lots of labeled data of different disease severities. Obtaining such data is time consuming and requires considerable expertise. SSL methods use a few labeled samples, and leverage the information from many unlabeled samples to train an accurate classifier. AL queries labels of most informative samples and maximizes gain from the labeling effort. Our primary contribution is in designing a query strategy that combines novel context information with classification uncertainty and feature similarity. Combining SSL and AL gives a robust segmentation method that: (1) optimally uses few labeled samples and many unlabeled samples; and (2) requires lower training time. Experimental results show our method achieves higher segmentation accuracy than FSL methods with fewer samples and reduced training effort. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Rigorous Training of Dogs Leads to High Accuracy in Human Scent Matching-To-Sample Performance

PubMed Central

Marchal, Sophie; Bregeras, Olivier; Puaux, Didier; Gervais, Rémi; Ferry, Barbara

2016-01-01

Human scent identification is based on a matching-to-sample task in which trained dogs are required to compare a scent sample collected from an object found at a crime scene to that of a suspect. Based on dogs’ greater olfactory ability to detect and process odours, this method has been used in forensic investigations to identify the odour of a suspect at a crime scene. The excellent reliability and reproducibility of the method largely depend on rigor in dog training. The present study describes the various steps of training that lead to high sensitivity scores, with dogs matching samples with 90% efficiency when the complexity of the scents presented during the task in the sample is similar to that presented in the in lineups, and specificity reaching a ceiling, with no false alarms in human scent matching-to-sample tasks. This high level of accuracy ensures reliable results in judicial human scent identification tests. Also, our data should convince law enforcement authorities to use these results as official forensic evidence when dogs are trained appropriately. PMID:26863620

Effect of finite sample size on feature selection and classification: a simulation study.

PubMed

Way, Ted W; Sahiner, Berkman; Hadjiiski, Lubomir M; Chan, Heang-Ping

2010-02-01

The small number of samples available for training and testing is often the limiting factor in finding the most effective features and designing an optimal computer-aided diagnosis (CAD) system. Training on a limited set of samples introduces bias and variance in the performance of a CAD system relative to that trained with an infinite sample size. In this work, the authors conducted a simulation study to evaluate the performances of various combinations of classifiers and feature selection techniques and their dependence on the class distribution, dimensionality, and the training sample size. The understanding of these relationships will facilitate development of effective CAD systems under the constraint of limited available samples. Three feature selection techniques, the stepwise feature selection (SFS), sequential floating forward search (SFFS), and principal component analysis (PCA), and two commonly used classifiers, Fisher's linear discriminant analysis (LDA) and support vector machine (SVM), were investigated. Samples were drawn from multidimensional feature spaces of multivariate Gaussian distributions with equal or unequal covariance matrices and unequal means, and with equal covariance matrices and unequal means estimated from a clinical data set. Classifier performance was quantified by the area under the receiver operating characteristic curve Az. The mean Az values obtained by resubstitution and hold-out methods were evaluated for training sample sizes ranging from 15 to 100 per class. The number of simulated features available for selection was chosen to be 50, 100, and 200. It was found that the relative performance of the different combinations of classifier and feature selection method depends on the feature space distributions, the dimensionality, and the available training sample sizes. The LDA and SVM with radial kernel performed similarly for most of the conditions evaluated in this study, although the SVM classifier showed a slightly higher hold-out performance than LDA for some conditions and vice versa for other conditions. PCA was comparable to or better than SFS and SFFS for LDA at small samples sizes, but inferior for SVM with polynomial kernel. For the class distributions simulated from clinical data, PCA did not show advantages over the other two feature selection methods. Under this condition, the SVM with radial kernel performed better than the LDA when few training samples were available, while LDA performed better when a large number of training samples were available. None of the investigated feature selection-classifier combinations provided consistently superior performance under the studied conditions for different sample sizes and feature space distributions. In general, the SFFS method was comparable to the SFS method while PCA may have an advantage for Gaussian feature spaces with unequal covariance matrices. The performance of the SVM with radial kernel was better than, or comparable to, that of the SVM with polynomial kernel under most conditions studied.

Consistently Sampled Correlation Filters with Space Anisotropic Regularization for Visual Tracking

PubMed Central

Shi, Guokai; Xu, Tingfa; Luo, Jiqiang; Li, Yuankun

2017-01-01

Most existing correlation filter-based tracking algorithms, which use fixed patches and cyclic shifts as training and detection measures, assume that the training samples are reliable and ignore the inconsistencies between training samples and detection samples. We propose to construct and study a consistently sampled correlation filter with space anisotropic regularization (CSSAR) to solve these two problems simultaneously. Our approach constructs a spatiotemporally consistent sample strategy to alleviate the redundancies in training samples caused by the cyclical shifts, eliminate the inconsistencies between training samples and detection samples, and introduce space anisotropic regularization to constrain the correlation filter for alleviating drift caused by occlusion. Moreover, an optimization strategy based on the Gauss-Seidel method was developed for obtaining robust and efficient online learning. Both qualitative and quantitative evaluations demonstrate that our tracker outperforms state-of-the-art trackers in object tracking benchmarks (OTBs). PMID:29231876

Method and system for laser-based formation of micro-shapes in surfaces of optical elements

DOEpatents

Bass, Isaac Louis; Guss, Gabriel Mark

2013-03-05

A method of forming a surface feature extending into a sample includes providing a laser operable to emit an output beam and modulating the output beam to form a pulse train having a plurality of pulses. The method also includes a) directing the pulse train along an optical path intersecting an exposed portion of the sample at a position i and b) focusing a first portion of the plurality of pulses to impinge on the sample at the position i. Each of the plurality of pulses is characterized by a spot size at the sample. The method further includes c) ablating at least a portion of the sample at the position i to form a portion of the surface feature and d) incrementing counter i. The method includes e) repeating steps a) through d) to form the surface feature. The sample is free of a rim surrounding the surface feature.

Methods for Integrating Environmental Awareness Training into Army Programs of Instruction

DTIC Science & Technology

1993-06-01

generations. iv NTIS CRA&I ) F -IC TAB U.a’mot’::ed El By .. . ... ....... By .......................... ...... . .. DiO t, ib., tion I CONTENTS...Training Support Package ................... E-1-E-19 Appendix F . Sample of Officer Basic Course Instructor’s Lesson Plan with Embedded Information... F -1- F -7 Appendix G. Samples of Situational Training Exercises ........... G-1-G 9 Appendix H. Samples of Pre-Command Course Guest Speaker

Field validation of the dnph method for aldehydes and ketones. Final report

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

Workman, G.S.; Steger, J.L.

1996-04-01

A stationary source emission test method for selected aldehydes and ketones has been validated. The method employs a sampling train with impingers containing 2,4-dinitrophenylhydrazine (DNPH) to derivatize the analytes. The resulting hydrazones are recovered and analyzed by high performance liquid chromatography. Nine analytes were studied; the method was validated for formaldehyde, acetaldehyde, propionaldehyde, acetophenone and isophorone. Acrolein, menthyl ethyl ketone, menthyl isobutyl ketone, and quinone did not meet the validation criteria. The study employed the validation techniques described in EPA method 301, which uses train spiking to determine bias, and collocated sampling trains to determine precision. The studies were carriedmore » out at a plywood veneer dryer and a polyester manufacturing plant.« less

CTEPP STANDARD OPERATING PROCEDURE FOR CONDUCTING STAFF AND PARTICIPANT TRAINING (SOP-2.27)

EPA Science Inventory

This SOP describes the method to train project staff and participants to collect various field samples and questionnaire data for the study. The training plan consists of two separate components: project staff training and participant training. Before project activities begin,...

Investigation of the Effluents Produced during the Functioning of Black and White Colored Smoke Devices.

DTIC Science & Technology

1986-01-31

and 4% diatomaceous earth (binder). Modified EPA Method 5 Sampling Train F The modified EPA Method 5 sampling train used was similar to the one...the fiber glass filter paper were taken by the Amberlite XAD-2. The XAD-2 is a porous polymer adsorbent used to sample organic vapors in effluents...from different kinds of combustion processes. Although a careful clean-up procedure was taken to wash the adsorbents before using, the polymer may still

Semi-Supervised Tripled Dictionary Learning for Standard-dose PET Image Prediction using Low-dose PET and Multimodal MRI

PubMed Central

Wang, Yan; Ma, Guangkai; An, Le; Shi, Feng; Zhang, Pei; Lalush, David S.; Wu, Xi; Pu, Yifei; Zhou, Jiliu; Shen, Dinggang

2017-01-01

Objective To obtain high-quality positron emission tomography (PET) image with low-dose tracer injection, this study attempts to predict the standard-dose PET (S-PET) image from both its low-dose PET (L-PET) counterpart and corresponding magnetic resonance imaging (MRI). Methods It was achieved by patch-based sparse representation (SR), using the training samples with a complete set of MRI, L-PET and S-PET modalities for dictionary construction. However, the number of training samples with complete modalities is often limited. In practice, many samples generally have incomplete modalities (i.e., with one or two missing modalities) that thus cannot be used in the prediction process. In light of this, we develop a semi-supervised tripled dictionary learning (SSTDL) method for S-PET image prediction, which can utilize not only the samples with complete modalities (called complete samples) but also the samples with incomplete modalities (called incomplete samples), to take advantage of the large number of available training samples and thus further improve the prediction performance. Results Validation was done on a real human brain dataset consisting of 18 subjects, and the results show that our method is superior to the SR and other baseline methods. Conclusion This work proposed a new S-PET prediction method, which can significantly improve the PET image quality with low-dose injection. Significance The proposed method is favorable in clinical application since it can decrease the potential radiation risk for patients. PMID:27187939

Key considerations for the experimental training and evaluation of cancer odour detection dogs: lessons learnt from a double-blind, controlled trial of prostate cancer detection

PubMed Central

2014-01-01

Background Cancer detection using sniffer dogs is a potential technology for clinical use and research. Our study sought to determine whether dogs could be trained to discriminate the odour of urine from men with prostate cancer from controls, using rigorous testing procedures and well-defined samples from a major research hospital. Methods We attempted to train ten dogs by initially rewarding them for finding and indicating individual prostate cancer urine samples (Stage 1). If dogs were successful in Stage 1, we then attempted to train them to discriminate prostate cancer samples from controls (Stage 2). The number of samples used to train each dog varied depending on their individual progress. Overall, 50 unique prostate cancer and 67 controls were collected and used during training. Dogs that passed Stage 2 were tested for their ability to discriminate 15 (Test 1) or 16 (Tests 2 and 3) unfamiliar prostate cancer samples from 45 (Test 1) or 48 (Tests 2 and 3) unfamiliar controls under double-blind conditions. Results Three dogs reached training Stage 2 and two of these learnt to discriminate potentially familiar prostate cancer samples from controls. However, during double-blind tests using new samples the two dogs did not indicate prostate cancer samples more frequently than expected by chance (Dog A sensitivity 0.13, specificity 0.71, Dog B sensitivity 0.25, specificity 0.75). The other dogs did not progress past Stage 1 as they did not have optimal temperaments for the sensitive odour discrimination training. Conclusions Although two dogs appeared to have learnt to select prostate cancer samples during training, they did not generalise on a prostate cancer odour during robust double-blind tests involving new samples. Our study illustrates that these rigorous tests are vital to avoid drawing misleading conclusions about the abilities of dogs to indicate certain odours. Dogs may memorise the individual odours of large numbers of training samples rather than generalise on a common odour. The results do not exclude the possibility that dogs could be trained to detect prostate cancer. We recommend that canine olfactory memory is carefully considered in all future studies and rigorous double-blind methods used to avoid confounding effects. PMID:24575737

A visual training tool for the Photoload sampling technique

Treesearch

Violet J. Holley; Robert E. Keane

2010-01-01

This visual training aid is designed to provide Photoload users a tool to increase the accuracy of fuel loading estimations when using the Photoload technique. The Photoload Sampling Technique (RMRS-GTR-190) provides fire managers a sampling method for obtaining consistent, accurate, inexpensive, and quick estimates of fuel loading. It is designed to require only one...

Support vector regression to predict porosity and permeability: Effect of sample size

NASA Astrophysics Data System (ADS)

Al-Anazi, A. F.; Gates, I. D.

2012-02-01

Porosity and permeability are key petrophysical parameters obtained from laboratory core analysis. Cores, obtained from drilled wells, are often few in number for most oil and gas fields. Porosity and permeability correlations based on conventional techniques such as linear regression or neural networks trained with core and geophysical logs suffer poor generalization to wells with only geophysical logs. The generalization problem of correlation models often becomes pronounced when the training sample size is small. This is attributed to the underlying assumption that conventional techniques employing the empirical risk minimization (ERM) inductive principle converge asymptotically to the true risk values as the number of samples increases. In small sample size estimation problems, the available training samples must span the complexity of the parameter space so that the model is able both to match the available training samples reasonably well and to generalize to new data. This is achieved using the structural risk minimization (SRM) inductive principle by matching the capability of the model to the available training data. One method that uses SRM is support vector regression (SVR) network. In this research, the capability of SVR to predict porosity and permeability in a heterogeneous sandstone reservoir under the effect of small sample size is evaluated. Particularly, the impact of Vapnik's ɛ-insensitivity loss function and least-modulus loss function on generalization performance was empirically investigated. The results are compared to the multilayer perception (MLP) neural network, a widely used regression method, which operates under the ERM principle. The mean square error and correlation coefficients were used to measure the quality of predictions. The results demonstrate that SVR yields consistently better predictions of the porosity and permeability with small sample size than the MLP method. Also, the performance of SVR depends on both kernel function type and loss functions used.

Classification of Parkinson's disease utilizing multi-edit nearest-neighbor and ensemble learning algorithms with speech samples.

PubMed

Zhang, He-Hua; Yang, Liuyang; Liu, Yuchuan; Wang, Pin; Yin, Jun; Li, Yongming; Qiu, Mingguo; Zhu, Xueru; Yan, Fang

2016-11-16

The use of speech based data in the classification of Parkinson disease (PD) has been shown to provide an effect, non-invasive mode of classification in recent years. Thus, there has been an increased interest in speech pattern analysis methods applicable to Parkinsonism for building predictive tele-diagnosis and tele-monitoring models. One of the obstacles in optimizing classifications is to reduce noise within the collected speech samples, thus ensuring better classification accuracy and stability. While the currently used methods are effect, the ability to invoke instance selection has been seldomly examined. In this study, a PD classification algorithm was proposed and examined that combines a multi-edit-nearest-neighbor (MENN) algorithm and an ensemble learning algorithm. First, the MENN algorithm is applied for selecting optimal training speech samples iteratively, thereby obtaining samples with high separability. Next, an ensemble learning algorithm, random forest (RF) or decorrelated neural network ensembles (DNNE), is used to generate trained samples from the collected training samples. Lastly, the trained ensemble learning algorithms are applied to the test samples for PD classification. This proposed method was examined using a more recently deposited public datasets and compared against other currently used algorithms for validation. Experimental results showed that the proposed algorithm obtained the highest degree of improved classification accuracy (29.44%) compared with the other algorithm that was examined. Furthermore, the MENN algorithm alone was found to improve classification accuracy by as much as 45.72%. Moreover, the proposed algorithm was found to exhibit a higher stability, particularly when combining the MENN and RF algorithms. This study showed that the proposed method could improve PD classification when using speech data and can be applied to future studies seeking to improve PD classification methods.

Detecting waste-combustion emissions: several advanced methods are useful for sampling air contaminants from hazardous-waste-incinerator stacks

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

Johnson, L.D.

1986-01-01

This paper is an overview of sampling methods being recommended to EPA regulatory programs, to EPA engineering research and development projects, and to interested parties in the industrial community. The methods discussed are generally applicable to both incineration and processes closely related to incineration (e.g., co-firing of waste in industrial boilers, and burning of contaminated heating oil). Although methods for inorganic hazardous compounds are very briefly outlined, the primary emphasis of the paper is on organic compounds that are likely to be chosen as principal organic hazardous constituents (POHCs) for a trial burn. Methods receiving major attention include: the Modifiedmore » Method 5 Train (MM5) which includes an XAD-2 sorbent module, the Source Assessment Sampling System (SASS), the recently developed Volatile Organic Sampling Train (VOST), and assorted containers such as glass bulbs and plastic bags.« less

New method for detection of gastric cancer by hyperspectral imaging: a pilot study

NASA Astrophysics Data System (ADS)

Kiyotoki, Shu; Nishikawa, Jun; Okamoto, Takeshi; Hamabe, Kouichi; Saito, Mari; Goto, Atsushi; Fujita, Yusuke; Hamamoto, Yoshihiko; Takeuchi, Yusuke; Satori, Shin; Sakaida, Isao

2013-02-01

We developed a new, easy, and objective method to detect gastric cancer using hyperspectral imaging (HSI) technology combining spectroscopy and imaging A total of 16 gastroduodenal tumors removed by endoscopic resection or surgery from 14 patients at Yamaguchi University Hospital, Japan, were recorded using a hyperspectral camera (HSC) equipped with HSI technology Corrected spectral reflectance was obtained from 10 samples of normal mucosa and 10 samples of tumors for each case The 16 cases were divided into eight training cases (160 training samples) and eight test cases (160 test samples) We established a diagnostic algorithm with training samples and evaluated it with test samples Diagnostic capability of the algorithm for each tumor was validated, and enhancement of tumors by image processing using the HSC was evaluated The diagnostic algorithm used the 726-nm wavelength, with a cutoff point established from training samples The sensitivity, specificity, and accuracy rates of the algorithm's diagnostic capability in the test samples were 78.8% (63/80), 92.5% (74/80), and 85.6% (137/160), respectively Tumors in HSC images of 13 (81.3%) cases were well enhanced by image processing Differences in spectral reflectance between tumors and normal mucosa suggested that tumors can be clearly distinguished from background mucosa with HSI technology.

Mining big data sets of plankton images: a zero-shot learning approach to retrieve labels without training data

NASA Astrophysics Data System (ADS)

Orenstein, E. C.; Morgado, P. M.; Peacock, E.; Sosik, H. M.; Jaffe, J. S.

2016-02-01

Technological advances in instrumentation and computing have allowed oceanographers to develop imaging systems capable of collecting extremely large data sets. With the advent of in situ plankton imaging systems, scientists must now commonly deal with "big data" sets containing tens of millions of samples spanning hundreds of classes, making manual classification untenable. Automated annotation methods are now considered to be the bottleneck between collection and interpretation. Typically, such classifiers learn to approximate a function that predicts a predefined set of classes for which a considerable amount of labeled training data is available. The requirement that the training data span all the classes of concern is problematic for plankton imaging systems since they sample such diverse, rapidly changing populations. These data sets may contain relatively rare, sparsely distributed, taxa that will not have associated training data; a classifier trained on a limited set of classes will miss these samples. The computer vision community, leveraging advances in Convolutional Neural Networks (CNNs), has recently attempted to tackle such problems using "zero-shot" object categorization methods. Under a zero-shot framework, a classifier is trained to map samples onto a set of attributes rather than a class label. These attributes can include visual and non-visual information such as what an organism is made out of, where it is distributed globally, or how it reproduces. A second stage classifier is then used to extrapolate a class. In this work, we demonstrate a zero-shot classifier, implemented with a CNN, to retrieve out-of-training-set labels from images. This method is applied to data from two continuously imaging, moored instruments: the Scripps Plankton Camera System (SPCS) and the Imaging FlowCytobot (IFCB). Results from simulated deployment scenarios indicate zero-shot classifiers could be successful at recovering samples of rare taxa in image sets. This capability will allow ecologists to identify trends in the distribution of difficult to sample organisms in their data.

Analysis of training sample selection strategies for regression-based quantitative landslide susceptibility mapping methods

NASA Astrophysics Data System (ADS)

Erener, Arzu; Sivas, A. Abdullah; Selcuk-Kestel, A. Sevtap; Düzgün, H. Sebnem

2017-07-01

All of the quantitative landslide susceptibility mapping (QLSM) methods requires two basic data types, namely, landslide inventory and factors that influence landslide occurrence (landslide influencing factors, LIF). Depending on type of landslides, nature of triggers and LIF, accuracy of the QLSM methods differs. Moreover, how to balance the number of 0 (nonoccurrence) and 1 (occurrence) in the training set obtained from the landslide inventory and how to select which one of the 1's and 0's to be included in QLSM models play critical role in the accuracy of the QLSM. Although performance of various QLSM methods is largely investigated in the literature, the challenge of training set construction is not adequately investigated for the QLSM methods. In order to tackle this challenge, in this study three different training set selection strategies along with the original data set is used for testing the performance of three different regression methods namely Logistic Regression (LR), Bayesian Logistic Regression (BLR) and Fuzzy Logistic Regression (FLR). The first sampling strategy is proportional random sampling (PRS), which takes into account a weighted selection of landslide occurrences in the sample set. The second method, namely non-selective nearby sampling (NNS), includes randomly selected sites and their surrounding neighboring points at certain preselected distances to include the impact of clustering. Selective nearby sampling (SNS) is the third method, which concentrates on the group of 1's and their surrounding neighborhood. A randomly selected group of landslide sites and their neighborhood are considered in the analyses similar to NNS parameters. It is found that LR-PRS, FLR-PRS and BLR-Whole Data set-ups, with order, yield the best fits among the other alternatives. The results indicate that in QLSM based on regression models, avoidance of spatial correlation in the data set is critical for the model's performance.

Progressive sparse representation-based classification using local discrete cosine transform evaluation for image recognition

NASA Astrophysics Data System (ADS)

Song, Xiaoning; Feng, Zhen-Hua; Hu, Guosheng; Yang, Xibei; Yang, Jingyu; Qi, Yunsong

2015-09-01

This paper proposes a progressive sparse representation-based classification algorithm using local discrete cosine transform (DCT) evaluation to perform face recognition. Specifically, the sum of the contributions of all training samples of each subject is first taken as the contribution of this subject, then the redundant subject with the smallest contribution to the test sample is iteratively eliminated. Second, the progressive method aims at representing the test sample as a linear combination of all the remaining training samples, by which the representation capability of each training sample is exploited to determine the optimal "nearest neighbors" for the test sample. Third, the transformed DCT evaluation is constructed to measure the similarity between the test sample and each local training sample using cosine distance metrics in the DCT domain. The final goal of the proposed method is to determine an optimal weighted sum of nearest neighbors that are obtained under the local correlative degree evaluation, which is approximately equal to the test sample, and we can use this weighted linear combination to perform robust classification. Experimental results conducted on the ORL database of faces (created by the Olivetti Research Laboratory in Cambridge), the FERET face database (managed by the Defense Advanced Research Projects Agency and the National Institute of Standards and Technology), AR face database (created by Aleix Martinez and Robert Benavente in the Computer Vision Center at U.A.B), and USPS handwritten digit database (gathered at the Center of Excellence in Document Analysis and Recognition at SUNY Buffalo) demonstrate the effectiveness of the proposed method.

The development of radioactive sample surrogates for training and exercises

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

Martha Finck; Bevin Brush; Dick Jansen

2012-03-01

The development of radioactive sample surrogates for training and exercises Source term information is required for to reconstruct a device used in a dispersed radiological dispersal device. Simulating a radioactive environment to train and exercise sampling and sample characterization methods with suitable sample materials is a continued challenge. The Idaho National Laboratory has developed and permitted a Radioactive Response Training Range (RRTR), an 800 acre test range that is approved for open air dispersal of activated KBr, for training first responders in the entry and exit from radioactively contaminated areas, and testing protocols for environmental sampling and field characterization. Membersmore » from the Department of Defense, Law Enforcement, and the Department of Energy participated in the first contamination exercise that was conducted at the RRTR in the July 2011. The range was contaminated using a short lived radioactive Br-82 isotope (activated KBr). Soil samples contaminated with KBr (dispersed as a solution) and glass particles containing activated potassium bromide that emulated dispersed radioactive materials (such as ceramic-based sealed source materials) were collected to assess environmental sampling and characterization techniques. This presentation summarizes the performance of a radioactive materials surrogate for use as a training aide for nuclear forensics.« less

Heart rate deceleration runs for postinfarction risk prediction.

PubMed

Guzik, Przemyslaw; Piskorski, Jaroslaw; Barthel, Petra; Bauer, Axel; Müller, Alexander; Junk, Nadine; Ulm, Kurt; Malik, Marek; Schmidt, Georg

2012-01-01

A method for counting episodes of uninterrupted beat-to-beat heart rate decelerations was developed. The method was set up and evaluated using 24-hour electrocardiogram Holter recordings of 1455 (training sample) and 946 (validation sample) postinfarction patients. During a median follow-up of 24 months, 70, 46, and 19 patients of the training sample suffered from total, cardiac, and sudden cardiac mortality, respectively. In the validation sample, these numbers were 39, 25, and 15. Episodes of consecutive beat-to-beat heart rate decelerations (deceleration runs [DRs]) were characterized by their length. Deceleration runs of 2 to 10 cycles were significantly less frequent in nonsurvivors. Multivariate model of DRs of 2, 4, and 8 cycles identified low-, intermediate-, and high-risk groups. In these groups of the training sample, the total mortalities were 1.8%, 6.1%, and 24%, respectively. In the validation sample, these numbers were 1.8%, 4.1%, and 21.9%. Infrequent DRs during 24-hour Holter indicate high risk of postinfarction mortality. Copyright © 2012 Elsevier Inc. All rights reserved.

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

PubMed

Zhang, Shanwen; Wu, Xiaowei; You, Zhuhong

2017-01-01

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

A Modified Sparse Representation Method for Facial Expression Recognition.

PubMed

Wang, Wei; Xu, LiHong

2016-01-01

In this paper, we carry on research on a facial expression recognition method, which is based on modified sparse representation recognition (MSRR) method. On the first stage, we use Haar-like+LPP to extract feature and reduce dimension. On the second stage, we adopt LC-K-SVD (Label Consistent K-SVD) method to train the dictionary, instead of adopting directly the dictionary from samples, and add block dictionary training into the training process. On the third stage, stOMP (stagewise orthogonal matching pursuit) method is used to speed up the convergence of OMP (orthogonal matching pursuit). Besides, a dynamic regularization factor is added to iteration process to suppress noises and enhance accuracy. We verify the proposed method from the aspect of training samples, dimension, feature extraction and dimension reduction methods and noises in self-built database and Japan's JAFFE and CMU's CK database. Further, we compare this sparse method with classic SVM and RVM and analyze the recognition effect and time efficiency. The result of simulation experiment has shown that the coefficient of MSRR method contains classifying information, which is capable of improving the computing speed and achieving a satisfying recognition result.

A Modified Sparse Representation Method for Facial Expression Recognition

PubMed Central

Wang, Wei; Xu, LiHong

2016-01-01

In this paper, we carry on research on a facial expression recognition method, which is based on modified sparse representation recognition (MSRR) method. On the first stage, we use Haar-like+LPP to extract feature and reduce dimension. On the second stage, we adopt LC-K-SVD (Label Consistent K-SVD) method to train the dictionary, instead of adopting directly the dictionary from samples, and add block dictionary training into the training process. On the third stage, stOMP (stagewise orthogonal matching pursuit) method is used to speed up the convergence of OMP (orthogonal matching pursuit). Besides, a dynamic regularization factor is added to iteration process to suppress noises and enhance accuracy. We verify the proposed method from the aspect of training samples, dimension, feature extraction and dimension reduction methods and noises in self-built database and Japan's JAFFE and CMU's CK database. Further, we compare this sparse method with classic SVM and RVM and analyze the recognition effect and time efficiency. The result of simulation experiment has shown that the coefficient of MSRR method contains classifying information, which is capable of improving the computing speed and achieving a satisfying recognition result. PMID:26880878

Family Therapy Training in Child and Adolescent Psychiatry Fellowship Programs

ERIC Educational Resources Information Center

Rait, Douglas Samuel

2012-01-01

Objective: This study describes the current state of family therapy training in a sample of child and adolescent psychiatry fellowship programs. Method: Child and adolescent psychiatry fellows (N = 66) from seven training programs completed a questionnaire assessing demographics, family therapy training experiences, common models of treatment and…

[Perceptions about continuous training of Chilean health care teachers].

PubMed

Pérez V, Cristhian; Fasce H, Eduardo; Coloma N, Katherine; Vaccarezza G, Giulietta; Ortega B, Javiera

2013-06-01

Continuous training of teachers, in discipline and pedagogical topics, is a key step to improve the quality of educational processes. To report the perception of Chilean teachers of undergraduate health care programs, about continuous training activities. Twenty teachers working at different undergraduate health care programs in Chile were interviewed. Maximum variation and theoretical sampling methods were used to select the sample. Data was analyzed by open coding, according to the Grounded Theory guidelines. Nine categories emerged from data analysis: Access to continuous training, meaning of training in discipline, activities of continuous training in discipline, meaning of continuous training in pedagogy, kinds of continuous training in pedagogy, quality of continuous training in pedagogy, ideal of continuous training in pedagogy, outcomes of continuous training in pedagogy and needs for continuous training in pedagogy. Teachers of health care programs prefer to participate in contextualized training activities. Also, they emphasize their need of training in evaluation and teaching strategies.

A semisupervised support vector regression method to estimate biophysical parameters from remotely sensed images

NASA Astrophysics Data System (ADS)

Castelletti, Davide; Demir, Begüm; Bruzzone, Lorenzo

2014-10-01

This paper presents a novel semisupervised learning (SSL) technique defined in the context of ɛ-insensitive support vector regression (SVR) to estimate biophysical parameters from remotely sensed images. The proposed SSL method aims to mitigate the problems of small-sized biased training sets without collecting any additional samples with reference measures. This is achieved on the basis of two consecutive steps. The first step is devoted to inject additional priors information in the learning phase of the SVR in order to adapt the importance of each training sample according to distribution of the unlabeled samples. To this end, a weight is initially associated to each training sample based on a novel strategy that defines higher weights for the samples located in the high density regions of the feature space while giving reduced weights to those that fall into the low density regions of the feature space. Then, in order to exploit different weights for training samples in the learning phase of the SVR, we introduce a weighted SVR (WSVR) algorithm. The second step is devoted to jointly exploit labeled and informative unlabeled samples for further improving the definition of the WSVR learning function. To this end, the most informative unlabeled samples that have an expected accurate target values are initially selected according to a novel strategy that relies on the distribution of the unlabeled samples in the feature space and on the WSVR function estimated at the first step. Then, we introduce a restructured WSVR algorithm that jointly uses labeled and unlabeled samples in the learning phase of the WSVR algorithm and tunes their importance by different values of regularization parameters. Experimental results obtained for the estimation of single-tree stem volume show the effectiveness of the proposed SSL method.

Sample Training Based Wildfire Segmentation by 2D Histogram θ-Division with Minimum Error

PubMed Central

Dong, Erqian; Sun, Mingui; Jia, Wenyan; Zhang, Dengyi; Yuan, Zhiyong

2013-01-01

A novel wildfire segmentation algorithm is proposed with the help of sample training based 2D histogram θ-division and minimum error. Based on minimum error principle and 2D color histogram, the θ-division methods were presented recently, but application of prior knowledge on them has not been explored. For the specific problem of wildfire segmentation, we collect sample images with manually labeled fire pixels. Then we define the probability function of error division to evaluate θ-division segmentations, and the optimal angle θ is determined by sample training. Performances in different color channels are compared, and the suitable channel is selected. To further improve the accuracy, the combination approach is presented with both θ-division and other segmentation methods such as GMM. Our approach is tested on real images, and the experiments prove its efficiency for wildfire segmentation. PMID:23878526

Domain Regeneration for Cross-Database Micro-Expression Recognition

NASA Astrophysics Data System (ADS)

Zong, Yuan; Zheng, Wenming; Huang, Xiaohua; Shi, Jingang; Cui, Zhen; Zhao, Guoying

2018-05-01

In this paper, we investigate the cross-database micro-expression recognition problem, where the training and testing samples are from two different micro-expression databases. Under this setting, the training and testing samples would have different feature distributions and hence the performance of most existing micro-expression recognition methods may decrease greatly. To solve this problem, we propose a simple yet effective method called Target Sample Re-Generator (TSRG) in this paper. By using TSRG, we are able to re-generate the samples from target micro-expression database and the re-generated target samples would share same or similar feature distributions with the original source samples. For this reason, we can then use the classifier learned based on the labeled source samples to accurately predict the micro-expression categories of the unlabeled target samples. To evaluate the performance of the proposed TSRG method, extensive cross-database micro-expression recognition experiments designed based on SMIC and CASME II databases are conducted. Compared with recent state-of-the-art cross-database emotion recognition methods, the proposed TSRG achieves more promising results.

Anomaly detection for machine learning redshifts applied to SDSS galaxies

NASA Astrophysics Data System (ADS)

Hoyle, Ben; Rau, Markus Michael; Paech, Kerstin; Bonnett, Christopher; Seitz, Stella; Weller, Jochen

2015-10-01

We present an analysis of anomaly detection for machine learning redshift estimation. Anomaly detection allows the removal of poor training examples, which can adversely influence redshift estimates. Anomalous training examples may be photometric galaxies with incorrect spectroscopic redshifts, or galaxies with one or more poorly measured photometric quantity. We select 2.5 million `clean' SDSS DR12 galaxies with reliable spectroscopic redshifts, and 6730 `anomalous' galaxies with spectroscopic redshift measurements which are flagged as unreliable. We contaminate the clean base galaxy sample with galaxies with unreliable redshifts and attempt to recover the contaminating galaxies using the Elliptical Envelope technique. We then train four machine learning architectures for redshift analysis on both the contaminated sample and on the preprocessed `anomaly-removed' sample and measure redshift statistics on a clean validation sample generated without any preprocessing. We find an improvement on all measured statistics of up to 80 per cent when training on the anomaly removed sample as compared with training on the contaminated sample for each of the machine learning routines explored. We further describe a method to estimate the contamination fraction of a base data sample.

Unsupervised Ensemble Anomaly Detection Using Time-Periodic Packet Sampling

NASA Astrophysics Data System (ADS)

Uchida, Masato; Nawata, Shuichi; Gu, Yu; Tsuru, Masato; Oie, Yuji

We propose an anomaly detection method for finding patterns in network traffic that do not conform to legitimate (i.e., normal) behavior. The proposed method trains a baseline model describing the normal behavior of network traffic without using manually labeled traffic data. The trained baseline model is used as the basis for comparison with the audit network traffic. This anomaly detection works in an unsupervised manner through the use of time-periodic packet sampling, which is used in a manner that differs from its intended purpose — the lossy nature of packet sampling is used to extract normal packets from the unlabeled original traffic data. Evaluation using actual traffic traces showed that the proposed method has false positive and false negative rates in the detection of anomalies regarding TCP SYN packets comparable to those of a conventional method that uses manually labeled traffic data to train the baseline model. Performance variation due to the probabilistic nature of sampled traffic data is mitigated by using ensemble anomaly detection that collectively exploits multiple baseline models in parallel. Alarm sensitivity is adjusted for the intended use by using maximum- and minimum-based anomaly detection that effectively take advantage of the performance variations among the multiple baseline models. Testing using actual traffic traces showed that the proposed anomaly detection method performs as well as one using manually labeled traffic data and better than one using randomly sampled (unlabeled) traffic data.

The Development of an Officer Training School Board Score Prediction Method Using a Multi-Board Approach

DTIC Science & Technology

1991-03-01

forms: ". ..application blanks, biographical inventories , interviews, work sample tests, and intelligence, aptitude, and personality tests" (1:11...the grouping method, 3) the task method, and 4) the knowledge , skills, abilities (KSA) method. The point method of measuring training/experience assigns... knowledge , skills, abilities, and other characteristics which relate specifically to each job element (3:131). Interview. According to N. Schmitt

Feature genes predicting the FLT3/ITD mutation in acute myeloid leukemia

PubMed Central

LI, CHENGLONG; ZHU, BIAO; CHEN, JIAO; HUANG, XIAOBING

2016-01-01

In the present study, gene expression profiles of acute myeloid leukemia (AML) samples were analyzed to identify feature genes with the capacity to predict the mutation status of FLT3/ITD. Two machine learning models, namely the support vector machine (SVM) and random forest (RF) methods, were used for classification. Four datasets were downloaded from the European Bioinformatics Institute, two of which (containing 371 samples, including 281 FLT3/ITD mutation-negative and 90 mutation-positive samples) were randomly defined as the training group, while the other two datasets (containing 488 samples, including 350 FLT3/ITD mutation-negative and 138 mutation-positive samples) were defined as the test group. Differentially expressed genes (DEGs) were identified by significance analysis of the micro-array data by using the training samples. The classification efficiency of the SCM and RF methods was evaluated using the following parameters: Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and the area under the receiver operating characteristic curve. Functional enrichment analysis was performed for the feature genes with DAVID. A total of 585 DEGs were identified in the training group, of which 580 were upregulated and five were downregulated. The classification accuracy rates of the two methods for the training group, the test group and the combined group using the 585 feature genes were >90%. For the SVM and RF methods, the rates of correct determination, specificity and PPV were >90%, while the sensitivity and NPV were >80%. The SVM method produced a slightly better classification effect than the RF method. A total of 13 biological pathways were overrepresented by the feature genes, mainly involving energy metabolism, chromatin organization and translation. The feature genes identified in the present study may be used to predict the mutation status of FLT3/ITD in patients with AML. PMID:27177049

Feature genes predicting the FLT3/ITD mutation in acute myeloid leukemia.

PubMed

Li, Chenglong; Zhu, Biao; Chen, Jiao; Huang, Xiaobing

2016-07-01

In the present study, gene expression profiles of acute myeloid leukemia (AML) samples were analyzed to identify feature genes with the capacity to predict the mutation status of FLT3/ITD. Two machine learning models, namely the support vector machine (SVM) and random forest (RF) methods, were used for classification. Four datasets were downloaded from the European Bioinformatics Institute, two of which (containing 371 samples, including 281 FLT3/ITD mutation-negative and 90 mutation‑positive samples) were randomly defined as the training group, while the other two datasets (containing 488 samples, including 350 FLT3/ITD mutation-negative and 138 mutation-positive samples) were defined as the test group. Differentially expressed genes (DEGs) were identified by significance analysis of the microarray data by using the training samples. The classification efficiency of the SCM and RF methods was evaluated using the following parameters: Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and the area under the receiver operating characteristic curve. Functional enrichment analysis was performed for the feature genes with DAVID. A total of 585 DEGs were identified in the training group, of which 580 were upregulated and five were downregulated. The classification accuracy rates of the two methods for the training group, the test group and the combined group using the 585 feature genes were >90%. For the SVM and RF methods, the rates of correct determination, specificity and PPV were >90%, while the sensitivity and NPV were >80%. The SVM method produced a slightly better classification effect than the RF method. A total of 13 biological pathways were overrepresented by the feature genes, mainly involving energy metabolism, chromatin organization and translation. The feature genes identified in the present study may be used to predict the mutation status of FLT3/ITD in patients with AML.

A comparison of machine learning and Bayesian modelling for molecular serotyping.

PubMed

Newton, Richard; Wernisch, Lorenz

2017-08-11

Streptococcus pneumoniae is a human pathogen that is a major cause of infant mortality. Identifying the pneumococcal serotype is an important step in monitoring the impact of vaccines used to protect against disease. Genomic microarrays provide an effective method for molecular serotyping. Previously we developed an empirical Bayesian model for the classification of serotypes from a molecular serotyping array. With only few samples available, a model driven approach was the only option. In the meanwhile, several thousand samples have been made available to us, providing an opportunity to investigate serotype classification by machine learning methods, which could complement the Bayesian model. We compare the performance of the original Bayesian model with two machine learning algorithms: Gradient Boosting Machines and Random Forests. We present our results as an example of a generic strategy whereby a preliminary probabilistic model is complemented or replaced by a machine learning classifier once enough data are available. Despite the availability of thousands of serotyping arrays, a problem encountered when applying machine learning methods is the lack of training data containing mixtures of serotypes; due to the large number of possible combinations. Most of the available training data comprises samples with only a single serotype. To overcome the lack of training data we implemented an iterative analysis, creating artificial training data of serotype mixtures by combining raw data from single serotype arrays. With the enhanced training set the machine learning algorithms out perform the original Bayesian model. However, for serotypes currently lacking sufficient training data the best performing implementation was a combination of the results of the Bayesian Model and the Gradient Boosting Machine. As well as being an effective method for classifying biological data, machine learning can also be used as an efficient method for revealing subtle biological insights, which we illustrate with an example.

Woodstove smoke and CO emissions: comparison of reference methods with the VIP sampler.

PubMed

Jaasma, D R; Champion, M C; Shelton, J W

1990-06-01

A new field sampler has been developed for measuring the particulate matter (PM) and carbon monoxide emissions of woodburning stoves. Particulate matter is determined by carbon balance and the workup of a sample train which is similar to a room-temperature EPA Method 5G train. A steel tank, initially evacuated, serves as the motive force for sampling and also accumulates a gas sample for post-test analysis of time-averaged stack CO and CO2 concentrations. Workup procedures can be completed within 72 hours of sampler retrieval. The system has been compared to reference methods in two laboratory test series involving six different woodburning appliances and two independent laboratories. The correlation of field sampler emission rates and reference method rates is strong.

Woodstove smoke and CO emissions: Comparison of reference methods with the VIP sampler

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

Jaasma, D.R.; Champion, M.C.; Shelton, J.W.

1990-06-01

A new field sampler has been developed for measuring the particulate matter (PM) and carbon monoxide emissions of woodburning stoves. Particulate matter is determined by carbon balance and the workup of a sample train which is similar to a room-temperature EPA Method 5G train. A steel tank, initially evacuated, serves as the motive force for sampling and also accumulates a gas sample for post-test analysis of time-averaged stack CO and CO{sub 2} concentrations. Workup procedures can be completed within 72 hours of sampler retrieval. The system has been compared to reference methods in two laboratory test series involving six differentmore » woodburning appliances and two independent laboratories. The correlation of field sampler emission rates and reference method rates is strong.« less

How large a training set is needed to develop a classifier for microarray data?

PubMed

Dobbin, Kevin K; Zhao, Yingdong; Simon, Richard M

2008-01-01

A common goal of gene expression microarray studies is the development of a classifier that can be used to divide patients into groups with different prognoses, or with different expected responses to a therapy. These types of classifiers are developed on a training set, which is the set of samples used to train a classifier. The question of how many samples are needed in the training set to produce a good classifier from high-dimensional microarray data is challenging. We present a model-based approach to determining the sample size required to adequately train a classifier. It is shown that sample size can be determined from three quantities: standardized fold change, class prevalence, and number of genes or features on the arrays. Numerous examples and important experimental design issues are discussed. The method is adapted to address ex post facto determination of whether the size of a training set used to develop a classifier was adequate. An interactive web site for performing the sample size calculations is provided. We showed that sample size calculations for classifier development from high-dimensional microarray data are feasible, discussed numerous important considerations, and presented examples.

Recognition of genetically modified product based on affinity propagation clustering and terahertz spectroscopy

NASA Astrophysics Data System (ADS)

Liu, Jianjun; Kan, Jianquan

2018-04-01

In this paper, based on the terahertz spectrum, a new identification method of genetically modified material by support vector machine (SVM) based on affinity propagation clustering is proposed. This algorithm mainly uses affinity propagation clustering algorithm to make cluster analysis and labeling on unlabeled training samples, and in the iterative process, the existing SVM training data are continuously updated, when establishing the identification model, it does not need to manually label the training samples, thus, the error caused by the human labeled samples is reduced, and the identification accuracy of the model is greatly improved.

Transfer Learning for Class Imbalance Problems with Inadequate Data.

PubMed

Al-Stouhi, Samir; Reddy, Chandan K

2016-07-01

A fundamental problem in data mining is to effectively build robust classifiers in the presence of skewed data distributions. Class imbalance classifiers are trained specifically for skewed distribution datasets. Existing methods assume an ample supply of training examples as a fundamental prerequisite for constructing an effective classifier. However, when sufficient data is not readily available, the development of a representative classification algorithm becomes even more difficult due to the unequal distribution between classes. We provide a unified framework that will potentially take advantage of auxiliary data using a transfer learning mechanism and simultaneously build a robust classifier to tackle this imbalance issue in the presence of few training samples in a particular target domain of interest. Transfer learning methods use auxiliary data to augment learning when training examples are not sufficient and in this paper we will develop a method that is optimized to simultaneously augment the training data and induce balance into skewed datasets. We propose a novel boosting based instance-transfer classifier with a label-dependent update mechanism that simultaneously compensates for class imbalance and incorporates samples from an auxiliary domain to improve classification. We provide theoretical and empirical validation of our method and apply to healthcare and text classification applications.

Professional Profile of National Health Service Physicians in Greece and Their Self-Expressed Training Needs

ERIC Educational Resources Information Center

Kyriopoulos, John; Gregory, Susan; Georgoussi, Eugenia; Dolgeras, Apostolos

2003-01-01

Introduction: Continuing medical education is not yet mandatory in Greece, but an increasing number of training courses is becoming available. In recent years, 32 training centers have been accredited. Method: A postal survey of a national sample of 500 National Health Service doctors, weighted toward hospitals with accredited training centers,…

Building Extraction Based on an Optimized Stacked Sparse Autoencoder of Structure and Training Samples Using LIDAR DSM and Optical Images.

PubMed

Yan, Yiming; Tan, Zhichao; Su, Nan; Zhao, Chunhui

2017-08-24

In this paper, a building extraction method is proposed based on a stacked sparse autoencoder with an optimized structure and training samples. Building extraction plays an important role in urban construction and planning. However, some negative effects will reduce the accuracy of extraction, such as exceeding resolution, bad correction and terrain influence. Data collected by multiple sensors, as light detection and ranging (LIDAR), optical sensor etc., are used to improve the extraction. Using digital surface model (DSM) obtained from LIDAR data and optical images, traditional method can improve the extraction effect to a certain extent, but there are some defects in feature extraction. Since stacked sparse autoencoder (SSAE) neural network can learn the essential characteristics of the data in depth, SSAE was employed to extract buildings from the combined DSM data and optical image. A better setting strategy of SSAE network structure is given, and an idea of setting the number and proportion of training samples for better training of SSAE was presented. The optical data and DSM were combined as input of the optimized SSAE, and after training by an optimized samples, the appropriate network structure can extract buildings with great accuracy and has good robustness.

Knowledge of Parents of Children with Autism Spectrum Disorder of Behavior Modification Methods and Their Training Needs Accordingly

ERIC Educational Resources Information Center

Deeb, Raid Mousa Al-Shaik

2016-01-01

The study aimed at identifying knowledge of parents of children with autism spectrum disorder of behavior modification methods and their training needs accordingly. The sample of the study consisted of (98) parents in Jordan. A scale of behavior modification methods was constructed, and then validated. The results of the study showed that the…

An Automated Algorithm to Screen Massive Training Samples for a Global Impervious Surface Classification

NASA Technical Reports Server (NTRS)

Tan, Bin; Brown de Colstoun, Eric; Wolfe, Robert E.; Tilton, James C.; Huang, Chengquan; Smith, Sarah E.

2012-01-01

An algorithm is developed to automatically screen the outliers from massive training samples for Global Land Survey - Imperviousness Mapping Project (GLS-IMP). GLS-IMP is to produce a global 30 m spatial resolution impervious cover data set for years 2000 and 2010 based on the Landsat Global Land Survey (GLS) data set. This unprecedented high resolution impervious cover data set is not only significant to the urbanization studies but also desired by the global carbon, hydrology, and energy balance researches. A supervised classification method, regression tree, is applied in this project. A set of accurate training samples is the key to the supervised classifications. Here we developed the global scale training samples from 1 m or so resolution fine resolution satellite data (Quickbird and Worldview2), and then aggregate the fine resolution impervious cover map to 30 m resolution. In order to improve the classification accuracy, the training samples should be screened before used to train the regression tree. It is impossible to manually screen 30 m resolution training samples collected globally. For example, in Europe only, there are 174 training sites. The size of the sites ranges from 4.5 km by 4.5 km to 8.1 km by 3.6 km. The amount training samples are over six millions. Therefore, we develop this automated statistic based algorithm to screen the training samples in two levels: site and scene level. At the site level, all the training samples are divided to 10 groups according to the percentage of the impervious surface within a sample pixel. The samples following in each 10% forms one group. For each group, both univariate and multivariate outliers are detected and removed. Then the screen process escalates to the scene level. A similar screen process but with a looser threshold is applied on the scene level considering the possible variance due to the site difference. We do not perform the screen process across the scenes because the scenes might vary due to the phenology, solar-view geometry, and atmospheric condition etc. factors but not actual landcover difference. Finally, we will compare the classification results from screened and unscreened training samples to assess the improvement achieved by cleaning up the training samples. Keywords:

Optimizing area under the ROC curve using semi-supervised learning

PubMed Central

Wang, Shijun; Li, Diana; Petrick, Nicholas; Sahiner, Berkman; Linguraru, Marius George; Summers, Ronald M.

2014-01-01

Receiver operating characteristic (ROC) analysis is a standard methodology to evaluate the performance of a binary classification system. The area under the ROC curve (AUC) is a performance metric that summarizes how well a classifier separates two classes. Traditional AUC optimization techniques are supervised learning methods that utilize only labeled data (i.e., the true class is known for all data) to train the classifiers. In this work, inspired by semi-supervised and transductive learning, we propose two new AUC optimization algorithms hereby referred to as semi-supervised learning receiver operating characteristic (SSLROC) algorithms, which utilize unlabeled test samples in classifier training to maximize AUC. Unlabeled samples are incorporated into the AUC optimization process, and their ranking relationships to labeled positive and negative training samples are considered as optimization constraints. The introduced test samples will cause the learned decision boundary in a multidimensional feature space to adapt not only to the distribution of labeled training data, but also to the distribution of unlabeled test data. We formulate the semi-supervised AUC optimization problem as a semi-definite programming problem based on the margin maximization theory. The proposed methods SSLROC1 (1-norm) and SSLROC2 (2-norm) were evaluated using 34 (determined by power analysis) randomly selected datasets from the University of California, Irvine machine learning repository. Wilcoxon signed rank tests showed that the proposed methods achieved significant improvement compared with state-of-the-art methods. The proposed methods were also applied to a CT colonography dataset for colonic polyp classification and showed promising results.1 PMID:25395692

Optimizing area under the ROC curve using semi-supervised learning.

PubMed

Wang, Shijun; Li, Diana; Petrick, Nicholas; Sahiner, Berkman; Linguraru, Marius George; Summers, Ronald M

2015-01-01

Receiver operating characteristic (ROC) analysis is a standard methodology to evaluate the performance of a binary classification system. The area under the ROC curve (AUC) is a performance metric that summarizes how well a classifier separates two classes. Traditional AUC optimization techniques are supervised learning methods that utilize only labeled data (i.e., the true class is known for all data) to train the classifiers. In this work, inspired by semi-supervised and transductive learning, we propose two new AUC optimization algorithms hereby referred to as semi-supervised learning receiver operating characteristic (SSLROC) algorithms, which utilize unlabeled test samples in classifier training to maximize AUC. Unlabeled samples are incorporated into the AUC optimization process, and their ranking relationships to labeled positive and negative training samples are considered as optimization constraints. The introduced test samples will cause the learned decision boundary in a multidimensional feature space to adapt not only to the distribution of labeled training data, but also to the distribution of unlabeled test data. We formulate the semi-supervised AUC optimization problem as a semi-definite programming problem based on the margin maximization theory. The proposed methods SSLROC1 (1-norm) and SSLROC2 (2-norm) were evaluated using 34 (determined by power analysis) randomly selected datasets from the University of California, Irvine machine learning repository. Wilcoxon signed rank tests showed that the proposed methods achieved significant improvement compared with state-of-the-art methods. The proposed methods were also applied to a CT colonography dataset for colonic polyp classification and showed promising results.

The impact of group therapy training on social communications of Afghan immigrants

PubMed Central

Mehrabi, Tayebeh; Musavi, Tayebeh; Ghazavi, Zahra; Zandieh, Zahra; Zamani, Ahmadreza

2011-01-01

BACKGROUND: Mental training considers sharing of mental health care information as the primary objective. The secondary objectives include facilitating dialogue about feelings such as isolation, sadness, labeling, loneliness and possible strategies for confronting with these feelings. Group therapy trainings have supportive functioning in accepting the environment so that the members are able to be part of the indigenous groups. However, no study has been ever done on the impact of this educational method on the communication problems of this group. This study aimed to determine the impact of group therapy training on the communication problems of Afghan immigrants. METHODS: This was a clinical trial study. Eighty-eight Afghan men were investigated. Sampling method was simple sampling method. Thereafter, the study subjects were divided randomly into two groups of test and control based on the inclusion criteria. Data collection tool was a self-made questionnaire about the social problems. For analyzing the data, software SPSS, independent t-test and paired t-test were used. RESULTS: Reviewing the data indicated lower mean score of the social problems after implementing the group therapy training in social communication compared with before implementing the group therapy training. Paired t-test showed a significant difference between mean scores of the social communication problems before and after the implementation of group therapy training. CONCLUSIONS: Given the effectiveness of the intervention, group therapy training on social problems in social communication of Afghan immigrants is recommended. This program should be part of continuous education and training of the Afghan immigrants. PMID:22224098

NHEXAS PHASE I MARYLAND STUDY--STANDARD OPERATING PROCEDURE FOR TRAINING OF FIELD TECHNICIANS (G07)

EPA Science Inventory

The purpose of this SOP is to describe the method used for training field technicians. The SOP outlines the responsibilities of the Field Technician (FT) and the Field Coordination Center Supervisor (FCC-S) before, during, and after sampling at residences, and the training syste...

The Efficacy of Relaxation Training in Treating Anxiety

ERIC Educational Resources Information Center

Francesco, Pagnini; Mauro, Manzoni Gian; Gianluca, Castelnuovo; Enrico, Molinari

2009-01-01

This paper provides a review of scientific literature about relaxation training and its effects on anxiety. Research investigating progressive relaxation, meditation, applied relaxation and autogenic training were considered. All these methods proved to be effective in reducing anxiety in all kind of samples, affected or not by physical or…

Multi-spectral brain tissue segmentation using automatically trained k-Nearest-Neighbor classification.

PubMed

Vrooman, Henri A; Cocosco, Chris A; van der Lijn, Fedde; Stokking, Rik; Ikram, M Arfan; Vernooij, Meike W; Breteler, Monique M B; Niessen, Wiro J

2007-08-01

Conventional k-Nearest-Neighbor (kNN) classification, which has been successfully applied to classify brain tissue in MR data, requires training on manually labeled subjects. This manual labeling is a laborious and time-consuming procedure. In this work, a new fully automated brain tissue classification procedure is presented, in which kNN training is automated. This is achieved by non-rigidly registering the MR data with a tissue probability atlas to automatically select training samples, followed by a post-processing step to keep the most reliable samples. The accuracy of the new method was compared to rigid registration-based training and to conventional kNN-based segmentation using training on manually labeled subjects for segmenting gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) in 12 data sets. Furthermore, for all classification methods, the performance was assessed when varying the free parameters. Finally, the robustness of the fully automated procedure was evaluated on 59 subjects. The automated training method using non-rigid registration with a tissue probability atlas was significantly more accurate than rigid registration. For both automated training using non-rigid registration and for the manually trained kNN classifier, the difference with the manual labeling by observers was not significantly larger than inter-observer variability for all tissue types. From the robustness study, it was clear that, given an appropriate brain atlas and optimal parameters, our new fully automated, non-rigid registration-based method gives accurate and robust segmentation results. A similarity index was used for comparison with manually trained kNN. The similarity indices were 0.93, 0.92 and 0.92, for CSF, GM and WM, respectively. It can be concluded that our fully automated method using non-rigid registration may replace manual segmentation, and thus that automated brain tissue segmentation without laborious manual training is feasible.

Explosive detection using high-volume vapor sampling and analysis by trained canines and ultra-trace detection equipment

NASA Astrophysics Data System (ADS)

Fisher, Mark; Sikes, John; Prather, Mark

2004-09-01

The dog's nose is an effective, highly-mobile sampling system, while the canine olfactory organs are an extremely sensitive detector. Having been trained to detect a wide variety of substances with exceptional results, canines are widely regarded as the 'gold standard' in chemical vapor detection. Historically, attempts to mimic the ability of dogs to detect vapors of explosives using electronic 'dogs noses' has proven difficult. However, recent advances in technology have resulted in development of detection (i.e., sampling and sensor) systems with performance that is rapidly approaching that of trained canines. The Nomadics Fido was the first sensor to demonstrate under field conditions the detection of landmines with performance approaching that of canines. More recently, comparative testing of Fido against canines has revealed that electronic vapor detection, when coupled with effective sampling methods, can produce results comparable to that of highly-trained canines. The results of these comparative tests will be presented, as will recent test results in which explosives hidden in cargo were detected using Fido with a high-volume sampling technique. Finally, the use of canines along with electronic sensors will be discussed as a means of improving the performance and expanding the capabilities of both methods.

Training sample selection based on self-training for liver cirrhosis classification using ultrasound images

NASA Astrophysics Data System (ADS)

Fujita, Yusuke; Mitani, Yoshihiro; Hamamoto, Yoshihiko; Segawa, Makoto; Terai, Shuji; Sakaida, Isao

2017-03-01

Ultrasound imaging is a popular and non-invasive tool used in the diagnoses of liver disease. Cirrhosis is a chronic liver disease and it can advance to liver cancer. Early detection and appropriate treatment are crucial to prevent liver cancer. However, ultrasound image analysis is very challenging, because of the low signal-to-noise ratio of ultrasound images. To achieve the higher classification performance, selection of training regions of interest (ROIs) is very important that effect to classification accuracy. The purpose of our study is cirrhosis detection with high accuracy using liver ultrasound images. In our previous works, training ROI selection by MILBoost and multiple-ROI classification based on the product rule had been proposed, to achieve high classification performance. In this article, we propose self-training method to select training ROIs effectively. Evaluation experiments were performed to evaluate effect of self-training, using manually selected ROIs and also automatically selected ROIs. Experimental results show that self-training for manually selected ROIs achieved higher classification performance than other approaches, including our conventional methods. The manually ROI definition and sample selection are important to improve classification accuracy in cirrhosis detection using ultrasound images.

Measurement of atmospheric mercury species with manual sampling and analysis methods in a case study in Indiana

USGS Publications Warehouse

Risch, M.R.; Prestbo, E.M.; Hawkins, L.

2007-01-01

Ground-level concentrations of three atmospheric mercury species were measured using manual sampling and analysis to provide data for estimates of mercury dry deposition. Three monitoring stations were operated simultaneously during winter, spring, and summer 2004, adjacent to three mercury wet-deposition monitoring stations in northern, central, and southern Indiana. The monitoring locations differed in land-use setting and annual mercury-emissions level from nearby sources. A timer-controlled air-sampling system that contained a three-part sampling train was used to isolate reactive gaseous mercury, particulate-bound mercury, and elemental mercury. The sampling trains were exchanged every 6 days, and the mercury species were quantified in a laboratory. A quality-assurance study indicated the sampling trains could be held at least 120 h without a significant change in reactive gaseous or particulate-bound mercury concentrations. The manual sampling method was able to provide valid mercury concentrations in 90 to 95% of samples. Statistical differences in mercury concentrations were observed during the project. Concentrations of reactive gaseous and elemental mercury were higher in the daytime samples than in the nighttime samples. Concentrations of reactive gaseous mercury were higher in winter than in summer and were highest at the urban monitoring location. The results of this case study indicated manual sampling and analysis could be a reliable method for measurement of atmospheric mercury species and has the capability for supplying representative concentrations in an effective manner from a long-term deposition-monitoring network. ?? 2007 Springer Science+Business Media B.V.

Dynamic stress effects in technical superconductors and the ''training'' problem of superconducting magnets

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

Pasztor, G.; Schmidt, C.

The behavior of NbTi superconductors under dynamic mechanical stress was investigated. A training effect was found in short-sample tests when the conductor was strained in a magnetic field and with a transport current applied. Possible mechanisms are discussed which were proposed to explain training in short samples and in magnets. A stress-induced microplastic as well as an incomplete pseudoelastic behavior of NbTi was detected by monitoring acoustic emission. The experiments support the hypothesis that microplastic or shape memory effects in NbTi involving dislocation processes are responsible for training. The minimum energy needed to induce a normal transition in short-sample testsmore » is calculated with a computer program, which gives the exact solution of the heat equation. A prestrain treatment of the conductor at room temperature is shown to be a simple method of reducing training of short samples and of magnets. This is a direct proof that the same mechanisms are involved in both cases.« less

System and method for resolving gamma-ray spectra

DOEpatents

Gentile, Charles A.; Perry, Jason; Langish, Stephen W.; Silber, Kenneth; Davis, William M.; Mastrovito, Dana

2010-05-04

A system for identifying radionuclide emissions is described. The system includes at least one processor for processing output signals from a radionuclide detecting device, at least one training algorithm run by the at least one processor for analyzing data derived from at least one set of known sample data from the output signals, at least one classification algorithm derived from the training algorithm for classifying unknown sample data, wherein the at least one training algorithm analyzes the at least one sample data set to derive at least one rule used by said classification algorithm for identifying at least one radionuclide emission detected by the detecting device.

Learning from Past Classification Errors: Exploring Methods for Improving the Performance of a Deep Learning-based Building Extraction Model through Quantitative Analysis of Commission Errors for Optimal Sample Selection

NASA Astrophysics Data System (ADS)

Swan, B.; Laverdiere, M.; Yang, L.

2017-12-01

In the past five years, deep Convolutional Neural Networks (CNN) have been increasingly favored for computer vision applications due to their high accuracy and ability to generalize well in very complex problems; however, details of how they function and in turn how they may be optimized are still imperfectly understood. In particular, their complex and highly nonlinear network architecture, including many hidden layers and self-learned parameters, as well as their mathematical implications, presents open questions about how to effectively select training data. Without knowledge of the exact ways the model processes and transforms its inputs, intuition alone may fail as a guide to selecting highly relevant training samples. Working in the context of improving a CNN-based building extraction model used for the LandScan USA gridded population dataset, we have approached this problem by developing a semi-supervised, highly-scalable approach to select training samples from a dataset of identified commission errors. Due to the large scope this project, tens of thousands of potential samples could be derived from identified commission errors. To efficiently trim those samples down to a manageable and effective set for creating additional training sample, we statistically summarized the spectral characteristics of areas with rates of commission errors at the image tile level and grouped these tiles using affinity propagation. Highly representative members of each commission error cluster were then used to select sites for training sample creation. The model will be incrementally re-trained with the new training data to allow for an assessment of how the addition of different types of samples affects the model performance, such as precision and recall rates. By using quantitative analysis and data clustering techniques to select highly relevant training samples, we hope to improve model performance in a manner that is resource efficient, both in terms of training process and in sample creation.

Image Augmentation for Object Image Classification Based On Combination of Pre-Trained CNN and SVM

NASA Astrophysics Data System (ADS)

Shima, Yoshihiro

2018-04-01

Neural networks are a powerful means of classifying object images. The proposed image category classification method for object images combines convolutional neural networks (CNNs) and support vector machines (SVMs). A pre-trained CNN, called Alex-Net, is used as a pattern-feature extractor. Alex-Net is pre-trained for the large-scale object-image dataset ImageNet. Instead of training, Alex-Net, pre-trained for ImageNet is used. An SVM is used as trainable classifier. The feature vectors are passed to the SVM from Alex-Net. The STL-10 dataset are used as object images. The number of classes is ten. Training and test samples are clearly split. STL-10 object images are trained by the SVM with data augmentation. We use the pattern transformation method with the cosine function. We also apply some augmentation method such as rotation, skewing and elastic distortion. By using the cosine function, the original patterns were left-justified, right-justified, top-justified, or bottom-justified. Patterns were also center-justified and enlarged. Test error rate is decreased by 0.435 percentage points from 16.055% by augmentation with cosine transformation. Error rates are increased by other augmentation method such as rotation, skewing and elastic distortion, compared without augmentation. Number of augmented data is 30 times that of the original STL-10 5K training samples. Experimental test error rate for the test 8k STL-10 object images was 15.620%, which shows that image augmentation is effective for image category classification.

40 CFR 745.225 - Accreditation of training programs: target housing and child-occupied facilities.

Code of Federal Regulations, 2010 CFR

2010-07-01

... equipment to be used for lecture and hands-on training. (B) A copy of the course test blueprint for each..., the delivery of the lecture, course test, hands-on training, and assessment activities. This includes... containment and cleanup methods, and post-renovation cleaning verification. (vii) The dust sampling technician...

40 CFR 745.225 - Accreditation of training programs: target housing and child-occupied facilities.

Code of Federal Regulations, 2011 CFR

2011-07-01

... equipment to be used for lecture and hands-on training. (B) A copy of the course test blueprint for each..., the delivery of the lecture, course test, hands-on training, and assessment activities. This includes... containment and cleanup methods, and post-renovation cleaning verification. (vii) The dust sampling technician...

Training in Psychiatric Genomics during Residency: A New Challenge

ERIC Educational Resources Information Center

Winner, Joel G.; Goebert, Deborah; Matsu, Courtenay; Mrazek, David A.

2010-01-01

Objective: The authors ascertained the amount of training in psychiatric genomics that is provided in North American psychiatric residency programs. Methods: A sample of 217 chief residents in psychiatric residency programs in the United States and Canada were identified by e-mail and surveyed to assess their training in psychiatric genetics and…

Comparing the Effect of Thinking Maps Training Package Developed by the Thinking Maps Method on the Reading Performance of Dyslexic Students.

PubMed

Faramarzi, Salar; Moradi, Mohammadreza; Abedi, Ahmad

2018-06-01

The present study aimed to develop the thinking maps training package and compare its training effect with the thinking maps method on the reading performance of second and fifth grade of elementary school male dyslexic students. For this mixed method exploratory study, from among the above mentioned grades' students in Isfahan, 90 students who met the inclusion criteria were selected by multistage sampling and randomly assigned into six experimental and control groups. The data were collected by reading and dyslexia test and Wechsler Intelligence Scale for Children-fourth edition. The results of covariance analysis indicated a significant difference between the reading performance of the experimental (thinking maps training package and thinking maps method groups) and control groups ([Formula: see text]). Moreover, there were significant differences between the thinking maps training package group and thinking maps method group in some of the subtests ([Formula: see text]). It can be concluded that thinking maps training package and the thinking maps method exert a positive influence on the reading performance of dyslexic students; therefore, thinking maps can be used as an effective training and treatment method.

Evaluating the High Risk Groups for Suicide: A Comparison of Logistic Regression, Support Vector Machine, Decision Tree and Artificial Neural Network

PubMed Central

AMINI, Payam; AHMADINIA, Hasan; POOROLAJAL, Jalal; MOQADDASI AMIRI, Mohammad

2016-01-01

Background: We aimed to assess the high-risk group for suicide using different classification methods includinglogistic regression (LR), decision tree (DT), artificial neural network (ANN), and support vector machine (SVM). Methods: We used the dataset of a study conducted to predict risk factors of completed suicide in Hamadan Province, the west of Iran, in 2010. To evaluate the high-risk groups for suicide, LR, SVM, DT and ANN were performed. The applied methods were compared using sensitivity, specificity, positive predicted value, negative predicted value, accuracy and the area under curve. Cochran-Q test was implied to check differences in proportion among methods. To assess the association between the observed and predicted values, Ø coefficient, contingency coefficient, and Kendall tau-b were calculated. Results: Gender, age, and job were the most important risk factors for fatal suicide attempts in common for four methods. SVM method showed the highest accuracy 0.68 and 0.67 for training and testing sample, respectively. However, this method resulted in the highest specificity (0.67 for training and 0.68 for testing sample) and the highest sensitivity for training sample (0.85), but the lowest sensitivity for the testing sample (0.53). Cochran-Q test resulted in differences between proportions in different methods (P<0.001). The association of SVM predictions and observed values, Ø coefficient, contingency coefficient, and Kendall tau-b were 0.239, 0.232 and 0.239, respectively. Conclusion: SVM had the best performance to classify fatal suicide attempts comparing to DT, LR and ANN. PMID:27957463

AUDIT MATERIALS FOR SEMIVOLATILE ORGANIC MEASUREMENTS DURING HAZARDOUS WASTE TRIAL BURNS

EPA Science Inventory

Two new performance audit materials utilizing different sorbents have neen developed to assess the overall accuracy and precision of the sampling, desorption, and analysis of semivolatile organic compounds by EPA, SW 846 Method 0010 (i.e., the Modified Method 5 sampling train). h...

Metadynamics for training neural network model chemistries: A competitive assessment

NASA Astrophysics Data System (ADS)

Herr, John E.; Yao, Kun; McIntyre, Ryker; Toth, David W.; Parkhill, John

2018-06-01

Neural network model chemistries (NNMCs) promise to facilitate the accurate exploration of chemical space and simulation of large reactive systems. One important path to improving these models is to add layers of physical detail, especially long-range forces. At short range, however, these models are data driven and data limited. Little is systematically known about how data should be sampled, and "test data" chosen randomly from some sampling techniques can provide poor information about generality. If the sampling method is narrow, "test error" can appear encouragingly tiny while the model fails catastrophically elsewhere. In this manuscript, we competitively evaluate two common sampling methods: molecular dynamics (MD), normal-mode sampling, and one uncommon alternative, Metadynamics (MetaMD), for preparing training geometries. We show that MD is an inefficient sampling method in the sense that additional samples do not improve generality. We also show that MetaMD is easily implemented in any NNMC software package with cost that scales linearly with the number of atoms in a sample molecule. MetaMD is a black-box way to ensure samples always reach out to new regions of chemical space, while remaining relevant to chemistry near kbT. It is a cheap tool to address the issue of generalization.

Police training in interviewing and interrogation methods: A comparison of techniques used with adult and juvenile suspects.

PubMed

Cleary, Hayley M D; Warner, Todd C

2016-06-01

Despite empirical progress in documenting and classifying various interrogation techniques, very little is known about how police are trained in interrogation methods, how frequently they use various techniques, and whether they employ techniques differentially with adult versus juvenile suspects. This study reports the nature and extent of formal (e.g., Reid Technique, PEACE, HUMINT) and informal interrogation training as well as self-reported technique usage in a diverse national sample (N = 340) of experienced American police officers. Officers were trained in a variety of different techniques ranging from comparatively benign pre-interrogation strategies (e.g., building rapport, observing body language or speech patterns) to more psychologically coercive techniques (e.g., blaming the victim, discouraging denials). Over half the sample reported being trained to use psychologically coercive techniques with both adults and juveniles. The majority (91%) receive informal, "on the job" interrogation training. Technique usage patterns indicate a spectrum of psychological intensity where information-gathering approaches were used most frequently and high-pressure tactics less frequently. Reid-trained officers (56%) were significantly more likely than officers without Reid training to use pre-interrogation and manipulation techniques. Across all analyses and techniques, usage patterns were identical for adult and juvenile suspects, suggesting that police interrogate youth in the same manner as adults. Overall, results suggest that training in specific interrogation methods is strongly associated with usage. Findings underscore the need for more law enforcement interrogation training in general, especially with juvenile suspects, and highlight the value of training as an avenue for reducing interrogation-induced miscarriages of justice. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

SU-F-E-09: Respiratory Signal Prediction Based On Multi-Layer Perceptron Neural Network Using Adjustable Training Samples

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

Sun, W; Jiang, M; Yin, F

Purpose: Dynamic tracking of moving organs, such as lung and liver tumors, under radiation therapy requires prediction of organ motions prior to delivery. The shift of moving organ may change a lot due to huge transform of respiration at different periods. This study aims to reduce the influence of that changes using adjustable training signals and multi-layer perceptron neural network (ASMLP). Methods: Respiratory signals obtained using a Real-time Position Management(RPM) device were used for this study. The ASMLP uses two multi-layer perceptron neural networks(MLPs) to infer respiration position alternately and the training sample will be updated with time. Firstly, amore » Savitzky-Golay finite impulse response smoothing filter was established to smooth the respiratory signal. Secondly, two same MLPs were developed to estimate respiratory position from its previous positions separately. Weights and thresholds were updated to minimize network errors according to Leverberg-Marquart optimization algorithm through backward propagation method. Finally, MLP 1 was used to predict 120∼150s respiration position using 0∼120s training signals. At the same time, MLP 2 was trained using 30∼150s training signals. Then MLP is used to predict 150∼180s training signals according to 30∼150s training signals. The respiration position is predicted as this way until it was finished. Results: In this experiment, the two methods were used to predict 2.5 minute respiratory signals. For predicting 1s ahead of response time, correlation coefficient was improved from 0.8250(MLP method) to 0.8856(ASMLP method). Besides, a 30% improvement of mean absolute error between MLP(0.1798 on average) and ASMLP(0.1267 on average) was achieved. For predicting 2s ahead of response time, correlation coefficient was improved from 0.61415 to 0.7098.Mean absolute error of MLP method(0.3111 on average) was reduced by 35% using ASMLP method(0.2020 on average). Conclusion: The preliminary results demonstrate that the ASMLP respiratory prediction method is more accurate than MLP method and can improve the respiration forecast accuracy.« less

Segmentation of knee cartilage by using a hierarchical active shape model based on multi-resolution transforms in magnetic resonance images

NASA Astrophysics Data System (ADS)

León, Madeleine; Escalante-Ramirez, Boris

2013-11-01

Knee osteoarthritis (OA) is characterized by the morphological degeneration of cartilage. Efficient segmentation of cartilage is important for cartilage damage diagnosis and to support therapeutic responses. We present a method for knee cartilage segmentation in magnetic resonance images (MRI). Our method incorporates the Hermite Transform to obtain a hierarchical decomposition of contours which describe knee cartilage shapes. Then, we compute a statistical model of the contour of interest from a set of training images. Thereby, our Hierarchical Active Shape Model (HASM) captures a large range of shape variability even from a small group of training samples, improving segmentation accuracy. The method was trained with a training set of 16- MRI of knee and tested with leave-one-out method.

Training set optimization under population structure in genomic selection.

PubMed

Isidro, Julio; Jannink, Jean-Luc; Akdemir, Deniz; Poland, Jesse; Heslot, Nicolas; Sorrells, Mark E

2015-01-01

Population structure must be evaluated before optimization of the training set population. Maximizing the phenotypic variance captured by the training set is important for optimal performance. The optimization of the training set (TRS) in genomic selection has received much interest in both animal and plant breeding, because it is critical to the accuracy of the prediction models. In this study, five different TRS sampling algorithms, stratified sampling, mean of the coefficient of determination (CDmean), mean of predictor error variance (PEVmean), stratified CDmean (StratCDmean) and random sampling, were evaluated for prediction accuracy in the presence of different levels of population structure. In the presence of population structure, the most phenotypic variation captured by a sampling method in the TRS is desirable. The wheat dataset showed mild population structure, and CDmean and stratified CDmean methods showed the highest accuracies for all the traits except for test weight and heading date. The rice dataset had strong population structure and the approach based on stratified sampling showed the highest accuracies for all traits. In general, CDmean minimized the relationship between genotypes in the TRS, maximizing the relationship between TRS and the test set. This makes it suitable as an optimization criterion for long-term selection. Our results indicated that the best selection criterion used to optimize the TRS seems to depend on the interaction of trait architecture and population structure.

Designs for the Evaluation of Teacher Training Materials. Report No. 2.

ERIC Educational Resources Information Center

Okey, James R.; Ciesla, Jerome L.

This paper describes methods to assess the impact on students of a teacher using skills learned in a training program. Three designs for assessing the effects of teacher training materials are presented: time series design, equivalent time-samples design, and posttest-only control group design. Data obtained by classroom teachers while using the…

Multivariate Methods for Prediction of Geologic Sample Composition with Laser-Induced Breakdown Spectroscopy

NASA Technical Reports Server (NTRS)

Morris, Richard; Anderson, R.; Clegg, S. M.; Bell, J. F., III

2010-01-01

Laser-induced breakdown spectroscopy (LIBS) uses pulses of laser light to ablate a material from the surface of a sample and produce an expanding plasma. The optical emission from the plasma produces a spectrum which can be used to classify target materials and estimate their composition. The ChemCam instrument on the Mars Science Laboratory (MSL) mission will use LIBS to rapidly analyze targets remotely, allowing more resource- and time-intensive in-situ analyses to be reserved for targets of particular interest. ChemCam will also be used to analyze samples that are not reachable by the rover's in-situ instruments. Due to these tactical and scientific roles, it is important that ChemCam-derived sample compositions are as accurate as possible. We have compared the results of partial least squares (PLS), multilayer perceptron (MLP) artificial neural networks (ANNs), and cascade correlation (CC) ANNs to determine which technique yields better estimates of quantitative element abundances in rock and mineral samples. The number of hidden nodes in the MLP ANNs was optimized using a genetic algorithm. The influence of two data preprocessing techniques were also investigated: genetic algorithm feature selection and averaging the spectra for each training sample prior to training the PLS and ANN algorithms. We used a ChemCam-like laboratory stand-off LIBS system to collect spectra of 30 pressed powder geostandards and a diverse suite of 196 geologic slab samples of known bulk composition. We tested the performance of PLS and ANNs on a subset of these samples, choosing to focus on silicate rocks and minerals with a loss on ignition of less than 2 percent. This resulted in a set of 22 pressed powder geostandards and 80 geologic samples. Four of the geostandards were used as a validation set and 18 were used as the training set for the algorithms. We found that PLS typically resulted in the lowest average absolute error in its predictions, but that the optimized MLP ANN and the CC ANN often gave results comparable to PLS. Averaging the spectra for each training sample and/or using feature selection to choose a small subset of wavelengths to use for predictions gave mixed results, with degraded performance in some cases and similar or slightly improved performance in other cases. However, training time was significantly reduced for both PLS and ANN methods by implementing feature selection, making this a potentially appealing method for initial, rapid-turn-around analyses necessary for Chemcam's tactical role on MSL. Choice of training samples has a strong influence on the accuracy of predictions. We are currently investigating the use of clustering algorithms (e.g. k-means, neural gas, etc.) to identify training sets that are spectrally similar to the unknown samples that are being predicted, and therefore result in improved predictions

A Hierarchical Feature and Sample Selection Framework and Its Application for Alzheimer’s Disease Diagnosis

NASA Astrophysics Data System (ADS)

An, Le; Adeli, Ehsan; Liu, Mingxia; Zhang, Jun; Lee, Seong-Whan; Shen, Dinggang

2017-03-01

Classification is one of the most important tasks in machine learning. Due to feature redundancy or outliers in samples, using all available data for training a classifier may be suboptimal. For example, the Alzheimer’s disease (AD) is correlated with certain brain regions or single nucleotide polymorphisms (SNPs), and identification of relevant features is critical for computer-aided diagnosis. Many existing methods first select features from structural magnetic resonance imaging (MRI) or SNPs and then use those features to build the classifier. However, with the presence of many redundant features, the most discriminative features are difficult to be identified in a single step. Thus, we formulate a hierarchical feature and sample selection framework to gradually select informative features and discard ambiguous samples in multiple steps for improved classifier learning. To positively guide the data manifold preservation process, we utilize both labeled and unlabeled data during training, making our method semi-supervised. For validation, we conduct experiments on AD diagnosis by selecting mutually informative features from both MRI and SNP, and using the most discriminative samples for training. The superior classification results demonstrate the effectiveness of our approach, as compared with the rivals.

Adaptive swarm cluster-based dynamic multi-objective synthetic minority oversampling technique algorithm for tackling binary imbalanced datasets in biomedical data classification.

PubMed

Li, Jinyan; Fong, Simon; Sung, Yunsick; Cho, Kyungeun; Wong, Raymond; Wong, Kelvin K L

2016-01-01

An imbalanced dataset is defined as a training dataset that has imbalanced proportions of data in both interesting and uninteresting classes. Often in biomedical applications, samples from the stimulating class are rare in a population, such as medical anomalies, positive clinical tests, and particular diseases. Although the target samples in the primitive dataset are small in number, the induction of a classification model over such training data leads to poor prediction performance due to insufficient training from the minority class. In this paper, we use a novel class-balancing method named adaptive swarm cluster-based dynamic multi-objective synthetic minority oversampling technique (ASCB_DmSMOTE) to solve this imbalanced dataset problem, which is common in biomedical applications. The proposed method combines under-sampling and over-sampling into a swarm optimisation algorithm. It adaptively selects suitable parameters for the rebalancing algorithm to find the best solution. Compared with the other versions of the SMOTE algorithm, significant improvements, which include higher accuracy and credibility, are observed with ASCB_DmSMOTE. Our proposed method tactfully combines two rebalancing techniques together. It reasonably re-allocates the majority class in the details and dynamically optimises the two parameters of SMOTE to synthesise a reasonable scale of minority class for each clustered sub-imbalanced dataset. The proposed methods ultimately overcome other conventional methods and attains higher credibility with even greater accuracy of the classification model.

A sampling approach for predicting the eating quality of apples using visible-near infrared spectroscopy.

PubMed

Martínez Vega, Mabel V; Sharifzadeh, Sara; Wulfsohn, Dvoralai; Skov, Thomas; Clemmensen, Line Harder; Toldam-Andersen, Torben B

2013-12-01

Visible-near infrared spectroscopy remains a method of increasing interest as a fast alternative for the evaluation of fruit quality. The success of the method is assumed to be achieved by using large sets of samples to produce robust calibration models. In this study we used representative samples of an early and a late season apple cultivar to evaluate model robustness (in terms of prediction ability and error) on the soluble solids content (SSC) and acidity prediction, in the wavelength range 400-1100 nm. A total of 196 middle-early season and 219 late season apples (Malus domestica Borkh.) cvs 'Aroma' and 'Holsteiner Cox' samples were used to construct spectral models for SSC and acidity. Partial least squares (PLS), ridge regression (RR) and elastic net (EN) models were used to build prediction models. Furthermore, we compared three sub-sample arrangements for forming training and test sets ('smooth fractionator', by date of measurement after harvest and random). Using the 'smooth fractionator' sampling method, fewer spectral bands (26) and elastic net resulted in improved performance for SSC models of 'Aroma' apples, with a coefficient of variation CVSSC = 13%. The model showed consistently low errors and bias (PLS/EN: R(2) cal = 0.60/0.60; SEC = 0.88/0.88°Brix; Biascal = 0.00/0.00; R(2) val = 0.33/0.44; SEP = 1.14/1.03; Biasval = 0.04/0.03). However, the prediction acidity and for SSC (CV = 5%) of the late cultivar 'Holsteiner Cox' produced inferior results as compared with 'Aroma'. It was possible to construct local SSC and acidity calibration models for early season apple cultivars with CVs of SSC and acidity around 10%. The overall model performance of these data sets also depend on the proper selection of training and test sets. The 'smooth fractionator' protocol provided an objective method for obtaining training and test sets that capture the existing variability of the fruit samples for construction of visible-NIR prediction models. The implication is that by using such 'efficient' sampling methods for obtaining an initial sample of fruit that represents the variability of the population and for sub-sampling to form training and test sets it should be possible to use relatively small sample sizes to develop spectral predictions of fruit quality. Using feature selection and elastic net appears to improve the SSC model performance in terms of R(2), RMSECV and RMSEP for 'Aroma' apples. © 2013 Society of Chemical Industry.

Training and Maintaining System-Wide Reliability in Outcome Management.

PubMed

Barwick, Melanie A; Urajnik, Diana J; Moore, Julia E

2014-01-01

The Child and Adolescent Functional Assessment Scale (CAFAS) is widely used for outcome management, for providing real time client and program level data, and the monitoring of evidence-based practices. Methods of reliability training and the assessment of rater drift are critical for service decision-making within organizations and systems of care. We assessed two approaches for CAFAS training: external technical assistance and internal technical assistance. To this end, we sampled 315 practitioners trained by external technical assistance approach from 2,344 Ontario practitioners who had achieved reliability on the CAFAS. To assess the internal technical assistance approach as a reliable alternative training method, 140 practitioners trained internally were selected from the same pool of certified raters. Reliabilities were high for both practitioners trained by external technical assistance and internal technical assistance approaches (.909-.995, .915-.997, respectively). 1 and 3-year estimates showed some drift on several scales. High and consistent reliabilities over time and training method has implications for CAFAS training of behavioral health care practitioners, and the maintenance of CAFAS as a global outcome management tool in systems of care.

A Novel Calibration-Minimum Method for Prediction of Mole Fraction in Non-Ideal Mixture.

PubMed

Shibayama, Shojiro; Kaneko, Hiromasa; Funatsu, Kimito

2017-04-01

This article proposes a novel concentration prediction model that requires little training data and is useful for rapid process understanding. Process analytical technology is currently popular, especially in the pharmaceutical industry, for enhancement of process understanding and process control. A calibration-free method, iterative optimization technology (IOT), was proposed to predict pure component concentrations, because calibration methods such as partial least squares, require a large number of training samples, leading to high costs. However, IOT cannot be applied to concentration prediction in non-ideal mixtures because its basic equation is derived from the Beer-Lambert law, which cannot be applied to non-ideal mixtures. We proposed a novel method that realizes prediction of pure component concentrations in mixtures from a small number of training samples, assuming that spectral changes arising from molecular interactions can be expressed as a function of concentration. The proposed method is named IOT with virtual molecular interaction spectra (IOT-VIS) because the method takes spectral change as a virtual spectrum x nonlin,i into account. It was confirmed through the two case studies that the predictive accuracy of IOT-VIS was the highest among existing IOT methods.

Statistical technique for analysing functional connectivity of multiple spike trains.

PubMed

Masud, Mohammad Shahed; Borisyuk, Roman

2011-03-15

A new statistical technique, the Cox method, used for analysing functional connectivity of simultaneously recorded multiple spike trains is presented. This method is based on the theory of modulated renewal processes and it estimates a vector of influence strengths from multiple spike trains (called reference trains) to the selected (target) spike train. Selecting another target spike train and repeating the calculation of the influence strengths from the reference spike trains enables researchers to find all functional connections among multiple spike trains. In order to study functional connectivity an "influence function" is identified. This function recognises the specificity of neuronal interactions and reflects the dynamics of postsynaptic potential. In comparison to existing techniques, the Cox method has the following advantages: it does not use bins (binless method); it is applicable to cases where the sample size is small; it is sufficiently sensitive such that it estimates weak influences; it supports the simultaneous analysis of multiple influences; it is able to identify a correct connectivity scheme in difficult cases of "common source" or "indirect" connectivity. The Cox method has been thoroughly tested using multiple sets of data generated by the neural network model of the leaky integrate and fire neurons with a prescribed architecture of connections. The results suggest that this method is highly successful for analysing functional connectivity of simultaneously recorded multiple spike trains. Copyright © 2011 Elsevier B.V. All rights reserved.

A non-invasive tool for detecting cervical cancer odor by trained scent dogs.

PubMed

Guerrero-Flores, Héctor; Apresa-García, Teresa; Garay-Villar, Ónix; Sánchez-Pérez, Alejandro; Flores-Villegas, David; Bandera-Calderón, Artfy; García-Palacios, Raúl; Rojas-Sánchez, Teresita; Romero-Morelos, Pablo; Sánchez-Albor, Verónica; Mata, Osvaldo; Arana-Conejo, Víctor; Badillo-Romero, Jesús; Taniguchi, Keiko; Marrero-Rodríguez, Daniel; Mendoza-Rodríguez, Mónica; Rodríguez-Esquivel, Miriam; Huerta-Padilla, Víctor; Martínez-Castillo, Andrea; Hernández-Gallardo, Irma; López-Romero, Ricardo; Bandala, Cindy; Rosales-Guevara, Juan; Salcedo, Mauricio

2017-01-26

Cervical Cancer (CC) has become a public health concern of alarming proportions in many developing countries such as Mexico, particularly in low income sectors and marginalized regions. As such, an early detection is a key medical factor in improving not only their population's quality of life but also its life expectancy. Interestingly, there has been an increase in the number of reports describing successful attempts at detecting cancer cells in human tissues or fluids using trained (sniffer) dogs. The great odor detection threshold exhibited by dogs is not unheard of. However, this represented a potential opportunity to develop an affordable, accessible, and non-invasive method for detection of CC. Using clicker training, a male beagle was trained to recognize CC odor. During training, fresh CC biopsies were used as a reference point. Other samples used included cervical smears on glass slides and medical surgical bandages used as intimate sanitary pads by CC patients. A double-blind procedure was exercised when testing the beagle's ability to discriminate CC from control samples. The beagle was proven able to detect CC-specific volatile organic compounds (VOC) contained in both fresh cervical smear samples and adsorbent material samples. Beagle's success rate at detecting and discriminating CC and non-CC odors, as indicated by specificity and sensitivity values recorded during the experiment, stood at an overall high (>90%). CC-related VOC in adsorbent materials were detectable after only eight hours of use by CC patients. Present data suggests different applications for VOC from the uterine cervix to be used in the detection and diagnosis of CC. Furthermore, data supports the use of trained dogs as a viable, affordable, non-invasive and, therefore, highly relevant alternative method for detection of CC lesions. Additional benefits of this method include its quick turnaround time and ease of use while remaining highly accurate and robust.

Application of Deep Learning in GLOBELAND30-2010 Product Refinement

NASA Astrophysics Data System (ADS)

Liu, T.; Chen, X.

2018-04-01

GlobeLand30, as one of the best Global Land Cover (GLC) product at 30-m resolution, has been widely used in many research fields. Due to the significant spectral confusion among different land cover types and limited textual information of Landsat data, the overall accuracy of GlobeLand30 is about 80 %. Although such accuracy is much higher than most other global land cover products, it cannot satisfy various applications. There is still a great need of an effective method to improve the quality of GlobeLand30. The explosive high-resolution satellite images and remarkable performance of Deep Learning on image classification provide a new opportunity to refine GlobeLand30. However, the performance of deep leaning depends on quality and quantity of training samples as well as model training strategy. Therefore, this paper 1) proposed an automatic training sample generation method via Google earth to build a large training sample set; and 2) explore the best training strategy for land cover classification using GoogleNet (Inception V3), one of the most widely used deep learning network. The result shows that the fine-tuning from first layer of Inception V3 using rough large sample set is the best strategy. The retrained network was then applied in one selected area from Xi'an city as a case study of GlobeLand30 refinement. The experiment results indicate that the proposed approach with Deep Learning and google earth imagery is a promising solution for further improving accuracy of GlobeLand30.

Analysing the 21 cm signal from the epoch of reionization with artificial neural networks

NASA Astrophysics Data System (ADS)

Shimabukuro, Hayato; Semelin, Benoit

2017-07-01

The 21 cm signal from the epoch of reionization should be observed within the next decade. While a simple statistical detection is expected with Square Kilometre Array (SKA) pathfinders, the SKA will hopefully produce a full 3D mapping of the signal. To extract from the observed data constraints on the parameters describing the underlying astrophysical processes, inversion methods must be developed. For example, the Markov Chain Monte Carlo method has been successfully applied. Here, we test another possible inversion method: artificial neural networks (ANNs). We produce a training set that consists of 70 individual samples. Each sample is made of the 21 cm power spectrum at different redshifts produced with the 21cmFast code plus the value of three parameters used in the seminumerical simulations that describe astrophysical processes. Using this set, we train the network to minimize the error between the parameter values it produces as an output and the true values. We explore the impact of the architecture of the network on the quality of the training. Then we test the trained network on the new set of 54 test samples with different values of the parameters. We find that the quality of the parameter reconstruction depends on the sensitivity of the power spectrum to the different parameters at a given redshift, that including thermal noise and sample variance decreases the quality of the reconstruction and that using the power spectrum at several redshifts as an input to the ANN improves the quality of the reconstruction. We conclude that ANNs are a viable inversion method whose main strength is that they require a sparse exploration of the parameter space and thus should be usable with full numerical simulations.

Effects of Two Modes of Exercise Training on Physical Fitness of 10 Year-Old Children

ERIC Educational Resources Information Center

Ribeiro, Ligia G. dos Santos Chaves; Portal, Maria de Nazare Dias; da Silva, Joao Bittencourt; Saraiva, Alan; da Cruz Monte, Gerson, Jr.; Dantas, Estelio H. M.

2010-01-01

Study aim: To compare two exercise training modes on the physical fitness of 10 year-old children. Material and methods: A sample of 60 schoolboys aged 10 years were randomly divided into 3 groups: Traditional (TG), trained according to the Brazilian national curricular parameters, Maturational (MG), in which the degree of difficulty of the…

Working Memory Training for Children with Cochlear Implants: A Pilot Study

ERIC Educational Resources Information Center

Kronenberger, William G.; Pisoni, David B.; Henning, Shirley C.; Colson, Bethany G.; Hazzard, Lindsey M.

2011-01-01

Purpose: This study investigated the feasibility and efficacy of a working memory training program for improving memory and language skills in a sample of 9 children who are deaf (age 7-15 years) with cochlear implants (CIs). Method: All children completed the Cogmed Working Memory Training program on a home computer over a 5-week period.…

Phase I Forest Area Estimation Using Landsat TM and Iterative Guided Spectral Class Rejection: Assessment of Possible Training Data Protocols

Treesearch

John A. Scrivani; Randolph H. Wynne; Christine E. Blinn; Rebecca F. Musy

2001-01-01

Two methods of training data collection for automated image classification were tested in Virginia as part of a larger effort to develop an objective, repeatable, and low-cost method to provide forest area classification from satellite imagery. The derived forest area estimates were compared to estimates derived from a traditional photo-interpreted, double sample. One...

Usage of the back-propagation method for alphabet recognition

NASA Astrophysics Data System (ADS)

Shaila Sree, R. N.; Eswaran, Kumar; Sundararajan, N.

1999-03-01

Artificial Neural Networks play a pivotal role in the branch of Artificial Intelligence. They can be trained efficiently for a variety of tasks using different methods, of which the Back Propagation method is one among them. The paper studies the choosing of various design parameters of a neural network for the Back Propagation method. The study shows that when these parameters are properly assigned, the training task of the net is greatly simplified. The character recognition problem has been chosen as a test case for this study. A sample space of different handwritten characters of the English alphabet was gathered. A Neural net is finally designed taking many the design aspects into consideration and trained for different styles of writing. Experimental results are reported and discussed. It has been found that an appropriate choice of the design parameters of the neural net for the Back Propagation method reduces the training time and improves the performance of the net.

Study on the Classification of GAOFEN-3 Polarimetric SAR Images Using Deep Neural Network

NASA Astrophysics Data System (ADS)

Zhang, J.; Zhang, J.; Zhao, Z.

2018-04-01

Polarimetric Synthetic Aperture Radar (POLSAR) imaging principle determines that the image quality will be affected by speckle noise. So the recognition accuracy of traditional image classification methods will be reduced by the effect of this interference. Since the date of submission, Deep Convolutional Neural Network impacts on the traditional image processing methods and brings the field of computer vision to a new stage with the advantages of a strong ability to learn deep features and excellent ability to fit large datasets. Based on the basic characteristics of polarimetric SAR images, the paper studied the types of the surface cover by using the method of Deep Learning. We used the fully polarimetric SAR features of different scales to fuse RGB images to the GoogLeNet model based on convolution neural network Iterative training, and then use the trained model to test the classification of data validation.First of all, referring to the optical image, we mark the surface coverage type of GF-3 POLSAR image with 8m resolution, and then collect the samples according to different categories. To meet the GoogLeNet model requirements of 256 × 256 pixel image input and taking into account the lack of full-resolution SAR resolution, the original image should be pre-processed in the process of resampling. In this paper, POLSAR image slice samples of different scales with sampling intervals of 2 m and 1 m to be trained separately and validated by the verification dataset. Among them, the training accuracy of GoogLeNet model trained with resampled 2-m polarimetric SAR image is 94.89 %, and that of the trained SAR image with resampled 1 m is 92.65 %.

Clustering redshift distributions for the Dark Energy Survey

NASA Astrophysics Data System (ADS)

Helsby, Jennifer

Accurate determination of photometric redshifts and their errors is critical for large scale structure and weak lensing studies for constraining cosmology from deep, wide imaging surveys. Current photometric redshift methods suffer from bias and scatter due to incomplete training sets. Exploiting the clustering between a sample of galaxies for which we have spectroscopic redshifts and a sample of galaxies for which the redshifts are unknown can allow us to reconstruct the true redshift distribution of the unknown sample. Here we use this method in both simulations and early data from the Dark Energy Survey (DES) to determine the true redshift distributions of galaxies in photometric redshift bins. We find that cross-correlating with the spectroscopic samples currently used for training provides a useful test of photometric redshifts and provides reliable estimates of the true redshift distribution in a photometric redshift bin. We discuss the use of the cross-correlation method in validating template- or learning-based approaches to redshift estimation and its future use in Stage IV surveys.

Portable automation of static chamber sample collection for quantifying soil gas flux

USDA-ARS?s Scientific Manuscript database

The collection of soil gas flux using the static chamber method is labor intensive. The number of chambers that can be sampled in a given time period is limited by the spacing between chambers and the availability of trained research technicians. However, the static chamber method can limit spatial ...

ISSUES RELATED TO SOLUTION CHEMISTRY IN MERCURY SAMPLING IMPINGERS

EPA Science Inventory

Analysis of mercury (Hg) speciation in combustion flue gases is often accomplished in standardized sampling trains in which the sample is passed sequentially through a series of aqueous solutions to capture and separate oxidized Hg (Hg2+) and elemental Hg (Hgo). Such methods incl...

Multiview boosting digital pathology analysis of prostate cancer.

PubMed

Kwak, Jin Tae; Hewitt, Stephen M

2017-04-01

Various digital pathology tools have been developed to aid in analyzing tissues and improving cancer pathology. The multi-resolution nature of cancer pathology, however, has not been fully analyzed and utilized. Here, we develop an automated, cooperative, and multi-resolution method for improving prostate cancer diagnosis. Digitized tissue specimen images are obtained from 5 tissue microarrays (TMAs). The TMAs include 70 benign and 135 cancer samples (TMA1), 74 benign and 89 cancer samples (TMA2), 70 benign and 115 cancer samples (TMA3), 79 benign and 82 cancer samples (TMA4), and 72 benign and 86 cancer samples (TMA5). The tissue specimen images are segmented using intensity- and texture-based features. Using the segmentation results, a number of morphological features from lumens and epithelial nuclei are computed to characterize tissues at different resolutions. Applying a multiview boosting algorithm, tissue characteristics, obtained from differing resolutions, are cooperatively combined to achieve accurate cancer detection. In segmenting prostate tissues, the multiview boosting method achieved≥ 0.97 AUC using TMA1. For detecting cancers, the multiview boosting method achieved an AUC of 0.98 (95% CI: 0.97-0.99) as trained on TMA2 and tested on TMA3, TMA4, and TMA5. The proposed method was superior to single-view approaches, utilizing features from a single resolution or merging features from all the resolutions. Moreover, the performance of the proposed method was insensitive to the choice of the training dataset. Trained on TMA3, TMA4, and TMA5, the proposed method obtained an AUC of 0.97 (95% CI: 0.96-0.98), 0.98 (95% CI: 0.96-0.99), and 0.97 (95% CI: 0.96-0.98), respectively. The multiview boosting method is capable of integrating information from multiple resolutions in an effective and efficient fashion and identifying cancers with high accuracy. The multiview boosting method holds a great potential for improving digital pathology tools and research. Copyright © 2017 Elsevier B.V. All rights reserved.

An Exemplar-Based Multi-View Domain Generalization Framework for Visual Recognition.

PubMed

Niu, Li; Li, Wen; Xu, Dong; Cai, Jianfei

2018-02-01

In this paper, we propose a new exemplar-based multi-view domain generalization (EMVDG) framework for visual recognition by learning robust classifier that are able to generalize well to arbitrary target domain based on the training samples with multiple types of features (i.e., multi-view features). In this framework, we aim to address two issues simultaneously. First, the distribution of training samples (i.e., the source domain) is often considerably different from that of testing samples (i.e., the target domain), so the performance of the classifiers learnt on the source domain may drop significantly on the target domain. Moreover, the testing data are often unseen during the training procedure. Second, when the training data are associated with multi-view features, the recognition performance can be further improved by exploiting the relation among multiple types of features. To address the first issue, considering that it has been shown that fusing multiple SVM classifiers can enhance the domain generalization ability, we build our EMVDG framework upon exemplar SVMs (ESVMs), in which a set of ESVM classifiers are learnt with each one trained based on one positive training sample and all the negative training samples. When the source domain contains multiple latent domains, the learnt ESVM classifiers are expected to be grouped into multiple clusters. To address the second issue, we propose two approaches under the EMVDG framework based on the consensus principle and the complementary principle, respectively. Specifically, we propose an EMVDG_CO method by adding a co-regularizer to enforce the cluster structures of ESVM classifiers on different views to be consistent based on the consensus principle. Inspired by multiple kernel learning, we also propose another EMVDG_MK method by fusing the ESVM classifiers from different views based on the complementary principle. In addition, we further extend our EMVDG framework to exemplar-based multi-view domain adaptation (EMVDA) framework when the unlabeled target domain data are available during the training procedure. The effectiveness of our EMVDG and EMVDA frameworks for visual recognition is clearly demonstrated by comprehensive experiments on three benchmark data sets.

A Classification of Remote Sensing Image Based on Improved Compound Kernels of Svm

NASA Astrophysics Data System (ADS)

Zhao, Jianing; Gao, Wanlin; Liu, Zili; Mou, Guifen; Lu, Lin; Yu, Lina

The accuracy of RS classification based on SVM which is developed from statistical learning theory is high under small number of train samples, which results in satisfaction of classification on RS using SVM methods. The traditional RS classification method combines visual interpretation with computer classification. The accuracy of the RS classification, however, is improved a lot based on SVM method, because it saves much labor and time which is used to interpret images and collect training samples. Kernel functions play an important part in the SVM algorithm. It uses improved compound kernel function and therefore has a higher accuracy of classification on RS images. Moreover, compound kernel improves the generalization and learning ability of the kernel.

Collaborative filtering for brain-computer interaction using transfer learning and active class selection.

PubMed

Wu, Dongrui; Lance, Brent J; Parsons, Thomas D

2013-01-01

Brain-computer interaction (BCI) and physiological computing are terms that refer to using processed neural or physiological signals to influence human interaction with computers, environment, and each other. A major challenge in developing these systems arises from the large individual differences typically seen in the neural/physiological responses. As a result, many researchers use individually-trained recognition algorithms to process this data. In order to minimize time, cost, and barriers to use, there is a need to minimize the amount of individual training data required, or equivalently, to increase the recognition accuracy without increasing the number of user-specific training samples. One promising method for achieving this is collaborative filtering, which combines training data from the individual subject with additional training data from other, similar subjects. This paper describes a successful application of a collaborative filtering approach intended for a BCI system. This approach is based on transfer learning (TL), active class selection (ACS), and a mean squared difference user-similarity heuristic. The resulting BCI system uses neural and physiological signals for automatic task difficulty recognition. TL improves the learning performance by combining a small number of user-specific training samples with a large number of auxiliary training samples from other similar subjects. ACS optimally selects the classes to generate user-specific training samples. Experimental results on 18 subjects, using both k nearest neighbors and support vector machine classifiers, demonstrate that the proposed approach can significantly reduce the number of user-specific training data samples. This collaborative filtering approach will also be generalizable to handling individual differences in many other applications that involve human neural or physiological data, such as affective computing.

Collaborative Filtering for Brain-Computer Interaction Using Transfer Learning and Active Class Selection

PubMed Central

Wu, Dongrui; Lance, Brent J.; Parsons, Thomas D.

2013-01-01

Brain-computer interaction (BCI) and physiological computing are terms that refer to using processed neural or physiological signals to influence human interaction with computers, environment, and each other. A major challenge in developing these systems arises from the large individual differences typically seen in the neural/physiological responses. As a result, many researchers use individually-trained recognition algorithms to process this data. In order to minimize time, cost, and barriers to use, there is a need to minimize the amount of individual training data required, or equivalently, to increase the recognition accuracy without increasing the number of user-specific training samples. One promising method for achieving this is collaborative filtering, which combines training data from the individual subject with additional training data from other, similar subjects. This paper describes a successful application of a collaborative filtering approach intended for a BCI system. This approach is based on transfer learning (TL), active class selection (ACS), and a mean squared difference user-similarity heuristic. The resulting BCI system uses neural and physiological signals for automatic task difficulty recognition. TL improves the learning performance by combining a small number of user-specific training samples with a large number of auxiliary training samples from other similar subjects. ACS optimally selects the classes to generate user-specific training samples. Experimental results on 18 subjects, using both nearest neighbors and support vector machine classifiers, demonstrate that the proposed approach can significantly reduce the number of user-specific training data samples. This collaborative filtering approach will also be generalizable to handling individual differences in many other applications that involve human neural or physiological data, such as affective computing. PMID:23437188

Application of Super-Resolution Convolutional Neural Network for Enhancing Image Resolution in Chest CT.

PubMed

Umehara, Kensuke; Ota, Junko; Ishida, Takayuki

2017-10-18

In this study, the super-resolution convolutional neural network (SRCNN) scheme, which is the emerging deep-learning-based super-resolution method for enhancing image resolution in chest CT images, was applied and evaluated using the post-processing approach. For evaluation, 89 chest CT cases were sampled from The Cancer Imaging Archive. The 89 CT cases were divided randomly into 45 training cases and 44 external test cases. The SRCNN was trained using the training dataset. With the trained SRCNN, a high-resolution image was reconstructed from a low-resolution image, which was down-sampled from an original test image. For quantitative evaluation, two image quality metrics were measured and compared to those of the conventional linear interpolation methods. The image restoration quality of the SRCNN scheme was significantly higher than that of the linear interpolation methods (p < 0.001 or p < 0.05). The high-resolution image reconstructed by the SRCNN scheme was highly restored and comparable to the original reference image, in particular, for a ×2 magnification. These results indicate that the SRCNN scheme significantly outperforms the linear interpolation methods for enhancing image resolution in chest CT images. The results also suggest that SRCNN may become a potential solution for generating high-resolution CT images from standard CT images.

Training Parents with Videotapes: Recognizing Limitations

ERIC Educational Resources Information Center

Foster, Brandon W.; Roberts, Mark W.

2007-01-01

Among the many methods of teaching skills to parents of disruptive children, videotape modeling of specific parent-child interaction sequences has been particularly effective. Given the likelihood of timeout resistance in defiant children, the authors tested the effectiveness of videotape parent training with a sample of clinic referred,…

Plyometrics: A Legitimate Form of Power Training?

ERIC Educational Resources Information Center

Duda, Marty

1988-01-01

Plyometric exercises or drills combine speed and strength to produce an explosive-reactive movement or increased power. Some world-class athletes have used plyometric-training in sports such as high-jumping, hurdles, football, baseball, and hockey. The method is still considered experimental. Sample exercises are described. (JL)

Water Quality & Pollutant Source Monitoring: Field and Laboratory Procedures. Training Manual.

ERIC Educational Resources Information Center

Office of Water Program Operations (EPA), Cincinnati, OH. National Training and Operational Technology Center.

This training manual presents material on techniques and instrumentation used to develop data in field monitoring programs and related laboratory operations concerned with water quality and pollution monitoring. Topics include: collection and handling of samples; bacteriological, biological, and chemical field and laboratory methods; field…

Artificial neural networks applied to quantitative elemental analysis of organic material using PIXE

NASA Astrophysics Data System (ADS)

Correa, R.; Chesta, M. A.; Morales, J. R.; Dinator, M. I.; Requena, I.; Vila, I.

2006-08-01

An artificial neural network (ANN) has been trained with real-sample PIXE (particle X-ray induced emission) spectra of organic substances. Following the training stage ANN was applied to a subset of similar samples thus obtaining the elemental concentrations in muscle, liver and gills of Cyprinus carpio. Concentrations obtained with the ANN method are in full agreement with results from one standard analytical procedure, showing the high potentiality of ANN in PIXE quantitative analyses.

Improved military air traffic controller selection methods as measured by subsequent training performance.

PubMed

Carretta, Thomas R; King, Raymond E

2008-01-01

Over the past decade, the U.S. military has conducted several studies to evaluate determinants of enlisted air traffic controller (ATC) performance. Research has focused on validation of the Armed Services Vocational Aptitude Battery (ASVAB) and has shown it to be a good predictor of training performance. Despite this, enlisted ATC training and post-training attrition is higher than desirable, prompting interest in alternate selection methods to augment current procedures. The current study examined the utility of the FAA Air Traffic Selection and Training (AT-SAT) battery for incrementing the predictiveness of the ASVAB versus several enlisted ATC training criteria. Subjects were 448 USAF enlisted ATC students who were administered the ASVAB and FAA AT-SAT subtests and subsequently graduated or were eliminated from apprentice-level training. Training criteria were a dichotomous graduation/elimination training score, average ATC fundamentals course score, and FAA certified tower operator test score. Results confirmed the predictive validity of the ASVAB and showed that one of the AT-SAT subtests resembling a low-fidelity ATC work sample significantly improved prediction of training performance beyond the ASVAB alone. Results suggested training attrition could be reduced by raising the current ASVAB minimum qualifying score. However, this approach may make it difficult to identify sufficient numbers of trainees and lead to adverse impact. Although the AT-SAT ATC work sample subtest showed incremental validity to the ASVAB, its length (95 min) may be problematic in operational testing. Recommendations are made for additional studies to address issues affecting operational implementation.

A Remote Sensing Image Fusion Method based on adaptive dictionary learning

NASA Astrophysics Data System (ADS)

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.

Comparison of Feature Selection Techniques in Machine Learning for Anatomical Brain MRI in Dementia.

PubMed

Tohka, Jussi; Moradi, Elaheh; Huttunen, Heikki

2016-07-01

We present a comparative split-half resampling analysis of various data driven feature selection and classification methods for the whole brain voxel-based classification analysis of anatomical magnetic resonance images. We compared support vector machines (SVMs), with or without filter based feature selection, several embedded feature selection methods and stability selection. While comparisons of the accuracy of various classification methods have been reported previously, the variability of the out-of-training sample classification accuracy and the set of selected features due to independent training and test sets have not been previously addressed in a brain imaging context. We studied two classification problems: 1) Alzheimer's disease (AD) vs. normal control (NC) and 2) mild cognitive impairment (MCI) vs. NC classification. In AD vs. NC classification, the variability in the test accuracy due to the subject sample did not vary between different methods and exceeded the variability due to different classifiers. In MCI vs. NC classification, particularly with a large training set, embedded feature selection methods outperformed SVM-based ones with the difference in the test accuracy exceeding the test accuracy variability due to the subject sample. The filter and embedded methods produced divergent feature patterns for MCI vs. NC classification that suggests the utility of the embedded feature selection for this problem when linked with the good generalization performance. The stability of the feature sets was strongly correlated with the number of features selected, weakly correlated with the stability of classification accuracy, and uncorrelated with the average classification accuracy.

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

PubMed

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

2013-08-01

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

Variables Related to MDTA Trainee Employment Success in Minnesota.

ERIC Educational Resources Information Center

Pucel, David J.

To predict a person's use of his Manpower Development and Training Act (MDTA) training, this study attempted to supplement existing methods of evaluation, using personal descriptive data about trainees and General Aptitude Test Battery Scores. The sample under study included all students enrolled in ten MDTA projects, representing a geographical…

The effect of sample size and disease prevalence on supervised machine learning of narrative data.

PubMed Central

McKnight, Lawrence K.; Wilcox, Adam; Hripcsak, George

2002-01-01

This paper examines the independent effects of outcome prevalence and training sample sizes on inductive learning performance. We trained 3 inductive learning algorithms (MC4, IB, and Naïve-Bayes) on 60 simulated datasets of parsed radiology text reports labeled with 6 disease states. Data sets were constructed to define positive outcome states at 4 prevalence rates (1, 5, 10, 25, and 50%) in training set sizes of 200 and 2,000 cases. We found that the effect of outcome prevalence is significant when outcome classes drop below 10% of cases. The effect appeared independent of sample size, induction algorithm used, or class label. Work is needed to identify methods of improving classifier performance when output classes are rare. PMID:12463878

Decision Tree Repository and Rule Set Based Mingjiang River Estuarine Wetlands Classifaction

NASA Astrophysics Data System (ADS)

Zhang, W.; Li, X.; Xiao, W.

2018-05-01

The increasing urbanization and industrialization have led to wetland losses in estuarine area of Mingjiang River over past three decades. There has been increasing attention given to produce wetland inventories using remote sensing and GIS technology. Due to inconsistency training site and training sample, traditionally pixel-based image classification methods can't achieve a comparable result within different organizations. Meanwhile, object-oriented image classification technique shows grate potential to solve this problem and Landsat moderate resolution remote sensing images are widely used to fulfill this requirement. Firstly, the standardized atmospheric correct, spectrally high fidelity texture feature enhancement was conducted before implementing the object-oriented wetland classification method in eCognition. Secondly, we performed the multi-scale segmentation procedure, taking the scale, hue, shape, compactness and smoothness of the image into account to get the appropriate parameters, using the top and down region merge algorithm from single pixel level, the optimal texture segmentation scale for different types of features is confirmed. Then, the segmented object is used as the classification unit to calculate the spectral information such as Mean value, Maximum value, Minimum value, Brightness value and the Normalized value. The Area, length, Tightness and the Shape rule of the image object Spatial features and texture features such as Mean, Variance and Entropy of image objects are used as classification features of training samples. Based on the reference images and the sampling points of on-the-spot investigation, typical training samples are selected uniformly and randomly for each type of ground objects. The spectral, texture and spatial characteristics of each type of feature in each feature layer corresponding to the range of values are used to create the decision tree repository. Finally, with the help of high resolution reference images, the random sampling method is used to conduct the field investigation, achieve an overall accuracy of 90.31 %, and the Kappa coefficient is 0.88. The classification method based on decision tree threshold values and rule set developed by the repository, outperforms the results obtained from the traditional methodology. Our decision tree repository and rule set based object-oriented classification technique was an effective method for producing comparable and consistency wetlands data set.

Blood vessels segmentation of hatching eggs based on fully convolutional networks

NASA Astrophysics Data System (ADS)

Geng, Lei; Qiu, Ling; Wu, Jun; Xiao, Zhitao

2018-04-01

FCN, trained end-to-end, pixels-to-pixels, predict result of each pixel. It has been widely used for semantic segmentation. In order to realize the blood vessels segmentation of hatching eggs, a method based on FCN is proposed in this paper. The training datasets are composed of patches extracted from very few images to augment data. The network combines with lower layer and deconvolution to enables precise segmentation. The proposed method frees from the problem that training deep networks need large scale samples. Experimental results on hatching eggs demonstrate that this method can yield more accurate segmentation outputs than previous researches. It provides a convenient reference for fertility detection subsequently.

A method for feature selection of APT samples based on entropy

NASA Astrophysics Data System (ADS)

Du, Zhenyu; Li, Yihong; Hu, Jinsong

2018-05-01

By studying the known APT attack events deeply, this paper propose a feature selection method of APT sample and a logic expression generation algorithm IOCG (Indicator of Compromise Generate). The algorithm can automatically generate machine readable IOCs (Indicator of Compromise), to solve the existing IOCs logical relationship is fixed, the number of logical items unchanged, large scale and cannot generate a sample of the limitations of the expression. At the same time, it can reduce the redundancy and useless APT sample processing time consumption, and improve the sharing rate of information analysis, and actively respond to complex and volatile APT attack situation. The samples were divided into experimental set and training set, and then the algorithm was used to generate the logical expression of the training set with the IOC_ Aware plug-in. The contrast expression itself was different from the detection result. The experimental results show that the algorithm is effective and can improve the detection effect.

Joint learning of labels and distance metric.

PubMed

Liu, Bo; Wang, Meng; Hong, Richang; Zha, Zhengjun; Hua, Xian-Sheng

2010-06-01

Machine learning algorithms frequently suffer from the insufficiency of training data and the usage of inappropriate distance metric. In this paper, we propose a joint learning of labels and distance metric (JLLDM) approach, which is able to simultaneously address the two difficulties. In comparison with the existing semi-supervised learning and distance metric learning methods that focus only on label prediction or distance metric construction, the JLLDM algorithm optimizes the labels of unlabeled samples and a Mahalanobis distance metric in a unified scheme. The advantage of JLLDM is multifold: 1) the problem of training data insufficiency can be tackled; 2) a good distance metric can be constructed with only very few training samples; and 3) no radius parameter is needed since the algorithm automatically determines the scale of the metric. Extensive experiments are conducted to compare the JLLDM approach with different semi-supervised learning and distance metric learning methods, and empirical results demonstrate its effectiveness.

Consistency of Pilot Trainee Cognitive Ability, Personality, and Training Performance in Undergraduate Pilot Training

DTIC Science & Technology

2013-09-09

multivariate correction method (Lawley, 1943) was used for all scores except the MAB FSIQ which used the univariate ( Thorndike , 1949) method. FSIQ... Thorndike , R. L. (1949). Personnel selection. NY: Wiley. Tupes, E. C., & Christal, R. C. (1961). Recurrent personality factors based on trait ratings... Thorndike , 1949). aThe correlations for 1995 were not corrected due to the small sample size (N = 17). *p< .05 Consistency of Pilot Attributes

Temporal Correlations and Neural Spike Train Entropy

NASA Astrophysics Data System (ADS)

Schultz, Simon R.; Panzeri, Stefano

2001-06-01

Sampling considerations limit the experimental conditions under which information theoretic analyses of neurophysiological data yield reliable results. We develop a procedure for computing the full temporal entropy and information of ensembles of neural spike trains, which performs reliably for limited samples of data. This approach also yields insight to the role of correlations between spikes in temporal coding mechanisms. The method, when applied to recordings from complex cells of the monkey primary visual cortex, results in lower rms error information estimates in comparison to a ``brute force'' approach.

A Novel User Classification Method for Femtocell Network by Using Affinity Propagation Algorithm and Artificial Neural Network

PubMed Central

Ahmed, Afaz Uddin; Tariqul Islam, Mohammad; Ismail, Mahamod; Kibria, Salehin; Arshad, Haslina

2014-01-01

An artificial neural network (ANN) and affinity propagation (AP) algorithm based user categorization technique is presented. The proposed algorithm is designed for closed access femtocell network. ANN is used for user classification process and AP algorithm is used to optimize the ANN training process. AP selects the best possible training samples for faster ANN training cycle. The users are distinguished by using the difference of received signal strength in a multielement femtocell device. A previously developed directive microstrip antenna is used to configure the femtocell device. Simulation results show that, for a particular house pattern, the categorization technique without AP algorithm takes 5 indoor users and 10 outdoor users to attain an error-free operation. While integrating AP algorithm with ANN, the system takes 60% less training samples reducing the training time up to 50%. This procedure makes the femtocell more effective for closed access operation. PMID:25133214

A novel user classification method for femtocell network by using affinity propagation algorithm and artificial neural network.

PubMed

Ahmed, Afaz Uddin; Islam, Mohammad Tariqul; Ismail, Mahamod; Kibria, Salehin; Arshad, Haslina

2014-01-01

An artificial neural network (ANN) and affinity propagation (AP) algorithm based user categorization technique is presented. The proposed algorithm is designed for closed access femtocell network. ANN is used for user classification process and AP algorithm is used to optimize the ANN training process. AP selects the best possible training samples for faster ANN training cycle. The users are distinguished by using the difference of received signal strength in a multielement femtocell device. A previously developed directive microstrip antenna is used to configure the femtocell device. Simulation results show that, for a particular house pattern, the categorization technique without AP algorithm takes 5 indoor users and 10 outdoor users to attain an error-free operation. While integrating AP algorithm with ANN, the system takes 60% less training samples reducing the training time up to 50%. This procedure makes the femtocell more effective for closed access operation.

Level 1 environmental assessment performance evaluation. Final report jun 77-oct 78

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

Estes, E.D.; Smith, F.; Wagoner, D.E.

1979-02-01

The report gives results of a two-phased evaluation of Level 1 environmental assessment procedures. Results from Phase I, a field evaluation of the Source Assessment Sampling System (SASS), showed that the SASS train performed well within the desired factor of 3 Level 1 accuracy limit. Three sample runs were made with two SASS trains sampling simultaneously and from approximately the same sampling point in a horizontal duct. A Method-5 train was used to estimate the 'true' particulate loading. The sampling systems were upstream of the control devices to ensure collection of sufficient material for comparison of total particulate, particle sizemore » distribution, organic classes, and trace elements. Phase II consisted of providing each of three organizations with three types of control samples to challenge the spectrum of Level 1 analytical procedures: an artificial sample in methylene chloride, an artificial sample on a flyash matrix, and a real sample composed of the combined XAD-2 resin extracts from all Phase I runs. Phase II results showed that when the Level 1 analytical procedures are carefully applied, data of acceptable accuracy is obtained. Estimates of intralaboratory and interlaboratory precision are made.« less

Application of Machine Learning in Urban Greenery Land Cover Extraction

NASA Astrophysics Data System (ADS)

Qiao, X.; Li, L. L.; Li, D.; Gan, Y. L.; Hou, A. Y.

2018-04-01

Urban greenery is a critical part of the modern city and the greenery coverage information is essential for land resource management, environmental monitoring and urban planning. It is a challenging work to extract the urban greenery information from remote sensing image as the trees and grassland are mixed with city built-ups. In this paper, we propose a new automatic pixel-based greenery extraction method using multispectral remote sensing images. The method includes three main steps. First, a small part of the images is manually interpreted to provide prior knowledge. Secondly, a five-layer neural network is trained and optimised with the manual extraction results, which are divided to serve as training samples, verification samples and testing samples. Lastly, the well-trained neural network will be applied to the unlabelled data to perform the greenery extraction. The GF-2 and GJ-1 high resolution multispectral remote sensing images were used to extract greenery coverage information in the built-up areas of city X. It shows a favourable performance in the 619 square kilometers areas. Also, when comparing with the traditional NDVI method, the proposed method gives a more accurate delineation of the greenery region. Due to the advantage of low computational load and high accuracy, it has a great potential for large area greenery auto extraction, which saves a lot of manpower and resources.

Burnout and Competency Development in Pre-Service Teacher Training

ERIC Educational Resources Information Center

Rodríguez-Hidalgo, Antonio J.; Calmaestra, Juan; Dios, Irene

2014-01-01

Introduction: The burnout syndrome negatively affects the students' academic performance. The relation between academic burnout and the self-perception of skills in initial teacher training is subjected to analysis. Method: A sample of 274 students (average age = 20,61 years old) from the Bachelor Degree in Early Childhood Education and the…

Evaluation of an In-Service Training Program for Child Welfare Practitioners

ERIC Educational Resources Information Center

Turcotte, Daniel; Lamonde, Genevieve; Beaudoin, Andre

2009-01-01

Objective: To test the effectiveness of an in-training program for practitioners in public child welfare organizations. Method: The sample consists of practitioners (N = 945) working in youth centers or in local community service centers. Data are collected through self-administered questionnaires prior to and after the program. Results: The data…

Counseling Psychology Trainees' Perceptions of Training and Commitments to Social Justice

ERIC Educational Resources Information Center

Beer, Amanda M.; Spanierman, Lisa B.; Greene, Jennifer C.; Todd, Nathan R.

2012-01-01

This mixed methods study examined social justice commitments of counseling psychology graduate trainees. In the quantitative portion of the study, a national sample of trainees (n = 260) completed a web-based survey assessing their commitments to social justice and related personal and training variables. Results suggested that students desired…

Cigarette Smoking and Anti-Smoking Counseling Practices among Physicians in Wuhan, China

ERIC Educational Resources Information Center

Gong, Jie; Zhang, Zhifeng; Zhu, Zhaoyang; Wan, Jun; Yang, Niannian; Li, Fang; Sun, Huiling; Li, Weiping; Xia, Jiang; Zhou, Dunjin; Chen, Xinguang

2012-01-01

Purpose: The paper seeks to report data on cigarette smoking, anti-smoking practices, physicians' receipt of anti-smoking training, and the association between receipt of the training and anti-smoking practice among physicians in Wuhan, China. Design/methodology/approach: Participants were selected through the stratified random sampling method.…

Bamboo Classification Using WorldView-2 Imagery of Giant Panda Habitat in a Large Shaded Area in Wolong, Sichuan Province, China.

PubMed

Tang, Yunwei; Jing, Linhai; Li, Hui; Liu, Qingjie; Yan, Qi; Li, Xiuxia

2016-11-22

This study explores the ability of WorldView-2 (WV-2) imagery for bamboo mapping in a mountainous region in Sichuan Province, China. A large area of this place is covered by shadows in the image, and only a few sampled points derived were useful. In order to identify bamboos based on sparse training data, the sample size was expanded according to the reflectance of multispectral bands selected using the principal component analysis (PCA). Then, class separability based on the training data was calculated using a feature space optimization method to select the features for classification. Four regular object-based classification methods were applied based on both sets of training data. The results show that the k -nearest neighbor ( k -NN) method produced the greatest accuracy. A geostatistically-weighted k -NN classifier, accounting for the spatial correlation between classes, was then applied to further increase the accuracy. It achieved 82.65% and 93.10% of the producer's and user's accuracies respectively for the bamboo class. The canopy densities were estimated to explain the result. This study demonstrates that the WV-2 image can be used to identify small patches of understory bamboos given limited known samples, and the resulting bamboo distribution facilitates the assessments of the habitats of giant pandas.

Anadolu University, Open Education Faculty, Turkish Language and Literature Department Graduated Students' Views towards Pedagogical Formation Training Certificate, Special Teaching Methods Courses and Turkish Language and Literature Education from: Sample of Turkey

ERIC Educational Resources Information Center

Bulut, Mesut

2016-01-01

The aim of this study is to find out Anadolu University Open Education Faculty Turkish Language and Literature graduated students' views towards Pedagogical Formation Training certificate and their opinions about special teaching methods. This study has been done in one of the universities of East Karadeniz in Turkey in which the 20 Turkish…

Hot flue-gas spiking and recovery study for tetrachlorodibenzodioxins (TCDD) using Modified Method 5 and SASS (Source Assessment Sampling System) sampling with a simulated incinerator. Final report, May 1981-February 1982

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

Cooke, M.; DeRoos, F.; Rising, B.

1984-10-01

The report gives results of an evaluation of the sampling and analysis of ultratrace levels of dibenzodioxins using EPA's recommended source sampling procedures (Modified Method 5 (MM5) train and the Source Assessment Sampling System--SASS). A gas-fired combustion system was used to simulate incineration flue gas, and a precision liquid injection system was designed for the program. The precision liquid injector was used to administer dilute solutions of 1,2,3,4-tetrachlorodibenzo-p-dioxin (1,2,3,4-TCDD) directly into a hot--260C (500F)--flue gas stream. Injections occurred continuously during the sampling episode so that very low gas-phase concentrations of 1,2,3,4-TCDD were continuously mixed with the flue gases. Recoveries weremore » measured for eight burn experiments. For all but one, the recoveries could be considered quantitative, demonstrating efficient collection by the EPA sampling systems. In one study, the components and connecting lines from a sampling device were analyzed separately to show where the 1,2,3,4-TCDD deposited in the train.« less

Cross-Domain Semi-Supervised Learning Using Feature Formulation.

PubMed

Xingquan Zhu

2011-12-01

Semi-Supervised Learning (SSL) traditionally makes use of unlabeled samples by including them into the training set through an automated labeling process. Such a primitive Semi-Supervised Learning (pSSL) approach suffers from a number of disadvantages including false labeling and incapable of utilizing out-of-domain samples. In this paper, we propose a formative Semi-Supervised Learning (fSSL) framework which explores hidden features between labeled and unlabeled samples to achieve semi-supervised learning. fSSL regards that both labeled and unlabeled samples are generated from some hidden concepts with labeling information partially observable for some samples. The key of the fSSL is to recover the hidden concepts, and take them as new features to link labeled and unlabeled samples for semi-supervised learning. Because unlabeled samples are only used to generate new features, but not to be explicitly included in the training set like pSSL does, fSSL overcomes the inherent disadvantages of the traditional pSSL methods, especially for samples not within the same domain as the labeled instances. Experimental results and comparisons demonstrate that fSSL significantly outperforms pSSL-based methods for both within-domain and cross-domain semi-supervised learning.

Estimating a Logistic Discrimination Functions When One of the Training Samples Is Subject to Misclassification: A Maximum Likelihood Approach.

PubMed

Nagelkerke, Nico; Fidler, Vaclav

2015-01-01

The problem of discrimination and classification is central to much of epidemiology. Here we consider the estimation of a logistic regression/discrimination function from training samples, when one of the training samples is subject to misclassification or mislabeling, e.g. diseased individuals are incorrectly classified/labeled as healthy controls. We show that this leads to zero-inflated binomial model with a defective logistic regression or discrimination function, whose parameters can be estimated using standard statistical methods such as maximum likelihood. These parameters can be used to estimate the probability of true group membership among those, possibly erroneously, classified as controls. Two examples are analyzed and discussed. A simulation study explores properties of the maximum likelihood parameter estimates and the estimates of the number of mislabeled observations.

Prediction of space sickness in astronauts from preflight fluid, electrolyte, and cardiovascular variables and Weightless Environmental Training Facility (WETF) training

NASA Technical Reports Server (NTRS)

Simanonok, K.; Mosely, E.; Charles, J.

1992-01-01

Nine preflight variables related to fluid, electrolyte, and cardiovascular status from 64 first-time Shuttle crewmembers were differentially weighted by discrimination analysis to predict the incidence and severity of each crewmember's space sickness as rated by NASA flight surgeons. The nine variables are serum uric acid, red cell count, environmental temperature at the launch site, serum phosphate, urine osmolality, serum thyroxine, sitting systolic blood pressure, calculated blood volume, and serum chloride. Using two methods of cross-validation on the original samples (jackknife and a stratefied random subsample), these variables enable the prediction of space sickness incidence (NONE or SICK) with 80 percent sickness and space severity (NONE, MILD, MODERATE, of SEVERE) with 59 percent success by one method of cross-validation and 67 percent by another method. Addition of a tenth variable, hours spent in the Weightlessness Environment Training Facility (WETF) did not improve the prediction of space sickness incidences but did improve the prediction of space sickness severity to 66 percent success by the first method of cross-validation of original samples and to 71 percent by the second method. Results to date suggest the presence of predisposing physiologic factors to space sickness that implicate fluid shift etiology. The data also suggest that prior exposure to fluid shift during WETF training may produce some circulatory pre-adaption to fluid shifts in weightlessness that results in a reduction of space sickness severity.

Multiple Point Statistics algorithm based on direct sampling and multi-resolution images

NASA Astrophysics Data System (ADS)

Julien, S.; Renard, P.; Chugunova, T.

2017-12-01

Multiple Point Statistics (MPS) has become popular for more than one decade in Earth Sciences, because these methods allow to generate random fields reproducing highly complex spatial features given in a conceptual model, the training image, while classical geostatistics techniques based on bi-point statistics (covariance or variogram) fail to generate realistic models. Among MPS methods, the direct sampling consists in borrowing patterns from the training image to populate a simulation grid. This latter is sequentially filled by visiting each of these nodes in a random order, and then the patterns, whose the number of nodes is fixed, become narrower during the simulation process, as the simulation grid is more densely informed. Hence, large scale structures are caught in the beginning of the simulation and small scale ones in the end. However, MPS may mix spatial characteristics distinguishable at different scales in the training image, and then loose the spatial arrangement of different structures. To overcome this limitation, we propose to perform MPS simulation using a decomposition of the training image in a set of images at multiple resolutions. Applying a Gaussian kernel onto the training image (convolution) results in a lower resolution image, and iterating this process, a pyramid of images depicting fewer details at each level is built, as it can be done in image processing for example to lighten the space storage of a photography. The direct sampling is then employed to simulate the lowest resolution level, and then to simulate each level, up to the finest resolution, conditioned to the level one rank coarser. This scheme helps reproduce the spatial structures at any scale of the training image and then generate more realistic models. We illustrate the method with aerial photographies (satellite images) and natural textures. Indeed, these kinds of images often display typical structures at different scales and are well-suited for MPS simulation techniques.

A method for the analysis of perfluorinated compounds in environmental and drinking waters and the determination of their lowest concentration minimal reporting levels.

PubMed

Boone, J Scott; Guan, Bing; Vigo, Craig; Boone, Tripp; Byrne, Christian; Ferrario, Joseph

2014-06-06

A trace analytical method was developed for the determination of seventeen specific perfluorinated chemicals (PFCs) in environmental and drinking waters. The objectives were to optimize an isotope-dilution method to increase the precision and accuracy of the analysis of the PFCs and to eliminate the need for matrix-matched standards. A 250 mL sample of environmental or drinking water was buffered to a pH of 4, spiked with labeled surrogate standards, extracted through solid phase extraction cartridges, and eluted with ammonium hydroxide in methyl tert-butyl ether: methanol solution. The sample eluents were concentrated to volume and analyzed by liquid chromatography/tandem mass spectrometry (LC-MS/MS). The lowest concentration minimal reporting levels (LCMRLs) for the seventeen PFCs were calculated and ranged from 0.034 to 0.600 ng/L for surface water and from 0.033 to 0.640 ng/L for drinking water. The relative standard deviations (RSDs) for all compounds were <20% for all concentrations above the LCMRL. The method proved effective and cost efficient and addressed the problems with the recovery of perfluorobutanoic acid (PFBA) and other short chain PFCs. Various surface water and drinking water samples were used during method development to optimize this method. The method was used to evaluate samples from the Mississippi River at New Orleans and drinking water samples from a private residence in that same city. The method was also used to determine PFC contamination in well water samples from a fire training area where perfluorinated foams were used in training to extinguish fires. Published by Elsevier B.V.

An exploratory study of a text classification framework for Internet-based surveillance of emerging epidemics

PubMed Central

Torii, Manabu; Yin, Lanlan; Nguyen, Thang; Mazumdar, Chand T.; Liu, Hongfang; Hartley, David M.; Nelson, Noele P.

2014-01-01

Purpose Early detection of infectious disease outbreaks is crucial to protecting the public health of a society. Online news articles provide timely information on disease outbreaks worldwide. In this study, we investigated automated detection of articles relevant to disease outbreaks using machine learning classifiers. In a real-life setting, it is expensive to prepare a training data set for classifiers, which usually consists of manually labeled relevant and irrelevant articles. To mitigate this challenge, we examined the use of randomly sampled unlabeled articles as well as labeled relevant articles. Methods Naïve Bayes and Support Vector Machine (SVM) classifiers were trained on 149 relevant and 149 or more randomly sampled unlabeled articles. Diverse classifiers were trained by varying the number of sampled unlabeled articles and also the number of word features. The trained classifiers were applied to 15 thousand articles published over 15 days. Top-ranked articles from each classifier were pooled and the resulting set of 1337 articles was reviewed by an expert analyst to evaluate the classifiers. Results Daily averages of areas under ROC curves (AUCs) over the 15-day evaluation period were 0.841 and 0.836, respectively, for the naïve Bayes and SVM classifier. We referenced a database of disease outbreak reports to confirm that this evaluation data set resulted from the pooling method indeed covered incidents recorded in the database during the evaluation period. Conclusions The proposed text classification framework utilizing randomly sampled unlabeled articles can facilitate a cost-effective approach to training machine learning classifiers in a real-life Internet-based biosurveillance project. We plan to examine this framework further using larger data sets and using articles in non-English languages. PMID:21134784

Measurement of trained speech patterns in stuttering: interjudge and intrajudge agreement of experts by means of modified time-interval analysis.

PubMed

Alpermann, Anke; Huber, Walter; Natke, Ulrich; Willmes, Klaus

2010-09-01

Improved fluency after stuttering therapy is usually measured by the percentage of stuttered syllables. However, outcome studies rarely evaluate the use of trained speech patterns that speakers use to manage stuttering. This study investigated whether the modified time interval analysis can distinguish between trained speech patterns, fluent speech, and stuttered speech. Seventeen German experts on stuttering judged a speech sample on two occasions. Speakers of the sample were stuttering adults, who were not undergoing therapy, as well as participants in a fluency shaping and a stuttering modification therapy. Results showed satisfactory inter-judge and intra-judge agreement above 80%. Intervals with trained speech patterns were identified as consistently as stuttered and fluent intervals. We discuss limitations of the study, as well as implications of our findings for the development of training for identification of trained speech patterns and future outcome studies. The reader will be able to (a) explain different methods to measure the use of trained speech patterns, (b) evaluate whether German experts are able to discriminate intervals with trained speech patterns reliably from fluent and stuttered intervals and (c) describe how the measurement of trained speech patterns can contribute to outcome studies.

Confidence Preserving Machine for Facial Action Unit Detection

PubMed Central

Zeng, Jiabei; Chu, Wen-Sheng; De la Torre, Fernando; Cohn, Jeffrey F.; Xiong, Zhang

2016-01-01

Facial action unit (AU) detection from video has been a long-standing problem in automated facial expression analysis. While progress has been made, accurate detection of facial AUs remains challenging due to ubiquitous sources of errors, such as inter-personal variability, pose, and low-intensity AUs. In this paper, we refer to samples causing such errors as hard samples, and the remaining as easy samples. To address learning with the hard samples, we propose the Confidence Preserving Machine (CPM), a novel two-stage learning framework that combines multiple classifiers following an “easy-to-hard” strategy. During the training stage, CPM learns two confident classifiers. Each classifier focuses on separating easy samples of one class from all else, and thus preserves confidence on predicting each class. During the testing stage, the confident classifiers provide “virtual labels” for easy test samples. Given the virtual labels, we propose a quasi-semi-supervised (QSS) learning strategy to learn a person-specific (PS) classifier. The QSS strategy employs a spatio-temporal smoothness that encourages similar predictions for samples within a spatio-temporal neighborhood. In addition, to further improve detection performance, we introduce two CPM extensions: iCPM that iteratively augments training samples to train the confident classifiers, and kCPM that kernelizes the original CPM model to promote nonlinearity. Experiments on four spontaneous datasets GFT [15], BP4D [56], DISFA [42], and RU-FACS [3] illustrate the benefits of the proposed CPM models over baseline methods and state-of-the-art semisupervised learning and transfer learning methods. PMID:27479964

Transfer Learning with Convolutional Neural Networks for SAR Ship Recognition

NASA Astrophysics Data System (ADS)

Zhang, Di; Liu, Jia; Heng, Wang; Ren, Kaijun; Song, Junqiang

2018-03-01

Ship recognition is the backbone of marine surveillance systems. Recent deep learning methods, e.g. Convolutional Neural Networks (CNNs), have shown high performance for optical images. Learning CNNs, however, requires a number of annotated samples to estimate numerous model parameters, which prevents its application to Synthetic Aperture Radar (SAR) images due to the limited annotated training samples. Transfer learning has been a promising technique for applications with limited data. To this end, a novel SAR ship recognition method based on CNNs with transfer learning has been developed. In this work, we firstly start with a CNNs model that has been trained in advance on Moving and Stationary Target Acquisition and Recognition (MSTAR) database. Next, based on the knowledge gained from this image recognition task, we fine-tune the CNNs on a new task to recognize three types of ships in the OpenSARShip database. The experimental results show that our proposed approach can obviously increase the recognition rate comparing with the result of merely applying CNNs. In addition, compared to existing methods, the proposed method proves to be very competitive and can learn discriminative features directly from training data instead of requiring pre-specification or pre-selection manually.

Short-Term Effects of Different Loading Schemes in Fitness-Related Resistance Training.

PubMed

Eifler, Christoph

2016-07-01

Eifler, C. Short-term effects of different loading schemes in fitness-related resistance training. J Strength Cond Res 30(7): 1880-1889, 2016-The purpose of this investigation was to analyze the short-term effects of different loading schemes in fitness-related resistance training and to identify the most effective loading method for advanced recreational athletes. The investigation was designed as a longitudinal field-test study. Two hundred healthy mature subjects with at least 12 months' experience in resistance training were randomized in 4 samples of 50 subjects each. Gender distribution was homogenous in all samples. Training effects were quantified by 10 repetition maximum (10RM) and 1 repetition maximum (1RM) testing (pre-post-test design). Over a period of 6 weeks, a standardized resistance training protocol with 3 training sessions per week was realized. Testing and training included 8 resistance training exercises in a standardized order. The following loading schemes were randomly matched to each sample: constant load (CL) with constant volume of repetitions, increasing load (IL) with decreasing volume of repetitions, decreasing load (DL) with increasing volume of repetitions, daily changing load (DCL), and volume of repetitions. For all loading schemes, significant strength gains (p < 0.001) could be noted for all resistance training exercises and both dependent variables (10RM, 1RM). In all cases, DCL obtained significantly higher strength gains (p < 0.001) than CL, IL, and DL. There were no significant differences in strength gains between CL, IL, and DL. The present data indicate that resistance training following DCL is more effective for advanced recreational athletes than CL, IL, or DL. Considering that DCL is widely unknown in fitness-related resistance training, the present data indicate, there is potential for improving resistance training in commercial fitness clubs.

The Influence of Training Strategy and Physical Condition toward Forehand Drive Ability in Table Tennis

NASA Astrophysics Data System (ADS)

Langitan, F. W.

2018-02-01

The objective of this research is to find out the influence of training strategy and physical condition toward forehand drive ability in table tennis of student in faculty of sport in university of Manado, department of health and recreation education. The method used in this research was factorial 2x2 design method. The population was taken from the student of Faculty of Sport at Manado State University, Indonesia, in 2017 of 76 students for sample research. The result of this research shows that: In general, this training strategy of wall bounce gives better influence toward forehand drive ability compare with the strategy of pair training in table tennis. For the students who have strong forehand muscle, the wall bounce training strategy give better influence to their ability of forehand drive in table tennis. For the student who have weak forehand muscle, pair training strategy give better influence than wall bound training toward forehand drive ability in table tennis. There is an interaction between training using hand muscle strength to the training result in table tennis using forehand drive.

Satellite Image Classification of Building Damages Using Airborne and Satellite Image Samples in a Deep Learning Approach

NASA Astrophysics Data System (ADS)

Duarte, D.; Nex, F.; Kerle, N.; Vosselman, G.

2018-05-01

The localization and detailed assessment of damaged buildings after a disastrous event is of utmost importance to guide response operations, recovery tasks or for insurance purposes. Several remote sensing platforms and sensors are currently used for the manual detection of building damages. However, there is an overall interest in the use of automated methods to perform this task, regardless of the used platform. Owing to its synoptic coverage and predictable availability, satellite imagery is currently used as input for the identification of building damages by the International Charter, as well as the Copernicus Emergency Management Service for the production of damage grading and reference maps. Recently proposed methods to perform image classification of building damages rely on convolutional neural networks (CNN). These are usually trained with only satellite image samples in a binary classification problem, however the number of samples derived from these images is often limited, affecting the quality of the classification results. The use of up/down-sampling image samples during the training of a CNN, has demonstrated to improve several image recognition tasks in remote sensing. However, it is currently unclear if this multi resolution information can also be captured from images with different spatial resolutions like satellite and airborne imagery (from both manned and unmanned platforms). In this paper, a CNN framework using residual connections and dilated convolutions is used considering both manned and unmanned aerial image samples to perform the satellite image classification of building damages. Three network configurations, trained with multi-resolution image samples are compared against two benchmark networks where only satellite image samples are used. Combining feature maps generated from airborne and satellite image samples, and refining these using only the satellite image samples, improved nearly 4 % the overall satellite image classification of building damages.

Assessing family medicine trainees--what can we learn from the European neighbours?

PubMed

Flum, Elisabeth; Maagaard, Roar; Godycki-Cwirko, Maciek; Scarborough, Nigel; Scherpbier, Nynke; Ledig, Thomas; Roos, Marco; Steinhäuser, Jost

2015-01-01

Although demands on family physicians (FP) are to a large extent similar in the European Union, uniform assessment standards for family medicine (FM) specialty training and assessment do not exist. Aim of this pilot study was to elicit and compare the different modalities and assessment methods of FM specialty training in five European countries. A semi structured survey was undertaken based on a convenient sample in five European countries (Denmark, Germany, Poland, the Netherlands and the United Kingdom). The respondents were asked to respond to ten items about aspects of FM specialty training and assessment methods in their respective countries. If available, this data was completed with information from official websites of the countries involved. FM specialty training is performed heterogeneously in the surveyed countries. Training time periods range from three to five years, in some countries requiring a foundation program of up to two years. Most countries perform longitudinal assessment during FM specialty training using a combination of competence-based approach with additional formative and summative assessment. There is some evidence on the assessments methods used, however the assessment method used and costs of assessment differs remarkably between the participating countries. Longitudinal and competence-based assessment is the presently preferred approach for FM specialty training. Countries which use less multifaceted methods for assessment could learn from best practice. Potential changes have significant cost implications.

Pre-Test and Post-Test Applications to Shape the Education of Phlebotomists in A Quality Management Program: An Experience in a Training Hospital

PubMed Central

Keşapli, Mustafa; Aydin, Özgür; Esen, Hatice; Yeğin, Ayşenur; Güngör, Faruk; Yilmaz, Necat

2016-01-01

Summary Background After the introduction of modern laboratory instruments and information systems, preanalytic phase is the new field of battle. Errors in preanalytical phase account for approximately half of total errors in clinical laboratory. The objective of this study was to share an experience of an education program that was believed to be successful in decreasing the number of rejected samples received from the Emergency Department (ED). Methods An education program about laboratory procedures, quality requirements in the laboratory, patient and health-care worker safety was planned by the quality team to be performed on 36 people who were responsible for sample collection in the ED. A questionary which included 11 questions about the preanalytic phase was applied to all the attendees before and after training. The number of rejected samples per million was discovered with right proportion account over the number of accepted and rejected samples to laboratory after and before the training period. Results Most of the attendees were nurses (n: 22/55%), with over 12 years of experience in general and 2–4 years experience in the ED. Knowledge level of the attendees was calculated before training as 58.9% and after training as 91.8%. While the total rate of sample rejection before training was 2.35% (sigma value 3.37–3.50), the rate after training was 1.56% (sigma value 3.62–3.75). Conclusions Increasing the knowledge of staff has a direct positive impact on the preanalytic phase. The application of a pre-test was observed to be a feasible tool to shape group specific education programs. PMID:28356887

Evaluation of a segment-based LANDSAT full-frame approach to corp area estimation

NASA Technical Reports Server (NTRS)

Bauer, M. E. (Principal Investigator); Hixson, M. M.; Davis, S. M.

1981-01-01

As the registration of LANDSAT full frames enters the realm of current technology, sampling methods should be examined which utilize other than the segment data used for LACIE. The effect of separating the functions of sampling for training and sampling for area estimation. The frame selected for analysis was acquired over north central Iowa on August 9, 1978. A stratification of he full-frame was defined. Training data came from segments within the frame. Two classification and estimation procedures were compared: statistics developed on one segment were used to classify that segment, and pooled statistics from the segments were used to classify a systematic sample of pixels. Comparisons to USDA/ESCS estimates illustrate that the full-frame sampling approach can provide accurate and precise area estimates.

Adaptive model predictive process control using neural networks

DOEpatents

Buescher, K.L.; Baum, C.C.; Jones, R.D.

1997-08-19

A control system for controlling the output of at least one plant process output parameter is implemented by adaptive model predictive control using a neural network. An improved method and apparatus provides for sampling plant output and control input at a first sampling rate to provide control inputs at the fast rate. The MPC system is, however, provided with a network state vector that is constructed at a second, slower rate so that the input control values used by the MPC system are averaged over a gapped time period. Another improvement is a provision for on-line training that may include difference training, curvature training, and basis center adjustment to maintain the weights and basis centers of the neural in an updated state that can follow changes in the plant operation apart from initial off-line training data. 46 figs.

Adaptive model predictive process control using neural networks

DOEpatents

Buescher, Kevin L.; Baum, Christopher C.; Jones, Roger D.

1997-01-01

A control system for controlling the output of at least one plant process output parameter is implemented by adaptive model predictive control using a neural network. An improved method and apparatus provides for sampling plant output and control input at a first sampling rate to provide control inputs at the fast rate. The MPC system is, however, provided with a network state vector that is constructed at a second, slower rate so that the input control values used by the MPC system are averaged over a gapped time period. Another improvement is a provision for on-line training that may include difference training, curvature training, and basis center adjustment to maintain the weights and basis centers of the neural in an updated state that can follow changes in the plant operation apart from initial off-line training data.

Use of Unlabeled Samples for Mitigating the Hughes Phenomenon

NASA Technical Reports Server (NTRS)

Landgrebe, David A.; Shahshahani, Behzad M.

1993-01-01

The use of unlabeled samples in improving the performance of classifiers is studied. When the number of training samples is fixed and small, additional feature measurements may reduce the performance of a statistical classifier. It is shown that by using unlabeled samples, estimates of the parameters can be improved and therefore this phenomenon may be mitigated. Various methods for using unlabeled samples are reviewed and experimental results are provided.

Rapid characterization of transgenic and non-transgenic soybean oils by chemometric methods using NIR spectroscopy

NASA Astrophysics Data System (ADS)

Luna, Aderval S.; da Silva, Arnaldo P.; Pinho, Jéssica S. A.; Ferré, Joan; Boqué, Ricard

Near infrared (NIR) spectroscopy and multivariate classification were applied to discriminate soybean oil samples into non-transgenic and transgenic. Principal Component Analysis (PCA) was applied to extract relevant features from the spectral data and to remove the anomalous samples. The best results were obtained when with Support Vectors Machine-Discriminant Analysis (SVM-DA) and Partial Least Squares-Discriminant Analysis (PLS-DA) after mean centering plus multiplicative scatter correction. For SVM-DA the percentage of successful classification was 100% for the training group and 100% and 90% in validation group for non transgenic and transgenic soybean oil samples respectively. For PLS-DA the percentage of successful classification was 95% and 100% in training group for non transgenic and transgenic soybean oil samples respectively and 100% and 80% in validation group for non transgenic and transgenic respectively. The results demonstrate that NIR spectroscopy can provide a rapid, nondestructive and reliable method to distinguish non-transgenic and transgenic soybean oils.

Multimodal manifold-regularized transfer learning for MCI conversion prediction.

PubMed

Cheng, Bo; Liu, Mingxia; Suk, Heung-Il; Shen, Dinggang; Zhang, Daoqiang

2015-12-01

As the early stage of Alzheimer's disease (AD), mild cognitive impairment (MCI) has high chance to convert to AD. Effective prediction of such conversion from MCI to AD is of great importance for early diagnosis of AD and also for evaluating AD risk pre-symptomatically. Unlike most previous methods that used only the samples from a target domain to train a classifier, in this paper, we propose a novel multimodal manifold-regularized transfer learning (M2TL) method that jointly utilizes samples from another domain (e.g., AD vs. normal controls (NC)) as well as unlabeled samples to boost the performance of the MCI conversion prediction. Specifically, the proposed M2TL method includes two key components. The first one is a kernel-based maximum mean discrepancy criterion, which helps eliminate the potential negative effect induced by the distributional difference between the auxiliary domain (i.e., AD and NC) and the target domain (i.e., MCI converters (MCI-C) and MCI non-converters (MCI-NC)). The second one is a semi-supervised multimodal manifold-regularized least squares classification method, where the target-domain samples, the auxiliary-domain samples, and the unlabeled samples can be jointly used for training our classifier. Furthermore, with the integration of a group sparsity constraint into our objective function, the proposed M2TL has a capability of selecting the informative samples to build a robust classifier. Experimental results on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database validate the effectiveness of the proposed method by significantly improving the classification accuracy of 80.1 % for MCI conversion prediction, and also outperforming the state-of-the-art methods.

Optimized mixed Markov models for motif identification

PubMed Central

Huang, Weichun; Umbach, David M; Ohler, Uwe; Li, Leping

2006-01-01

Background Identifying functional elements, such as transcriptional factor binding sites, is a fundamental step in reconstructing gene regulatory networks and remains a challenging issue, largely due to limited availability of training samples. Results We introduce a novel and flexible model, the Optimized Mixture Markov model (OMiMa), and related methods to allow adjustment of model complexity for different motifs. In comparison with other leading methods, OMiMa can incorporate more than the NNSplice's pairwise dependencies; OMiMa avoids model over-fitting better than the Permuted Variable Length Markov Model (PVLMM); and OMiMa requires smaller training samples than the Maximum Entropy Model (MEM). Testing on both simulated and actual data (regulatory cis-elements and splice sites), we found OMiMa's performance superior to the other leading methods in terms of prediction accuracy, required size of training data or computational time. Our OMiMa system, to our knowledge, is the only motif finding tool that incorporates automatic selection of the best model. OMiMa is freely available at [1]. Conclusion Our optimized mixture of Markov models represents an alternative to the existing methods for modeling dependent structures within a biological motif. Our model is conceptually simple and effective, and can improve prediction accuracy and/or computational speed over other leading methods. PMID:16749929

Exploring Academic Achievement in Males Trained in Self-Assessment Skills

ERIC Educational Resources Information Center

McDonald, Betty

2009-01-01

This paper examines academic achievement of males following formal training in self-assessment. It adds to current literature by proposing a tried-and-tested method of improving academic achievement in males at a time when they appear to be marginalised. The sample comprised 515 participants (233 males), representing 25.2% of that high school…

Efficiency in Assessment: Can Trained Student Interns Rate Essays as Well as Faculty Members?

ERIC Educational Resources Information Center

Cole, Tracy L.; Cochran, Loretta F.; Troboy, L. Kim; Roach, David W.

2012-01-01

What are the most efficient and effective methods in measuring outcomes for assurance of learning in higher education? This study examines the merits of outsourcing part of the assessment workload by comparing ratings completed by trained student interns to ratings completed by faculty. Faculty evaluation of students' written work samples provides…

Influence of vine training and sunlight exposure on the 3-alkyl-2-methoxypyrazines content in musts and wines from the Vitis vinifera variety cabernet sauvignon.

PubMed

Sala, Cristina; Busto, Olga; Guasch, Josep; Zamora, Fernando

2004-06-02

The influence of vine training and sunlight exposure on the 3-alkyl-2-methoxypyrazines contents in musts and wines was studied by means of two previously reported methods based on headspace solid-phase micro-extraction. Experimental samples were monitored throughout grape ripening and wine making. 3-Isobutyl-2-methoxypyrazine, 3-sec-butyl-2-methoxypyrazine and 3-isopropyl-2-methoxypyrazine were identified. The 3-isobutyl-2-methoxypyrazine content decreased throughout grape ripening in all of the sample types studied. After 1 day of maceration with the skins, there was an increase, but after racking, no further increase was observed. No significant differences between samples were found during grape ripening. Wines from goblet-trained vines, however, contained significantly less 3-isobutyl-2-methoxypyrazine. Clusters protected from sunlight since the beginning of the veraison resulted in wines with a significantly lower content of this compound than the control samples.

The effect of sampling techniques used in the multiconfigurational Ehrenfest method

NASA Astrophysics Data System (ADS)

Symonds, C.; Kattirtzi, J. A.; Shalashilin, D. V.

2018-05-01

In this paper, we compare and contrast basis set sampling techniques recently developed for use in the ab initio multiple cloning method, a direct dynamics extension to the multiconfigurational Ehrenfest approach, used recently for the quantum simulation of ultrafast photochemistry. We demonstrate that simultaneous use of basis set cloning and basis function trains can produce results which are converged to the exact quantum result. To demonstrate this, we employ these sampling methods in simulations of quantum dynamics in the spin boson model with a broad range of parameters and compare the results to accurate benchmarks.

The effect of sampling techniques used in the multiconfigurational Ehrenfest method.

PubMed

Symonds, C; Kattirtzi, J A; Shalashilin, D V

2018-05-14

In this paper, we compare and contrast basis set sampling techniques recently developed for use in the ab initio multiple cloning method, a direct dynamics extension to the multiconfigurational Ehrenfest approach, used recently for the quantum simulation of ultrafast photochemistry. We demonstrate that simultaneous use of basis set cloning and basis function trains can produce results which are converged to the exact quantum result. To demonstrate this, we employ these sampling methods in simulations of quantum dynamics in the spin boson model with a broad range of parameters and compare the results to accurate benchmarks.

Semi-supervised learning for photometric supernova classification

NASA Astrophysics Data System (ADS)

Richards, Joseph W.; Homrighausen, Darren; Freeman, Peter E.; Schafer, Chad M.; Poznanski, Dovi

2012-01-01

We present a semi-supervised method for photometric supernova typing. Our approach is to first use the non-linear dimension reduction technique diffusion map to detect structure in a data base of supernova light curves and subsequently employ random forest classification on a spectroscopically confirmed training set to learn a model that can predict the type of each newly observed supernova. We demonstrate that this is an effective method for supernova typing. As supernova numbers increase, our semi-supervised method efficiently utilizes this information to improve classification, a property not enjoyed by template-based methods. Applied to supernova data simulated by Kessler et al. to mimic those of the Dark Energy Survey, our methods achieve (cross-validated) 95 per cent Type Ia purity and 87 per cent Type Ia efficiency on the spectroscopic sample, but only 50 per cent Type Ia purity and 50 per cent efficiency on the photometric sample due to their spectroscopic follow-up strategy. To improve the performance on the photometric sample, we search for better spectroscopic follow-up procedures by studying the sensitivity of our machine-learned supernova classification on the specific strategy used to obtain training sets. With a fixed amount of spectroscopic follow-up time, we find that, despite collecting data on a smaller number of supernovae, deeper magnitude-limited spectroscopic surveys are better for producing training sets. For supernova Ia (II-P) typing, we obtain a 44 per cent (1 per cent) increase in purity to 72 per cent (87 per cent) and 30 per cent (162 per cent) increase in efficiency to 65 per cent (84 per cent) of the sample using a 25th (24.5th) magnitude-limited survey instead of the shallower spectroscopic sample used in the original simulations. When redshift information is available, we incorporate it into our analysis using a novel method of altering the diffusion map representation of the supernovae. Incorporating host redshifts leads to a 5 per cent improvement in Type Ia purity and 13 per cent improvement in Type Ia efficiency. A web service for the supernova classification method used in this paper can be found at .

Sampling and data handling methods for inhalable particulate sampling. Final report nov 78-dec 80

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

Smith, W.B.; Cushing, K.M.; Johnson, J.W.

1982-05-01

The report reviews the objectives of a research program on sampling and measuring particles in the inhalable particulate (IP) size range in emissions from stationary sources, and describes methods and equipment required. A computer technique was developed to analyze data on particle-size distributions of samples taken with cascade impactors from industrial process streams. Research in sampling systems for IP matter included concepts for maintaining isokinetic sampling conditions, necessary for representative sampling of the larger particles, while flowrates in the particle-sizing device were constant. Laboratory studies were conducted to develop suitable IP sampling systems with overall cut diameters of 15 micrometersmore » and conforming to a specified collection efficiency curve. Collection efficiencies were similarly measured for a horizontal elutriator. Design parameters were calculated for horizontal elutriators to be used with impactors, the EPA SASS train, and the EPA FAS train. Two cyclone systems were designed and evaluated. Tests on an Andersen Size Selective Inlet, a 15-micrometer precollector for high-volume samplers, showed its performance to be with the proposed limits for IP samplers. A stack sampling system was designed in which the aerosol is diluted in flow patterns and with mixing times simulating those in stack plumes.« less

The Influence of Cognitive Training on Older Adults’ Recall for Short Stories

PubMed Central

Sisco, S. M.; Marsiske, M; Gross, A. L.; Rebok, G. W.

2013-01-01

Objectives This paper investigated how a multi-component memory intervention affected memory for prose. We compared verbatim and paraphrased recall for short stories immediately and 1-, 2-, 3- and 5-years post-intervention in the ACTIVE (Advanced Cognitive Training for Independent and Vital Elderly) sample. Methods We studied 1,912 ACTIVE participants aged 65–91. Participants were randomized into one of three training arms (Memory, Reasoning, Speed of Processing) or a no-contact Control group; about half of the trained participants received additional booster training 1 and 3 years post-intervention. Results Memory-trained participants showed higher verbatim recall than non-memory-trained participants. Booster memory training led to higher verbatim recall. Memory training effects were evident immediately following training and not after one year following training. Discussion Results suggest that multi-factorial memory training can improve verbatim recall for prose, but the effect does not last without continued intervention. PMID:24385636

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

PubMed

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

2016-02-01

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

Intelligent diagnosis of short hydraulic signal based on improved EEMD and SVM with few low-dimensional training samples

NASA Astrophysics Data System (ADS)

Zhang, Meijun; Tang, Jian; Zhang, Xiaoming; Zhang, Jiaojiao

2016-03-01

The high accurate classification ability of an intelligent diagnosis method often needs a large amount of training samples with high-dimensional eigenvectors, however the characteristics of the signal need to be extracted accurately. Although the existing EMD(empirical mode decomposition) and EEMD(ensemble empirical mode decomposition) are suitable for processing non-stationary and non-linear signals, but when a short signal, such as a hydraulic impact signal, is concerned, their decomposition accuracy become very poor. An improve EEMD is proposed specifically for short hydraulic impact signals. The improvements of this new EEMD are mainly reflected in four aspects, including self-adaptive de-noising based on EEMD, signal extension based on SVM(support vector machine), extreme center fitting based on cubic spline interpolation, and pseudo component exclusion based on cross-correlation analysis. After the energy eigenvector is extracted from the result of the improved EEMD, the fault pattern recognition based on SVM with small amount of low-dimensional training samples is studied. At last, the diagnosis ability of improved EEMD+SVM method is compared with the EEMD+SVM and EMD+SVM methods, and its diagnosis accuracy is distinctly higher than the other two methods no matter the dimension of the eigenvectors are low or high. The improved EEMD is very propitious for the decomposition of short signal, such as hydraulic impact signal, and its combination with SVM has high ability for the diagnosis of hydraulic impact faults.

Incremental concept learning with few training examples and hierarchical classification

NASA Astrophysics Data System (ADS)

Bouma, Henri; Eendebak, Pieter T.; Schutte, Klamer; Azzopardi, George; Burghouts, Gertjan J.

2015-10-01

Object recognition and localization are important to automatically interpret video and allow better querying on its content. We propose a method for object localization that learns incrementally and addresses four key aspects. Firstly, we show that for certain applications, recognition is feasible with only a few training samples. Secondly, we show that novel objects can be added incrementally without retraining existing objects, which is important for fast interaction. Thirdly, we show that an unbalanced number of positive training samples leads to biased classifier scores that can be corrected by modifying weights. Fourthly, we show that the detector performance can deteriorate due to hard-negative mining for similar or closely related classes (e.g., for Barbie and dress, because the doll is wearing a dress). This can be solved by our hierarchical classification. We introduce a new dataset, which we call TOSO, and use it to demonstrate the effectiveness of the proposed method for the localization and recognition of multiple objects in images.

Efficient Detection of Copy Number Mutations in PMS2 Exons with a Close Homolog.

PubMed

Herman, Daniel S; Smith, Christina; Liu, Chang; Vaughn, Cecily P; Palaniappan, Selvi; Pritchard, Colin C; Shirts, Brian H

2018-07-01

Detection of 3' PMS2 copy-number mutations that cause Lynch syndrome is difficult because of highly homologous pseudogenes. To improve the accuracy and efficiency of clinical screening for these mutations, we developed a new method to analyze standard capture-based, next-generation sequencing data to identify deletions and duplications in PMS2 exons 9 to 15. The approach captures sequences using PMS2 targets, maps sequences randomly among regions with equal mapping quality, counts reads aligned to homologous exons and introns, and flags read count ratios outside of empirically derived reference ranges. The method was trained on 1352 samples, including 8 known positives, and tested on 719 samples, including 17 known positives. Clinical implementation of the first version of this method detected new mutations in the training (N = 7) and test (N = 2) sets that had not been identified by our initial clinical testing pipeline. The described final method showed complete sensitivity in both sample sets and false-positive rates of 5% (training) and 7% (test), dramatically decreasing the number of cases needing additional mutation evaluation. This approach leveraged the differences between gene and pseudogene to distinguish between PMS2 and PMS2CL copy-number mutations. These methods enable efficient and sensitive Lynch syndrome screening for 3' PMS2 copy-number mutations and may be applied similarly to other genomic regions with highly homologous pseudogenes. Copyright © 2018 American Society for Investigative Pathology and the Association for Molecular Pathology. Published by Elsevier Inc. All rights reserved.

Effectiveness of training in organizations: a meta-analysis of design and evaluation features.

PubMed

Arthur, Winfred; Bennett, Winston; Edens, Pamela S; Bell, Suzanne T

2003-04-01

The authors used meta-analytic procedures to examine the relationship between specified training design and evaluation features and the effectiveness of training in organizations. Results of the meta-analysis revealed training effectiveness sample-weighted mean ds of 0.60 (k = 15, N = 936) for reaction criteria, 0.63 (k = 234, N = 15,014) for learning criteria, 0.62 (k = 122, N = 15,627) for behavioral criteria, and 0.62 (k = 26, N = 1,748) for results criteria. These results suggest a medium to large effect size for organizational training. In addition, the training method used, the skill or task characteristic trained, and the choice of evaluation criteria were related to the effectiveness of training programs. Limitations of the study along with suggestions for future research are discussed.

Lessons learned from implementing a wet laboratory molecular training workshop for beach water quality monitoring.

PubMed

Verhougstraete, Marc Paul; Brothers, Sydney; Litaker, Wayne; Blackwood, A Denene; Noble, Rachel

2015-01-01

Rapid molecular testing methods are poised to replace many of the conventional, culture-based tests currently used in fields such as water quality and food science. Rapid qPCR methods have the benefit of being faster than conventional methods and provide a means to more accurately protect public health. However, many scientists and technicians in water and food quality microbiology laboratories have limited experience using these molecular tests. To ensure that practitioners can use and implement qPCR techniques successfully, we developed a week long workshop to provide hands-on training and exposure to rapid molecular methods for water quality management. This workshop trained academic professors, government employees, private industry representatives, and graduate students in rapid qPCR methods for monitoring recreational water quality. Attendees were immersed in these new methods with hands-on laboratory sessions, lectures, and one-on-one training. Upon completion, the attendees gained sufficient knowledge and practice to teach and share these new molecular techniques with colleagues at their respective laboratories. Key findings from this workshop demonstrated: 1) participants with no prior experience could be effectively trained to conduct highly repeatable qPCR analysis in one week; 2) participants with different desirable outcomes required exposure to a range of different platforms and sample processing approaches; and 3) the collaborative interaction amongst newly trained practitioners, workshop leaders, and members of the water quality community helped foster a cohesive cohort of individuals which can advocate powerful cohort for proper implementation of molecular methods.

Lessons Learned from Implementing a Wet Laboratory Molecular Training Workshop for Beach Water Quality Monitoring

PubMed Central

Verhougstraete, Marc Paul; Brothers, Sydney; Litaker, Wayne; Blackwood, A. Denene; Noble, Rachel

2015-01-01

Rapid molecular testing methods are poised to replace many of the conventional, culture-based tests currently used in fields such as water quality and food science. Rapid qPCR methods have the benefit of being faster than conventional methods and provide a means to more accurately protect public health. However, many scientists and technicians in water and food quality microbiology laboratories have limited experience using these molecular tests. To ensure that practitioners can use and implement qPCR techniques successfully, we developed a week long workshop to provide hands-on training and exposure to rapid molecular methods for water quality management. This workshop trained academic professors, government employees, private industry representatives, and graduate students in rapid qPCR methods for monitoring recreational water quality. Attendees were immersed in these new methods with hands-on laboratory sessions, lectures, and one-on-one training. Upon completion, the attendees gained sufficient knowledge and practice to teach and share these new molecular techniques with colleagues at their respective laboratories. Key findings from this workshop demonstrated: 1) participants with no prior experience could be effectively trained to conduct highly repeatable qPCR analysis in one week; 2) participants with different desirable outcomes required exposure to a range of different platforms and sample processing approaches; and 3) the collaborative interaction amongst newly trained practitioners, workshop leaders, and members of the water quality community helped foster a cohesive cohort of individuals which can advocate powerful cohort for proper implementation of molecular methods. PMID:25822486

The Successful Diagnosis and Typing of Systemic Amyloidosis Using A Microwave-Assisted Filter-Aided Fast Sample Preparation Method and LC/MS/MS Analysis

PubMed Central

Zou, Lili; Shen, Kaini; Zhong, Dingrong; Zhou, Daobin; Sun, Wei; Li, Jian

2015-01-01

Laser microdissection followed by mass spectrometry has been successfully used for amyloid typing. However, sample contamination can interfere with proteomic analysis, and overnight digestion limits the analytical throughput. Moreover, current quantitative analysis methods are based on the spectrum count, which ignores differences in protein length and may lead to misdiagnoses. Here, we developed a microwave-assisted filter-aided sample preparation (maFASP) method that can efficiently remove contaminants with a 10-kDa cutoff ultrafiltration unit and can accelerate the digestion process with the assistance of a microwave. Additionally, two parameters (P- and D-scores) based on the exponentially modified protein abundance index were developed to define the existence of amyloid deposits and those causative proteins with the greatest abundance. Using our protocol, twenty cases of systemic amyloidosis that were well-typed according to clinical diagnostic standards (training group) and another twenty-four cases without subtype diagnoses (validation group) were analyzed. Using this approach, sample preparation could be completed within four hours. We successfully subtyped 100% of the cases in the training group, and the diagnostic success rate in the validation group was 91.7%. This maFASP-aided proteomic protocol represents an efficient approach for amyloid diagnosis and subtyping, particularly for serum-contaminated samples. PMID:25984759

Nanophotonic particle simulation and inverse design using artificial neural networks.

PubMed

Peurifoy, John; Shen, Yichen; Jing, Li; Yang, Yi; Cano-Renteria, Fidel; DeLacy, Brendan G; Joannopoulos, John D; Tegmark, Max; Soljačić, Marin

2018-06-01

We propose a method to use artificial neural networks to approximate light scattering by multilayer nanoparticles. We find that the network needs to be trained on only a small sampling of the data to approximate the simulation to high precision. Once the neural network is trained, it can simulate such optical processes orders of magnitude faster than conventional simulations. Furthermore, the trained neural network can be used to solve nanophotonic inverse design problems by using back propagation, where the gradient is analytical, not numerical.

76 FR 28786 - Proposed Data Collections Submitted for Public Comment and Recommendations

Federal Register 2010, 2011, 2012, 2013, 2014

2011-05-18

.... The sample size is based on recommendations related to qualitative interview methods and the research... than 10 employees (CPWR, 2007), and this establishment size experiences the highest fatality rate... out occupational safety and health training. This interview will be administered to a sample of...

Bamboo Classification Using WorldView-2 Imagery of Giant Panda Habitat in a Large Shaded Area in Wolong, Sichuan Province, China

PubMed Central

Tang, Yunwei; Jing, Linhai; Li, Hui; Liu, Qingjie; Yan, Qi; Li, Xiuxia

2016-01-01

This study explores the ability of WorldView-2 (WV-2) imagery for bamboo mapping in a mountainous region in Sichuan Province, China. A large area of this place is covered by shadows in the image, and only a few sampled points derived were useful. In order to identify bamboos based on sparse training data, the sample size was expanded according to the reflectance of multispectral bands selected using the principal component analysis (PCA). Then, class separability based on the training data was calculated using a feature space optimization method to select the features for classification. Four regular object-based classification methods were applied based on both sets of training data. The results show that the k-nearest neighbor (k-NN) method produced the greatest accuracy. A geostatistically-weighted k-NN classifier, accounting for the spatial correlation between classes, was then applied to further increase the accuracy. It achieved 82.65% and 93.10% of the producer’s and user’s accuracies respectively for the bamboo class. The canopy densities were estimated to explain the result. This study demonstrates that the WV-2 image can be used to identify small patches of understory bamboos given limited known samples, and the resulting bamboo distribution facilitates the assessments of the habitats of giant pandas. PMID:27879661

Simulation techniques for estimating error in the classification of normal patterns

NASA Technical Reports Server (NTRS)

Whitsitt, S. J.; Landgrebe, D. A.

1974-01-01

Methods of efficiently generating and classifying samples with specified multivariate normal distributions were discussed. Conservative confidence tables for sample sizes are given for selective sampling. Simulation results are compared with classified training data. Techniques for comparing error and separability measure for two normal patterns are investigated and used to display the relationship between the error and the Chernoff bound.

A Survey of Hospice Volunteer Coordinators: Training Methods and Objectives of Current Hospice Volunteer Training Programs.

PubMed

Brock, Cara M; Herndon, Christopher M

2017-06-01

Currently more than 5800 hospice organizations operate in the United States. 1 Hospice organizations are required by the Centers for Medicare and Medicaid Services (CMS) to use volunteers for services provided to patients. 2 Although CMS regulates the amount of hours hospice volunteers should provide, there are currently no national requirements for objectives of training. 3 The purpose of this study was to gather information from a sample of hospices regarding volunteer coordinator background, current training for volunteers, importance of training objectives, and any comments regarding additional objectives. Representative state hospice organizations were contacted by e-mail requesting their participation and distribution of the survey throughout their member hospices. The survey asked demographical questions, along with ratings of training components based on perceived level of importance and time spent on each objective. A total of 90 surveys were received, and the response rate was undeterminable. Results showed the majority of hospices were nonprofit, had less than 100 currently trained volunteers, and maintained an average daily patient census of less than 50. Questions regarding training programs indicated that most use live lecture methods of approximately 19 hours or less in duration. Overall, responding hospice organizations agreed that all objectives surveyed were important in training volunteers. The small number of respondents to this survey makes generalization nationwide difficult, however it is a strong starting point for the development of further surveys on hospice volunteer training and achieving a standardized set of training objectives and delivery methods.

Crowdsourcing for translational research: analysis of biomarker expression using cancer microarrays

PubMed Central

Lawson, Jonathan; Robinson-Vyas, Rupesh J; McQuillan, Janette P; Paterson, Andy; Christie, Sarah; Kidza-Griffiths, Matthew; McDuffus, Leigh-Anne; Moutasim, Karwan A; Shaw, Emily C; Kiltie, Anne E; Howat, William J; Hanby, Andrew M; Thomas, Gareth J; Smittenaar, Peter

2017-01-01

Background: Academic pathology suffers from an acute and growing lack of workforce resource. This especially impacts on translational elements of clinical trials, which can require detailed analysis of thousands of tissue samples. We tested whether crowdsourcing – enlisting help from the public – is a sufficiently accurate method to score such samples. Methods: We developed a novel online interface to train and test lay participants on cancer detection and immunohistochemistry scoring in tissue microarrays. Lay participants initially performed cancer detection on lung cancer images stained for CD8, and we measured how extending a basic tutorial by annotated example images and feedback-based training affected cancer detection accuracy. We then applied this tutorial to additional cancer types and immunohistochemistry markers – bladder/ki67, lung/EGFR, and oesophageal/CD8 – to establish accuracy compared with experts. Using this optimised tutorial, we then tested lay participants' accuracy on immunohistochemistry scoring of lung/EGFR and bladder/p53 samples. Results: We observed that for cancer detection, annotated example images and feedback-based training both improved accuracy compared with a basic tutorial only. Using this optimised tutorial, we demonstrate highly accurate (>0.90 area under curve) detection of cancer in samples stained with nuclear, cytoplasmic and membrane cell markers. We also observed high Spearman correlations between lay participants and experts for immunohistochemistry scoring (0.91 (0.78, 0.96) and 0.97 (0.91, 0.99) for lung/EGFR and bladder/p53 samples, respectively). Conclusions: These results establish crowdsourcing as a promising method to screen large data sets for biomarkers in cancer pathology research across a range of cancers and immunohistochemical stains. PMID:27959886

Identifying presence of correlated errors in GRACE monthly harmonic coefficients using machine learning algorithms

NASA Astrophysics Data System (ADS)

Piretzidis, Dimitrios; Sra, Gurveer; Karantaidis, George; Sideris, Michael G.

2017-04-01

A new method for identifying correlated errors in Gravity Recovery and Climate Experiment (GRACE) monthly harmonic coefficients has been developed and tested. Correlated errors are present in the differences between monthly GRACE solutions, and can be suppressed using a de-correlation filter. In principle, the de-correlation filter should be implemented only on coefficient series with correlated errors to avoid losing useful geophysical information. In previous studies, two main methods of implementing the de-correlation filter have been utilized. In the first one, the de-correlation filter is implemented starting from a specific minimum order until the maximum order of the monthly solution examined. In the second one, the de-correlation filter is implemented only on specific coefficient series, the selection of which is based on statistical testing. The method proposed in the present study exploits the capabilities of supervised machine learning algorithms such as neural networks and support vector machines (SVMs). The pattern of correlated errors can be described by several numerical and geometric features of the harmonic coefficient series. The features of extreme cases of both correlated and uncorrelated coefficients are extracted and used for the training of the machine learning algorithms. The trained machine learning algorithms are later used to identify correlated errors and provide the probability of a coefficient series to be correlated. Regarding SVMs algorithms, an extensive study is performed with various kernel functions in order to find the optimal training model for prediction. The selection of the optimal training model is based on the classification accuracy of the trained SVM algorithm on the same samples used for training. Results show excellent performance of all algorithms with a classification accuracy of 97% - 100% on a pre-selected set of training samples, both in the validation stage of the training procedure and in the subsequent use of the trained algorithms to classify independent coefficients. This accuracy is also confirmed by the external validation of the trained algorithms using the hydrology model GLDAS NOAH. The proposed method meet the requirement of identifying and de-correlating only coefficients with correlated errors. Also, there is no need of applying statistical testing or other techniques that require prior de-correlation of the harmonic coefficients.

The Relationship between Self-Appraisal, Professional Training, and Diversity Awareness among Forensic Psychology Students: A Pilot Formative Evaluation

ERIC Educational Resources Information Center

Chandler, Donald S., Jr.; Chandler, Michele D.; Clark, Quelanda C.

2009-01-01

Currently, there is a growing need for formal training in forensic psychology. This pilot study examines the relational-behavior model (RBM) as a method of intrinsic motivational instruction, perceived academic competence, and program competency among a sample of forensic psychology students. In theory, the RBM suggests that self-appraisal,…

The Impact of Novice Counselors' Note-Taking Behavior on Recall and Judgment

ERIC Educational Resources Information Center

Lo, Chu-Ling; Wadsworth, John

2014-01-01

Purpose: To examine the effect of note-taking on novice counselors' recall and judgment of interview information in four situations: no notes, taking notes, taking notes and reviewing these notes, and reviewing notes taken by others. Method: The sample included 13 counselors-in-training recruited from a master's level training program in…

Effectiveness of Teaching Approaches of In-Service Training Courses for EFL Teachers in Jordanian Schools

ERIC Educational Resources Information Center

Al-Wreikat, Yazan Abdel Aziz Semreen; Bin Abdullah, Muhamad Kamarul Kabilan

2011-01-01

This paper focuses on the impact of the organization of teaching approaches on the effectiveness of training for Jordanian EFL teachers. The sample for this study is drawn from all government schools in the Hashemite Kingdom of Jordan. The study uses a mixed-method approach whereby findings are triangulated throughout (in interviews, observations,…

The Effects of Computerized and Traditional Ear Training Programs on Aural Skills of Elementary Students

ERIC Educational Resources Information Center

Kariuki, Patrick N.; Ross, Zachary R.

2017-01-01

The purpose of this study was to investigate the effects of computerized and traditional ear training methods on the aural skills abilities of elementary music students. The sample consisted of 20 students who were randomly assigned to either an experimental or control group. The experimental group was taught for five sessions using computerized…

The Importance of Training and Previous Contact in University Students' Opinion about Persons with Mental Disorder

ERIC Educational Resources Information Center

Barroso-Hurtado, Domingo; Mendo-Lázaro, Santiago

2016-01-01

Introduction: The present study analyzes differences in university students' opinions towards persons with mental disorder, as a function of whether they have had previous contact with them and whether they have received training about them. Method: The Opinions about Mental Illness Scale for Spanish population (OMI-S) was applied to a sample of…

The Effect of Asymmetrical Sample Training on Retention Functions for Hedonic Samples in Rats

ERIC Educational Resources Information Center

Simmons, Sabrina; Santi, Angelo

2012-01-01

Rats were trained in a symbolic delayed matching-to-sample task to discriminate sample stimuli that consisted of the presence of food or the absence of food. Asymmetrical sample training was provided in which one group was initially trained with only the food sample and the other group was initially trained with only the no-food sample. In…

Student Teachers' Emotional Teaching Experiences in Relation to Different Teaching Methods

ERIC Educational Resources Information Center

Timoštšuk, I.; Kikas, E.; Normak, M.

2016-01-01

The role of emotional experiences in teacher training is acknowledged, but the role of emotions during first experiences of classroom teaching has not been examined in large samples. This study examines the teaching methods used by student teachers in early teaching practice and the relationship between these methods and emotions experienced. We…

Routine programs of health care systems as an opportunity toward communication skills training for family physicians: A randomized field trial

PubMed Central

Zamani, Ahmad Reza; Motamedi, Narges; Farajzadegan, Ziba

2015-01-01

Background: To have high-quality primary health care services, an adequate doctor–patient communication is necessary. Because of time restrictions and limited budget in health system, an effective, feasible, and continuous training approach is important. The aim of this study is to assess the appropriateness of a communication skills training program simultaneously with routine programs of health care system. Materials and Methods: It was a randomized field trial in two health network settings during 2013. Twenty-eight family physicians through simple random sampling and 140 patients through convenience sampling participated as intervention and control group. The physicians in the intervention group (n = 14) attended six educational sessions, simultaneous organization meeting, with case discussion and peer education method. In both the groups, physicians completed communication skills knowledge and attitude questionnaires, and patients completed patient satisfaction of medical interview questionnaire at baseline, immediately after intervention, and four months postintervention. Physicians and health network administrators (stakeholders), completed a set of program evaluation forms. Descriptive statistics and Chi-square test, t-test, and repeated measure analysis of variance were used to analyze the data. Results: Use of routine program as a strategy of training was rated by stakeholders highly on “feasibility” (80.5%), “acceptability” (93.5%), “educational content and method appropriateness” (80.75%), and “ability to integrating in the health system programs” (approximate 60%). Significant improvements were found in physicians’ knowledge (P < 0.001), attitude (P < 0.001), and patients’ satisfaction (P = 0.002) in intervention group. Conclusions: Communication skills training program, simultaneous organization meeting was successfully implemented and well received by stakeholders, without considering extra time and manpower. Therefore it can be a valuable opportunity toward communication skills training. PMID:27462613

Discrimination of whisky brands and counterfeit identification by UV-Vis spectroscopy and multivariate data analysis.

PubMed

Martins, Angélica Rocha; Talhavini, Márcio; Vieira, Maurício Leite; Zacca, Jorge Jardim; Braga, Jez Willian Batista

2017-08-15

The discrimination of whisky brands and counterfeit identification were performed by UV-Vis spectroscopy combined with partial least squares for discriminant analysis (PLS-DA). In the proposed method all spectra were obtained with no sample preparation. The discrimination models were built with the employment of seven whisky brands: Red Label, Black Label, White Horse, Chivas Regal (12years), Ballantine's Finest, Old Parr and Natu Nobilis. The method was validated with an independent test set of authentic samples belonging to the seven selected brands and another eleven brands not included in the training samples. Furthermore, seventy-three counterfeit samples were also used to validate the method. Results showed correct classification rates for genuine and false samples over 98.6% and 93.1%, respectively, indicating that the method can be helpful for the forensic analysis of whisky samples. Copyright © 2017 Elsevier Ltd. All rights reserved.

Classification of weld defect based on information fusion technology for radiographic testing system

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

Jiang, Hongquan; Liang, Zeming, E-mail: heavenlzm@126.com; Gao, Jianmin

Improving the efficiency and accuracy of weld defect classification is an important technical problem in developing the radiographic testing system. This paper proposes a novel weld defect classification method based on information fusion technology, Dempster–Shafer evidence theory. First, to characterize weld defects and improve the accuracy of their classification, 11 weld defect features were defined based on the sub-pixel level edges of radiographic images, four of which are presented for the first time in this paper. Second, we applied information fusion technology to combine different features for weld defect classification, including a mass function defined based on the weld defectmore » feature information and the quartile-method-based calculation of standard weld defect class which is to solve a sample problem involving a limited number of training samples. A steam turbine weld defect classification case study is also presented herein to illustrate our technique. The results show that the proposed method can increase the correct classification rate with limited training samples and address the uncertainties associated with weld defect classification.« less

Classification of weld defect based on information fusion technology for radiographic testing system.

PubMed

Jiang, Hongquan; Liang, Zeming; Gao, Jianmin; Dang, Changying

2016-03-01

Improving the efficiency and accuracy of weld defect classification is an important technical problem in developing the radiographic testing system. This paper proposes a novel weld defect classification method based on information fusion technology, Dempster-Shafer evidence theory. First, to characterize weld defects and improve the accuracy of their classification, 11 weld defect features were defined based on the sub-pixel level edges of radiographic images, four of which are presented for the first time in this paper. Second, we applied information fusion technology to combine different features for weld defect classification, including a mass function defined based on the weld defect feature information and the quartile-method-based calculation of standard weld defect class which is to solve a sample problem involving a limited number of training samples. A steam turbine weld defect classification case study is also presented herein to illustrate our technique. The results show that the proposed method can increase the correct classification rate with limited training samples and address the uncertainties associated with weld defect classification.

Competitive Deep-Belief Networks for Underwater Acoustic Target Recognition

PubMed Central

Shen, Sheng; Yao, Xiaohui; Sheng, Meiping; Wang, Chen

2018-01-01

Underwater acoustic target recognition based on ship-radiated noise belongs to the small-sample-size recognition problems. A competitive deep-belief network is proposed to learn features with more discriminative information from labeled and unlabeled samples. The proposed model consists of four stages: (1) A standard restricted Boltzmann machine is pretrained using a large number of unlabeled data to initialize its parameters; (2) the hidden units are grouped according to categories, which provides an initial clustering model for competitive learning; (3) competitive training and back-propagation algorithms are used to update the parameters to accomplish the task of clustering; (4) by applying layer-wise training and supervised fine-tuning, a deep neural network is built to obtain features. Experimental results show that the proposed method can achieve classification accuracy of 90.89%, which is 8.95% higher than the accuracy obtained by the compared methods. In addition, the highest accuracy of our method is obtained with fewer features than other methods. PMID:29570642

Extracting information in spike time patterns with wavelets and information theory.

PubMed

Lopes-dos-Santos, Vítor; Panzeri, Stefano; Kayser, Christoph; Diamond, Mathew E; Quian Quiroga, Rodrigo

2015-02-01

We present a new method to assess the information carried by temporal patterns in spike trains. The method first performs a wavelet decomposition of the spike trains, then uses Shannon information to select a subset of coefficients carrying information, and finally assesses timing information in terms of decoding performance: the ability to identify the presented stimuli from spike train patterns. We show that the method allows: 1) a robust assessment of the information carried by spike time patterns even when this is distributed across multiple time scales and time points; 2) an effective denoising of the raster plots that improves the estimate of stimulus tuning of spike trains; and 3) an assessment of the information carried by temporally coordinated spikes across neurons. Using simulated data, we demonstrate that the Wavelet-Information (WI) method performs better and is more robust to spike time-jitter, background noise, and sample size than well-established approaches, such as principal component analysis, direct estimates of information from digitized spike trains, or a metric-based method. Furthermore, when applied to real spike trains from monkey auditory cortex and from rat barrel cortex, the WI method allows extracting larger amounts of spike timing information. Importantly, the fact that the WI method incorporates multiple time scales makes it robust to the choice of partly arbitrary parameters such as temporal resolution, response window length, number of response features considered, and the number of available trials. These results highlight the potential of the proposed method for accurate and objective assessments of how spike timing encodes information. Copyright © 2015 the American Physiological Society.

Reduced kernel recursive least squares algorithm for aero-engine degradation prediction

NASA Astrophysics Data System (ADS)

Zhou, Haowen; Huang, Jinquan; Lu, Feng

2017-10-01

Kernel adaptive filters (KAFs) generate a linear growing radial basis function (RBF) network with the number of training samples, thereby lacking sparseness. To deal with this drawback, traditional sparsification techniques select a subset of original training data based on a certain criterion to train the network and discard the redundant data directly. Although these methods curb the growth of the network effectively, it should be noted that information conveyed by these redundant samples is omitted, which may lead to accuracy degradation. In this paper, we present a novel online sparsification method which requires much less training time without sacrificing the accuracy performance. Specifically, a reduced kernel recursive least squares (RKRLS) algorithm is developed based on the reduced technique and the linear independency. Unlike conventional methods, our novel methodology employs these redundant data to update the coefficients of the existing network. Due to the effective utilization of the redundant data, the novel algorithm achieves a better accuracy performance, although the network size is significantly reduced. Experiments on time series prediction and online regression demonstrate that RKRLS algorithm requires much less computational consumption and maintains the satisfactory accuracy performance. Finally, we propose an enhanced multi-sensor prognostic model based on RKRLS and Hidden Markov Model (HMM) for remaining useful life (RUL) estimation. A case study in a turbofan degradation dataset is performed to evaluate the performance of the novel prognostic approach.

Illumination estimation via thin-plate spline interpolation.

PubMed

Shi, Lilong; Xiong, Weihua; Funt, Brian

2011-05-01

Thin-plate spline interpolation is used to interpolate the chromaticity of the color of the incident scene illumination across a training set of images. Given the image of a scene under unknown illumination, the chromaticity of the scene illumination can be found from the interpolated function. The resulting illumination-estimation method can be used to provide color constancy under changing illumination conditions and automatic white balancing for digital cameras. A thin-plate spline interpolates over a nonuniformly sampled input space, which in this case is a training set of image thumbnails and associated illumination chromaticities. To reduce the size of the training set, incremental k medians are applied. Tests on real images demonstrate that the thin-plate spline method can estimate the color of the incident illumination quite accurately, and the proposed training set pruning significantly decreases the computation.

Nearest neighbor density ratio estimation for large-scale applications in astronomy

NASA Astrophysics Data System (ADS)

Kremer, J.; Gieseke, F.; Steenstrup Pedersen, K.; Igel, C.

2015-09-01

In astronomical applications of machine learning, the distribution of objects used for building a model is often different from the distribution of the objects the model is later applied to. This is known as sample selection bias, which is a major challenge for statistical inference as one can no longer assume that the labeled training data are representative. To address this issue, one can re-weight the labeled training patterns to match the distribution of unlabeled data that are available already in the training phase. There are many examples in practice where this strategy yielded good results, but estimating the weights reliably from a finite sample is challenging. We consider an efficient nearest neighbor density ratio estimator that can exploit large samples to increase the accuracy of the weight estimates. To solve the problem of choosing the right neighborhood size, we propose to use cross-validation on a model selection criterion that is unbiased under covariate shift. The resulting algorithm is our method of choice for density ratio estimation when the feature space dimensionality is small and sample sizes are large. The approach is simple and, because of the model selection, robust. We empirically find that it is on a par with established kernel-based methods on relatively small regression benchmark datasets. However, when applied to large-scale photometric redshift estimation, our approach outperforms the state-of-the-art.

Crop identification and area estimation over large geographic areas using LANDSAT MSS data

NASA Technical Reports Server (NTRS)

Bauer, M. E. (Principal Investigator)

1977-01-01

The author has identified the following significant results. LANDSAT MSS data was adequate to accurately identify wheat in Kansas; corn and soybean estimates in Indiana were less accurate. Computer-aided analysis techniques were effectively used to extract crop identification information from LANDSAT data. Systematic sampling of entire counties made possible by computer classification methods resulted in very precise area estimates at county, district, and state levels. Training statistics were successfully extended from one county to other counties having similar crops and soils if the training areas sampled the total variation of the area to be classified.

Effectiveness of Stress Management Skill Training on the Depression, Anxiety and Stress Levels in Drug Addicts after Drug Withdrawal

PubMed Central

Habibi, Zahra; Tourani, Somayeh; Sadeghi, Hasan; Abolghasemi, Abbass

2013-01-01

Background Stressful life events may cause initiation of drug use among people. The main purpose of this study was to evaluate the effectiveness of stress management skill training on depression, anxiety and stress levels in drug addicts after withdrawal. Objectives The population included all drug addicts after withdrawal in 2012 in Alborz province. Materials and Methods The study was quasi-experimental with pretest-posttest design with a control group. Levels of emotional reactions (depression, anxiety and stress) in all referrals to a counseling center for drug withdrawal in 2012 using the Depression, Anxiety, Stress (DASS-21) questionnaire was assessed. The study population included drug addicts after withdrawal. The sampling method was available sampling and random assignment. Thirty people who had higher emotional reactions were randomly selected and divided into two test (n = 15) and control (n = 15) groups. For the test group, a stress management skill training course was held in twelve 90-minute sessions, but the control group received no intervention. The obtained data were analyzed using SPSS-19 software with analysis of covariance. Results The results showed that stress management skill training has a significant effect on reducing emotional reactions (P < 0.01). It was noted that after 2 months test group follow-up, stress management training has retained its effect. Conclusion Apparently, training addicts about life skills, particularly stress management seems to be a good idea. PMID:24971280

An empirical identification and categorisation of training best practices for ERP implementation projects

NASA Astrophysics Data System (ADS)

Esteves, Jose Manuel

2014-11-01

Although training is one of the most cited critical success factors in Enterprise Resource Planning (ERP) systems implementations, few empirical studies have attempted to examine the characteristics of management of the training process within ERP implementation projects. Based on the data gathered from a sample of 158 respondents across four stakeholder groups involved in ERP implementation projects, and using a mixed method design, we have assembled a derived set of training best practices. Results suggest that the categorised list of ERP training best practices can be used to better understand training activities in ERP implementation projects. Furthermore, the results reveal that the company size and location have an impact on the relevance of training best practices. This empirical study also highlights the need to investigate the role of informal workplace trainers in ERP training activities.

a Comparison of Two Strategies for Avoiding Negative Transfer in Domain Adaptation Based on Logistic Regression

NASA Astrophysics Data System (ADS)

Paul, A.; Vogt, K.; Rottensteiner, F.; Ostermann, J.; Heipke, C.

2018-05-01

In this paper we deal with the problem of measuring the similarity between training and tests datasets in the context of transfer learning (TL) for image classification. TL tries to transfer knowledge from a source domain, where labelled training samples are abundant but the data may follow a different distribution, to a target domain, where labelled training samples are scarce or even unavailable, assuming that the domains are related. Thus, the requirements w.r.t. the availability of labelled training samples in the target domain are reduced. In particular, if no labelled target data are available, it is inherently difficult to find a robust measure of relatedness between the source and target domains. This is of crucial importance for the performance of TL, because the knowledge transfer between unrelated data may lead to negative transfer, i.e. to a decrease of classification performance after transfer. We address the problem of measuring the relatedness between source and target datasets and investigate three different strategies to predict and, consequently, to avoid negative transfer in this paper. The first strategy is based on circular validation. The second strategy relies on the Maximum Mean Discrepancy (MMD) similarity metric, whereas the third one is an extension of MMD which incorporates the knowledge about the class labels in the source domain. Our method is evaluated using two different benchmark datasets. The experiments highlight the strengths and weaknesses of the investigated methods. We also show that it is possible to reduce the amount of negative transfer using these strategies for a TL method and to generate a consistent performance improvement over the whole dataset.

Distance Metric Learning via Iterated Support Vector Machines.

PubMed

Zuo, Wangmeng; Wang, Faqiang; Zhang, David; Lin, Liang; Huang, Yuchi; Meng, Deyu; Zhang, Lei

2017-07-11

Distance metric learning aims to learn from the given training data a valid distance metric, with which the similarity between data samples can be more effectively evaluated for classification. Metric learning is often formulated as a convex or nonconvex optimization problem, while most existing methods are based on customized optimizers and become inefficient for large scale problems. In this paper, we formulate metric learning as a kernel classification problem with the positive semi-definite constraint, and solve it by iterated training of support vector machines (SVMs). The new formulation is easy to implement and efficient in training with the off-the-shelf SVM solvers. Two novel metric learning models, namely Positive-semidefinite Constrained Metric Learning (PCML) and Nonnegative-coefficient Constrained Metric Learning (NCML), are developed. Both PCML and NCML can guarantee the global optimality of their solutions. Experiments are conducted on general classification, face verification and person re-identification to evaluate our methods. Compared with the state-of-the-art approaches, our methods can achieve comparable classification accuracy and are efficient in training.

An approach for evaluating the repeatability of rapid wetland assessment methods: The effects of training and experience

EPA Science Inventory

We sampled 92 wetlands from four different basins in the United States to quantify observer repeatability in rapid wetland condition assessment using the Delaware Rapid Assessment Protocol (DERAP). In the Inland Bays basin of Delaware, 58 wetland sites were sampled by multiple ob...

Collegiate Aviation Maintenance Training Programs Certified under 14CFR Part 147 that Are Members of the Aviation Technician Education Council

ERIC Educational Resources Information Center

Hunt, Terry Lile

2010-01-01

Scope and method of study: The purpose of this study was to construct a descriptive analysis of aviation maintenance training programs that confer the Bachelor of Science degree and who are members of the Aviation Technician Education Council. The sample was comprised of the 11 educational programs within the population that met these criteria.…

Training of polyp staging systems using mixed imaging modalities.

PubMed

Wimmer, Georg; Gadermayr, Michael; Kwitt, Roland; Häfner, Michael; Tamaki, Toru; Yoshida, Shigeto; Tanaka, Shinji; Merhof, Dorit; Uhl, Andreas

2018-05-04

In medical image data sets, the number of images is usually quite small. The small number of training samples does not allow to properly train classifiers which leads to massive overfitting to the training data. In this work, we investigate whether increasing the number of training samples by merging datasets from different imaging modalities can be effectively applied to improve predictive performance. Further, we investigate if the extracted features from the employed image representations differ between different imaging modalities and if domain adaption helps to overcome these differences. We employ twelve feature extraction methods to differentiate between non-neoplastic and neoplastic lesions. Experiments are performed using four different classifier training strategies, each with a different combination of training data. The specifically designed setup for these experiments enables a fair comparison between the four training strategies. Combining high definition with high magnification training data and chromoscopic with non-chromoscopic training data partly improved the results. The usage of domain adaptation has only a small effect on the results compared to just using non-adapted training data. Merging datasets from different imaging modalities turned out to be partially beneficial for the case of combining high definition endoscopic data with high magnification endoscopic data and for combining chromoscopic with non-chromoscopic data. NBI and chromoendoscopy on the other hand are mostly too different with respect to the extracted features to combine images of these two modalities for classifier training. Copyright © 2018 Elsevier Ltd. All rights reserved.

Community‐Based Participatory Research Skills and Training Needs in a Sample of Academic Researchers from a Clinical and Translational Science Center in the Northeast

PubMed Central

DiGirolamo, Ann; Geller, Alan C.; Tendulkar, Shalini A.; Patil, Pratima; Hacker, Karen

2012-01-01

Abstract Purpose: To determine the community‐based participatory research (CBPR) training interests and needs of researchers interested in CBPR to inform efforts to build infrastructure for conducting community‐engaged research. Method: A 20‐item survey was completed by 127 academic health researchers at Harvard Medical School, Harvard School of Public Health, and Harvard affiliated hospitals. Results: Slightly more than half of the participants reported current or prior experience with CBPR (58 %). Across all levels of academic involvement, approximately half of the participants with CBPR experience reported lacking skills in research methods and dissemination, with even fewer reporting skills in training of community partners. Regardless of prior CBPR experience, about half of the respondents reported having training needs in funding, partnership development, evaluation, and dissemination of CBPR projects. Among those with CBPR experience, more than one‐third of the participants wanted a mentor in CBPR; however only 19 % were willing to act as a mentor. Conclusions: Despite having experience with CBPR, many respondents did not have the comprehensive package of CBPR skills, reporting a need for training in a variety of CBPR skill sets. Further, the apparent mismatch between the need for mentors and availability in this sample suggests an important area for development. Clin Trans Sci 2012; Volume #: 1–5 PMID:22686211

Using the Project Method in Distributive Education. Teacher's Manual.

ERIC Educational Resources Information Center

Maletta, Edwin

The document explains how to integrate the project training methods into a distributive education curriculum for grades 10 or 11. The purpose of this teacher's manual is to give an overall picture of the project method in use. Ten sample projects are included which could apply to any distributive education student concentrating on the major areas…

Discrimination of Oil Slicks and Lookalikes in Polarimetric SAR Images Using CNN.

PubMed

Guo, Hao; Wu, Danni; An, Jubai

2017-08-09

Oil slicks and lookalikes (e.g., plant oil and oil emulsion) all appear as dark areas in polarimetric Synthetic Aperture Radar (SAR) images and are highly heterogeneous, so it is very difficult to use a single feature that can allow classification of dark objects in polarimetric SAR images as oil slicks or lookalikes. We established multi-feature fusion to support the discrimination of oil slicks and lookalikes. In the paper, simple discrimination analysis is used to rationalize a preferred features subset. The features analyzed include entropy, alpha, and Single-bounce Eigenvalue Relative Difference (SERD) in the C-band polarimetric mode. We also propose a novel SAR image discrimination method for oil slicks and lookalikes based on Convolutional Neural Network (CNN). The regions of interest are selected as the training and testing samples for CNN on the three kinds of polarimetric feature images. The proposed method is applied to a training data set of 5400 samples, including 1800 crude oil, 1800 plant oil, and 1800 oil emulsion samples. In the end, the effectiveness of the method is demonstrated through the analysis of some experimental results. The classification accuracy obtained using 900 samples of test data is 91.33%. It is here observed that the proposed method not only can accurately identify the dark spots on SAR images but also verify the ability of the proposed algorithm to classify unstructured features.

Discrimination of Oil Slicks and Lookalikes in Polarimetric SAR Images Using CNN

PubMed Central

An, Jubai

2017-01-01

Oil slicks and lookalikes (e.g., plant oil and oil emulsion) all appear as dark areas in polarimetric Synthetic Aperture Radar (SAR) images and are highly heterogeneous, so it is very difficult to use a single feature that can allow classification of dark objects in polarimetric SAR images as oil slicks or lookalikes. We established multi-feature fusion to support the discrimination of oil slicks and lookalikes. In the paper, simple discrimination analysis is used to rationalize a preferred features subset. The features analyzed include entropy, alpha, and Single-bounce Eigenvalue Relative Difference (SERD) in the C-band polarimetric mode. We also propose a novel SAR image discrimination method for oil slicks and lookalikes based on Convolutional Neural Network (CNN). The regions of interest are selected as the training and testing samples for CNN on the three kinds of polarimetric feature images. The proposed method is applied to a training data set of 5400 samples, including 1800 crude oil, 1800 plant oil, and 1800 oil emulsion samples. In the end, the effectiveness of the method is demonstrated through the analysis of some experimental results. The classification accuracy obtained using 900 samples of test data is 91.33%. It is here observed that the proposed method not only can accurately identify the dark spots on SAR images but also verify the ability of the proposed algorithm to classify unstructured features. PMID:28792477

Active Learning to Overcome Sample Selection Bias: Application to Photometric Variable Star Classification

NASA Astrophysics Data System (ADS)

Richards, Joseph W.; Starr, Dan L.; Brink, Henrik; Miller, Adam A.; Bloom, Joshua S.; Butler, Nathaniel R.; James, J. Berian; Long, James P.; Rice, John

2012-01-01

Despite the great promise of machine-learning algorithms to classify and predict astrophysical parameters for the vast numbers of astrophysical sources and transients observed in large-scale surveys, the peculiarities of the training data often manifest as strongly biased predictions on the data of interest. Typically, training sets are derived from historical surveys of brighter, more nearby objects than those from more extensive, deeper surveys (testing data). This sample selection bias can cause catastrophic errors in predictions on the testing data because (1) standard assumptions for machine-learned model selection procedures break down and (2) dense regions of testing space might be completely devoid of training data. We explore possible remedies to sample selection bias, including importance weighting, co-training, and active learning (AL). We argue that AL—where the data whose inclusion in the training set would most improve predictions on the testing set are queried for manual follow-up—is an effective approach and is appropriate for many astronomical applications. For a variable star classification problem on a well-studied set of stars from Hipparcos and Optical Gravitational Lensing Experiment, AL is the optimal method in terms of error rate on the testing data, beating the off-the-shelf classifier by 3.4% and the other proposed methods by at least 3.0%. To aid with manual labeling of variable stars, we developed a Web interface which allows for easy light curve visualization and querying of external databases. Finally, we apply AL to classify variable stars in the All Sky Automated Survey, finding dramatic improvement in our agreement with the ASAS Catalog of Variable Stars, from 65.5% to 79.5%, and a significant increase in the classifier's average confidence for the testing set, from 14.6% to 42.9%, after a few AL iterations.

A Novel Method to Detect Early Colorectal Cancer Based on Chromosome Copy Number Variation in Plasma.

PubMed

Xu, Jun-Feng; Kang, Qian; Ma, Xing-Yong; Pan, Yuan-Ming; Yang, Lang; Jin, Peng; Wang, Xin; Li, Chen-Guang; Chen, Xiao-Chen; Wu, Chao; Jiao, Shao-Zhuo; Sheng, Jian-Qiu

2018-01-01

Colonoscopy screening has been accepted broadly to evaluate the risk and incidence of colorectal cancer (CRC) during health examination in outpatients. However, the intrusiveness, complexity and discomfort of colonoscopy may limit its application and the compliance of patients. Thus, more reliable and convenient diagnostic methods are necessary for CRC screening. Genome instability, especially copy-number variation (CNV), is a hallmark of cancer and has been proved to have potential in clinical application. We determined the diagnostic potential of chromosomal CNV at the arm level by whole-genome sequencing of CRC plasma samples (n = 32) and healthy controls (n = 38). Arm level CNV was determined and the consistence of arm-level CNV between plasma and tissue was further analyzed. Two methods including regular z score and trained Support Vector Machine (SVM) classifier were applied for detection of colorectal cancer. In plasma samples of CRC patients, the most frequent deletions were detected on chromosomes 6, 8p, 14q and 1p, and the most frequent amplifications occurred on chromosome 19, 5, 2, 9p and 20p. These arm-level alterations detected in plasma were also observed in tumor tissues. We showed that the specificity of regular z score analysis for the detection of colorectal cancer was 86.8% (33/38), whereas its sensitivity was only 56.3% (18/32). Applying a trained SVM classifier (n = 40 in trained group) as the standard to detect colorectal cancer relevance ratio in the test samples (n = 30), a sensitivity of 91.7% (11/12) and a specificity 88.9% (16/18) were finally reached. Furthermore, all five early CRC patients in stages I and II were successfully detected. Trained SVM classifier based on arm-level CNVs can be used as a promising method to screen early-stage CRC. © 2018 The Author(s). Published by S. Karger AG, Basel.

The Great Acting Teachers and Their Methods.

ERIC Educational Resources Information Center

Brestoff, Richard

This book explores the acting theories and teaching methods of great teachers of acting--among them, the Europeans Stanislavski, Meyerhold, Brecht, and Grotowski; the Japanese Suzuki (who trained in Europe); and the contemporary Americans, Stella Adler, Lee Strasberg, and Sanford Meisner. Each chapter of the book includes a sample class, which…

Involving junior doctors in medical article publishing: is it an effective method of teaching?

PubMed

Oyibo, Samson O

2017-01-01

Having peer-reviewed articles published in medical journals is important for career progression in many medical specialties. Despite this, only a minority of junior doctors have the skills in the area of medical article publishing. The aim of this study was to assess junior doctors' views concerning being involved in medical article publishing and whether they perceive involvement as an effective method of teaching. A cross-sectional survey was administered to a convenience sample of doctors who had been involved in medical article publishing. Questions concerned training and involvement in publishing as junior doctors, effects on education and training, is it an effective method of teaching and should publishing be part of their education and training program. Questions used the 5-point Likert scale. Of the 39 doctors, 37 (94.9%) doctors responded. Only one-third of respondents agreed that they had adequate training or involvement in medical article publishing during their undergraduate medical training. Many (78.4%) agreed that it was difficult to get published as a junior doctor. Publishing as a junior doctor improved knowledge about publishing, understanding of the topic and interest in the field of study for 92, 92 and 73% of respondents, respectively. Many (89%) agreed that publishing made them eager to publish more. Most (76%) agreed that it was likely to encourage interest in a postgraduate career in that field of study. A majority (92%) felt that involvement in medical article publishing is an effective method of teaching and it should be a part of the junior doctors' education and training program. Junior doctors feel that involvement in medical article publishing contributes to learning and education and is an effective method of teaching. This supports the need to incorporate such training into the junior doctors' education and training program.

Rapid and Portable Methods for Identification of Bacterially Influenced Calcite: Application of Laser-Induced Breakdown Spectroscopy and AOTF Reflectance Spectroscopy, Fort Stanton Cave, New Mexico

NASA Astrophysics Data System (ADS)

McMillan, N. J.; Chavez, A.; Chanover, N.; Voelz, D.; Uckert, K.; Tawalbeh, R.; Gariano, J.; Dragulin, I.; Xiao, X.; Hull, R.

2014-12-01

Rapid, in-situ methods for identification of biologic and non-biologic mineral precipitation sites permit mapping of biological hot spots. Two portable spectrometers, Laser-Induced Breakdown Spectroscopy (LIBS) and Acoustic-Optic Tunable Filter Reflectance Spectroscopy (AOTFRS) were used to differentiate between bacterially influenced and inorganically precipitated calcite specimens from Fort Stanton Cave, NM, USA. LIBS collects light emitted from the decay of excited electrons in a laser ablation plasma; the spectrum is a chemical fingerprint of the analyte. AOTFRS collects light reflected from the surface of a specimen and provides structural information about the material (i.e., the presence of O-H bonds). These orthogonal data sets provide a rigorous method to determine the origin of calcite in cave deposits. This study used a set of 48 calcite samples collected from Fort Stanton cave. Samples were examined in SEM for the presence of biologic markers; these data were used to separate the samples into biologic and non-biologic groups. Spectra were modeled using the multivariate technique Partial Least Squares Regression (PLSR). Half of the spectra were used to train a PLSR model, in which biologic samples were assigned to the independent variable "0" and non-biologic samples were assigned the variable "1". Values of the independent variable were calculated for each of the training samples, which were close to 0 for the biologic samples (-0.09 - 0.23) and close to 1 for the non-biologic samples (0.57 - 1.14). A Value of Apparent Distinction (VAD) of 0.55 was used to numerically distinguish between the two groups; any sample with an independent variable value < 0.55 was classified as having a biologic origin; a sample with a value > 0.55 was determined to be non-biologic in origin. After the model was trained, independent variable values for the remaining half of the samples were calculated. Biologic or non-biologic origin was assigned by comparison to the VAD. Using LIBS data alone, the model has a 92% success rate, correctly identifying 23 of 25 samples. Modeling of AOTFRS spectra and the combined LIBS-AOTFRS data set have similar success rates. This study demonstrates that rapid, portable LIBS and AOTFRS instruments can be used to map the spatial distribution of biologic precipitation in caves.

[Identification of spill oil species based on low concentration synchronous fluorescence spectra and RBF neural network].

PubMed

Liu, Qian-qian; Wang, Chun-yan; Shi, Xiao-feng; Li, Wen-dong; Luan, Xiao-ning; Hou, Shi-lin; Zhang, Jin-liang; Zheng, Rong-er

2012-04-01

In this paper, a new method was developed to differentiate the spill oil samples. The synchronous fluorescence spectra in the lower nonlinear concentration range of 10(-2) - 10(-1) g x L(-1) were collected to get training data base. Radial basis function artificial neural network (RBF-ANN) was used to identify the samples sets, along with principal component analysis (PCA) as the feature extraction method. The recognition rate of the closely-related oil source samples is 92%. All the results demonstrated that the proposed method could identify the crude oil samples effectively by just one synchronous spectrum of the spill oil sample. The method was supposed to be very suitable to the real-time spill oil identification, and can also be easily applied to the oil logging and the analysis of other multi-PAHs or multi-fluorescent mixtures.

Nanophotonic particle simulation and inverse design using artificial neural networks

PubMed Central

Peurifoy, John; Shen, Yichen; Jing, Li; Cano-Renteria, Fidel; DeLacy, Brendan G.; Joannopoulos, John D.; Tegmark, Max

2018-01-01

We propose a method to use artificial neural networks to approximate light scattering by multilayer nanoparticles. We find that the network needs to be trained on only a small sampling of the data to approximate the simulation to high precision. Once the neural network is trained, it can simulate such optical processes orders of magnitude faster than conventional simulations. Furthermore, the trained neural network can be used to solve nanophotonic inverse design problems by using back propagation, where the gradient is analytical, not numerical. PMID:29868640

Recognition Using Hybrid Classifiers.

PubMed

Osadchy, Margarita; Keren, Daniel; Raviv, Dolev

2016-04-01

A canonical problem in computer vision is category recognition (e.g., find all instances of human faces, cars etc., in an image). Typically, the input for training a binary classifier is a relatively small sample of positive examples, and a huge sample of negative examples, which can be very diverse, consisting of images from a large number of categories. The difficulty of the problem sharply increases with the dimension and size of the negative example set. We propose to alleviate this problem by applying a "hybrid" classifier, which replaces the negative samples by a prior, and then finds a hyperplane which separates the positive samples from this prior. The method is extended to kernel space and to an ensemble-based approach. The resulting binary classifiers achieve an identical or better classification rate than SVM, while requiring far smaller memory and lower computational complexity to train and apply.

Unsupervised Learning —A Novel Clustering Method for Rolling Bearing Faults Identification

NASA Astrophysics Data System (ADS)

Kai, Li; Bo, Luo; Tao, Ma; Xuefeng, Yang; Guangming, Wang

2017-12-01

To promptly process the massive fault data and automatically provide accurate diagnosis results, numerous studies have been conducted on intelligent fault diagnosis of rolling bearing. Among these studies, such as artificial neural networks, support vector machines, decision trees and other supervised learning methods are used commonly. These methods can detect the failure of rolling bearing effectively, but to achieve better detection results, it often requires a lot of training samples. Based on above, a novel clustering method is proposed in this paper. This novel method is able to find the correct number of clusters automatically the effectiveness of the proposed method is validated using datasets from rolling element bearings. The diagnosis results show that the proposed method can accurately detect the fault types of small samples. Meanwhile, the diagnosis results are also relative high accuracy even for massive samples.

Assessing deep and shallow learning methods for quantitative prediction of acute chemical toxicity.

PubMed

Liu, Ruifeng; Madore, Michael; Glover, Kyle P; Feasel, Michael G; Wallqvist, Anders

2018-05-02

Animal-based methods for assessing chemical toxicity are struggling to meet testing demands. In silico approaches, including machine-learning methods, are promising alternatives. Recently, deep neural networks (DNNs) were evaluated and reported to outperform other machine-learning methods for quantitative structure-activity relationship modeling of molecular properties. However, most of the reported performance evaluations relied on global performance metrics, such as the root mean squared error (RMSE) between the predicted and experimental values of all samples, without considering the impact of sample distribution across the activity spectrum. Here, we carried out an in-depth analysis of DNN performance for quantitative prediction of acute chemical toxicity using several datasets. We found that the overall performance of DNN models on datasets of up to 30,000 compounds was similar to that of random forest (RF) models, as measured by the RMSE and correlation coefficients between the predicted and experimental results. However, our detailed analyses demonstrated that global performance metrics are inappropriate for datasets with a highly uneven sample distribution, because they show a strong bias for the most populous compounds along the toxicity spectrum. For highly toxic compounds, DNN and RF models trained on all samples performed much worse than the global performance metrics indicated. Surprisingly, our variable nearest neighbor method, which utilizes only structurally similar compounds to make predictions, performed reasonably well, suggesting that information of close near neighbors in the training sets is a key determinant of acute toxicity predictions.

Realistic sampling of amino acid geometries for a multipolar polarizable force field

PubMed Central

Hughes, Timothy J.; Cardamone, Salvatore

2015-01-01

The Quantum Chemical Topological Force Field (QCTFF) uses the machine learning method kriging to map atomic multipole moments to the coordinates of all atoms in the molecular system. It is important that kriging operates on relevant and realistic training sets of molecular geometries. Therefore, we sampled single amino acid geometries directly from protein crystal structures stored in the Protein Databank (PDB). This sampling enhances the conformational realism (in terms of dihedral angles) of the training geometries. However, these geometries can be fraught with inaccurate bond lengths and valence angles due to artefacts of the refinement process of the X‐ray diffraction patterns, combined with experimentally invisible hydrogen atoms. This is why we developed a hybrid PDB/nonstationary normal modes (NM) sampling approach called PDB/NM. This method is superior over standard NM sampling, which captures only geometries optimized from the stationary points of single amino acids in the gas phase. Indeed, PDB/NM combines the sampling of relevant dihedral angles with chemically correct local geometries. Geometries sampled using PDB/NM were used to build kriging models for alanine and lysine, and their prediction accuracy was compared to models built from geometries sampled from three other sampling approaches. Bond length variation, as opposed to variation in dihedral angles, puts pressure on prediction accuracy, potentially lowering it. Hence, the larger coverage of dihedral angles of the PDB/NM method does not deteriorate the predictive accuracy of kriging models, compared to the NM sampling around local energetic minima used so far in the development of QCTFF. © 2015 The Authors. Journal of Computational Chemistry Published by Wiley Periodicals, Inc. PMID:26235784

[Use of the reliable change index to evaluate the effectiveness of clinical interventions: Application of an asthma training program].

PubMed

Montero, Mikel; Iraurgi, Ioseba; Matellanes, Begoña; Montero, José Manuel

2015-12-01

To compare two methods for the evaluation of outcomes to assess effectiveness of a therapeutic intervention of a professional education program on asthma control. A naturalistic, intervention study in which asthmatic patients were attended by clinicians (IG group) who Had taken part in a special education program and a control group (CG) that received medical assistance from clinicians still waiting to be trained. Five urban Primary Care Health Centres of the same region. From an initial sample of 100 patients, 76 formed the final sample for analysis. The study included 37 males and 39 females, aged between 18 and 65 years (M=41.2 years). The two study groups were found to be homogeneous except for the sex variable. Training program for clinical treatment adherence. Peak flow as spirometric index, and structured interview. The results were initially analysed using classical techniques based on robust ANOVA models, and then by calculating the Reliable Change Index (RCI). ANOVA models, conducted separately for each sex, showed no significant differences, due to sample size. RCI methodology showed significant differences in the percentage of patients improved in both groups, as well as clinically relevant changes being observed individually. The RCI method is presented as an attractive alternative as regards the classical methods of analysis that can help in the clinical decision. Copyright © 2014 Elsevier España, S.L.U. All rights reserved.

40 CFR Appendix A-8 to Part 60 - Test Methods 26 through 30B

Code of Federal Regulations, 2013 CFR

2013-07-01

...), and 8.4.4 (Post-Test Leak-Check). 8.1.5Sampling Train Operation. Follow the general procedure given in... 40 Protection of Environment 8 2013-07-01 2013-07-01 false Test Methods 26 through 30B A Appendix... Part 60—Test Methods 26 through 30B Method 26—Determination of Hydrogen Chloride Emissions From...

40 CFR Appendix A-8 to Part 60 - Test Methods 26 through 30B

Code of Federal Regulations, 2014 CFR

2014-07-01

...), and 8.4.4 (Post-Test Leak-Check). 8.1.5Sampling Train Operation. Follow the general procedure given in... 40 Protection of Environment 8 2014-07-01 2014-07-01 false Test Methods 26 through 30B A Appendix... Part 60—Test Methods 26 through 30B Method 26—Determination of Hydrogen Chloride Emissions From...

40 CFR Appendix A-8 to Part 60 - Test Methods 26 through 30B

Code of Federal Regulations, 2011 CFR

2011-07-01

...), and 8.4.4 (Post-Test Leak-Check). 8.1.5Sampling Train Operation. Follow the general procedure given in... 40 Protection of Environment 7 2011-07-01 2011-07-01 false Test Methods 26 through 30B A Appendix... Part 60—Test Methods 26 through 30B Method 26—Determination of Hydrogen Chloride Emissions From...

40 CFR Appendix A-8 to Part 60 - Test Methods 26 through 30B

Code of Federal Regulations, 2012 CFR

2012-07-01

...), and 8.4.4 (Post-Test Leak-Check). 8.1.5Sampling Train Operation. Follow the general procedure given in... 40 Protection of Environment 8 2012-07-01 2012-07-01 false Test Methods 26 through 30B A Appendix... Part 60—Test Methods 26 through 30B Method 26—Determination of Hydrogen Chloride Emissions From...

Sampling algorithms for validation of supervised learning models for Ising-like systems

NASA Astrophysics Data System (ADS)

Portman, Nataliya; Tamblyn, Isaac

2017-12-01

In this paper, we build and explore supervised learning models of ferromagnetic system behavior, using Monte-Carlo sampling of the spin configuration space generated by the 2D Ising model. Given the enormous size of the space of all possible Ising model realizations, the question arises as to how to choose a reasonable number of samples that will form physically meaningful and non-intersecting training and testing datasets. Here, we propose a sampling technique called ;ID-MH; that uses the Metropolis-Hastings algorithm creating Markov process across energy levels within the predefined configuration subspace. We show that application of this method retains phase transitions in both training and testing datasets and serves the purpose of validation of a machine learning algorithm. For larger lattice dimensions, ID-MH is not feasible as it requires knowledge of the complete configuration space. As such, we develop a new ;block-ID; sampling strategy: it decomposes the given structure into square blocks with lattice dimension N ≤ 5 and uses ID-MH sampling of candidate blocks. Further comparison of the performance of commonly used machine learning methods such as random forests, decision trees, k nearest neighbors and artificial neural networks shows that the PCA-based Decision Tree regressor is the most accurate predictor of magnetizations of the Ising model. For energies, however, the accuracy of prediction is not satisfactory, highlighting the need to consider more algorithmically complex methods (e.g., deep learning).

Improved artificial neural networks in prediction of malignancy of lesions in contrast-enhanced MR-mammography.

PubMed

Vomweg, T W; Buscema, M; Kauczor, H U; Teifke, A; Intraligi, M; Terzi, S; Heussel, C P; Achenbach, T; Rieker, O; Mayer, D; Thelen, M

2003-09-01

The aim of this study was to evaluate the capability of improved artificial neural networks (ANN) and additional novel training methods in distinguishing between benign and malignant breast lesions in contrast-enhanced magnetic resonance-mammography (MRM). A total of 604 histologically proven cases of contrast-enhanced lesions of the female breast at MRI were analyzed. Morphological, dynamic and clinical parameters were collected and stored in a database. The data set was divided into several groups using random or experimental methods [Training & Testing (T&T) algorithm] to train and test different ANNs. An additional novel computer program for input variable selection was applied. Sensitivity and specificity were calculated and compared with a statistical method and an expert radiologist. After optimization of the distribution of cases among the training and testing sets by the T & T algorithm and the reduction of input variables by the Input Selection procedure a highly sophisticated ANN achieved a sensitivity of 93.6% and a specificity of 91.9% in predicting malignancy of lesions within an independent prediction sample set. The best statistical method reached a sensitivity of 90.5% and a specificity of 68.9%. An expert radiologist performed better than the statistical method but worse than the ANN (sensitivity 92.1%, specificity 85.6%). Features extracted out of dynamic contrast-enhanced MRM and additional clinical data can be successfully analyzed by advanced ANNs. The quality of the resulting network strongly depends on the training methods, which are improved by the use of novel training tools. The best results of an improved ANN outperform expert radiologists.

Classifier performance prediction for computer-aided diagnosis using a limited dataset.

PubMed

Sahiner, Berkman; Chan, Heang-Ping; Hadjiiski, Lubomir

2008-04-01

In a practical classifier design problem, the true population is generally unknown and the available sample is finite-sized. A common approach is to use a resampling technique to estimate the performance of the classifier that will be trained with the available sample. We conducted a Monte Carlo simulation study to compare the ability of the different resampling techniques in training the classifier and predicting its performance under the constraint of a finite-sized sample. The true population for the two classes was assumed to be multivariate normal distributions with known covariance matrices. Finite sets of sample vectors were drawn from the population. The true performance of the classifier is defined as the area under the receiver operating characteristic curve (AUC) when the classifier designed with the specific sample is applied to the true population. We investigated methods based on the Fukunaga-Hayes and the leave-one-out techniques, as well as three different types of bootstrap methods, namely, the ordinary, 0.632, and 0.632+ bootstrap. The Fisher's linear discriminant analysis was used as the classifier. The dimensionality of the feature space was varied from 3 to 15. The sample size n2 from the positive class was varied between 25 and 60, while the number of cases from the negative class was either equal to n2 or 3n2. Each experiment was performed with an independent dataset randomly drawn from the true population. Using a total of 1000 experiments for each simulation condition, we compared the bias, the variance, and the root-mean-squared error (RMSE) of the AUC estimated using the different resampling techniques relative to the true AUC (obtained from training on a finite dataset and testing on the population). Our results indicated that, under the study conditions, there can be a large difference in the RMSE obtained using different resampling methods, especially when the feature space dimensionality is relatively large and the sample size is small. Under this type of conditions, the 0.632 and 0.632+ bootstrap methods have the lowest RMSE, indicating that the difference between the estimated and the true performances obtained using the 0.632 and 0.632+ bootstrap will be statistically smaller than those obtained using the other three resampling methods. Of the three bootstrap methods, the 0.632+ bootstrap provides the lowest bias. Although this investigation is performed under some specific conditions, it reveals important trends for the problem of classifier performance prediction under the constraint of a limited dataset.

Short-term music training enhances verbal intelligence and executive function.

PubMed

Moreno, Sylvain; Bialystok, Ellen; Barac, Raluca; Schellenberg, E Glenn; Cepeda, Nicholas J; Chau, Tom

2011-11-01

Researchers have designed training methods that can be used to improve mental health and to test the efficacy of education programs. However, few studies have demonstrated broad transfer from such training to performance on untrained cognitive activities. Here we report the effects of two interactive computerized training programs developed for preschool children: one for music and one for visual art. After only 20 days of training, only children in the music group exhibited enhanced performance on a measure of verbal intelligence, with 90% of the sample showing this improvement. These improvements in verbal intelligence were positively correlated with changes in functional brain plasticity during an executive-function task. Our findings demonstrate that transfer of a high-level cognitive skill is possible in early childhood.

Dependability of Data Derived from Time Sampling Methods with Multiple Observation Targets

ERIC Educational Resources Information Center

Johnson, Austin H.; Chafouleas, Sandra M.; Briesch, Amy M.

2017-01-01

In this study, generalizability theory was used to examine the extent to which (a) time-sampling methodology, (b) number of simultaneous behavior targets, and (c) individual raters influenced variance in ratings of academic engagement for an elementary-aged student. Ten graduate-student raters, with an average of 7.20 hr of previous training in…

The customer satisfaction towards the service quality of Tawang Alun Malang-Banyuwangi Train

NASA Astrophysics Data System (ADS)

Permatasari, D.

2017-06-01

Service sector which has quiet vital role in supporting people’s daily activities is transportation service. Transportation is one of the important and strategic developments in improving economy sector. One of the alternative ways to overcome people’s need of transportation is by providing trains. This research was conducted on the weekend that has objectives to analyze the work performance of Indonesian Railway Company towards the service quality that can determine the customers’ satisfaction of TawangAlun Malang-Banyuwangi train and to analyze the customers’ satisfaction itself towards the service quality of TawangAlun Malang-Banyuwangi train. This research used quantitative descriptive as the research method. There are two kinds of data that were used in this research; the first one is the primary data taken from questionnaire’s results and interview meanwhile the second one is the secondary data taken from literature and internet. The sample used in this research is nonprobability sampling using convenience sampling technique. Data analysis used in this research is Importance Performance Analysis (IPA) and Customer Satisfaction index (CSI). The results are the Indonesian Railway Company should make a new innovation to buy the ticket from the ticket machine and add more exhausts in every railway coach.

A fast learning method for large scale and multi-class samples of SVM

NASA Astrophysics Data System (ADS)

Fan, Yu; Guo, Huiming

2017-06-01

A multi-class classification SVM(Support Vector Machine) fast learning method based on binary tree is presented to solve its low learning efficiency when SVM processing large scale multi-class samples. This paper adopts bottom-up method to set up binary tree hierarchy structure, according to achieved hierarchy structure, sub-classifier learns from corresponding samples of each node. During the learning, several class clusters are generated after the first clustering of the training samples. Firstly, central points are extracted from those class clusters which just have one type of samples. For those which have two types of samples, cluster numbers of their positive and negative samples are set respectively according to their mixture degree, secondary clustering undertaken afterwards, after which, central points are extracted from achieved sub-class clusters. By learning from the reduced samples formed by the integration of extracted central points above, sub-classifiers are obtained. Simulation experiment shows that, this fast learning method, which is based on multi-level clustering, can guarantee higher classification accuracy, greatly reduce sample numbers and effectively improve learning efficiency.

Benchmark of Machine Learning Methods for Classification of a SENTINEL-2 Image

NASA Astrophysics Data System (ADS)

Pirotti, F.; Sunar, F.; Piragnolo, M.

2016-06-01

Thanks to mainly ESA and USGS, a large bulk of free images of the Earth is readily available nowadays. One of the main goals of remote sensing is to label images according to a set of semantic categories, i.e. image classification. This is a very challenging issue since land cover of a specific class may present a large spatial and spectral variability and objects may appear at different scales and orientations. In this study, we report the results of benchmarking 9 machine learning algorithms tested for accuracy and speed in training and classification of land-cover classes in a Sentinel-2 dataset. The following machine learning methods (MLM) have been tested: linear discriminant analysis, k-nearest neighbour, random forests, support vector machines, multi layered perceptron, multi layered perceptron ensemble, ctree, boosting, logarithmic regression. The validation is carried out using a control dataset which consists of an independent classification in 11 land-cover classes of an area about 60 km2, obtained by manual visual interpretation of high resolution images (20 cm ground sampling distance) by experts. In this study five out of the eleven classes are used since the others have too few samples (pixels) for testing and validating subsets. The classes used are the following: (i) urban (ii) sowable areas (iii) water (iv) tree plantations (v) grasslands. Validation is carried out using three different approaches: (i) using pixels from the training dataset (train), (ii) using pixels from the training dataset and applying cross-validation with the k-fold method (kfold) and (iii) using all pixels from the control dataset. Five accuracy indices are calculated for the comparison between the values predicted with each model and control values over three sets of data: the training dataset (train), the whole control dataset (full) and with k-fold cross-validation (kfold) with ten folds. Results from validation of predictions of the whole dataset (full) show the random forests method with the highest values; kappa index ranging from 0.55 to 0.42 respectively with the most and least number pixels for training. The two neural networks (multi layered perceptron and its ensemble) and the support vector machines - with default radial basis function kernel - methods follow closely with comparable performance.

Weighted Discriminative Dictionary Learning based on Low-rank Representation

NASA Astrophysics Data System (ADS)

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.

Effect of information literacy training course on information literacy skills of undergraduate students of Isfahan University of Medical Sciences based on ACRL standards

PubMed Central

Karimi, Zohreh; Ashrafi-rizi, Hasan; Papi, Ahmad; Shahrzadi, Leila; Hassanzadeh, Akbar

2015-01-01

Background: Information literacy is the basis for lifelong learning. Information literacy skills, especially for student in an environment that is full of information from multiple technologies are being developed is equally important. Information literacy is a set of cognitive and practical skills and like any other science, proper training is needed, and standard-based education is definitely better and evaluation would be easier. This study aimed to determine the impact of information literacy training course on information literacy skills of Isfahan University of Medical Sciences students based on ACRL standard in 2012. Materials and Methods: The study method is semi-experience with two group design (with pre-test and post-test) and applied. The data collection toll was a questionnaire assessing student's information literacy that developed by Davarpanah and Siamak and validity was confirmed by professional librarians and reliability as measured by Cronbach's alpha, was 0.83. The sample consisted of 50 undergraduate students from Isfahan University of Medical Sciences that by random sampling method was perch in both case and control groups. Before and after the training (once a week), a questionnaire was distributed between the two groups. This training was held in a classroom equipped with computers with internet access and in addition to training using brochures and librarian presentation, interactive methods such as discussion and exercises were used. The data were analyzed using SPSS version 20 software and two level of descriptive (mean and SD) and inferential statistics (t-test and t-paired). Results: The results showed that the students’ information literacy scores before the training was lower than average, so that in the control group was 32.96 and in the case group was 33.24; while information literacy scores in the case group significantly increased after the training (46.68). Also, the effect of education, respectively had a greater impact on the ability to access information (the second standard), ethics and legal use of information (the third standard), effective use of information (the fourth standard), critically evaluate information and its sources (the fifth standard). Conclusion: This study showed that the training was effective on enhancing students’ information literacy skills as the greatest impact was on increasing the ability to access information. Due to low mean score information literacy in the context of object recognition, there is a need for more training in this area. PMID:27462618

Rule Extraction Based on Extreme Learning Machine and an Improved Ant-Miner Algorithm for Transient Stability Assessment.

PubMed

Li, Yang; Li, Guoqing; Wang, Zhenhao

2015-01-01

In order to overcome the problems of poor understandability of the pattern recognition-based transient stability assessment (PRTSA) methods, a new rule extraction method based on extreme learning machine (ELM) and an improved Ant-miner (IAM) algorithm is presented in this paper. First, the basic principles of ELM and Ant-miner algorithm are respectively introduced. Then, based on the selected optimal feature subset, an example sample set is generated by the trained ELM-based PRTSA model. And finally, a set of classification rules are obtained by IAM algorithm to replace the original ELM network. The novelty of this proposal is that transient stability rules are extracted from an example sample set generated by the trained ELM-based transient stability assessment model by using IAM algorithm. The effectiveness of the proposed method is shown by the application results on the New England 39-bus power system and a practical power system--the southern power system of Hebei province.

Comparison of four approaches to a rock facies classification problem

USGS Publications Warehouse

Dubois, M.K.; Bohling, Geoffrey C.; Chakrabarti, S.

2007-01-01

In this study, seven classifiers based on four different approaches were tested in a rock facies classification problem: classical parametric methods using Bayes' rule, and non-parametric methods using fuzzy logic, k-nearest neighbor, and feed forward-back propagating artificial neural network. Determining the most effective classifier for geologic facies prediction in wells without cores in the Panoma gas field, in Southwest Kansas, was the objective. Study data include 3600 samples with known rock facies class (from core) with each sample having either four or five measured properties (wire-line log curves), and two derived geologic properties (geologic constraining variables). The sample set was divided into two subsets, one for training and one for testing the ability of the trained classifier to correctly assign classes. Artificial neural networks clearly outperformed all other classifiers and are effective tools for this particular classification problem. Classical parametric models were inadequate due to the nature of the predictor variables (high dimensional and not linearly correlated), and feature space of the classes (overlapping). The other non-parametric methods tested, k-nearest neighbor and fuzzy logic, would need considerable improvement to match the neural network effectiveness, but further work, possibly combining certain aspects of the three non-parametric methods, may be justified. ?? 2006 Elsevier Ltd. All rights reserved.

Turboprop: improved PROPELLER imaging.

PubMed

Pipe, James G; Zwart, Nicholas

2006-02-01

A variant of periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) MRI, called turboprop, is introduced. This method employs an oscillating readout gradient during each spin echo of the echo train to collect more lines of data per echo train, which reduces the minimum scan time, motion-related artifact, and specific absorption rate (SAR) while increasing sampling efficiency. It can be applied to conventional fast spin-echo (FSE) imaging; however, this article emphasizes its application in diffusion-weighted imaging (DWI). The method is described and compared with conventional PROPELLER imaging, and clinical images collected with this PROPELLER variant are shown. Copyright 2006 Wiley-Liss, Inc.

Active machine learning for rapid landslide inventory mapping with VHR satellite images (Invited)

NASA Astrophysics Data System (ADS)

Stumpf, A.; Lachiche, N.; Malet, J.; Kerle, N.; Puissant, A.

2013-12-01

VHR satellite images have become a primary source for landslide inventory mapping after major triggering events such as earthquakes and heavy rainfalls. Visual image interpretation is still the prevailing standard method for operational purposes but is time-consuming and not well suited to fully exploit the increasingly better supply of remote sensing data. Recent studies have addressed the development of more automated image analysis workflows for landslide inventory mapping. In particular object-oriented approaches that account for spatial and textural image information have been demonstrated to be more adequate than pixel-based classification but manually elaborated rule-based classifiers are difficult to adapt under changing scene characteristics. Machine learning algorithm allow learning classification rules for complex image patterns from labelled examples and can be adapted straightforwardly with available training data. In order to reduce the amount of costly training data active learning (AL) has evolved as a key concept to guide the sampling for many applications. The underlying idea of AL is to initialize a machine learning model with a small training set, and to subsequently exploit the model state and data structure to iteratively select the most valuable samples that should be labelled by the user. With relatively few queries and labelled samples, an AL strategy yields higher accuracies than an equivalent classifier trained with many randomly selected samples. This study addressed the development of an AL method for landslide mapping from VHR remote sensing images with special consideration of the spatial distribution of the samples. Our approach [1] is based on the Random Forest algorithm and considers the classifier uncertainty as well as the variance of potential sampling regions to guide the user towards the most valuable sampling areas. The algorithm explicitly searches for compact regions and thereby avoids a spatially disperse sampling pattern inherent to most other AL methods. The accuracy, the sampling time and the computational runtime of the algorithm were evaluated on multiple satellite images capturing recent large scale landslide events. Sampling between 1-4% of the study areas the accuracies between 74% and 80% were achieved, whereas standard sampling schemes yielded only accuracies between 28% and 50% with equal sampling costs. Compared to commonly used point-wise AL algorithm the proposed approach significantly reduces the number of iterations and hence the computational runtime. Since the user can focus on relatively few compact areas (rather than on hundreds of distributed points) the overall labeling time is reduced by more than 50% compared to point-wise queries. An experimental evaluation of multiple expert mappings demonstrated strong relationships between the uncertainties of the experts and the machine learning model. It revealed that the achieved accuracies are within the range of the inter-expert disagreement and that it will be indispensable to consider ground truth uncertainties to truly achieve further enhancements in the future. The proposed method is generally applicable to a wide range of optical satellite images and landslide types. [1] A. Stumpf, N. Lachiche, J.-P. Malet, N. Kerle, and A. Puissant, Active learning in the spatial domain for remote sensing image classification, IEEE Transactions on Geosciece and Remote Sensing. 2013, DOI 10.1109/TGRS.2013.2262052.

ASM Based Synthesis of Handwritten Arabic Text Pages

PubMed Central

Al-Hamadi, Ayoub; Elzobi, Moftah; El-etriby, Sherif; Ghoneim, Ahmed

2015-01-01

Document analysis tasks, as text recognition, word spotting, or segmentation, are highly dependent on comprehensive and suitable databases for training and validation. However their generation is expensive in sense of labor and time. As a matter of fact, there is a lack of such databases, which complicates research and development. This is especially true for the case of Arabic handwriting recognition, that involves different preprocessing, segmentation, and recognition methods, which have individual demands on samples and ground truth. To bypass this problem, we present an efficient system that automatically turns Arabic Unicode text into synthetic images of handwritten documents and detailed ground truth. Active Shape Models (ASMs) based on 28046 online samples were used for character synthesis and statistical properties were extracted from the IESK-arDB database to simulate baselines and word slant or skew. In the synthesis step ASM based representations are composed to words and text pages, smoothed by B-Spline interpolation and rendered considering writing speed and pen characteristics. Finally, we use the synthetic data to validate a segmentation method. An experimental comparison with the IESK-arDB database encourages to train and test document analysis related methods on synthetic samples, whenever no sufficient natural ground truthed data is available. PMID:26295059

ASM Based Synthesis of Handwritten Arabic Text Pages.

PubMed

Dinges, Laslo; Al-Hamadi, Ayoub; Elzobi, Moftah; El-Etriby, Sherif; Ghoneim, Ahmed

2015-01-01

Document analysis tasks, as text recognition, word spotting, or segmentation, are highly dependent on comprehensive and suitable databases for training and validation. However their generation is expensive in sense of labor and time. As a matter of fact, there is a lack of such databases, which complicates research and development. This is especially true for the case of Arabic handwriting recognition, that involves different preprocessing, segmentation, and recognition methods, which have individual demands on samples and ground truth. To bypass this problem, we present an efficient system that automatically turns Arabic Unicode text into synthetic images of handwritten documents and detailed ground truth. Active Shape Models (ASMs) based on 28046 online samples were used for character synthesis and statistical properties were extracted from the IESK-arDB database to simulate baselines and word slant or skew. In the synthesis step ASM based representations are composed to words and text pages, smoothed by B-Spline interpolation and rendered considering writing speed and pen characteristics. Finally, we use the synthetic data to validate a segmentation method. An experimental comparison with the IESK-arDB database encourages to train and test document analysis related methods on synthetic samples, whenever no sufficient natural ground truthed data is available.

Improved semi-supervised online boosting for object tracking

NASA Astrophysics Data System (ADS)

Li, Yicui; Qi, Lin; Tan, Shukun

2016-10-01

The advantage of an online semi-supervised boosting method which takes object tracking problem as a classification problem, is training a binary classifier from labeled and unlabeled examples. Appropriate object features are selected based on real time changes in the object. However, the online semi-supervised boosting method faces one key problem: The traditional self-training using the classification results to update the classifier itself, often leads to drifting or tracking failure, due to the accumulated error during each update of the tracker. To overcome the disadvantages of semi-supervised online boosting based on object tracking methods, the contribution of this paper is an improved online semi-supervised boosting method, in which the learning process is guided by positive (P) and negative (N) constraints, termed P-N constraints, which restrict the labeling of the unlabeled samples. First, we train the classification by an online semi-supervised boosting. Then, this classification is used to process the next frame. Finally, the classification is analyzed by the P-N constraints, which are used to verify if the labels of unlabeled data assigned by the classifier are in line with the assumptions made about positive and negative samples. The proposed algorithm can effectively improve the discriminative ability of the classifier and significantly alleviate the drifting problem in tracking applications. In the experiments, we demonstrate real-time tracking of our tracker on several challenging test sequences where our tracker outperforms other related on-line tracking methods and achieves promising tracking performance.

Effect of information literacy training course on information literacy skills of undergraduate students of Isfahan University of Medical Sciences based on ACRL standards.

PubMed

Karimi, Zohreh; Ashrafi-Rizi, Hasan; Papi, Ahmad; Shahrzadi, Leila; Hassanzadeh, Akbar

2015-01-01

Information literacy is the basis for lifelong learning. Information literacy skills, especially for student in an environment that is full of information from multiple technologies are being developed is equally important. Information literacy is a set of cognitive and practical skills and like any other science, proper training is needed, and standard-based education is definitely better and evaluation would be easier. This study aimed to determine the impact of information literacy training course on information literacy skills of Isfahan University of Medical Sciences students based on ACRL standard in 2012. The study method is semi-experience with two group design (with pre-test and post-test) and applied. The data collection toll was a questionnaire assessing student's information literacy that developed by Davarpanah and Siamak and validity was confirmed by professional librarians and reliability as measured by Cronbach's alpha, was 0.83. The sample consisted of 50 undergraduate students from Isfahan University of Medical Sciences that by random sampling method was perch in both case and control groups. Before and after the training (once a week), a questionnaire was distributed between the two groups. This training was held in a classroom equipped with computers with internet access and in addition to training using brochures and librarian presentation, interactive methods such as discussion and exercises were used. The data were analyzed using SPSS version 20 software and two level of descriptive (mean and SD) and inferential statistics (t-test and t-paired). The results showed that the students' information literacy scores before the training was lower than average, so that in the control group was 32.96 and in the case group was 33.24; while information literacy scores in the case group significantly increased after the training (46.68). Also, the effect of education, respectively had a greater impact on the ability to access information (the second standard), ethics and legal use of information (the third standard), effective use of information (the fourth standard), critically evaluate information and its sources (the fifth standard). This study showed that the training was effective on enhancing students' information literacy skills as the greatest impact was on increasing the ability to access information. Due to low mean score information literacy in the context of object recognition, there is a need for more training in this area.

Student Teachers' Views about Assessment and Evaluation Methods in Mathematics

ERIC Educational Resources Information Center

Dogan, Mustafa

2011-01-01

This study aimed to find out assessment and evaluation approaches in a Mathematics Teacher Training Department based on the views and experiences of student teachers. The study used a descriptive survey method, with the research sample consisting of 150 third- and fourth-year Primary Mathematics student teachers. Data were collected using a…

Space sickness predictors suggest fluid shift involvement and possible countermeasures

NASA Technical Reports Server (NTRS)

Simanonok, K. E.; Moseley, E. C.; Charles, J. B.

1992-01-01

Preflight data from 64 first time Shuttle crew members were examined retrospectively to predict space sickness severity (NONE, MILD, MODERATE, or SEVERE) by discriminant analysis. From 9 input variables relating to fluid, electrolyte, and cardiovascular status, 8 variables were chosen by discriminant analysis that correctly predicted space sickness severity with 59 pct. success by one method of cross validation on the original sample and 67 pct. by another method. The 8 variables in order of their importance for predicting space sickness severity are sitting systolic blood pressure, serum uric acid, calculated blood volume, serum phosphate, urine osmolality, environmental temperature at the launch site, red cell count, and serum chloride. These results suggest the presence of predisposing physiologic factors to space sickness that implicate a fluid shift etiology. Addition of a 10th input variable, hours spent in the Weightless Environment Training Facility (WETF), improved the prediction of space sickness severity to 66 pct. success by the first method of cross validation on the original sample and to 71 pct. by the second method. The data suggest that WETF training may reduce space sickness severity.

Decoder calibration with ultra small current sample set for intracortical brain-machine interface

NASA Astrophysics Data System (ADS)

Zhang, Peng; Ma, Xuan; Chen, Luyao; Zhou, Jin; Wang, Changyong; Li, Wei; He, Jiping

2018-04-01

Objective. Intracortical brain-machine interfaces (iBMIs) aim to restore efficient communication and movement ability for paralyzed patients. However, frequent recalibration is required for consistency and reliability, and every recalibration will require relatively large most current sample set. The aim in this study is to develop an effective decoder calibration method that can achieve good performance while minimizing recalibration time. Approach. Two rhesus macaques implanted with intracortical microelectrode arrays were trained separately on movement and sensory paradigm. Neural signals were recorded to decode reaching positions or grasping postures. A novel principal component analysis-based domain adaptation (PDA) method was proposed to recalibrate the decoder with only ultra small current sample set by taking advantage of large historical data, and the decoding performance was compared with other three calibration methods for evaluation. Main results. The PDA method closed the gap between historical and current data effectively, and made it possible to take advantage of large historical data for decoder recalibration in current data decoding. Using only ultra small current sample set (five trials of each category), the decoder calibrated using the PDA method could achieve much better and more robust performance in all sessions than using other three calibration methods in both monkeys. Significance. (1) By this study, transfer learning theory was brought into iBMIs decoder calibration for the first time. (2) Different from most transfer learning studies, the target data in this study were ultra small sample set and were transferred to the source data. (3) By taking advantage of historical data, the PDA method was demonstrated to be effective in reducing recalibration time for both movement paradigm and sensory paradigm, indicating a viable generalization. By reducing the demand for large current training data, this new method may facilitate the application of intracortical brain-machine interfaces in clinical practice.

Evaluation of Athletic Training Students' Clinical Proficiencies

PubMed Central

Walker, Stacy E; Weidner, Thomas G; Armstrong, Kirk J

2008-01-01

Context: Appropriate methods for evaluating clinical proficiencies are essential in ensuring entry-level competence. Objective: To investigate the common methods athletic training education programs use to evaluate student performance of clinical proficiencies. Design: Cross-sectional design. Setting: Public and private institutions nationwide. Patients or Other Participants: All program directors of athletic training education programs accredited by the Commission on Accreditation of Allied Health Education Programs as of January 2006 (n  =  337); 201 (59.6%) program directors responded. Data Collection and Analysis: The institutional survey consisted of 11 items regarding institutional and program demographics. The 14-item Methods of Clinical Proficiency Evaluation in Athletic Training survey consisted of respondents' demographic characteristics and Likert-scale items regarding clinical proficiency evaluation methods and barriers, educational content areas, and clinical experience settings. We used analyses of variance and independent t tests to assess differences among athletic training education program characteristics and the barriers, methods, content areas, and settings regarding clinical proficiency evaluation. Results: Of the 3 methods investigated, simulations (n  =  191, 95.0%) were the most prevalent method of clinical proficiency evaluation. An independent-samples t test revealed that more opportunities existed for real-time evaluations in the college or high school athletic training room (t189  =  2.866, P  =  .037) than in other settings. Orthopaedic clinical examination and diagnosis (4.37 ± 0.826) and therapeutic modalities (4.36 ± 0.738) content areas were scored the highest in sufficient opportunities for real-time clinical proficiency evaluations. An inadequate volume of injuries or conditions (3.99 ± 1.033) and injury/condition occurrence not coinciding with the clinical proficiency assessment timetable (4.06 ± 0.995) were barriers to real-time evaluation. One-way analyses of variance revealed no difference between athletic training education program characteristics and the opportunities for and barriers to real-time evaluations among the various clinical experience settings. Conclusions: No one primary barrier hindered real-time clinical proficiency evaluation. To determine athletic training students' clinical proficiency for entry-level employment, athletic training education programs must incorporate standardized patients or take a disciplined approach to using simulation for instruction and evaluation. PMID:18668172

Application of Digital Image Analysis to Determine Pancreatic Islet Mass and Purity in Clinical Islet Isolation and Transplantation

PubMed Central

Wang, Ling-jia; Kissler, Hermann J; Wang, Xiaojun; Cochet, Olivia; Krzystyniak, Adam; Misawa, Ryosuke; Golab, Karolina; Tibudan, Martin; Grzanka, Jakub; Savari, Omid; Grose, Randall; Kaufman, Dixon B; Millis, Michael; Witkowski, Piotr

2015-01-01

Pancreatic islet mass, represented by islet equivalent (IEQ), is the most important parameter in decision making for clinical islet transplantation. To obtain IEQ, the sample of islets is routinely counted manually under a microscope and discarded thereafter. Islet purity, another parameter in islet processing, is routinely acquired by estimation only. In this study, we validated our digital image analysis (DIA) system developed using the software of Image Pro Plus for islet mass and purity assessment. Application of the DIA allows to better comply with current good manufacturing practice (cGMP) standards. Human islet samples were captured as calibrated digital images for the permanent record. Five trained technicians participated in determination of IEQ and purity by manual counting method and DIA. IEQ count showed statistically significant correlations between the manual method and DIA in all sample comparisons (r >0.819 and p < 0.0001). Statistically significant difference in IEQ between both methods was found only in High purity 100μL sample group (p = 0.029). As far as purity determination, statistically significant differences between manual assessment and DIA measurement was found in High and Low purity 100μL samples (p<0.005), In addition, islet particle number (IPN) and the IEQ/IPN ratio did not differ statistically between manual counting method and DIA. In conclusion, the DIA used in this study is a reliable technique in determination of IEQ and purity. Islet sample preserved as a digital image and results produced by DIA can be permanently stored for verification, technical training and islet information exchange between different islet centers. Therefore, DIA complies better with cGMP requirements than the manual counting method. We propose DIA as a quality control tool to supplement the established standard manual method for islets counting and purity estimation. PMID:24806436

Outcome Evaluation of "Cool and Clean", a Sports-Based Substance Use Prevention Programme for Young People in Switzerland

ERIC Educational Resources Information Center

Wicki, Matthias; Kuntsche, Sandra; Stucki, Stephanie; Marmet, Simon; Annaheim, Beatrice

2018-01-01

Aims: The aim of this study was to evaluate the outcomes of Cool and Clean, Switzerland's largest substance use prevention programme, targeted specifically at 10- to 20-year-olds who belong to a sports club and train as part of a team. Method: Based on a representative sample of young people who belong to a sports club and train as part of a team…

The effects of training married men about premenstrual syndrome by pamphlets and short messages on marital satisfaction.

PubMed

Morowatisharifabad, Mohammad Ali; Karimiankakolaki, Zohreh; Bokaie, Mahshid; Fallahzadeh, Hossein; Gerayllo, Sakineh

2014-12-01

Premenstrual syndrome (PMS), which includes physical, psychological and emotional symptoms that occur during the luteal phase of the menstrual cycle, has a negative impact on the quality of the relationship among married couples. The purpose of the study was to examine the effects of educating married men by two methods, pamphlet and short messages (SMS), on marital satisfaction of the couples. The study was experimental in nature. The sample consisted of 80 couples who had visited health centers in Yazd, Iran. The subjects were randomly assigned to the two training methods and pretested and post-tested on the outcome measures. The before to after the training increase in knowledge and practice in men and marital satisfaction of couples were statistically significant. The differences between the two training methods were not statistically significant. Pamphlets and SMS, if designed properly and based on the principles of psychology, can act as influential and almost equally effective educational tools in the context of PMS. © The Author 2014. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Characteristics of speaking style and implications for speech recognition.

PubMed

Shinozaki, Takahiro; Ostendorf, Mari; Atlas, Les

2009-09-01

Differences in speaking style are associated with more or less spectral variability, as well as different modulation characteristics. The greater variation in some styles (e.g., spontaneous speech and infant-directed speech) poses challenges for recognition but possibly also opportunities for learning more robust models, as evidenced by prior work and motivated by child language acquisition studies. In order to investigate this possibility, this work proposes a new method for characterizing speaking style (the modulation spectrum), examines spontaneous, read, adult-directed, and infant-directed styles in this space, and conducts pilot experiments in style detection and sampling for improved speech recognizer training. Speaking style classification is improved by using the modulation spectrum in combination with standard pitch and energy variation. Speech recognition experiments on a small vocabulary conversational speech recognition task show that sampling methods for training with a small amount of data benefit from the new features.

Prediction of near-surface soil moisture at large scale by digital terrain modeling and neural networks.

PubMed

Lavado Contador, J F; Maneta, M; Schnabel, S

2006-10-01

The capability of Artificial Neural Network models to forecast near-surface soil moisture at fine spatial scale resolution has been tested for a 99.5 ha watershed located in SW Spain using several easy to achieve digital models of topographic and land cover variables as inputs and a series of soil moisture measurements as training data set. The study methods were designed in order to determining the potentials of the neural network model as a tool to gain insight into soil moisture distribution factors and also in order to optimize the data sampling scheme finding the optimum size of the training data set. Results suggest the efficiency of the methods in forecasting soil moisture, as a tool to assess the optimum number of field samples, and the importance of the variables selected in explaining the final map obtained.

Increasing complexity of clinical research in gastroenterology: implications for the training of clinician-scientists.

PubMed

Scott, Frank I; McConnell, Ryan A; Lewis, Matthew E; Lewis, James D

2012-04-01

Significant advances have been made in clinical and epidemiologic research methods over the past 30 years. We sought to demonstrate the impact of these advances on published gastroenterology research from 1980 to 2010. Twenty original clinical articles were randomly selected from each of three journals from 1980, 1990, 2000, and 2010. Each article was assessed for topic, whether the outcome was clinical or physiologic, study design, sample size, number of authors and centers collaborating, reporting of various statistical methods, and external funding. From 1980 to 2010, there was a significant increase in analytic studies, clinical outcomes, number of authors per article, multicenter collaboration, sample size, and external funding. There was increased reporting of P values, confidence intervals, and power calculations, and increased use of large multicenter databases, multivariate analyses, and bioinformatics. The complexity of clinical gastroenterology and hepatology research has increased dramatically, highlighting the need for advanced training of clinical investigators.

Support vector machines-based fault diagnosis for turbo-pump rotor

NASA Astrophysics Data System (ADS)

Yuan, Sheng-Fa; Chu, Fu-Lei

2006-05-01

Most artificial intelligence methods used in fault diagnosis are based on empirical risk minimisation principle and have poor generalisation when fault samples are few. Support vector machines (SVM) is a new general machine-learning tool based on structural risk minimisation principle that exhibits good generalisation even when fault samples are few. Fault diagnosis based on SVM is discussed. Since basic SVM is originally designed for two-class classification, while most of fault diagnosis problems are multi-class cases, a new multi-class classification of SVM named 'one to others' algorithm is presented to solve the multi-class recognition problems. It is a binary tree classifier composed of several two-class classifiers organised by fault priority, which is simple, and has little repeated training amount, and the rate of training and recognition is expedited. The effectiveness of the method is verified by the application to the fault diagnosis for turbo pump rotor.

A computer-based program to teach braille reading to sighted individuals.

PubMed

Scheithauer, Mindy C; Tiger, Jeffrey H

2012-01-01

Instructors of the visually impaired need efficient braille-training methods. This study conducted a preliminary evaluation of a computer-based program intended to teach the relation between braille characters and English letters using a matching-to-sample format with 4 sighted college students. Each participant mastered matching visual depictions of the braille alphabet to their printed-word counterparts. Further, each participant increased the number of words they read in a braille passage following this training. These gains were maintained at variable levels on a maintenance probe conducted 2 to 4 weeks after training.

A COMPUTER-BASED PROGRAM TO TEACH BRAILLE READING TO SIGHTED INDIVIDUALS

PubMed Central

Scheithauer, Mindy C; Tiger, Jeffrey H

2012-01-01

Instructors of the visually impaired need efficient braille-training methods. This study conducted a preliminary evaluation of a computer-based program intended to teach the relation between braille characters and English letters using a matching-to-sample format with 4 sighted college students. Each participant mastered matching visual depictions of the braille alphabet to their printed-word counterparts. Further, each participant increased the number of words they read in a braille passage following this training. These gains were maintained at variable levels on a maintenance probe conducted 2 to 4 weeks after training. PMID:22844139

3D active shape models of human brain structures: application to patient-specific mesh generation

NASA Astrophysics Data System (ADS)

Ravikumar, Nishant; Castro-Mateos, Isaac; Pozo, Jose M.; Frangi, Alejandro F.; Taylor, Zeike A.

2015-03-01

The use of biomechanics-based numerical simulations has attracted growing interest in recent years for computer-aided diagnosis and treatment planning. With this in mind, a method for automatic mesh generation of brain structures of interest, using statistical models of shape (SSM) and appearance (SAM), for personalised computational modelling is presented. SSMs are constructed as point distribution models (PDMs) while SAMs are trained using intensity profiles sampled from a training set of T1-weighted magnetic resonance images. The brain structures of interest are, the cortical surface (cerebrum, cerebellum & brainstem), lateral ventricles and falx-cerebri membrane. Two methods for establishing correspondences across the training set of shapes are investigated and compared (based on SSM quality): the Coherent Point Drift (CPD) point-set registration method and B-spline mesh-to-mesh registration method. The MNI-305 (Montreal Neurological Institute) average brain atlas is used to generate the template mesh, which is deformed and registered to each training case, to establish correspondence over the training set of shapes. 18 healthy patients' T1-weightedMRimages form the training set used to generate the SSM and SAM. Both model-training and model-fitting are performed over multiple brain structures simultaneously. Compactness and generalisation errors of the BSpline-SSM and CPD-SSM are evaluated and used to quantitatively compare the SSMs. Leave-one-out cross validation is used to evaluate SSM quality in terms of these measures. The mesh-based SSM is found to generalise better and is more compact, relative to the CPD-based SSM. Quality of the best-fit model instance from the trained SSMs, to test cases are evaluated using the Hausdorff distance (HD) and mean absolute surface distance (MASD) metrics.

[Discrimination of types of polyacrylamide based on near infrared spectroscopy coupled with least square support vector machine].

PubMed

Zhang, Hong-Guang; Yang, Qin-Min; Lu, Jian-Gang

2014-04-01

In this paper, a novel discriminant methodology based on near infrared spectroscopic analysis technique and least square support vector machine was proposed for rapid and nondestructive discrimination of different types of Polyacrylamide. The diffuse reflectance spectra of samples of Non-ionic Polyacrylamide, Anionic Polyacrylamide and Cationic Polyacrylamide were measured. Then principal component analysis method was applied to reduce the dimension of the spectral data and extract of the principal compnents. The first three principal components were used for cluster analysis of the three different types of Polyacrylamide. Then those principal components were also used as inputs of least square support vector machine model. The optimization of the parameters and the number of principal components used as inputs of least square support vector machine model was performed through cross validation based on grid search. 60 samples of each type of Polyacrylamide were collected. Thus a total of 180 samples were obtained. 135 samples, 45 samples for each type of Polyacrylamide, were randomly split into a training set to build calibration model and the rest 45 samples were used as test set to evaluate the performance of the developed model. In addition, 5 Cationic Polyacrylamide samples and 5 Anionic Polyacrylamide samples adulterated with different proportion of Non-ionic Polyacrylamide were also prepared to show the feasibilty of the proposed method to discriminate the adulterated Polyacrylamide samples. The prediction error threshold for each type of Polyacrylamide was determined by F statistical significance test method based on the prediction error of the training set of corresponding type of Polyacrylamide in cross validation. The discrimination accuracy of the built model was 100% for prediction of the test set. The prediction of the model for the 10 mixing samples was also presented, and all mixing samples were accurately discriminated as adulterated samples. The overall results demonstrate that the discrimination method proposed in the present paper can rapidly and nondestructively discriminate the different types of Polyacrylamide and the adulterated Polyacrylamide samples, and offered a new approach to discriminate the types of Polyacrylamide.

Classification of teeth in cone-beam CT using deep convolutional neural network.

PubMed

Miki, Yuma; Muramatsu, Chisako; Hayashi, Tatsuro; Zhou, Xiangrong; Hara, Takeshi; Katsumata, Akitoshi; Fujita, Hiroshi

2017-01-01

Dental records play an important role in forensic identification. To this end, postmortem dental findings and teeth conditions are recorded in a dental chart and compared with those of antemortem records. However, most dentists are inexperienced at recording the dental chart for corpses, and it is a physically and mentally laborious task, especially in large scale disasters. Our goal is to automate the dental filing process by using dental x-ray images. In this study, we investigated the application of a deep convolutional neural network (DCNN) for classifying tooth types on dental cone-beam computed tomography (CT) images. Regions of interest (ROIs) including single teeth were extracted from CT slices. Fifty two CT volumes were randomly divided into 42 training and 10 test cases, and the ROIs obtained from the training cases were used for training the DCNN. For examining the sampling effect, random sampling was performed 3 times, and training and testing were repeated. We used the AlexNet network architecture provided in the Caffe framework, which consists of 5 convolution layers, 3 pooling layers, and 2 full connection layers. For reducing the overtraining effect, we augmented the data by image rotation and intensity transformation. The test ROIs were classified into 7 tooth types by the trained network. The average classification accuracy using the augmented training data by image rotation and intensity transformation was 88.8%. Compared with the result without data augmentation, data augmentation resulted in an approximately 5% improvement in classification accuracy. This indicates that the further improvement can be expected by expanding the CT dataset. Unlike the conventional methods, the proposed method is advantageous in obtaining high classification accuracy without the need for precise tooth segmentation. The proposed tooth classification method can be useful in automatic filing of dental charts for forensic identification. Copyright © 2016 Elsevier Ltd. All rights reserved.

Molecular cancer classification using a meta-sample-based regularized robust coding method.

PubMed

Wang, Shu-Lin; Sun, Liuchao; Fang, Jianwen

2014-01-01

Previous studies have demonstrated that machine learning based molecular cancer classification using gene expression profiling (GEP) data is promising for the clinic diagnosis and treatment of cancer. Novel classification methods with high efficiency and prediction accuracy are still needed to deal with high dimensionality and small sample size of typical GEP data. Recently the sparse representation (SR) method has been successfully applied to the cancer classification. Nevertheless, its efficiency needs to be improved when analyzing large-scale GEP data. In this paper we present the meta-sample-based regularized robust coding classification (MRRCC), a novel effective cancer classification technique that combines the idea of meta-sample-based cluster method with regularized robust coding (RRC) method. It assumes that the coding residual and the coding coefficient are respectively independent and identically distributed. Similar to meta-sample-based SR classification (MSRC), MRRCC extracts a set of meta-samples from the training samples, and then encodes a testing sample as the sparse linear combination of these meta-samples. The representation fidelity is measured by the l2-norm or l1-norm of the coding residual. Extensive experiments on publicly available GEP datasets demonstrate that the proposed method is more efficient while its prediction accuracy is equivalent to existing MSRC-based methods and better than other state-of-the-art dimension reduction based methods.

Systems and methods for selective detection and imaging in coherent Raman microscopy by spectral excitation shaping

DOEpatents

Xie, Xiaoliang Sunney; Freudiger, Christian; Min, Wei

2016-03-15

A microscopy imaging system is disclosed that includes a light source system, a spectral shaper, a modulator system, an optics system, an optical detector and a processor. The light source system is for providing a first train of pulses and a second train of pulses. The spectral shaper is for spectrally modifying an optical property of at least some frequency components of the broadband range of frequency components such that the broadband range of frequency components is shaped producing a shaped first train of pulses to specifically probe a spectral feature of interest from a sample, and to reduce information from features that are not of interest from the sample. The modulator system is for modulating a property of at least one of the shaped first train of pulses and the second train of pulses at a modulation frequency. The optical detector is for detecting an integrated intensity of substantially all optical frequency components of a train of pulses of interest transmitted or reflected through the common focal volume. The processor is for detecting a modulation at the modulation frequency of the integrated intensity of substantially all of the optical frequency components of the train of pulses of interest due to the non-linear interaction of the shaped first train of pulses with the second train of pulses as modulated in the common focal volume, and for providing an output signal for a pixel of an image for the microscopy imaging system.

Co-Labeling for Multi-View Weakly Labeled Learning.

PubMed

Xu, Xinxing; Li, Wen; Xu, Dong; Tsang, Ivor W

2016-06-01

It is often expensive and time consuming to collect labeled training samples in many real-world applications. To reduce human effort on annotating training samples, many machine learning techniques (e.g., semi-supervised learning (SSL), multi-instance learning (MIL), etc.) have been studied to exploit weakly labeled training samples. Meanwhile, when the training data is represented with multiple types of features, many multi-view learning methods have shown that classifiers trained on different views can help each other to better utilize the unlabeled training samples for the SSL task. In this paper, we study a new learning problem called multi-view weakly labeled learning, in which we aim to develop a unified approach to learn robust classifiers by effectively utilizing different types of weakly labeled multi-view data from a broad range of tasks including SSL, MIL and relative outlier detection (ROD). We propose an effective approach called co-labeling to solve the multi-view weakly labeled learning problem. Specifically, we model the learning problem on each view as a weakly labeled learning problem, which aims to learn an optimal classifier from a set of pseudo-label vectors generated by using the classifiers trained from other views. Unlike traditional co-training approaches using a single pseudo-label vector for training each classifier, our co-labeling approach explores different strategies to utilize the predictions from different views, biases and iterations for generating the pseudo-label vectors, making our approach more robust for real-world applications. Moreover, to further improve the weakly labeled learning on each view, we also exploit the inherent group structure in the pseudo-label vectors generated from different strategies, which leads to a new multi-layer multiple kernel learning problem. Promising results for text-based image retrieval on the NUS-WIDE dataset as well as news classification and text categorization on several real-world multi-view datasets clearly demonstrate that our proposed co-labeling approach achieves state-of-the-art performance for various multi-view weakly labeled learning problems including multi-view SSL, multi-view MIL and multi-view ROD.

Tabu search and binary particle swarm optimization for feature selection using microarray data.

PubMed

Chuang, Li-Yeh; Yang, Cheng-Huei; Yang, Cheng-Hong

2009-12-01

Gene expression profiles have great potential as a medical diagnosis tool because they represent the state of a cell at the molecular level. In the classification of cancer type research, available training datasets generally have a fairly small sample size compared to the number of genes involved. This fact poses an unprecedented challenge to some classification methodologies due to training data limitations. Therefore, a good selection method for genes relevant for sample classification is needed to improve the predictive accuracy, and to avoid incomprehensibility due to the large number of genes investigated. In this article, we propose to combine tabu search (TS) and binary particle swarm optimization (BPSO) for feature selection. BPSO acts as a local optimizer each time the TS has been run for a single generation. The K-nearest neighbor method with leave-one-out cross-validation and support vector machine with one-versus-rest serve as evaluators of the TS and BPSO. The proposed method is applied and compared to the 11 classification problems taken from the literature. Experimental results show that our method simplifies features effectively and either obtains higher classification accuracy or uses fewer features compared to other feature selection methods.

PONS2train: tool for testing the MLP architecture and local traning methods for runoff forecast

NASA Astrophysics Data System (ADS)

Maca, P.; Pavlasek, J.; Pech, P.

2012-04-01

The purpose of presented poster is to introduce the PONS2train developed for runoff prediction via multilayer perceptron - MLP. The software application enables the implementation of 12 different MLP's transfer functions, comparison of 9 local training algorithms and finally the evaluation the MLP performance via 17 selected model evaluation metrics. The PONS2train software is written in C++ programing language. Its implementation consists of 4 classes. The NEURAL_NET and NEURON classes implement the MLP, the CRITERIA class estimates model evaluation metrics and for model performance evaluation via testing and validation datasets. The DATA_PATTERN class prepares the validation, testing and calibration datasets. The software application uses the LAPACK, BLAS and ARMADILLO C++ linear algebra libraries. The PONS2train implements the first order local optimization algorithms: standard on-line and batch back-propagation with learning rate combined with momentum and its variants with the regularization term, Rprop and standard batch back-propagation with variable momentum and learning rate. The second order local training algorithms represents: the Levenberg-Marquardt algorithm with and without regularization and four variants of scaled conjugate gradients. The other important PONS2train features are: the multi-run, the weight saturation control, early stopping of trainings, and the MLP weights analysis. The weights initialization is done via two different methods: random sampling from uniform distribution on open interval or Nguyen Widrow method. The data patterns can be transformed via linear and nonlinear transformation. The runoff forecast case study focuses on PONS2train implementation and shows the different aspects of the MLP training, the MLP architecture estimation, the neural network weights analysis and model uncertainty estimation.

Fusion of shallow and deep features for classification of high-resolution remote sensing images

NASA Astrophysics Data System (ADS)

Gao, Lang; Tian, Tian; Sun, Xiao; Li, Hang

2018-02-01

Effective spectral and spatial pixel description plays a significant role for the classification of high resolution remote sensing images. Current approaches of pixel-based feature extraction are of two main kinds: one includes the widelyused principal component analysis (PCA) and gray level co-occurrence matrix (GLCM) as the representative of the shallow spectral and shape features, and the other refers to the deep learning-based methods which employ deep neural networks and have made great promotion on classification accuracy. However, the former traditional features are insufficient to depict complex distribution of high resolution images, while the deep features demand plenty of samples to train the network otherwise over fitting easily occurs if only limited samples are involved in the training. In view of the above, we propose a GLCM-based convolution neural network (CNN) approach to extract features and implement classification for high resolution remote sensing images. The employment of GLCM is able to represent the original images and eliminate redundant information and undesired noises. Meanwhile, taking shallow features as the input of deep network will contribute to a better guidance and interpretability. In consideration of the amount of samples, some strategies such as L2 regularization and dropout methods are used to prevent over-fitting. The fine-tuning strategy is also used in our study to reduce training time and further enhance the generalization performance of the network. Experiments with popular data sets such as PaviaU data validate that our proposed method leads to a performance improvement compared to individual involved approaches.

ALCHEMY: a reliable method for automated SNP genotype calling for small batch sizes and highly homozygous populations

PubMed Central

Wright, Mark H.; Tung, Chih-Wei; Zhao, Keyan; Reynolds, Andy; McCouch, Susan R.; Bustamante, Carlos D.

2010-01-01

Motivation: The development of new high-throughput genotyping products requires a significant investment in testing and training samples to evaluate and optimize the product before it can be used reliably on new samples. One reason for this is current methods for automated calling of genotypes are based on clustering approaches which require a large number of samples to be analyzed simultaneously, or an extensive training dataset to seed clusters. In systems where inbred samples are of primary interest, current clustering approaches perform poorly due to the inability to clearly identify a heterozygote cluster. Results: As part of the development of two custom single nucleotide polymorphism genotyping products for Oryza sativa (domestic rice), we have developed a new genotype calling algorithm called ‘ALCHEMY’ based on statistical modeling of the raw intensity data rather than modelless clustering. A novel feature of the model is the ability to estimate and incorporate inbreeding information on a per sample basis allowing accurate genotyping of both inbred and heterozygous samples even when analyzed simultaneously. Since clustering is not used explicitly, ALCHEMY performs well on small sample sizes with accuracy exceeding 99% with as few as 18 samples. Availability: ALCHEMY is available for both commercial and academic use free of charge and distributed under the GNU General Public License at http://alchemy.sourceforge.net/ Contact: mhw6@cornell.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:20926420

Mark 4A project training evaluation

NASA Technical Reports Server (NTRS)

Stephenson, S. N.

1985-01-01

A participant evaluation of a Deep Space Network (DSN) is described. The Mark IVA project is an implementation to upgrade the tracking and data acquisition systems of the dSN. Approximately six hundred DSN operations and engineering maintenance personnel were surveyed. The survey obtained a convenience sample including trained people within the population in order to learn what training had taken place and to what effect. The survey questionnaire used modifications of standard rating scales to evaluate over one hundred items in four training dimensions. The scope of the evaluation included Mark IVA vendor training, a systems familiarization training seminar, engineering training classes, a on-the-job training. Measures of central tendency were made from participant rating responses. Chi square tests of statistical significance were performed on the data. The evaluation results indicated that the effects of different Mark INA training methods could be measured according to certain ratings of technical training effectiveness, and that the Mark IVA technical training has exhibited positive effects on the abilities of DSN personnel to operate and maintain new Mark IVA equipment systems.

Chemometric brand differentiation of commercial spices using direct analysis in real time mass spectrometry.

PubMed

Pavlovich, Matthew J; Dunn, Emily E; Hall, Adam B

2016-05-15

Commercial spices represent an emerging class of fuels for improvised explosives. Being able to classify such spices not only by type but also by brand would represent an important step in developing methods to analytically investigate these explosive compositions. Therefore, a combined ambient mass spectrometric/chemometric approach was developed to quickly and accurately classify commercial spices by brand. Direct analysis in real time mass spectrometry (DART-MS) was used to generate mass spectra for samples of black pepper, cayenne pepper, and turmeric, along with four different brands of cinnamon, all dissolved in methanol. Unsupervised learning techniques showed that the cinnamon samples clustered according to brand. Then, we used supervised machine learning algorithms to build chemometric models with a known training set and classified the brands of an unknown testing set of cinnamon samples. Ten independent runs of five-fold cross-validation showed that the training set error for the best-performing models (i.e., the linear discriminant and neural network models) was lower than 2%. The false-positive percentages for these models were 3% or lower, and the false-negative percentages were lower than 10%. In particular, the linear discriminant model perfectly classified the testing set with 0% error. Repeated iterations of training and testing gave similar results, demonstrating the reproducibility of these models. Chemometric models were able to classify the DART mass spectra of commercial cinnamon samples according to brand, with high specificity and low classification error. This method could easily be generalized to other classes of spices, and it could be applied to authenticating questioned commercial samples of spices or to examining evidence from improvised explosives. Copyright © 2016 John Wiley & Sons, Ltd.

Operation of a sampling train for the analysis of environmental species in coal gasification gas-phase process streams

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

Pochan, M.J.; Massey, M.J.

1979-02-01

This report discusses the results of actual raw product gas sampling efforts and includes: Rationale for raw product gas sampling efforts; design and operation of the CMU gas sampling train; development and analysis of a sampling train data base; and conclusions and future application of results. The results of sampling activities at the CO/sub 2/-Acceptor and Hygas pilot plants proved that: The CMU gas sampling train is a valid instrument for characterization of environmental parameters in coal gasification gas-phase process streams; depending on the particular process configuration, the CMU gas sampling train can reduce gasifier effluent characterization activity to amore » single location in the raw product gas line; and in contrast to the slower operation of the EPA SASS Train, CMU's gas sampling train can collect representative effluent data at a rapid rate (approx. 2 points per hour) consistent with the rate of change of process variables, and thus function as a tool for process engineering-oriented analysis of environmental characteristics.« less

Automated source classification of new transient sources

NASA Astrophysics Data System (ADS)

Oertel, M.; Kreikenbohm, A.; Wilms, J.; DeLuca, A.

2017-10-01

The EXTraS project harvests the hitherto unexplored temporal domain information buried in the serendipitous data collected by the European Photon Imaging Camera (EPIC) onboard the ESA XMM-Newton mission since its launch. This includes a search for fast transients, missed by standard image analysis, and a search and characterization of variability in hundreds of thousands of sources. We present an automated classification scheme for new transient sources in the EXTraS project. The method is as follows: source classification features of a training sample are used to train machine learning algorithms (performed in R; randomForest (Breiman, 2001) in supervised mode) which are then tested on a sample of known source classes and used for classification.

A Tester for Carbon Nanotube Mode Lockers

NASA Astrophysics Data System (ADS)

Song, Yong-Won; Yamashita, Shinji

2007-05-01

We propose and demonstrate a tester for laser pulsating operation of carbon nanotubes employing a circulator with the extra degree of freedom of the second port to access diversified nanotube samples. The nanotubes are deposited onto the end facet of a dummy optical fiber by spray method that guarantees simple sample loading along with the minimized perturbation of optimized laser cavity condition. Resultant optical spectra, autocorrelation traces and pulse train of the laser outputs with qualified samples are presented.

The relationship between hospital managers' leadership style and effectiveness with passing managerial training courses.

PubMed

Saleh Ardestani, Abbas; Sarabi Asiabar, Ali; Ebadifard Azar, Farbod; Abtahi, Seyyed Ali

2016-01-01

Background: Effective leadership that rises from managerial training courses is highly constructive in managing hospitals more effectively. This study aims at investigating the relationship between leadership effectiveness with providing management training courses for hospital managers. Methods: This was a cross-sectional study carried out on top and middle managers of 16 hospitals of Iran University of Medical Sciences. As a sample, 96 participants were selected through census method. Data were collected using leadership effectiveness and style questionnaire, whose validity and reliability were certified in previous studies. Pearson correlation coefficient and linear regressions were used for data analysis. Results: Leadership effectiveness score was estimated to be 4.36, showing a suitable status for managers' leadership effectiveness compared to the set criteria. No significant difference was found between leadership effectiveness and styles among managers who had passed the training courses with those who had not (p>0.05). Conclusion: Passing managerial training courses may have no significant effect on managers' leadership effectiveness, but there may be some other variables which should be meticulously studied.

The relationship between hospital managers' leadership style and effectiveness with passing managerial training courses

PubMed Central

Saleh Ardestani, Abbas; Sarabi Asiabar, Ali; Ebadifard Azar, Farbod; Abtahi, Seyyed Ali

2016-01-01

Background: Effective leadership that rises from managerial training courses is highly constructive in managing hospitals more effectively. This study aims at investigating the relationship between leadership effectiveness with providing management training courses for hospital managers. Methods: This was a cross-sectional study carried out on top and middle managers of 16 hospitals of Iran University of Medical Sciences. As a sample, 96 participants were selected through census method. Data were collected using leadership effectiveness and style questionnaire, whose validity and reliability were certified in previous studies. Pearson correlation coefficient and linear regressions were used for data analysis. Results: Leadership effectiveness score was estimated to be 4.36, showing a suitable status for managers' leadership effectiveness compared to the set criteria. No significant difference was found between leadership effectiveness and styles among managers who had passed the training courses with those who had not (p>0.05). Conclusion: Passing managerial training courses may have no significant effect on managers' leadership effectiveness, but there may be some other variables which should be meticulously studied. PMID:28491840

Understanding biological mechanisms underlying adverse birth outcomes in developing countries: protocol for a prospective cohort (AMANHI bio–banking) study

PubMed Central

Baqui, Abdullah H; Khanam, Rasheda; Rahman, Mohammad Sayedur; Ahmed, Aziz; Rahman, Hasna Hena; Moin, Mamun Ibne; Ahmed, Salahuddin; Jehan, Fyezah; Nisar, Imran; Hussain, Atiya; Ilyas, Muhammad; Hotwani, Aneeta; Sajid, Muhammad; Qureshi, Shahida; Zaidi, Anita; Sazawal, Sunil; Ali, Said M; Deb, Saikat; Juma, Mohammed Hamad; Dhingra, Usha; Dutta, Arup; Ame, Shaali Makame; Hayward, Caroline; Rudan, Igor; Zangenberg, Mike; Russell, Donna; Yoshida, Sachiyo; Polašek, Ozren; Manu, Alexander; Bahl, Rajiv

2017-01-01

Objectives The AMANHI study aims to seek for biomarkers as predictors of important pregnancy–related outcomes, and establish a biobank in developing countries for future research as new methods and technologies become available. Methods AMANHI is using harmonised protocols to enrol 3000 women in early pregnancies (8–19 weeks of gestation) for population–based follow–up in pregnancy up to 42 days postpartum in Bangladesh, Pakistan and Tanzania, with collection taking place between August 2014 and June 2016. Urine pregnancy tests will be used to confirm reported or suspected pregnancies for screening ultrasound by trained sonographers to accurately date the pregnancy. Trained study field workers will collect very detailed phenotypic and epidemiological data from the pregnant woman and her family at scheduled home visits during pregnancy (enrolment, 24–28 weeks, 32–36 weeks & 38+ weeks) and postpartum (days 0–6 or 42–60). Trained phlebotomists will collect maternal and umbilical blood samples, centrifuge and obtain aliquots of serum, plasma and the buffy coat for storage. They will also measure HbA1C and collect a dried spot sample of whole blood. Maternal urine samples will also be collected and stored, alongside placenta, umbilical cord tissue and membrane samples, which will both be frozen and prepared for histology examination. Maternal and newborn stool (for microbiota) as well as paternal and newborn saliva samples (for DNA extraction) will also be collected. All samples will be stored at –80°C in the biobank in each of the three sites. These samples will be linked to numerous epidemiological and phenotypic data with unique study identification numbers. Importance of the study AMANHI biobank proves that biobanking is feasible to implement in LMICs, but recognises that biobank creation is only the first step in addressing current global challenges. PMID:29163938

Stakeholder-focused evaluation of an online course for health care providers.

PubMed

Dunet, Diane O; Reyes, Michele

2006-01-01

Different people who have a stake or interest in a training course (stakeholders) may have markedly different definitions of what constitutes "training success" and how they will use evaluation results. Stakeholders at multiple levels within and outside of the organization guided the development of an evaluation plan for a Web-based training course on hemochromatosis. Stakeholder interests and values were reflected in the type, level, and rigor of evaluation methods selected. Our mixed-method evaluation design emphasized small sample sizes and repeated measures. Limited resources for evaluation were leveraged by focusing on the data needs of key stakeholders, understanding how they wanted to use evaluation results, and collecting data needed for stakeholder decision making. Regular feedback to key stakeholders provided opportunities for updating the course evaluation plan to meet emerging needs for new or different information. Early and repeated involvement of stakeholders in the evaluation process also helped build support for the final product. Involving patient advocacy groups, managers, and representative course participants improved the course and enhanced product dissemination. For training courses, evaluation planning is an opportunity to tailor methods and data collection to meet the information needs of particular stakeholders. Rigorous evaluation research of every training course may be infeasible or unwarranted; however, course evaluations can be improved by good planning. A stakeholder-focused approach can build a picture of the results and impact of training while fostering the practical use of evaluation data.

Comparison of the effect of web-based, simulation-based, and conventional training on the accuracy of visual estimation of postpartum hemorrhage volume on midwifery students: A randomized clinical trial

PubMed Central

Kordi, Masoumeh; Fakari, Farzaneh Rashidi; Mazloum, Seyed Reza; Khadivzadeh, Talaat; Akhlaghi, Farideh; Tara, Mahmoud

2016-01-01

Introduction: Delay in diagnosis of bleeding can be due to underestimation of the actual amount of blood loss during delivery. Therefore, this research aimed to compare the efficacy of web-based, simulation-based, and conventional training on the accuracy of visual estimation of postpartum hemorrhage volume. Materials and Methods: This three-group randomized clinical trial study was performed on 105 midwifery students in Mashhad School of Nursing and Midwifery in 2013. The samples were selected by the convenience method and were randomly divided into three groups of web-based, simulation-based, and conventional training. The three groups participated before and 1 week after the training course in eight station practical tests, then, the students of the web-based group were trained on-line for 1 week, the students of the simulation-based group were trained in the Clinical Skills Centre for 4 h, and the students of the conventional group were trained for 4 h presentation by researchers. The data gathering tool was a demographic questionnaire designed by the researchers and objective structured clinical examination. Data were analyzed by software version 11.5. Results: The accuracy of visual estimation of postpartum hemorrhage volume after training increased significantly in the three groups at all stations (1, 2, 4, 5, 6 and 7 (P = 0.001), 8 (P = 0.027)) except station 3 (blood loss of 20 cc, P = 0.095), but the mean score of blood loss estimation after training did not significantly different between the three groups (P = 0.95). Conclusion: Training increased the accuracy of estimation of postpartum hemorrhage, but no significant difference was found among the three training groups. We can use web-based training as a substitute or supplement of training along with two other more common simulation and conventional methods. PMID:27500175

Nurses' Lived Experience of Working with Nursing Students in Clinical Wards: a Phenomenological Study

PubMed Central

Parvan, Kobra; Shahbazi, Shahla; Ebrahimi, Hossein; Valizadeh, Susan; Rahmani, Azad; Jabbarzadeh Tabrizi, Faranak; Esmaili, Fariba

2018-01-01

Introduction: Despite being aware of the importance of nurses’ role in providing clinical training to nursing students, studies show that sufficient research has not yet been conducted on the experience of clinical nurses who are engaged in training nursing students outside their normal working hours. The present study aim to describe the experience of these nurses who are training outside their routine working hours. Methods: This study was conducted using descriptive-phenomenology method. Twelve nurses was participated in this research. Data were collected using purposive sampling method and face to face interviews based on nurses’ real life experience of students’ learning in clinical settings through answering open-ended questions. Spiegel burg analysis method was used to analyze the data. Results: The result of data analysis was the derivation of four themes and eight sub-themes. Themes included "nurses as teaching sources", "changes in the balance of doing routine tasks", "professional enthusiasm", and "nurses as students' professional socialization source of inspiration". Sub-themes included "efficient education", "poor education", "support", "interference in the role," "self-efficacy development", "inner satisfaction", "positive imaging" and "being a model". Conclusion: It is necessary that academic centers plan for teaching nurses working on a contractual basis in the field of the evaluation method and various methods of teaching. The findings also suggested the development of individual self-efficacy in clinical nurses who train students. PMID:29637056

Automatic detection of apical roots in oral radiographs

NASA Astrophysics Data System (ADS)

Wu, Yi; Xie, Fangfang; Yang, Jie; Cheng, Erkang; Megalooikonomou, Vasileios; Ling, Haibin

2012-03-01

The apical root regions play an important role in analysis and diagnosis of many oral diseases. Automatic detection of such regions is consequently the first step toward computer-aided diagnosis of these diseases. In this paper we propose an automatic method for periapical root region detection by using the state-of-theart machine learning approaches. Specifically, we have adapted the AdaBoost classifier for apical root detection. One challenge in the task is the lack of training cases especially for diseased ones. To handle this problem, we boost the training set by including more root regions that are close to the annotated ones and decompose the original images to randomly generate negative samples. Based on these training samples, the Adaboost algorithm in combination with Haar wavelets is utilized in this task to train an apical root detector. The learned detector usually generates a large amount of true and false positives. In order to reduce the number of false positives, a confidence score for each candidate detection result is calculated for further purification. We first merge the detected regions by combining tightly overlapped detected candidate regions and then we use the confidence scores from the Adaboost detector to eliminate the false positives. The proposed method is evaluated on a dataset containing 39 annotated digitized oral X-Ray images from 21 patients. The experimental results show that our approach can achieve promising detection accuracy.

Assessment of a prevention program for work-related stress among urban police officers

PubMed Central

Arnetz, Bengt B.; Backman, Lena; Lynch, Adam; Lublin, Ake

2013-01-01

Objective To determine the efficacy of a primary prevention program designed to improve psychobiological responses to stress among urban police officers. Methods A random sample of 37 police cadets received complementary training in psychological and technical techniques to reduce anxiety and enhance performance when facing a series of police critical incidents. Training was done by Special Forces officers, trained by the authors in imaging. A random sample of 38 cadets, receiving training as usual, was followed in parallel. Assessment of somatic and psychological health, and stress biomarkers, was done at baseline, immediately following training, and after 18 months as regular police officers. Comparison was done using two-way repeated analysis of variance (ANOVA) and logistic regression. Results The intervention group improved their general health and problem-based coping as compared to the control group. They also demonstrated lower levels of stomach problems, sleep difficulties, and exhaustion. Training was associated with an OR of 4.1 (95% CI, 1.3–13.7; p < 0.05) for improved GHQ scores during the study as compared to no changes or worsening score. Conclusions This first primary prevention study of high-risk professions demonstrates the validity and functional utility of the intervention. Beneficial effects lasted at least during the first 2 years on the police force. It is suggested that preventive imagery training in first responders might contribute to enhanced resiliency. PMID:22366986

Spatial-temporal discriminant analysis for ERP-based brain-computer interface.

PubMed

Zhang, Yu; Zhou, Guoxu; Zhao, Qibin; Jin, Jing; Wang, Xingyu; Cichocki, Andrzej

2013-03-01

Linear discriminant analysis (LDA) has been widely adopted to classify event-related potential (ERP) in brain-computer interface (BCI). Good classification performance of the ERP-based BCI usually requires sufficient data recordings for effective training of the LDA classifier, and hence a long system calibration time which however may depress the system practicability and cause the users resistance to the BCI system. In this study, we introduce a spatial-temporal discriminant analysis (STDA) to ERP classification. As a multiway extension of the LDA, the STDA method tries to maximize the discriminant information between target and nontarget classes through finding two projection matrices from spatial and temporal dimensions collaboratively, which reduces effectively the feature dimensionality in the discriminant analysis, and hence decreases significantly the number of required training samples. The proposed STDA method was validated with dataset II of the BCI Competition III and dataset recorded from our own experiments, and compared to the state-of-the-art algorithms for ERP classification. Online experiments were additionally implemented for the validation. The superior classification performance in using few training samples shows that the STDA is effective to reduce the system calibration time and improve the classification accuracy, thereby enhancing the practicability of ERP-based BCI.

30 CFR 75.338 - Training.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 30 Mineral Resources 1 2011-07-01 2011-07-01 false Training. 75.338 Section 75.338 Mineral... SAFETY STANDARDS-UNDERGROUND COAL MINES Ventilation § 75.338 Training. (a) Certified persons conducting sampling shall be trained in the use of appropriate sampling equipment, procedures, location of sampling...

30 CFR 75.338 - Training.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Training. 75.338 Section 75.338 Mineral... SAFETY STANDARDS-UNDERGROUND COAL MINES Ventilation § 75.338 Training. (a) Certified persons conducting sampling shall be trained in the use of appropriate sampling equipment, procedures, location of sampling...

Synthesis of Common Arabic Handwritings to Aid Optical Character Recognition Research.

PubMed

Dinges, Laslo; Al-Hamadi, Ayoub; Elzobi, Moftah; El-Etriby, Sherif

2016-03-11

Document analysis tasks such as pattern recognition, word spotting or segmentation, require comprehensive databases for training and validation. Not only variations in writing style but also the used list of words is of importance in the case that training samples should reflect the input of a specific area of application. However, generation of training samples is expensive in the sense of manpower and time, particularly if complete text pages including complex ground truth are required. This is why there is a lack of such databases, especially for Arabic, the second most popular language. However, Arabic handwriting recognition involves different preprocessing, segmentation and recognition methods. Each requires particular ground truth or samples to enable optimal training and validation, which are often not covered by the currently available databases. To overcome this issue, we propose a system that synthesizes Arabic handwritten words and text pages and generates corresponding detailed ground truth. We use these syntheses to validate a new, segmentation based system that recognizes handwritten Arabic words. We found that a modification of an Active Shape Model based character classifiers-that we proposed earlier-improves the word recognition accuracy. Further improvements are achieved, by using a vocabulary of the 50,000 most common Arabic words for error correction.

Synthesis of Common Arabic Handwritings to Aid Optical Character Recognition Research

PubMed Central

Dinges, Laslo; Al-Hamadi, Ayoub; Elzobi, Moftah; El-etriby, Sherif

2016-01-01

Document analysis tasks such as pattern recognition, word spotting or segmentation, require comprehensive databases for training and validation. Not only variations in writing style but also the used list of words is of importance in the case that training samples should reflect the input of a specific area of application. However, generation of training samples is expensive in the sense of manpower and time, particularly if complete text pages including complex ground truth are required. This is why there is a lack of such databases, especially for Arabic, the second most popular language. However, Arabic handwriting recognition involves different preprocessing, segmentation and recognition methods. Each requires particular ground truth or samples to enable optimal training and validation, which are often not covered by the currently available databases. To overcome this issue, we propose a system that synthesizes Arabic handwritten words and text pages and generates corresponding detailed ground truth. We use these syntheses to validate a new, segmentation based system that recognizes handwritten Arabic words. We found that a modification of an Active Shape Model based character classifiers—that we proposed earlier—improves the word recognition accuracy. Further improvements are achieved, by using a vocabulary of the 50,000 most common Arabic words for error correction. PMID:26978368

The role of legitimation in the professional socialization of second-year undergraduate athletic training students.

PubMed

Klossner, Joanne

2008-01-01

Professional socialization during formal educational preparation can help students learn professional roles and can lead to improved organizational socialization as students emerge as members of the occupation's culture. Professional socialization research in athletic training is limited. To present the role of legitimation and how it influences the professional socialization of second-year athletic training students. Modified constructivist grounded theory and case study methods were used for this qualitative study. An accredited undergraduate athletic training education program. Twelve second-year students were selected purposively. The primary sample group (n = 4) was selected according to theoretical sampling guidelines. The remaining students made up the cohort sample (n = 8). Theoretically relevant data were gathered from 14 clinical instructors to clarify emergent student data. Data collection included document examination, observations, and interviews during 1 academic semester. Data were collected and analyzed through constant comparative analysis. Data triangulation, member checking, and peer-review strategies were used to ensure trustworthiness. Legitimation from various socializing agents initiated professional socialization. Students viewed trust and team membership as rewards for role fulfillment. My findings are consistent with the socialization literature that shows how learning a social or professional role, using rewards to facilitate role performance, and building trusting relationships with socializing agents are important aspects of legitimation and, ultimately, professional socialization.

Development of a Self-Rated Mixed Methods Skills Assessment: The National Institutes of Health Mixed Methods Research Training Program for the Health Sciences.

PubMed

Guetterman, Timothy C; Creswell, John W; Wittink, Marsha; Barg, Fran K; Castro, Felipe G; Dahlberg, Britt; Watkins, Daphne C; Deutsch, Charles; Gallo, Joseph J

2017-01-01

Demand for training in mixed methods is high, with little research on faculty development or assessment in mixed methods. We describe the development of a self-rated mixed methods skills assessment and provide validity evidence. The instrument taps six research domains: "Research question," "Design/approach," "Sampling," "Data collection," "Analysis," and "Dissemination." Respondents are asked to rate their ability to define or explain concepts of mixed methods under each domain, their ability to apply the concepts to problems, and the extent to which they need to improve. We administered the questionnaire to 145 faculty and students using an internet survey. We analyzed descriptive statistics and performance characteristics of the questionnaire using the Cronbach alpha to assess reliability and an analysis of variance that compared a mixed methods experience index with assessment scores to assess criterion relatedness. Internal consistency reliability was high for the total set of items (0.95) and adequate (≥0.71) for all but one subscale. Consistent with establishing criterion validity, respondents who had more professional experiences with mixed methods (eg, published a mixed methods article) rated themselves as more skilled, which was statistically significant across the research domains. This self-rated mixed methods assessment instrument may be a useful tool to assess skills in mixed methods for training programs. It can be applied widely at the graduate and faculty level. For the learner, assessment may lead to enhanced motivation to learn and training focused on self-identified needs. For faculty, the assessment may improve curriculum and course content planning.

Semi-supervised SVM for individual tree crown species classification

NASA Astrophysics Data System (ADS)

Dalponte, Michele; Ene, Liviu Theodor; Marconcini, Mattia; Gobakken, Terje; Næsset, Erik

2015-12-01

In this paper a novel semi-supervised SVM classifier is presented, specifically developed for tree species classification at individual tree crown (ITC) level. In ITC tree species classification, all the pixels belonging to an ITC should have the same label. This assumption is used in the learning of the proposed semi-supervised SVM classifier (ITC-S3VM). This method exploits the information contained in the unlabeled ITC samples in order to improve the classification accuracy of a standard SVM. The ITC-S3VM method can be easily implemented using freely available software libraries. The datasets used in this study include hyperspectral imagery and laser scanning data acquired over two boreal forest areas characterized by the presence of three information classes (Pine, Spruce, and Broadleaves). The experimental results quantify the effectiveness of the proposed approach, which provides classification accuracies significantly higher (from 2% to above 27%) than those obtained by the standard supervised SVM and by a state-of-the-art semi-supervised SVM (S3VM). Particularly, by reducing the number of training samples (i.e. from 100% to 25%, and from 100% to 5% for the two datasets, respectively) the proposed method still exhibits results comparable to the ones of a supervised SVM trained with the full available training set. This property of the method makes it particularly suitable for practical forest inventory applications in which collection of in situ information can be very expensive both in terms of cost and time.

Experiences of patients with multiple sclerosis from group counseling.

PubMed

Mazaheri, Mina; Fanian, Nasrin; Zargham-Boroujeni, Ali

2011-01-01

Group counseling is one of the most important methods in somatic and psychological rehabilitation of the multiple sclerosis (M.S.) patients. Knowing these patients' experiences, feelings, believes and emotion based on learning in group is necessary to indicate the importance of group discussion on quality of life of the patients. This study was done to achieve experiences of M.S. patients from group training. This was a qualitative study using phenomenological method. The samples were selected using purposeful sampling. Ten patients from M.S. society who had passed group training were included in the study. The group training was done through seven sessions weekly and voluntarily. The participants were interviewed using in-depth interview. The average time of each interview was between 30-50 minutes which has been recorded digitally and moved to a compact disc to transcribe and analysis. The data analyzed using 7-step Colaizzi method. The data were transformed into 158 codes, 12 sub-concepts and 4 main concepts including emotional consequences, communication, quality of life and needs. M.S can lead to multiple problems in patients such as somatic, behavioral, emotional and social disorders. Group psychotherapy is one of the methods which can decrease these problems and improve rehabilitation of the patients. Group discussion helps patients to overcome adverse feelings, behaviors and thoughts and guides them to move in a meaningful life. It also can improve quality of life and mental health of the patients.

Sparse Representation with Spatio-Temporal Online Dictionary Learning for Efficient Video Coding.

PubMed

Dai, Wenrui; Shen, Yangmei; Tang, Xin; Zou, Junni; Xiong, Hongkai; Chen, Chang Wen

2016-07-27

Classical dictionary learning methods for video coding suer from high computational complexity and interfered coding eciency by disregarding its underlying distribution. This paper proposes a spatio-temporal online dictionary learning (STOL) algorithm to speed up the convergence rate of dictionary learning with a guarantee of approximation error. The proposed algorithm incorporates stochastic gradient descents to form a dictionary of pairs of 3-D low-frequency and highfrequency spatio-temporal volumes. In each iteration of the learning process, it randomly selects one sample volume and updates the atoms of dictionary by minimizing the expected cost, rather than optimizes empirical cost over the complete training data like batch learning methods, e.g. K-SVD. Since the selected volumes are supposed to be i.i.d. samples from the underlying distribution, decomposition coecients attained from the trained dictionary are desirable for sparse representation. Theoretically, it is proved that the proposed STOL could achieve better approximation for sparse representation than K-SVD and maintain both structured sparsity and hierarchical sparsity. It is shown to outperform batch gradient descent methods (K-SVD) in the sense of convergence speed and computational complexity, and its upper bound for prediction error is asymptotically equal to the training error. With lower computational complexity, extensive experiments validate that the STOL based coding scheme achieves performance improvements than H.264/AVC or HEVC as well as existing super-resolution based methods in ratedistortion performance and visual quality.

Method and apparatus for in-process sensing of manufacturing quality

DOEpatents

Hartman, Daniel A [Santa Fe, NM; Dave, Vivek R [Los Alamos, NM; Cola, Mark J [Santa Fe, NM; Carpenter, Robert W [Los Alamos, NM

2005-02-22

A method for determining the quality of an examined weld joint comprising the steps of providing acoustical data from the examined weld joint, and performing a neural network operation on the acoustical data determine the quality of the examined weld joint produced by a friction weld process. The neural network may be trained by the steps of providing acoustical data and observable data from at least one test weld joint, and training the neural network based on the acoustical data and observable data to form a trained neural network so that the trained neural network is capable of determining the quality of a examined weld joint based on acoustical data from the examined weld joint. In addition, an apparatus having a housing, acoustical sensors mounted therein, and means for mounting the housing on a friction weld device so that the acoustical sensors do not contact the weld joint. The apparatus may sample the acoustical data necessary for the neural network to determine the quality of a weld joint.

Method and Apparatus for In-Process Sensing of Manufacturing Quality

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

Hartman, D.A.; Dave, V.R.; Cola, M.J.

2005-02-22

A method for determining the quality of an examined weld joint comprising the steps of providing acoustical data from the examined weld joint, and performing a neural network operation on the acoustical data determine the quality of the examined weld joint produced by a friction weld process. The neural network may be trained by the steps of providing acoustical data and observable data from at least one test weld joint, and training the neural network based on the acoustical data and observable data to form a trained neural network so that the trained neural network is capable of determining themore » quality of a examined weld joint based on acoustical data from the examined weld joint. In addition, an apparatus having a housing, acoustical sensors mounted therein, and means for mounting the housing on a friction weld device so that the acoustical sensors do not contact the weld joint. The apparatus may sample the acoustical data necessary for the neural network to determine the quality of a weld joint.« less

Online Doppler Effect Elimination Based on Unequal Time Interval Sampling for Wayside Acoustic Bearing Fault Detecting System

PubMed Central

Ouyang, Kesai; Lu, Siliang; Zhang, Shangbin; Zhang, Haibin; He, Qingbo; Kong, Fanrang

2015-01-01

The railway occupies a fairly important position in transportation due to its high speed and strong transportation capability. As a consequence, it is a key issue to guarantee continuous running and transportation safety of trains. Meanwhile, time consumption of the diagnosis procedure is of extreme importance for the detecting system. However, most of the current adopted techniques in the wayside acoustic defective bearing detector system (ADBD) are offline strategies, which means that the signal is analyzed after the sampling process. This would result in unavoidable time latency. Besides, the acquired acoustic signal would be corrupted by the Doppler effect because of high relative speed between the train and the data acquisition system (DAS). Thus, it is difficult to effectively diagnose the bearing defects immediately. In this paper, a new strategy called online Doppler effect elimination (ODEE) is proposed to remove the Doppler distortion online by the introduced unequal interval sampling scheme. The steps of proposed strategy are as follows: The essential parameters are acquired in advance. Then, the introduced unequal time interval sampling strategy is used to restore the Doppler distortion signal, and the amplitude of the signal is demodulated as well. Thus, the restored Doppler-free signal is obtained online. The proposed ODEE method has been employed in simulation analysis. Ultimately, the ODEE method is implemented in the embedded system for fault diagnosis of the train bearing. The results are in good accordance with the bearing defects, which verifies the good performance of the proposed strategy. PMID:26343657

D Semantic Labeling of ALS Data Based on Domain Adaption by Transferring and Fusing Random Forest Models

NASA Astrophysics Data System (ADS)

Wu, J.; Yao, W.; Zhang, J.; Li, Y.

2018-04-01

Labeling 3D point cloud data with traditional supervised learning methods requires considerable labelled samples, the collection of which is cost and time expensive. This work focuses on adopting domain adaption concept to transfer existing trained random forest classifiers (based on source domain) to new data scenes (target domain), which aims at reducing the dependence of accurate 3D semantic labeling in point clouds on training samples from the new data scene. Firstly, two random forest classifiers were firstly trained with existing samples previously collected for other data. They were different from each other by using two different decision tree construction algorithms: C4.5 with information gain ratio and CART with Gini index. Secondly, four random forest classifiers adapted to the target domain are derived through transferring each tree in the source random forest models with two types of operations: structure expansion and reduction-SER and structure transfer-STRUT. Finally, points in target domain are labelled by fusing the four newly derived random forest classifiers using weights of evidence based fusion model. To validate our method, experimental analysis was conducted using 3 datasets: one is used as the source domain data (Vaihingen data for 3D Semantic Labelling); another two are used as the target domain data from two cities in China (Jinmen city and Dunhuang city). Overall accuracies of 85.5 % and 83.3 % for 3D labelling were achieved for Jinmen city and Dunhuang city data respectively, with only 1/3 newly labelled samples compared to the cases without domain adaption.

Effect of communication skill training using group psychoeducation method on the stress level of psychiatry ward nurses.

PubMed

Ghazavi, Zahra; Lohrasbi, Fatemeh; Mehrabi, Tayebeh

2010-12-01

Nursing is a dynamic and supportive job, with the main role of taking care of patients. Maintaining appropriate communication of the nurse with the patients is particularly known as the main core of care in mental health. However, in spite of the importance of providing communication, one of the main sources of stress in nurses of psychiatry wards is communication with the patients. Some important reasons for inappropriate relationship between the nurse and patient can be lack of necessary skills to communicate with patients because of insufficient training. Although training communication skills is an important part of the education of medical and paramedical students, in recent studies it has been demonstrated that the communication skills learned in theoretical courses would not necessarily be transferred to clinical settings, and proving training in clinical settings is a must. The present study was carried out to determine the effect of training communication skills using psychoeducation method on the stress level of nurses of psychiatry wards in 2010. This is a quasi-experimental study. The participants were 45 nurses; 23 and 22 in the experiment and control groups, respectively, working in psychiatry wards of Noor and Farabi hospitals, Isfahan, Iran. The sampling was carried out by the census method, and then the participants were randomly assigned to the two groups of experiment and control, using random number table. The two groups filled out the demographic data form and also the questionnaire on nurses' occupational stress, designed by the researcher. The questionnaire was filled out three times; before, immediately after, and one month after the training. Training of communication skills was carried out using group psychoeducation method, in six sessions, each lasted for 1.5 hours. The training sessions of the experiment group were held in Farabi Hospital. The findings indicated that before the intervention, the members of the two groups had a high level of occupational stress. Immediately after the training, the stress level of the experiment group decreased significantly, and the decrease was sustained for the following one month. Training communicative skills using group psychoeducation method can decrease the occupational stress of psychiatry ward nurses.

Random forests ensemble classifier trained with data resampling strategy to improve cardiac arrhythmia diagnosis.

PubMed

Ozçift, Akin

2011-05-01

Supervised classification algorithms are commonly used in the designing of computer-aided diagnosis systems. In this study, we present a resampling strategy based Random Forests (RF) ensemble classifier to improve diagnosis of cardiac arrhythmia. Random forests is an ensemble classifier that consists of many decision trees and outputs the class that is the mode of the class's output by individual trees. In this way, an RF ensemble classifier performs better than a single tree from classification performance point of view. In general, multiclass datasets having unbalanced distribution of sample sizes are difficult to analyze in terms of class discrimination. Cardiac arrhythmia is such a dataset that has multiple classes with small sample sizes and it is therefore adequate to test our resampling based training strategy. The dataset contains 452 samples in fourteen types of arrhythmias and eleven of these classes have sample sizes less than 15. Our diagnosis strategy consists of two parts: (i) a correlation based feature selection algorithm is used to select relevant features from cardiac arrhythmia dataset. (ii) RF machine learning algorithm is used to evaluate the performance of selected features with and without simple random sampling to evaluate the efficiency of proposed training strategy. The resultant accuracy of the classifier is found to be 90.0% and this is a quite high diagnosis performance for cardiac arrhythmia. Furthermore, three case studies, i.e., thyroid, cardiotocography and audiology, are used to benchmark the effectiveness of the proposed method. The results of experiments demonstrated the efficiency of random sampling strategy in training RF ensemble classification algorithm. Copyright © 2011 Elsevier Ltd. All rights reserved.

Drought Management Activities of the National Drought Mitigation Center (NDMC): Contributions Toward a Global Drought Early Warning System (GDEWS)

NASA Astrophysics Data System (ADS)

Stumpf, A.; Lachiche, N.; Malet, J.; Kerle, N.; Puissant, A.

2011-12-01

VHR satellite images have become a primary source for landslide inventory mapping after major triggering events such as earthquakes and heavy rainfalls. Visual image interpretation is still the prevailing standard method for operational purposes but is time-consuming and not well suited to fully exploit the increasingly better supply of remote sensing data. Recent studies have addressed the development of more automated image analysis workflows for landslide inventory mapping. In particular object-oriented approaches that account for spatial and textural image information have been demonstrated to be more adequate than pixel-based classification but manually elaborated rule-based classifiers are difficult to adapt under changing scene characteristics. Machine learning algorithm allow learning classification rules for complex image patterns from labelled examples and can be adapted straightforwardly with available training data. In order to reduce the amount of costly training data active learning (AL) has evolved as a key concept to guide the sampling for many applications. The underlying idea of AL is to initialize a machine learning model with a small training set, and to subsequently exploit the model state and data structure to iteratively select the most valuable samples that should be labelled by the user. With relatively few queries and labelled samples, an AL strategy yields higher accuracies than an equivalent classifier trained with many randomly selected samples. This study addressed the development of an AL method for landslide mapping from VHR remote sensing images with special consideration of the spatial distribution of the samples. Our approach [1] is based on the Random Forest algorithm and considers the classifier uncertainty as well as the variance of potential sampling regions to guide the user towards the most valuable sampling areas. The algorithm explicitly searches for compact regions and thereby avoids a spatially disperse sampling pattern inherent to most other AL methods. The accuracy, the sampling time and the computational runtime of the algorithm were evaluated on multiple satellite images capturing recent large scale landslide events. Sampling between 1-4% of the study areas the accuracies between 74% and 80% were achieved, whereas standard sampling schemes yielded only accuracies between 28% and 50% with equal sampling costs. Compared to commonly used point-wise AL algorithm the proposed approach significantly reduces the number of iterations and hence the computational runtime. Since the user can focus on relatively few compact areas (rather than on hundreds of distributed points) the overall labeling time is reduced by more than 50% compared to point-wise queries. An experimental evaluation of multiple expert mappings demonstrated strong relationships between the uncertainties of the experts and the machine learning model. It revealed that the achieved accuracies are within the range of the inter-expert disagreement and that it will be indispensable to consider ground truth uncertainties to truly achieve further enhancements in the future. The proposed method is generally applicable to a wide range of optical satellite images and landslide types. [1] A. Stumpf, N. Lachiche, J.-P. Malet, N. Kerle, and A. Puissant, Active learning in the spatial domain for remote sensing image classification, IEEE Transactions on Geosciece and Remote Sensing. 2013, DOI 10.1109/TGRS.2013.2262052.

Uncertainties in Air Exchange using Continuous-Injection, Long-Term Sampling Tracer-Gas Methods

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

Sherman, Max H.; Walker, Iain S.; Lunden, Melissa M.

2013-12-01

The PerFluorocarbon Tracer (PFT) method is a low-cost approach commonly used for measuring air exchange in buildings using tracer gases. It is a specific application of the more general Continuous-Injection, Long-Term Sampling (CILTS) method. The technique is widely used but there has been little work on understanding the uncertainties (both precision and bias) associated with its use, particularly given that it is typically deployed by untrained or lightly trained people to minimize experimental costs. In this article we will conduct a first-principles error analysis to estimate the uncertainties and then compare that analysis to CILTS measurements that were over-sampled, throughmore » the use of multiple tracers and emitter and sampler distribution patterns, in three houses. We find that the CILTS method can have an overall uncertainty of 10-15percent in ideal circumstances, but that even in highly controlled field experiments done by trained experimenters expected uncertainties are about 20percent. In addition, there are many field conditions (such as open windows) where CILTS is not likely to provide any quantitative data. Even avoiding the worst situations of assumption violations CILTS should be considered as having a something like a ?factor of two? uncertainty for the broad field trials that it is typically used in. We provide guidance on how to deploy CILTS and design the experiment to minimize uncertainties.« less

ERP and Behavioral Effects of Physical and Cognitive Training on Working Memory in Aging: A Randomized Controlled Study

PubMed Central

Falkenstein, Michael

2018-01-01

Working memory (WM) performance decreases with age. A promising method to improve WM is physical or cognitive training. The present randomized controlled study is aimed at evaluating the effects of different training methods on WM. A sample of 141 healthy older adults (mean age 70 years) was assigned to one of four groups: physical training, cognitive training, a social control group, and a no-contact control group. The participants trained for four months. Before and after the training, n-back task during an EEG recording was applied. The results show that cognitive training enhanced the target detection rate in the 2-back task. This was corroborated by an increased number of repeated digits in the backward digit-span test but not in other memory tests. The improvement of WM was supported by an increased P3a prior to a correct target and an increased P3b both in nontarget and target trials. No ERP effects in the physical and no-contact control groups were found, while a reduction of P3a and P3b was found in the social control group. Thus, cognitive training enhances frontal and parietal processing related to the maintenance of a stored stimulus for subsequent matching with an upcoming stimulus and increases allocation of cognitive resources. These results indicate that multidomain cognitive training may increase WM capacity and neuronal activity in older age. PMID:29796016

Self-similarity Clustering Event Detection Based on Triggers Guidance

NASA Astrophysics Data System (ADS)

Zhang, Xianfei; Li, Bicheng; Tian, Yuxuan

Traditional method of Event Detection and Characterization (EDC) regards event detection task as classification problem. It makes words as samples to train classifier, which can lead to positive and negative samples of classifier imbalance. Meanwhile, there is data sparseness problem of this method when the corpus is small. This paper doesn't classify event using word as samples, but cluster event in judging event types. It adopts self-similarity to convergence the value of K in K-means algorithm by the guidance of event triggers, and optimizes clustering algorithm. Then, combining with named entity and its comparative position information, the new method further make sure the pinpoint type of event. The new method avoids depending on template of event in tradition methods, and its result of event detection can well be used in automatic text summarization, text retrieval, and topic detection and tracking.

The Efficacy of Stuttering Measurement Training: Evaluating Two Training Programs

PubMed Central

Bainbridge, Lauren A.; Stavros, Candace; Ebrahimian, Mineh; Wang, Yuedong

2015-01-01

Purpose Two stuttering measurement training programs currently used for training clinicians were evaluated for their efficacy in improving the accuracy of total stuttering event counting. Method Four groups, each with 12 randomly allocated participants, completed a pretest–posttest design training study. They were evaluated by their counts of stuttering events on eight 3-min audiovisual speech samples from adults and children who stutter. Stuttering judgment training involved use of either the Stuttering Measurement System (SMS), Stuttering Measurement Assessment and Training (SMAAT) programs, or no training. To test for the reliability of any training effect, SMS training was repeated with the 4th group. Results Both SMS-trained groups produced approximately 34% improvement, significantly better than no training or the SMAAT program. The SMAAT program produced a mixed result. Conclusions The SMS program was shown to produce a “medium” effect size improvement in the accuracy of stuttering event counts, and this improvement was almost perfectly replicated in a 2nd group. Half of the SMAAT judges produced a 36% improvement in accuracy, but the other half showed no improvement. Additional studies are needed to demonstrate the durability of the reported improvements, but these positive effects justify the importance of stuttering measurement training. PMID:25629956

Research on Abnormal Detection Based on Improved Combination of K - means and SVDD

NASA Astrophysics Data System (ADS)

Hao, Xiaohong; Zhang, Xiaofeng

2018-01-01

In order to improve the efficiency of network intrusion detection and reduce the false alarm rate, this paper proposes an anomaly detection algorithm based on improved K-means and SVDD. The algorithm first uses the improved K-means algorithm to cluster the training samples of each class, so that each class is independent and compact in class; Then, according to the training samples, the SVDD algorithm is used to construct the minimum superspheres. The subordinate relationship of the samples is determined by calculating the distance of the minimum superspheres constructed by SVDD. If the test sample is less than the center of the hypersphere, the test sample belongs to this class, otherwise it does not belong to this class, after several comparisons, the final test of the effective detection of the test sample.In this paper, we use KDD CUP99 data set to simulate the proposed anomaly detection algorithm. The results show that the algorithm has high detection rate and low false alarm rate, which is an effective network security protection method.

The Effectiveness of Theory of Mind Training On the Social Skills of Children with High Functioning Autism Spectrum Disorders.

PubMed

Adibsereshki, Narges; Nesayan, Abbas; Asadi Gandomani, Roghayeh; Karimlou, Masood

2015-01-01

Children with Autism Spectrum Disorders (ASD) tend to have problems in establishing and maintaining their social relationships. Some professionals believe this social impairment is the result of deficit in Theory of Mind (ToM). This study was conducted to explore the effectiveness of ToM training on such children. A quasi-experimental method, pre- test, post-test with control group was used. The sample included of 12 girls and 12 boys with High Functioning Autism Spectrum Disorders (HFASD). Two instruments were used as follows: the Theory of Mind test and the social skills questionnaire (1). The samples were randomly placed in the experimental and control groups. The experimental groups had 15 sessions of ToM training and the control groups had just regular school program. The data were analyzed by Kolmogorov-Smirnov, independent t- and twoway- variance tests. The scores for social skills in the experimental group were significantly more than the control group. ToM training might improve the social skills of children with autism spectrum disorders.

Vehicle classification in WAMI imagery using deep network

NASA Astrophysics Data System (ADS)

Yi, Meng; Yang, Fan; Blasch, Erik; Sheaff, Carolyn; Liu, Kui; Chen, Genshe; Ling, Haibin

2016-05-01

Humans have always had a keen interest in understanding activities and the surrounding environment for mobility, communication, and survival. Thanks to recent progress in photography and breakthroughs in aviation, we are now able to capture tens of megapixels of ground imagery, namely Wide Area Motion Imagery (WAMI), at multiple frames per second from unmanned aerial vehicles (UAVs). WAMI serves as a great source for many applications, including security, urban planning and route planning. These applications require fast and accurate image understanding which is time consuming for humans, due to the large data volume and city-scale area coverage. Therefore, automatic processing and understanding of WAMI imagery has been gaining attention in both industry and the research community. This paper focuses on an essential step in WAMI imagery analysis, namely vehicle classification. That is, deciding whether a certain image patch contains a vehicle or not. We collect a set of positive and negative sample image patches, for training and testing the detector. Positive samples are 64 × 64 image patches centered on annotated vehicles. We generate two sets of negative images. The first set is generated from positive images with some location shift. The second set of negative patches is generated from randomly sampled patches. We also discard those patches if a vehicle accidentally locates at the center. Both positive and negative samples are randomly divided into 9000 training images and 3000 testing images. We propose to train a deep convolution network for classifying these patches. The classifier is based on a pre-trained AlexNet Model in the Caffe library, with an adapted loss function for vehicle classification. The performance of our classifier is compared to several traditional image classifier methods using Support Vector Machine (SVM) and Histogram of Oriented Gradient (HOG) features. While the SVM+HOG method achieves an accuracy of 91.2%, the accuracy of our deep network-based classifier reaches 97.9%.

Relationship between the components of on-site training and emotional intelligence in the librarians of Isfahan University of Medical Sciences and Isfahan University with moderating role of personality characteristics

PubMed Central

Sayadi, Saeed; Safdarian, Ali; Khayeri, Behnaz

2015-01-01

Introduction: Training the man power is an inevitable necessity that the organizations need in order to survive and develop in today changing world. Aims: The aim of the present study is to identify the relationship between the components of on-site training and emotional intelligence in librarians of Isfahan University of Medical Sciences and Isfahan University with moderating role of personality characteristics. Settings and Design: Descriptive correlation method was used in the present study. The statistical population of the study was all of the 157 librarians of Isfahan University of Medical Sciences and Isfahan University from whom the appointed individuals were selected through random sampling method. Subjects and Methods: The research tools included the researcher-made questionnaire of investigating the effectiveness of on-site training system and two other standard questionnaires of Shrink emotional intelligence, and NEO personality questionnaire, which all of them had the needed reliability and validity. Statistical Analysis: The descriptive indices (distribution and mean) and also the inferential methods (Pearson correlation, regression analysis and analysis of variance) were used through applying version 20 of SPSS software to analyze the obtained data. Results: There was a significant relationship with certainty level of 95% between the components of on-site training with emotional intelligence in those who obtained low grades in the features of being extrovert and between the individual aspects of on-site training with emotional intelligence in those who got higher grades in the characteristic of being extrovert. Conclusion: The emotional intelligence is a promotable skill and considering the existence of a significant relationship between some components of emotional intelligence and on-site training, these skills can be institutionalized through conducting mentioned educational courses. PMID:27462631

Prediction of Protein-Protein Interaction Sites with Machine-Learning-Based Data-Cleaning and Post-Filtering Procedures.

PubMed

Liu, Guang-Hui; Shen, Hong-Bin; Yu, Dong-Jun

2016-04-01

Accurately predicting protein-protein interaction sites (PPIs) is currently a hot topic because it has been demonstrated to be very useful for understanding disease mechanisms and designing drugs. Machine-learning-based computational approaches have been broadly utilized and demonstrated to be useful for PPI prediction. However, directly applying traditional machine learning algorithms, which often assume that samples in different classes are balanced, often leads to poor performance because of the severe class imbalance that exists in the PPI prediction problem. In this study, we propose a novel method for improving PPI prediction performance by relieving the severity of class imbalance using a data-cleaning procedure and reducing predicted false positives with a post-filtering procedure: First, a machine-learning-based data-cleaning procedure is applied to remove those marginal targets, which may potentially have a negative effect on training a model with a clear classification boundary, from the majority samples to relieve the severity of class imbalance in the original training dataset; then, a prediction model is trained on the cleaned dataset; finally, an effective post-filtering procedure is further used to reduce potential false positive predictions. Stringent cross-validation and independent validation tests on benchmark datasets demonstrated the efficacy of the proposed method, which exhibits highly competitive performance compared with existing state-of-the-art sequence-based PPIs predictors and should supplement existing PPI prediction methods.

Gene function prediction based on the Gene Ontology hierarchical structure.

PubMed

Cheng, Liangxi; Lin, Hongfei; Hu, Yuncui; Wang, Jian; Yang, Zhihao

2014-01-01

The information of the Gene Ontology annotation is helpful in the explanation of life science phenomena, and can provide great support for the research of the biomedical field. The use of the Gene Ontology is gradually affecting the way people store and understand bioinformatic data. To facilitate the prediction of gene functions with the aid of text mining methods and existing resources, we transform it into a multi-label top-down classification problem and develop a method that uses the hierarchical relationships in the Gene Ontology structure to relieve the quantitative imbalance of positive and negative training samples. Meanwhile the method enhances the discriminating ability of classifiers by retaining and highlighting the key training samples. Additionally, the top-down classifier based on a tree structure takes the relationship of target classes into consideration and thus solves the incompatibility between the classification results and the Gene Ontology structure. Our experiment on the Gene Ontology annotation corpus achieves an F-value performance of 50.7% (precision: 52.7% recall: 48.9%). The experimental results demonstrate that when the size of training set is small, it can be expanded via topological propagation of associated documents between the parent and child nodes in the tree structure. The top-down classification model applies to the set of texts in an ontology structure or with a hierarchical relationship.

Comparison of different deep learning approaches for parotid gland segmentation from CT images

NASA Astrophysics Data System (ADS)

Hänsch, Annika; Schwier, Michael; Gass, Tobias; Morgas, Tomasz; Haas, Benjamin; Klein, Jan; Hahn, Horst K.

2018-02-01

The segmentation of target structures and organs at risk is a crucial and very time-consuming step in radiotherapy planning. Good automatic methods can significantly reduce the time clinicians have to spend on this task. Due to its variability in shape and often low contrast to surrounding structures, segmentation of the parotid gland is especially challenging. Motivated by the recent success of deep learning, we study different deep learning approaches for parotid gland segmentation. Particularly, we compare 2D, 2D ensemble and 3D U-Net approaches and find that the 2D U-Net ensemble yields the best results with a mean Dice score of 0.817 on our test data. The ensemble approach reduces false positives without the need for an automatic region of interest detection. We also apply our trained 2D U-Net ensemble to segment the test data of the 2015 MICCAI head and neck auto-segmentation challenge. With a mean Dice score of 0.861, our classifier exceeds the highest mean score in the challenge. This shows that the method generalizes well onto data from independent sites. Since appropriate reference annotations are essential for training but often difficult and expensive to obtain, it is important to know how many samples are needed to properly train a neural network. We evaluate the classifier performance after training with differently sized training sets (50-450) and find that 250 cases (without using extensive data augmentation) are sufficient to obtain good results with the 2D ensemble. Adding more samples does not significantly improve the Dice score of the segmentations.

Prediction task guided representation learning of medical codes in EHR.

PubMed

Cui, Liwen; Xie, Xiaolei; Shen, Zuojun

2018-06-18

There have been rapidly growing applications using machine learning models for predictive analytics in Electronic Health Records (EHR) to improve the quality of hospital services and the efficiency of healthcare resource utilization. A fundamental and crucial step in developing such models is to convert medical codes in EHR to feature vectors. These medical codes are used to represent diagnoses or procedures. Their vector representations have a tremendous impact on the performance of machine learning models. Recently, some researchers have utilized representation learning methods from Natural Language Processing (NLP) to learn vector representations of medical codes. However, most previous approaches are unsupervised, i.e. the generation of medical code vectors is independent from prediction tasks. Thus, the obtained feature vectors may be inappropriate for a specific prediction task. Moreover, unsupervised methods often require a lot of samples to obtain reliable results, but most practical problems have very limited patient samples. In this paper, we develop a new method called Prediction Task Guided Health Record Aggregation (PTGHRA), which aggregates health records guided by prediction tasks, to construct training corpus for various representation learning models. Compared with unsupervised approaches, representation learning models integrated with PTGHRA yield a significant improvement in predictive capability of generated medical code vectors, especially for limited training samples. Copyright © 2018. Published by Elsevier Inc.

Fuzzy controller training using particle swarm optimization for nonlinear system control.

PubMed

Karakuzu, Cihan

2008-04-01

This paper proposes and describes an effective utilization of particle swarm optimization (PSO) to train a Takagi-Sugeno (TS)-type fuzzy controller. Performance evaluation of the proposed fuzzy training method using the obtained simulation results is provided with two samples of highly nonlinear systems: a continuous stirred tank reactor (CSTR) and a Van der Pol (VDP) oscillator. The superiority of the proposed learning technique is that there is no need for a partial derivative with respect to the parameter for learning. This fuzzy learning technique is suitable for real-time implementation, especially if the system model is unknown and a supervised training cannot be run. In this study, all parameters of the controller are optimized with PSO in order to prove that a fuzzy controller trained by PSO exhibits a good control performance.

The School of Posture as a postural training method for Paraíba Telecommunications Operators.

PubMed

Cardia, M C; Soares Màsculo, F

2001-01-01

This work proposes to show the experience of posture training accomplished in the Paraíba State Telecommunication Company, using the knowledge of the Back School. The sample was composed of 12 operators, employees of the company, representing 31% of this population. The model applied in TELPA (Paraíba Telecommunication Company, Brazil) was based on the models of Sherbrooke, Canada, and of the School of Posture of Paraìba Federal University. Fifty-eight point four percent of participants showed a reduction of column pain, 25% improved the quality of the rest and the received training was considered enough for the learning of correct postures at work in 75% of the cases. The whole population approved of the training, and 83.3% of the cases considered that this training influenced their lives very positively.

Randomized control trial of computer-based training targeting alertness in older adults: the ALERT trial protocol.

PubMed

VanVleet, Thomas; Voss, Michelle; Dabit, Sawsan; Mitko, Alex; DeGutis, Joseph

2018-05-03

Healthy aging is associated with a decline in multiple functional domains including perception, attention, short and long-term memory, reasoning, decision-making, as well as cognitive and motor control functions; all of which are significantly modulated by an individual's level of alertness. The control of alertness also significantly declines with age and contributes to increased lapses of attention in everyday life, ranging from minor memory slips to a lack of vigilance and increased risk of falls or motor-vehicle accidents. Several experimental behavioral therapies designed to remediate age-related cognitive decline have been developed, but differ widely in content, method and dose. Preliminary studies demonstrate that Tonic and Phasic Alertness Training (TAPAT) can improve executive functions in older adults and may be a useful adjunct treatment to enhance benefits gained in other clinically validated treatments. The purpose of the current trial (referred to as the Attention training for Learning Enhancement and Resilience Trial or ALERT) is to compare TAPAT to an active control training condition, include a larger sample of patients, and assess both cognitive and functional outcomes. We will employ a multi-site, longitudinal, blinded randomized controlled trial (RCT) design with a target sample of 120 patients with age-related cognitive decline. Patients will be asked to complete 36 training sessions remotely (30 min/day, 5 days a week, over 3 months) of either the experimental TAPAT training program or an active control computer games condition. Patients will be assessed on a battery of cognitive and functional outcomes at four time points, including: a) immediately before training, b) halfway through training, c) within forty-eight hours post completion of total training, and d) after a three-month no-contact period post completion of total training, to assess the longevity of potential training effects. The strengths of this protocol are that it tests an innovative, in-home administered treatment that targets a fundamental deficit in adults with age-related cognitive decline; employs highly sensitive computer-based assessments of cognition as well as functional abilities, and incorporates a large sample size in an RCT design. ClinicalTrials.gov identifier: NCT02416401.

A study of the effects of active listening on listening attitudes of middle managers.

PubMed

Kubota, Shinya; Mishima, Norio; Nagata, Shoji

2004-01-01

The present study was conducted to clarify the direct effects of active listening (AL) training given to middle managers in a local government. Altogether, 345 middle managers participated in 13 AL training sessions over two years. We developed the Inventive Experiential Learning (IEL) method, and used it as the central training method in this study. To investigate how well the participants learned AL, we asked the middle managers to answer a shorter version of the Active Listening Attitude Scale (ALAS) consisting of two subscales-i.e. "Listening Attitude" and "Listening Skill"-before training, one month after and three months after training. Altogether, 284 middle managers answered the questionnaire three times. The scores of each subscale were analyzed by repeated measurement analysis of variance. The participants were divided into three groups using the percentile values of the original sample of ALAS, i.e. low-score group (-24%), medium-score group (25-75%) and high-score group (76%-), and the proportionate changes were examined. The results showed both the "Listening Attitude" and "Listening Skill" subscales increased significantly after training. Analysis of the percentiles showed that the proportion of the low-score group decreased and that of the high-score group increased in both subscales, from one to three months after training. These changes are considered to indicate that the participants have learned AL although they attended AL training for only one day.

Effect of somatosensory and neurofeedback training on balance in older healthy adults: a preliminary investigation.

PubMed

Azarpaikan, Atefeh; Taheri Torbati, Hamidreza

2017-10-23

The aim of this study was to assess the effectiveness of balance training with somatosensory and neurofeedback training on dynamic and static balance in healthy, elderly adults. The sample group consisted of 45 healthy adults randomly assigned to one of the three test groups: somatosensory, neurofeedback, and a control. Individualization of the balance program started with pre-tests for static and dynamic balances. Each group had 15- and 30-min training sessions. All groups were tested for static (postural stability) and dynamic balances (Berg Balance Scale) in acquisition and transfer tests (fall risk of stability and timed up and go). Improvements in static and dynamic balances were assessed by somatosensory and neurofeedback groups and then compared with the control group. Results indicated significant improvements in static and dynamic balances in both test groups in the acquisition test. Results revealed a significant improvement in the transfer test in the neurofeedback and somatosensory groups, in static and dynamic conditions, respectively. The findings suggest that these methods of balance training had a significant influence on balance. Both the methods are appropriate to prevent falling in adults. Neurofeedback training helped the participants to learn static balance, while somatosensory training was effective on dynamic balance learning. Further research is needed to assess the effects of longer and discontinuous stimulation with somatosensory and neurofeedback training on balance in elderly adults.

Object Classification With Joint Projection and Low-Rank Dictionary Learning.

PubMed

Foroughi, Homa; Ray, Nilanjan; Hong Zhang

2018-02-01

For an object classification system, the most critical obstacles toward real-world applications are often caused by large intra-class variability, arising from different lightings, occlusion, and corruption, in limited sample sets. Most methods in the literature would fail when the training samples are heavily occluded, corrupted or have significant illumination or viewpoint variations. Besides, most of the existing methods and especially deep learning-based methods, need large training sets to achieve a satisfactory recognition performance. Although using the pre-trained network on a generic large-scale data set and fine-tune it to the small-sized target data set is a widely used technique, this would not help when the content of base and target data sets are very different. To address these issues simultaneously, we propose a joint projection and low-rank dictionary learning method using dual graph constraints. Specifically, a structured class-specific dictionary is learned in the low-dimensional space, and the discrimination is further improved by imposing a graph constraint on the coding coefficients, that maximizes the intra-class compactness and inter-class separability. We enforce structural incoherence and low-rank constraints on sub-dictionaries to reduce the redundancy among them, and also make them robust to variations and outliers. To preserve the intrinsic structure of data, we introduce a supervised neighborhood graph into the framework to make the proposed method robust to small-sized and high-dimensional data sets. Experimental results on several benchmark data sets verify the superior performance of our method for object classification of small-sized data sets, which include a considerable amount of different kinds of variation, and may have high-dimensional feature vectors.

Non-intrusive reduced order modeling of nonlinear problems using neural networks

NASA Astrophysics Data System (ADS)

Hesthaven, J. S.; Ubbiali, S.

2018-06-01

We develop a non-intrusive reduced basis (RB) method for parametrized steady-state partial differential equations (PDEs). The method extracts a reduced basis from a collection of high-fidelity solutions via a proper orthogonal decomposition (POD) and employs artificial neural networks (ANNs), particularly multi-layer perceptrons (MLPs), to accurately approximate the coefficients of the reduced model. The search for the optimal number of neurons and the minimum amount of training samples to avoid overfitting is carried out in the offline phase through an automatic routine, relying upon a joint use of the Latin hypercube sampling (LHS) and the Levenberg-Marquardt (LM) training algorithm. This guarantees a complete offline-online decoupling, leading to an efficient RB method - referred to as POD-NN - suitable also for general nonlinear problems with a non-affine parametric dependence. Numerical studies are presented for the nonlinear Poisson equation and for driven cavity viscous flows, modeled through the steady incompressible Navier-Stokes equations. Both physical and geometrical parametrizations are considered. Several results confirm the accuracy of the POD-NN method and show the substantial speed-up enabled at the online stage as compared to a traditional RB strategy.

Random Forest-Based Recognition of Isolated Sign Language Subwords Using Data from Accelerometers and Surface Electromyographic Sensors.

PubMed

Su, Ruiliang; Chen, Xiang; Cao, Shuai; Zhang, Xu

2016-01-14

Sign language recognition (SLR) has been widely used for communication amongst the hearing-impaired and non-verbal community. This paper proposes an accurate and robust SLR framework using an improved decision tree as the base classifier of random forests. This framework was used to recognize Chinese sign language subwords using recordings from a pair of portable devices worn on both arms consisting of accelerometers (ACC) and surface electromyography (sEMG) sensors. The experimental results demonstrated the validity of the proposed random forest-based method for recognition of Chinese sign language (CSL) subwords. With the proposed method, 98.25% average accuracy was obtained for the classification of a list of 121 frequently used CSL subwords. Moreover, the random forests method demonstrated a superior performance in resisting the impact of bad training samples. When the proportion of bad samples in the training set reached 50%, the recognition error rate of the random forest-based method was only 10.67%, while that of a single decision tree adopted in our previous work was almost 27.5%. Our study offers a practical way of realizing a robust and wearable EMG-ACC-based SLR systems.

[Using neural networks based template matching method to obtain redshifts of normal galaxies].

PubMed

Xu, Xin; Luo, A-li; Wu, Fu-chao; Zhao, Yong-heng

2005-06-01

Galaxies can be divided into two classes: normal galaxy (NG) and active galaxy (AG). In order to determine NG redshifts, an automatic effective method is proposed in this paper, which consists of the following three main steps: (1) From the template of normal galaxy, the two sets of samples are simulated, one with the redshift of 0.0-0.3, the other of 0.3-0.5, then the PCA is used to extract the main components, and train samples are projected to the main component subspace to obtain characteristic spectra. (2) The characteristic spectra are used to train a Probabilistic Neural Network to obtain a Bayes classifier. (3) An unknown real NG spectrum is first inputted to this Bayes classifier to determine the possible range of redshift, then the template matching is invoked to locate the redshift value within the estimated range. Compared with the traditional template matching technique with an unconstrained range, our proposed method not only halves the computational load, but also increases the estimation accuracy. As a result, the proposed method is particularly useful for automatic spectrum processing produced from a large-scale sky survey project.

A training rule which guarantees finite-region stability for a class of closed-loop neural-network control systems.

PubMed

Kuntanapreeda, S; Fullmer, R R

1996-01-01

A training method for a class of neural network controllers is presented which guarantees closed-loop system stability. The controllers are assumed to be nonlinear, feedforward, sampled-data, full-state regulators implemented as single hidden-layer neural networks. The controlled systems must be locally hermitian and observable. Stability of the closed-loop system is demonstrated by determining a Lyapunov function, which can be used to identify a finite stability region about the regulator point.

Developing soft skill training for salespersons to increase total sales

NASA Astrophysics Data System (ADS)

Mardatillah, A.; Budiman, I.; Tarigan, U. P. P.; Sembiring, A. C.; Hendi

2018-04-01

This research was conducted in the multilevel marketing industry. Unprofessional salespersons behavior and responsibility can ruin the image of the multilevel marketing industry and distrust to the multilevel marketing industry. This leads to decreased company revenue due to lack of public interest in multilevel marketing products. Seeing these conditions, researcher develop training programs to improve the competence of salespersons in making sales. It was done by looking at factors that affect the level of salespersons sales. The research analyzes several factors that influence the salesperson’s sales level: presentation skills, questioning ability, adaptability, technical knowledge, self-control, interaction involvement, sales environment, and intrapersonal skills. Through the analysis of these factors with One Sample T-Test and Multiple Linear Regression methods, researchers design a training program for salespersons to increase their sales. The developed training for salespersons is basic training and special training and before training was given, salespersons need to be assessed for the effectivity and efficiency reasons.

Improving everyday prospective memory performance in older adults: comparing cognitive process and strategy training.

PubMed

Brom, Sarah Susanne; Kliegel, Matthias

2014-09-01

Considering the importance of prospective memory for independence in old age recently, research has started to examine interventions to reduce prospective memory errors. Two general approaches can be proposed: (a) process training of executive control associated with prospective memory functioning, and/or (b) strategy training to reduce executive task demands. The present study was the first to combine and compare both training methods in a sample of 62 community-dwelling older adults (60-86 years) and to explore their effects on an ecologically valid everyday life prospective memory task (here: regular blood pressure monitoring). Even though the training of executive control was successful in enhancing the trained ability, clear transfer effects on prospective memory performance could only be found for the strategy training. However, participants with low executive abilities benefited particularly from the implementation intention strategy. Conceptually, this supports models suggesting interactions between task demands and individual differences in executive control in explaining individual differences in prospective memory performance. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Factors related to nonuse of seat belts in Michigan.

DOT National Transportation Integrated Search

1987-09-01

This study combined direct observation of seat belt use with interview methods to : identify factors related to seat belt use in a state with a mandatory seat belt use law. Trained : observers recorded restraint use for a probability sample of motori...

Dive In to Aquatic Circuits.

ERIC Educational Resources Information Center

Goldfarb, Joseph M.

1995-01-01

The article presents a method for swimming teachers and coaches to stave off workout boredom in their students by using a circuit in the pool. After explaining how to set up a training circuit, the article describes sample stations and notes important safety precautions. (SM)

A Noise-Filtered Under-Sampling Scheme for Imbalanced Classification.

PubMed

Kang, Qi; Chen, XiaoShuang; Li, SiSi; Zhou, MengChu

2017-12-01

Under-sampling is a popular data preprocessing method in dealing with class imbalance problems, with the purposes of balancing datasets to achieve a high classification rate and avoiding the bias toward majority class examples. It always uses full minority data in a training dataset. However, some noisy minority examples may reduce the performance of classifiers. In this paper, a new under-sampling scheme is proposed by incorporating a noise filter before executing resampling. In order to verify the efficiency, this scheme is implemented based on four popular under-sampling methods, i.e., Undersampling + Adaboost, RUSBoost, UnderBagging, and EasyEnsemble through benchmarks and significance analysis. Furthermore, this paper also summarizes the relationship between algorithm performance and imbalanced ratio. Experimental results indicate that the proposed scheme can improve the original undersampling-based methods with significance in terms of three popular metrics for imbalanced classification, i.e., the area under the curve, -measure, and -mean.

Development of a Self-Rated Mixed Methods Skills Assessment: The NIH Mixed Methods Research Training Program for the Health Sciences

PubMed Central

Guetterman, Timothy C.; Creswell, John W.; Wittink, Marsha; Barg, Fran K.; Castro, Felipe G.; Dahlberg, Britt; Watkins, Daphne C.; Deutsch, Charles; Gallo, Joseph J.

2017-01-01

Introduction Demand for training in mixed methods is high, with little research on faculty development or assessment in mixed methods. We describe the development of a Self-Rated Mixed Methods Skills Assessment and provide validity evidence. The instrument taps six research domains: “Research question,” “Design/approach,” “Sampling,” “Data collection,” “Analysis,” and “Dissemination.” Respondents are asked to rate their ability to define or explain concepts of mixed methods under each domain, their ability to apply the concepts to problems, and the extent to which they need to improve. Methods We administered the questionnaire to 145 faculty and students using an internet survey. We analyzed descriptive statistics and performance characteristics of the questionnaire using Cronbach’s alpha to assess reliability and an ANOVA that compared a mixed methods experience index with assessment scores to assess criterion-relatedness. Results Internal consistency reliability was high for the total set of items (.95) and adequate (>=.71) for all but one subscale. Consistent with establishing criterion validity, respondents who had more professional experiences with mixed methods (e.g., published a mixed methods paper) rated themselves as more skilled, which was statistically significant across the research domains. Discussion This Self-Rated Mixed Methods Assessment instrument may be a useful tool to assess skills in mixed methods for training programs. It can be applied widely at the graduate and faculty level. For the learner, assessment may lead to enhanced motivation to learn and training focused on self-identified needs. For faculty, the assessment may improve curriculum and course content planning. PMID:28562495

Installation Restoration Program Stage 3. McClellan Air Force Base, California. Remedial Investigation/Feasibility Study Groundwater Sampling and Analysis Program Data Summary

DTIC Science & Technology

1988-12-01

and do not refer to monitoring zones at McClellSn AFB. b Priority poLutant metals analyses also included U.S. EPA Methods 206.2, 245.1 and 270.2. EW a...sampling protocol, and the laboratory is audited routinely. Therefore, no corrective action other than good training and supervision is necessary. The same

Exercise training does not increase muscle FNDC5 protein or mRNA expression in pigs

PubMed Central

Fain, John N.; Company, Joseph M.; Booth, Frank W.; Laughlin, M. Harold; Padilla, Jaume; Jenkins, Nathan T.; Bahouth, Suleiman W.; Sacks, Harold S.

2013-01-01

Background Exercise training elevates circulating irisin and induces the expression of the FNDC5 gene in skeletal muscles of mice. Our objective was to determine whether exercise training also increases FNDC5 protein or mRNA expression in the skeletal muscles of pigs as well as plasma irisin. Methods Castrated male pigs of the Rapacz familial hypercholesterolemic (FHM) strain and normal (Yucatan miniature) pigs were sacrificed after 16–20 weeks of exercise training. Samples of cardiac muscle, deltoid and triceps brachii muscle, subcutaneous and epicardial fat were obtained and FNDC5 mRNA, along with that of 6 other genes, was measured in all tissues of FHM pigs by reverse transcription polymerase chain reaction. FNDC protein in deltoid and triceps brachii was determined by Western blotting in both FHM and normal pigs. Citrate synthase activity was measured in the muscle samples of all pigs as an index of exercise training. Irisin was measured by an ELISA assay. Results There was no statistically significant effect of exercise training on FNDC5 gene expression in epicardial or subcutaneous fat, deltoid muscle, triceps brachii muscle or heart muscle. Exercise-training elevated circulating levels of irisin in the FHM pigs and citrate synthase activity in deltoid and triceps brachii muscle. A similar increase in citrate synthase activity was seen in muscle extracts of exercise-trained normal pigs but there was no alteration in circulating irisin. Conclusion Exercise training in pigs does not increase FNDC5 mRNA or protein in the deltoid or triceps brachii of FHM or normal pigs while increasing circulating irisin only in the FHM pigs. These data indicate that the response to exercise training in normal pigs is not comparable to that seen in mice. PMID:23831442

Quantifying mineral abundances of complex mixtures by coupling spectral deconvolution of SWIR spectra (2.1-2.4 μm) and regression tree analysis

USGS Publications Warehouse

Mulder, V.L.; Plotze, Michael; de Bruin, Sytze; Schaepman, Michael E.; Mavris, C.; Kokaly, Raymond F.; Egli, Markus

2013-01-01

This paper presents a methodology for assessing mineral abundances of mixtures having more than two constituents using absorption features in the 2.1-2.4 μm wavelength region. In the first step, the absorption behaviour of mineral mixtures is parameterised by exponential Gaussian optimisation. Next, mineral abundances are predicted by regression tree analysis using these parameters as inputs. The approach is demonstrated on a range of prepared samples with known abundances of kaolinite, dioctahedral mica, smectite, calcite and quartz and on a set of field samples from Morocco. The latter contained varying quantities of other minerals, some of which did not have diagnostic absorption features in the 2.1-2.4 μm region. Cross validation showed that the prepared samples of kaolinite, dioctahedral mica, smectite and calcite were predicted with a root mean square error (RMSE) less than 9 wt.%. For the field samples, the RMSE was less than 8 wt.% for calcite, dioctahedral mica and kaolinite abundances. Smectite could not be well predicted, which was attributed to spectral variation of the cations within the dioctahedral layered smectites. Substitution of part of the quartz by chlorite at the prediction phase hardly affected the accuracy of the predicted mineral content; this suggests that the method is robust in handling the omission of minerals during the training phase. The degree of expression of absorption components was different between the field sample and the laboratory mixtures. This demonstrates that the method should be calibrated and trained on local samples. Our method allows the simultaneous quantification of more than two minerals within a complex mixture and thereby enhances the perspectives of spectral analysis for mineral abundances.

Development of analytical cell support for vitrification at the West Valley Demonstration Project. Topical report

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

Barber, F.H.; Borek, T.T.; Christopher, J.Z.

1997-12-01

Analytical and Process Chemistry (A&PC) support is essential to the high-level waste vitrification campaign at the West Valley Demonstration Project (WVDP). A&PC characterizes the waste, providing information necessary to formulate the recipe for the target radioactive glass product. High-level waste (HLW) samples are prepared and analyzed in the analytical cells (ACs) and Sample Storage Cell (SSC) on the third floor of the main plant. The high levels of radioactivity in the samples require handling them in the shielded cells with remote manipulators. The analytical hot cells and third floor laboratories were refurbished to ensure optimal uninterrupted operation during the vitrificationmore » campaign. New and modified instrumentation, tools, sample preparation and analysis techniques, and equipment and training were required for A&PC to support vitrification. Analytical Cell Mockup Units (ACMUs) were designed to facilitate method development, scientist and technician training, and planning for analytical process flow. The ACMUs were fabricated and installed to simulate the analytical cell environment and dimensions. New techniques, equipment, and tools could be evaluated m in the ACMUs without the consequences of generating or handling radioactive waste. Tools were fabricated, handling and disposal of wastes was addressed, and spatial arrangements for equipment were refined. As a result of the work at the ACMUs the remote preparation and analysis methods and the equipment and tools were ready for installation into the ACs and SSC m in July 1995. Before use m in the hot cells, all remote methods had been validated and four to eight technicians were trained on each. Fine tuning of the procedures has been ongoing at the ACs based on input from A&PC technicians. Working at the ACs presents greater challenges than had development at the ACMUs. The ACMU work and further refinements m in the ACs have resulted m in a reduction m in analysis turnaround time (TAT).« less

Classification of urine sediment based on convolution neural network

NASA Astrophysics Data System (ADS)

Pan, Jingjing; Jiang, Cunbo; Zhu, Tiantian

2018-04-01

By designing a new convolution neural network framework, this paper breaks the constraints of the original convolution neural network framework requiring large training samples and samples of the same size. Move and cropping the input images, generate the same size of the sub-graph. And then, the generated sub-graph uses the method of dropout, increasing the diversity of samples and preventing the fitting generation. Randomly select some proper subset in the sub-graphic set and ensure that the number of elements in the proper subset is same and the proper subset is not the same. The proper subsets are used as input layers for the convolution neural network. Through the convolution layer, the pooling, the full connection layer and output layer, we can obtained the classification loss rate of test set and training set. In the red blood cells, white blood cells, calcium oxalate crystallization classification experiment, the classification accuracy rate of 97% or more.

Psychobiography Training in Psychology in North America: Mapping the Field and Charting a Course

PubMed Central

Ponterotto, Joseph G.; Reynolds, Jason D.; Morel, Samantha; Cheung, Linda

2015-01-01

Psychobiography holds an important position in the history of psychology, yet little is known about the status of psychobiographical training and dissertation research in psychology departments. This brief report identified psychobiography courses throughout North America and content analyzed a sample of 65 psychobiography dissertations to discern the theories and methods that have most commonly anchored this research. Results identified few psychology courses specifically in psychobiography, with a larger number of courses incorporating psychobiographical and/or narrative elements. With regard to psychobiography dissertations, the majority focused on artists, pioneering psychologists, and political leaders. Theories undergirding psychobiographical studies were most frequently psychoanalytic and psychodynamic. Methodologically, a majority of the dissertations were anchored in constructivist (discovery-oriented) qualitative procedures, with a minority incorporating mixed methods designs. The authors highlight the value of psychobiographical training to psychology students and present avenues and models for incorporating psychobiography into psychology curriculums. PMID:27247670

Transient classification in LIGO data using difference boosting neural network

NASA Astrophysics Data System (ADS)

Mukund, N.; Abraham, S.; Kandhasamy, S.; Mitra, S.; Philip, N. S.

2017-05-01

Detection and classification of transients in data from gravitational wave detectors are crucial for efficient searches for true astrophysical events and identification of noise sources. We present a hybrid method for classification of short duration transients seen in gravitational wave data using both supervised and unsupervised machine learning techniques. To train the classifiers, we use the relative wavelet energy and the corresponding entropy obtained by applying one-dimensional wavelet decomposition on the data. The prediction accuracy of the trained classifier on nine simulated classes of gravitational wave transients and also LIGO's sixth science run hardware injections are reported. Targeted searches for a couple of known classes of nonastrophysical signals in the first observational run of Advanced LIGO data are also presented. The ability to accurately identify transient classes using minimal training samples makes the proposed method a useful tool for LIGO detector characterization as well as searches for short duration gravitational wave signals.

[Evaluation of Image Quality of Readout Segmented EPI with Readout Partial Fourier Technique].

PubMed

Yoshimura, Yuuki; Suzuki, Daisuke; Miyahara, Kanae

Readout segmented EPI (readout segmentation of long variable echo-trains: RESOLVE) segmented k-space in the readout direction. By using the partial Fourier method in the readout direction, the imaging time was shortened. However, the influence on image quality due to insufficient data sampling is concerned. The setting of the partial Fourier method in the readout direction in each segment was changed. Then, we examined signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and distortion ratio for changes in image quality due to differences in data sampling. As the number of sampling segments decreased, SNR and CNR showed a low value. In addition, the distortion ratio did not change. The image quality of minimum sampling segments is greatly different from full data sampling, and caution is required when using it.

Is it time for studying real-life debiasing? Evaluation of the effectiveness of an analogical intervention technique

PubMed Central

Aczel, Balazs; Bago, Bence; Szollosi, Aba; Foldes, Andrei; Lukacs, Bence

2015-01-01

The aim of this study was to initiate the exploration of debiasing methods applicable in real-life settings for achieving lasting improvement in decision making competence regarding multiple decision biases. Here, we tested the potentials of the analogical encoding method for decision debiasing. The advantage of this method is that it can foster the transfer from learning abstract principles to improving behavioral performance. For the purpose of the study, we devised an analogical debiasing technique for 10 biases (covariation detection, insensitivity to sample size, base rate neglect, regression to the mean, outcome bias, sunk cost fallacy, framing effect, anchoring bias, overconfidence bias, planning fallacy) and assessed the susceptibility of the participants (N = 154) to these biases before and 4 weeks after the training. We also compared the effect of the analogical training to the effect of ‘awareness training’ and a ‘no-training’ control group. Results suggested improved performance of the analogical training group only on tasks where the violations of statistical principles are measured. The interpretation of these findings require further investigation, yet it is possible that analogical training may be the most effective in the case of learning abstract concepts, such as statistical principles, which are otherwise difficult to master. The study encourages a systematic research of debiasing trainings and the development of intervention assessment methods to measure the endurance of behavior change in decision debiasing. PMID:26300816

Comparison of texture synthesis methods for content generation in ultrasound simulation for training

NASA Astrophysics Data System (ADS)

Mattausch, Oliver; Ren, Elizabeth; Bajka, Michael; Vanhoey, Kenneth; Goksel, Orcun

2017-03-01

Navigation and interpretation of ultrasound (US) images require substantial expertise, the training of which can be aided by virtual-reality simulators. However, a major challenge in creating plausible simulated US images is the generation of realistic ultrasound speckle. Since typical ultrasound speckle exhibits many properties of Markov Random Fields, it is conceivable to use texture synthesis for generating plausible US appearance. In this work, we investigate popular classes of texture synthesis methods for generating realistic US content. In a user study, we evaluate their performance for reproducing homogeneous tissue regions in B-mode US images from small image samples of similar tissue and report the best-performing synthesis methods. We further show that regression trees can be used on speckle texture features to learn a predictor for US realism.

Interface Prostheses With Classifier-Feedback-Based User Training.

PubMed

Fang, Yinfeng; Zhou, Dalin; Li, Kairu; Liu, Honghai

2017-11-01

It is evident that user training significantly affects performance of pattern-recognition-based myoelectric prosthetic device control. Despite plausible classification accuracy on offline datasets, online accuracy usually suffers from the changes in physiological conditions and electrode displacement. The user ability in generating consistent electromyographic (EMG) patterns can be enhanced via proper user training strategies in order to improve online performance. This study proposes a clustering-feedback strategy that provides real-time feedback to users by means of a visualized online EMG signal input as well as the centroids of the training samples, whose dimensionality is reduced to minimal number by dimension reduction. Clustering feedback provides a criterion that guides users to adjust motion gestures and muscle contraction forces intentionally. The experiment results have demonstrated that hand motion recognition accuracy increases steadily along the progress of the clustering-feedback-based user training, while conventional classifier-feedback methods, i.e., label feedback, hardly achieve any improvement. The result concludes that the use of proper classifier feedback can accelerate the process of user training, and implies prosperous future for the amputees with limited or no experience in pattern-recognition-based prosthetic device manipulation.It is evident that user training significantly affects performance of pattern-recognition-based myoelectric prosthetic device control. Despite plausible classification accuracy on offline datasets, online accuracy usually suffers from the changes in physiological conditions and electrode displacement. The user ability in generating consistent electromyographic (EMG) patterns can be enhanced via proper user training strategies in order to improve online performance. This study proposes a clustering-feedback strategy that provides real-time feedback to users by means of a visualized online EMG signal input as well as the centroids of the training samples, whose dimensionality is reduced to minimal number by dimension reduction. Clustering feedback provides a criterion that guides users to adjust motion gestures and muscle contraction forces intentionally. The experiment results have demonstrated that hand motion recognition accuracy increases steadily along the progress of the clustering-feedback-based user training, while conventional classifier-feedback methods, i.e., label feedback, hardly achieve any improvement. The result concludes that the use of proper classifier feedback can accelerate the process of user training, and implies prosperous future for the amputees with limited or no experience in pattern-recognition-based prosthetic device manipulation.

An Accurate Framework for Arbitrary View Pedestrian Detection in Images

NASA Astrophysics Data System (ADS)

Fan, Y.; Wen, G.; Qiu, S.

2018-01-01

We consider the problem of detect pedestrian under from images collected under various viewpoints. This paper utilizes a novel framework called locality-constrained affine subspace coding (LASC). Firstly, the positive training samples are clustered into similar entities which represent similar viewpoint. Then Principal Component Analysis (PCA) is used to obtain the shared feature of each viewpoint. Finally, the samples that can be reconstructed by linear approximation using their top- k nearest shared feature with a small error are regarded as a correct detection. No negative samples are required for our method. Histograms of orientated gradient (HOG) features are used as the feature descriptors, and the sliding window scheme is adopted to detect humans in images. The proposed method exploits the sparse property of intrinsic information and the correlations among the multiple-views samples. Experimental results on the INRIA and SDL human datasets show that the proposed method achieves a higher performance than the state-of-the-art methods in form of effect and efficiency.

Toward accelerating landslide mapping with interactive machine learning techniques

NASA Astrophysics Data System (ADS)

Stumpf, André; Lachiche, Nicolas; Malet, Jean-Philippe; Kerle, Norman; Puissant, Anne

2013-04-01

Despite important advances in the development of more automated methods for landslide mapping from optical remote sensing images, the elaboration of inventory maps after major triggering events still remains a tedious task. Image classification with expert defined rules typically still requires significant manual labour for the elaboration and adaption of rule sets for each particular case. Machine learning algorithm, on the contrary, have the ability to learn and identify complex image patterns from labelled examples but may require relatively large amounts of training data. In order to reduce the amount of required training data active learning has evolved as key concept to guide the sampling for applications such as document classification, genetics and remote sensing. The general underlying idea of most active learning approaches is to initialize a machine learning model with a small training set, and to subsequently exploit the model state and/or the data structure to iteratively select the most valuable samples that should be labelled by the user and added in the training set. With relatively few queries and labelled samples, an active learning strategy should ideally yield at least the same accuracy than an equivalent classifier trained with many randomly selected samples. Our study was dedicated to the development of an active learning approach for landslide mapping from VHR remote sensing images with special consideration of the spatial distribution of the samples. The developed approach is a region-based query heuristic that enables to guide the user attention towards few compact spatial batches rather than distributed points resulting in time savings of 50% and more compared to standard active learning techniques. The approach was tested with multi-temporal and multi-sensor satellite images capturing recent large scale triggering events in Brazil and China and demonstrated balanced user's and producer's accuracies between 74% and 80%. The assessment also included an experimental evaluation of the uncertainties of manual mappings from multiple experts and demonstrated strong relationships between the uncertainty of the experts and the machine learning model.

Combining high-speed SVM learning with CNN feature encoding for real-time target recognition in high-definition video for ISR missions

NASA Astrophysics Data System (ADS)

Kroll, Christine; von der Werth, Monika; Leuck, Holger; Stahl, Christoph; Schertler, Klaus

2017-05-01

For Intelligence, Surveillance, Reconnaissance (ISR) missions of manned and unmanned air systems typical electrooptical payloads provide high-definition video data which has to be exploited with respect to relevant ground targets in real-time by automatic/assisted target recognition software. Airbus Defence and Space is developing required technologies for real-time sensor exploitation since years and has combined the latest advances of Deep Convolutional Neural Networks (CNN) with a proprietary high-speed Support Vector Machine (SVM) learning method into a powerful object recognition system with impressive results on relevant high-definition video scenes compared to conventional target recognition approaches. This paper describes the principal requirements for real-time target recognition in high-definition video for ISR missions and the Airbus approach of combining an invariant feature extraction using pre-trained CNNs and the high-speed training and classification ability of a novel frequency-domain SVM training method. The frequency-domain approach allows for a highly optimized implementation for General Purpose Computation on a Graphics Processing Unit (GPGPU) and also an efficient training of large training samples. The selected CNN which is pre-trained only once on domain-extrinsic data reveals a highly invariant feature extraction. This allows for a significantly reduced adaptation and training of the target recognition method for new target classes and mission scenarios. A comprehensive training and test dataset was defined and prepared using relevant high-definition airborne video sequences. The assessment concept is explained and performance results are given using the established precision-recall diagrams, average precision and runtime figures on representative test data. A comparison to legacy target recognition approaches shows the impressive performance increase by the proposed CNN+SVM machine-learning approach and the capability of real-time high-definition video exploitation.

HLA imputation in an admixed population: An assessment of the 1000 Genomes data as a training set.

PubMed

Nunes, Kelly; Zheng, Xiuwen; Torres, Margareth; Moraes, Maria Elisa; Piovezan, Bruno Z; Pontes, Gerlandia N; Kimura, Lilian; Carnavalli, Juliana E P; Mingroni Netto, Regina C; Meyer, Diogo

2016-03-01

Methods to impute HLA alleles based on dense single nucleotide polymorphism (SNP) data provide a valuable resource to association studies and evolutionary investigation of the MHC region. The availability of appropriate training sets is critical to the accuracy of HLA imputation, and the inclusion of samples with various ancestries is an important pre-requisite in studies of admixed populations. We assess the accuracy of HLA imputation using 1000 Genomes Project data as a training set, applying it to a highly admixed Brazilian population, the Quilombos from the state of São Paulo. To assess accuracy, we compared imputed and experimentally determined genotypes for 146 samples at 4 HLA classical loci. We found imputation accuracies of 82.9%, 81.8%, 94.8% and 86.6% for HLA-A, -B, -C and -DRB1 respectively (two-field resolution). Accuracies were improved when we included a subset of Quilombo individuals in the training set. We conclude that the 1000 Genomes data is a valuable resource for construction of training sets due to the diversity of ancestries and the potential for a large overlap of SNPs with the target population. We also show that tailoring training sets to features of the target population substantially enhances imputation accuracy. Copyright © 2016 American Society for Histocompatibility and Immunogenetics. Published by Elsevier Inc. All rights reserved.

Stochastic subset selection for learning with kernel machines.

PubMed

Rhinelander, Jason; Liu, Xiaoping P

2012-06-01

Kernel machines have gained much popularity in applications of machine learning. Support vector machines (SVMs) are a subset of kernel machines and generalize well for classification, regression, and anomaly detection tasks. The training procedure for traditional SVMs involves solving a quadratic programming (QP) problem. The QP problem scales super linearly in computational effort with the number of training samples and is often used for the offline batch processing of data. Kernel machines operate by retaining a subset of observed data during training. The data vectors contained within this subset are referred to as support vectors (SVs). The work presented in this paper introduces a subset selection method for the use of kernel machines in online, changing environments. Our algorithm works by using a stochastic indexing technique when selecting a subset of SVs when computing the kernel expansion. The work described here is novel because it separates the selection of kernel basis functions from the training algorithm used. The subset selection algorithm presented here can be used in conjunction with any online training technique. It is important for online kernel machines to be computationally efficient due to the real-time requirements of online environments. Our algorithm is an important contribution because it scales linearly with the number of training samples and is compatible with current training techniques. Our algorithm outperforms standard techniques in terms of computational efficiency and provides increased recognition accuracy in our experiments. We provide results from experiments using both simulated and real-world data sets to verify our algorithm.

Artificial neural networks for processing fluorescence spectroscopy data in skin cancer diagnostics

NASA Astrophysics Data System (ADS)

Lenhardt, L.; Zeković, I.; Dramićanin, T.; Dramićanin, M. D.

2013-11-01

Over the years various optical spectroscopic techniques have been widely used as diagnostic tools in the discrimination of many types of malignant diseases. Recently, synchronous fluorescent spectroscopy (SFS) coupled with chemometrics has been applied in cancer diagnostics. The SFS method involves simultaneous scanning of both emission and excitation wavelengths while keeping the interval of wavelengths (constant-wavelength mode) or frequencies (constant-energy mode) between them constant. This method is fast, relatively inexpensive, sensitive and non-invasive. Total synchronous fluorescence spectra of normal skin, nevus and melanoma samples were used as input for training of artificial neural networks. Two different types of artificial neural networks were trained, the self-organizing map and the feed-forward neural network. Histopathology results of investigated skin samples were used as the gold standard for network output. Based on the obtained classification success rate of neural networks, we concluded that both networks provided high sensitivity with classification errors between 2 and 4%.

Discriminant WSRC for Large-Scale Plant Species Recognition.

PubMed

Zhang, Shanwen; Zhang, Chuanlei; Zhu, Yihai; You, Zhuhong

2017-01-01

In sparse representation based classification (SRC) and weighted SRC (WSRC), it is time-consuming to solve the global sparse representation problem. A discriminant WSRC (DWSRC) is proposed for large-scale plant species recognition, including two stages. Firstly, several subdictionaries are constructed by dividing the dataset into several similar classes, and a subdictionary is chosen by the maximum similarity between the test sample and the typical sample of each similar class. Secondly, the weighted sparse representation of the test image is calculated with respect to the chosen subdictionary, and then the leaf category is assigned through the minimum reconstruction error. Different from the traditional SRC and its improved approaches, we sparsely represent the test sample on a subdictionary whose base elements are the training samples of the selected similar class, instead of using the generic overcomplete dictionary on the entire training samples. Thus, the complexity to solving the sparse representation problem is reduced. Moreover, DWSRC is adapted to newly added leaf species without rebuilding the dictionary. Experimental results on the ICL plant leaf database show that the method has low computational complexity and high recognition rate and can be clearly interpreted.

Purification of Training Samples Based on Spectral Feature and Superpixel Segmentation

NASA Astrophysics Data System (ADS)

Guan, X.; Qi, W.; He, J.; Wen, Q.; Chen, T.; Wang, Z.

2018-04-01

Remote sensing image classification is an effective way to extract information from large volumes of high-spatial resolution remote sensing images. Generally, supervised image classification relies on abundant and high-precision training data, which is often manually interpreted by human experts to provide ground truth for training and evaluating the performance of the classifier. Remote sensing enterprises accumulated lots of manually interpreted products from early lower-spatial resolution remote sensing images by executing their routine research and business programs. However, these manually interpreted products may not match the very high resolution (VHR) image properly because of different dates or spatial resolution of both data, thus, hindering suitability of manually interpreted products in training classification models, or small coverage area of these manually interpreted products. We also face similar problems in our laboratory in 21st Century Aerospace Technology Co. Ltd (short for 21AT). In this work, we propose a method to purify the interpreted product to match newly available VHRI data and provide the best training data for supervised image classifiers in VHR image classification. And results indicate that our proposed method can efficiently purify the input data for future machine learning use.

LCC: Light Curves Classifier

NASA Astrophysics Data System (ADS)

Vo, Martin

2017-08-01

Light Curves Classifier uses data mining and machine learning to obtain and classify desired objects. This task can be accomplished by attributes of light curves or any time series, including shapes, histograms, or variograms, or by other available information about the inspected objects, such as color indices, temperatures, and abundances. After specifying features which describe the objects to be searched, the software trains on a given training sample, and can then be used for unsupervised clustering for visualizing the natural separation of the sample. The package can be also used for automatic tuning parameters of used methods (for example, number of hidden neurons or binning ratio). Trained classifiers can be used for filtering outputs from astronomical databases or data stored locally. The Light Curve Classifier can also be used for simple downloading of light curves and all available information of queried stars. It natively can connect to OgleII, OgleIII, ASAS, CoRoT, Kepler, Catalina and MACHO, and new connectors or descriptors can be implemented. In addition to direct usage of the package and command line UI, the program can be used through a web interface. Users can create jobs for ”training” methods on given objects, querying databases and filtering outputs by trained filters. Preimplemented descriptors, classifier and connectors can be picked by simple clicks and their parameters can be tuned by giving ranges of these values. All combinations are then calculated and the best one is used for creating the filter. Natural separation of the data can be visualized by unsupervised clustering.

A Study on Capabilities Required In Military Medicine to Develop Modular Training Courses: A Qualitative Study

PubMed Central

DANA, ALI; MOHAMMADIMEHR, MOJGAN

2017-01-01

Introduction: The main mission of military medicine in the world is to support the health and treatment of the military in relation to issues, risks, injuries and diseases that arise due to the specific occupational conditions. The current study was carried out with the aim of determining the required skills of military physicians to define and determine the required training modules. Methods: The study was a qualitative research. Semi-structured interviews were used to collect the data and qualitative content analysis was used to analyze the data. The study population included all the professors and experts in the field of military medicine and medical sciences at the medical universities of Tehran. Snowball sampling technique was used to sample the study participants. Results: Based on the results, the required skills of military physicians in 5 categories and 29 sub- categories were identified; then based on the identified skills, 60 training modules at two introductory and advanced levels were determined including 39 introductory levels and 21 advanced levels. Conclusion: We can conclude that some of the important skills that military physicians need and can achieved through training have not been provided in any educational program and to achieve such skills and capabilities, other programs should be developed and modular training can be one of them. PMID:28761887

Oregon Indigenous Farmworkers: Results of Promotor Intervention on Pesticide Knowledge and Organophosphate Metabolite Levels

PubMed Central

McCauley, Linda; Runkle, Jennifer D.; Samples, Julie; Williams, Bryan; Muniz, Juan F; Semple, Marie; Shadbeh, Nargess

2013-01-01

Objectives Examine changes in health beliefs, pesticide safety knowledge, and biomarkers of pesticide exposure in indigenous farmworker who received enhanced pesticide safety training compared to those receiving the standard training. Methods Farmworkers in Oregon were randomly assigned to either a promotores pesticide safety training program or a standard video-based training. Spot urine samples were analyzed for dialkylphosphate (DAP) urinary metabolites. Pre/post intervention questionnaires were used to measure pesticide safety knowledge, health beliefs and work practices. Results Baseline to follow-up improvements in total pesticide knowledge scores were higher in the promotor group compared to the video. Pairwise differences in mean concentrations of DAP metabolite levels showed declines from baseline to follow-up for both intervention groups. Conclusions Results showed reductions in pesticide exposure in indigenous-language speaking farmworkers who receive enhanced pesticide safety training. PMID:24064776

Evaluation of SLAR and thematic mapper MSS data for forest cover mapping using computer-aided analysis techniques

NASA Technical Reports Server (NTRS)

Hoffer, R. M. (Principal Investigator); Knowlton, D. J.; Dean, M. E.

1981-01-01

A set of training statistics for the 30 meter resolution simulated thematic mapper MSS data was generated based on land use/land cover classes. In addition to this supervised data set, a nonsupervised multicluster block of training statistics is being defined in order to compare the classification results and evaluate the effect of the different training selection methods on classification performance. Two test data sets, defined using a stratified sampling procedure incorporating a grid system with dimensions of 50 lines by 50 columns, and another set based on an analyst supervised set of test fields were used to evaluate the classifications of the TMS data. The supervised training data set generated training statistics, and a per point Gaussian maximum likelihood classification of the 1979 TMS data was obtained. The August 1980 MSS data was radiometrically adjusted. The SAR data was redigitized and the SAR imagery was qualitatively analyzed.

Pharmacist supply of sildenafil: pharmacists' experiences and perceptions on training and tools for supply.

PubMed

Braund, Rhiannon; Ratnayake, Kaushalya; Tong, Katie; Song, Jackie; Chai, Stephen; Gauld, Natalie

2018-06-01

Background In 2014, New Zealand reclassified sildenafil (for erectile dysfunction) to allow supply by specially trained pharmacists under strict criteria. Objective The study aimed to determine pharmacists' experiences and perspectives on the training for, and supply of sildenafil under this model. Setting New Zealand community pharmacy. Method This qualitative study captured data with a semi-structured interview used with purposively-sampled participants. A maximum variation sample was used to select a wide range of pharmacists working in various pharmacies, including pharmacists who were trained to provide sildenafil and those not trained to supply sildenafil. Consenting pharmacists were interviewed, with interviews audio-recorded and transcribed. Analysis used a framework approach. Main outcome measures Topics explored included: satisfaction and experience of the training; suitability and usability of the screening tools; experiences of the supply process and why some pharmacists chose not to become trained. Results Thirty-five pharmacists were interviewed. Training was seen as uncomplicated and the screening tools provided confidence that key consultation areas were covered. Most consultations reportedly took 15-20 min, some up to 60 min. Pharmacists reported being comfortable with the consultations. Many men requesting supply fell outside of the parameters, resulting in medical referral. This new model of supply was seen as a positive for pharmacists and their patients. Unaccredited pharmacists reported a perceived lack of interest from men, or ability to provide the service as reasons for not seeking accreditation. Conclusion New Zealand's model of pharmacist supply of sildenafil appears workable with some areas for improvement identified.

The influence of listener training on the perceptual assessment of hypernasality.

PubMed

Oliveira, Adriana Cristina de Almeida Santos Furlan de; Scarmagnani, Rafaeli Higa; Fukushiro, Ana Paula; Yamashita, Renata Paciello

2016-04-01

Introduction A high agreement in the perceptual assessment of hypernasality among different listeners is difficult to achieve. Prior listener training and the standardization of analysis criteria may be effective strategies to decrease the effect of perceptual assessment subjectivity and increase the agreement among listeners. Objective To investigate the influence of prior training on agreement among different listeners in the perceptual assessment of hypernasality. Methods Three experienced speech-language pathologists analyzed 77 audio-recorded speech samples of individuals with repaired cleft palate. During the first phase, the listeners classified hypernasality according to their own criteria, using a 4-point scale. Seventy days later, they were required to complete the training to define the stimuli to be used as anchors for the assessment in the following phase. During the second phase, the listeners analyzed the same samples and rated hypernasality in a 4-point scale, using the anchors defined during training as the criteria. Intra- and interrater agreement in both the phases were calculated by the kappa coefficient. These values were statistically compared using the Z-test. Results The intrarater agreement obtained between the two phases of the study ranged from 0.38 to 0.92, with a statistically significant difference for one of the listeners (p=0.004). The agreement for the hypernasality degree obtained among the three listeners after training (0.54) was significantly higher than that obtained before training (0.37; p=0.044). Conclusion Listener training and the definition of criteria to rate hypernasality lead to the increase of intra- and interrater agreement.

DNA Everywhere. A Guide for Simplified Environmental Genomic DNA Extraction Suitable for Use in Remote Areas

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

Gabrielle N. Pecora; Francine C. Reid; Lauren M. Tom

2016-05-01

Collecting field samples from remote or geographically distant areas can be a financially and logistically challenging. With participation of a local organization where the samples are originated from, gDNA samples can be extracted from the field and shipped to a research institution for further processing and analysis. The ability to set up gDNA extraction capabilities in the field can drastically reduce cost and time when running long-term microbial studies with a large sample set. The method outlined here has developed a compact and affordable method for setting up a “laboratory” and extracting and shipping gDNA samples from anywhere in themore » world. This white paper explains the process of setting up the “laboratory”, choosing and training individuals with no prior scientific experience how to perform gDNA extractions and safe methods for shipping extracts to any research institution. All methods have been validated by the Andersen group at Lawrence Berkeley National Laboratory using the Berkeley Lab PhyloChip.« less

Development of a Moodle Course for Schoolchildren's Table Tennis Learning Based on Competence Motivation Theory: Its Effectiveness in Comparison to Traditional Training Method

ERIC Educational Resources Information Center

Zou, Junhua; Liu, Qingtang; Yang, Zongkai

2012-01-01

Based on Competence Motivation Theory (CMT), a Moodle course for schoolchildren's table tennis learning was developed (The URL is http://www.bssepp.com, and this course allows guest access). The effects of the course on students' knowledge, perceived competence and interest were evaluated through quantitative methods. The sample of the study…

Application of Response Surface Methods To Determine Conditions for Optimal Genomic Prediction

PubMed Central

Howard, Réka; Carriquiry, Alicia L.; Beavis, William D.

2017-01-01

An epistatic genetic architecture can have a significant impact on prediction accuracies of genomic prediction (GP) methods. Machine learning methods predict traits comprised of epistatic genetic architectures more accurately than statistical methods based on additive mixed linear models. The differences between these types of GP methods suggest a diagnostic for revealing genetic architectures underlying traits of interest. In addition to genetic architecture, the performance of GP methods may be influenced by the sample size of the training population, the number of QTL, and the proportion of phenotypic variability due to genotypic variability (heritability). Possible values for these factors and the number of combinations of the factor levels that influence the performance of GP methods can be large. Thus, efficient methods for identifying combinations of factor levels that produce most accurate GPs is needed. Herein, we employ response surface methods (RSMs) to find the experimental conditions that produce the most accurate GPs. We illustrate RSM with an example of simulated doubled haploid populations and identify the combination of factors that maximize the difference between prediction accuracies of best linear unbiased prediction (BLUP) and support vector machine (SVM) GP methods. The greatest impact on the response is due to the genetic architecture of the population, heritability of the trait, and the sample size. When epistasis is responsible for all of the genotypic variance and heritability is equal to one and the sample size of the training population is large, the advantage of using the SVM method vs. the BLUP method is greatest. However, except for values close to the maximum, most of the response surface shows little difference between the methods. We also determined that the conditions resulting in the greatest prediction accuracy for BLUP occurred when genetic architecture consists solely of additive effects, and heritability is equal to one. PMID:28720710

Binning in Gaussian Kernel Regularization

DTIC Science & Technology

2005-04-01

OSU-SVM Matlab package, the SVM trained on 966 bins has a comparable test classification rate as the SVM trained on 27,179 samples, but reduces the...71.40%) on 966 randomly sampled data. Using the OSU-SVM Matlab package, the SVM trained on 966 bins has a comparable test classification rate as the...the OSU-SVM Matlab package, the SVM trained on 966 bins has a comparable test classification rate as the SVM trained on 27,179 samples, and reduces

Pitfalls in statistical landslide susceptibility modelling

NASA Astrophysics Data System (ADS)

Schröder, Boris; Vorpahl, Peter; Märker, Michael; Elsenbeer, Helmut

2010-05-01

The use of statistical methods is a well-established approach to predict landslide occurrence probabilities and to assess landslide susceptibility. This is achieved by applying statistical methods relating historical landslide inventories to topographic indices as predictor variables. In our contribution, we compare several new and powerful methods developed in machine learning and well-established in landscape ecology and macroecology for predicting the distribution of shallow landslides in tropical mountain rainforests in southern Ecuador (among others: boosted regression trees, multivariate adaptive regression splines, maximum entropy). Although these methods are powerful, we think it is necessary to follow a basic set of guidelines to avoid some pitfalls regarding data sampling, predictor selection, and model quality assessment, especially if a comparison of different models is contemplated. We therefore suggest to apply a novel toolbox to evaluate approaches to the statistical modelling of landslide susceptibility. Additionally, we propose some methods to open the "black box" as an inherent part of machine learning methods in order to achieve further explanatory insights into preparatory factors that control landslides. Sampling of training data should be guided by hypotheses regarding processes that lead to slope failure taking into account their respective spatial scales. This approach leads to the selection of a set of candidate predictor variables considered on adequate spatial scales. This set should be checked for multicollinearity in order to facilitate model response curve interpretation. Model quality assesses how well a model is able to reproduce independent observations of its response variable. This includes criteria to evaluate different aspects of model performance, i.e. model discrimination, model calibration, and model refinement. In order to assess a possible violation of the assumption of independency in the training samples or a possible lack of explanatory information in the chosen set of predictor variables, the model residuals need to be checked for spatial auto¬correlation. Therefore, we calculate spline correlograms. In addition to this, we investigate partial dependency plots and bivariate interactions plots considering possible interactions between predictors to improve model interpretation. Aiming at presenting this toolbox for model quality assessment, we investigate the influence of strategies in the construction of training datasets for statistical models on model quality.

Measurement of glucose concentration by image processing of thin film slides

NASA Astrophysics Data System (ADS)

Piramanayagam, Sankaranaryanan; Saber, Eli; Heavner, David

2012-02-01

Measurement of glucose concentration is important for diagnosis and treatment of diabetes mellitus and other medical conditions. This paper describes a novel image-processing based approach for measuring glucose concentration. A fluid drop (patient sample) is placed on a thin film slide. Glucose, present in the sample, reacts with reagents on the slide to produce a color dye. The color intensity of the dye formed varies with glucose at different concentration levels. Current methods use spectrophotometry to determine the glucose level of the sample. Our proposed algorithm uses an image of the slide, captured at a specific wavelength, to automatically determine glucose concentration. The algorithm consists of two phases: training and testing. Training datasets consist of images at different concentration levels. The dye-occupied image region is first segmented using a Hough based technique and then an intensity based feature is calculated from the segmented region. Subsequently, a mathematical model that describes a relationship between the generated feature values and the given concentrations is obtained. During testing, the dye region of a test slide image is segmented followed by feature extraction. These two initial steps are similar to those done in training. However, in the final step, the algorithm uses the model (feature vs. concentration) obtained from the training and feature generated from test image to predict the unknown concentration. The performance of the image-based analysis was compared with that of a standard glucose analyzer.

The Role of Legitimation in the Professional Socialization of Second-Year Undergraduate Athletic Training Students

PubMed Central

Klossner, Joanne

2008-01-01

Context: Professional socialization during formal educational preparation can help students learn professional roles and can lead to improved organizational socialization as students emerge as members of the occupation's culture. Professional socialization research in athletic training is limited. Objective: To present the role of legitimation and how it influences the professional socialization of second-year athletic training students. Design: Modified constructivist grounded theory and case study methods were used for this qualitative study. Setting: An accredited undergraduate athletic training education program. Patients or Other Participants: Twelve second-year students were selected purposively. The primary sample group (n  =  4) was selected according to theoretical sampling guidelines. The remaining students made up the cohort sample (n  =  8). Theoretically relevant data were gathered from 14 clinical instructors to clarify emergent student data. Data Collection and Analysis: Data collection included document examination, observations, and interviews during 1 academic semester. Data were collected and analyzed through constant comparative analysis. Data triangulation, member checking, and peer-review strategies were used to ensure trustworthiness. Results: Legitimation from various socializing agents initiated professional socialization. Students viewed trust and team membership as rewards for role fulfillment. Conclusions: My findings are consistent with the socialization literature that shows how learning a social or professional role, using rewards to facilitate role performance, and building trusting relationships with socializing agents are important aspects of legitimation and, ultimately, professional socialization. PMID:18668171

Asymmetrical Sample Training and Asymmetrical Retention Functions in One-to-One and Many-to-One Matching in Pigeons

ERIC Educational Resources Information Center

Grant, Douglas S.

2006-01-01

Pigeons were trained in a matching task with either color (group color-first) or line (group line-first) samples. After asymmetrical training in which each group was initially trained with the same sample on all trials, marked retention asymmetries were obtained. In both groups, accuracy dropped precipitously on trials involving the initially…

Training in metabolomics research. I. Designing the experiment, collecting and extracting samples and generating metabolomics data.

PubMed

Barnes, Stephen; Benton, H Paul; Casazza, Krista; Cooper, Sara J; Cui, Xiangqin; Du, Xiuxia; Engler, Jeffrey; Kabarowski, Janusz H; Li, Shuzhao; Pathmasiri, Wimal; Prasain, Jeevan K; Renfrow, Matthew B; Tiwari, Hemant K

2016-07-01

The study of metabolism has had a long history. Metabolomics, a systems biology discipline representing analysis of known and unknown pathways of metabolism, has grown tremendously over the past 20 years. Because of its comprehensive nature, metabolomics requires careful consideration of the question(s) being asked, the scale needed to answer the question(s), collection and storage of the sample specimens, methods for extraction of the metabolites from biological matrices, the analytical method(s) to be employed and the quality control of the analyses, how collected data are correlated, the statistical methods to determine metabolites undergoing significant change, putative identification of metabolites and the use of stable isotopes to aid in verifying metabolite identity and establishing pathway connections and fluxes. The National Institutes of Health Common Fund Metabolomics Program was established in 2012 to stimulate interest in the approaches and technologies of metabolomics. To deliver one of the program's goals, the University of Alabama at Birmingham has hosted an annual 4-day short course in metabolomics for faculty, postdoctoral fellows and graduate students from national and international institutions. This paper is the first part of a summary of the training materials presented in the course to be used as a resource for all those embarking on metabolomics research. The complete set of training materials including slide sets and videos can be viewed at http://www.uab.edu/proteomics/metabolomics/workshop/workshop_june_2015.php. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Deep learning with domain adaptation for accelerated projection-reconstruction MR.

PubMed

Han, Yoseob; Yoo, Jaejun; Kim, Hak Hee; Shin, Hee Jung; Sung, Kyunghyun; Ye, Jong Chul

2018-09-01

The radial k-space trajectory is a well-established sampling trajectory used in conjunction with magnetic resonance imaging. However, the radial k-space trajectory requires a large number of radial lines for high-resolution reconstruction. Increasing the number of radial lines causes longer acquisition time, making it more difficult for routine clinical use. On the other hand, if we reduce the number of radial lines, streaking artifact patterns are unavoidable. To solve this problem, we propose a novel deep learning approach with domain adaptation to restore high-resolution MR images from under-sampled k-space data. The proposed deep network removes the streaking artifacts from the artifact corrupted images. To address the situation given the limited available data, we propose a domain adaptation scheme that employs a pre-trained network using a large number of X-ray computed tomography (CT) or synthesized radial MR datasets, which is then fine-tuned with only a few radial MR datasets. The proposed method outperforms existing compressed sensing algorithms, such as the total variation and PR-FOCUSS methods. In addition, the calculation time is several orders of magnitude faster than the total variation and PR-FOCUSS methods. Moreover, we found that pre-training using CT or MR data from similar organ data is more important than pre-training using data from the same modality for different organ. We demonstrate the possibility of a domain-adaptation when only a limited amount of MR data is available. The proposed method surpasses the existing compressed sensing algorithms in terms of the image quality and computation time. © 2018 International Society for Magnetic Resonance in Medicine.

Landslide Inventory Mapping from Bitemporal 10 m SENTINEL-2 Images Using Change Detection Based Markov Random Field

NASA Astrophysics Data System (ADS)

Qin, Y.; Lu, P.; Li, Z.

2018-04-01

Landslide inventory mapping is essential for hazard assessment and mitigation. In most previous studies, landslide mapping was achieved by visual interpretation of aerial photos and remote sensing images. However, such method is labor-intensive and time-consuming, especially over large areas. Although a number of semi-automatic landslide mapping methods have been proposed over the past few years, limitations remain in terms of their applicability over different study areas and data, and there is large room for improvement in terms of the accuracy and automation degree. For these reasons, we developed a change detection-based Markov Random Field (CDMRF) method for landslide inventory mapping. The proposed method mainly includes two steps: 1) change detection-based multi-threshold for training samples generation and 2) MRF for landslide inventory mapping. Compared with the previous methods, the proposed method in this study has three advantages: 1) it combines multiple image difference techniques with multi-threshold method to generate reliable training samples; 2) it takes the spectral characteristics of landslides into account; and 3) it is highly automatic with little parameter tuning. The proposed method was applied for regional landslides mapping from 10 m Sentinel-2 images in Western China. Results corroborated the effectiveness and applicability of the proposed method especially the capability of rapid landslide mapping. Some directions for future research are offered. This study to our knowledge is the first attempt to map landslides from free and medium resolution satellite (i.e., Sentinel-2) images in China.

Material discovery by combining stochastic surface walking global optimization with a neural network.

PubMed

Huang, Si-Da; Shang, Cheng; Zhang, Xiao-Jie; Liu, Zhi-Pan

2017-09-01

While the underlying potential energy surface (PES) determines the structure and other properties of a material, it has been frustrating to predict new materials from theory even with the advent of supercomputing facilities. The accuracy of the PES and the efficiency of PES sampling are two major bottlenecks, not least because of the great complexity of the material PES. This work introduces a "Global-to-Global" approach for material discovery by combining for the first time a global optimization method with neural network (NN) techniques. The novel global optimization method, named the stochastic surface walking (SSW) method, is carried out massively in parallel for generating a global training data set, the fitting of which by the atom-centered NN produces a multi-dimensional global PES; the subsequent SSW exploration of large systems with the analytical NN PES can provide key information on the thermodynamics and kinetics stability of unknown phases identified from global PESs. We describe in detail the current implementation of the SSW-NN method with particular focuses on the size of the global data set and the simultaneous energy/force/stress NN training procedure. An important functional material, TiO 2 , is utilized as an example to demonstrate the automated global data set generation, the improved NN training procedure and the application in material discovery. Two new TiO 2 porous crystal structures are identified, which have similar thermodynamics stability to the common TiO 2 rutile phase and the kinetics stability for one of them is further proved from SSW pathway sampling. As a general tool for material simulation, the SSW-NN method provides an efficient and predictive platform for large-scale computational material screening.

PhyloPythiaS+: a self-training method for the rapid reconstruction of low-ranking taxonomic bins from metagenomes.

PubMed

Gregor, Ivan; Dröge, Johannes; Schirmer, Melanie; Quince, Christopher; McHardy, Alice C

2016-01-01

Background. Metagenomics is an approach for characterizing environmental microbial communities in situ, it allows their functional and taxonomic characterization and to recover sequences from uncultured taxa. This is often achieved by a combination of sequence assembly and binning, where sequences are grouped into 'bins' representing taxa of the underlying microbial community. Assignment to low-ranking taxonomic bins is an important challenge for binning methods as is scalability to Gb-sized datasets generated with deep sequencing techniques. One of the best available methods for species bins recovery from deep-branching phyla is the expert-trained PhyloPythiaS package, where a human expert decides on the taxa to incorporate in the model and identifies 'training' sequences based on marker genes directly from the sample. Due to the manual effort involved, this approach does not scale to multiple metagenome samples and requires substantial expertise, which researchers who are new to the area do not have. Results. We have developed PhyloPythiaS+, a successor to our PhyloPythia(S) software. The new (+) component performs the work previously done by the human expert. PhyloPythiaS+ also includes a new k-mer counting algorithm, which accelerated the simultaneous counting of 4-6-mers used for taxonomic binning 100-fold and reduced the overall execution time of the software by a factor of three. Our software allows to analyze Gb-sized metagenomes with inexpensive hardware, and to recover species or genera-level bins with low error rates in a fully automated fashion. PhyloPythiaS+ was compared to MEGAN, taxator-tk, Kraken and the generic PhyloPythiaS model. The results showed that PhyloPythiaS+ performs especially well for samples originating from novel environments in comparison to the other methods. Availability. PhyloPythiaS+ in a virtual machine is available for installation under Windows, Unix systems or OS X on: https://github.com/algbioi/ppsp/wiki.

Mapping stand-age distribution of Russian forests from satellite data

NASA Astrophysics Data System (ADS)

Chen, D.; Loboda, T. V.; Hall, A.; Channan, S.; Weber, C. Y.

2013-12-01

Russian boreal forest is a critical component of the global boreal biome as approximately two thirds of the boreal forest is located in Russia. Numerous studies have shown that wildfire and logging have led to extensive modifications of forest cover in the region since 2000. Forest disturbance and subsequent regrowth influences carbon and energy budgets and, in turn, affect climate. Several global and regional satellite-based data products have been developed from coarse (>100m) and moderate (10-100m) resolution imagery to monitor forest cover change over the past decade, record of forest cover change pre-dating year 2000 is very fragmented. Although by using stacks of Landsat images, some information regarding the past disturbances can be obtained, the quantity and locations of such stacks with sufficient number of images are extremely limited, especially in Eastern Siberia. This paper describes a modified method which is built upon previous work to hindcast the disturbance history and map stand-age distribution in the Russian boreal forest. Utilizing data from both Landsat and the Moderate Resolution Imaging Spectroradiometer (MODIS), a wall-to-wall map indicating the estimated age of forest in the Russian boreal forest is created. Our previous work has shown that disturbances can be mapped successfully up to 30 years in the past as the spectral signature of regrowing forests is statistically significantly different from that of mature forests. The presented algorithm ingests 55 multi-temporal stacks of Landsat imagery available over Russian forest before 2001 and processes through a standardized and semi-automated approach to extract training and validation data samples. Landsat data, dating back to 1984, are used to generate maps of forest disturbance using temporal shifts in Disturbance Index through the multi-temporal stack of imagery in selected locations. These maps are then used as reference data to train a decision tree classifier on 50 MODIS-based indices. The resultant map provides an estimate of forest age based on the regrowth curves observed from Landsat imagery. The accuracy of the resultant map is assessed against three datasets: 1) subset of the disturbance maps developed within the algorithm, 2) independent disturbance maps created by the Northern Eurasia Land Dynamics Analysis (NELDA) project, and 3) field-based stand-age distribution from forestry inventory units. The current version of the product presents a considerable improvement on the previous version which used Landsat data samples at a set of randomly selected locations, resulting a strong bias of the training samples towards the Landsat-rich regions (e.g. European Russia) whereas regions such as Siberia were under-sampled. Aiming at improving accuracy, the current method significantly increases the number of training Landsat samples compared to the previous work. Aside from the previously used data, the current method uses all available Landsat data for the under-sampled regions in order to increase the representativeness of the total samples. The finial accuracy assessment is still ongoing, however, the initial results suggested an overall accuracy expressed in Kappa > 0.8. We plan to release both the training data and the final disturbance map of the Russian boreal forest to the public after the validation is completed.

Factors influencing training transfer in nursing profession: a qualitative study.

PubMed

Ma, Fang; Bai, Yangjing; Bai, Yangjuan; Ma, Weiguang; Yang, Xiangyu; Li, Jiping

2018-03-20

There is a growing recognition that training is not translated into performance and the 'transfer problem' exists in organization training today. Although factors contributing to training transfer have been identified in business and industry, the factors influencing training transfer in nursing profession remain less clear. A qualitative descriptive study was undertaken in two tertiary referral hospitals in China from February 2013 to September 2013. Purposeful sampling of 24 nursing staffs were interviewed about the factors influencing training transfer. Seven themes evolved from the analysis, categorized in 4 main domains, which described the factors influencing training transfer in nursing profession in trainee characteristics, training design, work environment and profession domain. The trainee characteristics domain included attitude and ability. The training design domain included training content and instruction method. The work environment domain included supports as facilitators and opposition as hindrance. The theme pertaining to the profession domain was professional development. Health care managers need to understand the factors influencing training transfer for maximizing the benefits of training. The right beliefs and values about training, the rigorous employee selection for training, the relevance of training content, training instructions facilitating learning and transfer, supports from peer, supervisors and the organization, organizational culture such as change, sharing, learning and support, and professional development are key to successful training transfer. Furthermore, managers should be aware of the opposition from co-workers and find ways to prevent it.

S-CNN: Subcategory-aware convolutional networks for object detection.

PubMed

Chen, Tao; Lu, Shijian; Fan, Jiayuan

2017-09-26

The marriage between the deep convolutional neural network (CNN) and region proposals has made breakthroughs for object detection in recent years. While the discriminative object features are learned via a deep CNN for classification, the large intra-class variation and deformation still limit the performance of the CNN based object detection. We propose a subcategory-aware CNN (S-CNN) to solve the object intra-class variation problem. In the proposed technique, the training samples are first grouped into multiple subcategories automatically through a novel instance sharing maximum margin clustering process. A multi-component Aggregated Channel Feature (ACF) detector is then trained to produce more latent training samples, where each ACF component corresponds to one clustered subcategory. The produced latent samples together with their subcategory labels are further fed into a CNN classifier to filter out false proposals for object detection. An iterative learning algorithm is designed for the joint optimization of image subcategorization, multi-component ACF detector, and subcategory-aware CNN classifier. Experiments on INRIA Person dataset, Pascal VOC 2007 dataset and MS COCO dataset show that the proposed technique clearly outperforms the state-of-the-art methods for generic object detection.

Psychological Aspects of Cosmetic Surgery among Females: A Media Literacy Training Intervention

PubMed Central

Khazir, Zahra; Dehdari, Tahereh; Majdabad, Mahmood Mahmoodi; Tehrani, Said Pournaghash

2016-01-01

Introduction: The present study examined the favorable attitude of a sample of female university students regarding elective cosmetic surgery, body dysmorphic disorder, self-esteem and body dissatisfaction following a media literacy training intervention. Methods: This study was a quasi-experimental type. The study sample included 140 female university students who were allocated to either the intervention (n=70) or the control group (n=70). Attitude toward cosmetic surgery, body dysmorphic disorder, self-esteem and, body satisfaction was measured in both groups before the intervention and 4 weeks later. Four media literacy training sessions were conducted over 4 weeks for the intervention group. The data was analyzed through analysis of covariance, student’s paired-samples t test, and Pearson correlation. Results: Our findings showed that favorable attitude, body dysmorphic disorder and body dissatisfaction scores were significantly lower (p<0.05) in the intervention group than the control group. Furthermore, self-esteem score increased significantly in the intervention group. Conclusions: Our results underscores the importance of media literacy intervention in decreasing female’s favorable attitude towards elective cosmetic surgery, body dysmorphic disorder and body dissatisfaction as well as increasing self-esteem. PMID:26383204

Robust BMPM training based on second-order cone programming and its application in medical diagnosis.

PubMed

Peng, Xiang; King, Irwin

2008-01-01

The Biased Minimax Probability Machine (BMPM) constructs a classifier which deals with the imbalanced learning tasks. It provides a worst-case bound on the probability of misclassification of future data points based on reliable estimates of means and covariance matrices of the classes from the training data samples, and achieves promising performance. In this paper, we develop a novel yet critical extension training algorithm for BMPM that is based on Second-Order Cone Programming (SOCP). Moreover, we apply the biased classification model to medical diagnosis problems to demonstrate its usefulness. By removing some crucial assumptions in the original solution to this model, we make the new method more accurate and robust. We outline the theoretical derivatives of the biased classification model, and reformulate it into an SOCP problem which could be efficiently solved with global optima guarantee. We evaluate our proposed SOCP-based BMPM (BMPMSOCP) scheme in comparison with traditional solutions on medical diagnosis tasks where the objectives are to focus on improving the sensitivity (the accuracy of the more important class, say "ill" samples) instead of the overall accuracy of the classification. Empirical results have shown that our method is more effective and robust to handle imbalanced classification problems than traditional classification approaches, and the original Fractional Programming-based BMPM (BMPMFP).

Potential of near-infrared hyperspectral reflectance imaging for screening of farm feed contamination

NASA Astrophysics Data System (ADS)

Wang, Wenbo; Paliwal, Jitendra

2005-09-01

With the outbreak of Bovine Spongiform Encephalopathy (BSE) (commonly known as mad cow disease) in 1987 in the United Kingdom and a recent case discovered in Alberta, more and more emphasis is placed on food and farm feed quality and safety issues internationally. The disease is believed to be spread through farm feed contamination by animal byproducts in the form of meat-and-bone-meal (MBM). The paper reviewed the available techniques necessary to the enforcement of legislation concerning the feed safety issues. The standard microscopy method, although highly sensitive, is laborious and costly. A method to routinely screen farm feed contamination certainly helps to reduce the complexity of safety inspection. A hyperspectral imaging system working in the near-infrared wavelength region of 1100-1600 nm was used to study the possibility of detection of ground broiler feed contamination by ground pork. Hyperspectral images of raw broiler feed, ground broiler feed, ground pork, and contaminated feed samples were acquired. Raw broiler feed samples were found to possess comparatively large spectral variations due to light scattering effect. Ground feed adulterated with 1%, 3%, 5%, and 10% of ground pork was tested to identify feed contamination. Discriminant analysis using Mahalanobis distance showed that the model trained using pure ground feed samples and pure ground pork samples resulted in 100% false negative errors for all test replicates of contaminated samples. A discriminant model trained with pure ground feed samples and 10% contamination level samples resulted in 12.5% false positive error and 0% false negative error.

An integrative machine learning strategy for improved prediction of essential genes in Escherichia coli metabolism using flux-coupled features.

PubMed

Nandi, Sutanu; Subramanian, Abhishek; Sarkar, Ram Rup

2017-07-25

Prediction of essential genes helps to identify a minimal set of genes that are absolutely required for the appropriate functioning and survival of a cell. The available machine learning techniques for essential gene prediction have inherent problems, like imbalanced provision of training datasets, biased choice of the best model for a given balanced dataset, choice of a complex machine learning algorithm, and data-based automated selection of biologically relevant features for classification. Here, we propose a simple support vector machine-based learning strategy for the prediction of essential genes in Escherichia coli K-12 MG1655 metabolism that integrates a non-conventional combination of an appropriate sample balanced training set, a unique organism-specific genotype, phenotype attributes that characterize essential genes, and optimal parameters of the learning algorithm to generate the best machine learning model (the model with the highest accuracy among all the models trained for different sample training sets). For the first time, we also introduce flux-coupled metabolic subnetwork-based features for enhancing the classification performance. Our strategy proves to be superior as compared to previous SVM-based strategies in obtaining a biologically relevant classification of genes with high sensitivity and specificity. This methodology was also trained with datasets of other recent supervised classification techniques for essential gene classification and tested using reported test datasets. The testing accuracy was always high as compared to the known techniques, proving that our method outperforms known methods. Observations from our study indicate that essential genes are conserved among homologous bacterial species, demonstrate high codon usage bias, GC content and gene expression, and predominantly possess a tendency to form physiological flux modules in metabolism.

Vegetation as an Agronomic Method of Dust Control on Helicopter Training Areas at Fort Rucker, Alabama

DTIC Science & Technology

1991-09-01

effectiveness and cost of these materials vary greatly. Admix methods include the mixing of cement, hydrated lime , cutback asphalt, and similar...the information needed to determine fertilizer and lime requirements for the soil. The mowing height should be raised to keep the vegetation at a mini...Methods Year 1 In February 1988, soil samples were collected from the site for analysis to determine lime and fertilizer requirements. Area RT14 was

A systematic review and meta-analysis of online versus alternative methods for training licensed health care professionals to deliver clinical interventions.

PubMed

Richmond, Helen; Copsey, Bethan; Hall, Amanda M; Davies, David; Lamb, Sarah E

2017-11-23

Online training is growing in popularity and yet its effectiveness for training licensed health professionals (HCPs) in clinical interventions is not clear. We aimed to systematically review the literature on the effectiveness of online versus alternative training methods in clinical interventions for licensed Health Care Professionals (HCPs) on outcomes of knowledge acquisition, practical skills, clinical behaviour, self-efficacy and satisfaction. Seven databases were searched for randomised controlled trials (RCTs) from January 2000 to June 2015. Two independent reviewers rated trial quality and extracted trial data. Comparative effects were summarised as standardised mean differences (SMD) and 95% confidence intervals. Pooled effect sizes were calculated using a random-effects model for three contrasts of online versus (i) interactive workshops (ii) taught lectures and (iii) written/electronic manuals. We included 14 studies with a total of 1089 participants. Most trials studied medical professionals, used a workshop or lecture comparison, were of high risk of bias and had small sample sizes (range 21-183). Using the GRADE approach, we found low quality evidence that there was no difference between online training and an interactive workshop for clinical behaviour SMD 0.12 (95% CI -0.13 to 0.37). We found very low quality evidence of no difference between online methods and both a workshop and lecture for knowledge (workshop: SMD 0.04 (95% CI -0.28 to 0.36); lecture: SMD 0.22 (95% CI: -0.08, 0.51)). Lastly, compared to a manual (n = 3/14), we found very low quality evidence that online methods were superior for knowledge SMD 0.99 (95% CI 0.02 to 1.96). There were too few studies to draw any conclusions on the effects of online training for practical skills, self-efficacy, and satisfaction across all contrasts. It is likely that online methods may be as effective as alternative methods for training HCPs in clinical interventions for the outcomes of knowledge and clinical behaviour. However, the low quality of the evidence precludes drawing firm conclusions on the relative effectiveness of these training methods. Moreover, the confidence intervals around our effect sizes were large and could encompass important differences in effectiveness. More robust, adequately powered RCTs are needed.

Memory Self-Efficacy Predicts Responsiveness to Inductive Reasoning Training in Older Adults

PubMed Central

Jackson, Joshua J.; Hill, Patrick L.; Gao, Xuefei; Roberts, Brent W.; Stine-Morrow, Elizabeth A. L.

2012-01-01

Objectives. In the current study, we assessed the relationship between memory self-efficacy at pretest and responsiveness to inductive reasoning training in a sample of older adults. Methods. Participants completed a measure of self-efficacy assessing beliefs about memory capacity. Participants were then randomly assigned to a waitlist control group or an inductive reasoning training intervention. Latent change score models were used to examine the moderators of change in inductive reasoning. Results. Inductive reasoning showed clear improvements in the training group compared with the control. Within the training group, initial memory capacity beliefs significantly predicted change in inductive reasoning such that those with higher levels of capacity beliefs showed greater responsiveness to the intervention. Further analyses revealed that self-efficacy had effects on how trainees allocated time to the training materials over the course of the intervention. Discussion. Results indicate that self-referential beliefs about cognitive potential may be an important factor contributing to plasticity in adulthood. PMID:21743037

Application of Convolutional Neural Network in Classification of High Resolution Agricultural Remote Sensing Images

NASA Astrophysics Data System (ADS)

Yao, C.; Zhang, Y.; Zhang, Y.; Liu, H.

2017-09-01

With the rapid development of Precision Agriculture (PA) promoted by high-resolution remote sensing, it makes significant sense in management and estimation of agriculture through crop classification of high-resolution remote sensing image. Due to the complex and fragmentation of the features and the surroundings in the circumstance of high-resolution, the accuracy of the traditional classification methods has not been able to meet the standard of agricultural problems. In this case, this paper proposed a classification method for high-resolution agricultural remote sensing images based on convolution neural networks(CNN). For training, a large number of training samples were produced by panchromatic images of GF-1 high-resolution satellite of China. In the experiment, through training and testing on the CNN under the toolbox of deep learning by MATLAB, the crop classification finally got the correct rate of 99.66 % after the gradual optimization of adjusting parameter during training. Through improving the accuracy of image classification and image recognition, the applications of CNN provide a reference value for the field of remote sensing in PA.

Decision tree and PCA-based fault diagnosis of rotating machinery

NASA Astrophysics Data System (ADS)

Sun, Weixiang; Chen, Jin; Li, Jiaqing

2007-04-01

After analysing the flaws of conventional fault diagnosis methods, data mining technology is introduced to fault diagnosis field, and a new method based on C4.5 decision tree and principal component analysis (PCA) is proposed. In this method, PCA is used to reduce features after data collection, preprocessing and feature extraction. Then, C4.5 is trained by using the samples to generate a decision tree model with diagnosis knowledge. At last the tree model is used to make diagnosis analysis. To validate the method proposed, six kinds of running states (normal or without any defect, unbalance, rotor radial rub, oil whirl, shaft crack and a simultaneous state of unbalance and radial rub), are simulated on Bently Rotor Kit RK4 to test C4.5 and PCA-based method and back-propagation neural network (BPNN). The result shows that C4.5 and PCA-based diagnosis method has higher accuracy and needs less training time than BPNN.

Voxel-based Gaussian naïve Bayes classification of ischemic stroke lesions in individual T1-weighted MRI scans.

PubMed

Griffis, Joseph C; Allendorfer, Jane B; Szaflarski, Jerzy P

2016-01-15

Manual lesion delineation by an expert is the standard for lesion identification in MRI scans, but it is time-consuming and can introduce subjective bias. Alternative methods often require multi-modal MRI data, user interaction, scans from a control population, and/or arbitrary statistical thresholding. We present an approach for automatically identifying stroke lesions in individual T1-weighted MRI scans using naïve Bayes classification. Probabilistic tissue segmentation and image algebra were used to create feature maps encoding information about missing and abnormal tissue. Leave-one-case-out training and cross-validation was used to obtain out-of-sample predictions for each of 30 cases with left hemisphere stroke lesions. Our method correctly predicted lesion locations for 30/30 un-trained cases. Post-processing with smoothing (8mm FWHM) and cluster-extent thresholding (100 voxels) was found to improve performance. Quantitative evaluations of post-processed out-of-sample predictions on 30 cases revealed high spatial overlap (mean Dice similarity coefficient=0.66) and volume agreement (mean percent volume difference=28.91; Pearson's r=0.97) with manual lesion delineations. Our automated approach agrees with manual tracing. It provides an alternative to automated methods that require multi-modal MRI data, additional control scans, or user interaction to achieve optimal performance. Our fully trained classifier has applications in neuroimaging and clinical contexts. Copyright © 2015 Elsevier B.V. All rights reserved.

LiDAR point classification based on sparse representation

NASA Astrophysics Data System (ADS)

Li, Nan; Pfeifer, Norbert; Liu, Chun

2017-04-01

In order to combine the initial spatial structure and features of LiDAR data for accurate classification. The LiDAR data is represented as a 4-order tensor. Sparse representation for classification(SRC) method is used for LiDAR tensor classification. It turns out SRC need only a few of training samples from each class, meanwhile can achieve good classification result. Multiple features are extracted from raw LiDAR points to generate a high-dimensional vector at each point. Then the LiDAR tensor is built by the spatial distribution and feature vectors of the point neighborhood. The entries of LiDAR tensor are accessed via four indexes. Each index is called mode: three spatial modes in direction X ,Y ,Z and one feature mode. Sparse representation for classification(SRC) method is proposed in this paper. The sparsity algorithm is to find the best represent the test sample by sparse linear combination of training samples from a dictionary. To explore the sparsity of LiDAR tensor, the tucker decomposition is used. It decomposes a tensor into a core tensor multiplied by a matrix along each mode. Those matrices could be considered as the principal components in each mode. The entries of core tensor show the level of interaction between the different components. Therefore, the LiDAR tensor can be approximately represented by a sparse tensor multiplied by a matrix selected from a dictionary along each mode. The matrices decomposed from training samples are arranged as initial elements in the dictionary. By dictionary learning, a reconstructive and discriminative structure dictionary along each mode is built. The overall structure dictionary composes of class-specified sub-dictionaries. Then the sparse core tensor is calculated by tensor OMP(Orthogonal Matching Pursuit) method based on dictionaries along each mode. It is expected that original tensor should be well recovered by sub-dictionary associated with relevant class, while entries in the sparse tensor associated with other classed should be nearly zero. Therefore, SRC use the reconstruction error associated with each class to do data classification. A section of airborne LiDAR points of Vienna city is used and classified into 6classes: ground, roofs, vegetation, covered ground, walls and other points. Only 6 training samples from each class are taken. For the final classification result, ground and covered ground are merged into one same class(ground). The classification accuracy for ground is 94.60%, roof is 95.47%, vegetation is 85.55%, wall is 76.17%, other object is 20.39%.

Implementing psychological first-aid training for medical reserve corps volunteers.

PubMed

Chandra, Anita; Kim, Jee; Pieters, Huibrie C; Tang, Jennifer; McCreary, Michael; Schreiber, Merritt; Wells, Kenneth

2014-02-01

We assessed the feasibility and impact on knowledge, attitudes, and reported practices of psychological first-aid (PFA) training in a sample of Medical Reserve Corps (MRC) members. Data have been limited on the uptake of PFA training in surge responders (eg, MRC) who are critical to community response. Our mixed-methods approach involved self-administered pre- and post-training surveys and within-training focus group discussions of 76 MRC members attending a PFA training and train-the-trainer workshop. Listen, protect, connect (a PFA model for lay persons) focuses on listening and understanding both verbal and nonverbal cues; protecting the individual by determining realistic ways to help while providing reassurance; and connecting the individual with resources in the community. From pre- to post-training, perceived confidence and capability in using PFA after an emergency or disaster increased from 71% to 90% (P < .01), but no significant increase was found in PFA-related knowledge. Qualitative analyses suggest that knowledge and intentions to use PFA increased with training. Brief training was feasible, and while results were modest, the PFA training resulted in greater reported confidence and perceived capability in addressing psychological distress of persons affected by public health threats. PFA training is a promising approach to improve surge responder confidence and competency in addressing postdisaster needs.

Stigmatization of Illicit Drug Use among Puerto Rican Health Professionals in Training1

PubMed Central

Varas-Díaz, Nelson; Negrón, Salvador Santiago; Neilands, Torsten B.; Bou, Francheska Cintrón; Rivera, Souhail Malavé

2010-01-01

Social stigma continues to be a barrier for health promotion in our society. One of the most stigmatized health conditions in our time continues to be addiction to illicit drug use. Although it has been widely recognized as a health concern, criminalizing approaches continue to be common in Puerto Rico. Health professionals need to engage in challenging the stigma of illicit drug use in order to foster policies and government efforts with health-oriented approaches. Still, personal stigmatizing attitudes among them continue to be a barrier for the implementation of this agenda. Therefore, the main objectives of this study were to document stigma towards illicit drug use among a sample of health professionals in training, and explore differences in such attitudes among participants from different areas of training. In order to achieve this objective we carried out a sequential mixed method approach with a sample of 501 health professionals in training or practice from the disciplines of medicine, nursing, psychology and social work. Results evidence the continued existence of stigmatizing attitudes among this population. We discuss some of the implications for public health and potential strategies for action. PMID:20496525

Maximizing lipocalin prediction through balanced and diversified training set and decision fusion.

PubMed

Nath, Abhigyan; Subbiah, Karthikeyan

2015-12-01

Lipocalins are short in sequence length and perform several important biological functions. These proteins are having less than 20% sequence similarity among paralogs. Experimentally identifying them is an expensive and time consuming process. The computational methods based on the sequence similarity for allocating putative members to this family are also far elusive due to the low sequence similarity existing among the members of this family. Consequently, the machine learning methods become a viable alternative for their prediction by using the underlying sequence/structurally derived features as the input. Ideally, any machine learning based prediction method must be trained with all possible variations in the input feature vector (all the sub-class input patterns) to achieve perfect learning. A near perfect learning can be achieved by training the model with diverse types of input instances belonging to the different regions of the entire input space. Furthermore, the prediction performance can be improved through balancing the training set as the imbalanced data sets will tend to produce the prediction bias towards majority class and its sub-classes. This paper is aimed to achieve (i) the high generalization ability without any classification bias through the diversified and balanced training sets as well as (ii) enhanced the prediction accuracy by combining the results of individual classifiers with an appropriate fusion scheme. Instead of creating the training set randomly, we have first used the unsupervised Kmeans clustering algorithm to create diversified clusters of input patterns and created the diversified and balanced training set by selecting an equal number of patterns from each of these clusters. Finally, probability based classifier fusion scheme was applied on boosted random forest algorithm (which produced greater sensitivity) and K nearest neighbour algorithm (which produced greater specificity) to achieve the enhanced predictive performance than that of individual base classifiers. The performance of the learned models trained on Kmeans preprocessed training set is far better than the randomly generated training sets. The proposed method achieved a sensitivity of 90.6%, specificity of 91.4% and accuracy of 91.0% on the first test set and sensitivity of 92.9%, specificity of 96.2% and accuracy of 94.7% on the second blind test set. These results have established that diversifying training set improves the performance of predictive models through superior generalization ability and balancing the training set improves prediction accuracy. For smaller data sets, unsupervised Kmeans based sampling can be an effective technique to increase generalization than that of the usual random splitting method. Copyright © 2015 Elsevier Ltd. All rights reserved.

A review of the effectiveness of stress management skills training on academic vitality and psychological well-being of college students.

PubMed

Alborzkouh, P; Nabati, M; Zainali, M; Abed, Y; Shahgholy Ghahfarokhi, F

2015-01-01

Objective: Carrying out the appropriate psychological interventions to improve vitality and mental well-being is critical. The study was carried out to review the effectiveness of stress management training on the academic life and mental well-being of the students of Shahed University. Methodology: The method used was quasi-experimental with a pretest-posttest plan and control group. Therefore, a total of 40 students of Shahed University of Tehran were selected by a convenience sampling method and were organized into two groups: experimental and control group. Both groups were pretested by using an academic vitality inventory and an 84-question psychological well-being inventory. Then, the experimental group received stress management skills training for ten sessions, and the control group did not receive any intervention. Next, both groups were post-tested, and the data were analyzed with SPSS-21 software by using descriptive and inferential statistical methods. Findings: The findings showed that the stress management skills training significantly contributed to promoting the academic vitality and psychological well-being of students (p < 0.001). Conclusions: It was concluded from this research that teaching the methods for dealing with stress was an effective strategy to help students exposed to high stress and pressure, and this was due to its high efficiency, especially when it was held in groups, had a small cost, and it was accepted by the individuals.

Novel and successful free comments method for sensory characterization of chocolate ice cream: A comparative study between pivot profile and comment analysis.

PubMed

Fonseca, Fernando G A; Esmerino, Erick A; Filho, Elson R Tavares; Ferraz, Juliana P; da Cruz, Adriano G; Bolini, Helena M A

2016-05-01

Rapid sensory profiling methods have gained space in the sensory evaluation field. Techniques using direct analysis of the terms generated by consumers are considered easy to perform, without specific training requirements, thus improving knowledge about consumer perceptions on various products. This study aimed to determine the sensory profile of different commercial samples of chocolate ice cream, labeled as conventional and light or diet, using the "comment analysis" and "pivot profile" methods, based on consumers' perceptions. In the comment analysis task, consumers responded to 2 separate open questions describing the sensory attributes they liked or disliked in each sample. In the pivot profile method, samples were served in pairs (consisting of a coded sample and pivot), and consumers indicated the higher and lower intensity attributes in the target sample compared with the pivot. We observed that both methods were able to characterize the different chocolate ice cream samples using consumer perception, with high correlation results and configurational similarity (regression vector coefficient=0.917) between them. However, it is worth emphasizing that comment analysis is performed intuitively by consumers, whereas the pivot profile method showed high analytical and discriminative power even using consumers, proving to be a promising technique for routine application when classical descriptive methods cannot be used. Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Static versus dynamic sampling for data mining

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

John, G.H.; Langley, P.

1996-12-31

As data warehouses grow to the point where one hundred gigabytes is considered small, the computational efficiency of data-mining algorithms on large databases becomes increasingly important. Using a sample from the database can speed up the datamining process, but this is only acceptable if it does not reduce the quality of the mined knowledge. To this end, we introduce the {open_quotes}Probably Close Enough{close_quotes} criterion to describe the desired properties of a sample. Sampling usually refers to the use of static statistical tests to decide whether a sample is sufficiently similar to the large database, in the absence of any knowledgemore » of the tools the data miner intends to use. We discuss dynamic sampling methods, which take into account the mining tool being used and can thus give better samples. We describe dynamic schemes that observe a mining tool`s performance on training samples of increasing size and use these results to determine when a sample is sufficiently large. We evaluate these sampling methods on data from the UCI repository and conclude that dynamic sampling is preferable.« less

Porosity estimation by semi-supervised learning with sparsely available labeled samples

NASA Astrophysics Data System (ADS)

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

2017-09-01

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

Comparing the NIOSH Method 5040 to a Diesel Particulate Matter Meter for Elemental Carbon

NASA Astrophysics Data System (ADS)

Ayers, David Matthew

Introduction: The sampling of elemental carbon has been associated with monitoring exposures in the trucking and mining industries. Recently, in the field of engineered nanomaterials, single wall and muti-wall carbon nanotubes (MWCNTs) are being produced in ever increasing quantities. The only approved atmospheric sampling for multi-wall carbon nanotubes in NIOSH Method 5040. These results are accurate but can take up to 30 days for sample results to be received. Objectives: Compare the results of elemental carbon sampling from the NIOSH Method 5040 to a Diesel Particulate Matter (DPM) Meter. Methods: MWCNTs were transferred and weighed between several trays placed on a scale. The NIOSH Method 5040 and DPM sampling train was hung 6 inches above the receiving tray. The transferring and weighing of the MWCNTs created an aerosol containing elemental carbon. Twenty-one total samples using both meters type were collected. Results: The assumptions for a Two-Way ANOVA were violated therefore, Mann-Whitney U Tests and a Kruskal-Wallis Test were performed. The hypotheses for both research questions were rejected. There was a significant difference in the EC concentrations obtained by the NIOSH Method 5040 and the DPM meter. There were also significant differences in elemental carbon level concentrations when sampled using a DPM meter versus a sampling pump based upon the three concentration levels (low, medium and high). Conclusions: The differences in the EC concentrations were statistically significant therefore, the two methods (NIOSH Method 5040 and DPM) are not the same. The NIOSH Method 5040 should continue to be the only authorized method of establishing an EC concentration for MWCNTs until a MWCNT specific method or an instantaneous meter is invented.

SELDI-TOF-MS proteomic profiling of serum, urine, and amniotic fluid in neural tube defects.

PubMed

Liu, Zhenjiang; Yuan, Zhengwei; Zhao, Qun

2014-01-01

Neural tube defects (NTDs) are common birth defects, whose specific biomarkers are needed. The purpose of this pilot study is to determine whether protein profiling in NTD-mothers differ from normal controls using SELDI-TOF-MS. ProteinChip Biomarker System was used to evaluate 82 maternal serum samples, 78 urine samples and 76 amniotic fluid samples. The validity of classification tree was then challenged with a blind test set including another 20 NTD-mothers and 18 controls in serum samples, and another 19 NTD-mothers and 17 controls in urine samples, and another 20 NTD-mothers and 17 controls in amniotic fluid samples. Eight proteins detected in serum samples were up-regulated and four proteins were down-regulated in the NTD group. Four proteins detected in urine samples were up-regulated and one protein was down-regulated in the NTD group. Six proteins detected in amniotic fluid samples were up-regulated and one protein was down-regulated in the NTD group. The classification tree for serum samples separated NTDs from healthy individuals, achieving a sensitivity of 91% and a specificity of 97% in the training set, and achieving a sensitivity of 90% and a specificity of 97% and a positive predictive value of 95% in the test set. The classification tree for urine samples separated NTDs from controls, achieving a sensitivity of 95% and a specificity of 94% in the training set, and achieving a sensitivity of 89% and a specificity of 82% and a positive predictive value of 85% in the test set. The classification tree for amniotic fluid samples separated NTDs from controls, achieving a sensitivity of 93% and a specificity of 89% in the training set, and achieving a sensitivity of 90% and a specificity of 88% and a positive predictive value of 90% in the test set. These suggest that SELDI-TOF-MS is an additional method for NTDs pregnancies detection.

The predictive validity of a situational judgement test, a clinical problem solving test and the core medical training selection methods for performance in specialty training .

PubMed

Patterson, Fiona; Lopes, Safiatu; Harding, Stephen; Vaux, Emma; Berkin, Liz; Black, David

2017-02-01

The aim of this study was to follow up a sample of physicians who began core medical training (CMT) in 2009. This paper examines the long-term validity of CMT and GP selection methods in predicting performance in the Membership of Royal College of Physicians (MRCP(UK)) examinations. We performed a longitudinal study, examining the extent to which the GP and CMT selection methods (T1) predict performance in the MRCP(UK) examinations (T2). A total of 2,569 applicants from 2008-09 who completed CMT and GP selection methods were included in the study. Looking at MRCP(UK) part 1, part 2 written and PACES scores, both CMT and GP selection methods show evidence of predictive validity for the outcome variables, and hierarchical regressions show the GP methods add significant value to the CMT selection process. CMT selection methods predict performance in important outcomes and have good evidence of validity; the GP methods may have an additional role alongside the CMT selection methods. © Royal College of Physicians 2017. All rights reserved.

Oral vocabulary training program for Spanish third-graders with low socio-economic status: A randomized controlled trial

PubMed Central

Simpson, Ian Craig; Valle, Araceli; Defior, Sylvia

2017-01-01

Although the importance of vocabulary training in English speaking countries is well recognized and has been extensively studied, the same is not true for Spanish–few evidence based vocabulary studies for Spanish-speaking children have been reported. Here, two rich oral vocabulary training programs (definition and context), based on literature about vocabulary instruction for English-speaking children, were developed and applied in a sample of 100 Spanish elementary school third-graders recruited from areas of predominantly low socio-economic status (SES). Compared to an alternative read-aloud method which served as the control, both explicit methods were more effective in teaching word meanings when assessed immediately after the intervention. Nevertheless, five months later, only the definition group continued to demonstrate significant vocabulary knowledge gains. The definition method was more effective in specifically teaching children word meanings and, more broadly, in helping children organize and express knowledge of words. We recommend the explicit and rich vocabulary instruction as a means to fostering vocabulary knowledge in low SES children. PMID:29186175

Mapping species distributions with MAXENT using a geographically biased sample of presence data: a performance assessment of methods for correcting sampling bias.

PubMed

Fourcade, Yoan; Engler, Jan O; Rödder, Dennis; Secondi, Jean

2014-01-01

MAXENT is now a common species distribution modeling (SDM) tool used by conservation practitioners for predicting the distribution of a species from a set of records and environmental predictors. However, datasets of species occurrence used to train the model are often biased in the geographical space because of unequal sampling effort across the study area. This bias may be a source of strong inaccuracy in the resulting model and could lead to incorrect predictions. Although a number of sampling bias correction methods have been proposed, there is no consensual guideline to account for it. We compared here the performance of five methods of bias correction on three datasets of species occurrence: one "virtual" derived from a land cover map, and two actual datasets for a turtle (Chrysemys picta) and a salamander (Plethodon cylindraceus). We subjected these datasets to four types of sampling biases corresponding to potential types of empirical biases. We applied five correction methods to the biased samples and compared the outputs of distribution models to unbiased datasets to assess the overall correction performance of each method. The results revealed that the ability of methods to correct the initial sampling bias varied greatly depending on bias type, bias intensity and species. However, the simple systematic sampling of records consistently ranked among the best performing across the range of conditions tested, whereas other methods performed more poorly in most cases. The strong effect of initial conditions on correction performance highlights the need for further research to develop a step-by-step guideline to account for sampling bias. However, this method seems to be the most efficient in correcting sampling bias and should be advised in most cases.

Mapping Species Distributions with MAXENT Using a Geographically Biased Sample of Presence Data: A Performance Assessment of Methods for Correcting Sampling Bias

PubMed Central

Fourcade, Yoan; Engler, Jan O.; Rödder, Dennis; Secondi, Jean

2014-01-01

MAXENT is now a common species distribution modeling (SDM) tool used by conservation practitioners for predicting the distribution of a species from a set of records and environmental predictors. However, datasets of species occurrence used to train the model are often biased in the geographical space because of unequal sampling effort across the study area. This bias may be a source of strong inaccuracy in the resulting model and could lead to incorrect predictions. Although a number of sampling bias correction methods have been proposed, there is no consensual guideline to account for it. We compared here the performance of five methods of bias correction on three datasets of species occurrence: one “virtual” derived from a land cover map, and two actual datasets for a turtle (Chrysemys picta) and a salamander (Plethodon cylindraceus). We subjected these datasets to four types of sampling biases corresponding to potential types of empirical biases. We applied five correction methods to the biased samples and compared the outputs of distribution models to unbiased datasets to assess the overall correction performance of each method. The results revealed that the ability of methods to correct the initial sampling bias varied greatly depending on bias type, bias intensity and species. However, the simple systematic sampling of records consistently ranked among the best performing across the range of conditions tested, whereas other methods performed more poorly in most cases. The strong effect of initial conditions on correction performance highlights the need for further research to develop a step-by-step guideline to account for sampling bias. However, this method seems to be the most efficient in correcting sampling bias and should be advised in most cases. PMID:24818607

Exploring Career Success of Late Bloomers from the TVET Background

ERIC Educational Resources Information Center

Omar, Zoharah; Krauss, Steven Eric; Sail, Rahim M.; Ismail, Ismi Arif

2011-01-01

Purpose: The purpose of this paper is to explore objective and subjective career success and to identify factors contributing to career success among a sample of technical and vocational education and training (TVET) "late bloomers" working in Malaysia. Design/methodology/approach: Incorporating a mixed method design, the authors…

Suicide and Firearm Means Restriction: Can Training Make a Difference?

ERIC Educational Resources Information Center

Slovak, Karen; Brewer, Thomas W.

2010-01-01

Along with physician education in depression recognition and treatment, restricting lethal methods is an effective suicide prevention strategy. The present study surveyed a random sample (N = 697) of Ohio licensed social workers regarding client firearm assessment and safety counseling. Analyses sought to determine what independent factors would…

Automated Speech Rate Measurement in Dysarthria

ERIC Educational Resources Information Center

Martens, Heidi; Dekens, Tomas; Van Nuffelen, Gwen; Latacz, Lukas; Verhelst, Werner; De Bodt, Marc

2015-01-01

Purpose: In this study, a new algorithm for automated determination of speech rate (SR) in dysarthric speech is evaluated. We investigated how reliably the algorithm calculates the SR of dysarthric speech samples when compared with calculation performed by speech-language pathologists. Method: The new algorithm was trained and tested using Dutch…

Texture Classification by Texton: Statistical versus Binary

PubMed Central

Guo, Zhenhua; Zhang, Zhongcheng; Li, Xiu; Li, Qin; You, Jane

2014-01-01

Using statistical textons for texture classification has shown great success recently. The maximal response 8 (Statistical_MR8), image patch (Statistical_Joint) and locally invariant fractal (Statistical_Fractal) are typical statistical texton algorithms and state-of-the-art texture classification methods. However, there are two limitations when using these methods. First, it needs a training stage to build a texton library, thus the recognition accuracy will be highly depended on the training samples; second, during feature extraction, local feature is assigned to a texton by searching for the nearest texton in the whole library, which is time consuming when the library size is big and the dimension of feature is high. To address the above two issues, in this paper, three binary texton counterpart methods were proposed, Binary_MR8, Binary_Joint, and Binary_Fractal. These methods do not require any training step but encode local feature into binary representation directly. The experimental results on the CUReT, UIUC and KTH-TIPS databases show that binary texton could get sound results with fast feature extraction, especially when the image size is not big and the quality of image is not poor. PMID:24520346

Using transfer learning to detect galaxy mergers

NASA Astrophysics Data System (ADS)

Ackermann, Sandro; Schawinksi, Kevin; Zhang, Ce; Weigel, Anna K.; Turp, M. Dennis

2018-05-01

We investigate the use of deep convolutional neural networks (deep CNNs) for automatic visual detection of galaxy mergers. Moreover, we investigate the use of transfer learning in conjunction with CNNs, by retraining networks first trained on pictures of everyday objects. We test the hypothesis that transfer learning is useful for improving classification performance for small training sets. This would make transfer learning useful for finding rare objects in astronomical imaging datasets. We find that these deep learning methods perform significantly better than current state-of-the-art merger detection methods based on nonparametric systems like CAS and GM20. Our method is end-to-end and robust to image noise and distortions; it can be applied directly without image preprocessing. We also find that transfer learning can act as a regulariser in some cases, leading to better overall classification accuracy (p = 0.02). Transfer learning on our full training set leads to a lowered error rate from 0.0381 down to 0.0321, a relative improvement of 15%. Finally, we perform a basic sanity-check by creating a merger sample with our method, and comparing with an already existing, manually created merger catalogue in terms of colour-mass distribution and stellar mass function.

Transfer learning for bimodal biometrics recognition

NASA Astrophysics Data System (ADS)

Dan, Zhiping; Sun, Shuifa; Chen, Yanfei; Gan, Haitao

2013-10-01

Biometrics recognition aims to identify and predict new personal identities based on their existing knowledge. As the use of multiple biometric traits of the individual may enables more information to be used for recognition, it has been proved that multi-biometrics can produce higher accuracy than single biometrics. However, a common problem with traditional machine learning is that the training and test data should be in the same feature space, and have the same underlying distribution. If the distributions and features are different between training and future data, the model performance often drops. In this paper, we propose a transfer learning method for face recognition on bimodal biometrics. The training and test samples of bimodal biometric images are composed of the visible light face images and the infrared face images. Our algorithm transfers the knowledge across feature spaces, relaxing the assumption of same feature space as well as same underlying distribution by automatically learning a mapping between two different but somewhat similar face images. According to the experiments in the face images, the results show that the accuracy of face recognition has been greatly improved by the proposed method compared with the other previous methods. It demonstrates the effectiveness and robustness of our method.

Training in metabolomics research. I. Designing the experiment, collecting and extracting samples and generating metabolomics data

PubMed Central

Barnes, Stephen; Benton, H. Paul; Casazza, Krista; Cooper, Sara J.; Cui, Xiangqin; Du, Xiuxia; Engler, Jeffrey; Kabarowski, Janusz H.; Li, Shuzhao; Pathmasiri, Wimal; Prasain, Jeevan K.; Renfrow, Matthew B.; Tiwari, Hemant K.

2016-01-01

The study of metabolism has had a long history. Metabolomics, a systems biology discipline representing analysis of known and unknown pathways of metabolism, has grown tremendously over the past 20 years. Because of its comprehensive nature, metabolomics requires careful consideration of the question(s) being asked, the scale needed to answer the question(s), collection and storage of the sample specimens, methods for extraction of the metabolites from biological matrices, the analytical method(s) to be employed and the quality control of the analyses, how collected data are correlated, the statistical methods to determine metabolites undergoing significant change, putative identification of metabolites and the use of stable isotopes to aid in verifying metabolite identity and establishing pathway connections and fluxes. The National Institutes of Health Common Fund Metabolomics Program was established in 2012 to stimulate interest in the approaches and technologies of metabolomics. To deliver one of the program’s goals, the University of Alabama at Birmingham has hosted an annual 4-day short course in metabolomics for faculty, postdoctoral fellows and graduate students from national and international institutions. This paper is the first part of a summary of the training materials presented in the course to be used as a resource for all those embarking on metabolomics research. PMID:27434804

Quality of Documentation as a Surrogate Marker for Awareness and Training Effectiveness of PHTLS-Courses. Part of the Prospective Longitudinal Mixed-Methods EPPTC-Trial.

PubMed

Häske, David; Beckers, Stefan K; Hofmann, Marzellus; Lefering, Rolf; Gliwitzky, Bernhard; Wölfl, Christoph C; Grützner, Paul; Stöckle, Ulrich; Dieroff, Marc; Münzberg, Matthias

2017-01-01

Care for severely injured patients requires multidisciplinary teamwork. A decrease in the number of accident victims ultimately affects the routine and skills. PHTLS ("Pre-Hospital Trauma Life Support") courses are established two-day courses for medical and non-medical rescue service personnel, aimed at improving the pre-hospital care of trauma patients worldwide. The study aims the examination of the quality of documentation before and after PHTLS courses as a surrogate endpoint of training effectiveness and awareness. This was a prospective pre-post intervention trial and was part of the mixed-method longitudinal EPPTC (Effect of Paramedic Training on Pre-Hospital Trauma Care) study, evaluating subjective and objective changes among participants and real patient care, as a result of PHTLS courses. The courses provide an overview of the SAMPLE approach for interrogation of anamnestic information, which is believed to be responsible for patient safety as relevant, among others, "Allergies," "Medication," and "Patient History" (AMP). The focus of the course is not the documentation. In total, 320 protocols were analyzed before and after the training. The PHTLS course led to a significant increase (p < 0.001) in the "AMP" information in the documentation. The subgroups analysis of "allergies" (+47.2%), "drugs" (+38.1%), and "medical history" (+27.8%) before and after the PHTLS course showed a significant increase in the information content. In summary, we showed that PHTLS training improves documentation quality, which we used as a surrogate endpoint for learning effectiveness and awareness. In this regard, we demonstrated that participants use certain parts of training in real life, thereby suggesting that the learning methods of PHTLS training are effective. These results, however, do not indicate whether patient care has changed.

Study on the medical meteorological forecast of the number of hypertension inpatient based on SVR

NASA Astrophysics Data System (ADS)

Zhai, Guangyu; Chai, Guorong; Zhang, Haifeng

2017-06-01

The purpose of this study is to build a hypertension prediction model by discussing the meteorological factors for hypertension incidence. The research method is selecting the standard data of relative humidity, air temperature, visibility, wind speed and air pressure of Lanzhou from 2010 to 2012(calculating the maximum, minimum and average value with 5 days as a unit ) as the input variables of Support Vector Regression(SVR) and the standard data of hypertension incidence of the same period as the output dependent variables to obtain the optimal prediction parameters by cross validation algorithm, then by SVR algorithm learning and training, a SVR forecast model for hypertension incidence is built. The result shows that the hypertension prediction model is composed of 15 input independent variables, the training accuracy is 0.005, the final error is 0.0026389. The forecast accuracy based on SVR model is 97.1429%, which is higher than statistical forecast equation and neural network prediction method. It is concluded that SVR model provides a new method for hypertension prediction with its simple calculation, small error as well as higher historical sample fitting and Independent sample forecast capability.

Integrative genetic risk prediction using non-parametric empirical Bayes classification.

PubMed

Zhao, Sihai Dave

2017-06-01

Genetic risk prediction is an important component of individualized medicine, but prediction accuracies remain low for many complex diseases. A fundamental limitation is the sample sizes of the studies on which the prediction algorithms are trained. One way to increase the effective sample size is to integrate information from previously existing studies. However, it can be difficult to find existing data that examine the target disease of interest, especially if that disease is rare or poorly studied. Furthermore, individual-level genotype data from these auxiliary studies are typically difficult to obtain. This article proposes a new approach to integrative genetic risk prediction of complex diseases with binary phenotypes. It accommodates possible heterogeneity in the genetic etiologies of the target and auxiliary diseases using a tuning parameter-free non-parametric empirical Bayes procedure, and can be trained using only auxiliary summary statistics. Simulation studies show that the proposed method can provide superior predictive accuracy relative to non-integrative as well as integrative classifiers. The method is applied to a recent study of pediatric autoimmune diseases, where it substantially reduces prediction error for certain target/auxiliary disease combinations. The proposed method is implemented in the R package ssa. © 2016, The International Biometric Society.

Trends in Characteristics and Country of Origin Among Foreign-Trained Nurses in the United States, 1990 and 2000

PubMed Central

Polsky, Daniel; Ross, Sara J.; Brush, Barbara L.; Sochalski, Julie

2007-01-01

Objectives. We describe long-term trends in the characteristics of foreign-trained new entrants to the registered nurse (RN) workforce in the United States. Methods. Using the 1990 and 2000 US Census 5% Public Use Microdata Sample files, we compared trends in characteristics of US- and foreign-trained new entrants to the RN labor force (n=40827) and identified trends in the country of origin of the foreign-trained new entrants. Results. Foreign-trained RNs grew as a percentage of new entrants to the RN workforce, from 8.8% in 1990 to 15.2% in 2000. Compared with US-trained RNs, foreign-trained RNs were 3 times as likely to work in nursing homes and were more likely to have earned a bachelor’s degree. In 2000, 21% of foreign-trained RNs originated from low-income countries, a doubling of the rate since 1990. Conclusions. Foreign-trained RNs now account for a substantial and growing proportion of the US RN workforce. Our findings suggest foreign-trained RNs entering the United States are not of lower quality than US-trained RNs. However, growth in the proportion of RNs from low-income countries may have negative consequences in those countries. PMID:17395844

‘End of life could be on any ward really’: A qualitative study of hospital volunteers’ end-of-life care training needs and learning preferences

PubMed Central

Brighton, Lisa Jane; Koffman, Jonathan; Robinson, Vicky; Khan, Shaheen A; George, Rob; Burman, Rachel; Selman, Lucy Ellen

2017-01-01

Background: Over half of all deaths in Europe occur in hospital, a location associated with many complaints. Initiatives to improve inpatient end-of-life care are therefore a priority. In England, over 78,000 volunteers provide a potentially cost-effective resource to hospitals. Many work with people who are dying and their families, yet little is known about their training in end-of-life care. Aims: To explore hospital volunteers’ end-of-life care training needs and learning preferences, and the acceptability of training evaluation methods. Design: Qualitative focus groups. Setting/participants: Volunteers from a large teaching hospital were purposively sampled. Results: Five focus groups were conducted with 25 hospital volunteers (aged 19–80 years). Four themes emerged as follows: preparation for the volunteering role, training needs, training preferences and evaluation preferences. Many described encounters with patients with life-threatening illness and their families. Perceived training needs in end-of-life care included communication skills, grief and bereavement, spiritual diversity, common symptoms, and self-care. Volunteers valued learning from peers and end-of-life care specialists using interactive teaching methods including real-case examples and role plays. A chance to ‘refresh’ training at a later date was suggested to enhance learning. Evaluation through self-reports or observations were acceptable, but ratings by patients, families and staff were thought to be pragmatically unsuitable owing to sporadic contact with each. Conclusion: Gaps in end-of-life care training for hospital volunteers indicate scope to maximise on this resource. This evidence will inform development of training and evaluations which could better enable volunteers to make positive, cost-effective contributions to end-of-life care in hospitals. PMID:28056642

End-of-Life Conversation Game Increases Confidence for Having End-of-Life Conversations for Chaplains-in-Training.

PubMed

Van Scoy, Lauren Jodi; Watson-Martin, Elizabeth; Bohr, Tiffany A; Levi, Benjamin H; Green, Michael J

2018-04-01

Discussing end-of-life issues with patients is an essential role for chaplains. Few tools are available to help chaplains-in-training develop end-of-life communication skills. This study aimed to determine whether playing an end-of-life conversation game increases the confidence for chaplain-in-trainings to discuss end-of-life issues with patients. We used a convergent mixed methods design. Chaplains-in-training played the end-of-life conversation game twice over 2 weeks. For each game, pre- and postgame questionnaires measured confidence discussing end-of-life issues with patients and emotional affect. Between games, chaplains-in-training discussed end-of-life issues with an inpatient. One week after game 2, chaplains-in-training were individually interviewed. Quantitative data were analyzed using descriptive statistics and Wilcoxon rank-sum t tests. Content analysis identified interview themes. Quantitative and qualitative data sets were then integrated using a joint display. Twenty-three chaplains-in-training (52% female; 87% Caucasian; 70% were in year 1 of training) completed the study. Confidence scores (scale: 15-75; 75 = very confident) increased significantly after each game, increasing by 10.0 points from pregame 1 to postgame 2 ( P < .001). Positive affect subscale scores also increased significantly after each game, and shyness subscale scores decreased significantly after each game. Content analysis found that chaplains-in-training found the game to be a positive, useful experience and reported that playing twice was beneficial (not redundant). Mixed methods analysis suggest that an end-of-life conversation game is a useful tool that can increase chaplain-in-trainings' confidence for initiating end-of-life discussions with patients. A larger sample size is needed to confirm these findings.

'End of life could be on any ward really': A qualitative study of hospital volunteers' end-of-life care training needs and learning preferences.

PubMed

Brighton, Lisa Jane; Koffman, Jonathan; Robinson, Vicky; Khan, Shaheen A; George, Rob; Burman, Rachel; Selman, Lucy Ellen

2017-10-01

Over half of all deaths in Europe occur in hospital, a location associated with many complaints. Initiatives to improve inpatient end-of-life care are therefore a priority. In England, over 78,000 volunteers provide a potentially cost-effective resource to hospitals. Many work with people who are dying and their families, yet little is known about their training in end-of-life care. To explore hospital volunteers' end-of-life care training needs and learning preferences, and the acceptability of training evaluation methods. Qualitative focus groups. Volunteers from a large teaching hospital were purposively sampled. Five focus groups were conducted with 25 hospital volunteers (aged 19-80 years). Four themes emerged as follows: preparation for the volunteering role, training needs, training preferences and evaluation preferences. Many described encounters with patients with life-threatening illness and their families. Perceived training needs in end-of-life care included communication skills, grief and bereavement, spiritual diversity, common symptoms, and self-care. Volunteers valued learning from peers and end-of-life care specialists using interactive teaching methods including real-case examples and role plays. A chance to 'refresh' training at a later date was suggested to enhance learning. Evaluation through self-reports or observations were acceptable, but ratings by patients, families and staff were thought to be pragmatically unsuitable owing to sporadic contact with each. Gaps in end-of-life care training for hospital volunteers indicate scope to maximise on this resource. This evidence will inform development of training and evaluations which could better enable volunteers to make positive, cost-effective contributions to end-of-life care in hospitals.

Validation of Sampling Protocol and the Promulgation of Method Modifications for the Characterization of Energetic Residues on Military Testing and Training Ranges

DTIC Science & Technology

2009-06-01

Figure 2. Examples of surface vegetation at a firing point (inset) and near the crater of an 81-mm mortar projectile low-order detonation on an artillery... mortar impact range.......................... 7 Figure 3. Fort Richardson and surrounding areas...crater where an 81-mm mortar projectile had low-ordered on an impact range. If vegetation is removed or avoided during sampling, energetic residue

Comparison of gel column, card, and cartridge techniques for dog erythrocyte antigen 1.1 blood typing

PubMed Central

Seth, Mayank; Jackson, Karen V.; Winzelberg, Sarah; Giger, Urs

2012-01-01

Objective To compare accuracy and ease of use of a card agglutination assay, an immunochromatographic cartridge method, and a gel-based method for canine blood typing. Sample Blood samples from 52 healthy blood donor dogs, 10 dogs with immune-mediated hemolytic anemia (IMHA), and 29 dogs with other diseases. Procedures Blood samples were tested in accordance with manufacturer guidelines. Samples with low PCVs were created by the addition of autologous plasma to separately assess the effects of anemia on test results. Results Compared with a composite reference standard of agreement between 2 methods, the gel-based method was found to be 100% accurate. The card agglutination assay was 89% to 91% accurate, depending on test interpretation, and the immunochromatographic cartridge method was 93% accurate but 100% specific. Errors were observed more frequently in samples from diseased dogs, particularly those with IMHA. In the presence of persistent autoagglutination, dog erythrocyte antigen (DEA) 1.1 typing was not possible, except with the immunochromatographic cartridge method. Conclusions and Clinical Relevance The card agglutination assay and immunochromatographic cartridge method, performed by trained personnel, were suitable for in-clinic emergency DEA 1.1 blood typing. There may be errors, particularly for samples from dogs with IMHA, and the immunochromatographic cartridge method may have an advantage of allowing typing of samples with persistent autoagglutination. The laboratory gel-based method would be preferred for routine DEA 1.1 typing of donors and patients if it is available and time permits. Current DEA 1.1 typing techniques appear to be appropriately standardized and easy to use. PMID:22280380

[Current panorama of the teaching of microbiology and parasitology in Spain].

PubMed

Cantón, Rafael; Sánchez-Romero, María Isabel; Gómez-Mampaso, Enrique

2010-10-01

The training program of residents in microbiology and parasitology in Spain includes clinical skills, ranging from the diagnostic approach to the patient and adequate sample collection for diagnosis of infectious diseases to antimicrobial therapy and infection control measures. Training also includes new challenges in clinical microbiology that ensure residents' participation in infection control programs of health-care associated infections, training in the resolution of public health problems, and application of new molecular microbiology methods. Specialization in clinical microbiology may be undertaken by graduates in Medicine, Biology, Biochemistry and Chemistry. The training is performed in accredited microbiology laboratories at different hospitals (n = 61) across the country through 4-year residency programs. In the last few years, there has been a major imbalance between the number of intended residents (0.17 per 100,000 inhabitants) and those graduating as specialists in clinical microbiology (0.13 per 100,000 inhabitants), with wide variations across the country. The current tendency in Europe is to strengthen the role of clinical microbiologists as key figures in the diagnosis of infectious diseases and in public health microbiology. Training programs have been hampered by the practice of sending samples for microbiological tests to external, centralized multipurpose laboratories with few clinical microbiologists and without a core curriculum. Essential elements in the training of specialists in clinical microbiology are a close relationship between the laboratory and the clinical center and collaboration with other specialists. Copyright © 2010 Elsevier España S.L. All rights reserved.

What was retained? The assessment of the training for the peer trainers' course on short and long term basis

PubMed Central

Mevsim, Vildan; Guldal, Dilek; Ozcakar, Nilgun; Saygin, Ozge

2008-01-01

Background In Turkey, the studies have reported that the age at which sexual intercourse and sexual activity starts has been steadily declining. There is an urgent need to increase social and health services for young people in order to provide them with a healthy life by changing their risky behaviors, avoiding unwanted pregnancies and sexually transmitted diseases (STDs). Sexual and reproductive health training particularly for adolescents warrants special attention and consideration. The objective of our study is to find out the short and long term effectiveness of a training course on peer education. Methods The study was conducted on 237 students who participated in a 40 hour Peer Trainer Training course. We utilized two types of evaluation methods to measure the effectiveness of the training on students' knowledge and attitude. The first method consisted of administering 3 tests comprised of the same 45 questions at 3 separate time intervals. Prior to the training a pre-test was given to obtain a measurement of base knowledge, and then an immediate post-test was given to evaluate the change in the knowledge and opinion of the participants. Finally, 6 months later the same test was administered to measure the retention of knowledge by the students. In the second type of evaluation, the participants' assessment of the training itself was sought by asking them to complete a Short Course Evaluation Form. We utilized SPSS 12.0 for descriptive analysis, and the Wilcoxon two related sample t-test were run. Results According to the pre and immediate post-test results, the training resulted in an increase in knowledge learned by an average of 21.6% (p < 0.05). Whereas, according to the immediate post test and the late post-test which was given six month later, there was a 1.8% decrease in the knowledge and attitude of the participants (p > 0.05). Participants thought that they had fun during training, and they became aware of what they knew and what they did not know. Conclusion Peer trainers with the training methods utilized, the knowledge and counseling acquired during training sessions will be able to provide counseling to their peers on reproductive health. PMID:18211713

Residential scene classification for gridded population sampling in developing countries using deep convolutional neural networks on satellite imagery.

PubMed

Chew, Robert F; Amer, Safaa; Jones, Kasey; Unangst, Jennifer; Cajka, James; Allpress, Justine; Bruhn, Mark

2018-05-09

Conducting surveys in low- and middle-income countries is often challenging because many areas lack a complete sampling frame, have outdated census information, or have limited data available for designing and selecting a representative sample. Geosampling is a probability-based, gridded population sampling method that addresses some of these issues by using geographic information system (GIS) tools to create logistically manageable area units for sampling. GIS grid cells are overlaid to partition a country's existing administrative boundaries into area units that vary in size from 50 m × 50 m to 150 m × 150 m. To avoid sending interviewers to unoccupied areas, researchers manually classify grid cells as "residential" or "nonresidential" through visual inspection of aerial images. "Nonresidential" units are then excluded from sampling and data collection. This process of manually classifying sampling units has drawbacks since it is labor intensive, prone to human error, and creates the need for simplifying assumptions during calculation of design-based sampling weights. In this paper, we discuss the development of a deep learning classification model to predict whether aerial images are residential or nonresidential, thus reducing manual labor and eliminating the need for simplifying assumptions. On our test sets, the model performs comparable to a human-level baseline in both Nigeria (94.5% accuracy) and Guatemala (96.4% accuracy), and outperforms baseline machine learning models trained on crowdsourced or remote-sensed geospatial features. Additionally, our findings suggest that this approach can work well in new areas with relatively modest amounts of training data. Gridded population sampling methods like geosampling are becoming increasingly popular in countries with outdated or inaccurate census data because of their timeliness, flexibility, and cost. Using deep learning models directly on satellite images, we provide a novel method for sample frame construction that identifies residential gridded aerial units. In cases where manual classification of satellite images is used to (1) correct for errors in gridded population data sets or (2) classify grids where population estimates are unavailable, this methodology can help reduce annotation burden with comparable quality to human analysts.

MAPPING THE GALAXY COLOR–REDSHIFT RELATION: OPTIMAL PHOTOMETRIC REDSHIFT CALIBRATION STRATEGIES FOR COSMOLOGY SURVEYS

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

Masters, Daniel; Steinhardt, Charles; Faisst, Andreas

2015-11-01

Calibrating the photometric redshifts of ≳10{sup 9} galaxies for upcoming weak lensing cosmology experiments is a major challenge for the astrophysics community. The path to obtaining the required spectroscopic redshifts for training and calibration is daunting, given the anticipated depths of the surveys and the difficulty in obtaining secure redshifts for some faint galaxy populations. Here we present an analysis of the problem based on the self-organizing map, a method of mapping the distribution of data in a high-dimensional space and projecting it onto a lower-dimensional representation. We apply this method to existing photometric data from the COSMOS survey selectedmore » to approximate the anticipated Euclid weak lensing sample, enabling us to robustly map the empirical distribution of galaxies in the multidimensional color space defined by the expected Euclid filters. Mapping this multicolor distribution lets us determine where—in galaxy color space—redshifts from current spectroscopic surveys exist and where they are systematically missing. Crucially, the method lets us determine whether a spectroscopic training sample is representative of the full photometric space occupied by the galaxies in a survey. We explore optimal sampling techniques and estimate the additional spectroscopy needed to map out the color–redshift relation, finding that sampling the galaxy distribution in color space in a systematic way can efficiently meet the calibration requirements. While the analysis presented here focuses on the Euclid survey, similar analysis can be applied to other surveys facing the same calibration challenge, such as DES, LSST, and WFIRST.« less

Local classification: Locally weighted-partial least squares-discriminant analysis (LW-PLS-DA).

PubMed

Bevilacqua, Marta; Marini, Federico

2014-08-01

The possibility of devising a simple, flexible and accurate non-linear classification method, by extending the locally weighted partial least squares (LW-PLS) approach to the cases where the algorithm is used in a discriminant way (partial least squares discriminant analysis, PLS-DA), is presented. In particular, to assess which category an unknown sample belongs to, the proposed algorithm operates by identifying which training objects are most similar to the one to be predicted and building a PLS-DA model using these calibration samples only. Moreover, the influence of the selected training samples on the local model can be further modulated by adopting a not uniform distance-based weighting scheme which allows the farthest calibration objects to have less impact than the closest ones. The performances of the proposed locally weighted-partial least squares-discriminant analysis (LW-PLS-DA) algorithm have been tested on three simulated data sets characterized by a varying degree of non-linearity: in all cases, a classification accuracy higher than 99% on external validation samples was achieved. Moreover, when also applied to a real data set (classification of rice varieties), characterized by a high extent of non-linearity, the proposed method provided an average correct classification rate of about 93% on the test set. By the preliminary results, showed in this paper, the performances of the proposed LW-PLS-DA approach have proved to be comparable and in some cases better than those obtained by other non-linear methods (k nearest neighbors, kernel-PLS-DA and, in the case of rice, counterpropagation neural networks). Copyright © 2014 Elsevier B.V. All rights reserved.

Generic Learning-Based Ensemble Framework for Small Sample Size Face Recognition in Multi-Camera Networks.

PubMed

Zhang, Cuicui; Liang, Xuefeng; Matsuyama, Takashi

2014-12-08

Multi-camera networks have gained great interest in video-based surveillance systems for security monitoring, access control, etc. Person re-identification is an essential and challenging task in multi-camera networks, which aims to determine if a given individual has already appeared over the camera network. Individual recognition often uses faces as a trial and requires a large number of samples during the training phrase. This is difficult to fulfill due to the limitation of the camera hardware system and the unconstrained image capturing conditions. Conventional face recognition algorithms often encounter the "small sample size" (SSS) problem arising from the small number of training samples compared to the high dimensionality of the sample space. To overcome this problem, interest in the combination of multiple base classifiers has sparked research efforts in ensemble methods. However, existing ensemble methods still open two questions: (1) how to define diverse base classifiers from the small data; (2) how to avoid the diversity/accuracy dilemma occurring during ensemble. To address these problems, this paper proposes a novel generic learning-based ensemble framework, which augments the small data by generating new samples based on a generic distribution and introduces a tailored 0-1 knapsack algorithm to alleviate the diversity/accuracy dilemma. More diverse base classifiers can be generated from the expanded face space, and more appropriate base classifiers are selected for ensemble. Extensive experimental results on four benchmarks demonstrate the higher ability of our system to cope with the SSS problem compared to the state-of-the-art system.

Generic Learning-Based Ensemble Framework for Small Sample Size Face Recognition in Multi-Camera Networks

PubMed Central

Zhang, Cuicui; Liang, Xuefeng; Matsuyama, Takashi

2014-01-01

Multi-camera networks have gained great interest in video-based surveillance systems for security monitoring, access control, etc. Person re-identification is an essential and challenging task in multi-camera networks, which aims to determine if a given individual has already appeared over the camera network. Individual recognition often uses faces as a trial and requires a large number of samples during the training phrase. This is difficult to fulfill due to the limitation of the camera hardware system and the unconstrained image capturing conditions. Conventional face recognition algorithms often encounter the “small sample size” (SSS) problem arising from the small number of training samples compared to the high dimensionality of the sample space. To overcome this problem, interest in the combination of multiple base classifiers has sparked research efforts in ensemble methods. However, existing ensemble methods still open two questions: (1) how to define diverse base classifiers from the small data; (2) how to avoid the diversity/accuracy dilemma occurring during ensemble. To address these problems, this paper proposes a novel generic learning-based ensemble framework, which augments the small data by generating new samples based on a generic distribution and introduces a tailored 0–1 knapsack algorithm to alleviate the diversity/accuracy dilemma. More diverse base classifiers can be generated from the expanded face space, and more appropriate base classifiers are selected for ensemble. Extensive experimental results on four benchmarks demonstrate the higher ability of our system to cope with the SSS problem compared to the state-of-the-art system. PMID:25494350

Associations between owner personality and psychological status and the prevalence of canine behavior problems

PubMed Central

Dodman, Nicholas H.; Brown, Dorothy C.

2018-01-01

Behavioral problems are a major source of poor welfare and premature mortality in companion dogs. Previous studies have demonstrated associations between owners’ personality and psychological status and the prevalence and/or severity of their dogs’ behavior problems. However, the mechanisms responsible for these associations are currently unknown. Other studies have detected links between the tendency of dogs to display behavior problems and their owners’ use of aversive or confrontational training methods. This raises the possibility that the effects of owner personality and psychological status on dog behavior are mediated via their influence on the owner’s choice of training methods. We investigated this hypothesis in a self-selected, convenience sample of 1564 current dog owners using an online battery of questionnaires designed to measure, respectively, owner personality, depression, emotion regulation, use of aversive/confrontational training methods, and owner-reported dog behavior. Multivariate linear and logistic regression analyses identified modest, positive associations between owners’ use of aversive/confrontational training methods and the prevalence/severity of the following dog behavior problems: owner-directed aggression, stranger-directed aggression, separation problems, chasing, persistent barking, and house-soiling (urination and defecation when left alone). The regression models also detected modest associations between owners’ low scores on four of the ‘Big Five’ personality dimensions (Agreeableness, Emotional Stability, Extraversion & Conscientiousness) and their dogs’ tendency to display higher rates of owner-directed aggression, stranger-directed fear, and/or urination when left alone. The study found only weak evidence to support the hypothesis that these relationships between owner personality and dog behavior were mediated via the owners’ use of punitive training methods, but it did detect a more than five-fold increase in the use of aversive/confrontational training techniques among men with moderate depression. Further research is needed to clarify the causal relationship between owner personality and psychological status and the behavioral problems of companion dogs. PMID:29444154

Associations between owner personality and psychological status and the prevalence of canine behavior problems.

PubMed

Dodman, Nicholas H; Brown, Dorothy C; Serpell, James A

2018-01-01

Behavioral problems are a major source of poor welfare and premature mortality in companion dogs. Previous studies have demonstrated associations between owners' personality and psychological status and the prevalence and/or severity of their dogs' behavior problems. However, the mechanisms responsible for these associations are currently unknown. Other studies have detected links between the tendency of dogs to display behavior problems and their owners' use of aversive or confrontational training methods. This raises the possibility that the effects of owner personality and psychological status on dog behavior are mediated via their influence on the owner's choice of training methods. We investigated this hypothesis in a self-selected, convenience sample of 1564 current dog owners using an online battery of questionnaires designed to measure, respectively, owner personality, depression, emotion regulation, use of aversive/confrontational training methods, and owner-reported dog behavior. Multivariate linear and logistic regression analyses identified modest, positive associations between owners' use of aversive/confrontational training methods and the prevalence/severity of the following dog behavior problems: owner-directed aggression, stranger-directed aggression, separation problems, chasing, persistent barking, and house-soiling (urination and defecation when left alone). The regression models also detected modest associations between owners' low scores on four of the 'Big Five' personality dimensions (Agreeableness, Emotional Stability, Extraversion & Conscientiousness) and their dogs' tendency to display higher rates of owner-directed aggression, stranger-directed fear, and/or urination when left alone. The study found only weak evidence to support the hypothesis that these relationships between owner personality and dog behavior were mediated via the owners' use of punitive training methods, but it did detect a more than five-fold increase in the use of aversive/confrontational training techniques among men with moderate depression. Further research is needed to clarify the causal relationship between owner personality and psychological status and the behavioral problems of companion dogs.

Performance Evaluation of Machine Learning Methods for Leaf Area Index Retrieval from Time-Series MODIS Reflectance Data

PubMed Central

Wang, Tongtong; Xiao, Zhiqiang; Liu, Zhigang

2017-01-01

Leaf area index (LAI) is an important biophysical parameter and the retrieval of LAI from remote sensing data is the only feasible method for generating LAI products at regional and global scales. However, most LAI retrieval methods use satellite observations at a specific time to retrieve LAI. Because of the impacts of clouds and aerosols, the LAI products generated by these methods are spatially incomplete and temporally discontinuous, and thus they cannot meet the needs of practical applications. To generate high-quality LAI products, four machine learning algorithms, including back-propagation neutral network (BPNN), radial basis function networks (RBFNs), general regression neutral networks (GRNNs), and multi-output support vector regression (MSVR) are proposed to retrieve LAI from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data in this study and performance of these machine learning algorithms is evaluated. The results demonstrated that GRNNs, RBFNs, and MSVR exhibited low sensitivity to training sample size, whereas BPNN had high sensitivity. The four algorithms performed slightly better with red, near infrared (NIR), and short wave infrared (SWIR) bands than red and NIR bands, and the results were significantly better than those obtained using single band reflectance data (red or NIR). Regardless of band composition, GRNNs performed better than the other three methods. Among the four algorithms, BPNN required the least training time, whereas MSVR needed the most for any sample size. PMID:28045443

Performance Evaluation of Machine Learning Methods for Leaf Area Index Retrieval from Time-Series MODIS Reflectance Data.

PubMed

Wang, Tongtong; Xiao, Zhiqiang; Liu, Zhigang

2017-01-01

Leaf area index (LAI) is an important biophysical parameter and the retrieval of LAI from remote sensing data is the only feasible method for generating LAI products at regional and global scales. However, most LAI retrieval methods use satellite observations at a specific time to retrieve LAI. Because of the impacts of clouds and aerosols, the LAI products generated by these methods are spatially incomplete and temporally discontinuous, and thus they cannot meet the needs of practical applications. To generate high-quality LAI products, four machine learning algorithms, including back-propagation neutral network (BPNN), radial basis function networks (RBFNs), general regression neutral networks (GRNNs), and multi-output support vector regression (MSVR) are proposed to retrieve LAI from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data in this study and performance of these machine learning algorithms is evaluated. The results demonstrated that GRNNs, RBFNs, and MSVR exhibited low sensitivity to training sample size, whereas BPNN had high sensitivity. The four algorithms performed slightly better with red, near infrared (NIR), and short wave infrared (SWIR) bands than red and NIR bands, and the results were significantly better than those obtained using single band reflectance data (red or NIR). Regardless of band composition, GRNNs performed better than the other three methods. Among the four algorithms, BPNN required the least training time, whereas MSVR needed the most for any sample size.

DES Science Portal: Computing Photometric Redshifts

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

Gschwend, Julia

An important challenge facing photometric surveys for cosmological purposes, such as the Dark Energy Survey (DES), is the need to produce reliable photometric redshifts (photo-z). The choice of adequate algorithms and configurations and the maintenance of an up-to-date spectroscopic database to build training sets, for example, are challenging tasks when dealing with large amounts of data that are regularly updated and constantly growing. In this paper, we present the first of a series of tools developed by DES, provided as part of the DES Science Portal, an integrated web-based data portal developed to facilitate the scientific analysis of the data,more » while ensuring the reproducibility of the analysis. We present the DES Science Portal photometric redshift tools, starting from the creation of a spectroscopic sample to training the neural network photo-z codes, to the final estimation of photo-zs for a large photometric catalog. We illustrate this operation by calculating well calibrated photo-zs for a galaxy sample extracted from the DES first year (Y1A1) data. The series of processes mentioned above is run entirely within the Portal environment, which automatically produces validation metrics, and maintains the provenance between the different steps. This system allows us to fine tune the many steps involved in the process of calculating photo-zs, making sure that we do not lose the information on the configurations and inputs of the previous processes. By matching the DES Y1A1 photometry to a spectroscopic sample, we define different training sets that we use to feed the photo-z algorithms already installed at the Portal. Finally, we validate the results under several conditions, including the case of a sample limited to i<22.5 with the color properties close to the full DES Y1A1 photometric data. This way we compare the performance of multiple methods and training configurations. The infrastructure presented here is an effcient way to test several methods of calculating photo-zs and use them to create different catalogs for portal science workflows« less

Sentiment Analysis of Health Care Tweets: Review of the Methods Used.

PubMed

Gohil, Sunir; Vuik, Sabine; Darzi, Ara

2018-04-23

Twitter is a microblogging service where users can send and read short 140-character messages called "tweets." There are several unstructured, free-text tweets relating to health care being shared on Twitter, which is becoming a popular area for health care research. Sentiment is a metric commonly used to investigate the positive or negative opinion within these messages. Exploring the methods used for sentiment analysis in Twitter health care research may allow us to better understand the options available for future research in this growing field. The first objective of this study was to understand which tools would be available for sentiment analysis of Twitter health care research, by reviewing existing studies in this area and the methods they used. The second objective was to determine which method would work best in the health care settings, by analyzing how the methods were used to answer specific health care questions, their production, and how their accuracy was analyzed. A review of the literature was conducted pertaining to Twitter and health care research, which used a quantitative method of sentiment analysis for the free-text messages (tweets). The study compared the types of tools used in each case and examined methods for tool production, tool training, and analysis of accuracy. A total of 12 papers studying the quantitative measurement of sentiment in the health care setting were found. More than half of these studies produced tools specifically for their research, 4 used open source tools available freely, and 2 used commercially available software. Moreover, 4 out of the 12 tools were trained using a smaller sample of the study's final data. The sentiment method was trained against, on an average, 0.45% (2816/627,024) of the total sample data. One of the 12 papers commented on the analysis of accuracy of the tool used. Multiple methods are used for sentiment analysis of tweets in the health care setting. These range from self-produced basic categorizations to more complex and expensive commercial software. The open source and commercial methods are developed on product reviews and generic social media messages. None of these methods have been extensively tested against a corpus of health care messages to check their accuracy. This study suggests that there is a need for an accurate and tested tool for sentiment analysis of tweets trained using a health care setting-specific corpus of manually annotated tweets first. ©Sunir Gohil, Sabine Vuik, Ara Darzi. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 23.04.2018.

Understanding biological mechanisms underlying adverse birth outcomes in developing countries: protocol for a prospective cohort (AMANHI bio-banking) study.

PubMed

Baqui, Abdullah H; Khanam, Rasheda; Rahman, Mohammad Sayedur; Ahmed, Aziz; Rahman, Hasna Hena; Moin, Mamun Ibne; Ahmed, Salahuddin; Jehan, Fyezah; Nisar, Imran; Hussain, Atiya; Ilyas, Muhammad; Hotwani, Aneeta; Sajid, Muhammad; Qureshi, Shahida; Zaidi, Anita; Sazawal, Sunil; Ali, Said M; Deb, Saikat; Juma, Mohammed Hamad; Dhingra, Usha; Dutta, Arup; Ame, Shaali Makame; Hayward, Caroline; Rudan, Igor; Zangenberg, Mike; Russell, Donna; Yoshida, Sachiyo; Polašek, Ozren; Manu, Alexander; Bahl, Rajiv

2017-12-01

The AMANHI study aims to seek for biomarkers as predictors of important pregnancy-related outcomes, and establish a biobank in developing countries for future research as new methods and technologies become available. AMANHI is using harmonised protocols to enrol 3000 women in early pregnancies (8-19 weeks of gestation) for population-based follow-up in pregnancy up to 42 days postpartum in Bangladesh, Pakistan and Tanzania, with collection taking place between August 2014 and June 2016. Urine pregnancy tests will be used to confirm reported or suspected pregnancies for screening ultrasound by trained sonographers to accurately date the pregnancy. Trained study field workers will collect very detailed phenotypic and epidemiological data from the pregnant woman and her family at scheduled home visits during pregnancy (enrolment, 24-28 weeks, 32-36 weeks & 38+ weeks) and postpartum (days 0-6 or 42-60). Trained phlebotomists will collect maternal and umbilical blood samples, centrifuge and obtain aliquots of serum, plasma and the buffy coat for storage. They will also measure HbA1C and collect a dried spot sample of whole blood. Maternal urine samples will also be collected and stored, alongside placenta, umbilical cord tissue and membrane samples, which will both be frozen and prepared for histology examination. Maternal and newborn stool (for microbiota) as well as paternal and newborn saliva samples (for DNA extraction) will also be collected. All samples will be stored at -80°C in the biobank in each of the three sites. These samples will be linked to numerous epidemiological and phenotypic data with unique study identification numbers. AMANHI biobank proves that biobanking is feasible to implement in LMICs, but recognises that biobank creation is only the first step in addressing current global challenges.

Accurate in silico identification of protein succinylation sites using an iterative semi-supervised learning technique.

PubMed

Zhao, Xiaowei; Ning, Qiao; Chai, Haiting; Ma, Zhiqiang

2015-06-07

As a widespread type of protein post-translational modifications (PTMs), succinylation plays an important role in regulating protein conformation, function and physicochemical properties. Compared with the labor-intensive and time-consuming experimental approaches, computational predictions of succinylation sites are much desirable due to their convenient and fast speed. Currently, numerous computational models have been developed to identify PTMs sites through various types of two-class machine learning algorithms. These methods require both positive and negative samples for training. However, designation of the negative samples of PTMs was difficult and if it is not properly done can affect the performance of computational models dramatically. So that in this work, we implemented the first application of positive samples only learning (PSoL) algorithm to succinylation sites prediction problem, which was a special class of semi-supervised machine learning that used positive samples and unlabeled samples to train the model. Meanwhile, we proposed a novel succinylation sites computational predictor called SucPred (succinylation site predictor) by using multiple feature encoding schemes. Promising results were obtained by the SucPred predictor with an accuracy of 88.65% using 5-fold cross validation on the training dataset and an accuracy of 84.40% on the independent testing dataset, which demonstrated that the positive samples only learning algorithm presented here was particularly useful for identification of protein succinylation sites. Besides, the positive samples only learning algorithm can be applied to build predictors for other types of PTMs sites with ease. A web server for predicting succinylation sites was developed and was freely accessible at http://59.73.198.144:8088/SucPred/. Copyright © 2015 Elsevier Ltd. All rights reserved.

Sequential Classifier Training for Rice Mapping with Multitemporal Remote Sensing Imagery

NASA Astrophysics Data System (ADS)

Guo, Y.; Jia, X.; Paull, D.

2017-10-01

Most traditional methods for rice mapping with remote sensing data are effective when they are applied to the initial growing stage of rice, as the practice of flooding during this period makes the spectral characteristics of rice fields more distinguishable. In this study, we propose a sequential classifier training approach for rice mapping that can be used over the whole growing period of rice for monitoring various growth stages. Rice fields are firstly identified during the initial flooding period. The identified rice fields are used as training data to train a classifier that separates rice and non-rice pixels. The classifier is then used as a priori knowledge to assist the training of classifiers for later rice growing stages. This approach can be applied progressively to sequential image data, with only a small amount of training samples being required from each image. In order to demonstrate the effectiveness of the proposed approach, experiments were conducted at one of the major rice-growing areas in Australia. The proposed approach was applied to a set of multitemporal remote sensing images acquired by the Sentinel-2A satellite. Experimental results show that, compared with traditional spectral-indexbased algorithms, the proposed method is able to achieve more stable and consistent rice mapping accuracies and it reaches higher than 80% during the whole rice growing period.

Reframing implementation as an organisational behaviour problem.

PubMed

Clay-Williams, Robyn; Braithwaite, Jeffrey

2015-01-01

The purpose of this paper is to report on a process evaluation of a randomised controlled trial (RCT) intervention study that tested the effectiveness of classroom- and simulation-based crew resource management courses, alone and in combination, and identifies organisational barriers and facilitators to implementation of team training programmes in healthcare. The RCT design consisted of a before and after study with a team training intervention. Quantitative data were gathered on utility and affective reactions to training, and on teamwork knowledge, attitudes, and behaviours of the learners. A sample of participants was interviewed at the conclusion of the study. Interview responses were analysed, alongside qualitative elements of the classroom course critique, to search for evidence, context, and facilitation clues to the implementation process. The RCT method provided scientifically robust data that supported the benefits of classroom training. Qualitative data identified a number of facilitators to implementation of team training, and shed light on some of the ways that learning was diffused throughout the organisation. Barriers to successful implementation were also identified, including hospital time and resource constraints and poor organisational communication. Quantitative randomised methods have intermittently been used to evaluate team training interventions in healthcare. Despite two decades of team training trials, however, the authors do not know as well as the authors would like what goes on inside the "black box" of such RCTs. While results are usually centred on outcomes, this study also provides insight into the context and mechanisms associated with those outcomes and identifies barriers and facilitators to successful intervention implementation.

ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c6sc05720a Click here for additional data file.

PubMed Central

Smith, J. S.

2017-01-01

Deep learning is revolutionizing many areas of science and technology, especially image, text, and speech recognition. In this paper, we demonstrate how a deep neural network (NN) trained on quantum mechanical (QM) DFT calculations can learn an accurate and transferable potential for organic molecules. We introduce ANAKIN-ME (Accurate NeurAl networK engINe for Molecular Energies) or ANI for short. ANI is a new method designed with the intent of developing transferable neural network potentials that utilize a highly-modified version of the Behler and Parrinello symmetry functions to build single-atom atomic environment vectors (AEV) as a molecular representation. AEVs provide the ability to train neural networks to data that spans both configurational and conformational space, a feat not previously accomplished on this scale. We utilized ANI to build a potential called ANI-1, which was trained on a subset of the GDB databases with up to 8 heavy atoms in order to predict total energies for organic molecules containing four atom types: H, C, N, and O. To obtain an accelerated but physically relevant sampling of molecular potential surfaces, we also proposed a Normal Mode Sampling (NMS) method for generating molecular conformations. Through a series of case studies, we show that ANI-1 is chemically accurate compared to reference DFT calculations on much larger molecular systems (up to 54 atoms) than those included in the training data set. PMID:28507695

Smartphone-based colorimetric analysis for detection of saliva alcohol concentration.

PubMed

Jung, Youngkee; Kim, Jinhee; Awofeso, Olumide; Kim, Huisung; Regnier, Fred; Bae, Euiwon

2015-11-01

A simple device and associated analytical methods are reported. We provide objective and accurate determination of saliva alcohol concentrations using smartphone-based colorimetric imaging. The device utilizes any smartphone with a miniature attachment that positions the sample and provides constant illumination for sample imaging. Analyses of histograms based on channel imaging of red-green-blue (RGB) and hue-saturation-value (HSV) color space provide unambiguous determination of blood alcohol concentration from color changes on sample pads. A smartphone-based sample analysis by colorimetry was developed and tested with blind samples that matched with the training sets. This technology can be adapted to any smartphone and used to conduct color change assays.

Feature learning and change feature classification based on deep learning for ternary change detection in SAR images

NASA Astrophysics Data System (ADS)

Gong, Maoguo; Yang, Hailun; Zhang, Puzhao

2017-07-01

Ternary change detection aims to detect changes and group the changes into positive change and negative change. It is of great significance in the joint interpretation of spatial-temporal synthetic aperture radar images. In this study, sparse autoencoder, convolutional neural networks (CNN) and unsupervised clustering are combined to solve ternary change detection problem without any supervison. Firstly, sparse autoencoder is used to transform log-ratio difference image into a suitable feature space for extracting key changes and suppressing outliers and noise. And then the learned features are clustered into three classes, which are taken as the pseudo labels for training a CNN model as change feature classifier. The reliable training samples for CNN are selected from the feature maps learned by sparse autoencoder with certain selection rules. Having training samples and the corresponding pseudo labels, the CNN model can be trained by using back propagation with stochastic gradient descent. During its training procedure, CNN is driven to learn the concept of change, and more powerful model is established to distinguish different types of changes. Unlike the traditional methods, the proposed framework integrates the merits of sparse autoencoder and CNN to learn more robust difference representations and the concept of change for ternary change detection. Experimental results on real datasets validate the effectiveness and superiority of the proposed framework.

Face recognition via sparse representation of SIFT feature on hexagonal-sampling image

NASA Astrophysics Data System (ADS)

Zhang, Daming; Zhang, Xueyong; Li, Lu; Liu, Huayong

2018-04-01

This paper investigates a face recognition approach based on Scale Invariant Feature Transform (SIFT) feature and sparse representation. The approach takes advantage of SIFT which is local feature other than holistic feature in classical Sparse Representation based Classification (SRC) algorithm and possesses strong robustness to expression, pose and illumination variations. Since hexagonal image has more inherit merits than square image to make recognition process more efficient, we extract SIFT keypoint in hexagonal-sampling image. Instead of matching SIFT feature, firstly the sparse representation of each SIFT keypoint is given according the constructed dictionary; secondly these sparse vectors are quantized according dictionary; finally each face image is represented by a histogram and these so-called Bag-of-Words vectors are classified by SVM. Due to use of local feature, the proposed method achieves better result even when the number of training sample is small. In the experiments, the proposed method gave higher face recognition rather than other methods in ORL and Yale B face databases; also, the effectiveness of the hexagonal-sampling in the proposed method is verified.

Effectiveness of stress management training on the psychological well-being of the nurses.

PubMed

Pahlevani, M; Ebrahimi, M; Radmehr, S; Amini, F; Bahraminasab, M; Yazdani, M

2015-01-01

Objective: an appropriate psychological intervention to promote the level of the public health and mental well-being of nurses has a great importance. This investigation was aimed to study the effectiveness of stress management training on the psychological welfare of nurses in Imam Khomeini Hospital. Methodology: this study was quasi-experimental with pretest-posttest that used a control group. Hence, 40 of the nurses in Imam Khomeini Hospital were selected by using a convenience sampling method and placed in the experimental group and the control group. Both groups were pretested by using psychological well-being 84-question scale. Afterwards, the experimental group was trained for ten sessions under stress management skill exercise, and the check group got no intervention. Next, both societies were post-tested, and the acquired data were analyzed by using inferential and descriptive statistical methods accompanied by SPSS 21 software. Findings: the results indicated that stress management training significantly led to the promotion of psychological well-being in nurses (p < 0.001). Conclusion: it was found from the research that due to the high level of effectiveness of stress management training, its low cost, and its high acceptability by the patients, especially when it was performed in a group, had a significant positive impact on the promotion of psychological well-being in nurses.

Successes and failures of using the cell phone as a main mode of communication between participants and facilitators from a distance: an innovative method of training rural health facility managers in Papua New Guinea.

PubMed

Au, Lucy

2012-01-01

Rural Health Facility Management Training is a training program developed by the National Department of Health in collaboration with AUSAID through the office of the Capacity Building Service Centre. The purpose of the training is to train officers-in-charge who did not acquire knowledge and skills of managing a health facility. As part of this study, it is essential to assess whether the cell phone is a better mode of communication between the participants and the facilitators compared with other modes of communication from a distance. The study used the cross-sectional method to collect 160 samples from 12 provinces and the statistical software Stata (version 8) was used to analyse the data. The results showed that mobile coverage is not very effective in most rural areas, though, it is efficient and accessible. Furthermore, it is expensive to make a call compared with sending text massages. In spite of the high cost involved, most health managers prefer to use the cell phone compared to normal post, email, or fax. This clearly shows that the mobile phone is a better device for distant learning in rural Papua New Guinea compared to other modes of communication.

Case Study: Does training of private networks of Family Planning clinicians in urban Pakistan affect service utilization?

PubMed Central

2010-01-01

Background To determine whether training of providers participating in franchise clinic networks is associated with increased Family Planning service use among low-income urban families in Pakistan. Methods The study uses 2001 survey data consisting of interviews with 1113 clinical and non-clinical providers working in public and private hospitals/clinics. Data analysis excludes non-clinical providers reducing sample size to 822. Variables for the analysis are divided into client volume, and training in family planning. Regression models are used to compute the association between training and service use in franchise versus private non-franchise clinics. Results In franchise clinic networks, staff are 6.5 times more likely to receive family planning training (P = 0.00) relative to private non-franchises. Service use was significantly associated with training (P = 0.00), franchise affiliation (P = 0.01), providers' years of family planning experience (P = 0.02) and the number of trained staff working at government owned clinics (P = 0.00). In this setting, nurses are significantly less likely to receive training compared to doctors (P = 0.00). Conclusions These findings suggest that franchises recruit and train various cadres of health workers and training maybe associated with increased service use through improvement in quality of services. PMID:21062460

Task oriented training improves the balance outcome & reducing fall risk in diabetic population

PubMed Central

Ghazal, Javeria; Malik, Arshad Nawaz; Amjad, Imran

2016-01-01

Objectives: The objective was to determine the balance impairments and to compare task oriented versus traditional balance training in fall reduction among diabetic patients. Methods: The randomized control trial with descriptive survey and 196 diabetic patients were recruited to assess balance impairments through purposive sampling technique. Eighteen patients were randomly allocated into two groups; task oriented balance training group TOB (n=8) and traditional balance training group TBT (n=10). The inclusion criteria were 30-50 years age bracket and diagnosed cases of Diabetes Mellitus with neuropathy. The demographics were taken through standardized & valid assessment tools include Berg Balance Scale and Functional Reach Test. The measurements were obtained at baseline, after 04 and 08 weeks of training. Results: The mean age of the participants was 49 ±6.79. The result shows that 165(84%) were at moderate risk of fall and 31(15%) were at mild risk of fall among total 196 diabetic patients. There was significant improvement (p <0.05) in task oriented balance training group for dynamic balance, anticipatory balance and reactive balance after 8 weeks of training as compare to traditional balance training. Conclusion: Task oriented balance training is effective in improving the dynamic, anticipator and reactive balance. The task oriented training reduces the risk of falling through enhancing balance outcome. PMID:27648053

Cleft audit protocol for speech (CAPS-A): a comprehensive training package for speech analysis.

PubMed

Sell, D; John, A; Harding-Bell, A; Sweeney, T; Hegarty, F; Freeman, J

2009-01-01

The previous literature has largely focused on speech analysis systems and ignored process issues, such as the nature of adequate speech samples, data acquisition, recording and playback. Although there has been recognition of the need for training on tools used in speech analysis associated with cleft palate, little attention has been paid to this issue. To design, execute, and evaluate a training programme for speech and language therapists on the systematic and reliable use of the Cleft Audit Protocol for Speech-Augmented (CAPS-A), addressing issues of standardized speech samples, data acquisition, recording, playback, and listening guidelines. Thirty-six specialist speech and language therapists undertook the training programme over four days. This consisted of two days' training on the CAPS-A tool followed by a third day, making independent ratings and transcriptions on ten new cases which had been previously recorded during routine audit data collection. This task was repeated on day 4, a minimum of one month later. Ratings were made using the CAPS-A record form with the CAPS-A definition table. An analysis was made of the speech and language therapists' CAPS-A ratings at occasion 1 and occasion 2 and the intra- and inter-rater reliability calculated. Trained therapists showed consistency in individual judgements on specific sections of the tool. Intraclass correlation coefficients were calculated for each section with good agreement on eight of 13 sections. There were only fair levels of agreement on anterior oral cleft speech characteristics, non-cleft errors/immaturities and voice. This was explained, at least in part, by their low prevalence which affects the calculation of the intraclass correlation coefficient statistic. Speech and language therapists benefited from training on the CAPS-A, focusing on specific aspects of speech using definitions of parameters and scalar points, in order to apply the tool systematically and reliably. Ratings are enhanced by ensuring a high degree of attention to the nature of the data, standardizing the speech sample, data acquisition, the listening process together with the use of high-quality recording and playback equipment. In addition, a method is proposed for maintaining listening skills following training as part of an individual's continuing education.

Deep convolutional networks for pancreas segmentation in CT imaging

NASA Astrophysics Data System (ADS)

Roth, Holger R.; Farag, Amal; Lu, Le; Turkbey, Evrim B.; Summers, Ronald M.

2015-03-01

Automatic organ segmentation is an important prerequisite for many computer-aided diagnosis systems. The high anatomical variability of organs in the abdomen, such as the pancreas, prevents many segmentation methods from achieving high accuracies when compared to state-of-the-art segmentation of organs like the liver, heart or kidneys. Recently, the availability of large annotated training sets and the accessibility of affordable parallel computing resources via GPUs have made it feasible for "deep learning" methods such as convolutional networks (ConvNets) to succeed in image classification tasks. These methods have the advantage that used classification features are trained directly from the imaging data. We present a fully-automated bottom-up method for pancreas segmentation in computed tomography (CT) images of the abdomen. The method is based on hierarchical coarse-to-fine classification of local image regions (superpixels). Superpixels are extracted from the abdominal region using Simple Linear Iterative Clustering (SLIC). An initial probability response map is generated, using patch-level confidences and a two-level cascade of random forest classifiers, from which superpixel regions with probabilities larger 0.5 are retained. These retained superpixels serve as a highly sensitive initial input of the pancreas and its surroundings to a ConvNet that samples a bounding box around each superpixel at different scales (and random non-rigid deformations at training time) in order to assign a more distinct probability of each superpixel region being pancreas or not. We evaluate our method on CT images of 82 patients (60 for training, 2 for validation, and 20 for testing). Using ConvNets we achieve maximum Dice scores of an average 68% +/- 10% (range, 43-80%) in testing. This shows promise for accurate pancreas segmentation, using a deep learning approach and compares favorably to state-of-the-art methods.

Supervised target detection in hyperspectral images using one-class Fukunaga-Koontz Transform

NASA Astrophysics Data System (ADS)

Binol, Hamidullah; Bal, Abdullah

2016-05-01

A novel hyperspectral target detection technique based on Fukunaga-Koontz transform (FKT) is presented. FKT offers significant properties for feature selection and ordering. However, it can only be used to solve multi-pattern classification problems. Target detection may be considered as a two-class classification problem, i.e., target versus background clutter. Nevertheless, background clutter typically contains different types of materials. That's why; target detection techniques are different than classification methods by way of modeling clutter. To avoid the modeling of the background clutter, we have improved one-class FKT (OC-FKT) for target detection. The statistical properties of target training samples are used to define tunnel-like boundary of the target class. Non-target samples are then created synthetically as to be outside of the boundary. Thus, only limited target samples become adequate for training of FKT. The hyperspectral image experiments confirm that the proposed OC-FKT technique provides an effective means for target detection.

Human sensory response to acetone/air mixtures.

PubMed

Salthammer, T; Schulz, N; Stolte, R; Uhde, E

2016-10-01

The release of organic compounds from building products may influence the perceived air quality in the indoor environment. Consequently, building products are assessed for chemical emissions and for the acceptability of emitted odors. A procedure for odor evaluations in test chambers is described by the standard ISO 16000-28. A panel of eight or more trained subjects directly determines the perceived intensity Π (unit pi) of an air sample via diffusers. For the training of the panelists, a comparative Π-scale is applied. The panelists can use acetone/air mixtures in a concentration range between 20 mg/m(3) (0 pi) and 320 mg/m(3) (15 pi) as reference. However, the training and calibration procedure itself can substantially contribute to the method uncertainty. This concerns the assumed odor threshold of acetone, the variability of panelist responses, and the analytical determination of acetone concentrations in air with online methods as well as the influence of the diffuser geometry and the airflow profile. © 2015 The Authors. Indoor Air published by John Wiley & Sons Ltd.

LQAS: User Beware.

PubMed

Rhoda, Dale A; Fernandez, Soledad A; Fitch, David J; Lemeshow, Stanley

2010-02-01

Researchers around the world are using Lot Quality Assurance Sampling (LQAS) techniques to assess public health parameters and evaluate program outcomes. In this paper, we report that there are actually two methods being called LQAS in the world today, and that one of them is badly flawed. This paper reviews fundamental LQAS design principles, and compares and contrasts the two LQAS methods. We raise four concerns with the simply-written, freely-downloadable training materials associated with the second method. The first method is founded on sound statistical principles and is carefully designed to protect the vulnerable populations that it studies. The language used in the training materials for the second method is simple, but not at all clear, so the second method sounds very much like the first. On close inspection, however, the second method is found to promote study designs that are biased in favor of finding programmatic or intervention success, and therefore biased against the interests of the population being studied. We outline several recommendations, and issue a call for a new high standard of clarity and face validity for those who design, conduct, and report LQAS studies.

Methodological integrative review of the work sampling technique used in nursing workload research.

PubMed

Blay, Nicole; Duffield, Christine M; Gallagher, Robyn; Roche, Michael

2014-11-01

To critically review the work sampling technique used in nursing workload research. Work sampling is a technique frequently used by researchers and managers to explore and measure nursing activities. However, work sampling methods used are diverse making comparisons of results between studies difficult. Methodological integrative review. Four electronic databases were systematically searched for peer-reviewed articles published between 2002-2012. Manual scanning of reference lists and Rich Site Summary feeds from contemporary nursing journals were other sources of data. Articles published in the English language between 2002-2012 reporting on research which used work sampling to examine nursing workload. Eighteen articles were reviewed. The review identified that the work sampling technique lacks a standardized approach, which may have an impact on the sharing or comparison of results. Specific areas needing a shared understanding included the training of observers and subjects who self-report, standardization of the techniques used to assess observer inter-rater reliability, sampling methods and reporting of outcomes. Work sampling is a technique that can be used to explore the many facets of nursing work. Standardized reporting measures would enable greater comparison between studies and contribute to knowledge more effectively. Author suggestions for the reporting of results may act as guidelines for researchers considering work sampling as a research method. © 2014 John Wiley & Sons Ltd.

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

Rottmann, Joerg; Berbeco, Ross

Purpose: Precise prediction of respiratory motion is a prerequisite for real-time motion compensation techniques such as beam, dynamic couch, or dynamic multileaf collimator tracking. Collection of tumor motion data to train the prediction model is required for most algorithms. To avoid exposure of patients to additional dose from imaging during this procedure, the feasibility of training a linear respiratory motion prediction model with an external surrogate signal is investigated and its performance benchmarked against training the model with tumor positions directly. Methods: The authors implement a lung tumor motion prediction algorithm based on linear ridge regression that is suitable tomore » overcome system latencies up to about 300 ms. Its performance is investigated on a data set of 91 patient breathing trajectories recorded from fiducial marker tracking during radiotherapy delivery to the lung of ten patients. The expected 3D geometric error is quantified as a function of predictor lookahead time, signal sampling frequency and history vector length. Additionally, adaptive model retraining is evaluated, i.e., repeatedly updating the prediction model after initial training. Training length for this is gradually increased with incoming (internal) data availability. To assess practical feasibility model calculation times as well as various minimum data lengths for retraining are evaluated. Relative performance of model training with external surrogate motion data versus tumor motion data is evaluated. However, an internal–external motion correlation model is not utilized, i.e., prediction is solely driven by internal motion in both cases. Results: Similar prediction performance was achieved for training the model with external surrogate data versus internal (tumor motion) data. Adaptive model retraining can substantially boost performance in the case of external surrogate training while it has little impact for training with internal motion data. A minimum adaptive retraining data length of 8 s and history vector length of 3 s achieve maximal performance. Sampling frequency appears to have little impact on performance confirming previously published work. By using the linear predictor, a relative geometric 3D error reduction of about 50% was achieved (using adaptive retraining, a history vector length of 3 s and with results averaged over all investigated lookahead times and signal sampling frequencies). The absolute mean error could be reduced from (2.0 ± 1.6) mm when using no prediction at all to (0.9 ± 0.8) mm and (1.0 ± 0.9) mm when using the predictor trained with internal tumor motion training data and external surrogate motion training data, respectively (for a typical lookahead time of 250 ms and sampling frequency of 15 Hz). Conclusions: A linear prediction model can reduce latency induced tracking errors by an average of about 50% in real-time image guided radiotherapy systems with system latencies of up to 300 ms. Training a linear model for lung tumor motion prediction with an external surrogate signal alone is feasible and results in similar performance as training with (internal) tumor motion. Particularly for scenarios where motion data are extracted from fluoroscopic imaging with ionizing radiation, this may alleviate the need for additional imaging dose during the collection of model training data.« less

Performance Evaluation of a Salivary Amylase Biosensor for Stress Assessment in Military Field Research.

PubMed

Peng, Henry T; Savage, Erin; Vartanian, Oshin; Smith, Shane; Rhind, Shawn G; Tenn, Catherine; Bjamason, Stephen

2016-05-01

A convenient biosensor for real-time measurement of biomarkers for in-field psychophysiological stress research and military operations is desirable. We evaluated a hand-held device for measuring salivary amylase as a stress marker in medical technicians undergoing combat casualty care training using two different modalities in operating room and field settings. Salivary amylase activity was measured by two biosensor methods: directly sampling saliva with a test strip placed under the tongue or pipetting a fixed volume of precollected saliva onto the test strip, followed by analyzing the sample on the strip using a biosensor. The two methods were compared for their accuracy and sensitivity to detect the stress response using an enzyme assay method as a standard. The measurements from the under-the-tongue method were not as consistent with those from the standard assay method as the values obtained from the pipetting method. The under-the-tongue method did not detect any significant increase in the amylase activity due to stress in the operating room (P > 0.1), in contrast to the significant increases observed using the pipetting method and assay method with a significance level less than 0.05 and 0.1, respectively. Furthermore, the under-the-tongue method showed no increased amylase activity in the field testing, while both the pipetting method and assay method showed increased amylase activity in the same group (P < 0.1). The accuracy and consistency of the biosensors need to be improved when used to directly measure salivary amylase activity under the tongue for stress assessment in military medical training. © 2015 Her Majesty the Queen in Right of Canada. Journal of Clinical Laboratory Analysis published by Wiley Periodicals, Inc. Reproduced with the permission DRDC Editorial Board.

Psycho-Social Factors Causing Stress: A Study of Teacher Educators

ERIC Educational Resources Information Center

Jain, Geetika; Tyagi, Harish Kumar; Kumar, Anil

2015-01-01

Purpose: The present investigation was planned to determine the influence of type of personality, gender, age, qualification and experience causing stress among teacher educators at work. Method: A sample of 100 subjects from male and female teachers teaching in teacher training colleges, Delhi, India was drawn randomly. The data was collected by…

Stroke Knowledge among Urban and Frontier First Responders and Emergency Medical Technicians in Montana

ERIC Educational Resources Information Center

McNamara, Michael J.; Oser, Carrie; Gohdes, Dorothy; Fogle, Crystelle C.; Dietrich, Dennis W.; Burnett, Anne; Okon, Nicholas; Russell, Joseph A.; DeTienne, James; Harwell, Todd S.; Helgerson, Steven D.

2008-01-01

Purpose: To assess stroke knowledge and practice among frontier and urban emergency medical services (EMS) providers and to evaluate the need for additional prehospital stroke training opportunities in Montana. Methods: In 2006, a telephone survey of a representative sample of EMS providers was conducted in Montana. Respondents were stratified…

Mega-Analysis of School Psychology Blueprint for Training and Practice Domains

ERIC Educational Resources Information Center

Burns, Matthew K.; Kanive, Rebecca; Zaslofsky, Anne F.; Parker, David C.

2013-01-01

Meta-analytic research is an effective method for synthesizing existing research and for informing practice and policy. Hattie (2009) suggested that meta-analytic procedures could be employed to existing meta-analyses to create a mega-analysis. The current mega-analysis examined a sample of 47 meta-analyses according to the "School…

The Impact of Behavioral Signs of Intoxication on Bartender Service

ERIC Educational Resources Information Center

Goodsite, Billie; Klear, Lacey; Rosenberg, Harold

2008-01-01

Objective: The present study was designed to assess whether the serving practices of a sample of bartenders in an American university town would vary as a function of the number of behavioral cues of intoxication displayed by apparently real patrons (who were actually experimental confederates). Method: We trained two male and three female…

Nutrient Intake and Dietary Habits of Women Endurance Athletes.

ERIC Educational Resources Information Center

Wiseman, Juliet

Dietary information was collected from a sample of women endurance athletes (n=16). Seven-day food intake records were taken using a semiweighted method. Questionnaires were used to obtain additional information on training, supplements, and attitudes toward diet. Notable features of the diets were a low average energy intake while mean intakes of…

Issues of Perception Post 9/11 and Implications for Antiterrorism Education

ERIC Educational Resources Information Center

Barner, Rayford E.

2015-01-01

This research study sought to determine a connection between antiterrorism education training and police officer's perception of the cultural communities implicated in the September 11, 2001 terrorist attacks. The study employs a mixed-methods research design using surveys and interviews. The sample is taken from 52 police officers of a police…

A Computer-Based Program to Teach Braille Reading to Sighted Individuals

ERIC Educational Resources Information Center

Scheithauer, Mindy C.; Tiger, Jeffrey H.

2012-01-01

Instructors of the visually impaired need efficient braille-training methods. This study conducted a preliminary evaluation of a computer-based program intended to teach the relation between braille characters and English letters using a matching-to-sample format with 4 sighted college students. Each participant mastered matching visual depictions…

Manganese Analysis in Water Samples. Training Module 5.211.2.77.

ERIC Educational Resources Information Center

Bonte, John L.; Davidson, Arnold C.

This document is an instructional module package prepared in objective form for use by an instructor familiar with the spectrophotometric analysis of manganese in water using the persulfate method. Included are objectives, an instructor guide, student handouts, and transparency masters. A video tape is also available from the author. This module…

Interpretation Training in Individuals with Generalized Social Anxiety Disorder: A Randomized Controlled Trial

ERIC Educational Resources Information Center

Amir, Nader; Taylor, Charles T.

2012-01-01

Objective: To examine the efficacy of a multisession computerized interpretation modification program (IMP) in the treatment of generalized social anxiety disorder (GSAD). Method: The sample comprised 49 individuals meeting diagnostic criteria for GSAD who were enrolled in a randomized, double-blind placebo-controlled trial comparing IMP (n = 23)…

Using Support Vector Machine Ensembles for Target Audience Classification on Twitter

PubMed Central

Lo, Siaw Ling; Chiong, Raymond; Cornforth, David

2015-01-01

The vast amount and diversity of the content shared on social media can pose a challenge for any business wanting to use it to identify potential customers. In this paper, our aim is to investigate the use of both unsupervised and supervised learning methods for target audience classification on Twitter with minimal annotation efforts. Topic domains were automatically discovered from contents shared by followers of an account owner using Twitter Latent Dirichlet Allocation (LDA). A Support Vector Machine (SVM) ensemble was then trained using contents from different account owners of the various topic domains identified by Twitter LDA. Experimental results show that the methods presented are able to successfully identify a target audience with high accuracy. In addition, we show that using a statistical inference approach such as bootstrapping in over-sampling, instead of using random sampling, to construct training datasets can achieve a better classifier in an SVM ensemble. We conclude that such an ensemble system can take advantage of data diversity, which enables real-world applications for differentiating prospective customers from the general audience, leading to business advantage in the crowded social media space. PMID:25874768

Using support vector machine ensembles for target audience classification on Twitter.

PubMed

Lo, Siaw Ling; Chiong, Raymond; Cornforth, David

2015-01-01

The vast amount and diversity of the content shared on social media can pose a challenge for any business wanting to use it to identify potential customers. In this paper, our aim is to investigate the use of both unsupervised and supervised learning methods for target audience classification on Twitter with minimal annotation efforts. Topic domains were automatically discovered from contents shared by followers of an account owner using Twitter Latent Dirichlet Allocation (LDA). A Support Vector Machine (SVM) ensemble was then trained using contents from different account owners of the various topic domains identified by Twitter LDA. Experimental results show that the methods presented are able to successfully identify a target audience with high accuracy. In addition, we show that using a statistical inference approach such as bootstrapping in over-sampling, instead of using random sampling, to construct training datasets can achieve a better classifier in an SVM ensemble. We conclude that such an ensemble system can take advantage of data diversity, which enables real-world applications for differentiating prospective customers from the general audience, leading to business advantage in the crowded social media space.

Classification of prostate cancer grade using temporal ultrasound: in vivo feasibility study

NASA Astrophysics Data System (ADS)

Ghavidel, Sahar; Imani, Farhad; Khallaghi, Siavash; Gibson, Eli; Khojaste, Amir; Gaed, Mena; Moussa, Madeleine; Gomez, Jose A.; Siemens, D. Robert; Leveridge, Michael; Chang, Silvia; Fenster, Aaron; Ward, Aaron D.; Abolmaesumi, Purang; Mousavi, Parvin

2016-03-01

Temporal ultrasound has been shown to have high classification accuracy in differentiating cancer from benign tissue. In this paper, we extend the temporal ultrasound method to classify lower grade Prostate Cancer (PCa) from all other grades. We use a group of nine patients with mostly lower grade PCa, where cancerous regions are also limited. A critical challenge is to train a classifier with limited aggressive cancerous tissue compared to low grade cancerous tissue. To resolve the problem of imbalanced data, we use Synthetic Minority Oversampling Technique (SMOTE) to generate synthetic samples for the minority class. We calculate spectral features of temporal ultrasound data and perform feature selection using Random Forests. In leave-one-patient-out cross-validation strategy, an area under receiver operating characteristic curve (AUC) of 0.74 is achieved with overall sensitivity and specificity of 70%. Using an unsupervised learning approach prior to proposed method improves sensitivity and AUC to 80% and 0.79. This work represents promising results to classify lower and higher grade PCa with limited cancerous training samples, using temporal ultrasound.

Histopathological Image Classification using Discriminative Feature-oriented Dictionary Learning

PubMed Central

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

2016-01-01

In histopathological image analysis, feature extraction for classification is a challenging task due to the diversity of histology features suitable for each problem as well as presence of rich geometrical structures. In this paper, we propose an automatic feature discovery framework via learning class-specific dictionaries and present a low-complexity method for classification and disease grading in histopathology. Essentially, our Discriminative Feature-oriented Dictionary Learning (DFDL) method learns class-specific dictionaries such that under a sparsity constraint, the learned dictionaries allow representing a new image sample parsimoniously via the dictionary corresponding to the class identity of the sample. At the same time, the dictionary is designed to be poorly capable of representing samples from other classes. Experiments on three challenging real-world image databases: 1) histopathological images of intraductal breast lesions, 2) mammalian kidney, lung and spleen images provided by the Animal Diagnostics Lab (ADL) at Pennsylvania State University, and 3) brain tumor images from The Cancer Genome Atlas (TCGA) database, reveal the merits of our proposal over state-of-the-art alternatives. Moreover, we demonstrate that DFDL exhibits a more graceful decay in classification accuracy against the number of training images which is highly desirable in practice where generous training is often not available. PMID:26513781

A Comparison of Match-to-Sample and Respondent-Type Training of Equivalence Classes

ERIC Educational Resources Information Center

Clayton, Michael C.; Hayes, Linda J.

2004-01-01

Throughout the 25-year history of research on stimulus equivalence, one feature of the training procedure has remained constant, namely, the requirement of operant responding during the training procedures. The present investigation compared the traditional match-to-sample (MTS) training with a more recent respondent-type (ReT) procedure. Another…

Modeling landslide susceptibility in data-scarce environments using optimized data mining and statistical methods

NASA Astrophysics Data System (ADS)

Lee, Jung-Hyun; Sameen, Maher Ibrahim; Pradhan, Biswajeet; Park, Hyuck-Jin

2018-02-01

This study evaluated the generalizability of five models to select a suitable approach for landslide susceptibility modeling in data-scarce environments. In total, 418 landslide inventories and 18 landslide conditioning factors were analyzed. Multicollinearity and factor optimization were investigated before data modeling, and two experiments were then conducted. In each experiment, five susceptibility maps were produced based on support vector machine (SVM), random forest (RF), weight-of-evidence (WoE), ridge regression (Rid_R), and robust regression (RR) models. The highest accuracy (AUC = 0.85) was achieved with the SVM model when either the full or limited landslide inventories were used. Furthermore, the RF and WoE models were severely affected when less landslide samples were used for training. The other models were affected slightly when the training samples were limited.

Effect of Exercise Program Speed, Agility, and Quickness (SAQ) in Improving Speed, Agility, and Acceleration

NASA Astrophysics Data System (ADS)

Azmi, K.; Kusnanik, N. W.

2018-01-01

This study aimed to analyze the effect of speed, agility and quickness training program to increase in speed, agility and acceleration. This study was conducted at 26 soccer players and divided into 2 groups with 13 players each group. Group 1 was given SAQ training program, and Group 2 conventional training program for 8 weeks. This study used a quantitative approach with quasi-experimental method. The design of this study used a matching-only design. Data was collected by testing 30-meter sprint (speed), agility t-test (agility), and run 10 meters (acceleration) during the pretest and posttest. Furthermore, the data was analyzed using paired sample t-test and independent t-test. The results showed: that there was a significant effect of speed, agility and quickness training program in improving in speed, agility and acceleration. In summary, it can be concluded that the speed, agility and quickness training program can improve the speed, agility and acceleration of the soccer players.

The 2-degree Field Lensing Survey: photometric redshifts from a large new training sample to r < 19.5

NASA Astrophysics Data System (ADS)

Wolf, C.; Johnson, A. S.; Bilicki, M.; Blake, C.; Amon, A.; Erben, T.; Glazebrook, K.; Heymans, C.; Hildebrandt, H.; Joudaki, S.; Klaes, D.; Kuijken, K.; Lidman, C.; Marin, F.; Parkinson, D.; Poole, G.

2017-04-01

We present a new training set for estimating empirical photometric redshifts of galaxies, which was created as part of the 2-degree Field Lensing Survey project. This training set is located in a ˜700 deg2 area of the Kilo-Degree-Survey South field and is randomly selected and nearly complete at r < 19.5. We investigate the photometric redshift performance obtained with ugriz photometry from VST-ATLAS and W1/W2 from WISE, based on several empirical and template methods. The best redshift errors are obtained with kernel-density estimation (KDE), as are the lowest biases, which are consistent with zero within statistical noise. The 68th percentiles of the redshift scatter for magnitude-limited samples at r < (15.5, 17.5, 19.5) are (0.014, 0.017, 0.028). In this magnitude range, there are no known ambiguities in the colour-redshift map, consistent with a small rate of redshift outliers. In the fainter regime, the KDE method produces p(z) estimates per galaxy that represent unbiased and accurate redshift frequency expectations. The p(z) sum over any subsample is consistent with the true redshift frequency plus Poisson noise. Further improvements in redshift precision at r < 20 would mostly be expected from filter sets with narrower passbands to increase the sensitivity of colours to small changes in redshift.

Comparing Pattern Recognition Feature Sets for Sorting Triples in the FIRST Database

NASA Astrophysics Data System (ADS)

Proctor, D. D.

2006-07-01

Pattern recognition techniques have been used with increasing success for coping with the tremendous amounts of data being generated by automated surveys. Usually this process involves construction of training sets, the typical examples of data with known classifications. Given a feature set, along with the training set, statistical methods can be employed to generate a classifier. The classifier is then applied to process the remaining data. Feature set selection, however, is still an issue. This paper presents techniques developed for accommodating data for which a substantive portion of the training set cannot be classified unambiguously, a typical case for low-resolution data. Significance tests on the sort-ordered, sample-size-normalized vote distribution of an ensemble of decision trees is introduced as a method of evaluating relative quality of feature sets. The technique is applied to comparing feature sets for sorting a particular radio galaxy morphology, bent-doubles, from the Faint Images of the Radio Sky at Twenty Centimeters (FIRST) database. Also examined are alternative functional forms for feature sets. Associated standard deviations provide the means to evaluate the effect of the number of folds, the number of classifiers per fold, and the sample size on the resulting classifications. The technique also may be applied to situations for which, although accurate classifications are available, the feature set is clearly inadequate, but is desired nonetheless to make the best of available information.

Contraceptive counseling among pediatric primary care providers in Western Pennsylvania: A survey-based study.

PubMed

Papas, Beth Ann; Shaikh, Nader; Watson, Katherine; Sucato, Gina S

2017-01-01

Data suggest that adolescents in the United States receive inadequate contraceptive counseling. This study sought to determine factors affecting pediatricians' discussion of contraception with adolescent patients, with a specific focus on long-acting reversible contraception-implantable contraception and intrauterine devices. A cross-sectional survey was sent via email to a convenience sample of pediatric residents and pediatric primary care providers in Western Pennsylvania. Self-reported contraceptive counseling and prescribing practices in response to clinical vignettes were assessed. Of potential participants (287), 88 (31%) responded. Younger providers and providers who had received contraceptive training were significantly more likely to discuss long-acting reversible contraception methods. Discussion of contraceptive methods also varied by both the age and the sexual history of the patient. Variation in contraceptive counseling potentially results in missed opportunities to counsel about and provide the most effective contraceptive methods. More uniform, universal provider training might alleviate some of these inconsistencies.

Decision tree methods: applications for classification and prediction.

PubMed

Song, Yan-Yan; Lu, Ying

2015-04-25

Decision tree methodology is a commonly used data mining method for establishing classification systems based on multiple covariates or for developing prediction algorithms for a target variable. This method classifies a population into branch-like segments that construct an inverted tree with a root node, internal nodes, and leaf nodes. The algorithm is non-parametric and can efficiently deal with large, complicated datasets without imposing a complicated parametric structure. When the sample size is large enough, study data can be divided into training and validation datasets. Using the training dataset to build a decision tree model and a validation dataset to decide on the appropriate tree size needed to achieve the optimal final model. This paper introduces frequently used algorithms used to develop decision trees (including CART, C4.5, CHAID, and QUEST) and describes the SPSS and SAS programs that can be used to visualize tree structure.

Spectral-spatial classification of hyperspectral image using three-dimensional convolution network

NASA Astrophysics Data System (ADS)

Liu, Bing; Yu, Xuchu; Zhang, Pengqiang; Tan, Xiong; Wang, Ruirui; Zhi, Lu

2018-01-01

Recently, hyperspectral image (HSI) classification has become a focus of research. However, the complex structure of an HSI makes feature extraction difficult to achieve. Most current methods build classifiers based on complex handcrafted features computed from the raw inputs. The design of an improved 3-D convolutional neural network (3D-CNN) model for HSI classification is described. This model extracts features from both the spectral and spatial dimensions through the application of 3-D convolutions, thereby capturing the important discrimination information encoded in multiple adjacent bands. The designed model views the HSI cube data altogether without relying on any pre- or postprocessing. In addition, the model is trained in an end-to-end fashion without any handcrafted features. The designed model was applied to three widely used HSI datasets. The experimental results demonstrate that the 3D-CNN-based method outperforms conventional methods even with limited labeled training samples.

Wavefront sensing and control aspects in a high energy laser optical train

NASA Astrophysics Data System (ADS)

Bartosewcz, M.; Bareket, N.

1981-01-01

In this paper we review the major elements of a HEL (high energy laser) wavefront sensing and control system with particular emphasis on experimental demonstrations and hardware components developed at Lockheed Missiles & Space Company, Inc. The review concentrates on three important elements of wavefront control: wavefront sampling, wavefront sensing and active mirrors. Methods of wavefront sampling by diffraction gratings are described. Some new developments in wavefront sensing are explored. Hardware development efforts of fast steering mirrors and edge controlled deformable mirrors are described.

Effect of short-term research training programs on medical students' attitudes toward aging.

PubMed

Jeste, Dilip V; Avanzino, Julie; Depp, Colin A; Gawronska, Maja; Tu, Xin; Sewell, Daniel D; Huege, Steven F

2018-01-01

Strategies to build a larger workforce of physicians dedicated to research on aging are needed. One method to address this shortage of physician scientists in geriatrics is short-term training in aging research for early-stage medical students. The authors examined the effects of two summer research training programs, funded by the National Institutes of Health, on medical students' attitudes toward aging, using the Carolina Opinions on Care of Older Adults (COCOA). The programs combined mentored research, didactics, and some clinical exposure. In a sample of 134 participants, COCOA scores improved significantly after completion of the research training program. There was a significant interaction of gender, such that female students had higher baseline scores than males, but this gender difference in COCOA scores was attenuated following the program. Four of the six COCOA subscales showed significant improvement from baseline: early interest in geriatrics, empathy/compassion, attitudes toward geriatrics careers, and ageism.

Methods employed for chest radiograph interpretation education for radiographers: A systematic review of the literature.

PubMed

McLaughlin, L; McConnell, J; McFadden, S; Bond, R; Hughes, C

2017-11-01

This systematic review aimed to determine the strength of evidence available in the literature on the effect of training to develop the skills required by radiographers to interpret plain radiography chest images. Thirteen articles feature within the review. Sample size varied from one reporting radiographer to 148 radiography students/experienced radiographers. The quality of the articles achieved a mean score of 7.5/10, indicating the evidence is strong and the quality of studies in this field is high. Investigative approaches included audit of participants' performance in clinical practice post formal training, evaluation of informal training and the impact of short feedback sessions on performance. All studies demonstrated positive attributions on user performance. Using a combination of training techniques can help maximise learning and accommodate those with different preferred learning types. Copyright © 2017 The College of Radiographers. Published by Elsevier Ltd. All rights reserved.

Macroscopic inhomogeneous deformation behavior arising in single crystal Ni-Mn-Ga foils under tensile loading

NASA Astrophysics Data System (ADS)

Murasawa, Go; Yeduru, Srinivasa R.; Kohl, Manfred

2016-12-01

This study investigated macroscopic inhomogeneous deformation occurring in single-crystal Ni-Mn-Ga foils under uniaxial tensile loading. Two types of single-crystal Ni-Mn-Ga foil samples were examined as-received and after thermo-mechanical training. Local strain and the strain field were measured under tensile loading using laser speckle and digital image correlation. The as-received sample showed a strongly inhomogeneous strain field with intermittence under progressive deformation, but the trained sample result showed strain field homogeneity throughout the specimen surface. The as-received sample is a mainly polycrystalline-like state composed of the domain structure. The sample contains many domain boundaries and large domain structures in the body. Its structure would cause large local strain band nucleation with intermittence. However, the trained one is an ideal single-crystalline state with a transformation preferential orientation of variants after almost all domain boundary and large domain structures vanish during thermo-mechanical training. As a result, macroscopic homogeneous deformation occurs on the trained sample surface during deformation.

The Effect of Multiprofessional Simulation-Based Obstetric Team Training on Patient-Reported Quality of Care: A Pilot Study.

PubMed

Truijens, Sophie E M; Banga, Franyke R; Fransen, Annemarie F; Pop, Victor J M; van Runnard Heimel, Pieter J; Oei, S Guid

2015-08-01

This study aimed to explore whether multiprofessional simulation-based obstetric team training improves patient-reported quality of care during pregnancy and childbirth. Multiprofessional teams from a large obstetric collaborative network in the Netherlands were trained in teamwork skills using the principles of crew resource management. Patient-reported quality of care was measured with the validated Pregnancy and Childbirth Questionnaire (PCQ) at 6 weeks postpartum. Before the training, 76 postpartum women (sample I) completed the questionnaire 6 weeks postpartum. Three months after the training, another sample of 68 postpartum women (sample II) completed the questionnaire. In sample II (after the training), the mean (SD) score of 108.9 (10.9) on the PCQ questionnaire was significantly higher than the score of 103.5 (11.6) in sample I (before training) (t = 2.75, P = 0.007). The effect size of the increase in PCQ total score was 0.5. Moreover, the subscales "personal treatment during pregnancy" and "educational information" showed a significant increase after the team training (P < 0.001). Items with the largest increase in mean scores included communication between health care professionals, clear leadership, involvement in planning, and better provision of information. Despite the methodological restrictions of a pilot study, the preliminary results indicate that multiprofessional simulation-based obstetric team training seems to improve patient-reported quality of care. The possibility that this improvement relates to the training is supported by the fact that the items with the largest increase are about the principles of crew resource management, used in the training.

Classification without labels: learning from mixed samples in high energy physics

NASA Astrophysics Data System (ADS)

Metodiev, Eric M.; Nachman, Benjamin; Thaler, Jesse

2017-10-01

Modern machine learning techniques can be used to construct powerful models for difficult collider physics problems. In many applications, however, these models are trained on imperfect simulations due to a lack of truth-level information in the data, which risks the model learning artifacts of the simulation. In this paper, we introduce the paradigm of classification without labels (CWoLa) in which a classifier is trained to distinguish statistical mixtures of classes, which are common in collider physics. Crucially, neither individual labels nor class proportions are required, yet we prove that the optimal classifier in the CWoLa paradigm is also the optimal classifier in the traditional fully-supervised case where all label information is available. After demonstrating the power of this method in an analytical toy example, we consider a realistic benchmark for collider physics: distinguishing quark- versus gluon-initiated jets using mixed quark/gluon training samples. More generally, CWoLa can be applied to any classification problem where labels or class proportions are unknown or simulations are unreliable, but statistical mixtures of the classes are available.

Classification without labels: learning from mixed samples in high energy physics

DOE PAGES

Metodiev, Eric M.; Nachman, Benjamin; Thaler, Jesse

2017-10-25

Modern machine learning techniques can be used to construct powerful models for difficult collider physics problems. In many applications, however, these models are trained on imperfect simulations due to a lack of truth-level information in the data, which risks the model learning artifacts of the simulation. In this paper, we introduce the paradigm of classification without labels (CWoLa) in which a classifier is trained to distinguish statistical mixtures of classes, which are common in collider physics. Crucially, neither individual labels nor class proportions are required, yet we prove that the optimal classifier in the CWoLa paradigm is also the optimalmore » classifier in the traditional fully-supervised case where all label information is available. After demonstrating the power of this method in an analytical toy example, we consider a realistic benchmark for collider physics: distinguishing quark- versus gluon-initiated jets using mixed quark/gluon training samples. More generally, CWoLa can be applied to any classification problem where labels or class proportions are unknown or simulations are unreliable, but statistical mixtures of the classes are available.« less

Self-Paced Prioritized Curriculum Learning With Coverage Penalty in Deep Reinforcement Learning.

PubMed

Ren, Zhipeng; Dong, Daoyi; Li, Huaxiong; Chen, Chunlin; Zhipeng Ren; Daoyi Dong; Huaxiong Li; Chunlin Chen; Dong, Daoyi; Li, Huaxiong; Chen, Chunlin; Ren, Zhipeng

2018-06-01

In this paper, a new training paradigm is proposed for deep reinforcement learning using self-paced prioritized curriculum learning with coverage penalty. The proposed deep curriculum reinforcement learning (DCRL) takes the most advantage of experience replay by adaptively selecting appropriate transitions from replay memory based on the complexity of each transition. The criteria of complexity in DCRL consist of self-paced priority as well as coverage penalty. The self-paced priority reflects the relationship between the temporal-difference error and the difficulty of the current curriculum for sample efficiency. The coverage penalty is taken into account for sample diversity. With comparison to deep Q network (DQN) and prioritized experience replay (PER) methods, the DCRL algorithm is evaluated on Atari 2600 games, and the experimental results show that DCRL outperforms DQN and PER on most of these games. More results further show that the proposed curriculum training paradigm of DCRL is also applicable and effective for other memory-based deep reinforcement learning approaches, such as double DQN and dueling network. All the experimental results demonstrate that DCRL can achieve improved training efficiency and robustness for deep reinforcement learning.

Classification without labels: learning from mixed samples in high energy physics

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

Metodiev, Eric M.; Nachman, Benjamin; Thaler, Jesse

Modern machine learning techniques can be used to construct powerful models for difficult collider physics problems. In many applications, however, these models are trained on imperfect simulations due to a lack of truth-level information in the data, which risks the model learning artifacts of the simulation. In this paper, we introduce the paradigm of classification without labels (CWoLa) in which a classifier is trained to distinguish statistical mixtures of classes, which are common in collider physics. Crucially, neither individual labels nor class proportions are required, yet we prove that the optimal classifier in the CWoLa paradigm is also the optimalmore » classifier in the traditional fully-supervised case where all label information is available. After demonstrating the power of this method in an analytical toy example, we consider a realistic benchmark for collider physics: distinguishing quark- versus gluon-initiated jets using mixed quark/gluon training samples. More generally, CWoLa can be applied to any classification problem where labels or class proportions are unknown or simulations are unreliable, but statistical mixtures of the classes are available.« less

Integrated Design of Basic Training, Practicum and End-of-Course Assignment Modules in the Teacher Training Degree: Perception of University Teachers, Students, and School Teachers

NASA Astrophysics Data System (ADS)

Torremorell, Maria Carme Boqué; de Nicolás, Montserrat Alguacil; Valls, Mercè Pañellas

Teacher training at the Blanquerna Faculty of Psychology and Educational and Sports Sciences (FPCEE), in Barcelona, has a long pedagogical tradition based on teaching innovation. Its educational style is characterised by methods focused on the students' involvement and on close collaboration with teaching practice centres. Within a core subject in the Teacher Training diploma course, students were asked to assess different methodological proposals aimed at promoting the development of their personal, social, and professional competences. In the assessment surveys, from a sample of 145 students, scores for variables very satisfactory or satisfactory ranged from 95.8 % to 83.4 % for the entire set of methodological actions under analysis. Data obtained in this first research phase were very useful to design basic training modules for the new Teacher Training Degree. In the second phase (in process), active teachers are asked for their perception on the orientation of the practicum, its connection with the end-of-course assignment, and the in-service student's incidence on innovation processes at school.

[User friendliness of computer-based cognitive training for psychogeriatric patients with mild to moderate cognitive impairments].

PubMed

van der Ploeg, Eva S; Hoorweg, Angela; van der Lee, Jacqueline

2016-04-01

Cognitive impairment associated with dementia is characterized by a continuous decline. Cognitive training is a method to train specific brain functions such as memory and attention to prevent or slow down cognitive decline. A small number of studies has shown that cognitive training on a computer has a positive effect on both cognition and mood in people with cognitive impairment. This pilot study tested if serious games could be integrated in a psychogeriatric rehabilitation center. Fourteen psychogeriatric patients participated twice weekly in cognitive training sessions on a computer. Both the participants and the facilitator reported positive interactions and outcomes. However, after five weeks only half of the sample still participated in the training. This was partly because of patient turn-over as well as incorporating this new task in the facilitators' daily work. Fear of failure, physical limitations and rapidly decreasing cognitive function led to drop out according to the facilitator. The engagement of patients in the games and the role of the facilitator seemed essential for success, especially monitoring (and adjusting) the difficulty level of the program for every individual participant.

Interpretation of correlations in clinical research.

PubMed

Hung, Man; Bounsanga, Jerry; Voss, Maren Wright

2017-11-01

Critically analyzing research is a key skill in evidence-based practice and requires knowledge of research methods, results interpretation, and applications, all of which rely on a foundation based in statistics. Evidence-based practice makes high demands on trained medical professionals to interpret an ever-expanding array of research evidence. As clinical training emphasizes medical care rather than statistics, it is useful to review the basics of statistical methods and what they mean for interpreting clinical studies. We reviewed the basic concepts of correlational associations, violations of normality, unobserved variable bias, sample size, and alpha inflation. The foundations of causal inference were discussed and sound statistical analyses were examined. We discuss four ways in which correlational analysis is misused, including causal inference overreach, over-reliance on significance, alpha inflation, and sample size bias. Recent published studies in the medical field provide evidence of causal assertion overreach drawn from correlational findings. The findings present a primer on the assumptions and nature of correlational methods of analysis and urge clinicians to exercise appropriate caution as they critically analyze the evidence before them and evaluate evidence that supports practice. Critically analyzing new evidence requires statistical knowledge in addition to clinical knowledge. Studies can overstate relationships, expressing causal assertions when only correlational evidence is available. Failure to account for the effect of sample size in the analyses tends to overstate the importance of predictive variables. It is important not to overemphasize the statistical significance without consideration of effect size and whether differences could be considered clinically meaningful.

Reconstruction of three-dimensional porous media using generative adversarial neural networks

NASA Astrophysics Data System (ADS)

Mosser, Lukas; Dubrule, Olivier; Blunt, Martin J.

2017-10-01

To evaluate the variability of multiphase flow properties of porous media at the pore scale, it is necessary to acquire a number of representative samples of the void-solid structure. While modern x-ray computer tomography has made it possible to extract three-dimensional images of the pore space, assessment of the variability in the inherent material properties is often experimentally not feasible. We present a method to reconstruct the solid-void structure of porous media by applying a generative neural network that allows an implicit description of the probability distribution represented by three-dimensional image data sets. We show, by using an adversarial learning approach for neural networks, that this method of unsupervised learning is able to generate representative samples of porous media that honor their statistics. We successfully compare measures of pore morphology, such as the Euler characteristic, two-point statistics, and directional single-phase permeability of synthetic realizations with the calculated properties of a bead pack, Berea sandstone, and Ketton limestone. Results show that generative adversarial networks can be used to reconstruct high-resolution three-dimensional images of porous media at different scales that are representative of the morphology of the images used to train the neural network. The fully convolutional nature of the trained neural network allows the generation of large samples while maintaining computational efficiency. Compared to classical stochastic methods of image reconstruction, the implicit representation of the learned data distribution can be stored and reused to generate multiple realizations of the pore structure very rapidly.

Automated novelty detection in the WISE survey with one-class support vector machines

NASA Astrophysics Data System (ADS)

Solarz, A.; Bilicki, M.; Gromadzki, M.; Pollo, A.; Durkalec, A.; Wypych, M.

2017-10-01

Wide-angle photometric surveys of previously uncharted sky areas or wavelength regimes will always bring in unexpected sources - novelties or even anomalies - whose existence and properties cannot be easily predicted from earlier observations. Such objects can be efficiently located with novelty detection algorithms. Here we present an application of such a method, called one-class support vector machines (OCSVM), to search for anomalous patterns among sources preselected from the mid-infrared AllWISE catalogue covering the whole sky. To create a model of expected data we train the algorithm on a set of objects with spectroscopic identifications from the SDSS DR13 database, present also in AllWISE. The OCSVM method detects as anomalous those sources whose patterns - WISE photometric measurements in this case - are inconsistent with the model. Among the detected anomalies we find artefacts, such as objects with spurious photometry due to blending, but more importantly also real sources of genuine astrophysical interest. Among the latter, OCSVM has identified a sample of heavily reddened AGN/quasar candidates distributed uniformly over the sky and in a large part absent from other WISE-based AGN catalogues. It also allowed us to find a specific group of sources of mixed types, mostly stars and compact galaxies. By combining the semi-supervised OCSVM algorithm with standard classification methods it will be possible to improve the latter by accounting for sources which are not present in the training sample, but are otherwise well-represented in the target set. Anomaly detection adds flexibility to automated source separation procedures and helps verify the reliability and representativeness of the training samples. It should be thus considered as an essential step in supervised classification schemes to ensure completeness and purity of produced catalogues. The catalogues of outlier data are only available at the CDS via anonymous ftp to http://cdsarc.u-strasbg.fr (http://130.79.128.5) or via http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/A+A/606/A39

Selective Transfer Machine for Personalized Facial Expression Analysis

PubMed Central

Chu, Wen-Sheng; De la Torre, Fernando; Cohn, Jeffrey F.

2017-01-01

Automatic facial action unit (AU) and expression detection from videos is a long-standing problem. The problem is challenging in part because classifiers must generalize to previously unknown subjects that differ markedly in behavior and facial morphology (e.g., heavy versus delicate brows, smooth versus deeply etched wrinkles) from those on which the classifiers are trained. While some progress has been achieved through improvements in choices of features and classifiers, the challenge occasioned by individual differences among people remains. Person-specific classifiers would be a possible solution but for a paucity of training data. Sufficient training data for person-specific classifiers typically is unavailable. This paper addresses the problem of how to personalize a generic classifier without additional labels from the test subject. We propose a transductive learning method, which we refer as a Selective Transfer Machine (STM), to personalize a generic classifier by attenuating person-specific mismatches. STM achieves this effect by simultaneously learning a classifier and re-weighting the training samples that are most relevant to the test subject. We compared STM to both generic classifiers and cross-domain learning methods on four benchmarks: CK+ [44], GEMEP-FERA [67], RU-FACS [4] and GFT [57]. STM outperformed generic classifiers in all. PMID:28113267

Semi-supervised anomaly detection - towards model-independent searches of new physics

NASA Astrophysics Data System (ADS)

Kuusela, Mikael; Vatanen, Tommi; Malmi, Eric; Raiko, Tapani; Aaltonen, Timo; Nagai, Yoshikazu

2012-06-01

Most classification algorithms used in high energy physics fall under the category of supervised machine learning. Such methods require a training set containing both signal and background events and are prone to classification errors should this training data be systematically inaccurate for example due to the assumed MC model. To complement such model-dependent searches, we propose an algorithm based on semi-supervised anomaly detection techniques, which does not require a MC training sample for the signal data. We first model the background using a multivariate Gaussian mixture model. We then search for deviations from this model by fitting to the observations a mixture of the background model and a number of additional Gaussians. This allows us to perform pattern recognition of any anomalous excess over the background. We show by a comparison to neural network classifiers that such an approach is a lot more robust against misspecification of the signal MC than supervised classification. In cases where there is an unexpected signal, a neural network might fail to correctly identify it, while anomaly detection does not suffer from such a limitation. On the other hand, when there are no systematic errors in the training data, both methods perform comparably.

Effects of High Intensity Interval Training on Increasing Explosive Power, Speed, and Agility

NASA Astrophysics Data System (ADS)

Fajrin, F.; Kusnanik, N. W.; Wijono

2018-01-01

High Intensity Interval Training (HIIT) is a type of exercise that combines high-intensity exercise and low intensity exercise in a certain time interval. This type of training is very effective and efficient to improve the physical components. The process of improving athletes achievement related to how the process of improving the physical components, so the selection of a good practice method will be very helpful. This study aims to analyze how is the effects of HIIT on increasing explosive power, speed, and agility. This type of research is quantitative with quasi-experimental methods. The design of this study used the Matching-Only Design, with data analysis using the t-test (paired sample t-test). After being given the treatment for six weeks, the results showed there are significant increasing in explosive power, speed, and agility. HIIT in this study used a form of exercise plyometric as high-intensity exercise and jogging as mild or moderate intensity exercise. Increase was due to the improvement of neuromuscular characteristics that affect the increase in muscle strength and performance. From the data analysis, researchers concluded that, Exercises of High Intensity Interval Training significantly effect on the increase in Power Limbs, speed, and agility.

Polyphonic sonification of electrocardiography signals for diagnosis of cardiac pathologies

NASA Astrophysics Data System (ADS)

Kather, Jakob Nikolas; Hermann, Thomas; Bukschat, Yannick; Kramer, Tilmann; Schad, Lothar R.; Zöllner, Frank Gerrit

2017-03-01

Electrocardiography (ECG) data are multidimensional temporal data with ubiquitous applications in the clinic. Conventionally, these data are presented visually. It is presently unclear to what degree data sonification (auditory display), can enable the detection of clinically relevant cardiac pathologies in ECG data. In this study, we introduce a method for polyphonic sonification of ECG data, whereby different ECG channels are simultaneously represented by sound of different pitch. We retrospectively applied this method to 12 samples from a publicly available ECG database. We and colleagues from our professional environment then analyzed these data in a blinded way. Based on these analyses, we found that the sonification technique can be intuitively understood after a short training session. On average, the correct classification rate for observers trained in cardiology was 78%, compared to 68% and 50% for observers not trained in cardiology or not trained in medicine at all, respectively. These values compare to an expected random guessing performance of 25%. Strikingly, 27% of all observers had a classification accuracy over 90%, indicating that sonification can be very successfully used by talented individuals. These findings can serve as a baseline for potential clinical applications of ECG sonification.

GIS-based support vector machine modeling of earthquake-triggered landslide susceptibility in the Jianjiang River watershed, China

NASA Astrophysics Data System (ADS)

Xu, Chong; Dai, Fuchu; Xu, Xiwei; Lee, Yuan Hsi

2012-04-01

Support vector machine (SVM) modeling is based on statistical learning theory. It involves a training phase with associated input and target output values. In recent years, the method has become increasingly popular. The main purpose of this study is to evaluate the mapping power of SVM modeling in earthquake triggered landslide-susceptibility mapping for a section of the Jianjiang River watershed using a Geographic Information System (GIS) software. The river was affected by the Wenchuan earthquake of May 12, 2008. Visual interpretation of colored aerial photographs of 1-m resolution and extensive field surveys provided a detailed landslide inventory map containing 3147 landslides related to the 2008 Wenchuan earthquake. Elevation, slope angle, slope aspect, distance from seismogenic faults, distance from drainages, and lithology were used as the controlling parameters. For modeling, three groups of positive and negative training samples were used in concert with four different kernel functions. Positive training samples include the centroids of 500 large landslides, those of all 3147 landslides, and 5000 randomly selected points in landslide polygons. Negative training samples include 500, 3147, and 5000 randomly selected points on slopes that remained stable during the Wenchuan earthquake. The four kernel functions are linear, polynomial, radial basis, and sigmoid. In total, 12 cases of landslide susceptibility were mapped. Comparative analyses of landslide-susceptibility probability and area relation curves show that both the polynomial and radial basis functions suitably classified the input data as either landslide positive or negative though the radial basis function was more successful. The 12 generated landslide-susceptibility maps were compared with known landslide centroid locations and landslide polygons to verify the success rate and predictive accuracy of each model. The 12 results were further validated using area-under-curve analysis. Group 3 with 5000 randomly selected points on the landslide polygons, and 5000 randomly selected points along stable slopes gave the best results with a success rate of 79.20% and predictive accuracy of 79.13% under the radial basis function. Of all the results, the sigmoid kernel function was the least skillful when used in concert with the centroid data of all 3147 landslides as positive training samples, and the negative training samples of 3147 randomly selected points in regions of stable slope (success rate = 54.95%; predictive accuracy = 61.85%). This paper also provides suggestions and reference data for selecting appropriate training samples and kernel function types for earthquake triggered landslide-susceptibility mapping using SVM modeling. Predictive landslide-susceptibility maps could be useful in hazard mitigation by helping planners understand the probability of landslides in different regions.

Statistical generation of training sets for measuring NO3(-), NH4(+) and major ions in natural waters using an ion selective electrode array.

PubMed

Mueller, Amy V; Hemond, Harold F

2016-05-18

Knowledge of ionic concentrations in natural waters is essential to understand watershed processes. Inorganic nitrogen, in the form of nitrate and ammonium ions, is a key nutrient as well as a participant in redox, acid-base, and photochemical processes of natural waters, leading to spatiotemporal patterns of ion concentrations at scales as small as meters or hours. Current options for measurement in situ are costly, relying primarily on instruments adapted from laboratory methods (e.g., colorimetric, UV absorption); free-standing and inexpensive ISE sensors for NO3(-) and NH4(+) could be attractive alternatives if interferences from other constituents were overcome. Multi-sensor arrays, coupled with appropriate non-linear signal processing, offer promise in this capacity but have not yet successfully achieved signal separation for NO3(-) and NH4(+)in situ at naturally occurring levels in unprocessed water samples. A novel signal processor, underpinned by an appropriate sensor array, is proposed that overcomes previous limitations by explicitly integrating basic chemical constraints (e.g., charge balance). This work further presents a rationalized process for the development of such in situ instrumentation for NO3(-) and NH4(+), including a statistical-modeling strategy for instrument design, training/calibration, and validation. Statistical analysis reveals that historical concentrations of major ionic constituents in natural waters across New England strongly covary and are multi-modal. This informs the design of a statistically appropriate training set, suggesting that the strong covariance of constituents across environmental samples can be exploited through appropriate signal processing mechanisms to further improve estimates of minor constituents. Two artificial neural network architectures, one expanded to incorporate knowledge of basic chemical constraints, were tested to process outputs of a multi-sensor array, trained using datasets of varying degrees of statistical representativeness to natural water samples. The accuracy of ANN results improves monotonically with the statistical representativeness of the training set (error decreases by ∼5×), while the expanded neural network architecture contributes a further factor of 2-3.5 decrease in error when trained with the most representative sample set. Results using the most statistically accurate set of training samples (which retain environmentally relevant ion concentrations but avoid the potential interference of humic acids) demonstrated accurate, unbiased quantification of nitrate and ammonium at natural environmental levels (±20% down to <10 μM), as well as the major ions Na(+), K(+), Ca(2+), Mg(2+), Cl(-), and SO4(2-), in unprocessed samples. These results show promise for the development of new in situ instrumentation for the support of scientific field work.

Leadership training design, delivery, and implementation: A meta-analysis.

PubMed

Lacerenza, Christina N; Reyes, Denise L; Marlow, Shannon L; Joseph, Dana L; Salas, Eduardo

2017-12-01

Recent estimates suggest that although a majority of funds in organizational training budgets tend to be allocated to leadership training (Ho, 2016; O'Leonard, 2014), only a small minority of organizations believe their leadership training programs are highly effective (Schwartz, Bersin, & Pelster, 2014), calling into question the effectiveness of current leadership development initiatives. To help address this issue, this meta-analysis estimates the extent to which leadership training is effective and identifies the conditions under which these programs are most effective. In doing so, we estimate the effectiveness of leadership training across four criteria (reactions, learning, transfer, and results; Kirkpatrick, 1959) using only employee data and we examine 15 moderators of training design and delivery to determine which elements are associated with the most effective leadership training interventions. Data from 335 independent samples suggest that leadership training is substantially more effective than previously thought, leading to improvements in reactions (δ = .63), learning (δ = .73), transfer (δ = .82), and results (δ = .72), the strength of these effects differs based on various design, delivery, and implementation characteristics. Moderator analyses support the use of needs analysis, feedback, multiple delivery methods (especially practice), spaced training sessions, a location that is on-site, and face-to-face delivery that is not self-administered. Results also suggest that the content of training, attendance policy, and duration influence the effectiveness of the training program. Practical implications for training development and theoretical implications for leadership and training literatures are discussed. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

System and Method for Outlier Detection via Estimating Clusters

NASA Technical Reports Server (NTRS)

Iverson, David J. (Inventor)

2016-01-01

An efficient method and system for real-time or offline analysis of multivariate sensor data for use in anomaly detection, fault detection, and system health monitoring is provided. Models automatically derived from training data, typically nominal system data acquired from sensors in normally operating conditions or from detailed simulations, are used to identify unusual, out of family data samples (outliers) that indicate possible system failure or degradation. Outliers are determined through analyzing a degree of deviation of current system behavior from the models formed from the nominal system data. The deviation of current system behavior is presented as an easy to interpret numerical score along with a measure of the relative contribution of each system parameter to any off-nominal deviation. The techniques described herein may also be used to "clean" the training data.

Parents or School Health Trainers, which of them is Appropriate for Menstrual Health Education?

PubMed Central

Djalalinia, Shirin; Tehrani, Fahimeh Ramezani; Afzali, Hossein Malek; Hejazi, Farzaneh; Peykari, Niloofar

2012-01-01

Objectives: The purpose of this community-based participatory research was to compare different training sources for adolescents’ menstrual health education. Methods: From 15 middle schools in Tehran, through quota random sampling, 1823 female students were selected proportionally and allocated randomly to three groups (parent trained, schools’ health trainers trained, and control). Following a two-year training program, the adolescents’ menstrual health was compared. Results: In the present study, the school health trainers trained group showed a better feeling for menarche, compared to the two other groups (P < 0.001). The need for adolescent health training was emphasized by 82% of the participants; they also believed that the appropriate age for such empowerment courses was about 12 years. In the school health trainers trained group, the offered age was significantly lower than in other groups (P < 0.001). The adolescents trained by the school health trainers had a better practice of habits related to menstrual and hygiene practices, like having a bath during menstruation and the use of sanitary pads or cotton, compared to their counterpart groups (P > 0.05). Conclusion: It is suggested that school-based health training leads to better menstrual health promotion and healthy puberty transition, and school health trainers play a key role in this regard. PMID:23024851

Working memory training in healthy young adults: Support for the null from a randomized comparison to active and passive control groups.

PubMed

Clark, Cameron M; Lawlor-Savage, Linette; Goghari, Vina M

2017-01-01

Training of working memory as a method of increasing working memory capacity and fluid intelligence has received much attention in recent years. This burgeoning field remains highly controversial with empirically-backed disagreements at all levels of evidence, including individual studies, systematic reviews, and even meta-analyses. The current study investigated the effect of a randomized six week online working memory intervention on untrained cognitive abilities in a community-recruited sample of healthy young adults, in relation to both a processing speed training active control condition, as well as a no-contact control condition. Results of traditional null hypothesis significance testing, as well as Bayesian factor analyses, revealed support for the null hypothesis across all cognitive tests administered before and after training. Importantly, all three groups were similar at pre-training for a variety of individual variables purported to moderate transfer of training to fluid intelligence, including personality traits, motivation to train, and expectations of cognitive improvement from training. Because these results are consistent with experimental trials of equal or greater methodological rigor, we suggest that future research re-focus on: 1) other promising interventions known to increase memory performance in healthy young adults, and; 2) examining sub-populations or alternative populations in which working memory training may be efficacious.

Acoustic changes in student actors' voices after 12 months of training.

PubMed

Walzak, Peta; McCabe, Patricia; Madill, Cate; Sheard, Christine

2008-05-01

This study was to evaluate acoustic changes in student actors' voices after 12 months of actor training. The design used was a longitudinal study. Eighteen students enrolled in an Australian tertiary 3-year acting program (nine male and nine female) were assessed at the beginning of their acting course and again 12 months later using a questionnaire, interview, maximum phonation time (MPT), reading, spontaneous speaking, sustained phonation tasks, and a pitch range task. Samples were analyzed for MPT, fundamental frequency across tasks, pitch range for speaking and reading, singing pitch range, noise-to-harmonic ratio, shimmer, and jitter. After training, measures of shimmer significantly increased for both male and female participants. Female participants' pitch range significantly increased after training, with a significantly lower mean frequency for their lowest pitch. The finding of limited or negative changes for some measures indicate that further investigation is required into the long-term effects of actor voice training and which parameters of voicing are most targeted and valued in training. Particular investigation into the relationship between training targets and outcomes could more reliably inform acting programs about changes in teaching methodologies. Further research into the relationship between specific training techniques, physiological changes, and vocal changes may also provide information on implementing more evidence-based training methods.

32 CFR Appendix E to Part 110 - Application of 4-Week Summer Field Training Formula (Sample)

Code of Federal Regulations, 2014 CFR

2014-07-01

... 32 National Defense 1 2014-07-01 2014-07-01 false Application of 4-Week Summer Field Training Formula (Sample) E Appendix E to Part 110 National Defense Department of Defense OFFICE OF THE SECRETARY... Appendix E to Part 110—Application of 4-Week Summer Field Training Formula (Sample) Zone I Zone II Total...

32 CFR Appendix E to Part 110 - Application of 4-Week Summer Field Training Formula (Sample)

Code of Federal Regulations, 2013 CFR

2013-07-01

... 32 National Defense 1 2013-07-01 2013-07-01 false Application of 4-Week Summer Field Training Formula (Sample) E Appendix E to Part 110 National Defense Department of Defense OFFICE OF THE SECRETARY... Appendix E to Part 110—Application of 4-Week Summer Field Training Formula (Sample) Zone I Zone II Total...

32 CFR Appendix E to Part 110 - Application of 4-Week Summer Field Training Formula (Sample)

Code of Federal Regulations, 2012 CFR

2012-07-01

... 32 National Defense 1 2012-07-01 2012-07-01 false Application of 4-Week Summer Field Training Formula (Sample) E Appendix E to Part 110 National Defense Department of Defense OFFICE OF THE SECRETARY... Appendix E to Part 110—Application of 4-Week Summer Field Training Formula (Sample) Zone I Zone II Total...

32 CFR Appendix E to Part 110 - Application of 4-Week Summer Field Training Formula (Sample)

Code of Federal Regulations, 2011 CFR

2011-07-01

... 32 National Defense 1 2011-07-01 2011-07-01 false Application of 4-Week Summer Field Training Formula (Sample) E Appendix E to Part 110 National Defense Department of Defense OFFICE OF THE SECRETARY... Appendix E to Part 110—Application of 4-Week Summer Field Training Formula (Sample) Zone I Zone II Total...

32 CFR Appendix E to Part 110 - Application of 4-Week Summer Field Training Formula (Sample)

Code of Federal Regulations, 2010 CFR

2010-07-01

... Formula (Sample) E Appendix E to Part 110 National Defense Department of Defense OFFICE OF THE SECRETARY... COMMUTATION INSTEAD OF UNIFORMS FOR MEMBERS OF THE SENIOR RESERVE OFFICERS' TRAINING CORPS Pt. 110, App. E Appendix E to Part 110—Application of 4-Week Summer Field Training Formula (Sample) Zone I Zone II Total...

Determination of Minimum Training Sample Size for Microarray-Based Cancer Outcome Prediction–An Empirical Assessment

PubMed Central

Cheng, Ningtao; Wu, Leihong; Cheng, Yiyu

2013-01-01

The promise of microarray technology in providing prediction classifiers for cancer outcome estimation has been confirmed by a number of demonstrable successes. However, the reliability of prediction results relies heavily on the accuracy of statistical parameters involved in classifiers. It cannot be reliably estimated with only a small number of training samples. Therefore, it is of vital importance to determine the minimum number of training samples and to ensure the clinical value of microarrays in cancer outcome prediction. We evaluated the impact of training sample size on model performance extensively based on 3 large-scale cancer microarray datasets provided by the second phase of MicroArray Quality Control project (MAQC-II). An SSNR-based (scale of signal-to-noise ratio) protocol was proposed in this study for minimum training sample size determination. External validation results based on another 3 cancer datasets confirmed that the SSNR-based approach could not only determine the minimum number of training samples efficiently, but also provide a valuable strategy for estimating the underlying performance of classifiers in advance. Once translated into clinical routine applications, the SSNR-based protocol would provide great convenience in microarray-based cancer outcome prediction in improving classifier reliability. PMID:23861920

STAR-GALAXY CLASSIFICATION IN MULTI-BAND OPTICAL IMAGING

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

Fadely, Ross; Willman, Beth; Hogg, David W.

2012-11-20

Ground-based optical surveys such as PanSTARRS, DES, and LSST will produce large catalogs to limiting magnitudes of r {approx}> 24. Star-galaxy separation poses a major challenge to such surveys because galaxies-even very compact galaxies-outnumber halo stars at these depths. We investigate photometric classification techniques on stars and galaxies with intrinsic FWHM <0.2 arcsec. We consider unsupervised spectral energy distribution template fitting and supervised, data-driven support vector machines (SVMs). For template fitting, we use a maximum likelihood (ML) method and a new hierarchical Bayesian (HB) method, which learns the prior distribution of template probabilities from the data. SVM requires training datamore » to classify unknown sources; ML and HB do not. We consider (1) a best-case scenario (SVM{sub best}) where the training data are (unrealistically) a random sampling of the data in both signal-to-noise and demographics and (2) a more realistic scenario where training is done on higher signal-to-noise data (SVM{sub real}) at brighter apparent magnitudes. Testing with COSMOS ugriz data, we find that HB outperforms ML, delivering {approx}80% completeness, with purity of {approx}60%-90% for both stars and galaxies. We find that no algorithm delivers perfect performance and that studies of metal-poor main-sequence turnoff stars may be challenged by poor star-galaxy separation. Using the Receiver Operating Characteristic curve, we find a best-to-worst ranking of SVM{sub best}, HB, ML, and SVM{sub real}. We conclude, therefore, that a well-trained SVM will outperform template-fitting methods. However, a normally trained SVM performs worse. Thus, HB template fitting may prove to be the optimal classification method in future surveys.« less