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.

Play Therapy Training among School Psychology, Social Work, and School Counseling Graduate Training Programs

ERIC Educational Resources Information Center

Pascarella, Christina Bechle

2012-01-01

This study examined play therapy training across the nation among school psychology, social work, and school counseling graduate training programs. It also compared current training to previous training among school psychology and school counseling programs. A random sample of trainers was selected from lists of graduate programs provided by…

Does On-the-Job Training Improve an Employee's Job Performance?

ERIC Educational Resources Information Center

Duff, Juanita

A study examined the link between on-the-job training (OJT) and job performance in a randomly selected sample of 50 skilled maintenance craftpersons employed by the city of Chicago. The sample was identified from the training sheets signed by 160 employees who participated in OJT in a 1-month period. The majority of the employees agreed with…

Appearance-based representative samples refining method for palmprint recognition

NASA Astrophysics Data System (ADS)

Wen, Jiajun; Chen, Yan

2012-07-01

The sparse representation can deal with the lack of sample problem due to utilizing of all the training samples. However, the discrimination ability will degrade when more training samples are used for representation. We propose a novel appearance-based palmprint recognition method. We aim to find a compromise between the discrimination ability and the lack of sample problem so as to obtain a proper representation scheme. Under the assumption that the test sample can be well represented by a linear combination of a certain number of training samples, we first select the representative training samples according to the contributions of the samples. Then we further refine the training samples by an iteration procedure, excluding the training sample with the least contribution to the test sample for each time. Experiments on PolyU multispectral palmprint database and two-dimensional and three-dimensional palmprint database show that the proposed method outperforms the conventional appearance-based palmprint recognition methods. Moreover, we also explore and find out the principle of the usage for the key parameters in the proposed algorithm, which facilitates to obtain high-recognition accuracy.

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.

Generating virtual training samples for sparse representation of face images and face recognition

NASA Astrophysics Data System (ADS)

Du, Yong; Wang, Yu

2016-03-01

There are many challenges in face recognition. In real-world scenes, images of the same face vary with changing illuminations, different expressions and poses, multiform ornaments, or even altered mental status. Limited available training samples cannot convey these possible changes in the training phase sufficiently, and this has become one of the restrictions to improve the face recognition accuracy. In this article, we view the multiplication of two images of the face as a virtual face image to expand the training set and devise a representation-based method to perform face recognition. The generated virtual samples really reflect some possible appearance and pose variations of the face. By multiplying a training sample with another sample from the same subject, we can strengthen the facial contour feature and greatly suppress the noise. Thus, more human essential information is retained. Also, uncertainty of the training data is simultaneously reduced with the increase of the training samples, which is beneficial for the training phase. The devised representation-based classifier uses both the original and new generated samples to perform the classification. In the classification phase, we first determine K nearest training samples for the current test sample by calculating the Euclidean distances between the test sample and training samples. Then, a linear combination of these selected training samples is used to represent the test sample, and the representation result is used to classify the test sample. The experimental results show that the proposed method outperforms some state-of-the-art face recognition methods.

Manifold Regularized Experimental Design for Active Learning.

PubMed

Zhang, Lining; Shum, Hubert P H; Shao, Ling

2016-12-02

Various machine learning and data mining tasks in classification require abundant data samples to be labeled for training. Conventional active learning methods aim at labeling the most informative samples for alleviating the labor of the user. Many previous studies in active learning select one sample after another in a greedy manner. However, this is not very effective because the classification models has to be retrained for each newly labeled sample. Moreover, many popular active learning approaches utilize the most uncertain samples by leveraging the classification hyperplane of the classifier, which is not appropriate since the classification hyperplane is inaccurate when the training data are small-sized. The problem of insufficient training data in real-world systems limits the potential applications of these approaches. This paper presents a novel method of active learning called manifold regularized experimental design (MRED), which can label multiple informative samples at one time for training. In addition, MRED gives an explicit geometric explanation for the selected samples to be labeled by the user. Different from existing active learning methods, our method avoids the intrinsic problems caused by insufficiently labeled samples in real-world applications. Various experiments on synthetic datasets, the Yale face database and the Corel image database have been carried out to show how MRED outperforms existing methods.

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.

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.

Multicultural Training Experiences as Predictors of Multicultural Competencies: Students' Perspectives

ERIC Educational Resources Information Center

Dickson, Ginger L.; Jepsen, David A.

2007-01-01

The authors surveyed a national sample of master's-level counseling students regarding their multicultural training experiences and their multicultural counseling competencies. A series of hierarchical regression models tested the prediction of inventoried competencies from measures of selected training experiences: (a) program cultural ambience…

Brief Report: The Effect of Delayed Matching to Sample on Stimulus Over-Selectivity

ERIC Educational Resources Information Center

Reed, Phil

2012-01-01

Stimulus over-selectivity occurs when one aspect of the environment controls behavior at the expense of other equally salient aspects. Participants were trained on a match-to-sample (MTS) discrimination task. Levels of over-selectivity in a group of children (4-18 years) with Autism Spectrum Disorders (ASD) were compared with a mental-aged matched…

Re-Emergence of Under-Selected Stimuli, after the Extinction of Over-Selected Stimuli in an Automated Match to Samples Procedure

ERIC Educational Resources Information Center

Broomfield, Laura; McHugh, Louise; Reed, Phil

2008-01-01

Stimulus over-selectivity occurs when one of potentially many aspects of the environment comes to control behaviour. In two experiments, adults with no developmental disabilities, were trained and tested in an automated match to samples (MTS) paradigm. In Experiment 1, participants completed two conditions, in one of which the over-selected…

Distribution-Preserving Stratified Sampling for Learning Problems.

PubMed

Cervellera, Cristiano; Maccio, Danilo

2017-06-09

The need for extracting a small sample from a large amount of real data, possibly streaming, arises routinely in learning problems, e.g., for storage, to cope with computational limitations, obtain good training/test/validation sets, and select minibatches for stochastic gradient neural network training. Unless we have reasons to select the samples in an active way dictated by the specific task and/or model at hand, it is important that the distribution of the selected points is as similar as possible to the original data. This is obvious for unsupervised learning problems, where the goal is to gain insights on the distribution of the data, but it is also relevant for supervised problems, where the theory explains how the training set distribution influences the generalization error. In this paper, we analyze the technique of stratified sampling from the point of view of distances between probabilities. This allows us to introduce an algorithm, based on recursive binary partition of the input space, aimed at obtaining samples that are distributed as much as possible as the original data. A theoretical analysis is proposed, proving the (greedy) optimality of the procedure together with explicit error bounds. An adaptive version of the algorithm is also introduced to cope with streaming data. Simulation tests on various data sets and different learning tasks are also provided.

Multi-color space threshold segmentation and self-learning k-NN algorithm for surge test EUT status identification

NASA Astrophysics Data System (ADS)

Huang, Jian; Liu, Gui-xiong

2016-09-01

The identification of targets varies in different surge tests. A multi-color space threshold segmentation and self-learning k-nearest neighbor algorithm ( k-NN) for equipment under test status identification was proposed after using feature matching to identify equipment status had to train new patterns every time before testing. First, color space (L*a*b*, hue saturation lightness (HSL), hue saturation value (HSV)) to segment was selected according to the high luminance points ratio and white luminance points ratio of the image. Second, the unknown class sample S r was classified by the k-NN algorithm with training set T z according to the feature vector, which was formed from number of pixels, eccentricity ratio, compactness ratio, and Euler's numbers. Last, while the classification confidence coefficient equaled k, made S r as one sample of pre-training set T z '. The training set T z increased to T z+1 by T z ' if T z ' was saturated. In nine series of illuminant, indicator light, screen, and disturbances samples (a total of 21600 frames), the algorithm had a 98.65%identification accuracy, also selected five groups of samples to enlarge the training set from T 0 to T 5 by itself.

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.

[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.

Skills Acquisition in Plantain Flour Processing Enterprises: A Validation of Training Modules for Senior Secondary Schools

ERIC Educational Resources Information Center

Udofia, Nsikak-Abasi; Nlebem, Bernard S.

2013-01-01

This study was to validate training modules that can help provide requisite skills for Senior Secondary school students in plantain flour processing enterprises for self-employment and to enable them pass their examination. The study covered Rivers State. Purposive sampling technique was used to select a sample size of 205. Two sets of structured…

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.

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.

A Fast Algorithm of Convex Hull Vertices Selection for Online Classification.

PubMed

Ding, Shuguang; Nie, Xiangli; Qiao, Hong; Zhang, Bo

2018-04-01

Reducing samples through convex hull vertices selection (CHVS) within each class is an important and effective method for online classification problems, since the classifier can be trained rapidly with the selected samples. However, the process of CHVS is NP-hard. In this paper, we propose a fast algorithm to select the convex hull vertices, based on the convex hull decomposition and the property of projection. In the proposed algorithm, the quadratic minimization problem of computing the distance between a point and a convex hull is converted into a linear equation problem with a low computational complexity. When the data dimension is high, an approximate, instead of exact, convex hull is allowed to be selected by setting an appropriate termination condition in order to delete more nonimportant samples. In addition, the impact of outliers is also considered, and the proposed algorithm is improved by deleting the outliers in the initial procedure. Furthermore, a dimension convention technique via the kernel trick is used to deal with nonlinearly separable problems. An upper bound is theoretically proved for the difference between the support vector machines based on the approximate convex hull vertices selected and all the training samples. Experimental results on both synthetic and real data sets show the effectiveness and validity of the proposed algorithm.

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.

Multicultural Training in Doctoral School Psychology Programs: In Search of the Model Program?

ERIC Educational Resources Information Center

Kearns, Tori; Ford, Laurie; Brown, Kimberly

The multicultural training (MCT) of APA-accredited School Psychology programs was studied. The sample included faculty and students from five programs nominated for strong MCT and five comparison programs randomly selected from the list of remaining APA-accredited programs. Program training was evaluated using a survey based on APA guidelines for…

Stimulus Equivalence, Generalization, and Contextual Stimulus Control in Verbal Classes

PubMed Central

Sigurðardóttir, Zuilma Gabriela; Mackay, Harry A; Green, Gina

2012-01-01

Stimulus generalization and contextual control affect the development of equivalence classes. Experiment 1 demonstrated primary stimulus generalization from the members of trained equivalence classes. Adults were taught to match six spoken Icelandic nouns and corresponding printed words and pictures to one another in computerized three-choice matching-to-sample tasks. Tests confirmed that six equivalence classes had formed. Without further training, plural forms of the stimuli were presented in tests for all matching performances. All participants demonstrated virtually errorless performances. In Experiment 2, classifications of the nouns used in Experiment 1 were brought under contextual control. Three nouns were feminine and three were masculine. The match-to-sample training taught participants to select a comparison of the same number as the sample (i.e., singular or plural) in the presence of contextual stimulus A regardless of noun gender. Concurrently, in the presence of contextual stimulus B, participants were taught to select a comparison of the same gender as the sample (i.e., feminine or masculine), regardless of number. Generalization was assessed using a card-sorting test. All participants eventually sorted the cards correctly into gender and number stimulus classes. When printed words used in training were replaced by their picture equivalents, participants demonstrated almost errorless performances. PMID:22754102

The Efficiency of a Selective Training Program on the Development of Some Social Skills of Saudi Students with Autism

ERIC Educational Resources Information Center

Alothman, Ibrahim A.

2016-01-01

The objective of the present study is to find out the efficiency of a selective training program on the development of some social skills of Saudi students with Autism. The study sample comprised of (6) male students with Autism who aged (9-12) years, with an average age of (10.58) years, and a standard deviation of (1.16) years. Their IQ ranged…

Evaluation of a Traffic Sign Detector by Synthetic Image Data for Advanced Driver Assistance Systems

NASA Astrophysics Data System (ADS)

Hanel, A.; Kreuzpaintner, D.; Stilla, U.

2018-05-01

Recently, several synthetic image datasets of street scenes have been published. These datasets contain various traffic signs and can therefore be used to train and test machine learning-based traffic sign detectors. In this contribution, selected datasets are compared regarding ther applicability for traffic sign detection. The comparison covers the process to produce the synthetic images and addresses the virtual worlds, needed to produce the synthetic images, and their environmental conditions. The comparison covers variations in the appearance of traffic signs and the labeling strategies used for the datasets, as well. A deep learning traffic sign detector is trained with multiple training datasets with different ratios between synthetic and real training samples to evaluate the synthetic SYNTHIA dataset. A test of the detector on real samples only has shown that an overall accuracy and ROC AUC of more than 95 % can be achieved for both a small rate of synthetic samples and a large rate of synthetic samples in the training dataset.

An Evaluation of the National Teachers' Institute's Manpower Training Program for Teaching Personnel in Mid-Western Nigeria

ERIC Educational Resources Information Center

Osunde, A. U.; Omoruyi, F. E. O.

2004-01-01

This study evaluated the manpower-training program for teaching personnel in mid-western Nigeria by the National Teachers' Institute. Overall, 240 participants involved in the training program who were randomly selected from the area constituted the sample for the study. A questionnaire designed by the authors was the major instrument used for…

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

Assessing Generative Braille Responding Following Training in a Matching-to-Sample Format

ERIC Educational Resources Information Center

Putnam, Brittany C.; Tiger, Jeffrey H.

2016-01-01

We evaluated the effects of teaching sighted college students to select printed text letters given a braille sample stimulus in a matching-to-sample (MTS) format on the emergence of untrained (a) construction of print characters given braille samples, (b) construction of braille characters given print samples, (c) transcription of print characters…

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.

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.

A Comparison of the Career Maturity, Self Concept and Academic Achievement of Female Cooperative Vocational Office Training Students, Intensive Business Training Students, and Regular Business Education Students in Selected High Schools in Mississippi.

ERIC Educational Resources Information Center

Seaward, Marty Robertson

The purpose of this study was to compare the career maturity, self concept, and academic achievement of female students enrolled in intensive business training (IBT), cooperative vocational office training (CVOT), and regular business education programs. A sample of 240 students, equalized into three groups on the basis of IQ scores, were given…

The Effect of Observing Response Procedures on the Reduction of Over-Selectivity in a Match to Sample Task: Immediate but Not Long Term Benefits

ERIC Educational Resources Information Center

Broomfield, Laura; McHugh, Louise; Reed, Phil

2008-01-01

Stimulus over-selectivity occurs when only one of potentially many aspects of the environment comes to control behavior. In three experiments, adult participants with no developmental disabilities were trained and tested in a match to samples (MTS) paradigm. Participants in Experiment 1 were assigned to one of two conditions, which differed on…

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.

Evaluation of LEAA Funded Courts Training Programs. Volume I.

ERIC Educational Resources Information Center

McManis Associates, Inc., Washington, DC.

An impact evaluation of eight courts training project (CTP) institutes funded by the Law Enforcement Assistance Administration was conducted. After a literature search and visits to potential evaluation sites in all fifty states, twelve sites were selected from a random stratified sample of court systems. Data were obtained from 1047 respondents…

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.…

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.

Application of self-organizing feature maps to analyze the relationships between ignitable liquids and selected mass spectral ions.

PubMed

Frisch-Daiello, Jessica L; Williams, Mary R; Waddell, Erin E; Sigman, Michael E

2014-03-01

The unsupervised artificial neural networks method of self-organizing feature maps (SOFMs) is applied to spectral data of ignitable liquids to visualize the grouping of similar ignitable liquids with respect to their American Society for Testing and Materials (ASTM) class designations and to determine the ions associated with each group. The spectral data consists of extracted ion spectra (EIS), defined as the time-averaged mass spectrum across the chromatographic profile for select ions, where the selected ions are a subset of ions from Table 2 of the ASTM standard E1618-11. Utilization of the EIS allows for inter-laboratory comparisons without the concern of retention time shifts. The trained SOFM demonstrates clustering of the ignitable liquid samples according to designated ASTM classes. The EIS of select samples designated as miscellaneous or oxygenated as well as ignitable liquid residues from fire debris samples are projected onto the SOFM. The results indicate the similarities and differences between the variables of the newly projected data compared to those of the data used to train the SOFM. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Improvement of Predictive Ability by Uniform Coverage of the Target Genetic Space

PubMed Central

Bustos-Korts, Daniela; Malosetti, Marcos; Chapman, Scott; Biddulph, Ben; van Eeuwijk, Fred

2016-01-01

Genome-enabled prediction provides breeders with the means to increase the number of genotypes that can be evaluated for selection. One of the major challenges in genome-enabled prediction is how to construct a training set of genotypes from a calibration set that represents the target population of genotypes, where the calibration set is composed of a training and validation set. A random sampling protocol of genotypes from the calibration set will lead to low quality coverage of the total genetic space by the training set when the calibration set contains population structure. As a consequence, predictive ability will be affected negatively, because some parts of the genotypic diversity in the target population will be under-represented in the training set, whereas other parts will be over-represented. Therefore, we propose a training set construction method that uniformly samples the genetic space spanned by the target population of genotypes, thereby increasing predictive ability. To evaluate our method, we constructed training sets alongside with the identification of corresponding genomic prediction models for four genotype panels that differed in the amount of population structure they contained (maize Flint, maize Dent, wheat, and rice). Training sets were constructed using uniform sampling, stratified-uniform sampling, stratified sampling and random sampling. We compared these methods with a method that maximizes the generalized coefficient of determination (CD). Several training set sizes were considered. We investigated four genomic prediction models: multi-locus QTL models, GBLUP models, combinations of QTL and GBLUPs, and Reproducing Kernel Hilbert Space (RKHS) models. For the maize and wheat panels, construction of the training set under uniform sampling led to a larger predictive ability than under stratified and random sampling. The results of our methods were similar to those of the CD method. For the rice panel, all training set construction methods led to similar predictive ability, a reflection of the very strong population structure in this panel. PMID:27672112

How to Select a Good Training-data Subset for Transcription: Submodular Active Selection for Sequences

DTIC Science & Technology

2009-01-01

selection and uncertainty sampling signif- icantly. Index Terms: Transcription, labeling, submodularity, submod- ular selection, active learning , sequence...name of batch active learning , where a subset of data that is most informative and represen- tative of the whole is selected for labeling. Often...representative subset. Note that our Fisher ker- nel is over an unsupervised generative model, which enables us to bootstrap our active learning approach

Prevalence and correlates of resistance training in a regional Australian population.

PubMed

Humphries, B; Duncan, M J; Mummery, W K

2010-07-01

The core components of physical activity, cardiovascular endurance, muscular strength, balance and flexibility can provide many health benefits and potentially slow declines associated with aging. Aerobic exercise message to the public has been widely promoted by national health authorities, although the promotion of resistance training has received far less attention. In this research, the prevalence of resistance training in a sample of adults living in regional Australia was primarily assessed. A computer-assisted telephone interview survey (n=1230) was conducted by the Population Research Laboratory at Central Queensland University on Queensland adults in October to November 2006. Respondents were asked to report the frequency with which they engaged in resistance training. Respondents were 18 years or older that could be contacted by direct-dialled, land-based telephone service. A telephone database using a computer program to select, with replacement, a simple random sample of phone numbers selected respondents. Almost 14% of the population did some form of gym-based resistance training in the week before the survey. There was a significant (p<0.05) reduction in participation levels with age. Participation was highest amongst the youngest 18-34-year-olds (23.8%), steadily declining with age to a low of 7% in the 55 years and older age group. There was no significant association between sexes and participation in resistance training. The findings underscore the need to increase overall education on the benefits of resistance training with an emphasis among targeted adult populations to increase participation in resistance training.

Exploring the Use of Information Communication Technologies by Selected Caribbean Extension Officers

ERIC Educational Resources Information Center

Strong, Robert; Ganpat, Wayne; Harder, Amy; Irby, Travis L.; Lindner, James R.

2014-01-01

Purpose: The purpose of this study was to describe selected Caribbean extension officers' technology preferences and examine factors that may affect their technology preferences. Design/methodology/approach: The sample consisted of extension officers (N = 119) participating in professional development training sessions in Grenada, Belize and Saint…

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

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

Evaluating candidate reactions to selection practices using organisational justice theory.

PubMed

Patterson, Fiona; Zibarras, Lara; Carr, Victoria; Irish, Bill; Gregory, Simon

2011-03-01

This study aimed to examine candidate reactions to selection practices in postgraduate medical training using organisational justice theory. We carried out three independent cross-sectional studies using samples from three consecutive annual recruitment rounds. Data were gathered from candidates applying for entry into UK general practice (GP) training during 2007, 2008 and 2009. Participants completed an evaluation questionnaire immediately after the short-listing stage and after the selection centre (interview) stage. Participants were doctors applying for GP training in the UK. Main outcome measures were participants' evaluations of the selection methods and perceptions of the overall fairness of each selection stage (short-listing and selection centre). A total of 23,855 evaluation questionnaires were completed (6893 in 2007, 10,497 in 2008 and 6465 in 2009). Absolute levels of perceptions of fairness of all the selection methods at both the short-listing and selection centre stages were consistently high over the 3years. Similarly, all selection methods were considered to be job-related by candidates. However, in general, candidates considered the selection centre stage to be significantly fairer than the short-listing stage. Of all the selection methods, the simulated patient consultation completed at the selection centre stage was rated as the most job-relevant. This is the first study to use a model of organisational justice theory to evaluate candidate reactions during selection into postgraduate specialty training. The high-fidelity selection methods are consistently viewed as more job-relevant and fairer by candidates. This has important implications for the design of recruitment systems for all specialties and, potentially, for medical school admissions. Using this approach, recruiters can systematically compare perceptions of the fairness and job relevance of various selection methods. © Blackwell Publishing Ltd 2011.

Fuzzy Nonlinear Proximal Support Vector Machine for Land Extraction Based on Remote Sensing Image

PubMed Central

Zhong, Xiaomei; Li, Jianping; Dou, Huacheng; Deng, Shijun; Wang, Guofei; Jiang, Yu; Wang, Yongjie; Zhou, Zebing; Wang, Li; Yan, Fei

2013-01-01

Currently, remote sensing technologies were widely employed in the dynamic monitoring of the land. This paper presented an algorithm named fuzzy nonlinear proximal support vector machine (FNPSVM) by basing on ETM+ remote sensing image. This algorithm is applied to extract various types of lands of the city Da’an in northern China. Two multi-category strategies, namely “one-against-one” and “one-against-rest” for this algorithm were described in detail and then compared. A fuzzy membership function was presented to reduce the effects of noises or outliers on the data samples. The approaches of feature extraction, feature selection, and several key parameter settings were also given. Numerous experiments were carried out to evaluate its performances including various accuracies (overall accuracies and kappa coefficient), stability, training speed, and classification speed. The FNPSVM classifier was compared to the other three classifiers including the maximum likelihood classifier (MLC), back propagation neural network (BPN), and the proximal support vector machine (PSVM) under different training conditions. The impacts of the selection of training samples, testing samples and features on the four classifiers were also evaluated in these experiments. PMID:23936016

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.

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

Assessing the Tailored Adaptive Personality Assessment System

DTIC Science & Technology

2014-01-01

and selection course. TAPAS data were collected from 1,216 Soldier-candidates attending the ARSOF course and were used to predict selection for ARSOF...were selected for ARSOF training following the course. Data were collected from February to June 2012. All respondents completed the TAPAS...the TAPAS for this purpose. Collecting additional data from ARSOF candidates would provide larger participant samples that could be used to cross

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.

Accuracy of genomic selection for BCWD resistance in rainbow trout

USDA-ARS?s Scientific Manuscript database

Bacterial cold water disease (BCWD) causes significant economic losses in salmonids. In this study, we aimed to (1) predict genomic breeding values (GEBV) by genotyping training (n=583) and validation samples (n=53) with a SNP50K chip; and (2) assess the accuracy of genomic selection (GS) for BCWD r...

Peace Corps Rural Energy Survey: Senegal. Training for Development Series. Training Manual No. T-27.

ERIC Educational Resources Information Center

Peace Corps, Washington, DC. Information Collection and Exchange Div.

This survey was undertaken by the Peace Corps to assist Senegal in identifying energy needs in rural areas and in implementing alternative, renewable energy projects at the community level. This book describes the sample, energy use, energy needs, and resources. Fifteen villages of fewer than 5,000 people were selected for data collection. The…

Assessment of Leadership Training of Head Teachers and Secondary School Performance in Mubende District, Uganda

ERIC Educational Resources Information Center

Benson, Kayiwa

2011-01-01

The purpose of the study was to establish the relationship between leadership training of head teachers and school performance in secondary schools in Mubende district, Uganda. Descriptive-correlational research design was used. Six schools out of 32 were selected and the sample size of head teachers, teachers and students leaders was 287 out of…

Long-term application of computer-based pleoptics in home therapy: selected results of a prospective multicenter study.

PubMed

Kämpf, Uwe; Shamshinova, Angelika; Kaschtschenko, Tamara; Mascolus, Wilfried; Pillunat, Lutz; Haase, Wolfgang

2008-01-01

The paper presents selected results of a prospective multicenter study. The reported study was aimed at the evaluation of a software-based stimulation method of computer training applied in addition to occlusion as a complementary treatment for therapy-resistant cases of amblyopia. The stimulus was a drifting sinusoidal grating of a spatial frequency of 0.3 cyc/deg and a temporal frequency of 1 cyc/sec, reciprocally coordinated with each other to a drift of 0.33 deg/sec. This pattern was implemented as a background stimulus into simple computer games to bind attention by sensory-motor coordination tasks. According to an earlier proposed hypothesis, the stimulation aims at the provocation of stimulus-induced phase-coupling in order to contribute to the refreshment of synchronization and coordination processes in the visual transmission channels. To assess the outcome of the therapy, we studied the development of the visual acuity during a period of 6 months. Our cooperating partners of this prospective multicenter study were strabologic departments in ophthalmic clinics and private practices as well. For the issue of therapy control, a partial sample of 55 patients from an overall sample of 198 patients was selected, according to the criterion of strong therapy resistance. The visual acuity was increased about two logarithmic steps by an occlusion combined with computer training in addition to the earlier obtained gain of the same amount by occlusion alone. Recalculated relatively to the duration of the therapy periods, the computer training combined with occlusion was found to be about twice as effective as the preceding occlusion alone. The results of combined computer training and occlusion show an additional increase of the same amount as the preceding occlusion alone, which yielded at its end no further advantage to the development of visual acuity in the selected sample of our 55 therapy-resistant patients. In a concluding theoretical note, a preliminary hypothesis about the neuronal mechanisms of the stimulus-induced treatment effect is discussed.

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.

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.

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

The Effect of a Training Programme in Creativity on Developing the Creative Abilities among Children with Visual Impairment

ERIC Educational Resources Information Center

Al-Dababneh, Kholoud A.; al-Masa'deh, Mu'tasem M.; Oliemat, Enass M.

2015-01-01

This study aims to investigate the effects of a training programme in creativity on developing creative abilities among 9-10-year-old children with visual impairment in Jordan. The study sample consisted of 41 students from fourth and fifth grades, who were randomly selected and divided into two experimental groups and two control groups. To…

People's Need for Additional Job Training: Development and Evaluation of an Assessment Procedure.

ERIC Educational Resources Information Center

Copa, George H.; Maurice, Clyde F.

A procedure was developed and evaluated for assessing the self-perceived educational needs of people as one input to the process of planning, approving, and implementing relevant educational programs. The method of data collection involved selecting samples of people by randomly selecting households in a given geographic area, and then contacting…

White wines aroma recovery and enrichment: Sensory-led aroma selection and consumer perception.

PubMed

Lezaeta, Alvaro; Bordeu, Edmundo; Agosin, Eduardo; Pérez-Correa, J Ricardo; Varela, Paula

2018-06-01

We developed a sensory-based methodology to aromatically enrich wines using different aromatic fractions recovered during fermentations of Sauvignon Blanc must. By means of threshold determination and generic descriptive analysis using a trained sensory panel, the aromatic fractions were characterized, selected, and clustered. The selected fractions were grouped, re-assessed, and validated by the trained panel. A consumer panel assessed overall liking and answered a CATA question on some enriched wines and their ideal sample. Differences in elicitation rates between non-enriched and enriched wines with respect to the ideal product highlighted product optimization and the role of aromatic enrichment. Enrichment with aromatic fractions increased the aromatic quality of wines and enhanced consumer appreciation. Copyright © 2018. Published by Elsevier Ltd.

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.

Salivary testosterone is related to self-selected training load in elite female athletes.

PubMed

Cook, Christian J; Beaven, C Martyn

2013-05-27

Testosterone has been related to improved acute neuromuscular performance in athletic populations. It is our contention that testosterone may also contribute to improved volitional motivation and, when monitored longitudinally, may provide one proxy marker for readiness to perform. Twelve female netball players provided saliva samples prior to five standardized training sessions in which they completed a maximal-distance medicine ball throw, and then 3 sets of bench press and then back squat using a self-selected load perceived to equal a 3-repetition maximum load. Additional repetitions were encouraged when possible and total voluntary workload was calculated from the product of the load lifted and repetitions performed. Relative salivary testosterone levels as a group were correlated with bench press (r=0.8399; p=0.0007) and squat (r=0.6703; p=0.0171) self-selected workload, as well as maximal medicine ball throw performance (r=0.7062; p=0.0103). Individual salivary testosterone, when viewed relatively over time, demonstrated strong relationships with self-selected workloads during an in-season training period in female netball players. As such, daily variations in testosterone may provide information regarding voluntary training motivation and readiness to perform in elite athletic populations. Psychological and behavioral aspects of testosterone may have the potential to enhance training adaptation by complementing the known anabolic and permissive properties of testosterone. Copyright © 2013 Elsevier Inc. All rights reserved.

Evaluation of two selection tests for recruitment into radiology specialty training.

PubMed

Patterson, Fiona; Knight, Alec; McKnight, Liam; Booth, Thomas C

2016-07-11

This study evaluated whether two selection tests previously validated for primary care General Practice (GP) trainee selection could provide a valid shortlisting selection method for entry into specialty training for the secondary care specialty of radiology. We conducted a retrospective analysis of data from radiology applicants who also applied to UK GP specialty training or Core Medical Training. The psychometric properties of the two selection tests, a clinical problem solving (CPS) test and situational judgement test (SJT), were analysed to evaluate their reliability. Predictive validity of the tests was analysed by comparing them with the current radiology selection assessments, and the licensure examination results taken after the first stage of training (Fellowship of the Royal College of Radiologists (FRCR) Part 1). The internal reliability of the two selection tests in the radiology applicant sample was good (α ≥ 0.80). The average correlation with radiology shortlisting selection scores was r = 0.26 for the CPS (with p < 0.05 in 5 of 11 shortlisting centres), r = 0.15 for the SJT (with p < 0.05 in 2 of 11 shortlisting centres) and r = 0.25 (with p < 0.05 in 5 of 11 shortlisting centres) for the two tests combined. The CPS test scores significantly correlated with performance in both components of the FRCR Part 1 examinations (r = 0.5 anatomy; r = 0.4 physics; p < 0.05 for both). The SJT did not correlate with either component of the examination. The current CPS test may be an appropriate selection method for shortlisting in radiology but would benefit from further refinement for use in radiology to ensure that the test specification is relevant. The evidence on whether the SJT may be appropriate for shortlisting in radiology is limited. However, these results may be expected to some extent since the SJT is designed to measure non-academic attributes. Further validation work (e.g. with non-academic outcome variables) is required to evaluate whether an SJT will add value in recruitment for radiology specialty training and will further inform construct validity of SJTs as a selection methodology.

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.

Transformation of the Discriminative and Eliciting Functions of Generalized Relational Stimuli

ERIC Educational Resources Information Center

Dougher, Michael J.; Hamilton, Derek; Fink, Brandi; Harrington, Jennifer

2007-01-01

In three experiments, match-to-sample procedures were used with undergraduates to establish arbitrary relational functions for three abstract visual stimuli. In the presence of samples A, B, and C, participants were trained to select the smallest, middle, and largest member, respectively, of a series of three-comparison arrays. In Experiment 1,…

Relations among Acute and Chronic Nicotine Administration, Short-Term Memory, and Tactics of Data Analysis

ERIC Educational Resources Information Center

Kangas, Brian D.; Branch, Marc N.

2012-01-01

Emerging evidence suggests that nicotine may enhance short-term memory. Some of this evidence comes from nonhuman primate research using a procedure called delayed matching-to-sample, wherein the monkey is trained to select a comparison stimulus that matches some physical property of a previously presented sample stimulus. Delays between sample…

Scene recognition based on integrating active learning with dictionary learning

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

Assessing generative braille responding following training in a matching-to-sample format.

PubMed

Putnam, Brittany C; Tiger, Jeffrey H

2016-12-01

We evaluated the effects of teaching sighted college students to select printed text letters given a braille sample stimulus in a matching-to-sample (MTS) format on the emergence of untrained (a) construction of print characters given braille samples, (b) construction of braille characters given print samples, (c) transcription of print characters given braille sample sentences, and (d) vocal reading given braille sample passages. The results demonstrated the generative development of these repertoires given MTS instruction. © 2016 Society for the Experimental Analysis of Behavior.

User-Driven Sampling Strategies in Image Exploitation

DOE PAGES

Harvey, Neal R.; Porter, Reid B.

2013-12-23

Visual analytics and interactive machine learning both try to leverage the complementary strengths of humans and machines to solve complex data exploitation tasks. These fields overlap most significantly when training is involved: the visualization or machine learning tool improves over time by exploiting observations of the human-computer interaction. This paper focuses on one aspect of the human-computer interaction that we call user-driven sampling strategies. Unlike relevance feedback and active learning sampling strategies, where the computer selects which data to label at each iteration, we investigate situations where the user selects which data is to be labeled at each iteration. User-drivenmore » sampling strategies can emerge in many visual analytics applications but they have not been fully developed in machine learning. We discovered that in user-driven sampling strategies suggest new theoretical and practical research questions for both visualization science and machine learning. In this paper we identify and quantify the potential benefits of these strategies in a practical image analysis application. We find user-driven sampling strategies can sometimes provide significant performance gains by steering tools towards local minima that have lower error than tools trained with all of the data. Furthermore, in preliminary experiments we find these performance gains are particularly pronounced when the user is experienced with the tool and application domain.« less

User-driven sampling strategies in image exploitation

NASA Astrophysics Data System (ADS)

Harvey, Neal; Porter, Reid

2013-12-01

Visual analytics and interactive machine learning both try to leverage the complementary strengths of humans and machines to solve complex data exploitation tasks. These fields overlap most significantly when training is involved: the visualization or machine learning tool improves over time by exploiting observations of the human-computer interaction. This paper focuses on one aspect of the human-computer interaction that we call user-driven sampling strategies. Unlike relevance feedback and active learning sampling strategies, where the computer selects which data to label at each iteration, we investigate situations where the user selects which data is to be labeled at each iteration. User-driven sampling strategies can emerge in many visual analytics applications but they have not been fully developed in machine learning. User-driven sampling strategies suggest new theoretical and practical research questions for both visualization science and machine learning. In this paper we identify and quantify the potential benefits of these strategies in a practical image analysis application. We find user-driven sampling strategies can sometimes provide significant performance gains by steering tools towards local minima that have lower error than tools trained with all of the data. In preliminary experiments we find these performance gains are particularly pronounced when the user is experienced with the tool and application domain.

Effects of Video Game Training on Measures of Selective Attention and Working Memory in Older Adults: Results from a Randomized Controlled Trial.

PubMed

Ballesteros, Soledad; Mayas, Julia; Prieto, Antonio; Ruiz-Marquez, Eloísa; Toril, Pilar; Reales, José M

2017-01-01

Video game training with older adults potentially enhances aspects of cognition that decline with aging and could therefore offer a promising training approach. Although, previous published studies suggest that training can produce transfer, many of them have certain shortcomings. This randomized controlled trial (RCT; Clinicaltrials.gov ID: NCT02796508) tried to overcome some of these limitations by incorporating an active control group and the assessment of motivation and expectations. Seventy-five older volunteers were randomly assigned to the experimental group trained for 16 sessions with non-action video games from Lumosity , a commercial platform (http://www.lumosity.com/) or to an active control group trained for the same number of sessions with simulation strategy games. The final sample included 55 older adults (30 in the experimental group and 25 in the active control group). Participants were tested individually before and after training to assess working memory (WM) and selective attention and also reported their perceived improvement, motivation and engagement. The results showed improved performance across the training sessions. The main results were: (1) the experimental group did not show greater improvements in measures of selective attention and working memory than the active control group (the opposite occurred in the oddball task); (2) a marginal training effect was observed for the N -back task, but not for the Stroop task while both groups improved in the Corsi Blocks task. Based on these results, one can conclude that training with non-action games provide modest benefits for untrained tasks. The effect is not specific for that kind of training as a similar effect was observed for strategy video games. Groups did not differ in motivation, engagement or expectations.

Effects of Video Game Training on Measures of Selective Attention and Working Memory in Older Adults: Results from a Randomized Controlled Trial

PubMed Central

Ballesteros, Soledad; Mayas, Julia; Prieto, Antonio; Ruiz-Marquez, Eloísa; Toril, Pilar; Reales, José M.

2017-01-01

Video game training with older adults potentially enhances aspects of cognition that decline with aging and could therefore offer a promising training approach. Although, previous published studies suggest that training can produce transfer, many of them have certain shortcomings. This randomized controlled trial (RCT; Clinicaltrials.gov ID: NCT02796508) tried to overcome some of these limitations by incorporating an active control group and the assessment of motivation and expectations. Seventy-five older volunteers were randomly assigned to the experimental group trained for 16 sessions with non-action video games from Lumosity, a commercial platform (http://www.lumosity.com/) or to an active control group trained for the same number of sessions with simulation strategy games. The final sample included 55 older adults (30 in the experimental group and 25 in the active control group). Participants were tested individually before and after training to assess working memory (WM) and selective attention and also reported their perceived improvement, motivation and engagement. The results showed improved performance across the training sessions. The main results were: (1) the experimental group did not show greater improvements in measures of selective attention and working memory than the active control group (the opposite occurred in the oddball task); (2) a marginal training effect was observed for the N-back task, but not for the Stroop task while both groups improved in the Corsi Blocks task. Based on these results, one can conclude that training with non-action games provide modest benefits for untrained tasks. The effect is not specific for that kind of training as a similar effect was observed for strategy video games. Groups did not differ in motivation, engagement or expectations. PMID:29163136

[Hyperspectral remote sensing image classification based on SVM optimized by clonal selection].

PubMed

Liu, Qing-Jie; Jing, Lin-Hai; Wang, Meng-Fei; Lin, Qi-Zhong

2013-03-01

Model selection for support vector machine (SVM) involving kernel and the margin parameter values selection is usually time-consuming, impacts training efficiency of SVM model and final classification accuracies of SVM hyperspectral remote sensing image classifier greatly. Firstly, based on combinatorial optimization theory and cross-validation method, artificial immune clonal selection algorithm is introduced to the optimal selection of SVM (CSSVM) kernel parameter a and margin parameter C to improve the training efficiency of SVM model. Then an experiment of classifying AVIRIS in India Pine site of USA was performed for testing the novel CSSVM, as well as a traditional SVM classifier with general Grid Searching cross-validation method (GSSVM) for comparison. And then, evaluation indexes including SVM model training time, classification overall accuracy (OA) and Kappa index of both CSSVM and GSSVM were all analyzed quantitatively. It is demonstrated that OA of CSSVM on test samples and whole image are 85.1% and 81.58, the differences from that of GSSVM are both within 0.08% respectively; And Kappa indexes reach 0.8213 and 0.7728, the differences from that of GSSVM are both within 0.001; While the ratio of model training time of CSSVM and GSSVM is between 1/6 and 1/10. Therefore, CSSVM is fast and accurate algorithm for hyperspectral image classification and is superior to GSSVM.

Consumer and trained panel evaluation of beef strip steaks of varying marbling and enhancement levels cooked to three degrees of doneness.

PubMed

Lucherk, L W; O'Quinn, T G; Legako, J F; Rathmann, R J; Brooks, J C; Miller, M F

2016-12-01

The palatability of USDA graded beef strip loins of seven treatments [High Enhanced (HE: 112% of raw weight) Select, Low Enhanced (LE: 107% of raw weight) Select, Prime, upper 2/3 Choice (Top Choice), lower 1/3 Choice (Low Choice), Select, and Standard] cooked to three degrees of doneness [DOD; rare (60°C), medium (71°C), or well-done (77°C)] was evaluated by consumer and trained sensory panelists. For consumers, Select HE steaks rated higher (P<0.05) for juiciness, tenderness, flavor identity, flavor liking, and overall liking than all non-enhanced treatments other than Prime. No differences (P>0.05) were observed between Select LE and Prime samples for most traits evaluated. The effect of USDA grade and enhancement on trained panel palatability scores was independent of DOD for all traits other than juiciness, with the role of marbling in juiciness increasing as DOD increased from rare to well-done. These results indicate enhancement as an effective method to improve the palatability of lower grading beef. Copyright © 2016 Elsevier Ltd. All rights reserved.

Accuracy of genomic prediction for BCWD resistance in rainbow trout using different genotyping platforms and genomic selection models

USDA-ARS?s Scientific Manuscript database

In this study, we aimed to (1) predict genomic estimated breeding value (GEBV) for bacterial cold water disease (BCWD) resistance by genotyping training (n=583) and validation samples (n=53) with two genotyping platforms (24K RAD-SNP and 49K SNP) and using different genomic selection (GS) models (Ba...

An Actuarial Model for Selecting Participants for a Special Medical Education Program.

ERIC Educational Resources Information Center

Walker-Bartnick, Leslie; And Others

An actuarial model applied to the selection process of a special medical school program at the University of Maryland School of Medicine was tested. The 77 students in the study sample were admitted to the university's Fifth Pathway Program, which is designed for U.S. citizens who completed their medical school training, except for internship and…

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.

Running Performance, VO2max, and Running Economy: The Widespread Issue of Endogenous Selection Bias.

PubMed

Borgen, Nicolai T

2018-05-01

Studies in sport and exercise medicine routinely use samples of highly trained individuals in order to understand what characterizes elite endurance performance, such as running economy and maximal oxygen uptake VO 2max . However, it is not well understood in the literature that using such samples most certainly leads to biased findings and accordingly potentially erroneous conclusions because of endogenous selection bias. In this paper, I review the current literature on running economy and VO 2max , and discuss the literature in light of endogenous selection bias. I demonstrate that the results in a large part of the literature may be misleading, and provide some practical suggestions as to how future studies may alleviate endogenous selection bias.

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.

Dealing with missing data in remote sensing images within land and crop classification

NASA Astrophysics Data System (ADS)

Skakun, Sergii; Kussul, Nataliia; Basarab, Ruslan

Optical remote sensing images from space provide valuable data for environmental monitoring, disaster management [1], agriculture mapping [2], so forth. In many cases, a time-series of satellite images is used to discriminate or estimate particular land parameters. One of the factors that influence the efficiency of satellite imagery is the presence of clouds. This leads to the occurrence of missing data that need to be addressed. Numerous approaches have been proposed to fill in missing data (or gaps) and can be categorized into inpainting-based, multispectral-based, and multitemporal-based. In [3], ancillary MODIS data are utilized for filling gaps and predicting Landsat data. In this paper we propose to use self-organizing Kohonen maps (SOMs) for missing data restoration in time-series of satellite imagery. Such approach was previously used for MODIS data [4], but applying this approach for finer spatial resolution data such as Sentinel-2 and Landsat-8 represents a challenge. Moreover, data for training the SOMs are selected manually in [4] that complicates the use of the method in an automatic mode. SOM is a type of artificial neural network that is trained using unsupervised learning to produce a discretised representation of the input space of the training samples, called a map. The map seeks to preserve the topological properties of the input space. The reconstruction of satellite images is performed for each spectral band separately, i.e. a separate SOM is trained for each spectral band. Pixels that have no missing values in the time-series are selected for training. Selecting the number of training pixels represent a trade-off, in particular increasing the number of training samples will lead to the increased time of SOM training while increasing the quality of restoration. Also, training data sets should be selected automatically. As such, we propose to select training samples on a regular grid of pixels. Therefore, the SOM seeks to project a large number of non-missing data to the subspace vectors in the map. Restoration of the missing values is performed in the following way. The multi-temporal pixel values (with gaps) are put to the neural network. A neuron-winner (or a best matching unit, BMU) in the SOM is selected based on the distance metric (for example, Euclidian). It should be noted that missing values are omitted from metric estimation when selecting BMU. When the BMU is selected, missing values are substituted by corresponding components of the BMU values. The efficiency of the proposed approach was tested on a time-series of Landsat-8 images over the JECAM test site in Ukraine and Sich-2 images over Crimea (Sich-2 is Ukrainian remote sensing satellite acquiring images at 8m spatial resolution). Landsat-8 images were first converted to the TOA reflectance, and then were atmospherically corrected so each pixel value represents a surface reflectance in the range from 0 to 1. The error of reconstruction (error of quantization) on training data was: band-2: 0.015; band-3: 0.020; band-4: 0.026; band-5: 0.070; band-6: 0.060; band-7: 0.055. The reconstructed images were also used for crop classification using a multi-layer perceptron (MLP). Overall accuracy was 85.98% and Cohen's kappa was 0.83. References. 1. Skakun, S., Kussul, N., Shelestov, A. and Kussul, O. “Flood Hazard and Flood Risk Assessment Using a Time Series of Satellite Images: A Case Study in Namibia,” Risk Analysis, 2013, doi: 10.1111/risa.12156. 2. Gallego, F.J., Kussul, N., Skakun, S., Kravchenko, O., Shelestov, A., Kussul, O. “Efficiency assessment of using satellite data for crop area estimation in Ukraine,” International Journal of Applied Earth Observation and Geoinformation, vol. 29, pp. 22-30, 2014. 3. Roy D.P., Ju, J., Lewis, P., Schaaf, C., Gao, F., Hansen, M., and Lindquist, E., “Multi-temporal MODIS-Landsat data fusion for relative radiometric normalization, gap filling, and prediction of Landsat data,” Remote Sensing of Environment, 112(6), pp. 3112-3130, 2008. 4. Latif, B.A., and Mercier, G., “Self-Organizing maps for processing of data with missing values and outliers: application to remote sensing images,” Self-Organizing Maps. InTech, pp. 189-210, 2010.

Machine learning from computer simulations with applications in rail vehicle dynamics

NASA Astrophysics Data System (ADS)

Taheri, Mehdi; Ahmadian, Mehdi

2016-05-01

The application of stochastic modelling for learning the behaviour of a multibody dynamics (MBD) models is investigated. Post-processing data from a simulation run are used to train the stochastic model that estimates the relationship between model inputs (suspension relative displacement and velocity) and the output (sum of suspension forces). The stochastic model can be used to reduce the computational burden of the MBD model by replacing a computationally expensive subsystem in the model (suspension subsystem). With minor changes, the stochastic modelling technique is able to learn the behaviour of a physical system and integrate its behaviour within MBD models. The technique is highly advantageous for MBD models where real-time simulations are necessary, or with models that have a large number of repeated substructures, e.g. modelling a train with a large number of railcars. The fact that the training data are acquired prior to the development of the stochastic model discards the conventional sampling plan strategies like Latin Hypercube sampling plans where simulations are performed using the inputs dictated by the sampling plan. Since the sampling plan greatly influences the overall accuracy and efficiency of the stochastic predictions, a sampling plan suitable for the process is developed where the most space-filling subset of the acquired data with ? number of sample points that best describes the dynamic behaviour of the system under study is selected as the training data.

Impact of auditory training for perceptual assessment of voice executed by undergraduate students in Speech-Language Pathology.

PubMed

Silva, Regiane Serafim Abreu; Simões-Zenari, Marcia; Nemr, Nair Kátia

2012-01-01

To analyze the impact of auditory training for auditory-perceptual assessment carried out by Speech-Language Pathology undergraduate students. During two semesters, 17 undergraduate students enrolled in theoretical subjects regarding phonation (Phonation/Phonation Disorders) analyzed samples of altered and unaltered voices (selected for this purpose), using the GRBAS scale. All subjects received auditory training during nine 15-minute meetings. In each meeting, a different parameter was presented using the different voices sample, with predominance of the trained aspect in each session. Sample assessment using the scale was carried out before and after training, and in other four opportunities throughout the meetings. Students' assessments were compared to an assessment carried out by three voice-experts speech-language pathologists who were the judges. To verify training effectiveness, the Friedman's test and the Kappa index were used. The rate of correct answers in the pre-training was considered between regular and good. It was observed maintenance of the number of correct answers throughout assessments, for most of the scale parameters. In the post-training moment, the students showed improvements in the analysis of asthenia, a parameter that was emphasized during training after the students reported difficulties analyzing it. There was a decrease in the number of correct answers for the roughness parameter after it was approached segmented into hoarseness and harshness, and observed in association with different diagnoses and acoustic parameters. Auditory training enhances students' initial abilities to perform the evaluation, aside from guiding adjustments in the dynamics of the university subject.

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.

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.

Comparison of Hybrid Classifiers for Crop Classification Using Normalized Difference Vegetation Index Time Series: A Case Study for Major Crops in North Xinjiang, China

PubMed Central

Hao, Pengyu; Wang, Li; Niu, Zheng

2015-01-01

A range of single classifiers have been proposed to classify crop types using time series vegetation indices, and hybrid classifiers are used to improve discriminatory power. Traditional fusion rules use the product of multi-single classifiers, but that strategy cannot integrate the classification output of machine learning classifiers. In this research, the performance of two hybrid strategies, multiple voting (M-voting) and probabilistic fusion (P-fusion), for crop classification using NDVI time series were tested with different training sample sizes at both pixel and object levels, and two representative counties in north Xinjiang were selected as study area. The single classifiers employed in this research included Random Forest (RF), Support Vector Machine (SVM), and See 5 (C 5.0). The results indicated that classification performance improved (increased the mean overall accuracy by 5%~10%, and reduced standard deviation of overall accuracy by around 1%) substantially with the training sample number, and when the training sample size was small (50 or 100 training samples), hybrid classifiers substantially outperformed single classifiers with higher mean overall accuracy (1%~2%). However, when abundant training samples (4,000) were employed, single classifiers could achieve good classification accuracy, and all classifiers obtained similar performances. Additionally, although object-based classification did not improve accuracy, it resulted in greater visual appeal, especially in study areas with a heterogeneous cropping pattern. PMID:26360597

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

Electron Microscopy and Image Analysis for Selected Materials

NASA Technical Reports Server (NTRS)

Williams, George

1999-01-01

This particular project was completed in collaboration with the metallurgical diagnostics facility. The objective of this research had four major components. First, we required training in the operation of the environmental scanning electron microscope (ESEM) for imaging of selected materials including biological specimens. The types of materials range from cyanobacteria and diatoms to cloth, metals, sand, composites and other materials. Second, to obtain training in surface elemental analysis technology using energy dispersive x-ray (EDX) analysis, and in the preparation of x-ray maps of these same materials. Third, to provide training for the staff of the metallurgical diagnostics and failure analysis team in the area of image processing and image analysis technology using NIH Image software. Finally, we were to assist in the sample preparation, observing, imaging, and elemental analysis for Mr. Richard Hoover, one of NASA MSFC's solar physicists and Marshall's principal scientist for the agency-wide virtual Astrobiology Institute. These materials have been collected from various places around the world including the Fox Tunnel in Alaska, Siberia, Antarctica, ice core samples from near Lake Vostoc, thermal vents in the ocean floor, hot springs and many others. We were successful in our efforts to obtain high quality, high resolution images of various materials including selected biological ones. Surface analyses (EDX) and x-ray maps were easily prepared with this technology. We also discovered and used some applications for NIH Image software in the metallurgical diagnostics facility.

Integrating Genetic, Neuropsychological and Neuroimaging Data to Model Early-Onset Obsessive Compulsive Disorder Severity

PubMed Central

Mas, Sergi; Gassó, Patricia; Morer, Astrid; Calvo, Anna; Bargalló, Nuria; Lafuente, Amalia; Lázaro, Luisa

2016-01-01

We propose an integrative approach that combines structural magnetic resonance imaging data (MRI), diffusion tensor imaging data (DTI), neuropsychological data, and genetic data to predict early-onset obsessive compulsive disorder (OCD) severity. From a cohort of 87 patients, 56 with complete information were used in the present analysis. First, we performed a multivariate genetic association analysis of OCD severity with 266 genetic polymorphisms. This association analysis was used to select and prioritize the SNPs that would be included in the model. Second, we split the sample into a training set (N = 38) and a validation set (N = 18). Third, entropy-based measures of information gain were used for feature selection with the training subset. Fourth, the selected features were fed into two supervised methods of class prediction based on machine learning, using the leave-one-out procedure with the training set. Finally, the resulting model was validated with the validation set. Nine variables were used for the creation of the OCD severity predictor, including six genetic polymorphisms and three variables from the neuropsychological data. The developed model classified child and adolescent patients with OCD by disease severity with an accuracy of 0.90 in the testing set and 0.70 in the validation sample. Above its clinical applicability, the combination of particular neuropsychological, neuroimaging, and genetic characteristics could enhance our understanding of the neurobiological basis of the disorder. PMID:27093171

Novice to expert practice via postprofessional athletic training education: a grounded theory.

PubMed

Neibert, Peter J

2009-01-01

To discover the theoretic constructs that confirm, disconfirm, or extend the principles and their applications appropriate for National Athletic Trainers' Association (NATA)-accredited postprofessional athletic training education programs. Interviews at the 2003 NATA Annual Meeting & Clinical Symposia. Qualitative study using grounded theory procedures. Thirteen interviews were conducted with postprofessional graduates. Participants were purposefully selected based on theoretic sampling and availability. The transcribed interviews were analyzed using open coding, axial coding, and selective coding procedures. Member checks, reflective journaling, and triangulation were used to ensure trustworthiness. The participants' comments confirmed and extended the current principles of postprofessional athletic training education programs and offered additional suggestions for more effective practical applications. The emergence of this central category of novice to expert practice is a paramount finding. The tightly woven fabric of the 10 processes, when interlaced with one another, provides a strong tapestry supporting novice to expert practice via postprofessional athletic training education. The emergence of this theoretic position pushes postprofessional graduate athletic training education forward to the future for further investigation into the theoretic constructs of novice to expert practice.

Mapping raised bogs with an iterative one-class classification approach

NASA Astrophysics Data System (ADS)

Mack, Benjamin; Roscher, Ribana; Stenzel, Stefanie; Feilhauer, Hannes; Schmidtlein, Sebastian; Waske, Björn

2016-10-01

Land use and land cover maps are one of the most commonly used remote sensing products. In many applications the user only requires a map of one particular class of interest, e.g. a specific vegetation type or an invasive species. One-class classifiers are appealing alternatives to common supervised classifiers because they can be trained with labeled training data of the class of interest only. However, training an accurate one-class classification (OCC) model is challenging, particularly when facing a large image, a small class and few training samples. To tackle these problems we propose an iterative OCC approach. The presented approach uses a biased Support Vector Machine as core classifier. In an iterative pre-classification step a large part of the pixels not belonging to the class of interest is classified. The remaining data is classified by a final classifier with a novel model and threshold selection approach. The specific objective of our study is the classification of raised bogs in a study site in southeast Germany, using multi-seasonal RapidEye data and a small number of training sample. Results demonstrate that the iterative OCC outperforms other state of the art one-class classifiers and approaches for model selection. The study highlights the potential of the proposed approach for an efficient and improved mapping of small classes such as raised bogs. Overall the proposed approach constitutes a feasible approach and useful modification of a regular one-class classifier.

A Proactive Approach to Building Security.

ERIC Educational Resources Information Center

Winters, Sharon

1994-01-01

Describes building security procedures developed at the Hampton Public Library (Virginia) to deal with problem patrons. Highlights include need for the library monitor program; staffing patterns; monitor selection criteria; training procedures; library behavior guidelines; library policy statements; theft detection systems; and sample job…

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

redMaGiC: Selecting luminous red galaxies from the DES Science Verification data

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

Rozo, E.; Rykoff, E. S.; Abate, A.

Here, we introduce redMaGiC, an automated algorithm for selecting luminous red galaxies (LRGs). The algorithm was specifically developed to minimize photometric redshift uncertainties in photometric large-scale structure studies. redMaGiC achieves this by self-training the colour cuts necessary to produce a luminosity-thresholded LRG sample of constant comoving density. We demonstrate that redMaGiC photo-zs are very nearly as accurate as the best machine learning-based methods, yet they require minimal spectroscopic training, do not suffer from extrapolation biases, and are very nearly Gaussian. We apply our algorithm to Dark Energy Survey (DES) Science Verification (SV) data to produce a redMaGiC catalogue sampling themore » redshift range z ϵ [0.2, 0.8]. Our fiducial sample has a comoving space density of 10 –3 (h –1 Mpc) –3, and a median photo-z bias (zspec – zphoto) and scatter (σz/(1 + z)) of 0.005 and 0.017, respectively. The corresponding 5σ outlier fraction is 1.4 per cent. We also test our algorithm with Sloan Digital Sky Survey Data Release 8 and Stripe 82 data, and discuss how spectroscopic training can be used to control photo-z biases at the 0.1 per cent level.« less

redMaGiC: Selecting luminous red galaxies from the DES Science Verification data

DOE PAGES

Rozo, E.; Rykoff, E. S.; Abate, A.; ...

2016-05-30

Here, we introduce redMaGiC, an automated algorithm for selecting luminous red galaxies (LRGs). The algorithm was specifically developed to minimize photometric redshift uncertainties in photometric large-scale structure studies. redMaGiC achieves this by self-training the colour cuts necessary to produce a luminosity-thresholded LRG sample of constant comoving density. We demonstrate that redMaGiC photo-zs are very nearly as accurate as the best machine learning-based methods, yet they require minimal spectroscopic training, do not suffer from extrapolation biases, and are very nearly Gaussian. We apply our algorithm to Dark Energy Survey (DES) Science Verification (SV) data to produce a redMaGiC catalogue sampling themore » redshift range z ϵ [0.2, 0.8]. Our fiducial sample has a comoving space density of 10 –3 (h –1 Mpc) –3, and a median photo-z bias (zspec – zphoto) and scatter (σz/(1 + z)) of 0.005 and 0.017, respectively. The corresponding 5σ outlier fraction is 1.4 per cent. We also test our algorithm with Sloan Digital Sky Survey Data Release 8 and Stripe 82 data, and discuss how spectroscopic training can be used to control photo-z biases at the 0.1 per cent level.« less

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.

Problems of psychological monitoring in astronaut training.

PubMed

Morgun, V V

1997-10-01

Monitoring of the goal-oriented psychological changes of a man during professional training is necessary. The level development of the astronaut psychic features is checked by means of psychological testing with the final aim to evaluate each professionally important psychological qualities and to evaluate in general. The list of psychological features needed for evaluation is determined and empirically selected weight factors based on wide statistical sampling is introduced. Accumulation of psychological test results can predict an astronaut's ability of solving complicated problems in a flight mission. It can help to correct the training process and reveal weakness.

Pilot personality and crew coordination - Implications for training and selection

NASA Technical Reports Server (NTRS)

Chidester, Thomas R.; Helmreich, Robert L.; Gregorich, Steven E.; Geis, Craig E.

1991-01-01

It is contended that past failures to find linkages between performance and personality were due to a combination of premature performance evaluation, inadequate statistical modeling, and/or the reliance on data gathered in contrived as opposed to realistic situations. The goal of the research presented is to isolate subgroups of pilots along performance-related personality dimensions and to document limits on the impact of crew coordination training between the groups. Three different profiles were identified through cluster analysis of personality scales that replicated across samples and predicted attitude change following training in crew coordination.

Landing and Population Hazard Analysis for Stardust Entry in Operations and Entry Planning

NASA Technical Reports Server (NTRS)

Tooley, Jeffrey; Desai, Prasun N.; Lynos, Daniel T.; Hirst, Edward A.; Wahl, Tom E.; Wawrzyniak, Georffery G.

2006-01-01

Stardust is a comet sample return mission that successfully returned to Earth on January 15, 2006. Stardust's targeted landing area was the Utah Test and Training Range in the Northwest corner of Utah. Requirements for the risks associated with landing were levied on Stardust by the Utah Test and Training Range and NASA. This paper describes the analysis to verify that these requirements were met and and includes calculation of debris survivability, generation of landing site selection plots, and identification of keep-out zones, as well as appropriate selection of the landing site. Operationally the risk requirements were all met for both of the GOMO-GO polls, so entry was authorized.

Training effect of the exchange bias in sputter deposited Fe3O4 thin films with varying thickness

NASA Astrophysics Data System (ADS)

Muhammed Shameem, P. V.; Senthil Kumar, M.

2018-07-01

The training effect property of the exchange bias in the reactively sputtered polycrystalline Fe3O4 thin films of varying thicknesses in the range 25-200 nm are studied. Structural studies by X-ray diffraction, X-ray photoelectron spectroscopy and selected area electron diffraction confirm the formation of single phase Fe3O4. The scanning electron spectroscopy images show that the grains are uniformly distributed. All the samples show clear and consistent exchange bias training behaviour due to the dynamics of the spins at the interface of the ferrimagnetic core and the spin glass-like surface of the grains. The analysis of the training effect data of the exchange bias field HE measured at 2 K by using three different models show that the model based on the relaxation of the frozen and rotatable spin components at the interface gives the best description for all the samples. From this model, it is found that the reversible interface spins relax around 7 times faster than the frozen interface spins at 2 K for all the samples and that their relative relaxation rates are independent of the sample thickness. This constancy show that the relative relaxation rates of the interfacial frozen and rotatable spin components is a material dependent property. The frozen component of the interfacial spins of each sample is found to be dominated at the initial stage of the training. A direct equivalence between the HE and remanence asymmetry ME is observed. Above the spin freezing temperature, the training effect measurements at 75 K show that the HE decreases sharply with successive field cycling as compared to the measurements made at 2 K and the HE vanishes after first few cycles.

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.

Wolves, dogs, rearing and reinforcement: complex interactions underlying species differences in training and problem-solving performance.

PubMed

Frank, Harry

2011-11-01

Frank and Frank et al. (1982-1987) administered a series of age-graded training and problem-solving tasks to samples of Eastern timber wolf (C. lupus lycaon) and Alaskan Malamute (C. familiaris) pups to test Frank's (Zeitschrift für Tierpsychologie 53:389-399, 1980) model of the evolution of information processing under conditions of natural and artificial selection. Results confirmed the model's prediction that wolves should perform better than dogs on problem-solving tasks and that dogs should perform better than wolves on training tasks. Further data collected at the University of Connecticut in 1983 revealed a more complex and refined picture, indicating that species differences can be mediated by a number of factors influencing wolf performance, including socialization regimen (hand-rearing vs. mother-rearing), interactive effects of socialization on the efficacy of both rewards and punishments, and the flexibility to select learning strategies that experimenters might not anticipate.

The hidden curriculum in undergraduate medical education: qualitative study of medical students' perceptions of teaching.

PubMed

Lempp, Heidi; Seale, Clive

2004-10-02

To study medical students' views about the quality of the teaching they receive during their undergraduate training, especially in terms of the hidden curriculum. Semistructured interviews with individual students. One medical school in the United Kingdom. 36 undergraduate medical students, across all stages of their training, selected by random and quota sampling, stratified by sex and ethnicity, with the whole medical school population as a sampling frame. Medical students' experiences and perceptions of the quality of teaching received during their undergraduate training. Students reported many examples of positive role models and effective, approachable teachers, with valued characteristics perceived according to traditional gendered stereotypes. They also described a hierarchical and competitive atmosphere in the medical school, in which haphazard instruction and teaching by humiliation occur, especially during the clinical training years. Following on from the recent reforms of the manifest curriculum, the hidden curriculum now needs attention to produce the necessary fundamental changes in the culture of undergraduate medical education.

Effectiveness of battlefield-ethics training during combat deployment: a programme assessment.

PubMed

Warner, Christopher H; Appenzeller, George N; Mobbs, Angela; Parker, Jessica R; Warner, Carolynn M; Grieger, Thomas; Hoge, Charles W

2011-09-03

Breakdowns in the ethical conduct of soldiers towards non-combatants on the battlefield are of grave concern in war. Evidence-based training approaches to prevent unethical conduct are scarce. We assessed the effectiveness of battlefield-ethics training and factors associated with unethical battlefield conduct. The training package, based on movie vignettes and leader-led discussions, was administered 7 to 8 months into a 15-month high-intensity combat deployment in Iraq, between Dec 11, 2007, and Jan 30, 2008. Soldiers from an infantry brigade combat team (total population about 3500) were randomly selected, on the basis of company and the last four digits of each soldier's social security number, and invited to complete an anonymous survey 3 months after completion of the training. Reports of unethical behaviour and attitudes in this sample were compared with a randomly selected pre-training sample from the same brigade. The response patterns for ethical behaviour and reporting of ethical violations were analysed with chi-square analyses. We developed two logistic regression models using self-reported unethical behaviours as dependent variables. Factors associated with unethical conduct, including combat experiences and post-traumatic stress disorder (PTSD), were assessed with validated scales. Of 500 randomly selected soldiers 421 agreed to participate in the anonymous post-training survey. A total of 397 soldiers of the same brigade completed the pre-training survey. Training was associated with significantly lower rates of unethical conduct of soldiers and greater willingness to report and address misconduct than in those before training. For example, reports of unnecessary damage or destruction of private property decreased from 13·6% (54 of 397; 95% CI 10·2-17·0) before training to 5·0% (21 of 421; 2·9-7·1) after training (percent difference -63·2%; p<0·0001), and willingness to report a unit member for mistreatment of a non-combatant increased from 36·0% (143 of 397; 31·3-40·7) to 58·9% (248 of 421; 54·2-63·6; percent difference 63·6; p<0·0001). Nearly all participants (410 [97%]) reported that training made it clear how to respond towards non-combatants. Combat frequency and intensity was the strongest predictor of unethical behaviour; PTSD was not a significant predictor of unethical behaviour after controlling for combat experiences. Leader-led battlefield ethics training positively influenced soldiers' understanding of how to interact with and treat non-combatants, and reduced reports of ethical misconduct. Unethical battlefield conduct was associated with high-intensity combat but not with PTSD. None. Copyright © 2011 Elsevier Ltd. All rights reserved.

Predicting Classifier Performance with Limited Training Data: Applications to Computer-Aided Diagnosis in Breast and Prostate Cancer

PubMed Central

Basavanhally, Ajay; Viswanath, Satish; Madabhushi, Anant

2015-01-01

Clinical trials increasingly employ medical imaging data in conjunction with supervised classifiers, where the latter require large amounts of training data to accurately model the system. Yet, a classifier selected at the start of the trial based on smaller and more accessible datasets may yield inaccurate and unstable classification performance. In this paper, we aim to address two common concerns in classifier selection for clinical trials: (1) predicting expected classifier performance for large datasets based on error rates calculated from smaller datasets and (2) the selection of appropriate classifiers based on expected performance for larger datasets. We present a framework for comparative evaluation of classifiers using only limited amounts of training data by using random repeated sampling (RRS) in conjunction with a cross-validation sampling strategy. Extrapolated error rates are subsequently validated via comparison with leave-one-out cross-validation performed on a larger dataset. The ability to predict error rates as dataset size increases is demonstrated on both synthetic data as well as three different computational imaging tasks: detecting cancerous image regions in prostate histopathology, differentiating high and low grade cancer in breast histopathology, and detecting cancerous metavoxels in prostate magnetic resonance spectroscopy. For each task, the relationships between 3 distinct classifiers (k-nearest neighbor, naive Bayes, Support Vector Machine) are explored. Further quantitative evaluation in terms of interquartile range (IQR) suggests that our approach consistently yields error rates with lower variability (mean IQRs of 0.0070, 0.0127, and 0.0140) than a traditional RRS approach (mean IQRs of 0.0297, 0.0779, and 0.305) that does not employ cross-validation sampling for all three datasets. PMID:25993029

Comparison of Sensorimotor Rhythm (SMR) and Beta Training on Selective Attention and Symptoms in Children with Attention Deficit/Hyperactivity Disorder (ADHD): A Trend Report.

PubMed

Mohammadi, Mohammad Reza; Malmir, Nastaran; Khaleghi, Ali; Aminiorani, Majd

2015-06-01

The aim of this study was to assess and compare the effect of two neurofeedback protocols (SMR/theta and beta/theta) on ADHD symptoms, selective attention and EEG (electroencephalogram) parameters in children with ADHD. The sample consisted of 16 children (9-15 year old: 13 boys; 3 girls) with ADHD-combined type (ADHD-C). All of children used methylphenidate (MPH) during the study. The neurofeedback training consisted of two phases of 15 sessions, each lasting 45 minutes. In the first phase, participants were trained to enhance sensorimotor rhythm (12-15 Hz) and reduce theta activity (4-8 Hz) at C4 and in the second phase; they had to increase beta (15-18 Hz) and reduce theta activity at C3. Assessments consisted of d2 attention endurance test, ADHD rating scale (parent form) at three time periods: before, middle and the end of the training. EEG signals were recorded just before and after the training. Based on parents' reports, inattention after beta/theta training, and hyperactivity/impulsivity were improved after the end of the training. All subscales of d2 test were improved except for the difference between maximum and minimum responses. However, EEG analysis showed no significant differences. Neurofeedback in conjunction with Methylphenidate may cause further improvement in ADHD symptoms reported by parents and selective attention without long-term impact on EEG patterns. However, determining the exact relationship between EEG parameters, neurofeedback protocols and ADHD symptoms remain unclear.

Game-based training of flexibility and attention improves task-switch performance: near and far transfer of cognitive training in an EEG study.

PubMed

Olfers, Kerwin J F; Band, Guido P H

2018-01-01

There is a demand for ways to enhance cognitive flexibility, as it can be a limiting factor for performance in daily life. Video game training has been linked to advantages in cognitive functioning, raising the question if training with video games can promote cognitive flexibility. In the current study, we investigated if game-based computerized cognitive training (GCCT) could enhance cognitive flexibility in a healthy young adult sample (N = 72), as measured by task-switch performance. Three GCCT schedules were contrasted, which targeted: (1) cognitive flexibility and task switching, (2) attention and working memory, or (3) an active control involving basic math games, in twenty 45-min sessions across 4-6 weeks. Performance on an alternating-runs task-switch paradigm during pretest and posttest sessions indicated greater overall reaction time improvements after both flexibility and attention training as compared to control, although not related to local switch cost. Flexibility training enhanced performance in the presence of distractor-related interference. In contrast, attention training was beneficial when low task difficulty undermined sustained selective attention. Furthermore, flexibility training improved response selection as indicated by a larger N2 amplitude after training as compared to control, and more efficient conflict monitoring as indicated by reduced Nc/CRN and larger Pe amplitude after training. These results provide tentative support for the efficacy of GCCT and suggest that an ideal training might include both task switching and attention components, with maximal task diversity both within and between training games.

The effect of note-taking skills training on the achievement motivation in learning on B.A students in Shahid Bahonar University of Kerman and Kerman University of Medical Sciences (Iran).

PubMed

Sharifi, Parvane; Rahmati, Abbas; Saber, Maryam

2013-10-01

To evaluate the effect of note-taking skills training on the achievement motivation in learning. The experimental study comprised graduate students of the 2010-11 batch at Kerman's Bahonar University and Kerman's Medical Sciences University, Iran. The study sample included 110 people; 55 in the test group, and 55 in the control group. They were randomly selected and replaced through the single-stage cluster sampling. To collect the data, a questionnaire was used. Pre-test was performed before the training session in two groups. After training course, a post-test was taken. For data analysis, the independent t-test, was used. The average pre-test score of the test group was 182 +/- 34.15, while for the control group it was 191 +/- 30.37 (p < 0.089). After the training, the post-test showed statistically significant change. The test group scored 220 +/- 20.94 against the controls who scored 195 +/- 27.26 (p < 0.001). The findings showed that achievement motivation in learning increased significantly after imparting training in note-taking skills. Authorities in the educational system should invest more for promotion of such skills.

Analysis of Retention of First-Term Enlisted Personnel in the Selected Reserves.

DTIC Science & Technology

1988-06-01

composed of questions related to education and training. The Kaiser - Meyer - Olkin measure of sampling adequacy was 0.887 and the number of cases was...status. The Kaiser - Meyer - Olkin measure of sampling adequacy was 0.901 and the number of cases was 1,889. These three factors were also used as...drills. The Kaiser - Meyer - Olkin measure of sampling adequacy was 0.841 and the number of cases was 2,507. These two factors were also used as

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.

Evaluation of three techniques for classifying urban land cover patterns using LANDSAT MSS data. [New Orleans, Louisiana

NASA Technical Reports Server (NTRS)

Baumann, P. R. (Principal Investigator)

1979-01-01

Three computer quantitative techniques for determining urban land cover patterns are evaluated. The techniques examined deal with the selection of training samples by an automated process, the overlaying of two scenes from different seasons of the year, and the use of individual pixels as training points. Evaluation is based on the number and type of land cover classes generated and the marks obtained from an accuracy test. New Orleans, Louisiana and its environs form the study area.

Key personality traits of sales managers.

PubMed

Lounsbury, John W; Foster, Nancy A; Levy, Jacob J; Gibson, Lucy W

2014-01-01

Sales managers are crucial for producing positive sales outcomes for companies. However, there has been a relative dearth of scholarly investigations into the personal attributes of sales managers. Such information could prove important in the recruitment, selection, training needs identification, career planning, counseling, and development of sales managers. Drawing on Holland's vocational theory, we sought to identify key personality traits that distinguish sales managers from other occupations and are related to their career satisfaction. The main sample was comprised of a total of 978 sales managers employed in a large number of companies across the United States (along with a comparison sample drawn from 79,512 individuals from other professional occupations). Participants completed an online version of Resource Associates' Personal Style Inventory as well a measure of career satisfaction. Our sample of 978 sales managers had higher levels of Assertiveness, Customer Service Orientation, Extraversion, Image Management, Optimism, and Visionary Style; and lower levels of Conscientiousness, Agreeableness, Intrinsic Motivation, Openness, and Tough-Mindedness than a sample of 79,512 individuals in a variety of other occupations. Nine of these traits were significantly correlated with sales managers' career satisfaction. Based on the results, a psychological profile of sales managers was presented as were implications for their recruitment, selection, training, development, and mentoring.

Monitoring Well Installation and Groundwater Sampling and Analysis Plan at the USARC Training Reserve, 84th Division, Milwaukee, Wisconsin

DTIC Science & Technology

1988-11-01

paint chips at the sampling site. 0 Clean water tanks, pumps, mud pans, hoses, including hoses and tanks used to transfer water from source to drill rig...TO’ LCA , Filll I F’APCr,;I~- € C/ " rKL2PIrlA , ATTFNrIGN TO SMOKING. ALCOHOLF MFDrICATIONP AND FXPOSI.RE TO CARCINOGENS.1 ENERAL MEDICAl. HISTORY...A. General: 1. Place samples in core trough for visual inspection. After logging, place selected samples in sample jars or wood core boxes. 2. Seal

Selection for optimal crew performance - Relative impact of selection and training

NASA Technical Reports Server (NTRS)

Chidester, Thomas R.

1987-01-01

An empirical study supporting Helmreich's (1986) theoretical work on the distinct manner in which training and selection impact crew coordination is presented. Training is capable of changing attitudes, while selection screens for stable personality characteristics. Training appears least effective for leadership, an area strongly influenced by personality. Selection is least effective for influencing attitudes about personal vulnerability to stress, which appear to be trained in resource management programs. Because personality correlates with attitudes before and after training, it is felt that selection may be necessary even with a leadership-oriented training cirriculum.

A Study of the Effects of Business Training on the Attitude towards Major Selection of Secondary Level Male Students in Iran

ERIC Educational Resources Information Center

Samiee, Fateme; Sadeghian, Zahra; Akrami, Nahid; Kiani, Mehdi Keikhosro; Golkian, Mina

2015-01-01

The present study aimed to study the effect of business education on the attitudes of the secondary school male student towards major selection. The population of the study were all male secondary students in Isfahan in the year 2013/2015. The sample was a group of 44 students were placed in the experimental and 44 in the control group. In this…

Novice to Expert Practice via Postprofessional Athletic Training Education: A Grounded Theory

PubMed Central

Neibert, Peter J

2009-01-01

Objective: To discover the theoretic constructs that confirm, disconfirm, or extend the principles and their applications appropriate for National Athletic Trainers' Association (NATA)–accredited postprofessional athletic training education programs. Design: Interviews at the 2003 NATA Annual Meeting & Clinical Symposia. Setting: Qualitative study using grounded theory procedures. Patients and Other Participants: Thirteen interviews were conducted with postprofessional graduates. Participants were purposefully selected based on theoretic sampling and availability. Data Collection and Analysis: The transcribed interviews were analyzed using open coding, axial coding, and selective coding procedures. Member checks, reflective journaling, and triangulation were used to ensure trustworthiness. Results: The participants' comments confirmed and extended the current principles of postprofessional athletic training education programs and offered additional suggestions for more effective practical applications. Conclusions: The emergence of this central category of novice to expert practice is a paramount finding. The tightly woven fabric of the 10 processes, when interlaced with one another, provides a strong tapestry supporting novice to expert practice via postprofessional athletic training education. The emergence of this theoretic position pushes postprofessional graduate athletic training education forward to the future for further investigation into the theoretic constructs of novice to expert practice. PMID:19593420

Effects of Meditation on Anxiety and Chemical Dependency.

ERIC Educational Resources Information Center

Wong, Martin R.; And Others

1981-01-01

Studied a non-self-selected sample of chemically dependent people instructed in meditation techniques. Differences established upon training termination were no longer evident in the instructed group after six months. Subjects who reported continuing at least minimal meditative practices, however, showed differences in social adjustment, work…

Enhancing Employee Skills.

ERIC Educational Resources Information Center

1999

This document contains four symposium papers on enhancing employee skills. "The Effect of Study Skills Training Intervention on United States Air Force Aeromedical Apprentices" (John C. Griffith) demonstrates how study skills intervention resulted in a significant increase in the end-of-course scores of a sample of 90 randomly selected Air Force…

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.

Basic forest cover mapping using digitized remote sensor data and automated data processing techniques

NASA Technical Reports Server (NTRS)

Coggeshall, M. E.; Hoffer, R. M.

1973-01-01

Remote sensing equipment and automatic data processing techniques were employed as aids in the institution of improved forest resource management methods. On the basis of automatically calculated statistics derived from manually selected training samples, the feature selection processor of LARSYS selected, upon consideration of various groups of the four available spectral regions, a series of channel combinations whose automatic classification performances (for six cover types, including both deciduous and coniferous forest) were tested, analyzed, and further compared with automatic classification results obtained from digitized color infrared photography.

Unmet demand for training among mature age Australians: Prevalence, differentials and perceived causes.

PubMed

Adair, Tim; Lourey, Emma; Taylor, Philip

2016-03-01

To explore the prevalence of unmet demand for training by mature age Australians and to identify the main barriers to accessing training. A total of 3007 Australians aged 45-74 years were surveyed using Computer Assisted Telephone Interviewing. The sample frame was randomly selected and stratified based on the capital city and the rest of the state, and data were weighted to be nationally representative. Over one-third (37%) of respondents who had worked in the past five years reported wanting to attend some form of training but were unable to; these were most likely women and those aged 45-54 year. Commonly cited reasons for not being able to attend training included not being able to fit it in with work commitments, affordability and employer reluctance. Reduction of these barriers to workplace training can improve mature age people's ability to remain engaged in the workforce. © 2015 AJA Inc.

Environmental effects of fog oil and CS usage at the Combat Maneuver Training Center, Hohenfels, Germany. [2-chlorophenylmethylene

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

Brubaker, K.L.; Rosenblatt, D.H.; Snyder, C.T.

1992-03-01

In response to environmental concerns at the Combat Maneuver Training Center (CMTC), Hohenfels, Germany, the US Army 7th Army Training Command commissioned a scientific study by Argonne National Laboratory to investigate specific issues. The study involved three parts: (1) a field study to determine if fog oil and CS (a compound named after its discoverers, B.B. Carson and R.W. Stoughton) were accumulating in the CMTC environment, (2) a screening of selected soil samples for the presence of US Environmental Protection Agency priority pollutants, and (3) a literature review of the health effects of fog oil and CS, as well asmore » a review of training practices at CMTC. No fog oil or fog oil degradation products were detected in any soil, sediment, or vegetation sample collected at CMTC. Trace quantities of one or more priority pollutants were tentatively detected in three of eight soil and sediment samples. However, the priority pollutant concentrations are so low that they pose no environmental or health hazards. No evidence of widespread or significant contamination in the training areas was found. Crucial data needed to fully evaluate both acute and chronic health effects of civilian exposures to CS at CMTC are not available. On the basis of the available literature, long-ten-n health effects in the civilian population near CMTC that could result from the use of fog oil and CS during training activities are believed to be negligible.« less

Environmental effects of fog oil and CS usage at the Combat Maneuver Training Center, Hohenfels, Germany

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

Brubaker, K.L.; Rosenblatt, D.H.; Snyder, C.T.

1992-03-01

In response to environmental concerns at the Combat Maneuver Training Center (CMTC), Hohenfels, Germany, the US Army 7th Army Training Command commissioned a scientific study by Argonne National Laboratory to investigate specific issues. The study involved three parts: (1) a field study to determine if fog oil and CS (a compound named after its discoverers, B.B. Carson and R.W. Stoughton) were accumulating in the CMTC environment, (2) a screening of selected soil samples for the presence of US Environmental Protection Agency priority pollutants, and (3) a literature review of the health effects of fog oil and CS, as well asmore » a review of training practices at CMTC. No fog oil or fog oil degradation products were detected in any soil, sediment, or vegetation sample collected at CMTC. Trace quantities of one or more priority pollutants were tentatively detected in three of eight soil and sediment samples. However, the priority pollutant concentrations are so low that they pose no environmental or health hazards. No evidence of widespread or significant contamination in the training areas was found. Crucial data needed to fully evaluate both acute and chronic health effects of civilian exposures to CS at CMTC are not available. On the basis of the available literature, long-ten-n health effects in the civilian population near CMTC that could result from the use of fog oil and CS during training activities are believed to be negligible.« less

Alcohol cognitive bias modification training for problem drinkers over the web.

PubMed

Wiers, Reinout W; Houben, Katrijn; Fadardi, Javad S; van Beek, Paul; Rhemtulla, Mijke; Cox, W Miles

2015-01-01

Following successful outcomes of cognitive bias modification (CBM) programs for alcoholism in clinical and community samples, the present study investigated whether different varieties of CBM (attention control training and approach-bias re-training) could be delivered successfully in a fully automated web-based way and whether these interventions would help self-selected problem drinkers to reduce their drinking. Participants were recruited through online advertising, which resulted in 697 interested participants, of whom 615 were screened in. Of the 314 who initiated training, 136 completed a pretest, four sessions of computerized training and a posttest. Participants were randomly assigned to one of four experimental conditions (attention control or one of three varieties of approach-bias re-training) or a sham-training control condition. The general pattern of findings was that participants in all conditions (including participants in the control-training condition) reduced their drinking. It is suggested that integrating CBM with online cognitive and motivational interventions could improve results. Copyright © 2014 Elsevier Ltd. All rights reserved.

Filtered selection coupled with support vector machines generate a functionally relevant prediction model for colorectal cancer

PubMed Central

Gabere, Musa Nur; Hussein, Mohamed Aly; Aziz, Mohammad Azhar

2016-01-01

Purpose There has been considerable interest in using whole-genome expression profiles for the classification of colorectal cancer (CRC). The selection of important features is a crucial step before training a classifier. Methods In this study, we built a model that uses support vector machine (SVM) to classify cancer and normal samples using Affymetrix exon microarray data obtained from 90 samples of 48 patients diagnosed with CRC. From the 22,011 genes, we selected the 20, 30, 50, 100, 200, 300, and 500 genes most relevant to CRC using the minimum-redundancy–maximum-relevance (mRMR) technique. With these gene sets, an SVM model was designed using four different kernel types (linear, polynomial, radial basis function [RBF], and sigmoid). Results The best model, which used 30 genes and RBF kernel, outperformed other combinations; it had an accuracy of 84% for both ten fold and leave-one-out cross validations in discriminating the cancer samples from the normal samples. With this 30 genes set from mRMR, six classifiers were trained using random forest (RF), Bayes net (BN), multilayer perceptron (MLP), naïve Bayes (NB), reduced error pruning tree (REPT), and SVM. Two hybrids, mRMR + SVM and mRMR + BN, were the best models when tested on other datasets, and they achieved a prediction accuracy of 95.27% and 91.99%, respectively, compared to other mRMR hybrid models (mRMR + RF, mRMR + NB, mRMR + REPT, and mRMR + MLP). Ingenuity pathway analysis was used to analyze the functions of the 30 genes selected for this model and their potential association with CRC: CDH3, CEACAM7, CLDN1, IL8, IL6R, MMP1, MMP7, and TGFB1 were predicted to be CRC biomarkers. Conclusion This model could be used to further develop a diagnostic tool for predicting CRC based on gene expression data from patient samples. PMID:27330311

Knowledge of the Nigerian Code of Health Research Ethics Among Biomedical Researchers in Southern Nigeria.

PubMed

Ogunrin, Olubunmi A; Daniel, Folasade; Ansa, Victor

2016-12-01

Responsibility for protection of research participants from harm and exploitation rests on Research Ethics Committees and principal investigators. The Nigerian National Code of Health Research Ethics defines responsibilities of stakeholders in research so its knowledge among researchers will likely aid ethical conduct of research. The levels of awareness and knowledge of the Code among biomedical researchers in southern Nigerian research institutions was assessed. Four institutions were selected using a stratified random sampling technique. Research participants were selected by purposive sampling and completed a pre-tested structured questionnaire. A total of 102 biomedical researchers completed the questionnaires. Thirty percent of the participants were aware of the National Code though 64% had attended at least one training seminar in research ethics. Twenty-five percent had a fairly acceptable knowledge (scores 50%-74%) and 10% had excellent knowledge of the code (score ≥75%). Ninety-five percent expressed intentions to learn more about the National Code and agreed that it is highly relevant to the ethical conduct of research. Awareness and knowledge of the Code were found to be very limited among biomedical researchers in southern Nigeria. There is need to improve awareness and knowledge through ethics seminars and training. Use of existing Nigeria-specific online training resources is also encouraged.

redMaGiC: selecting luminous red galaxies from the DES Science Verification data

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

Rozo, E.

We introduce redMaGiC, an automated algorithm for selecting Luminous Red Galaxies (LRGs). The algorithm was developed to minimize photometric redshift uncertainties in photometric large-scale structure studies. redMaGiC achieves this by self-training the color-cuts necessary to produce a luminosity-thresholded LRG sam- ple of constant comoving density. Additionally, we demonstrate that redMaGiC photo-zs are very nearly as accurate as the best machine-learning based methods, yet they require minimal spectroscopic training, do not suffer from extrapolation biases, and are very nearly Gaussian. We apply our algorithm to Dark Energy Survey (DES) Science Verification (SV) data to produce a redMaGiC catalog sampling the redshiftmore » range z ϵ [0.2,0.8]. Our fiducial sample has a comoving space density of 10 -3 (h -1Mpc) -3, and a median photo-z bias (z spec z photo) and scatter (σ z=(1 + z)) of 0.005 and 0.017 respectively.The corresponding 5σ outlier fraction is 1.4%. We also test our algorithm with Sloan Digital Sky Survey (SDSS) Data Release 8 (DR8) and Stripe 82 data, and discuss how spectroscopic training can be used to control photo-z biases at the 0.1% level.« less

Limits in decision making arise from limits in memory retrieval.

PubMed

Giguère, Gyslain; Love, Bradley C

2013-05-07

Some decisions, such as predicting the winner of a baseball game, are challenging in part because outcomes are probabilistic. When making such decisions, one view is that humans stochastically and selectively retrieve a small set of relevant memories that provides evidence for competing options. We show that optimal performance at test is impossible when retrieving information in this fashion, no matter how extensive training is, because limited retrieval introduces noise into the decision process that cannot be overcome. One implication is that people should be more accurate in predicting future events when trained on idealized rather than on the actual distributions of items. In other words, we predict the best way to convey information to people is to present it in a distorted, idealized form. Idealization of training distributions is predicted to reduce the harmful noise induced by immutable bottlenecks in people's memory retrieval processes. In contrast, machine learning systems that selectively weight (i.e., retrieve) all training examples at test should not benefit from idealization. These conjectures are strongly supported by several studies and supporting analyses. Unlike machine systems, people's test performance on a target distribution is higher when they are trained on an idealized version of the distribution rather than on the actual target distribution. Optimal machine classifiers modified to selectively and stochastically sample from memory match the pattern of human performance. These results suggest firm limits on human rationality and have broad implications for how to train humans tasked with important classification decisions, such as radiologists, baggage screeners, intelligence analysts, and gamblers.

Limits in decision making arise from limits in memory retrieval

PubMed Central

Giguère, Gyslain; Love, Bradley C.

2013-01-01

Some decisions, such as predicting the winner of a baseball game, are challenging in part because outcomes are probabilistic. When making such decisions, one view is that humans stochastically and selectively retrieve a small set of relevant memories that provides evidence for competing options. We show that optimal performance at test is impossible when retrieving information in this fashion, no matter how extensive training is, because limited retrieval introduces noise into the decision process that cannot be overcome. One implication is that people should be more accurate in predicting future events when trained on idealized rather than on the actual distributions of items. In other words, we predict the best way to convey information to people is to present it in a distorted, idealized form. Idealization of training distributions is predicted to reduce the harmful noise induced by immutable bottlenecks in people’s memory retrieval processes. In contrast, machine learning systems that selectively weight (i.e., retrieve) all training examples at test should not benefit from idealization. These conjectures are strongly supported by several studies and supporting analyses. Unlike machine systems, people’s test performance on a target distribution is higher when they are trained on an idealized version of the distribution rather than on the actual target distribution. Optimal machine classifiers modified to selectively and stochastically sample from memory match the pattern of human performance. These results suggest firm limits on human rationality and have broad implications for how to train humans tasked with important classification decisions, such as radiologists, baggage screeners, intelligence analysts, and gamblers. PMID:23610402

Survey of Employers.

ERIC Educational Resources Information Center

European Social Fund, Dublin (Ireland).

A study examined attitudes of Irish employers toward vocational training (VT) activities, state agencies responsible for administering VT, and the skills that employees would need in the future. Of a sample of 500 firms that were selected as being representative from the standpoints of size, sector, location, and form of ownership, 219 were…

34 CFR 200.89 - MEP allocations; Re-interviewing; Eligibility documentation; and Quality control.

Code of Federal Regulations, 2011 CFR

2011-07-01

... determinations being tested) trained to conduct personal interviews and to understand and apply program... child eligibility determinations through the re-interview of a randomly selected sample of children previously identified as migratory. In conducting these re-interviews, an SEA must— (i) Use, at least once...

34 CFR 200.89 - MEP allocations; Re-interviewing; Eligibility documentation; and Quality control.

Code of Federal Regulations, 2010 CFR

2010-07-01

... determinations being tested) trained to conduct personal interviews and to understand and apply program... child eligibility determinations through the re-interview of a randomly selected sample of children previously identified as migratory. In conducting these re-interviews, an SEA must— (i) Use, at least once...

Hair Mineral Analysis and Disruptive Behavior in Clinically Normal Young Men.

ERIC Educational Resources Information Center

Struempler, Richard E.; And Others

1985-01-01

Forty young navy recruits were selected for hair mineral analysis on the basis of three criteria: mental test scores, demerits during training, and premature discharge from the navy. Statistical analysis revealed several significant relationships between behavioral criteria and mineral measures. Findings confirmed, in a nonclinical sample, hair…

Sensitivity of the central visual field in 70- to 81-year-old male athletes and in a population sample.

PubMed

Era, P; Pärssinen, O; Pykälä, P; Jokela, J; Suominen, H

1994-10-01

The sensitivity of the central visual field (0 degree-30 degrees) was studied using an automatic Octopus 500E perimeter in elderly male athletes and in a population sample of men of corresponding age. The athletes (N = 96) were endurance and power athletes, who were still active in competitive sports with training histories spanning tens of years. The athletes' results were compared with those of a sample of men of the same age (70-81 years, N = 41) randomly selected from the local population register. The sensitivity values of the athletes, and the endurance athletes in particular, were significantly better than those of the controls, with differences varying from 1 to 2.5 dB in the different areas of the central visual field. Multivariate analyses of the background factors of visual field sensitivity showed that the most important were age, amount of annual training, number of chronic diseases, HDL-cholesterol level, and vital capacity. The results suggest that a long training history, especially of the aerobic type, may be beneficial with respect to the sensitivity of the visual system.

Effects of consensus training on the reliability of auditory perceptual ratings of voice quality.

PubMed

Iwarsson, Jenny; Reinholt Petersen, Niels

2012-05-01

This study investigates the effect of consensus training of listeners on intrarater and interrater reliability and agreement of perceptual voice analysis. The use of such training, including a reference voice sample, could be assumed to make the internal standards held in memory common and more robust, which is of great importance to reduce the variability of auditory perceptual ratings. A prospective design with testing before and after training. Thirteen students of audiologopedics served as listening subjects. The ratings were made using a multidimensional protocol with four-point equal-appearing interval scales. The stimuli consisted of text reading by authentic dysphonic patients. The consensus training for each perceptual voice parameter included (1) definition, (2) underlying physiology, (3) presentation of carefully selected sound examples representing the parameter in three different grades followed by group discussions of perceived characteristics, and (4) practical exercises including imitation to make use of the listeners' proprioception. Intrarater reliability and agreement showed a marked improvement for intermittent aphonia but not for vocal fry. Interrater reliability was high for most parameters before training with a slight increase after training. Interrater agreement showed marked increases for most voice quality parameters as a result of the training. The results support the recommendation of specific consensus training, including use of a reference voice sample material, to calibrate, equalize, and stabilize the internal standards held in memory by the listeners. Copyright © 2012 The Voice Foundation. Published by Mosby, Inc. All rights reserved.

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.

Community intervention to improve knowledge and practices on commonly used drugs.

PubMed

Kafle, K K; Karkee, S B; Shrestha, N; Prasad, R R; Bhuju, G B; Das, P L; Chataut, B D

2010-01-01

World Health Organisation (WHO) estimates that about half of all medicines are inappropriately prescribed, dispensed and sold and about half of all patients fail to take their medicines properly. The overall objective of the study was improving use of medicines in the community by creating awareness among different target groups. It was a pre-post comparison of intervention implemented at the community level in purposively selected Bhaktapur District of Kathmandu Valley, Nepal. The study was conducted in the private schools of the study district. Twelve schools were randomly selected. Thereafter, students from 6-9 grades were listed from the selected schools. Then 15% of the total students in each grade were randomly selected to get six students from each grade of the each school, totaling 288 students. The households of the selected students served as the sample households for the study. Thus, there were 288 households sampled for the study. The intervention and the targeted intermediary groups consisted of a. training of schools teachers b. training of journalists c. interactive discussions of trained school teachers with school children using key messages and c. communication of key messages through the local F.M. radio, newspaper/magazine. There was a significant increase in correct knowledge on action of antibiotics and excellent knowledge on the methods of administration of antibiotics of households after the intervention. Similarly, there was a significant increase in knowledge on cough as a disease and a significant decrease in the use of cough medicines after intervention. There was also a significant increase in excellent knowledge on the sources of vitamins and a significant decrease in the use of vitamin/tonics after the intervention. The participation of intermediary groups eg. school teachers, journalists and school children in the implementation of intervention were successful. The groups have fulfilled the commitments in implementing the plan of action. The key messages have effectively reached the households, and the knowledge and practices of the community members in drug use have improved.

NASA/BLM APT, phase 2. Volume 2: Technology demonstration. [Arizona

NASA Technical Reports Server (NTRS)

1981-01-01

Techniques described include: (1) steps in the preprocessing of LANDSAT data; (2) the training of a classifier; (3) maximum likelihood classification and precision; (4) geometric correction; (5) class description; (6) digitizing; (7) digital terrain data; (8) an overview of sample design; (9) allocation and selection of primary sample units; (10) interpretation of secondary sample units; (11) data collection ground plots; (12) data reductions; (13) analysis for productivity estimation and map verification; (14) cost analysis; and (150) LANDSAT digital products. The evaluation of the pre-inventory planning for P.J. is included.

Effectiveness of Group Training of Assertiveness on Social Anxiety among Deaf and Hard of Hearing Adolescents.

PubMed

Ahmadi, Hamed; Daramadi, Parviz Sharifi; Asadi-Samani, Majid; Givtaj, Hamed; Sani, Mohammad Reza Mahmoudian

2017-06-01

The present study was conducted to compare the effectiveness of assertiveness group training on social anxiety (SAD) between deaf and hearing impaired adolescents. Forty eight (24 deaf and 24 hearing impaired) people participated in this study. First, participants with SAD, i.e. attaining the scores above 40 for Connor's Social Inventory Scale 2000 (SPIN), were selected according to convenience sampling and randomly assigned to two groups, i.e. intervention and control. Then, assertiveness group training was conducted for intervention group within 10 sessions, and immediately after completion of the training sessions, SPIN was re-administered to the two groups. ANCOVA showed that the effectiveness of assertiveness group training on SAD is different between deaf and hearing impaired participants, i.e. assertiveness group training was effective on improvement of SAD in hearing impaired participants but not deaf ones. Therefore, it is recommended to incorporate assertiveness group training in the educational programs developed for adolescents with ear disorders especially hearing impairment.

Training set selection for the prediction of essential genes.

PubMed

Cheng, Jian; Xu, Zhao; Wu, Wenwu; Zhao, Li; Li, Xiangchen; Liu, Yanlin; Tao, Shiheng

2014-01-01

Various computational models have been developed to transfer annotations of gene essentiality between organisms. However, despite the increasing number of microorganisms with well-characterized sets of essential genes, selection of appropriate training sets for predicting the essential genes of poorly-studied or newly sequenced organisms remains challenging. In this study, a machine learning approach was applied reciprocally to predict the essential genes in 21 microorganisms. Results showed that training set selection greatly influenced predictive accuracy. We determined four criteria for training set selection: (1) essential genes in the selected training set should be reliable; (2) the growth conditions in which essential genes are defined should be consistent in training and prediction sets; (3) species used as training set should be closely related to the target organism; and (4) organisms used as training and prediction sets should exhibit similar phenotypes or lifestyles. We then analyzed the performance of an incomplete training set and an integrated training set with multiple organisms. We found that the size of the training set should be at least 10% of the total genes to yield accurate predictions. Additionally, the integrated training sets exhibited remarkable increase in stability and accuracy compared with single sets. Finally, we compared the performance of the integrated training sets with the four criteria and with random selection. The results revealed that a rational selection of training sets based on our criteria yields better performance than random selection. Thus, our results provide empirical guidance on training set selection for the identification of essential genes on a genome-wide scale.

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.

Progressive sampling-based Bayesian optimization for efficient and automatic machine learning model selection.

PubMed

Zeng, Xueqiang; Luo, Gang

2017-12-01

Machine learning is broadly used for clinical data analysis. Before training a model, a machine learning algorithm must be selected. Also, the values of one or more model parameters termed hyper-parameters must be set. Selecting algorithms and hyper-parameter values requires advanced machine learning knowledge and many labor-intensive manual iterations. To lower the bar to machine learning, miscellaneous automatic selection methods for algorithms and/or hyper-parameter values have been proposed. Existing automatic selection methods are inefficient on large data sets. This poses a challenge for using machine learning in the clinical big data era. To address the challenge, this paper presents progressive sampling-based Bayesian optimization, an efficient and automatic selection method for both algorithms and hyper-parameter values. We report an implementation of the method. We show that compared to a state of the art automatic selection method, our method can significantly reduce search time, classification error rate, and standard deviation of error rate due to randomization. This is major progress towards enabling fast turnaround in identifying high-quality solutions required by many machine learning-based clinical data analysis tasks.

Classifier transfer with data selection strategies for online support vector machine classification with class imbalance

NASA Astrophysics Data System (ADS)

Krell, Mario Michael; Wilshusen, Nils; Seeland, Anett; Kim, Su Kyoung

2017-04-01

Objective. Classifier transfers usually come with dataset shifts. To overcome dataset shifts in practical applications, we consider the limitations in computational resources in this paper for the adaptation of batch learning algorithms, like the support vector machine (SVM). Approach. We focus on data selection strategies which limit the size of the stored training data by different inclusion, exclusion, and further dataset manipulation criteria like handling class imbalance with two new approaches. We provide a comparison of the strategies with linear SVMs on several synthetic datasets with different data shifts as well as on different transfer settings with electroencephalographic (EEG) data. Main results. For the synthetic data, adding only misclassified samples performed astoundingly well. Here, balancing criteria were very important when the other criteria were not well chosen. For the transfer setups, the results show that the best strategy depends on the intensity of the drift during the transfer. Adding all and removing the oldest samples results in the best performance, whereas for smaller drifts, it can be sufficient to only add samples near the decision boundary of the SVM which reduces processing resources. Significance. For brain-computer interfaces based on EEG data, models trained on data from a calibration session, a previous recording session, or even from a recording session with another subject are used. We show, that by using the right combination of data selection criteria, it is possible to adapt the SVM classifier to overcome the performance drop from the transfer.

Chemometric classification of casework arson samples based on gasoline content.

PubMed

Sinkov, Nikolai A; Sandercock, P Mark L; Harynuk, James J

2014-02-01

Detection and identification of ignitable liquids (ILs) in arson debris is a critical part of arson investigations. The challenge of this task is due to the complex and unpredictable chemical nature of arson debris, which also contains pyrolysis products from the fire. ILs, most commonly gasoline, are complex chemical mixtures containing hundreds of compounds that will be consumed or otherwise weathered by the fire to varying extents depending on factors such as temperature, air flow, the surface on which IL was placed, etc. While methods such as ASTM E-1618 are effective, data interpretation can be a costly bottleneck in the analytical process for some laboratories. In this study, we address this issue through the application of chemometric tools. Prior to the application of chemometric tools such as PLS-DA and SIMCA, issues of chromatographic alignment and variable selection need to be addressed. Here we use an alignment strategy based on a ladder consisting of perdeuterated n-alkanes. Variable selection and model optimization was automated using a hybrid backward elimination (BE) and forward selection (FS) approach guided by the cluster resolution (CR) metric. In this work, we demonstrate the automated construction, optimization, and application of chemometric tools to casework arson data. The resulting PLS-DA and SIMCA classification models, trained with 165 training set samples, have provided classification of 55 validation set samples based on gasoline content with 100% specificity and sensitivity. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Identity matching in a patient with Alzheimer's disease.

PubMed

Steinunn Steingrimsdottir, Hanna; Arntzen, Erik

2011-05-01

Difficulties with short-term memory are one of the main problems in patients with dementia. Therefore, one purpose of this study was to examine the effects of simultaneous vs delayed presentation of comparison stimuli in a matching-to-sample (MTS) task using computerized training. By using an identity MTS procedure, the participant was trained to select a comparison stimulus identical to a sample stimulus. A 2nd purpose was to study the effect of the number of choices presented, thereby evaluating short-term memory deficits and possible deterioration of deficits over time. In this study, an 80-year-old-male with a Mini-Mental State Examination (MMSE) score of 10 was exposed to 4 experimental conditions. The results showed that using 3 comparison stimuli presented simultaneously with the sample stimulus on the screen resulted in more incorrect responding than when using 2 comparison stimuli. Furthermore, when adding a 0-second delay between the presentation of the sample stimulus and the 2 comparison stimuli, the number of correct responses did not exceed chance level.

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.

Wrappers for Performance Enhancement and Oblivious Decision Graphs

DTIC Science & Technology

1995-09-01

always select all relevant features. We test di erent search engines to search the space of feature subsets and introduce compound operators to speed...distinct instances from the original dataset appearing in the test set is thus 0:632m. The 0i accuracy estimate is derived by using bootstrap sample...i for training and the rest of the instances for testing . Given a number b, the number of bootstrap samples, let 0i be the accuracy estimate for

MPA and CPM Curriculum: An Analysis of the Views of Public Administrators.

ERIC Educational Resources Information Center

Rose, Bruce J.

Views of state public administrators about management education and training needs were investigated, as were administrator views concerning short-term management development workshops. Data was drawn from responses to questionnaires mailed to 5,980 state administrators who were selected from a national survey and from random samples using lists…

An Analysis of Self-Reported Graduates. Technical Paper

ERIC Educational Resources Information Center

Braysher, Ben

2012-01-01

The annual Student Outcomes Survey collects information on the outcomes of two groups of students--those that have completed a qualification (graduates) and those that have completed only part of a course and then left the vocational education and training (VET) system (module completers). At the time of selecting the survey sample, insufficient…

Student Teachers' Conceptions of Creativity in the Secondary Music Classroom

ERIC Educational Resources Information Center

Kokotsaki, Dimitra

2011-01-01

This study aims to explore the meaning of the concept of creativity from the perspective of student teachers pursuing a one year teacher training course following their first degree. Seventeen student teachers following a specialist music teaching route in secondary education were selected as the sample for this study to offer their understanding…

How Broad Liberal Arts Training Produces Phd Economists: Carleton's Story

ERIC Educational Resources Information Center

Bourne, Jenny; Grawe, Nathan D.

2015-01-01

Several recent studies point to strong performance in economics PhD programs of graduates from liberal arts colleges. While every undergraduate program is unique and the likelihood of selection bias combines with small sample sizes to caution against drawing strong conclusions, the authors reflect on their experience at Carleton College to…

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.

Geriatric dentistry education and context in a selection of countries in 5 continents.

PubMed

Marchini, Leonardo; Ettinger, Ronald; Chen, Xi; Kossioni, Anastassia; Tan, Haiping; Tada, Sayaka; Ikebe, Kazunori; Dosumu, Elizabeth Bosede; Oginni, Fadekemi O; Akeredolu, Patricia Adetokunbo; Butali, Azeez; Donnelly, Leeann; Brondani, Mario; Fritzsch, Bernd; Adeola, Henry A

2018-05-01

To summarize and discuss how geriatric dentistry has been addressed in dental schools of different countries regarding to (1) teaching students at the predoctoral level; (2) advanced training, and (3) research. A convenience sample of faculty members from a selection of high, upper-middle and lower-middle income countries were recruited to complete the survey. The survey had 5 open-ended main topics, and asked about (1) the size of their elderly population, (2) general information about dental education; (3) the number of dental schools teaching geriatric dentistry, and their teaching methods; (4) advanced training in geriatric dentistry; (5) scholarship/research in geriatric dentistry. (1) There is great variation in the size of elderly population; (2) duration of training and content of dental education curriculum varies; (3) geriatric dentistry has not been established as a standalone course in dental schools in the majority of the countries, (4) most countries, with the exception of Japan, lack adequate number of dentists trained in geriatric dentistry as well as training programs, and (5) geriatric dentistry-related research has increased in recent years in scope and content, although the majority of these papers are not in English. © 2018 Special Care Dentistry Association and Wiley Periodicals, Inc.

Optical method and apparatus for detection of surface and near-subsurface defects in dense ceramics

DOEpatents

Ellingson, William A.; Brada, Mark P.

1995-01-01

A laser is used in a non-destructive manner to detect surface and near-subsurface defects in dense ceramics and particularly in ceramic bodies with complex shapes such as ceramic bearings, turbine blades, races, and the like. The laser's wavelength is selected based upon the composition of the ceramic sample and the laser can be directed on the sample while the sample is static or in dynamic rotate or translate motion. Light is scattered off surface and subsurface defects using a preselected polarization. The change in polarization angle is used to select the depth and characteristics of surface/subsurface defects. The scattered light is detected by an optical train consisting of a charge coupled device (CCD), or vidicon, television camera which, in turn, is coupled to a video monitor and a computer for digitizing the image. An analyzing polarizer in the optical train allows scattered light at a given polarization angle to be observed for enhancing sensitivity to either surface or near-subsurface defects. Application of digital image processing allows subtraction of digitized images in near real-time providing enhanced sensitivity to subsurface defects. Storing known "feature masks" of identified defects in the computer and comparing the detected scatter pattern (Fourier images) with the stored feature masks allows for automatic classification of detected defects.

Assessment of soil and water contaminants from selected locations in and near the Idaho Army National Guard Orchard Training Area, Ada County, Idaho, 2001-2003

USGS Publications Warehouse

Parliman, D.J.

2004-01-01

In 2001, the National Guard Bureau and the U.S. Geological Survey began a project to compile hydrogeologic data and determine presence or absence of soil, surface-water, and ground-water contamination at the Idaho Army National Guard Orchard Training Area in southwestern Idaho. Between June 2002 and April 2003, a total of 114 soil, surface-water, ground-water, precipitation, or dust samples were collected from 68 sample sites (65 different locations) in the Orchard Training Area (OTA) or along the vehicle corridor to the OTA. Soil and water samples were analyzed for concentrations of selected total trace metals, major ions, nutrients, explosive compounds, semivolatile organics, and petroleum hydrocarbons. Water samples also were analyzed for concentrations of selected dissolved trace metals and major ions. Distinguishing naturally occurring large concentrations of trace metals, major ions, and nutrients from contamination related to land and water uses at the OTA was difficult. There were no historical analyses for this area to compare with modern data, and although samples were collected from 65 locations in and near the OTA, sampled areas represented only a small part of the complex OTA land-use areas and soil types. For naturally occurring compounds, several assumptions were made?anomalously large concentrations, when tied to known land uses, may indicate presence of contamination; naturally occurring concentrations cannot be separated from contamination concentrations in mid- and lower ranges of data; and smallest concentrations may represent the lowest naturally occurring range of concentrations and (or) the absence of contaminants related to land and water uses. Presence of explosive, semivolatile organic (SVOC), and petroleum hydrocarbon compounds in samples indicates contamination from land and water uses. In areas along the vehicle corridor and major access roads within the OTA, most trace metal, major ion, and nutrient concentrations in soil samples were not in the upper 10th percentile of data, but concentrations of 25 metals, ions, or nutrients were in the upper 10th percentile in a puddle sample near the heavy equipment maneuvering area, MPRC-H. The largest concentrations of tin, ammonia, and nitrite plus nitrate (as nitrogen) in water from the OTA were detected in a sample from this puddle. Petroleum hydrocarbons were the most common contaminant, detected in all soil and surface-water samples. An SVOC, bis (2-ethylhexyl) phthalate, a plasticizer, was detected at a site along the vehicle corridor. In Maneuver Areas within the OTA, many soil samples contained at least one trace metal, major ion, or nutrient in the upper 10th percentile of data, and the largest concentrations of cobalt, iron, mercury, titanium, sodium, ammonia, or total phosphorus were detected in 6 of 13 soil samples outside the Tadpole Lake area. The largest concentrations of aluminum, arsenic, beryllium, nickel, selenium, silver, strontium, thallium, vanadium, chloride, potassium, sulfate, and nitrite plus nitrate were detected in soil samples from the Tadpole Lake area. Water from Tadpole Lake contained the largest total concentrations of 19 trace metals, 4 major ions, and 1 nutrient. Petroleum hydrocarbons were detected in 5 soil samples and water from Tadpole Lake. SVOCs related to combustion of fuel or plasticizers were detected in 1 soil sample. Explosive compounds were detected in 1 precipitation sample.In the Impact Area within the OTA, most soil samples contained at least one trace metal, major ion, or nutrient in the upper 10th percentile of data, and the largest concentrations of barium, chromium, copper, manganese, lead, or orthophosphate were detected in 6 of the 18 soil samples. Petroleum hydrocarbons were detected in 4 soil samples, SVOCs in 6 samples, and explosive compounds in 4 samples. In the mobilization and training equipment site (MATES) compound adjacent to the OTA, all soil and water samples contained at lea

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.

Multistage classification of multispectral Earth observational data: The design approach

NASA Technical Reports Server (NTRS)

Bauer, M. E. (Principal Investigator); Muasher, M. J.; Landgrebe, D. A.

1981-01-01

An algorithm is proposed which predicts the optimal features at every node in a binary tree procedure. The algorithm estimates the probability of error by approximating the area under the likelihood ratio function for two classes and taking into account the number of training samples used in estimating each of these two classes. Some results on feature selection techniques, particularly in the presence of a very limited set of training samples, are presented. Results comparing probabilities of error predicted by the proposed algorithm as a function of dimensionality as compared to experimental observations are shown for aircraft and LANDSAT data. Results are obtained for both real and simulated data. Finally, two binary tree examples which use the algorithm are presented to illustrate the usefulness of the procedure.

Sparse feature selection for classification and prediction of metastasis in endometrial cancer.

PubMed

Ahsen, Mehmet Eren; Boren, Todd P; Singh, Nitin K; Misganaw, Burook; Mutch, David G; Moore, Kathleen N; Backes, Floor J; McCourt, Carolyn K; Lea, Jayanthi S; Miller, David S; White, Michael A; Vidyasagar, Mathukumalli

2017-03-27

Metastasis via pelvic and/or para-aortic lymph nodes is a major risk factor for endometrial cancer. Lymph-node resection ameliorates risk but is associated with significant co-morbidities. Incidence in patients with stage I disease is 4-22% but no mechanism exists to accurately predict it. Therefore, national guidelines for primary staging surgery include pelvic and para-aortic lymph node dissection for all patients whose tumor exceeds 2cm in diameter. We sought to identify a robust molecular signature that can accurately classify risk of lymph node metastasis in endometrial cancer patients. 86 tumors matched for age and race, and evenly distributed between lymph node-positive and lymph node-negative cases, were selected as a training cohort. Genomic micro-RNA expression was profiled for each sample to serve as the predictive feature matrix. An independent set of 28 tumor samples was collected and similarly characterized to serve as a test cohort. A feature selection algorithm was designed for applications where the number of samples is far smaller than the number of measured features per sample. A predictive miRNA expression signature was developed using this algorithm, which was then used to predict the metastatic status of the independent test cohort. A weighted classifier, using 18 micro-RNAs, achieved 100% accuracy on the training cohort. When applied to the testing cohort, the classifier correctly predicted 90% of node-positive cases, and 80% of node-negative cases (FDR = 6.25%). Results indicate that the evaluation of the quantitative sparse-feature classifier proposed here in clinical trials may lead to significant improvement in the prediction of lymphatic metastases in endometrial cancer patients.

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

The effectiveness of training acceptance / commitment and training emotion regulation on high-risk behaviors of students with dyscalculia.

PubMed

Narimani, Mohammad; Abbasi, Moslem; Abolghasemi, Abbas; Ahadi, Batoul

2013-09-01

Now a days the utilization of Acceptance / Commitment and Emotion Regulation Strategy as a comprehensive treatment plan has been discussed in both the prevention and the control of destructive and risky behaviors. Treatment based on Acceptance/Commitment and Emotion Regulation was effective in both the improvement and the control of high-risk behaviors of students with dyscalculia. The purpose of this study was to investigate the effectiveness of Acceptance and Commitment, and Emotional Regulation training in high-risk behaviors of students with dyscalculia. This research was experimental, with pre-test, post-test and a control group. The statistical universe of this study included all sixth-grade male students in Ardabil city in the academic year of 2012-2013 (A.H.). The subjects of this study involved 800 sixth-grade elementary students in Ardabil province, selected using a multi-stage cluster sampling. From among them, 60 students with dyscalculia were selected using random sampling method after the initial diagnosis by structured clinical interview and the Keymath Mathematic test. Twenty pupil were selected for either the experimental or the control group. To collect data, the questionnaires of "Keymath Mathematic test" and High-risk Behavior" were used. The results of Multivariate Analysis of Variance (MANOVA) showed that "Acceptance / Commitment and Emotion Regulation" treatment trainings were effective in reducing high-risk behaviors, in a manner that they led to a reduction in negative emotions, self-destructive and impulsive behaviors of students with math disorder (dyscalculia). It can be concluded that teaching these skills to the students has been influential in enhancing awareness level and change or positive attitude creation in the subjects. Therefore, it is essential to design and implement interventions based on "prevention caused by the peer group, in collaboration with the parents either at the school or at home among the family members".

Training self-assessment and task-selection skills to foster self-regulated learning: Do trained skills transfer across domains?

PubMed

Raaijmakers, Steven F; Baars, Martine; Paas, Fred; van Merriënboer, Jeroen J G; van Gog, Tamara

2018-01-01

Students' ability to accurately self-assess their performance and select a suitable subsequent learning task in response is imperative for effective self-regulated learning. Video modeling examples have proven effective for training self-assessment and task-selection skills, and-importantly-such training fostered self-regulated learning outcomes. It is unclear, however, whether trained skills would transfer across domains. We investigated whether skills acquired from training with either a specific, algorithmic task-selection rule or a more general heuristic task-selection rule in biology would transfer to self-regulated learning in math. A manipulation check performed after the training confirmed that both algorithmic and heuristic training improved task-selection skills on the biology problems compared with the control condition. However, we found no evidence that students subsequently applied the acquired skills during self-regulated learning in math. Future research should investigate how to support transfer of task-selection skills across domains.

Training self‐assessment and task‐selection skills to foster self‐regulated learning: Do trained skills transfer across domains?

PubMed Central

Baars, Martine; Paas, Fred; van Merriënboer, Jeroen J. G.; van Gog, Tamara

2018-01-01

Summary Students' ability to accurately self‐assess their performance and select a suitable subsequent learning task in response is imperative for effective self‐regulated learning. Video modeling examples have proven effective for training self‐assessment and task‐selection skills, and—importantly—such training fostered self‐regulated learning outcomes. It is unclear, however, whether trained skills would transfer across domains. We investigated whether skills acquired from training with either a specific, algorithmic task‐selection rule or a more general heuristic task‐selection rule in biology would transfer to self‐regulated learning in math. A manipulation check performed after the training confirmed that both algorithmic and heuristic training improved task‐selection skills on the biology problems compared with the control condition. However, we found no evidence that students subsequently applied the acquired skills during self‐regulated learning in math. Future research should investigate how to support transfer of task‐selection skills across domains. PMID:29610547

Improving the performance of extreme learning machine for hyperspectral image classification

NASA Astrophysics Data System (ADS)

Li, Jiaojiao; Du, Qian; Li, Wei; Li, Yunsong

2015-05-01

Extreme learning machine (ELM) and kernel ELM (KELM) can offer comparable performance as the standard powerful classifier―support vector machine (SVM), but with much lower computational cost due to extremely simple training step. However, their performance may be sensitive to several parameters, such as the number of hidden neurons. An empirical linear relationship between the number of training samples and the number of hidden neurons is proposed. Such a relationship can be easily estimated with two small training sets and extended to large training sets so as to greatly reduce computational cost. Other parameters, such as the steepness parameter in the sigmodal activation function and regularization parameter in the KELM, are also investigated. The experimental results show that classification performance is sensitive to these parameters; fortunately, simple selections will result in suboptimal performance.

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 Independent Living Movement in Asia: Solidarity from Japan

ERIC Educational Resources Information Center

Hayashi, Reiko; Okuhira, Masako

2008-01-01

Independent living movements of disabled people are emerging in Asian countries, supported by independent living centres (ILCs) in Japan. A study was conducted in Japan to explore the impact of the training program offered by Japanese ILCs to disabled people from other Asian countries. A sample of 35 people was selected by a snowballing method.…

AN EXPERIMENT IN TEACHING TOPOGRAPHICAL ORIENTATION AND SPATIAL ORGANIZATION TO CONGENITALLY BLIND CHILDREN.

ERIC Educational Resources Information Center

ASCARELLI, ANNA; GARRY, RALPH

THIS RESEARCH ATTEMPTED TO ESTABLISH A BETTER UNDERSTANDING OF THE PROBLEMS OF CONGENITALLY TOTALLY BLIND CHILDREN AND TO TEST THE POSSIBILITY OF MEETING THESE PROBLEMS WITH A SPECIAL TRAINING PROGRAM IN GENERAL ORIENTATION AND SPACE PERCEPTION. A SAMPLE OF 60 CHILDREN WAS SELECTED FOR THE EXPERIMENT. THESE SUBJECTS WERE WITHOUT ADDITIONAL…

New Directions in Apprentice Selection: Self-Perceived "On the Job" Literacy (Reading) Demands of Apprentices.

ERIC Educational Resources Information Center

Edwards, Peter; Gould, Warren

A study investigated the self-perceived, on-the-job literacy tasks of electrical mechanic apprentices in Victoria, Australia. A random sample of 401 apprentices from 19 locations representing all levels of apprenticeship training were questioned about their reading needs and the consequences of making a reading error in their work. Data were…

School Counselors' and School Psychologists' Bullying Prevention and Intervention Strategies: A Look into Real-World Practices

ERIC Educational Resources Information Center

Lund, Emily M.; Blake, Jamilia J.; Ewing, Heidi K.; Banks, Courtney S.

2012-01-01

A sample of 560 school psychologists and school counselors completed a Web-based survey regarding bullying in their schools, related training, and interventions used. Few school-based mental health professionals used evidence-based bullying interventions or were involved in the selection of interventions for their school, and administrators were…

Genomic selection models double the accuracy of predicted breeding values for bacterial cold water disease resistance compared to a traditional pedigree-based model in rainbow trout aquaculture.

PubMed

Vallejo, Roger L; Leeds, Timothy D; Gao, Guangtu; Parsons, James E; Martin, Kyle E; Evenhuis, Jason P; Fragomeni, Breno O; Wiens, Gregory D; Palti, Yniv

2017-02-01

Previously, we have shown that bacterial cold water disease (BCWD) resistance in rainbow trout can be improved using traditional family-based selection, but progress has been limited to exploiting only between-family genetic variation. Genomic selection (GS) is a new alternative that enables exploitation of within-family genetic variation. We compared three GS models [single-step genomic best linear unbiased prediction (ssGBLUP), weighted ssGBLUP (wssGBLUP), and BayesB] to predict genomic-enabled breeding values (GEBV) for BCWD resistance in a commercial rainbow trout population, and compared the accuracy of GEBV to traditional estimates of breeding values (EBV) from a pedigree-based BLUP (P-BLUP) model. We also assessed the impact of sampling design on the accuracy of GEBV predictions. For these comparisons, we used BCWD survival phenotypes recorded on 7893 fish from 102 families, of which 1473 fish from 50 families had genotypes [57 K single nucleotide polymorphism (SNP) array]. Naïve siblings of the training fish (n = 930 testing fish) were genotyped to predict their GEBV and mated to produce 138 progeny testing families. In the following generation, 9968 progeny were phenotyped to empirically assess the accuracy of GEBV predictions made on their non-phenotyped parents. The accuracy of GEBV from all tested GS models were substantially higher than the P-BLUP model EBV. The highest increase in accuracy relative to the P-BLUP model was achieved with BayesB (97.2 to 108.8%), followed by wssGBLUP at iteration 2 (94.4 to 97.1%) and 3 (88.9 to 91.2%) and ssGBLUP (83.3 to 85.3%). Reducing the training sample size to n = ~1000 had no negative impact on the accuracy (0.67 to 0.72), but with n = ~500 the accuracy dropped to 0.53 to 0.61 if the training and testing fish were full-sibs, and even substantially lower, to 0.22 to 0.25, when they were not full-sibs. Using progeny performance data, we showed that the accuracy of genomic predictions is substantially higher than estimates obtained from the traditional pedigree-based BLUP model for BCWD resistance. Overall, we found that using a much smaller training sample size compared to similar studies in livestock, GS can substantially improve the selection accuracy and genetic gains for this trait in a commercial rainbow trout breeding population.

Face recognition based on symmetrical virtual image and original training image

NASA Astrophysics Data System (ADS)

Ke, Jingcheng; Peng, Yali; Liu, Shigang; Li, Jun; Pei, Zhao

2018-02-01

In face representation-based classification methods, we are able to obtain high recognition rate if a face has enough available training samples. However, in practical applications, we only have limited training samples to use. In order to obtain enough training samples, many methods simultaneously use the original training samples and corresponding virtual samples to strengthen the ability of representing the test sample. One is directly using the original training samples and corresponding mirror samples to recognize the test sample. However, when the test sample is nearly symmetrical while the original training samples are not, the integration of the original training and mirror samples might not well represent the test samples. To tackle the above-mentioned problem, in this paper, we propose a novel method to obtain a kind of virtual samples which are generated by averaging the original training samples and corresponding mirror samples. Then, the original training samples and the virtual samples are integrated to recognize the test sample. Experimental results on five face databases show that the proposed method is able to partly overcome the challenges of the various poses, facial expressions and illuminations of original face image.

Selecting Strategies to Reduce High-Risk Unsafe Work Behaviors Using the Safety Behavior Sampling Technique and Bayesian Network Analysis.

PubMed

Ghasemi, Fakhradin; Kalatpour, Omid; Moghimbeigi, Abbas; Mohammadfam, Iraj

2017-03-04

High-risk unsafe behaviors (HRUBs) have been known as the main cause of occupational accidents. Considering the financial and societal costs of accidents and the limitations of available resources, there is an urgent need for managing unsafe behaviors at workplaces. The aim of the present study was to find strategies for decreasing the rate of HRUBs using an integrated approach of safety behavior sampling technique and Bayesian networks analysis. A cross-sectional study. The Bayesian network was constructed using a focus group approach. The required data was collected using the safety behavior sampling, and the parameters of the network were estimated using Expectation-Maximization algorithm. Using sensitivity analysis and belief updating, it was determined that which factors had the highest influences on unsafe behavior. Based on BN analyses, safety training was the most important factor influencing employees' behavior at the workplace. High quality safety training courses can reduce the rate of HRUBs about 10%. Moreover, the rate of HRUBs increased by decreasing the age of employees. The rate of HRUBs was higher in the afternoon and last days of a week. Among the investigated variables, training was the most important factor affecting safety behavior of employees. By holding high quality safety training courses, companies would be able to reduce the rate of HRUBs significantly.

Unsupervised active learning based on hierarchical graph-theoretic clustering.

PubMed

Hu, Weiming; Hu, Wei; Xie, Nianhua; Maybank, Steve

2009-10-01

Most existing active learning approaches are supervised. Supervised active learning has the following problems: inefficiency in dealing with the semantic gap between the distribution of samples in the feature space and their labels, lack of ability in selecting new samples that belong to new categories that have not yet appeared in the training samples, and lack of adaptability to changes in the semantic interpretation of sample categories. To tackle these problems, we propose an unsupervised active learning framework based on hierarchical graph-theoretic clustering. In the framework, two promising graph-theoretic clustering algorithms, namely, dominant-set clustering and spectral clustering, are combined in a hierarchical fashion. Our framework has some advantages, such as ease of implementation, flexibility in architecture, and adaptability to changes in the labeling. Evaluations on data sets for network intrusion detection, image classification, and video classification have demonstrated that our active learning framework can effectively reduce the workload of manual classification while maintaining a high accuracy of automatic classification. It is shown that, overall, our framework outperforms the support-vector-machine-based supervised active learning, particularly in terms of dealing much more efficiently with new samples whose categories have not yet appeared in the training samples.

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.

Selective enzymatic hydrolysis of chlorogenic acid lactones in a model system and in a coffee extract. Application to reduction of coffee bitterness.

PubMed

Kraehenbuehl, Karin; Page-Zoerkler, Nicole; Mauroux, Olivier; Gartenmann, Karin; Blank, Imre; Bel-Rhlid, Rachid

2017-03-01

Chlorogenic acid lactones have been identified as key contributors to coffee bitterness. These compounds are formed during roasting by dehydration and cyclization of their precursors, the chlorogenic acids (CGAs). In the present study, we investigated an approach to decompose these lactones in a selective way without affecting the positive coffee attributes developed during roasting. A model system composed of (3-caffeoylquinic acid lactone (3-CQAL), 4- caffeoyl quinic acid lactone (4-CQAL), and 4-feruloylquinic acid lactone (4-FQAL)) was used for the screening of enzymes before treatment of the coffee extracts. Hog liver esterase (HLE) hydrolyzed chlorogenic acid lactones (CQALs, FQALs) selectively, while chlorogenate esterase hydrolyzed all chlorogenic acids (CQAs, FQAs) and their corresponding lactones (CQALs, FQALs) in a non-selective way. Enzymatically treated coffee samples were evaluated for their bitterness by a trained sensory panel and were found significantly less bitter than the untreated samples. Copyright © 2016 Elsevier Ltd. All rights reserved.

Vibration and acoustic frequency spectra for industrial process modeling using selective fusion multi-condition samples and multi-source features

NASA Astrophysics Data System (ADS)

Tang, Jian; Qiao, Junfei; Wu, ZhiWei; Chai, Tianyou; Zhang, Jian; Yu, Wen

2018-01-01

Frequency spectral data of mechanical vibration and acoustic signals relate to difficult-to-measure production quality and quantity parameters of complex industrial processes. A selective ensemble (SEN) algorithm can be used to build a soft sensor model of these process parameters by fusing valued information selectively from different perspectives. However, a combination of several optimized ensemble sub-models with SEN cannot guarantee the best prediction model. In this study, we use several techniques to construct mechanical vibration and acoustic frequency spectra of a data-driven industrial process parameter model based on selective fusion multi-condition samples and multi-source features. Multi-layer SEN (MLSEN) strategy is used to simulate the domain expert cognitive process. Genetic algorithm and kernel partial least squares are used to construct the inside-layer SEN sub-model based on each mechanical vibration and acoustic frequency spectral feature subset. Branch-and-bound and adaptive weighted fusion algorithms are integrated to select and combine outputs of the inside-layer SEN sub-models. Then, the outside-layer SEN is constructed. Thus, "sub-sampling training examples"-based and "manipulating input features"-based ensemble construction methods are integrated, thereby realizing the selective information fusion process based on multi-condition history samples and multi-source input features. This novel approach is applied to a laboratory-scale ball mill grinding process. A comparison with other methods indicates that the proposed MLSEN approach effectively models mechanical vibration and acoustic signals.

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.

Naming and Categorization in Young Children: IV: Listener Behavior Training and Transfer of Function

PubMed Central

Horne, Pauline J; Hughes, J. Carl; Lowe, C. Fergus

2006-01-01

Following pretraining with everyday objects, 14 children aged from 1 to 4 years were trained, for each of three pairs of different arbitrary wooden shapes (Set 1), to select one stimulus in response to the spoken word /zog/, and the other to /vek/. When given a test for the corresponding tacts (“zog” and “vek”), 10 children passed, showing that they had learned common names for the stimuli, and 4 failed. All children were trained to clap to one stimulus of Pair 1 and wave to the other. All those who named showed either transfer of the novel functions to the remaining two pairs of stimuli in Test 1, or novel function comprehension for all three pairs in Test 2, or both. Three of these children next participated in, and passed, category match-to-sample tests. In contrast, all 4 children who had learned only listener behavior failed both the category transfer and category match-to-sample tests. When 3 of them were next trained to name the stimuli, they passed the category transfer and (for the 2 subjects tested) category match-to-sample tests. Three children were next trained on the common listener relations with another set of arbitrary stimuli (Set 2); all succeeded on the tact and category tests with the Set 2 stimuli. Taken together with the findings from the other studies in the series, the present experiment shows that (a) common listener training also establishes the corresponding names in some but not all children, and (b) only children who learn common names categorize; all those who learn only listener behavior fail. This is good evidence in support of the naming account of categorization. PMID:16673828

Derivation and validation of different machine-learning models in mortality prediction of trauma in motorcycle riders: a cross-sectional retrospective study in southern Taiwan

PubMed Central

Kuo, Pao-Jen; Wu, Shao-Chun; Chien, Peng-Chen; Rau, Cheng-Shyuan; Chen, Yi-Chun; Hsieh, Hsiao-Yun; Hsieh, Ching-Hua

2018-01-01

Objectives This study aimed to build and test the models of machine learning (ML) to predict the mortality of hospitalised motorcycle riders. Setting The study was conducted in a level-1 trauma centre in southern Taiwan. Participants Motorcycle riders who were hospitalised between January 2009 and December 2015 were classified into a training set (n=6306) and test set (n=946). Using the demographic information, injury characteristics and laboratory data of patients, logistic regression (LR), support vector machine (SVM) and decision tree (DT) analyses were performed to determine the mortality of individual motorcycle riders, under different conditions, using all samples or reduced samples, as well as all variables or selected features in the algorithm. Primary and secondary outcome measures The predictive performance of the model was evaluated based on accuracy, sensitivity, specificity and geometric mean, and an analysis of the area under the receiver operating characteristic curves of the two different models was carried out. Results In the training set, both LR and SVM had a significantly higher area under the receiver operating characteristic curve (AUC) than DT. No significant difference was observed in the AUC of LR and SVM, regardless of whether all samples or reduced samples and whether all variables or selected features were used. In the test set, the performance of the SVM model for all samples with selected features was better than that of all other models, with an accuracy of 98.73%, sensitivity of 86.96%, specificity of 99.02%, geometric mean of 92.79% and AUC of 0.9517, in mortality prediction. Conclusion ML can provide a feasible level of accuracy in predicting the mortality of motorcycle riders. Integration of the ML model, particularly the SVM algorithm in the trauma system, may help identify high-risk patients and, therefore, guide appropriate interventions by the clinical staff. PMID:29306885

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.

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.

Spectral signature selection for mapping unvegetated soils

NASA Technical Reports Server (NTRS)

May, G. A.; Petersen, G. W.

1975-01-01

Airborne multispectral scanner data covering the wavelength interval from 0.40-2.60 microns were collected at an altitude of 1000 m above the terrain in southeastern Pennsylvania. Uniform training areas were selected within three sites from this flightline. Soil samples were collected from each site and a procedure developed to allow assignment of scan line and element number from the multispectral scanner data to each sampling location. These soil samples were analyzed on a spectrophotometer and laboratory spectral signatures were derived. After correcting for solar radiation and atmospheric attenuation, the laboratory signatures were compared to the spectral signatures derived from these same soils using multispectral scanner data. Both signatures were used in supervised and unsupervised classification routines. Computer-generated maps using the laboratory and multispectral scanner derived signatures resulted in maps that were similar to maps resulting from field surveys. Approximately 90% agreement was obtained between classification maps produced using multispectral scanner derived signatures and laboratory derived signatures.

Improving Classification of Cancer and Mining Biomarkers from Gene Expression Profiles Using Hybrid Optimization Algorithms and Fuzzy Support Vector Machine

PubMed Central

Moteghaed, Niloofar Yousefi; Maghooli, Keivan; Garshasbi, Masoud

2018-01-01

Background: Gene expression data are characteristically high dimensional with a small sample size in contrast to the feature size and variability inherent in biological processes that contribute to difficulties in analysis. Selection of highly discriminative features decreases the computational cost and complexity of the classifier and improves its reliability for prediction of a new class of samples. Methods: The present study used hybrid particle swarm optimization and genetic algorithms for gene selection and a fuzzy support vector machine (SVM) as the classifier. Fuzzy logic is used to infer the importance of each sample in the training phase and decrease the outlier sensitivity of the system to increase the ability to generalize the classifier. A decision-tree algorithm was applied to the most frequent genes to develop a set of rules for each type of cancer. This improved the abilities of the algorithm by finding the best parameters for the classifier during the training phase without the need for trial-and-error by the user. The proposed approach was tested on four benchmark gene expression profiles. Results: Good results have been demonstrated for the proposed algorithm. The classification accuracy for leukemia data is 100%, for colon cancer is 96.67% and for breast cancer is 98%. The results show that the best kernel used in training the SVM classifier is the radial basis function. Conclusions: The experimental results show that the proposed algorithm can decrease the dimensionality of the dataset, determine the most informative gene subset, and improve classification accuracy using the optimal parameters of the classifier with no user interface. PMID:29535919

Sensory description of marine oils through development of a sensory wheel and vocabulary.

PubMed

Larssen, W E; Monteleone, E; Hersleth, M

2018-04-01

The Omega-3 industry lacks a defined methodology and a vocabulary for evaluating the sensory quality of marine oils. This study was conducted to identify the sensory descriptors of marine oils and organize them in a sensory wheel for use as a tool in quality assessment. Samples of marine oils were collected from six of the largest producers of omega-3 products in Norway. The oils were selected to cover as much variation in sensory characteristics as possible, i.e. oils with different fatty acid content originating from different species. Oils were evaluated by six industry expert panels and one trained sensory panel to build up a vocabulary through a series of language sessions. A total of 184 aroma (odor by nose), flavor, taste and mouthfeel descriptors were generated. A sensory wheel based on 60 selected descriptors grouped together in 21 defined categories was created to form a graphical presentation of the sensory vocabulary. A selection of the oil samples was also evaluated by a trained sensory panel using descriptive analysis. Chemical analysis showed a positive correlation between primary and secondary oxidation products and sensory properties such as rancidity, chemical flavor and process flavor and a negative correlation between primary oxidation products and acidic. This research is a first step towards the broader objective of standardizing the sensory terminology related to marine oils. Copyright © 2017 Elsevier Ltd. All rights reserved.

38 CFR 21.296 - Selecting a training establishment for on-job training.

Code of Federal Regulations, 2012 CFR

2012-07-01

... establishment for on-job training. 21.296 Section 21.296 Pensions, Bonuses, and Veterans' Relief DEPARTMENT OF... establishment for on-job training. (a) Additional criteria for selecting a training establishment. In addition... to provide on-job training to disabled veterans; (2) Provide continuous training for each veteran...

38 CFR 21.296 - Selecting a training establishment for on-job training.

Code of Federal Regulations, 2013 CFR

2013-07-01

... establishment for on-job training. 21.296 Section 21.296 Pensions, Bonuses, and Veterans' Relief DEPARTMENT OF... establishment for on-job training. (a) Additional criteria for selecting a training establishment. In addition... to provide on-job training to disabled veterans; (2) Provide continuous training for each veteran...

38 CFR 21.296 - Selecting a training establishment for on-job training.

Code of Federal Regulations, 2010 CFR

2010-07-01

... establishment for on-job training. 21.296 Section 21.296 Pensions, Bonuses, and Veterans' Relief DEPARTMENT OF... establishment for on-job training. (a) Additional criteria for selecting a training establishment. In addition... to provide on-job training to disabled veterans; (2) Provide continuous training for each veteran...

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.

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.

Cognitive training and selective attention in the aging brain: an electrophysiological study.

PubMed

O'Brien, Jennifer L; Edwards, Jerri D; Maxfield, Nathan D; Peronto, Carol L; Williams, Victoria A; Lister, Jennifer J

2013-11-01

Age-related deficits in selective attention are hypothesized to result from decrements in inhibition of task-irrelevant information. Speed of processing (SOP) training is an adaptive cognitive intervention designed to enhance processing speed for attention tasks. The effectiveness of SOP training to improve cognitive and everyday functional performance is well documented. However, underlying mechanisms of these training benefits are unknown. Participants completed a visual search task evaluated using event-related potentials (ERPs) before and after 10 weeks of SOP training or no contact. N2pc and P3b components were evaluated to determine SOP training effects on attentional resource allocation and capacity. Selective attention to a target was enhanced after SOP training compared to no training. N2pc and P3b amplitudes increased after training, reflecting attentional allocation and capacity enhancement, consistent with previous studies demonstrating behavioral improvements in selective attention following SOP training. Changes in ERPs related to attention allocation and capacity following SOP training support the idea that training leads to cognitive enhancement. Specifically, we provide electrophysiological evidence that SOP training may be successful in counteracting age-related declines in selective attention. This study provides important evidence of the underlying mechanisms by which SOP training improves cognitive function in older adults. Published by Elsevier Ireland Ltd.

Model training across multiple breeding cycles significantly improves genomic prediction accuracy in rye (Secale cereale L.).

PubMed

Auinger, Hans-Jürgen; Schönleben, Manfred; Lehermeier, Christina; Schmidt, Malthe; Korzun, Viktor; Geiger, Hartwig H; Piepho, Hans-Peter; Gordillo, Andres; Wilde, Peer; Bauer, Eva; Schön, Chris-Carolin

2016-11-01

Genomic prediction accuracy can be significantly increased by model calibration across multiple breeding cycles as long as selection cycles are connected by common ancestors. In hybrid rye breeding, application of genome-based prediction is expected to increase selection gain because of long selection cycles in population improvement and development of hybrid components. Essentially two prediction scenarios arise: (1) prediction of the genetic value of lines from the same breeding cycle in which model training is performed and (2) prediction of lines from subsequent cycles. It is the latter from which a reduction in cycle length and consequently the strongest impact on selection gain is expected. We empirically investigated genome-based prediction of grain yield, plant height and thousand kernel weight within and across four selection cycles of a hybrid rye breeding program. Prediction performance was assessed using genomic and pedigree-based best linear unbiased prediction (GBLUP and PBLUP). A total of 1040 S 2 lines were genotyped with 16 k SNPs and each year testcrosses of 260 S 2 lines were phenotyped in seven or eight locations. The performance gap between GBLUP and PBLUP increased significantly for all traits when model calibration was performed on aggregated data from several cycles. Prediction accuracies obtained from cross-validation were in the order of 0.70 for all traits when data from all cycles (N CS  = 832) were used for model training and exceeded within-cycle accuracies in all cases. As long as selection cycles are connected by a sufficient number of common ancestors and prediction accuracy has not reached a plateau when increasing sample size, aggregating data from several preceding cycles is recommended for predicting genetic values in subsequent cycles despite decreasing relatedness over time.

Prediction of lithium response in first-episode mania using the LITHium Intelligent Agent (LITHIA): Pilot data and proof-of-concept.

PubMed

Fleck, David E; Ernest, Nicholas; Adler, Caleb M; Cohen, Kelly; Eliassen, James C; Norris, Matthew; Komoroski, Richard A; Chu, Wen-Jang; Welge, Jeffrey A; Blom, Thomas J; DelBello, Melissa P; Strakowski, Stephen M

2017-06-01

Individualized treatment for bipolar disorder based on neuroimaging treatment targets remains elusive. To address this shortcoming, we developed a linguistic machine learning system based on a cascading genetic fuzzy tree (GFT) design called the LITHium Intelligent Agent (LITHIA). Using multiple objectively defined functional magnetic resonance imaging (fMRI) and proton magnetic resonance spectroscopy ( 1 H-MRS) inputs, we tested whether LITHIA could accurately predict the lithium response in participants with first-episode bipolar mania. We identified 20 subjects with first-episode bipolar mania who received an adequate trial of lithium over 8 weeks and both fMRI and 1 H-MRS scans at baseline pre-treatment. We trained LITHIA using 18 1 H-MRS and 90 fMRI inputs over four training runs to classify treatment response and predict symptom reductions. Each training run contained a randomly selected 80% of the total sample and was followed by a 20% validation run. Over a different randomly selected distribution of the sample, we then compared LITHIA to eight common classification methods. LITHIA demonstrated nearly perfect classification accuracy and was able to predict post-treatment symptom reductions at 8 weeks with at least 88% accuracy in training and 80% accuracy in validation. Moreover, LITHIA exceeded the predictive capacity of the eight comparator methods and showed little tendency towards overfitting. The results provided proof-of-concept that a novel GFT is capable of providing control to a multidimensional bioinformatics problem-namely, prediction of the lithium response-in a pilot data set. Future work on this, and similar machine learning systems, could help assign psychiatric treatments more efficiently, thereby optimizing outcomes and limiting unnecessary treatment. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

The Effect of Selected Intervention Tactics on Self-Sufficient Behaviors of the Homeless: An Application of the Theory of Planned Behavior.

ERIC Educational Resources Information Center

Moroz, Pauline

A sample of 24 voluntary participants in a federally funded vocational training and placement program for homeless people in El Paso, Texas, was studied to identify specific interventions that increase self-sufficient behaviors of homeless individuals. Case study data were collected from orientation discussions, career counseling sessions, and…

THE FARMER AND HIS EDUCATIONAL INVESTMENT--WHAT ARE THE RELATIONSHIPS OF THIS INVESTMENT TO FARM SUCCESS.

ERIC Educational Resources Information Center

PERSONS, EDGAR

A SAMPLE OF 528 FARMERS WHO HAD BEEN ENROLLED IN INSTRUCTIONAL ON-FARM TRAINING FOLLOWING WORLD WAR II WERE STUDIED TO DETERMINE THE RELATIONSHIP OF SELECTED ECONOMIC, EDUCATIONAL, AND BIOGRAPHICAL VARIABLES TO FARM SUCCESS AND TO EXAMINE THE RELATIONSHIP OF THE EDUCATIONAL INVESTMENT COMPONENT TO FARM SUCCESS. DATA WERE COLLECTED BY QUESTIONNAIRE…

Text Classification for Intelligent Portfolio Management

DTIC Science & Technology

2002-05-01

years including nearest neighbor classification [15], naive Bayes with EM (Ex- pectation Maximization) [11] [13], Winnow with active learning [10... Active Learning and Expectation Maximization (EM). In particular, active learning is used to actively select documents for labeling, then EM assigns...generalization with active learning . Machine Learning, 15(2):201–221, 1994. [3] I. Dagan and P. Engelson. Committee-based sampling for training

Rudimentary Reading Repertoires via Stimulus Equivalence and Recombination of Minimal Verbal Units

PubMed Central

Matos, Maria Amelia; Avanzi, Alessandra Lopes; McIlvane, William J

2006-01-01

We report a study with sixteen low-SES Brazilian children that sought to establish a repertoire of relations involving dictated words, printed words, and corresponding pictures. Children were taught: (1) in response to dictated words, to select corresponding pictures; (2) in response to syllables presented in both visual and auditory formats, to select words which contained a corresponding syllable in either the first or the last position; (3) in response to dictated-word samples, to “construct” corresponding printed words via arranging their constituent syllabic components; and (4) in response to printed word samples, to construct identical printed words by arranging their syllabic constituents. After training on the first two types of tasks, children were given tests for potentially emergent relations involving printed words and pictures. Almost all exhibited relations consistent with stimulus equivalence. They also displayed emergent naming performances––not only with training words but also with new words that were recombinations of their constituent syllables. The present work was inspired by Sidman's stimulus equivalence paradigm and by Skinner's functional analysis of verbal relations, particularly as applied to conceptions of minimal behavioral units and creativity (i.e., behavioral flexibility) in the analytical units applied to verbal relations. PMID:22477340

Predicting Kenya Short Rains Using the Indian Ocean SST

NASA Astrophysics Data System (ADS)

Peng, X.; Albertson, J. D.; Steinschneider, S.

2017-12-01

The rainfall over the Eastern Africa is charaterized by the typical bimodal monsoon system. Literatures have shown that the monsoon system is closely connected with the large-scale atmospheric motion which is believed to be driven by sea surface temperature anomalies (SSTA). Therefore, we may make use of the predictability of SSTA in estimating future Easter Africa monsoon. In this study, we tried predict the Kenya short rains (Oct, Nov and Dec rainfall) based on the Indian Ocean SSTA. The Least Absolute Shrinkage and Selection Operator (LASSO) regression is used to avoid over-fitting issues. Models for different lead times are trained using a 28-year training set (2006-1979) and are tested using a 10-year test set (2007-2016). Satisfying prediciton skills are achieved at relatively long lead times (i.e., 8 and 10 months) in terms of correlation coefficient and sign accuracy. Unlike some of the previous work, the prediction models are obtained from a data-driven method. Limited predictors are selected for each model and can be used in understanding the underlying physical connection. Still, further investigation is needed since the sampling variability issue cannot be excluded due to the limited sample size.

Effects of selection and training on unit-level performance over time: a latent growth modeling approach.

PubMed

Van Iddekinge, Chad H; Ferris, Gerald R; Perrewé, Pamela L; Blass, Fred R; Heetderks, Thomas D; Perryman, Alexa A

2009-07-01

Surprisingly few data exist concerning whether and how utilization of job-related selection and training procedures affects different aspects of unit or organizational performance over time. The authors used longitudinal data from a large fast-food organization (N = 861 units) to examine how change in use of selection and training relates to change in unit performance. Latent growth modeling analyses revealed significant variation in both the use and the change in use of selection and training across units. Change in selection and training was related to change in 2 proximal unit outcomes: customer service performance and retention. Change in service performance, in turn, was related to change in the more distal outcome of unit financial performance (i.e., profits). Selection and training also affected financial performance, both directly and indirectly (e.g., through service performance). Finally, results of a cross-lagged panel analysis suggested the existence of a reciprocal causal relationship between the utilization of the human resources practices and unit performance. However, there was some evidence to suggest that selection and training may be associated with different causal sequences, such that use of the training procedure appeared to lead to unit performance, whereas unit performance appeared to lead to use of the selection procedure.

Automating data analysis for two-dimensional gas chromatography/time-of-flight mass spectrometry non-targeted analysis of comparative samples.

PubMed

Titaley, Ivan A; Ogba, O Maduka; Chibwe, Leah; Hoh, Eunha; Cheong, Paul H-Y; Simonich, Staci L Massey

2018-03-16

Non-targeted analysis of environmental samples, using comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC × GC/ToF-MS), poses significant data analysis challenges due to the large number of possible analytes. Non-targeted data analysis of complex mixtures is prone to human bias and is laborious, particularly for comparative environmental samples such as contaminated soil pre- and post-bioremediation. To address this research bottleneck, we developed OCTpy, a Python™ script that acts as a data reduction filter to automate GC × GC/ToF-MS data analysis from LECO ® ChromaTOF ® software and facilitates selection of analytes of interest based on peak area comparison between comparative samples. We used data from polycyclic aromatic hydrocarbon (PAH) contaminated soil, pre- and post-bioremediation, to assess the effectiveness of OCTpy in facilitating the selection of analytes that have formed or degraded following treatment. Using datasets from the soil extracts pre- and post-bioremediation, OCTpy selected, on average, 18% of the initial suggested analytes generated by the LECO ® ChromaTOF ® software Statistical Compare feature. Based on this list, 63-100% of the candidate analytes identified by a highly trained individual were also selected by OCTpy. This process was accomplished in several minutes per sample, whereas manual data analysis took several hours per sample. OCTpy automates the analysis of complex mixtures of comparative samples, reduces the potential for human error during heavy data handling and decreases data analysis time by at least tenfold. Copyright © 2018 Elsevier B.V. All rights reserved.

Towards an Automatic Framework for Urban Settlement Mapping from Satellite Images: Applications of Geo-referenced Social Media and One Class Classification

NASA Astrophysics Data System (ADS)

Miao, Zelang

2017-04-01

Currently, urban dwellers comprise more than half of the world's population and this percentage is still dramatically increasing. The explosive urban growth over the next two decades poses long-term profound impact on people as well as the environment. Accurate and up-to-date delineation of urban settlements plays a fundamental role in defining planning strategies and in supporting sustainable development of urban settlements. In order to provide adequate data about urban extents and land covers, classifying satellite data has become a common practice, usually with accurate enough results. Indeed, a number of supervised learning methods have proven effective in urban area classification, but they usually depend on a large amount of training samples, whose collection is a time and labor expensive task. This issue becomes particularly serious when classifying large areas at the regional/global level. As an alternative to manual ground truth collection, in this work we use geo-referenced social media data. Cities and densely populated areas are an extremely fertile land for the production of individual geo-referenced data (such as GPS and social network data). Training samples derived from geo-referenced social media have several advantages: they are easy to collect, usually they are freely exploitable; and, finally, data from social media are spatially available in many locations, and with no doubt in most urban areas around the world. Despite these advantages, the selection of training samples from social media meets two challenges: 1) there are many duplicated points; 2) method is required to automatically label them as "urban/non-urban". The objective of this research is to validate automatic sample selection from geo-referenced social media and its applicability in one class classification for urban extent mapping from satellite images. The findings in this study shed new light on social media applications in the field of remote sensing.

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…

Optical method and apparatus for detection of surface and near-subsurface defects in dense ceramics

DOEpatents

Ellingson, W.A.; Brada, M.P.

1995-06-20

A laser is used in a non-destructive manner to detect surface and near-subsurface defects in dense ceramics and particularly in ceramic bodies with complex shapes such as ceramic bearings, turbine blades, races, and the like. The laser`s wavelength is selected based upon the composition of the ceramic sample and the laser can be directed on the sample while the sample is static or in dynamic rotate or translate motion. Light is scattered off surface and subsurface defects using a preselected polarization. The change in polarization angle is used to select the depth and characteristics of surface/subsurface defects. The scattered light is detected by an optical train consisting of a charge coupled device (CCD), or vidicon, television camera which, in turn, is coupled to a video monitor and a computer for digitizing the image. An analyzing polarizer in the optical train allows scattered light at a given polarization angle to be observed for enhancing sensitivity to either surface or near-subsurface defects. Application of digital image processing allows subtraction of digitized images in near real-time providing enhanced sensitivity to subsurface defects. Storing known ``feature masks`` of identified defects in the computer and comparing the detected scatter pattern (Fourier images) with the stored feature masks allows for automatic classification of detected defects. 29 figs.

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.

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

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.

Finding strong gravitational lenses in the Kilo Degree Survey with Convolutional Neural Networks

NASA Astrophysics Data System (ADS)

Petrillo, C. E.; Tortora, C.; Chatterjee, S.; Vernardos, G.; Koopmans, L. V. E.; Verdoes Kleijn, G.; Napolitano, N. R.; Covone, G.; Schneider, P.; Grado, A.; McFarland, J.

2017-11-01

The volume of data that will be produced by new-generation surveys requires automatic classification methods to select and analyse sources. Indeed, this is the case for the search for strong gravitational lenses, where the population of the detectable lensed sources is only a very small fraction of the full source population. We apply for the first time a morphological classification method based on a Convolutional Neural Network (CNN) for recognizing strong gravitational lenses in 255 deg2 of the Kilo Degree Survey (KiDS), one of the current-generation optical wide surveys. The CNN is currently optimized to recognize lenses with Einstein radii ≳1.4 arcsec, about twice the r-band seeing in KiDS. In a sample of 21 789 colour-magnitude selected luminous red galaxies (LRGs), of which three are known lenses, the CNN retrieves 761 strong-lens candidates and correctly classifies two out of three of the known lenses. The misclassified lens has an Einstein radius below the range on which the algorithm is trained. We down-select the most reliable 56 candidates by a joint visual inspection. This final sample is presented and discussed. A conservative estimate based on our results shows that with our proposed method it should be possible to find ∼100 massive LRG-galaxy lenses at z ≲ 0.4 in KiDS when completed. In the most optimistic scenario, this number can grow considerably (to maximally ∼2400 lenses), when widening the colour-magnitude selection and training the CNN to recognize smaller image-separation lens systems.

McTwo: a two-step feature selection algorithm based on maximal information coefficient.

PubMed

Ge, Ruiquan; Zhou, Manli; Luo, Youxi; Meng, Qinghan; Mai, Guoqin; Ma, Dongli; Wang, Guoqing; Zhou, Fengfeng

2016-03-23

High-throughput bio-OMIC technologies are producing high-dimension data from bio-samples at an ever increasing rate, whereas the training sample number in a traditional experiment remains small due to various difficulties. This "large p, small n" paradigm in the area of biomedical "big data" may be at least partly solved by feature selection algorithms, which select only features significantly associated with phenotypes. Feature selection is an NP-hard problem. Due to the exponentially increased time requirement for finding the globally optimal solution, all the existing feature selection algorithms employ heuristic rules to find locally optimal solutions, and their solutions achieve different performances on different datasets. This work describes a feature selection algorithm based on a recently published correlation measurement, Maximal Information Coefficient (MIC). The proposed algorithm, McTwo, aims to select features associated with phenotypes, independently of each other, and achieving high classification performance of the nearest neighbor algorithm. Based on the comparative study of 17 datasets, McTwo performs about as well as or better than existing algorithms, with significantly reduced numbers of selected features. The features selected by McTwo also appear to have particular biomedical relevance to the phenotypes from the literature. McTwo selects a feature subset with very good classification performance, as well as a small feature number. So McTwo may represent a complementary feature selection algorithm for the high-dimensional biomedical datasets.

Pre-Mission Input Requirements to Enable Successful Sample Collection by A Remote Field/EVA Team

NASA Technical Reports Server (NTRS)

Cohen, B. A.; Lim, D. S. S.; Young, K. E.; Brunner, A.; Elphic, R. E.; Horne, A.; Kerrigan, M. C.; Osinski, G. R.; Skok, J. R.; Squyres, S. W.;

Neuromorphic learning of continuous-valued mappings from noise-corrupted data

NASA Technical Reports Server (NTRS)

Troudet, T.; Merrill, W.

1991-01-01

The effect of noise on the learning performance of the backpropagation algorithm is analyzed. A selective sampling of the training set is proposed to maximize the learning of control laws by backpropagation, when the data have been corrupted by noise. The training scheme is applied to the nonlinear control of a cart-pole system in the presence of noise. The neural computation provides the neurocontroller with good noise-filtering properties. In the presence of plant noise, the neurocontroller is found to be more stable than the teacher. A novel perspective on the application of neural network technology to control engineering is presented.

Active learning for solving the incomplete data problem in facial age classification by the furthest nearest-neighbor criterion.

PubMed

Wang, Jian-Gang; Sung, Eric; Yau, Wei-Yun

2011-07-01

Facial age classification is an approach to classify face images into one of several predefined age groups. One of the difficulties in applying learning techniques to the age classification problem is the large amount of labeled training data required. Acquiring such training data is very costly in terms of age progress, privacy, human time, and effort. Although unlabeled face images can be obtained easily, it would be expensive to manually label them on a large scale and getting the ground truth. The frugal selection of the unlabeled data for labeling to quickly reach high classification performance with minimal labeling efforts is a challenging problem. In this paper, we present an active learning approach based on an online incremental bilateral two-dimension linear discriminant analysis (IB2DLDA) which initially learns from a small pool of labeled data and then iteratively selects the most informative samples from the unlabeled set to increasingly improve the classifier. Specifically, we propose a novel data selection criterion called the furthest nearest-neighbor (FNN) that generalizes the margin-based uncertainty to the multiclass case and which is easy to compute, so that the proposed active learning algorithm can handle a large number of classes and large data sizes efficiently. Empirical experiments on FG-NET and Morph databases together with a large unlabeled data set for age categorization problems show that the proposed approach can achieve results comparable or even outperform a conventionally trained active classifier that requires much more labeling effort. Our IB2DLDA-FNN algorithm can achieve similar results much faster than random selection and with fewer samples for age categorization. It also can achieve comparable results with active SVM but is much faster than active SVM in terms of training because kernel methods are not needed. The results on the face recognition database and palmprint/palm vein database showed that our approach can handle problems with large number of classes. Our contributions in this paper are twofold. First, we proposed the IB2DLDA-FNN, the FNN being our novel idea, as a generic on-line or active learning paradigm. Second, we showed that it can be another viable tool for active learning of facial age range classification.

SELECTING AND TRAINING THE TRAINING OFFICER.

ERIC Educational Resources Information Center

TAYLOR, NANCY

TO ACHIEVE THE OBJECTIVES OF TRAINING IN INDUSTRY--TECHNICAL AND LIBERAL EDUCATION, SPECIFIC JOB SKILLS, AND THE DEVELOPMENT OF ATTITUDES--THE TRAINING OFFICER MUST KNOW THE COMPANY WITHIN WHICH HE IS WORKING, AS WELL AS MANAGEMENT THEORY AND TRAINING METHODS. THE SELECTION OF TRAINING OFFICERS IS BASED ON A JOB SPECIFICATION, AN OUTGROWTH OF A…

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.

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.

Occupational Safety & Health. Inspectors' Opinions on Improving OSHA Effectiveness. Fact Sheet for Subcommittee on Health and Safety, Committee on Education and Labor, House of Representatives.

ERIC Educational Resources Information Center

General Accounting Office, Washington, DC. Div. of Human Resources.

Questionnaires gathered opinions of all Occupational Safety and Health Administration (OSHA) field supervisors and a randomly selected sample of one-third of the compliance officers about OSHA's approach to improving workplace safety and health. Major topics addressed were enforcement, safety and health standards, education and training, employer…

A Study on the Sources of Resources and Capacity Building in Resource Mobilization: Case of Private Chartered Universities in Nakuru Town, Kenya

ERIC Educational Resources Information Center

Kipchumba, Simon Kibet; Zhimin, Liu; Chelagat, Robert

2013-01-01

The purpose of this study was to review and analyze the resources needs and sources of resources and level of training and capacity building in resource mobilization in Kenyan private chartered universities. The study employed a descriptive survey research design. Purposeful sampling technique was used to select 63 respondents (staff) from three…

Resistance Training Effects on Metabolic Function Among Youth: A Systematic Review.

PubMed

Bea, Jennifer W; Blew, Robert M; Howe, Carol; Hetherington-Rauth, Megan; Going, Scott B

2017-08-01

This systematic review evaluates the relationship between resistance training and metabolic function in youth. PubMed, Embase, Cochrane Library, Web of Science, CINAHL, and ClinicalTrials. gov were searched for articles that (1): studied children (2); included resistance training (3); were randomized interventions; and (4) reported markers of metabolic function. The selected studies were analyzed using the Cochrane Risk-of-Bias Tool. Thirteen articles met inclusion criteria. Mean age ranged from 12.2 to 16.9 years, but most were limited to high school (n = 11) and overweight/obese (n = 12). Sample sizes (n = 22-304), session duration (40-60min), and intervention length (8-52 wks) varied. Exercise frequency was typically 2-3 d/wk. Resistance training was metabolically beneficial compared with control or resistance plus aerobic training in 5 studies overall and 3 out of the 4 studies with the fewest threats to bias (p ≤ .05); each was accompanied by beneficial changes in body composition, but only one study adjusted for change in body composition. Limited evidence suggests that resistance training may positively affect metabolic parameters in youth. Well-controlled resistance training interventions of varying doses are needed to definitively determine whether resistance training can mitigate metabolic dysfunction in youth and whether training benefits on metabolic parameters are independent of body composition changes.

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

ANALYSIS OF SAMPLING TECHNIQUES FOR IMBALANCED DATA: AN N=648 ADNI STUDY

PubMed Central

Dubey, Rashmi; Zhou, Jiayu; Wang, Yalin; Thompson, Paul M.; Ye, Jieping

2013-01-01

Many neuroimaging applications deal with imbalanced imaging data. For example, in Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, the mild cognitive impairment (MCI) cases eligible for the study are nearly two times the Alzheimer’s disease (AD) patients for structural magnetic resonance imaging (MRI) modality and six times the control cases for proteomics modality. Constructing an accurate classifier from imbalanced data is a challenging task. Traditional classifiers that aim to maximize the overall prediction accuracy tend to classify all data into the majority class. In this paper, we study an ensemble system of feature selection and data sampling for the class imbalance problem. We systematically analyze various sampling techniques by examining the efficacy of different rates and types of undersampling, oversampling, and a combination of over and under sampling approaches. We thoroughly examine six widely used feature selection algorithms to identify significant biomarkers and thereby reduce the complexity of the data. The efficacy of the ensemble techniques is evaluated using two different classifiers including Random Forest and Support Vector Machines based on classification accuracy, area under the receiver operating characteristic curve (AUC), sensitivity, and specificity measures. Our extensive experimental results show that for various problem settings in ADNI, (1). a balanced training set obtained with K-Medoids technique based undersampling gives the best overall performance among different data sampling techniques and no sampling approach; and (2). sparse logistic regression with stability selection achieves competitive performance among various feature selection algorithms. Comprehensive experiments with various settings show that our proposed ensemble model of multiple undersampled datasets yields stable and promising results. PMID:24176869

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.

Implementation of a training program for low-literacy promotoras in oral rehydration therapy.

PubMed

Amerson, Roxanne; Hall-Clifford, Rachel; Thompson, Beti; Comninellas, Nicholas

2015-01-01

The purpose of this study was to ascertain the effectiveness of a culturally appropriate promotora training program related to oral rehydration therapy and diarrheal management. Factors that influenced the development, implementation, and evaluation of the program provided to low-literacy women in Guatemala are explored. Promotora training was conducted with 15 Mayan women from a rural community in the highlands of Guatemala. Women were selected by leaders of the community to participate in the program. Quantitative data were collected and analyzed to determine descriptive statistics and reliability coefficients for the pretests and posttests. A nonparametric Wilcoxon test for paired-samples was conducted. The qualitative data from the program evaluations were analyzed for themes. Mean scores increased from 41.73 (SD = 9.65) to 70.33 (SD = 21.29) on the pretest and posttest. The Cronbach's alpha was 0.54 on the pretest with 0.65 on the posttest. The Wilcoxon test demonstrated a significant difference between the pretest and posttest scores (Z = 3.040, p < .05). Extremely low-literacy levels played a major role in the ability of the women to successfully complete the requirements of the training program. The curriculum demonstrated effectiveness, but will benefit from replication with a larger sample. © 2014 Wiley Periodicals, Inc.

Counseling psychology trainees' perceptions of training and commitments to social justice.

PubMed

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 greater social justice training than what they experienced in their programs. In the qualitative portion, we used a phenomenological approach to expand and elaborate upon quantitative results. A subsample (n = 7) of trainees who identified as strong social justice activists were interviewed regarding their personal, professional, and training experiences. Eleven themes related to participants' meanings of and experiences with social justice emerged within 4 broad categories: nature of social justice, motivation for activism, role of training, and personal and professional integration. Thematic findings as well as descriptive statistics informed the selection and ordering of variables in a hierarchical regression analysis that examined predictors of social justice commitment. Results indicated that trainees' perceptions of training environment significantly predicted their social justice commitment over and above their general activist orientation and spirituality. Findings are discussed collectively, and implications for training and future research are provided. (c) 2012 APA, all rights reserved.

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.

Science achievement of students in the Republic of Yemen and implications for improvement of science instruction

NASA Astrophysics Data System (ADS)

Ismail, Nageeb Kassem

The purpose of this study was to establish a research base from which strategies could be developed for improving science education in Yemen. The study measured the achievement in general science of Yemeni students attending primary, preparatory, and secondary schools, and their counterparts attending three- or five-year education programs in primary teacher training institutions. A sample of 1,984 students from six major cities in Yemen was given the Second International Science Study test in May 1988. Achievement scores of these selected groups were compared. The mean achievement in general science was 11.93 for science track students, 9.21 for three-year teacher training institution students, and 8.49 for five-year teacher training institution students. These mean scores were based on a total of 35 items. This low level of achievement was further verified by making comparisons of the achievement of selected groups from Yemeni high schools in six cities with each other. The following factors were measured in this study: location, grade level, gender and type of science program studied. Selected groups from Yemeni high schools were also compared to their peers in other nations. The researcher compared students of the science track and teacher training institutions to their counterparts in 13 nations and students of the literature track to their counterparts in eight nations. Fifth and ninth grade students' scores were compared with the scores of their counterparts in 15 and 17 nations respectively. In every comparison, every Yemeni group ranked at the bottom of the achievement list. (Jacobson W., & Doran, R. 1988) The outcomes of this research indicate the profound need for improving science programs in all grade levels in Yemen. The research recommendations for improvement in science education in Yemen fall into four areas: a change in attitudes toward education, a change in teacher education, a change in classroom conditions, and a change in educational opportunities for women. Because this research study was based on a sizable sample and many hypotheses were tested, this work has contributed appreciable to the base of data available to future researchers. This study also implemented use of the SISS instrument for the first time in Arabic.

Multiclass Continuous Correspondence Learning

NASA Technical Reports Server (NTRS)

Bue, Brian D,; Thompson, David R.

2011-01-01

We extend the Structural Correspondence Learning (SCL) domain adaptation algorithm of Blitzer er al. to the realm of continuous signals. Given a set of labeled examples belonging to a 'source' domain, we select a set of unlabeled examples in a related 'target' domain that play similar roles in both domains. Using these 'pivot samples, we map both domains into a common feature space, allowing us to adapt a classifier trained on source examples to classify target examples. We show that when between-class distances are relatively preserved across domains, we can automatically select target pivots to bring the domains into correspondence.

Adaptive training diminishes distractibility in aging across species.

PubMed

Mishra, Jyoti; de Villers-Sidani, Etienne; Merzenich, Michael; Gazzaley, Adam

2014-12-03

Aging is associated with deficits in the ability to ignore distractions, which has not yet been remediated by any neurotherapeutic approach. Here, in parallel auditory experiments with older rats and humans, we evaluated a targeted cognitive training approach that adaptively manipulated distractor challenge. Training resulted in enhanced discrimination abilities in the setting of irrelevant information in both species that was driven by selectively diminished distraction-related errors. Neural responses to distractors in auditory cortex were selectively reduced in both species, mimicking the behavioral effects. Sensory receptive fields in trained rats exhibited improved spectral and spatial selectivity. Frontal theta measures of top-down engagement with distractors were selectively restrained in trained humans. Finally, training gains generalized to group and individual level benefits in aspects of working memory and sustained attention. Thus, we demonstrate converging cross-species evidence for training-induced selective plasticity of distractor processing at multiple neural scales, benefitting distractor suppression and cognitive control. Copyright © 2014 Elsevier Inc. All rights reserved.

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.

The effects of sampling bias and model complexity on the predictive performance of MaxEnt species distribution models.

PubMed

Syfert, Mindy M; Smith, Matthew J; Coomes, David A

2013-01-01

Species distribution models (SDMs) trained on presence-only data are frequently used in ecological research and conservation planning. However, users of SDM software are faced with a variety of options, and it is not always obvious how selecting one option over another will affect model performance. Working with MaxEnt software and with tree fern presence data from New Zealand, we assessed whether (a) choosing to correct for geographical sampling bias and (b) using complex environmental response curves have strong effects on goodness of fit. SDMs were trained on tree fern data, obtained from an online biodiversity data portal, with two sources that differed in size and geographical sampling bias: a small, widely-distributed set of herbarium specimens and a large, spatially clustered set of ecological survey records. We attempted to correct for geographical sampling bias by incorporating sampling bias grids in the SDMs, created from all georeferenced vascular plants in the datasets, and explored model complexity issues by fitting a wide variety of environmental response curves (known as "feature types" in MaxEnt). In each case, goodness of fit was assessed by comparing predicted range maps with tree fern presences and absences using an independent national dataset to validate the SDMs. We found that correcting for geographical sampling bias led to major improvements in goodness of fit, but did not entirely resolve the problem: predictions made with clustered ecological data were inferior to those made with the herbarium dataset, even after sampling bias correction. We also found that the choice of feature type had negligible effects on predictive performance, indicating that simple feature types may be sufficient once sampling bias is accounted for. Our study emphasizes the importance of reducing geographical sampling bias, where possible, in datasets used to train SDMs, and the effectiveness and essentialness of sampling bias correction within MaxEnt.

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.

Improvements of the Vis-NIRS Model in the Prediction of Soil Organic Matter Content Using Spectral Pretreatments, Sample Selection, and Wavelength Optimization

NASA Astrophysics Data System (ADS)

Lin, Z. D.; Wang, Y. B.; Wang, R. J.; Wang, L. S.; Lu, C. P.; Zhang, Z. Y.; Song, L. T.; Liu, Y.

2017-07-01

A total of 130 topsoil samples collected from Guoyang County, Anhui Province, China, were used to establish a Vis-NIR model for the prediction of organic matter content (OMC) in lime concretion black soils. Different spectral pretreatments were applied for minimizing the irrelevant and useless information of the spectra and increasing the spectra correlation with the measured values. Subsequently, the Kennard-Stone (KS) method and sample set partitioning based on joint x-y distances (SPXY) were used to select the training set. Successive projection algorithm (SPA) and genetic algorithm (GA) were then applied for wavelength optimization. Finally, the principal component regression (PCR) model was constructed, in which the optimal number of principal components was determined using the leave-one-out cross validation technique. The results show that the combination of the Savitzky-Golay (SG) filter for smoothing and multiplicative scatter correction (MSC) can eliminate the effect of noise and baseline drift; the SPXY method is preferable to KS in the sample selection; both the SPA and the GA can significantly reduce the number of wavelength variables and favorably increase the accuracy, especially GA, which greatly improved the prediction accuracy of soil OMC with Rcc, RMSEP, and RPD up to 0.9316, 0.2142, and 2.3195, respectively.

A sampling-based method for ranking protein structural models by integrating multiple scores and features.

PubMed

Shi, Xiaohu; Zhang, Jingfen; He, Zhiquan; Shang, Yi; Xu, Dong

2011-09-01

One of the major challenges in protein tertiary structure prediction is structure quality assessment. In many cases, protein structure prediction tools generate good structural models, but fail to select the best models from a huge number of candidates as the final output. In this study, we developed a sampling-based machine-learning method to rank protein structural models by integrating multiple scores and features. First, features such as predicted secondary structure, solvent accessibility and residue-residue contact information are integrated by two Radial Basis Function (RBF) models trained from different datasets. Then, the two RBF scores and five selected scoring functions developed by others, i.e., Opus-CA, Opus-PSP, DFIRE, RAPDF, and Cheng Score are synthesized by a sampling method. At last, another integrated RBF model ranks the structural models according to the features of sampling distribution. We tested the proposed method by using two different datasets, including the CASP server prediction models of all CASP8 targets and a set of models generated by our in-house software MUFOLD. The test result shows that our method outperforms any individual scoring function on both best model selection, and overall correlation between the predicted ranking and the actual ranking of structural quality.

Acrylamide exposure among Turkish toddlers from selected cereal-based baby food samples.

PubMed

Cengiz, Mehmet Fatih; Gündüz, Cennet Pelin Boyacı

2013-10-01

In this study, acrylamide exposure from selected cereal-based baby food samples was investigated among toddlers aged 1-3 years in Turkey. The study contained three steps. The first step was collecting food consumption data and toddlers' physical properties, such as gender, age and body weight, using a questionnaire given to parents by a trained interviewer between January and March 2012. The second step was determining the acrylamide levels in food samples that were reported on by the parents in the questionnaire, using a gas chromatography-mass spectrometry (GC-MS) method. The last step was combining the determined acrylamide levels in selected food samples with individual food consumption and body weight data using a deterministic approach to estimate the acrylamide exposure levels. The mean acrylamide levels of baby biscuits, breads, baby bread-rusks, crackers, biscuits, breakfast cereals and powdered cereal-based baby foods were 153, 225, 121, 604, 495, 290 and 36 μg/kg, respectively. The minimum, mean and maximum acrylamide exposures were estimated to be 0.06, 1.43 and 6.41 μg/kg BW per day, respectively. The foods that contributed to acrylamide exposure were aligned from high to low as bread, crackers, biscuits, baby biscuits, powdered cereal-based baby foods, baby bread-rusks and breakfast cereals. Copyright © 2013 Elsevier Ltd. All rights reserved.

Through their eyes: selective attention in peahens during courtship

PubMed Central

Yorzinski, Jessica L.; Patricelli, Gail L.; Babcock, Jason S.; Pearson, John M.; Platt, Michael L.

2013-01-01

SUMMARY Conspicuous, multicomponent ornamentation in male animals can be favored by female mate choice but we know little about the cognitive processes females use to evaluate these traits. Sexual selection may favor attention mechanisms allowing the choosing females to selectively and efficiently acquire relevant information from complex male display traits and, in turn, may favor male display traits that effectively capture and hold female attention. Using a miniaturized telemetric gaze-tracker, we show that peahens (Pavo cristatus) selectively attend to specific components of peacock courtship displays and virtually ignore other, highly conspicuous components. Females gazed at the lower train but largely ignored the head, crest and upper train. When the lower train was obscured, however, females spent more time gazing at the upper train and approached the upper train from a distance. Our results suggest that peahens mainly evaluate the lower train during close-up courtship but use the upper train as a long-distance attraction signal. Furthermore, we found that behavioral display components (train rattling and wing shaking) captured and maintained female attention, indicating that interactions between display components may promote the evolution of multicomponent displays. Taken together, these findings suggest that selective attention plays a crucial role in sexual selection and likely influences the evolution of male display traits. PMID:23885088

Expression analysis of selected classes of circulating exosomal miRNAs in soccer players as an indicator of adaptation to physical activity

PubMed Central

Jastrzębski, Zbigniew; Kiszałkiewicz, Justyna; Brzeziański, Michał; Pastuszak-Lewandoska, Dorota; Radzimińki, Łukasz; Brzeziańska-Lasota, Ewa; Jegier, Anna

2017-01-01

Recently studies have shown that, depending on the type of training and its duration, the expression levels of selected circulating myomiRNAs (c-miR-27a,b, c-miR-29a,b,c, c-miR-133a) differ and correlate with the physiological indicators of adaptation to physical activity. To analyse the expression of selected classes of miRNAs in soccer players during different periods of their training cycle. The study involved 22 soccer players aged 17-18 years. The multi-stage 20-m shuttle run test was used to estimate VO2 max among the soccer players. Samples serum were collected at baseline (time point I), after one week (time point II), and after 2 months of training (time point III). The analysis of the relative quantification (RQ) level of three exosomal myomiRNAs, c-miRNA-27b, c-miR-29a, and c-miR-133, was performed by quantitative polymerase chain reaction (qPCR) at three time points – before the training, after 1 week of training and after the completion of two months of competition season training. The expression analysis showed low expression levels (according to references) of all evaluated myomiRNAs before the training cycle. Analysis performed after a week of the training cycle and after completion of the entire training cycle showed elevated expression of all tested myomiRNAs. Statistical analysis revealed significant differences between the first and the second time point in soccer players for c-miR-27b and c-miR-29a; between the first and the third time point for c-miR-27b and c-miR-29a; and between the second and the third time point for c-miR-27b. Statistical analysis showed a positive correlation between the levels of c-miR-29a and VO2 max. Two months of training affected the expression of c-miR-27b and miR-29a in soccer players. The increased expression of c-miR-27b and c-miR-29 with training could indicate their probable role in the adaptation process that takes place in the muscular system. Possibly, the expression of c-miR-29a will be found to be involved in cardiorespiratory fitness in future research. PMID:29472735

An examination of current practices and gender differences in strength and conditioning in a sample of varsity high school athletic programs.

PubMed

Reynolds, Monica L; Ransdell, Lynda B; Lucas, Shelley M; Petlichkoff, Linda M; Gao, Yong

2012-01-01

Currently, little is known about strength and conditioning programs at the high school level. Therefore, the purpose of this research was to explore current practices in strength and conditioning for varsity high school athletes in selected sports. The following were specifically examined: who administers programs for these athletes, what kinds of training activities are done, and whether the responsible party or emphasis changes depending on the gender of the athletes. Coaches of varsity soccer, basketball, softball, and baseball in 3 large Idaho school districts were asked to complete an online survey. Sixty-seven percent (32/48) of the questionnaires were completed and used for the study. The majority of coaches (84%) provided strength and conditioning opportunities for their athletes, although only 37% required participation. Strength training programs were designed and implemented primarily by either physical education teachers or head coaches. Compared with coaches of male athletes, coaches of female athletes were less likely to know the credentials of their strength coaches, and they were less likely to use certified coaches to plan and implement their strength and conditioning programs. Most programs included dynamic warm-ups and cool-downs, plyometrics, agility training, speed training, and conditioning, and most programs were conducted 3 d·wk(-1) (76%) for sessions lasting between 30 and 59 minutes (63%). Compared with their female counterparts, male athletes were more likely to have required training, participate in strength training year round, and train using more sessions per week. This study provides additional information related to the practice of strength and conditioning in a sample of high school athletic teams.

Training needs analysis for MSMEs: how to improve training effectiveness

NASA Astrophysics Data System (ADS)

Rohayati, Y.; Wulandari, S.

2017-12-01

The study aims to analyze training needs for MSMEs in the area of Kabupaten Bandung by selecting the case of MSMEs joined in Association for Agricultural Product Process, focusing on marketing as the main topic of the training. The needs analysis was required to improve training participation and effectiveness. Both aspects are important to notice since making MSMEs participate in training is not an easy task. Similarly, the needs analysis was carried out to anticipate participants’ thoughts that the training does not give any benefits for them or is ineffective because it does not meet their needs although it was actually to help MSMEs improve their marketing knowledge expected to lead to their success. This research involved 100 MSMEs with business ages starting from less than five years to more than 15 years. Those involved MSMEs were dominated by MSMEs targeting local marketing areas. The data were collected by survey and judgmental sampling technique. By conducting a descriptive analysis, it can be concluded that the needs of SMEs on marketing training materials should focus on improving marketing skills such as product development, sales, and use of marketing media as well as discussing legal aspects such as the need for certification and product brand. The results of the study also concluded that there is a need for training that is supplemented by making visits to more successful SMEs as well as practices with on the job training methods.

Establishment and operation of the National Accident Sampling System (NASS) team within the cities of Ft. Lauderdale/Hollywood, Florida

NASA Astrophysics Data System (ADS)

Beddow, B.; Roberts, C.; Rankin, J.; Bloch, A.; Peizer, J.

1981-01-01

The National Accident Sampling System (NASS) is described. The study area discussed is one of the original ten sites selected for NASS implementation. In addition to collecting data from the field, the original ten sites address questions of feasibility of the plan, projected results of the data collection effort, and specific operational topics, e.g., team size, sampling requirements, training approaches, quality control procedures, and field techniques. Activities and results of the first three years of the project, for both major tasks (establishment and operation) are addressed. Topics include: study area documentation; team description, function and activities; problems and solutions; and recommendations.

Train axle bearing fault detection using a feature selection scheme based multi-scale morphological filter

NASA Astrophysics Data System (ADS)

Li, Yifan; Liang, Xihui; Lin, Jianhui; Chen, Yuejian; Liu, Jianxin

2018-02-01

This paper presents a novel signal processing scheme, feature selection based multi-scale morphological filter (MMF), for train axle bearing fault detection. In this scheme, more than 30 feature indicators of vibration signals are calculated for axle bearings with different conditions and the features which can reflect fault characteristics more effectively and representatively are selected using the max-relevance and min-redundancy principle. Then, a filtering scale selection approach for MMF based on feature selection and grey relational analysis is proposed. The feature selection based MMF method is tested on diagnosis of artificially created damages of rolling bearings of railway trains. Experimental results show that the proposed method has a superior performance in extracting fault features of defective train axle bearings. In addition, comparisons are performed with the kurtosis criterion based MMF and the spectral kurtosis criterion based MMF. The proposed feature selection based MMF method outperforms these two methods in detection of train axle bearing faults.

A systematic comparison of different object-based classification techniques using high spatial resolution imagery in agricultural environments

NASA Astrophysics Data System (ADS)

Li, Manchun; Ma, Lei; Blaschke, Thomas; Cheng, Liang; Tiede, Dirk

2016-07-01

Geographic Object-Based Image Analysis (GEOBIA) is becoming more prevalent in remote sensing classification, especially for high-resolution imagery. Many supervised classification approaches are applied to objects rather than pixels, and several studies have been conducted to evaluate the performance of such supervised classification techniques in GEOBIA. However, these studies did not systematically investigate all relevant factors affecting the classification (segmentation scale, training set size, feature selection and mixed objects). In this study, statistical methods and visual inspection were used to compare these factors systematically in two agricultural case studies in China. The results indicate that Random Forest (RF) and Support Vector Machines (SVM) are highly suitable for GEOBIA classifications in agricultural areas and confirm the expected general tendency, namely that the overall accuracies decline with increasing segmentation scale. All other investigated methods except for RF and SVM are more prone to obtain a lower accuracy due to the broken objects at fine scales. In contrast to some previous studies, the RF classifiers yielded the best results and the k-nearest neighbor classifier were the worst results, in most cases. Likewise, the RF and Decision Tree classifiers are the most robust with or without feature selection. The results of training sample analyses indicated that the RF and adaboost. M1 possess a superior generalization capability, except when dealing with small training sample sizes. Furthermore, the classification accuracies were directly related to the homogeneity/heterogeneity of the segmented objects for all classifiers. Finally, it was suggested that RF should be considered in most cases for agricultural mapping.

Methodology of Global Adult Tobacco Survey (GATS), Malaysia, 2011

PubMed Central

Omar, Azahadi; Yusoff, Muhammad Fadhli Mohd; Hiong, Tee Guat; Aris, Tahir; Morton, Jeremy; Pujari, Sameer

2015-01-01

Introduction Malaysia participated in the second phase of the Global Adult Tobacco Survey (GATS) in 2011. GATS, a new component of the Global Tobacco Surveillance System, is a nationally representative household survey of adults 15 years old or above. The objectives of GATS Malaysia were to (i) systematically monitor tobacco use among adults and track key indicators of tobacco control and (ii) track the implementation of some of the Framework Convention of Tobacco Control (FCTC)-recommended demand related policies. Methods GATS Malaysia 2011 was a nationwide cross-sectional survey using multistage stratified sampling to select 5112 nationally representative households. One individual aged 15 years or older was randomly chosen from each selected household and interviewed using handheld device. GATS Core Questionnaire with optional questions was pre-tested and uploaded into handheld devices after repeated quality control processes. Data collectors were trained through a centralized training. Manuals and picture book were prepared to aid in the training of data collectors and during data collection. Field-level data were aggregated on a daily basis and analysed twice a week. Quality controls were instituted to ensure collection of high quality data. Sample weighting and analysis were conducted with the assistance of researchers from the Centers for Disease Control and Prevention, Atlanta, USA Results GATS Malaysia received a total response rate of 85.3% from 5112 adults surveyed. Majority of the respondents were 25–44 years old and Malays. Conclusions The robust methodology used in the GATS Malaysia provides national estimates for tobacco used classified by socio-demographic characteristics and reliable data on various dimensions of tobacco control. PMID:26451348

A Study to Determine if a Difference Exists Among the Cumulative Incidence of Acute Respiratory Disease Hospital Admissions of Three Groups of Army Basic Trainees as Defined by the Design of Barracks in Which They Are Housed

DTIC Science & Technology

1989-08-01

number) Using chi-square tests of homogeneity, a selected sample of Army Basic Trainees at Ft. Jackso was studied to determine if there was a...Period of training for sample soldiers was January to May 1985. Results of testing for the female trainees indicated no significant difference in incidence...of ARD among three barracks groups. Results of testing for male trainees indicated statistically significant dif -erences of ARD among each of three

An improved SRC method based on virtual samples for face recognition

NASA Astrophysics Data System (ADS)

Fu, Lijun; Chen, Deyun; Lin, Kezheng; Li, Ao

2018-07-01

The sparse representation classifier (SRC) performs classification by evaluating which class leads to the minimum representation error. However, in real world, the number of available training samples is limited due to noise interference, training samples cannot accurately represent the test sample linearly. Therefore, in this paper, we first produce virtual samples by exploiting original training samples at the aim of increasing the number of training samples. Then, we take the intra-class difference as data representation of partial noise, and utilize the intra-class differences and training samples simultaneously to represent the test sample in a linear way according to the theory of SRC algorithm. Using weighted score level fusion, the respective representation scores of the virtual samples and the original training samples are fused together to obtain the final classification results. The experimental results on multiple face databases show that our proposed method has a very satisfactory classification performance.

Provider-related barriers to rapid HIV testing in U.S. urban non-profit community clinics, community-based organizations (CBOs) and hospitals.

PubMed

Bogart, Laura M; Howerton, Devery; Lange, James; Setodji, Claude Messan; Becker, Kirsten; Klein, David J; Asch, Steven M

2010-06-01

We examined provider-reported barriers to rapid HIV testing in U.S. urban non-profit community clinics, community-based organizations (CBOs), and hospitals. 12 primary metropolitan statistical areas (PMSAs; three per region) were sampled randomly, with sampling weights proportional to AIDS case reports. Across PMSAs, all 671 hospitals and a random sample of 738 clinics/CBOs were telephoned for a survey on rapid HIV test availability. Of the 671 hospitals, 172 hospitals were randomly selected for barriers questions, for which 158 laboratory and 136 department staff were eligible and interviewed in 2005. Of the 738 clinics/CBOs, 276 were randomly selected for barriers questions, 206 were reached, and 118 were eligible and interviewed in 2005-2006. In multivariate models, barriers regarding translation of administrative/quality assurance policies into practice were significantly associated with rapid HIV testing availability. For greater rapid testing diffusion, policies are needed to reduce administrative barriers and provide quality assurance training to non-laboratory staff.

Active learning approach for detection of hard exudates, cotton wool spots, and drusen in retinal images

NASA Astrophysics Data System (ADS)

Sánchez, Clara I.; Niemeijer, Meindert; Kockelkorn, Thessa; Abràmoff, Michael D.; van Ginneken, Bram

2009-02-01

Computer-aided Diagnosis (CAD) systems for the automatic identification of abnormalities in retinal images are gaining importance in diabetic retinopathy screening programs. A huge amount of retinal images are collected during these programs and they provide a starting point for the design of machine learning algorithms. However, manual annotations of retinal images are scarce and expensive to obtain. This paper proposes a dynamic CAD system based on active learning for the automatic identification of hard exudates, cotton wool spots and drusen in retinal images. An uncertainty sampling method is applied to select samples that need to be labeled by an expert from an unlabeled set of 4000 retinal images. It reduces the number of training samples needed to obtain an optimum accuracy by dynamically selecting the most informative samples. Results show that the proposed method increases the classification accuracy compared to alternative techniques, achieving an area under the ROC curve of 0.87, 0.82 and 0.78 for the detection of hard exudates, cotton wool spots and drusen, respectively.

Use of a probabilistic neural network to reduce costs of selecting construction rock

USGS Publications Warehouse

Singer, Donald A.; Bliss, James D.

2003-01-01

Rocks used as construction aggregate in temperate climates deteriorate to differing degrees because of repeated freezing and thawing. The magnitude of the deterioration depends on the rock's properties. Aggregate, including crushed carbonate rock, is required to have minimum geotechnical qualities before it can be used in asphalt and concrete. In order to reduce chances of premature and expensive repairs, extensive freeze-thaw tests are conducted on potential construction rocks. These tests typically involve 300 freeze-thaw cycles and can take four to five months to complete. Less time consuming tests that (1) predict durability as well as the extended freeze-thaw test or that (2) reduce the number of rocks subject to the extended test, could save considerable amounts of money. Here we use a probabilistic neural network to try and predict durability as determined by the freeze-thaw test using four rock properties measured on 843 limestone samples from the Kansas Department of Transportation. Modified freeze-thaw tests and less time consuming specific gravity (dry), specific gravity (saturated), and modified absorption tests were conducted on each sample. Durability factors of 95 or more as determined from the extensive freeze-thaw tests are viewed as acceptable—rocks with values below 95 are rejected. If only the modified freeze-thaw test is used to predict which rocks are acceptable, about 45% are misclassified. When 421 randomly selected samples and all four standardized and scaled variables were used to train aprobabilistic neural network, the rate of misclassification of 422 independent validation samples dropped to 28%. The network was trained so that each class (group) and each variable had its own coefficient (sigma). In an attempt to reduce errors further, an additional class was added to the training data to predict durability values greater than 84 and less than 98, resulting in only 11% of the samples misclassified. About 43% of the test data was classed by the neural net into the middle group—these rocks should be subject to full freeze-thaw tests. Thus, use of the probabilistic neural network would meanthat the extended test would only need be applied to 43% of the samples, and 11% of the rocks classed as acceptable would fail early.

Unlocking Diversity in Germplasm Collections via Genomic Selection: A Case Study Based on Quantitative Adult Plant Resistance to Stripe Rust in Spring Wheat.

PubMed

Muleta, Kebede T; Bulli, Peter; Zhang, Zhiwu; Chen, Xianming; Pumphrey, Michael

2017-11-01

Harnessing diversity from germplasm collections is more feasible today because of the development of lower-cost and higher-throughput genotyping methods. However, the cost of phenotyping is still generally high, so efficient methods of sampling and exploiting useful diversity are needed. Genomic selection (GS) has the potential to enhance the use of desirable genetic variation in germplasm collections through predicting the genomic estimated breeding values (GEBVs) for all traits that have been measured. Here, we evaluated the effects of various scenarios of population genetic properties and marker density on the accuracy of GEBVs in the context of applying GS for wheat ( L.) germplasm use. Empirical data for adult plant resistance to stripe rust ( f. sp. ) collected on 1163 spring wheat accessions and genotypic data based on the wheat 9K single nucleotide polymorphism (SNP) iSelect assay were used for various genomic prediction tests. Unsurprisingly, the results of the cross-validation tests demonstrated that prediction accuracy increased with an increase in training population size and marker density. It was evident that using all the available markers (5619) was unnecessary for capturing the trait variation in the germplasm collection, with no further gain in prediction accuracy beyond 1 SNP per 3.2 cM (∼1850 markers), which is close to the linkage disequilibrium decay rate in this population. Collectively, our results suggest that larger germplasm collections may be efficiently sampled via lower-density genotyping methods, whereas genetic relationships between the training and validation populations remain critical when exploiting GS to select from germplasm collections. Copyright © 2017 Crop Science Society of America.

Change classification in SAR time series: a functional approach

NASA Astrophysics Data System (ADS)

Boldt, Markus; Thiele, Antje; Schulz, Karsten; Hinz, Stefan

2017-10-01

Change detection represents a broad field of research in SAR remote sensing, consisting of many different approaches. Besides the simple recognition of change areas, the analysis of type, category or class of the change areas is at least as important for creating a comprehensive result. Conventional strategies for change classification are based on supervised or unsupervised landuse / landcover classifications. The main drawback of such approaches is that the quality of the classification result directly depends on the selection of training and reference data. Additionally, supervised processing methods require an experienced operator who capably selects the training samples. This training step is not necessary when using unsupervised strategies, but nevertheless meaningful reference data must be available for identifying the resulting classes. Consequently, an experienced operator is indispensable. In this study, an innovative concept for the classification of changes in SAR time series data is proposed. Regarding the drawbacks of traditional strategies given above, it copes without using any training data. Moreover, the method can be applied by an operator, who does not have detailed knowledge about the available scenery yet. This knowledge is provided by the algorithm. The final step of the procedure, which main aspect is given by the iterative optimization of an initial class scheme with respect to the categorized change objects, is represented by the classification of these objects to the finally resulting classes. This assignment step is subject of this paper.

Efficient robust conditional random fields.

PubMed

Song, Dongjin; Liu, Wei; Zhou, Tianyi; Tao, Dacheng; Meyer, David A

2015-10-01

Conditional random fields (CRFs) are a flexible yet powerful probabilistic approach and have shown advantages for popular applications in various areas, including text analysis, bioinformatics, and computer vision. Traditional CRF models, however, are incapable of selecting relevant features as well as suppressing noise from noisy original features. Moreover, conventional optimization methods often converge slowly in solving the training procedure of CRFs, and will degrade significantly for tasks with a large number of samples and features. In this paper, we propose robust CRFs (RCRFs) to simultaneously select relevant features. An optimal gradient method (OGM) is further designed to train RCRFs efficiently. Specifically, the proposed RCRFs employ the l1 norm of the model parameters to regularize the objective used by traditional CRFs, therefore enabling discovery of the relevant unary features and pairwise features of CRFs. In each iteration of OGM, the gradient direction is determined jointly by the current gradient together with the historical gradients, and the Lipschitz constant is leveraged to specify the proper step size. We show that an OGM can tackle the RCRF model training very efficiently, achieving the optimal convergence rate [Formula: see text] (where k is the number of iterations). This convergence rate is theoretically superior to the convergence rate O(1/k) of previous first-order optimization methods. Extensive experiments performed on three practical image segmentation tasks demonstrate the efficacy of OGM in training our proposed RCRFs.

Rodent Research on the International Space Station - A Look Forward

NASA Technical Reports Server (NTRS)

Kapusta, A. B.; Smithwick, M.; Wigley, C. L.

2014-01-01

Rodent Research on the International Space Station (ISS) is one of the highest priority science activities being supported by NASA and is planned for up to two flights per year. The first Rodent Research flight, Rodent Research-1 (RR-1) validates the hardware and basic science operations (dissections and tissue preservation). Subsequent flights will add new capabilities to support rodent research on the ISS. RR-1 will validate the following capabilities: animal husbandry for up to 30 days, video downlink to support animal health checks and scientific analysis, on-orbit dissections, sample preservation in RNA. Later and formalin, sample transfer from formalin to ethanol (hindlimbs), rapid cool-down and subsequent freezing at -80 of tissues and carcasses, sample return and recovery. RR-2, scheduled for SpX-6 (Winter 20142015) will add the following capabilities: animal husbandry for up to 60 days, RFID chip reader for individual animal identification, water refill and food replenishment, anesthesia and recovery, bone densitometry, blood collection (via cardiac puncture), blood separation via centrifugation, soft tissue fixation in formalin with transfer to ethanol, and delivery of injectable drugs that require frozen storage prior to use. Additional capabilities are also planned for future flights and these include but are not limited to male mice, live animal return, and the development of experiment unique equipment to support science requirements for principal investigators that are selected for flight. In addition to the hardware capabilities to support rodent research the Crew Office has implemented a training program in generic rodent skills for all USOS crew members during their pre-assignment training rotation. This class includes training in general animal handling, euthanasia, injections, and dissections. The dissection portion of this training focuses on the dissection of the spleen, liver, kidney with adrenals, brain, eyes, and hindlimbs. By achieving and maintaining proficiency in these basic skills as part of the nominal astronaut training curriculum this allows the rodent research program to focus the mission specific crew training on scientific requirements of research and operations flow.

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

A comparative analysis of support vector machines and extreme learning machines.

PubMed

Liu, Xueyi; Gao, Chuanhou; Li, Ping

2012-09-01

The theory of extreme learning machines (ELMs) has recently become increasingly popular. As a new learning algorithm for single-hidden-layer feed-forward neural networks, an ELM offers the advantages of low computational cost, good generalization ability, and ease of implementation. Hence the comparison and model selection between ELMs and other kinds of state-of-the-art machine learning approaches has become significant and has attracted many research efforts. This paper performs a comparative analysis of the basic ELMs and support vector machines (SVMs) from two viewpoints that are different from previous works: one is the Vapnik-Chervonenkis (VC) dimension, and the other is their performance under different training sample sizes. It is shown that the VC dimension of an ELM is equal to the number of hidden nodes of the ELM with probability one. Additionally, their generalization ability and computational complexity are exhibited with changing training sample size. ELMs have weaker generalization ability than SVMs for small sample but can generalize as well as SVMs for large sample. Remarkably, great superiority in computational speed especially for large-scale sample problems is found in ELMs. The results obtained can provide insight into the essential relationship between them, and can also serve as complementary knowledge for their past experimental and theoretical comparisons. Copyright © 2012 Elsevier Ltd. All rights reserved.

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.

Neural Correlates of Selective Attention With Hearing Aid Use Followed by ReadMyQuips Auditory Training Program.

PubMed

Rao, Aparna; Rishiq, Dania; Yu, Luodi; Zhang, Yang; Abrams, Harvey

The objectives of this study were to investigate the effects of hearing aid use and the effectiveness of ReadMyQuips (RMQ), an auditory training program, on speech perception performance and auditory selective attention using electrophysiological measures. RMQ is an audiovisual training program designed to improve speech perception in everyday noisy listening environments. Participants were adults with mild to moderate hearing loss who were first-time hearing aid users. After 4 weeks of hearing aid use, the experimental group completed RMQ training in 4 weeks, and the control group received listening practice on audiobooks during the same period. Cortical late event-related potentials (ERPs) and the Hearing in Noise Test (HINT) were administered at prefitting, pretraining, and post-training to assess effects of hearing aid use and RMQ training. An oddball paradigm allowed tracking of changes in P3a and P3b ERPs to distractors and targets, respectively. Behavioral measures were also obtained while ERPs were recorded from participants. After 4 weeks of hearing aid use but before auditory training, HINT results did not show a statistically significant change, but there was a significant P3a reduction. This reduction in P3a was correlated with improvement in d prime (d') in the selective attention task. Increased P3b amplitudes were also correlated with improvement in d' in the selective attention task. After training, this correlation between P3b and d' remained in the experimental group, but not in the control group. Similarly, HINT testing showed improved speech perception post training only in the experimental group. The criterion calculated in the auditory selective attention task showed a reduction only in the experimental group after training. ERP measures in the auditory selective attention task did not show any changes related to training. Hearing aid use was associated with a decrement in involuntary attention switch to distractors in the auditory selective attention task. RMQ training led to gains in speech perception in noise and improved listener confidence in the auditory selective attention task.

Effectiveness of Healthy Menu Changes in a Nontrainee Military Dining Facility.

PubMed

Belanger, Bethany A; Kwon, Junehee

2016-01-01

The purpose of this study was to evaluate the impact of implementing the Initial Military Training (IMT) menu standards in nontrainee dining facilities (DFAC) on food selection, nutrient intake, and satisfaction of soldiers. Participants were recruited during lunch before and 3 weeks after the menu changes. Direct observations, digital photography, and plate waste methods were used to assess soldiers' food selection and consumption, along with a survey assessing soldiers' meal satisfaction under the two menu standards. Descriptive statistics and independent sample t-tests were used to summarize and compare the data. A total of 172 and 140 soldiers participated before and after menu changes, respectively. Soldiers consumed 886 kcals (38.6% from total fat and 11.2% from saturated fat) and 1,784 mg of sodium before the menu change. Three weeks after the change, all figures improved (p < 0.01). The percentage of healthier food selections mirrored food items served at the DFAC and improved after the intervention (p < 0.001). There were no differences observed in overall satisfaction and meal acceptability after the intervention. Our findings suggest implementing the Initial Military Training menu standards in nontrainee Army DFACs is feasible and has the potential to improve the overall healthfulness of soldiers' food selection and consumption. Reprint & Copyright © 2016 Association of Military Surgeons of the U.S.

Using Data Independent Acquisition (DIA) to Model High-responding Peptides for Targeted Proteomics Experiments*

PubMed Central

Searle, Brian C.; Egertson, Jarrett D.; Bollinger, James G.; Stergachis, Andrew B.; MacCoss, Michael J.

2015-01-01

Targeted mass spectrometry is an essential tool for detecting quantitative changes in low abundant proteins throughout the proteome. Although selected reaction monitoring (SRM) is the preferred method for quantifying peptides in complex samples, the process of designing SRM assays is laborious. Peptides have widely varying signal responses dictated by sequence-specific physiochemical properties; one major challenge is in selecting representative peptides to target as a proxy for protein abundance. Here we present PREGO, a software tool that predicts high-responding peptides for SRM experiments. PREGO predicts peptide responses with an artificial neural network trained using 11 minimally redundant, maximally relevant properties. Crucial to its success, PREGO is trained using fragment ion intensities of equimolar synthetic peptides extracted from data independent acquisition experiments. Because of similarities in instrumentation and the nature of data collection, relative peptide responses from data independent acquisition experiments are a suitable substitute for SRM experiments because they both make quantitative measurements from integrated fragment ion chromatograms. Using an SRM experiment containing 12,973 peptides from 724 synthetic proteins, PREGO exhibits a 40–85% improvement over previously published approaches at selecting high-responding peptides. These results also represent a dramatic improvement over the rules-based peptide selection approaches commonly used in the literature. PMID:26100116

Social Workers as Research Psychotherapists in an Investigation of Cognitive–Behavioral Therapy among Rural Older Adults

PubMed Central

Shah, Avani; Scogin, Forrest; Presnell, Andrew; Morthland, Martin; Kaufman, Allan V.

2013-01-01

This is a report on the treatment fidelity of in-home cognitive–behavioral therapy (CBT) delivered by a sample of clinically trained, master's-level social workers to a group of primarily rural, medically frail older adults as part of the Project to Enhance Aged Rural Living (PEARL) clinical trial. The social workers in this study received brief didactic and experiential CBT training. Audiotaped sessions were randomly selected and evaluated by independent reviewers. Results showed that the social workers adequately delivered CBT as measured by the Cognitive Therapy Scale. Older adult participants also evidenced pre- posttreatment improvements, suggesting that the social workers' delivery of CBT facilitated improvement. PMID:25949093

Sub-Selective Quantization for Learning Binary Codes in Large-Scale Image Search.

PubMed

Li, Yeqing; Liu, Wei; Huang, Junzhou

2018-06-01

Recently with the explosive growth of visual content on the Internet, large-scale image search has attracted intensive attention. It has been shown that mapping high-dimensional image descriptors to compact binary codes can lead to considerable efficiency gains in both storage and performing similarity computation of images. However, most existing methods still suffer from expensive training devoted to large-scale binary code learning. To address this issue, we propose a sub-selection based matrix manipulation algorithm, which can significantly reduce the computational cost of code learning. As case studies, we apply the sub-selection algorithm to several popular quantization techniques including cases using linear and nonlinear mappings. Crucially, we can justify the resulting sub-selective quantization by proving its theoretic properties. Extensive experiments are carried out on three image benchmarks with up to one million samples, corroborating the efficacy of the sub-selective quantization method in terms of image retrieval.

A neural network approach for enhancing information extraction from multispectral image data

USGS Publications Warehouse

Liu, J.; Shao, G.; Zhu, H.; Liu, S.

2005-01-01

A back-propagation artificial neural network (ANN) was applied to classify multispectral remote sensing imagery data. The classification procedure included four steps: (i) noisy training that adds minor random variations to the sampling data to make the data more representative and to reduce the training sample size; (ii) iterative or multi-tier classification that reclassifies the unclassified pixels by making a subset of training samples from the original training set, which means the neural model can focus on fewer classes; (iii) spectral channel selection based on neural network weights that can distinguish the relative importance of each channel in the classification process to simplify the ANN model; and (iv) voting rules that adjust the accuracy of classification and produce outputs of different confidence levels. The Purdue Forest, located west of Purdue University, West Lafayette, Indiana, was chosen as the test site. The 1992 Landsat thematic mapper imagery was used as the input data. High-quality airborne photographs of the same Lime period were used for the ground truth. A total of 11 land use and land cover classes were defined, including water, broadleaved forest, coniferous forest, young forest, urban and road, and six types of cropland-grassland. The experiment, indicated that the back-propagation neural network application was satisfactory in distinguishing different land cover types at US Geological Survey levels II-III. The single-tier classification reached an overall accuracy of 85%. and the multi-tier classification an overall accuracy of 95%. For the whole test, region, the final output of this study reached an overall accuracy of 87%. ?? 2005 CASI.

Effects of Plymetrics Training and Weight Training on selected Motor Ability Components among University Male Students

NASA Astrophysics Data System (ADS)

Shaikh, Alauddin; Mallick, Nazrul Islam

2012-11-01

Introduction: The aim of this study was to find out the effects of plyometrics training and weight training among university male students.Procedure: 60 male students from the different colleges of the Burdwan University were randomly selected as subjects and their age were 19-25 years served as Weight training Group (WTG), second group served as Plyometric Training Group (PTG) and the third group served as Control Group (CT). Eight weeks weight training and six weeks plyometric training were given for experiment accordingly. The control group was not given any training except of their routine. The selected subjects were measured of their motor ability components, speed, endurance, explosive power and agility. ANCOVA was calculation for statistical treatment.Finding: Plyometric training and weight training groups significantly increase speed, endurance, explosive power and agility.Conclusion: The plyometric training has significantly improved speed, explosive power, muscular endurance and agility. The weight training programme has significantly improved agility, muscular endurance, and explosive power. The plometric training is superior to weight training in improving explosive power, agility and muscular endurance.

Analysis of sampling techniques for imbalanced data: An n = 648 ADNI study.

PubMed

Dubey, Rashmi; Zhou, Jiayu; Wang, Yalin; Thompson, Paul M; Ye, Jieping

2014-02-15

Many neuroimaging applications deal with imbalanced imaging data. For example, in Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, the mild cognitive impairment (MCI) cases eligible for the study are nearly two times the Alzheimer's disease (AD) patients for structural magnetic resonance imaging (MRI) modality and six times the control cases for proteomics modality. Constructing an accurate classifier from imbalanced data is a challenging task. Traditional classifiers that aim to maximize the overall prediction accuracy tend to classify all data into the majority class. In this paper, we study an ensemble system of feature selection and data sampling for the class imbalance problem. We systematically analyze various sampling techniques by examining the efficacy of different rates and types of undersampling, oversampling, and a combination of over and undersampling approaches. We thoroughly examine six widely used feature selection algorithms to identify significant biomarkers and thereby reduce the complexity of the data. The efficacy of the ensemble techniques is evaluated using two different classifiers including Random Forest and Support Vector Machines based on classification accuracy, area under the receiver operating characteristic curve (AUC), sensitivity, and specificity measures. Our extensive experimental results show that for various problem settings in ADNI, (1) a balanced training set obtained with K-Medoids technique based undersampling gives the best overall performance among different data sampling techniques and no sampling approach; and (2) sparse logistic regression with stability selection achieves competitive performance among various feature selection algorithms. Comprehensive experiments with various settings show that our proposed ensemble model of multiple undersampled datasets yields stable and promising results. © 2013 Elsevier Inc. All rights reserved.

Factors predicting training transfer in health professionals participating in quality improvement educational interventions.

PubMed

Eid, Ahmed; Quinn, Doris

2017-01-31

Predictors of quality improvement (QI) training transfer are needed. This study aimed to identify these predictors among health professionals who participated in a QI training program held at a large hospital in the United States between 2005 and 2014. It also aimed to determine how these predictive factors facilitated or impeded QI training transfer. Following the Success Case Method, we used a screening survey to identify trainees with high and low levels of training transfer. We then conducted semistructured interviews with a sample of the survey respondents to document how training transfer was achieved and how lack of training transfer could be explained. The survey's response rate was 43%, with a Cronbach alpha of 0.89. We then conducted a thematic analysis of the interview transcripts of 16 physicians. The analysis revealed 3 categories of factors influencing the transfer of QI training: trainee characteristics, training course, and work environment. Relevant trainee characteristics included attitude toward change, motivation, mental processing skills, interpersonal skills, and the personality characteristics curiosity, humility, conscientiousness, resilience, wisdom, and positivity. The training project, team-based learning, and lectures were identified as relevant aspects of the training course. Work culture, work relationships, and resources were subthemes of the work environment category. We identified several QI training transfer predictors in our cohort of physicians. We hypothesize that some of these predictors may be more relevant to QI training transfer. Our results will help organizational leaders select trainees who are most likely to transfer QI training and to ensure that their work environments are conducive to QI training transfer.

Chile: Acceptability of a Training Program for Depression Management in Primary Care.

PubMed

Marín, Rigoberto; Martínez, Pablo; Cornejo, Juan P; Díaz, Berta; Peralta, José; Tala, Álvaro; Rojas, Graciela

2016-01-01

In Chile, there are inconsistencies in the management of depression in primary care settings, and the National Depression Program, currently in effect, was implemented without a standardized training program. The objective of this study is to evaluate the acceptability of a training program on the management of depression for primary care health teams. The study was a randomized controlled trial, and two primary centers from the Metropolitan Region of Santiago were randomly selected to carry out the intervention training program. Pre-post surveys were applied, to evaluate expectations and satisfaction with the intervention, respectively. Descriptive and content analysis was carried out. The sample consisted of 41 health professionals, 56.1% of who reported that their expectations for the intervention were met. All of the training activities were evaluated with scores higher than 6.4 (on a 1-7 scale). The trainers, the methodology, and the learning environment were considered strengths and facilitators of the program, while the limited duration of the training, the logistical problems faced during part of the program, and the lack of educational material were viewed as weaknesses. The intervention was well accepted by primary health care teams. However, the clinical impact in patients still has to be evaluated.

Effect of exercise training on walking mobility in multiple sclerosis: a meta-analysis.

PubMed

Snook, Erin M; Motl, Robert W

2009-02-01

The study used meta-analytic procedures to examine the overall effect of exercise training interventions on walking mobility among individuals with multiple sclerosis. A search was conducted for published exercise training studies from 1960 to November 2007 using MEDLINE, PsychINFO, CINAHL, and Current Contents Plus. Studies were selected if they measured walking mobility, using instruments identified as acceptable walking mobility constructs and outcome measures for individuals with neurologic disorders, before and after an intervention that included exercise training. Forty-two published articles were located and reviewed, and 22 provided enough data to compute effect sizes expressed as Cohen's d. Sixty-six effect sizes were retrieved from the 22 publications with 600 multiple sclerosis participants and yielded a weighted mean effect size of g = 0.19 (95% confidence interval, 0.09-0.28). There were larger effects associated with supervised exercise training ( g = 0.32), exercise programs that were less than 3 months in duration (g = 0.28), and mixed samples of relapsing-remitting and progressive multiple sclerosis (g = 0.52). The cumulative evidence supports that exercise training is associated with a small improvement in walking mobility among individuals with multiple sclerosis.

Issues in development, evaluation, and use of the NASA Preflight Adaptation Trainer (PAT)

NASA Technical Reports Server (NTRS)

Lane, Norman E.; Kennedy, Robert S.

1988-01-01

The Preflight Adaptation Trainer (PAT) is intended to reduce or alleviate space adaptation syndrome by providing opportunities for portions of that adaptation to occur under normal gravity conditions prior to space flight. Since the adaptation aspects of the PAT objectives involve modification not only of the behavior of the trainee, but also of sensiomotor skills which underly the behavioral generation, the defining of training objectives of the PAT utilizes four mechanisms: familiarization, demonstration, training and adaptation. These mechanisms serve as structural reference points for evaluation, drive the content and organization of the training procedures, and help to define the roles of the PAT instructors and operators. It was determined that three psychomotor properties are most critical for PAT evaluation: reliability; sensitivity; and relevance. It is cause for concern that the number of measures available to examine PAT effects exceed those that can be properly studied with the available sample sizes; special attention will be required in selection of the candidate measure set. The issues in PAT use and application within a training system context are addressed through linking the three training related mechanisms of familiarization, demonstration and training to the fourth mechanism, adaptation.

Multiple mini interview (MMI) for general practice training selection in Australia: interviewers' motivation.

PubMed

Burgess, Annette; Roberts, Chris; Sureshkumar, Premala; Mossman, Karyn

2018-01-25

Multiple Mini Interviews (MMIs) are being used by a growing number of postgraduate training programs and medical schools as their interview process for selection entry. The Australian General Practice and Training (AGPT) used a National Assessment Centre (NAC) approach to selection into General Practice (GP) Training, which include MMIs. Interviewing is a resource intensive process, and implementation of the MMI requires a large number of interviewers, with a number of candidates being interviewed simultaneously. In 2015, 308 interviewers participated in the MMI process - a decrease from 340 interviewers in 2014, and 310 in 2013. At the same time, the number of applicants has steadily increased, with 1930 applications received in 2013; 2254 in 2014; and 2360 in 2015. This has raised concerns regarding the increasing recruitment needs, and the need to retain interviewers for subsequent years of MMIs. In order to investigate interviewers' reasons for participating in MMIs, we utilised self-determination theory (SDT) to consider interviewers' motivation to take part in MMIs at national selection centres. In 2015, 308 interviewers were recruited from 17 Regional Training Providers (RTPs) to participate in the MMI process at one of 15 NACs. For this study, a convenience sample of NAC sites was used. Forty interviewers were interviewed (n = 40; 40/308 = 13%) from five NACs. Framework analysis was used to code and categorise data into themes. Interviewers' motivation to take part as interviewers were largely related to their sense of duty, their desire to contribute their expertise to the process, and their desire to have input into selection of GP Registrars; a sense of duty to their profession; and an opportunity to meet with colleagues and future trainees. Interviewers also highlighted factors hindering motivation, which sometimes included the large number of candidates seen in one day. Interviewers' motivation for contributing to the MMIs was largely related to their desire to contribute to their profession, and ultimately improve future patient care. Interviewers recognised the importance of interviewing, and felt their individual roles made a crucial contribution to the profession of general practice. Good administration and leadership at each NAC is needed. By gaining an understanding of interviewers' motivation, and enhancing this, engagement and retention of interviewers may be increased.

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.

Personal characteristics associated with resident physicians' self perceptions of preparedness to deliver cross-cultural care.

PubMed

Lopez, Lenny; Vranceanu, Ana-Maria; Cohen, Amy P; Betancourt, Joseph; Weissman, Joel S

2008-12-01

Recent reports from the Institute of Medicine emphasize patient-centered care and cross-cultural training as a means of improving the quality of medical care and eliminating racial and ethnic disparities. To determine whether, controlling for training received in medical school or during residency, resident physician socio-cultural characteristics influence self-perceived preparedness and skill in delivering cross-cultural care. National survey of resident physicians. A probability sample of residents in seven specialties in their final year of training at US academic health centers. Nine resident characteristics were analyzed. Differences in preparedness and skill were assessed using the chi(2) statistic and multivariate logistic regression. Fifty-eight percent (2047/3500) of residents responded. The most important factor associated with improved perceived skill level in performing selected tasks or services believed to be useful in treating culturally diverse patients was having received cross-cultural skills training during residency (OR range 1.71-4.22). Compared with white residents, African American physicians felt more prepared to deal with patients with distrust in the US healthcare system (OR 1.63) and with racial or ethnic minorities (OR 1.61), Latinos reported feeling more prepared to deal with new immigrants (OR 1.88) and Asians reported feeling more prepared to deal with patients with health beliefs at odds with Western medicine (1.43). Cross-cultural care skills training is associated with increased self-perceived preparedness to care for diverse patient populations providing support for the importance of such training in graduate medical education. In addition, selected resident characteristics are associated with being more or less prepared for different aspects of cross-cultural care. This underscores the need to both include medical residents from diverse backgrounds in all training programs and tailor such programs to individual resident needs in order to maximize the chances that such training is likely to have an impact on the quality of care.

"Taking Training to the Next Level": The American College of Surgeons Committee on Residency Training Survey.

PubMed

Damewood, Richard B; Blair, Patrice Gabler; Park, Yoon Soo; Lupi, Linda K; Newman, Rachel Williams; Sachdeva, Ajit K

The American College of Surgeons (ACS) appointed a committee of leaders from the ACS, Association of Program Directors in Surgery, Accreditation Council for Graduate Medical Education, and American Board of Surgery to define key challenges facing surgery resident training programs and to explore solutions. The committee wanted to solicit the perspectives of surgery resident program directors (PDs) given their pivotal role in residency training. Two surveys were developed, pilot tested, and administered to PDs following Institutional Review Board approval. PDs from 247 Accreditation Council for Graduate Medical Education-accredited general surgery programs were randomized to receive 1 of the 2 surveys. Bias analyses were conducted, and adjusted Pearson χ 2 tests were used to test for differences in response patterns by program type and size. All accredited general surgery programs in the United States were included in the sampling frame of the survey; 10 programs with initial or withdrawn accreditation were excluded from the sampling frame. A total of 135 PDs responded, resulting in a 54.7% response rate (Survey A: n = 67 and Survey B: n = 68). The respondent sample was determined to be representative of program type and size. Nearly 52% of PD responses were from university-based programs, and 41% had over 6 residents per graduating cohort. More than 61% of PDs reported that, compared to 10 years ago, both entering and graduating residents are less prepared in technical skills. PDs expressed significant concerns regarding the effect of duty-hour restrictions on the overall preparation of graduating residents (61%) and quality of patient care (57%). The current 5-year training structure was viewed as needing a significant or extensive increase in opportunities for resident autonomy (63%), and the greatest barriers to resident autonomy were viewed to be patient preferences not to be cared for by residents (68%), liability concerns (68%), and Centers for Medicare and Medicaid Services regulations (65%). Although 64% of PDs believe that moderate or significant changes are needed in the current structure of residency training, 35% believe that no changes in the structure are needed. When asked for their 1 best recommendation regarding the structure of surgical residency, only 22% of PDs selected retaining the current 5-year structure. The greatest percentage of PDs (28%) selected the "4 + 2" model as their 1 best recommendation for the structure to be used. In the area of faculty development, 56% of PDs supported a significant or extensive increase in Train the Teacher programs, and 41% supported a significant or extensive increase in faculty certification in education. Information regarding the valuable perspectives of PDs gathered through these surveys should help in implementing important changes in residency training and faculty development. These efforts will need to be pursued collaboratively with involvement of key stakeholders, including the organizations represented on this ACS committee. Copyright © 2017 Association of Program Directors in Surgery. Published by Elsevier Inc. All rights reserved.

[Electroencephalogram Feature Selection Based on Correlation Coefficient Analysis].

PubMed

Zhou, Jinzhi; Tang, Xiaofang

2015-08-01

In order to improve the accuracy of classification with small amount of motor imagery training data on the development of brain-computer interface (BCD systems, we proposed an analyzing method to automatically select the characteristic parameters based on correlation coefficient analysis. Throughout the five sample data of dataset IV a from 2005 BCI Competition, we utilized short-time Fourier transform (STFT) and correlation coefficient calculation to reduce the number of primitive electroencephalogram dimension, then introduced feature extraction based on common spatial pattern (CSP) and classified by linear discriminant analysis (LDA). Simulation results showed that the average rate of classification accuracy could be improved by using correlation coefficient feature selection method than those without using this algorithm. Comparing with support vector machine (SVM) optimization features algorithm, the correlation coefficient analysis can lead better selection parameters to improve the accuracy of classification.

Identification of a three-biomarker panel in urine for early detection of pancreatic adenocarcinoma

PubMed Central

Radon, Tomasz P; Massat, Nathalie J; Jones, Richard; Alrawashdeh, Wasfi; Dumartin, Laurent; Ennis, Darren; Duffy, Stephen W; Kocher, Hemant M; Pereira, Stephen P; Nascimento, Cristiane M; Real, Francisco X; Malats, Núria; Neoptolemos, John; Costello, Eithne; Greenhalf, William; Lemoine, Nick R; Crnogorac-Jurcevic, Tatjana

2015-01-01

Purpose Non-invasive biomarkers for early detection of pancreatic ductal adenocarcinoma (PDAC) are currently not available. Here, we aimed to identify a set of urine proteins able to distinguish patients with early stage PDAC from healthy individuals (H). Experimental design Proteomes of 18 urine samples from healthy controls, chronic pancreatitis and PDAC patients (six/group) were assayed using GeLC/MS/MS analysis. The selected biomarkers were subsequently validated using ELISA assays using multiple logistic regression applied to a training dataset in a multicentre cohort comprising 488 urine samples. Results LYVE-1, REG1A and TFF1 were selected as candidate biomarkers. When comparing PDAC (n=192) to healthy (n=87) urines, the resulting areas under the receiver operating characteristic curves (AUCs) of the panel were 0.89 (95%CI 0.84-0.94) in the training (70% of the data), and 0.92 (95%CI 0.86-0.98) in the validation (30% of the data) datasets. When comparing PDAC stage I-II (n=71) to healthy urines, the panel achieved AUCs of 0.90 (95%CI 0.84-0.96) and 0.93 (95%CI 0.84-1.00) in the training and validation datasets, respectively. In PDAC stage I-II and healthy samples with matching plasma CA19.9 the panel achieved a higher AUC of 0.97 (95%CI 0.94-0.99) than CA19.9 (AUC=0.88, 95%CI 0.81-0.95, p=0.005). Adding plasma CA19.9 to the panel increased the AUC from 0.97 (95%CI 0.94-0.99) to 0.99 (95%CI 0.97-1.00, p=0.04) but did not improve the comparison of stage I-IIA PDAC (n=17) to healthy urine. Conclusion We have established a novel, three-protein biomarker panel that is able to detect patients with early stage pancreatic cancer in urine specimens. PMID:26240291

34 CFR 361.32 - Use of profitmaking organizations for on-the-job training in connection with selected projects.

Code of Federal Regulations, 2011 CFR

2011-07-01

... individuals with disabilities under the Projects With Industry program, 34 CFR part 379, if the designated... training in connection with selected projects. 361.32 Section 361.32 Education Regulations of the Offices... on-the-job training in connection with selected projects. The State plan must assure that the...

Adoption of clinical and business trainings by child mental health clinics in New York State.

PubMed

Chor, Ka Ho Brian; Olin, Su-Chin Serene; Weaver, Jamie; Cleek, Andrew F; McKay, Mary M; Hoagwood, Kimberly E; Horwitz, Sarah M

2014-12-01

This study prospectively examined the naturalistic adoption of clinical and business evidence-informed training by all 346 outpatient mental health clinics licensed to treat children, adolescents, and their families in New York State. The study used attendance data (September 2011-August 2013) from the Clinic Technical Assistance Center, a training, consultation, and educational center funded by the state Office of Mental Health, to classify the clinics' adoption of 33 trainings. Adoption behavior was classified by number, type, and intensity of trainings. The clinics were classified into four adopter groups reflecting the highest training intensity in which they participated (low, medium, and high adopters and "super-adopters"). A total of 268 clinics adopted trainings (median=5); business and clinical trainings were about equally accessed (82% versus 78%). Participation was highest for hour-long Webinars (96%) followed by learning collaboratives, which take six to 18 months to complete (34%). Most (73%-94%) adopters of business learning collaboratives and all adopters of clinical learning collaboratives had previously sampled a Webinar, although maintaining participation in learning collaboratives was a challenge. The adopter groups captured meaningful adopter profiles: 41% of clinics were low adopters that selected fewer trainings and participated only in Webinars, and 34% were high or super-adopters that accessed more trainings and participated in at least one learning collaborative. More nuanced definitions of adoption behavior can improve the understanding of clinic adoption of training and hence promote the development of efficient rollout strategies by state systems.

Green Tea Consumption after Intense Taekwondo Training Enhances Salivary Defense Factors and Antibacterial Capacity

PubMed Central

Lin, Shiuan-Pey; Li, Chia-Yang; Suzuki, Katsuhiko; Chang, Chen-Kang; Chou, Kuei-Ming; Fang, Shih-Hua

2014-01-01

The aim of this study was to investigate the short-term effects of green tea consumption on selected salivary defense proteins, antibacterial capacity and anti-oxidation activity in taekwondo (TKD) athletes, following intensive training. Twenty-two TKD athletes performed a 2-hr TKD training session. After training, participants ingested green tea (T, caffeine 6 mg/kg and catechins 22 mg/kg) or an equal volume of water (W). Saliva samples were collected at three time points: before training (BT-T; BT-W), immediately after training (AT-T; AT-W), and 30 min after drinking green tea or water (Rec-T; Rec-W). Salivary total protein, immunoglobulin A (SIgA), lactoferrin, α-amylase activity, free radical scavenger activity (FRSA) and antibacterial capacity were measured. Salivary total protein, lactoferrin, SIgA concentrations and α-amylase activity increased significantly immediately after intensive TKD training. After tea drinking and 30 min rest, α-amylase activity and the ratio of α-amylase to total protein were significantly higher than before and after training. In addition, salivary antibacterial capacity was not affected by intense training, but green tea consumption after training enhanced salivary antibacterial capacity. Additionally, we observed that salivary FRSA was markedly suppressed immediately after training and quickly returned to pre-exercise values, regardless of which fluid was consumed. Our results show that green tea consumption significantly enhances the activity of α-amylase and salivary antibacterial capacity. PMID:24498143

Poor retention does not have to be the rule: retention of volunteer community health workers in Uganda.

PubMed

Ludwick, Teralynn; Brenner, Jennifer L; Kyomuhangi, Teddy; Wotton, Kathryn A; Kabakyenga, Jerome Kahuma

2014-05-01

Globally, health worker shortages continue to plague developing countries. Community health workers are increasingly being promoted to extend primary health care to underserved populations. Since 2004, Healthy Child Uganda (HCU) has trained volunteer community health workers in child health promotion in rural southwest Uganda. This study analyses the retention and motivation of volunteer community health workers trained by HCU. It presents retention rates over a 5-year period and provides insight into volunteer motivation. The findings are based on a 2010 retrospective review of the community health worker registry and the results of a survey on selection and motivation. The survey was comprised of qualitative and quantitative questions and verbally administered to a convenience sample of project participants. Between February 2004 and July 2009, HCU trained 404 community health workers (69% female) in 175 villages. Volunteers had an average age of 36.7 years, 4.9 children and some primary school education. Ninety-six per cent of volunteer community health workers were retained after 1 year (389/404), 91% after 2 years (386/404) and 86% after 5 years (101/117). Of the 54 'dropouts', main reasons cited for discontinuation included 'too busy' (12), moved (11), business/employment (8), death (6) and separation/divorce (6). Of 58 questionnaire respondents, most (87%) reported having been selected at an inclusive community meeting. Pair-wise ranking was used to assess the importance of seven 'motivational factors' among respondents. Those highest ranked were 'improved child health', 'education/training' and 'being asked for advice/assistance by peers', while the modest 'transport allowance' ranked lowest. Our findings suggest that in our rural, African setting, volunteer community health workers can be retained over the medium term. Community health worker programmes should invest in community involvement in selection, quality training, supportive supervision and incentives, which may promote improved retention.

Method for Automatic Selection of Parameters in Normal Tissue Complication Probability Modeling.

PubMed

Christophides, Damianos; Appelt, Ane L; Gusnanto, Arief; Lilley, John; Sebag-Montefiore, David

2018-07-01

To present a fully automatic method to generate multiparameter normal tissue complication probability (NTCP) models and compare its results with those of a published model, using the same patient cohort. Data were analyzed from 345 rectal cancer patients treated with external radiation therapy to predict the risk of patients developing grade 1 or ≥2 cystitis. In total, 23 clinical factors were included in the analysis as candidate predictors of cystitis. Principal component analysis was used to decompose the bladder dose-volume histogram into 8 principal components, explaining more than 95% of the variance. The data set of clinical factors and principal components was divided into training (70%) and test (30%) data sets, with the training data set used by the algorithm to compute an NTCP model. The first step of the algorithm was to obtain a bootstrap sample, followed by multicollinearity reduction using the variance inflation factor and genetic algorithm optimization to determine an ordinal logistic regression model that minimizes the Bayesian information criterion. The process was repeated 100 times, and the model with the minimum Bayesian information criterion was recorded on each iteration. The most frequent model was selected as the final "automatically generated model" (AGM). The published model and AGM were fitted on the training data sets, and the risk of cystitis was calculated. The 2 models had no significant differences in predictive performance, both for the training and test data sets (P value > .05) and found similar clinical and dosimetric factors as predictors. Both models exhibited good explanatory performance on the training data set (P values > .44), which was reduced on the test data sets (P values < .05). The predictive value of the AGM is equivalent to that of the expert-derived published model. It demonstrates potential in saving time, tackling problems with a large number of parameters, and standardizing variable selection in NTCP modeling. Crown Copyright © 2018. Published by Elsevier Inc. All rights reserved.

Selection of appropriate training and validation set chemicals for modelling dermal permeability by U-optimal design.

PubMed

Xu, G; Hughes-Oliver, J M; Brooks, J D; Yeatts, J L; Baynes, R E

2013-01-01

Quantitative structure-activity relationship (QSAR) models are being used increasingly in skin permeation studies. The main idea of QSAR modelling is to quantify the relationship between biological activities and chemical properties, and thus to predict the activity of chemical solutes. As a key step, the selection of a representative and structurally diverse training set is critical to the prediction power of a QSAR model. Early QSAR models selected training sets in a subjective way and solutes in the training set were relatively homogenous. More recently, statistical methods such as D-optimal design or space-filling design have been applied but such methods are not always ideal. This paper describes a comprehensive procedure to select training sets from a large candidate set of 4534 solutes. A newly proposed 'Baynes' rule', which is a modification of Lipinski's 'rule of five', was used to screen out solutes that were not qualified for the study. U-optimality was used as the selection criterion. A principal component analysis showed that the selected training set was representative of the chemical space. Gas chromatograph amenability was verified. A model built using the training set was shown to have greater predictive power than a model built using a previous dataset [1].

Automated lung sound analysis for detecting pulmonary abnormalities.

PubMed

Datta, Shreyasi; Dutta Choudhury, Anirban; Deshpande, Parijat; Bhattacharya, Sakyajit; Pal, Arpan

2017-07-01

Identification of pulmonary diseases comprises of accurate auscultation as well as elaborate and expensive pulmonary function tests. Prior arts have shown that pulmonary diseases lead to abnormal lung sounds such as wheezes and crackles. This paper introduces novel spectral and spectrogram features, which are further refined by Maximal Information Coefficient, leading to the classification of healthy and abnormal lung sounds. A balanced lung sound dataset, consisting of publicly available data and data collected with a low-cost in-house digital stethoscope are used. The performance of the classifier is validated over several randomly selected non-overlapping training and validation samples and tested on separate subjects for two separate test cases: (a) overlapping and (b) non-overlapping data sources in training and testing. The results reveal that the proposed method sustains an accuracy of 80% even for non-overlapping data sources in training and testing.

Qualitative Assessment of a Novel Efficacy-Focused Training Intervention for Public Health Workers in Disaster Recovery.

PubMed

Tower, Craig; Altman, Brian A; Strauss-Riggs, Kandra; Iversen, Annelise; Garrity, Stephanie; Thompson, Carol B; Walsh, Lauren; Rutkow, Lainie; Schor, Kenneth; Barnett, Daniel J

2016-08-01

We trained local public health workers on disaster recovery roles and responsibilities by using a novel curriculum based on a threat and efficacy framework and a training-of-trainers approach. This study used qualitative data to assess changes in perceptions of efficacy toward Hurricane Sandy recovery and willingness to participate in future disaster recoveries. Purposive and snowball sampling were used to select trainers and trainees from participating local public health departments in jurisdictions impacted by Hurricane Sandy in October 2012. Two focus groups totaling 29 local public health workers were held in April and May of 2015. Focus group participants discussed the content and quality of the curriculum, training logistics, and their willingness to engage in future disaster recovery efforts. The training curriculum improved participants' understanding of and confidence in their disaster recovery work and related roles within their agencies (self-efficacy); increased their individual- and agency-level sense of role-importance in disaster recovery (response-efficacy); and enhanced their sense of their agencies' effective functioning in disaster recovery. Participants suggested further training customization and inclusion of other recovery agencies. Threat- and efficacy-based disaster recovery trainings show potential to increase public health workers' sense of efficacy and willingness to participate in recovery efforts. (Disaster Med Public Health Preparedness. 2016;10:615-622).

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.

Raman spectroscopy applied to identify metabolites in urine of physically active subjects.

PubMed

Moreira, Letícia Parada; Silveira, Landulfo; da Silva, Alexandre Galvão; Fernandes, Adriana Barrinha; Pacheco, Marcos Tadeu Tavares; Rocco, Débora Dias Ferraretto Moura

2017-11-01

Raman spectroscopy is a rapid and non-destructive technique suitable for biological fluids analysis. In this work, dispersive Raman spectroscopy has been employed as a rapid and nondestructive technique to detect the metabolites in urine of physically active subjects before and after vigorous 30min pedaling or running compared to sedentary subjects. For so, urine samples from 9 subjects were obtained before and immediately after physical activities and submitted to Raman spectroscopy (830nm excitation, 250mW laser power, 20s integration time) and compared to urine from 5 sedentary subjects. The Raman spectra of urine from sedentary showed peaks related to urea, creatinine, ketone bodies, phosphate and other nitrogenous compounds. These metabolic biomarkers presented peaks with different intensities in the urine of physically active individuals after exercises compared to before, measured by the intensity of selected peaks the Raman spectra, which means different concentrations after training. These peaks presented different intensity values for each subject before physical activity, also behaving differently compared to the post-training: some subjects presented increase while others decrease the intensity. Raman spectroscopy may allow the development of a rapid and non-destructive test for metabolic evaluation of the physical training in active and trained subjects using urine samples, allowing nutrition adjustment with the sport's performance. Copyright © 2017 Elsevier B.V. All rights reserved.

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.

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.

Frequency selective detection of nuclear quadrupole resonance (NQR) spin echoes

NASA Astrophysics Data System (ADS)

Somasundaram, Samuel D.; Jakobsson, Andreas; Smith, John A. S.; Althoefer, Kaspar A.

2006-05-01

Nuclear Quadrupole Resonance (NQR) is a radio frequency (RF) technique that can be used to detect the presence of quadrupolar nuclei, such as the 14N nucleus prevalent in many explosives and narcotics. The technique has been hampered by low signal-to-noise ratios and is further aggravated by the presence of RF interference (RFI). To ensure accurate detection, proposed detectors should exploit the rich form of the NQR signal. Furthermore, the detectors should also be robust to any remaining residual interference, left after suitable RFI mitigation has been employed. In this paper, we propose a new NQR data model, particularly for the realistic case where multiple pulse sequences are used to generate trains of spin echoes. Furthermore, we refine two recently proposed approximative maximum likelihood (AML) detectors, enabling the algorithm to optimally exploit the data model of the entire echo train and also incorporate knowledge of the temperature dependent spin-echo decay time. The AML-based detectors ensure accurate detection and robustness against residual RFI, even when the temperature of the sample is not precisely known, by exploiting the dependencies of the NQR resonant lines on temperature. Further robustness against residual interference is gained as the proposed detector is frequency selective; exploiting only those regions of the spectrum where the NQR signal is expected. Extensive numerical evaluations based on both simulated and measured NQR data indicate that the proposed Frequency selective Echo Train AML (FETAML) detector offers a significant improvement as compared to other existing detectors.

CpG Methylation Signature Predicts Recurrence in Early-Stage Hepatocellular Carcinoma: Results From a Multicenter Study.

PubMed

Qiu, Jiliang; Peng, Baogang; Tang, Yunqiang; Qian, Yeben; Guo, Pi; Li, Mengfeng; Luo, Junhang; Chen, Bin; Tang, Hui; Lu, Canliang; Cai, Muyan; Ke, Zunfu; He, Wei; Zheng, Yun; Xie, Dan; Li, Binkui; Yuan, Yunfei

2017-03-01

Purpose Early-stage hepatocellular carcinoma (E-HCC) is being diagnosed increasingly, and in one half of diagnosed patients, recurrence will develop. Thus, it is urgent to identify recurrence-related markers. We investigated the effectiveness of CpG methylation in predicting recurrence for patients with E-HCCs. Patients and Methods In total, 576 patients with E-HCC from four independent centers were sorted by three phases. In the discovery phase, 66 tumor samples were analyzed using the Illumina Methylation 450k Beadchip. Two algorithms, Least Absolute Shrinkage and Selector Operation and Support Vector Machine-Recursive Feature Elimination, were used to select significant CpGs. In the training phase, penalized Cox regression was used to further narrow CpGs into 140 samples. In the validation phase, candidate CpGs were validated using an internal cohort (n = 141) and two external cohorts (n = 191 and n =104). Results After combining the 46 CpGs selected by the Least Absolute Shrinkage and Selector Operation and the Support Vector Machine-Recursive Feature Elimination algorithms, three CpGs corresponding to SCAN domain containing 3, Src homology 3-domain growth factor receptor-bound 2-like interacting protein 1, and peptidase inhibitor 3 were highlighted as candidate predictors in the training phase. On the basis of the three CpGs, a methylation signature for E-HCC (MSEH) was developed to classify patients into high- and low-risk recurrence groups in the training cohort ( P < .001). The performance of MSEH was validated in the internal cohort ( P < .001) and in the two external cohorts ( P < .001; P = .002). Furthermore, a nomogram comprising MSEH, tumor differentiation, cirrhosis, hepatitis B virus surface antigen, and antivirus therapy was generated to predict the 5-year recurrence-free survival in the training cohort, and it performed well in the three validation cohorts (concordance index: 0.725, 0.697, and 0.693, respectively). Conclusion MSEH, a three-CpG-based signature, is useful in predicting recurrence for patients with E-HCC.

Selecting for creativity and innovation potential: implications for practice in healthcare education.

PubMed

Patterson, Fiona; Zibarras, Lara Dawn

2017-05-01

The ability to innovate is an important requirement in many organisations. Despite this pressing need, few selection systems in healthcare focus on identifying the potential for creativity and innovation and so this area has been vastly under-researched. As a first step towards understanding how we might select for creativity and innovation, this paper explores the use of a trait-based measure of creativity and innovation potential, and evaluates its efficacy for use in selection for healthcare education. This study uses a sample of 188 postgraduate physicians applying for education and training in UK General Practice. Participants completed two questionnaires (a trait-based measure of creativity and innovation, and a measure of the Big Five personality dimensions) and were also rated by assessors on creative problem solving measured during a selection centre. In exploring the construct validity of the trait-based measure of creativity and innovation, our research clarifies the associations between personality, and creativity and innovation. In particular, our study highlights the importance of motivation in the creativity and innovation process. Results also suggest that Openness to Experience is positively related to creativity and innovation whereas some aspects of Conscientiousness are negatively associated with creativity and innovation. Results broadly support the utility of using a trait-based measure of creativity and innovation in healthcare selection processes, although practically this may be best delivered as part of an interview process, rather than as a screening tool. Findings are discussed in relation to broader implications for placing more priority on creativity and innovation as selection criteria within healthcare education and training in future.

Assessment of knowledge and practice of health workers towards tuberculosis infection control and associated factors in public health facilities of Addis Ababa, Ethiopia: A cross-sectional study.

PubMed

Demissie Gizaw, Girma; Aderaw Alemu, Zewdie; Kibret, Kelemu Tilahun

2015-01-01

Tuberculosis is the leading causes of mortality among infectious diseases worldwide. The risk of transmission from patients to health workers is doubles that of the general population. The close contact to the infectious case before diagnosis is the major risk for tuberculosis infection. The aim of the study was to assess knowledge and practice of health professionals towards tuberculosis infection control and its associated factors in health facilities of Addis Ababa, Ethiopia. A cross-sectional study was conducted from February 29 to April 15/2014 in selected health facilities in Addis Ababa. Five hundred ninety health workers were included in the study. The sample size was assigned to each health facility proportional to their number of health workers. Study subjects were selected from each stratum by simple random sampling technique. Interviewer administered structured questionnaire was used to collect information. Logistic regression was used to identify factors associated with knowledge and practice of health workers towards tuberculosis infection control. Five hundred eighty two participants with 98.6% response rate were involved in the study. Of these, 36.1% had poor knowledge and 51.7% unsatisfactory practice score towards tuberculosis infection control. Having more than six years working experience in health facility (AOR = 2.51; 95% CI: 1.5-4.1) and tuberculosis related training (AOR = 2.51 95% CI; 1.5, 4.1) were significantly associated with knowledge on tuberculosis infection control. Having experience in tuberculosis clinic (AOR =1.93; 95% CI: 1.12, 3.34) and tuberculosis related training (AOR = 1.48; 95% CI: 1.87, 2.51) were significantly associated with practice on tuberculosis infection control. One third of health workers had relatively poor knowledge and nearly half of them had unsatisfactory practice on tuberculosis infection control. Tuberculosis training and work experiences in health facility are determinant factor to knowledge. Whereas tuberculosis related training and experience in tuberculosis clinic are predictor to practice. So, training of the health professionals, on job orientations of junior health workers, and farther study including private health workers are recommended.

Selection and Training of Navy Recruit Company Commanders. Final Report.

ERIC Educational Resources Information Center

Curry, Thomas F., Jr.; And Others

This report addresses the selection, training, and utilization of Navy Recruit Company Commanders (Recruit Training Instructors). It represents one in a series of reports concerning the optimization of Navy Recruit Training to meet the needs of the post-1980 period. The report provides a comprehensive review of the Navy's Recruit Company Commander…

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

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.

Effects of Selection and Training on Unit-Level Performance over Time: A Latent Growth Modeling Approach

ERIC Educational Resources Information Center

Van Iddekinge, Chad H.; Ferris, Gerald R.; Perrewe, Pamela L.; Perryman, Alexa A.; Blass, Fred R.; Heetderks, Thomas D.

2009-01-01

Surprisingly few data exist concerning whether and how utilization of job-related selection and training procedures affects different aspects of unit or organizational performance over time. The authors used longitudinal data from a large fast-food organization (N = 861 units) to examine how change in use of selection and training relates to…

Active relearning for robust supervised classification of pulmonary emphysema

NASA Astrophysics Data System (ADS)

Raghunath, Sushravya; Rajagopalan, Srinivasan; Karwoski, Ronald A.; Bartholmai, Brian J.; Robb, Richard A.

2012-03-01

Radiologists are adept at recognizing the appearance of lung parenchymal abnormalities in CT scans. However, the inconsistent differential diagnosis, due to subjective aggregation, mandates supervised classification. Towards optimizing Emphysema classification, we introduce a physician-in-the-loop feedback approach in order to minimize uncertainty in the selected training samples. Using multi-view inductive learning with the training samples, an ensemble of Support Vector Machine (SVM) models, each based on a specific pair-wise dissimilarity metric, was constructed in less than six seconds. In the active relearning phase, the ensemble-expert label conflicts were resolved by an expert. This just-in-time feedback with unoptimized SVMs yielded 15% increase in classification accuracy and 25% reduction in the number of support vectors. The generality of relearning was assessed in the optimized parameter space of six different classifiers across seven dissimilarity metrics. The resultant average accuracy improved to 21%. The co-operative feedback method proposed here could enhance both diagnostic and staging throughput efficiency in chest radiology practice.

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.

Biomotor status and kinesiological education of girls aged 10 to 12 years--example: volleyball.

PubMed

Milić, Mirjana; Grgantov, Zoran; Katić, Ratko

2012-09-01

The aim of this study was to define processes of orientation and/or selection towards sports game of volleyball in schoolgirls of Kastela, aged 10-12, by examining the relations between regular classes of physical education (PE) and extracurricular sport activities. For this purpose, two morphological measures were used (body height and body mass) and a set of 11 motor tests (6 basic motor abilities tests and 5 motor achievement tests) on a sample of 242 girls aged 10-12 was used, divided into a subsample of 42 girls participating in volleyball training (Volleyball players) and a subsample of 200 girls who do not participate in volleyball training (volleyball non-players). Based on the comparison of test results of schoolgirls from Kastela and Croatian norms, factor analysis of applied variables and discriminant analysis of these variables between volleyball players and non-players, processes and/or phases of selection in forming quality volleyball players were defined. Selection processes are preceded by orientation processes in physical education classes, i.e. choosing those sport activities which are in accordance with the biomotor status of students. Results have shown that orientation and initial selection in female volleyball needs to be executed based on the motor set of psychomotor speed, repetitive strength of the trunk and flexibility (muscle tone regulation), and body height. Volleyball training has affected the muscle mass development and the development of strength factors, so that explosive strength of jumping and/or takeoff along with body height, has predominantly differentiated female volleyball players from non-players, aged 10 to 12, and serve and spike quality will have dominant influence on the match outcome.

Text Classification for Assisting Moderators in Online Health Communities

PubMed Central

Huh, Jina; Yetisgen-Yildiz, Meliha; Pratt, Wanda

2013-01-01

Objectives Patients increasingly visit online health communities to get help on managing health. The large scale of these online communities makes it impossible for the moderators to engage in all conversations; yet, some conversations need their expertise. Our work explores low-cost text classification methods to this new domain of determining whether a thread in an online health forum needs moderators’ help. Methods We employed a binary classifier on WebMD’s online diabetes community data. To train the classifier, we considered three feature types: (1) word unigram, (2) sentiment analysis features, and (3) thread length. We applied feature selection methods based on χ2 statistics and under sampling to account for unbalanced data. We then performed a qualitative error analysis to investigate the appropriateness of the gold standard. Results Using sentiment analysis features, feature selection methods, and balanced training data increased the AUC value up to 0.75 and the F1-score up to 0.54 compared to the baseline of using word unigrams with no feature selection methods on unbalanced data (0.65 AUC and 0.40 F1-score). The error analysis uncovered additional reasons for why moderators respond to patients’ posts. Discussion We showed how feature selection methods and balanced training data can improve the overall classification performance. We present implications of weighing precision versus recall for assisting moderators of online health communities. Our error analysis uncovered social, legal, and ethical issues around addressing community members’ needs. We also note challenges in producing a gold standard, and discuss potential solutions for addressing these challenges. Conclusion Social media environments provide popular venues in which patients gain health-related information. Our work contributes to understanding scalable solutions for providing moderators’ expertise in these large-scale, social media environments. PMID:24025513

High-order graph matching based feature selection for Alzheimer's disease identification.

PubMed

Liu, Feng; Suk, Heung-Il; Wee, Chong-Yaw; Chen, Huafu; Shen, Dinggang

2013-01-01

One of the main limitations of l1-norm feature selection is that it focuses on estimating the target vector for each sample individually without considering relations with other samples. However, it's believed that the geometrical relation among target vectors in the training set may provide useful information, and it would be natural to expect that the predicted vectors have similar geometric relations as the target vectors. To overcome these limitations, we formulate this as a graph-matching feature selection problem between a predicted graph and a target graph. In the predicted graph a node is represented by predicted vector that may describe regional gray matter volume or cortical thickness features, and in the target graph a node is represented by target vector that include class label and clinical scores. In particular, we devise new regularization terms in sparse representation to impose high-order graph matching between the target vectors and the predicted ones. Finally, the selected regional gray matter volume and cortical thickness features are fused in kernel space for classification. Using the ADNI dataset, we evaluate the effectiveness of the proposed method and obtain the accuracies of 92.17% and 81.57% in AD and MCI classification, respectively.

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...

Reducing children's social anxiety symptoms: exploring a novel parent-administered cognitive bias modification training intervention.

PubMed

Lau, Jennifer Y F; Pettit, Eleanor; Creswell, Cathy

2013-07-01

Social fears and worries in children are common and impairing. Yet, questions have been raised over the efficacy, suitability and accessibility of current frontline treatments. Here, we present data on the effectiveness of a novel parent-administered Cognitive Bias Modification of Interpretations (CBM-I) training tool. CBM-I capitalises on findings demonstrating an association between anxiety symptoms and biased interpretations, the tendency to interpret ambiguous situations negatively. Through CBM-I training, participants are exposed to benign resolutions, and reinforced for selecting these. In adults and adolescents, CBM-I training is effective at reducing symptoms and mood reactivity. In the present study, we developed a novel, child-appropriate form of CBM-I training, by presenting training materials within bedtime stories, read by a parent to the child across three consecutive evenings. Compared to a test-retest control group (n = 17), children receiving CBM-I (n = 19) reported greater endorsement of benign interpretations of ambiguous situations post-training (compared to pre-training). These participants (but not the test-retest control group) also showed a significant reduction in social anxiety symptoms. Pending replication and extensions to a clinical sample, these data may implicate a cost-effective, mechanism-driven and developmentally-appropriate resource for targeting social anxiety problems in children. Copyright © 2013 Elsevier Ltd. All rights reserved.

The influence of training on the attentional blink and psychological refractory period.

PubMed

Garner, K G; Tombu, M N; Dux, P E

2014-05-01

A growing body of research suggests that dual-task interference in sensory consolidation (e.g., the attentional blink, AB) and response selection (e.g., the psychological refractory period, PRP) stems from a common central bottleneck of information processing. With regard to response selection, it is well known that training reduces dual-task interference. We tested whether training that is known to be effective for response selection can also reduce dual-task interference in sensory consolidation. Over two experiments, performance on a PRP paradigm (Exp. 1) and on AB paradigms (differing in their stimuli and task demands, Exps. 1 and 2) was examined after participants had completed a relevant training regimen (T1 practice for both paradigms), an irrelevant training regimen (comparable sensorimotor training, not related to T1 for both tasks), a visual-search training regimen (Exp. 2 only), or after participants had been allocated to a no-training control group. Training that had shown to be effective for reducing dual-task interference in response selection was also found to be effective for reducing interference in sensory consolidation. In addition, we found some evidence that training benefits transferred to the sensory consolidation of untrained stimuli. Collectively, these findings show that training benefits can transfer across cognitive operations that draw on the central bottleneck in information processing. These findings have implications for theories of the AB and for the design of cognitive-training regimens that aim to produce transferable training benefits.

HIV rapid diagnostic testing by lay providers in a key population-led health service programme in Thailand.

PubMed

Wongkanya, Rapeeporn; Pankam, Tippawan; Wolf, Shauna; Pattanachaiwit, Supanit; Jantarapakde, Jureeporn; Pengnongyang, Supabhorn; Thapwong, Prasopsuk; Udomjirasirichot, Apichat; Churattanakraisri, Yutthana; Prawepray, Nanthika; Paksornsit, Apiluk; Sitthipau, Thidadaow; Petchaithong, Sarayut; Jitsakulchaidejt, Raruay; Nookhai, Somboon; Lertpiriyasuwat, Cheewanan; Ongwandee, Sumet; Phanuphak, Praphan; Phanuphak, Nittaya

2018-01-01

Introduction:  Rapid diagnostic testing (RDT) for HIV has a quick turn-around time, which increases the proportion of people testing who receive their result. HIV RDT in Thailand has traditionally been performed only by medical technologists (MTs), which is a barrier to its being scaled up. We evaluated the performance of HIV RDT conducted by trained lay providers who were members of, or worked closely with, a group of men who have sex with men (MSM) and with transgender women (TG) communities, and compared it to tests conducted by MTs. Methods:  Lay providers received a 3-day intensive training course on how to perform a finger-prick blood collection and an HIV RDT as part of the Key Population-led Health Services (KPLHS) programme among MSM and TG. All the samples were tested by lay providers using Alere Determine HIV 1/2. HIV-reactive samples were confirmed by DoubleCheckGold Ultra HIV 1&2 and SD Bioline HIV 1/2. All HIV-positive and 10% of HIV-negative samples were re-tested by MTs using Serodia HIV 1/2. Results:  Of 1680 finger-prick blood samples collected and tested using HIV RDT by lay providers in six drop-in centres in Bangkok, Chiang Mai, Chonburi and Songkhla, 252 (15%) were HIV-positive. MTs re-tested these HIV-positive samples and 143 randomly selected HIV-negative samples with 100% concordant test results. Conclusion:  Lay providers in Thailand can be trained and empowered to perform HIV RDT as they were found to achieve comparable results in sample testing with MTs. Based on the task-shifting concept, this rapid HIV testing performed by lay providers as part of the KPLHS programme has great potential to enhance HIV prevention and treatment programmes among key at-risk populations.

Combined radiogrammetry and texture analysis for early diagnosis of osteoporosis using Indian and Swiss data.

PubMed

Areeckal, Anu Shaju; Kamath, Jagannath; Zawadynski, Sophie; Kocher, Michel; S, Sumam David

2018-05-26

Osteoporosis is a bone disorder characterized by bone loss and decreased bone strength. The most widely used technique for detection of osteoporosis is the measurement of bone mineral density (BMD) using dual energy X-ray absorptiometry (DXA). But DXA scans are expensive and not widely available in low-income economies. In this paper, we propose a low cost pre-screening tool for the detection of low bone mass, using cortical radiogrammetry of third metacarpal bone and trabecular texture analysis of distal radius from hand and wrist radiographs. An automatic segmentation algorithm to automatically locate and segment the third metacarpal bone and distal radius region of interest (ROI) is proposed. Cortical measurements such as combined cortical thickness (CCT), cortical area (CA), percent cortical area (PCA) and Barnett Nordin index (BNI) were taken from the shaft of third metacarpal bone. Texture analysis of trabecular network at the distal radius was performed using features obtained from histogram, gray level Co-occurrence matrix (GLCM) and morphological gradient method (MGM). The significant cortical and texture features were selected using independent sample t-test and used to train classifiers to classify healthy subjects and people with low bone mass. The proposed pre-screening tool was validated on two ethnic groups, Indian sample population and Swiss sample population. Data of 134 subjects from Indian sample population and 65 subjects from Swiss sample population were analysed. The proposed automatic segmentation approach shows a detection accuracy of 86% in detecting the third metacarpal bone shaft and 90% in accurately locating the distal radius ROI. Comparison of the automatic radiogrammetry to the ground truth provided by experts show a mean absolute error of 0.04 mm for cortical width of healthy group, 0.12 mm for cortical width of low bone mass group, 0.22 mm for medullary width of healthy group, and 0.26 mm for medullary width of low bone mass group. Independent sample t-test was used to select the most discriminant features, to be used as input for training the classifiers. Pearson correlation analysis of the extracted features with DXA-BMD of lumbar spine (DXA-LS) shows significantly high correlation values. Classifiers were trained with the most significant features in the Indian and Swiss sample data. Weighted KNN classifier shows the best test accuracy of 78% for Indian sample data and 100% for Swiss sample data. Hence, combined automatic radiogrammetry and texture analysis is shown to be an effective low cost pre-screening tool for early diagnosis of osteoporosis. Copyright © 2018 Elsevier Ltd. All rights reserved.

Perceptual Learning Selectively Refines Orientation Representations in Early Visual Cortex

PubMed Central

Jehee, Janneke F.M.; Ling, Sam; Swisher, Jascha D.; van Bergen, Ruben S.; Tong, Frank

2013-01-01

Although practice has long been known to improve perceptual performance, the neural basis of this improvement in humans remains unclear. Using fMRI in conjunction with a novel signal detection-based analysis, we show that extensive practice selectively enhances the neural representation of trained orientations in the human visual cortex. Twelve observers practiced discriminating small changes in the orientation of a laterally presented grating over 20 or more daily one-hour training sessions. Training on average led to a two-fold improvement in discrimination sensitivity, specific to the trained orientation and the trained location, with minimal improvement found for untrained orthogonal orientations or for orientations presented in the untrained hemifield. We measured the strength of orientation-selective responses in individual voxels in early visual areas (V1–V4) using signal detection measures, both pre- and post-training. Although the overall amplitude of the BOLD response was no greater after training, practice nonetheless specifically enhanced the neural representation of the trained orientation at the trained location. This training-specific enhancement of orientation-selective responses was observed in the primary visual cortex (V1) as well as higher extrastriate visual areas V2–V4, and moreover, reliably predicted individual differences in the behavioral effects of perceptual learning. These results demonstrate that extensive training can lead to targeted functional reorganization of the human visual cortex, refining the cortical representation of behaviorally relevant information. PMID:23175828

Perceptual learning selectively refines orientation representations in early visual cortex.

PubMed

Jehee, Janneke F M; Ling, Sam; Swisher, Jascha D; van Bergen, Ruben S; Tong, Frank

2012-11-21

Although practice has long been known to improve perceptual performance, the neural basis of this improvement in humans remains unclear. Using fMRI in conjunction with a novel signal detection-based analysis, we show that extensive practice selectively enhances the neural representation of trained orientations in the human visual cortex. Twelve observers practiced discriminating small changes in the orientation of a laterally presented grating over 20 or more daily 1 h training sessions. Training on average led to a twofold improvement in discrimination sensitivity, specific to the trained orientation and the trained location, with minimal improvement found for untrained orthogonal orientations or for orientations presented in the untrained hemifield. We measured the strength of orientation-selective responses in individual voxels in early visual areas (V1-V4) using signal detection measures, both before and after training. Although the overall amplitude of the BOLD response was no greater after training, practice nonetheless specifically enhanced the neural representation of the trained orientation at the trained location. This training-specific enhancement of orientation-selective responses was observed in the primary visual cortex (V1) as well as higher extrastriate visual areas V2-V4, and moreover, reliably predicted individual differences in the behavioral effects of perceptual learning. These results demonstrate that extensive training can lead to targeted functional reorganization of the human visual cortex, refining the cortical representation of behaviorally relevant information.

Study on pattern recognition of Raman spectrum based on fuzzy neural network

NASA Astrophysics Data System (ADS)

Zheng, Xiangxiang; Lv, Xiaoyi; Mo, Jiaqing

2017-10-01

Hydatid disease is a serious parasitic disease in many regions worldwide, especially in Xinjiang, China. Raman spectrum of the serum of patients with echinococcosis was selected as the research object in this paper. The Raman spectrum of blood samples from healthy people and patients with echinococcosis are measured, of which the spectrum characteristics are analyzed. The fuzzy neural network not only has the ability of fuzzy logic to deal with uncertain information, but also has the ability to store knowledge of neural network, so it is combined with the Raman spectrum on the disease diagnosis problem based on Raman spectrum. Firstly, principal component analysis (PCA) is used to extract the principal components of the Raman spectrum, reducing the network input and accelerating the prediction speed and accuracy of Network based on remaining the original data. Then, the information of the extracted principal component is used as the input of the neural network, the hidden layer of the network is the generation of rules and the inference process, and the output layer of the network is fuzzy classification output. Finally, a part of samples are randomly selected for the use of training network, then the trained network is used for predicting the rest of the samples, and the predicted results are compared with general BP neural network to illustrate the feasibility and advantages of fuzzy neural network. Success in this endeavor would be helpful for the research work of spectroscopic diagnosis of disease and it can be applied in practice in many other spectral analysis technique fields.

Identifying deficiencies in national and foreign medical team responses through expert opinion surveys: implications for education and training.

PubMed

Djalali, Ahmadreza; Ingrassia, Pier Luigi; Corte, Francesco Della; Foletti, Marco; Gallardo, Alba Ripoll; Ragazzoni, Luca; Kaptan, Kubilay; Lupescu, Olivera; Arculeo, Chris; von Arnim, Gotz; Friedl, Tom; Ashkenazi, Michael; Heselmann, Deike; Hreckovski, Boris; Khorram-Manesh, Amir; Khorrram-Manesh, Amir; Komadina, Radko; Lechner, Kostanze; Patru, Cristina; Burkle, Frederick M; Fisher, Philipp

2014-08-01

Unacceptable practices in the delivery of international medical assistance are reported after every major international disaster; this raises concerns about the clinical competence and practice of some foreign medical teams (FMTs). The aim of this study is to explore and analyze the opinions of disaster management experts about potential deficiencies in the art and science of national and FMTs during disasters and the impact these opinions might have on competency-based education and training. This qualitative study was performed in 2013. A questionnaire-based evaluation of experts' opinions and experiences in responding to disasters was conducted. The selection of the experts was done using the purposeful sampling method, and the sample size was considered by data saturation. Content analysis was used to explore the implications of the data. This study shows that there is a lack of competency-based training for disaster responders. Developing and performing standardized training courses is influenced by shortcomings in budget, expertise, and standards. There is a lack of both coordination and integration among teams and their activities during disasters. The participants of this study emphasized problems concerning access to relevant resources during disasters. The major findings of this study suggest that teams often are not competent during the response phase because of education and training deficiencies. Foreign medical teams and medically related nongovernmental organizations (NGOs) do not always provide expected capabilities and services. Failures in leadership and in coordination among teams are also a problem. All deficiencies need to be applied to competency-based curricula.

Assessment of the noise annoyance among subway train conductors in Tehran, Iran.

PubMed

Hamidi, Mansoureh; Kavousi, Amir; Zaheri, Somayeh; Hamadani, Abolfazl; Mirkazemi, Roksana

2014-01-01

Subway transportation system is a new phenomenon in Iran. Noise annoyance interferes with the individual's task performance, and the required alertness in the driving of subway trains. This is the first study conducted to measure the level of noise and noise annoyance among conductors of subway organization in Tehran, Iran. This cross sectional study was conducted among 167 randomly selected train conductors. Information related to noise annoyance was collected by using a self-administered questionnaire. The dosimetry and sound metering was done for the conductors and inside the cabins. There were 41 sound metering measuring samples inside the conductors' cabin, and there were 12 samples of conductors' noise exposure. The results of sound level meter showed that the mean Leq was 73.0 dBA ± 8.7 dBA and the dosimetry mean measured Leq was 82.1 dBA ± 6.8 dBA. 80% of conductors were very annoyed/annoyed by noise in their work place. 53.9% of conductors reported that noise affected their work performance and 63.5% reported that noise causes that they lose their concentration. The noise related to movement of train wheels on rail was reported as the worst by 83.2% followed by the noise of brakes (74.3%) and the ventilation noise (71.9%). 56.9% of conductors reported that they are suffering from sleeplessness, 40.1% from tinnitus and 80.2% feeling fatigue and sleepy. The study results showed the high level of noise and noise annoyance among train conductors and the poor health outcome of their exposure to this level of noise.

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:

Evaluation of Wet Chemical ICP-AES Elemental Analysis Methods usingSimulated Hanford Waste Samples-Phase I Interim Report

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

Coleman, Charles J.; Edwards, Thomas B.

2005-04-30

The wet chemistry digestion method development for providing process control elemental analyses of the Hanford Tank Waste Treatment and Immobilization Plant (WTP) Melter Feed Preparation Vessel (MFPV) samples is divided into two phases: Phase I consists of: (1) optimizing digestion methods as a precursor to elemental analyses by ICP-AES techniques; (2) selecting methods with the desired analytical reliability and speed to support the nine-hour or less turnaround time requirement of the WTP; and (3) providing baseline comparison to the laser ablation (LA) sample introduction technique for ICP-AES elemental analyses that is being developed at the Savannah River National Laboratory (SRNL).more » Phase II consists of: (1) Time-and-Motion study of the selected methods from Phase I with actual Hanford waste or waste simulants in shielded cell facilities to ensure that the methods can be performed remotely and maintain the desired characteristics; and (2) digestion of glass samples prepared from actual Hanford Waste tank sludge for providing comparative results to the LA Phase II study. Based on the Phase I testing discussed in this report, a tandem digestion approach consisting of sodium peroxide fusion digestions carried out in nickel crucibles and warm mixed-acid digestions carried out in plastic bottles has been selected for Time-and-Motion study in Phase II. SRNL experience with performing this analytical approach in laboratory hoods indicates that well-trained cell operator teams will be able to perform the tandem digestions in five hours or less. The selected approach will produce two sets of solutions for analysis by ICP-AES techniques. Four hours would then be allocated for performing the ICP-AES analyses and reporting results to meet the nine-hour or less turnaround time requirement. The tandem digestion approach will need to be performed in two separate shielded analytical cells by two separate cell operator teams in order to achieve the nine-hour or less turnaround time. Because of the simplicity of the warm mixed-acid method, a well-trained cell operator team may in time be able to perform both sets of digestions. However, having separate shielded cells for each of the methods is prudent to avoid overcrowding problems that would impede a minimal turnaround time.« less

Gait parameters associated with responsiveness to treadmill training with body-weight support after stroke: an exploratory study.

PubMed

Mulroy, Sara J; Klassen, Tara; Gronley, JoAnne K; Eberly, Valerie J; Brown, David A; Sullivan, Katherine J

2010-02-01

Task-specific training programs after stroke improve walking function, but it is not clear which biomechanical parameters of gait are most associated with improved walking speed. The purpose of this study was to identify gait parameters associated with improved walking speed after a locomotor training program that included body-weight-supported treadmill training (BWSTT). A prospective, between-subjects design was used. Fifteen people, ranging from approximately 9 months to 5 years after stroke, completed 1 of 3 different 6-week training regimens. These regimens consisted of 12 sessions of BWSTT alternated with 12 sessions of: lower-extremity resistive cycling; lower-extremity progressive, resistive strengthening; or a sham condition of arm ergometry. Gait analysis was conducted before and after the 6-week intervention program. Kinematics, kinetics, and electromyographic (EMG) activity were recorded from the hemiparetic lower extremity while participants walked at a self-selected pace. Changes in gait parameters were compared in participants who showed an increase in self-selected walking speed of greater than 0.08 m/s (high-response group) and in those with less improvement (low-response group). Compared with participants in the low-response group, those in the high-response group displayed greater increases in terminal stance hip extension angle and hip flexion power (product of net joint moment and angular velocity) after the intervention. The intensity of soleus muscle EMG activity during walking also was significantly higher in participants in the high-response group after the intervention. Only sagittal-plane parameters were assessed, and the sample size was small. Task-specific locomotor training alternated with strength training resulted in kinematic, kinetic, and muscle activation adaptations that were strongly associated with improved walking speed. Changes in both hip and ankle biomechanics during late stance were associated with greater increases in gait speed.

Building capacity for skilled birth attendance: An evaluation of the Maternal and Child Health Aides training programme in Sierra Leone.

PubMed

Jones, Susan; Ameh, Charles A; Gopalakrishnan, Somasundari; Sam, Betty; Bull, Florence; Labicane, Roderick R; Dabo, Fatmata; van den Broek, Nynke

2015-12-01

Maternal and Child Health Aides (MCH Aide) in Sierra Leone provide the majority of maternity services at primary care level. To formulate recommendations for improving the quality and scale-up of MCH Aides training an evaluation of all schools across Sierra Leone was undertaken. Structured, direct observation of two randomly selected teaching sessions per school using pre-tested standardised review forms. Event sampling with random selection of timetabled sessions across all 14 MCH Aide Training Schools. All MCH Aide training schools across Sierra Leone. Tutors across 14 MCH Aide training schools observed in August 2013. Assessment of four key elements of teaching and learning: (1) teaching style, (2) use of visual aids, (3) teaching environment and (4) student involvement. In the majority of teaching schools there was over-crowding (11/14), lack of furniture and inconsistent electricity supply. Ten of 26 tutors used lesson plans and teaching was mostly tutor- rather than student-focused. Majority of tutors use a didactic approach rather than active learning methods. Teaching aides were rarely available (15% of lessons). Tutors were knowledgeable in their subject area and there was evidence of an excellent tutor-student relationship. Training for Maternal and Child health Aides relies on teacher focused didactic methods, which may hinder student learning. Teaching and learning within the schools needs to be enhanced by a combination of tutor development and improvements in the learning environment. Interventions to improve the quality of teaching are urgently needed and should include training on teaching techniques and student assessment for tutors, provision of audio visual equipment and teaching aides such as posters and mannequins. Monitoring and Evaluation of interventions is critical to be able to amend the programmes approach and address further challenges at an early stage. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

On the Value-Dependence of Value-Driven Attentional Capture

PubMed Central

Anderson, Brian A.; Halpern, Madeline

2017-01-01

Findings from an increasingly large number of studies have been used to argue that attentional capture can be dependent on the learned value of a stimulus, or value-driven. However, under certain circumstances attention can be biased to select stimuli that previously served as targets, independent of reward history. Value-driven attentional capture, as studied using the training phase-test phase design introduced by Anderson and colleagues, is widely presumed to reflect the combined influence of learned value and selection history. However, the degree to which attentional capture is at all dependent on value learning in this paradigm has recently been questioned. Support for value-dependence can be provided through one of two means: (1) greater attentional capture by prior targets following rewarded training than following unrewarded training, and (2) greater attentional capture by prior targets previously associated with high compared to low value. Using a variant of the original value-driven attentional capture paradigm, Sha and Jiang (2016) failed to find evidence of either, and raised criticisms regarding the adequacy of evidence provided by prior studies using this particular paradigm. To address this disparity, here we provided a stringent test of the value-dependence hypothesis using the traditional value-driven attentional capture paradigm. With a sufficiently large sample size, value-dependence was observed based on both criteria, with no evidence of attentional capture without rewards during training. Our findings support the validity of the traditional value-driven attentional capture paradigm in measuring what its name purports to measure. PMID:28176215

Mexican Pharmacies and Antibiotic Consumption at the US-Mexico Border.

PubMed

Homedes, Núria; Ugalde, Antonio

2012-12-01

To study antibiotic dispensing to US and Mexican residents, at Mexican pharmacies at the US-Mexico border, and the pharmacy clerks' capability to promote appropriate use. The site selected was Ciudad Juarez, Chihuahua (pop. 1.2 million) separated from El Paso, Texas (pop. 800,000) by the Rio Grande River. A convenience sample of 32 pharmacies located near the international bridges, major shopping centers, and interior neighborhoods was selected. Pharmacy clients were interviewed (n=230) and 152 interactions between clients and pharmacy clerks were observed. Information was obtained about education and pharmaceutical training of 113 clerks working in 25 pharmacies. A senior pharmacy clerk in each of the 25 pharmacies was interviewed and asked for their recommendations to clients presenting two clinical scenarios and seven diagnoses. Professionally trained pharmacists only spend a few hours a week in some pharmacies. Clerks' education levels are very low; some have only completed primary education. There is no required pharmaceutical training and their knowledge about pharmaceuticals comes mostly from representatives of the pharmaceutical industry. Clerks' knowledge of antibiotics, the most frequently sold class of medicines (65% without prescription), is very limited. Clients trust pharmacy clerks and tend to follow their advice. The findings raise concerns about dispensing of antibiotics at Mexican border pharmacies and antibiotic overuse due to lack of control. Because inappropriate antibiotic use contributes to increased resistance, pharmacy clerks should receive independent training to dispense antibiotics and promote their appropriate use.

Mexican Pharmacies and Antibiotic Consumption at the US-Mexico Border

PubMed Central

Homedes, Núria; Ugalde, Antonio

2012-01-01

Objective: To study antibiotic dispensing to US and Mexican residents, at Mexican pharmacies at the US-Mexico border, and the pharmacy clerks’ capability to promote appropriate use. Methods: The site selected was Ciudad Juarez, Chihuahua (pop. 1.2 million) separated from El Paso, Texas (pop. 800,000) by the Rio Grande River. A convenience sample of 32 pharmacies located near the international bridges, major shopping centers, and interior neighborhoods was selected. Pharmacy clients were interviewed (n=230) and 152 interactions between clients and pharmacy clerks were observed. Information was obtained about education and pharmaceutical training of 113 clerks working in 25 pharmacies. A senior pharmacy clerk in each of the 25 pharmacies was interviewed and asked for their recommendations to clients presenting two clinical scenarios and seven diagnoses. Findings: Professionally trained pharmacists only spend a few hours a week in some pharmacies. Clerks’ education levels are very low; some have only completed primary education. There is no required pharmaceutical training and their knowledge about pharmaceuticals comes mostly from representatives of the pharmaceutical industry. Clerks’ knowledge of antibiotics, the most frequently sold class of medicines (65% without prescription), is very limited. Clients trust pharmacy clerks and tend to follow their advice. Conclusions: The findings raise concerns about dispensing of antibiotics at Mexican border pharmacies and antibiotic overuse due to lack of control. Because inappropriate antibiotic use contributes to increased resistance, pharmacy clerks should receive independent training to dispense antibiotics and promote their appropriate use. PMID:23532456

Wearable Sensor Data Classification for Human Activity Recognition Based on an Iterative Learning Framework.

PubMed

Davila, Juan Carlos; Cretu, Ana-Maria; Zaremba, Marek

2017-06-07

The design of multiple human activity recognition applications in areas such as healthcare, sports and safety relies on wearable sensor technologies. However, when making decisions based on the data acquired by such sensors in practical situations, several factors related to sensor data alignment, data losses, and noise, among other experimental constraints, deteriorate data quality and model accuracy. To tackle these issues, this paper presents a data-driven iterative learning framework to classify human locomotion activities such as walk, stand, lie, and sit, extracted from the Opportunity dataset. Data acquired by twelve 3-axial acceleration sensors and seven inertial measurement units are initially de-noised using a two-stage consecutive filtering approach combining a band-pass Finite Impulse Response (FIR) and a wavelet filter. A series of statistical parameters are extracted from the kinematical features, including the principal components and singular value decomposition of roll, pitch, yaw and the norm of the axial components. The novel interactive learning procedure is then applied in order to minimize the number of samples required to classify human locomotion activities. Only those samples that are most distant from the centroids of data clusters, according to a measure presented in the paper, are selected as candidates for the training dataset. The newly built dataset is then used to train an SVM multi-class classifier. The latter will produce the lowest prediction error. The proposed learning framework ensures a high level of robustness to variations in the quality of input data, while only using a much lower number of training samples and therefore a much shorter training time, which is an important consideration given the large size of the dataset.

Sensory analysis of lipstick.

PubMed

Yap, K C S; Aminah, A

2011-06-01

Sensory analysis of lipstick product by trained panellists started with recruiting female panels who are lipstick users, in good health condition and willing to be a part of sensory members. This group of people was further scrutinized with duo-trio method using commercial lipstick samples that are commonly used among them. About 40% of the 15 panels recruited were unable to differentiate the lipstick samples they usually use better than chance. The balance of nine panels that were corrected at least with 65% across all trials in panels screening process was formed a working group to develop sensory languages as a means of describing product similarities and differences and a scoring system. Five sessions with each session took about 90 min were carried out using 10 types of lipsticks with different waxes mixture ratio in the formulation together with six commercial lipsticks that are the most common to the panels. First session was focus on listing out the panels' perception towards the characteristic of the lipstick samples after normal application on their lips. Second session was focus on the refining and categorizing the responses gathered from the first session and translated into sensory attributes with its definition. Third session was focus on the scoring system. Fourth and fifth sessions were repetition of the third session to ensure consistency. In a collective effort of the panels, sensory attributes developed for lipstick were Spreadability, Off flavour, Hardness, Smoothness, Moist, Not messy, Glossy and Greasy. Analysis of variance was able to provide ample evidence on gauging the panel performance. A proper panels selecting and training was able to produce a reliable and sensitive trained panel for evaluating the product based on the procedures being trained. © 2011 The Authors. ICS © 2011 Society of Cosmetic Scientists and the Société Française de Cosmétologie.

The partial-reinforcement extinction effect and the contingent-sampling hypothesis.

PubMed

Hochman, Guy; Erev, Ido

2013-12-01

The partial-reinforcement extinction effect (PREE) implies that learning under partial reinforcements is more robust than learning under full reinforcements. While the advantages of partial reinforcements have been well-documented in laboratory studies, field research has failed to support this prediction. In the present study, we aimed to clarify this pattern. Experiment 1 showed that partial reinforcements increase the tendency to select the promoted option during extinction; however, this effect is much smaller than the negative effect of partial reinforcements on the tendency to select the promoted option during the training phase. Experiment 2 demonstrated that the overall effect of partial reinforcements varies inversely with the attractiveness of the alternative to the promoted behavior: The overall effect is negative when the alternative is relatively attractive, and positive when the alternative is relatively unattractive. These results can be captured with a contingent-sampling model assuming that people select options that provided the best payoff in similar past experiences. The best fit was obtained under the assumption that similarity is defined by the sequence of the last four outcomes.

Automated training site selection for large-area remote-sensing image analysis

NASA Astrophysics Data System (ADS)

McCaffrey, Thomas M.; Franklin, Steven E.

1993-11-01

A computer program is presented to select training sites automatically from remotely sensed digital imagery. The basic ideas are to guide the image analyst through the process of selecting typical and representative areas for large-area image classifications by minimizing bias, and to provide an initial list of potential classes for which training sites are required to develop a classification scheme or to verify classification accuracy. Reducing subjectivity in training site selection is achieved by using a purely statistical selection of homogeneous sites which then can be compared to field knowledge, aerial photography, or other remote-sensing imagery and ancillary data to arrive at a final selection of sites to be used to train the classification decision rules. The selection of the homogeneous sites uses simple tests based on the coefficient of variance, the F-statistic, and the Student's i-statistic. Comparisons of site means are conducted with a linear growing list of previously located homogeneous pixels. The program supports a common pixel-interleaved digital image format and has been tested on aerial and satellite optical imagery. The program is coded efficiently in the C programming language and was developed under AIX-Unix on an IBM RISC 6000 24-bit color workstation.

Neutral degradates of chloroacetamide herbicides: occurrence in drinking water and removal during conventional water treatment.

PubMed

Hladik, Michelle L; Bouwer, Edward J; Roberts, A Lynn

2008-12-01

Treated drinking water samples from 12 water utilities in the Midwestern United States were collected during Fall 2003 and Spring 2004 and were analyzed for selected neutral degradates of chloroacetamide herbicides, along with related compounds. Target analytes included 20 neutral chloroacetamide degradates, six ionic chloroacetamide degradates, four parent chloroacetamide herbicides, three triazine herbicides, and two neutral triazine degradates. In the fall samples, 17 of 20 neutral chloroacetamide degradates were detected in the finished drinking water, while 19 of 20 neutral chloroacetamide degradates were detected in the spring. Median concentrations for the neutral chloroacetamide degradates were approximately 2-60ng/L during both sampling periods. Concentrations measured in the fall samples of treated water were nearly the same as those measured in source waters, despite the variety of treatment trains employed. Significant removals (average of 40% for all compounds) were only found in the spring samples at those utilities that employed activated carbon.

Using Ensemble Decisions and Active Selection to Improve Low-Cost Labeling for Multi-View Data

NASA Technical Reports Server (NTRS)

Rebbapragada, Umaa; Wagstaff, Kiri L.

2011-01-01

This paper seeks to improve low-cost labeling in terms of training set reliability (the fraction of correctly labeled training items) and test set performance for multi-view learning methods. Co-training is a popular multiview learning method that combines high-confidence example selection with low-cost (self) labeling. However, co-training with certain base learning algorithms significantly reduces training set reliability, causing an associated drop in prediction accuracy. We propose the use of ensemble labeling to improve reliability in such cases. We also discuss and show promising results on combining low-cost ensemble labeling with active (low-confidence) example selection. We unify these example selection and labeling strategies under collaborative learning, a family of techniques for multi-view learning that we are developing for distributed, sensor-network environments.

Body weight-supported treadmill training vs. overground walking training for persons with chronic stroke: a pilot randomized controlled trial.

PubMed

Combs-Miller, Stephanie A; Kalpathi Parameswaran, Anu; Colburn, Dawn; Ertel, Tara; Harmeyer, Amanda; Tucker, Lindsay; Schmid, Arlene A

2014-09-01

To compare the effects of body weight-supported treadmill training and overground walking training when matched for task and dose (duration/frequency/intensity) on improving walking function, activity, and participation after stroke. Single-blind, pilot randomized controlled trial with three-month follow-up. University and community settings. A convenience sample of participants (N = 20) at least six months post-stroke and able to walk independently were recruited. Thirty-minute walking interventions (body weight-supported treadmill training or overground walking training) were administered five times a week for two weeks. Intensity was monitored with the Borg Rating of Perceived Exertion Scale at five-minute increments to maintain a moderate training intensity. Walking speed (comfortable/fast 10-meter walk), walking endurance (6-minute walk), spatiotemporal symmetry, and the ICF Measure of Participation and ACTivity were assessed before, immediately after, and three months following the intervention. The overground walking training group demonstrated significantly greater improvements in comfortable walking speed compared with the body weight-supported treadmill training group immediately (change of 0.11 m/s vs. 0.06 m/s, respectively; p = 0.047) and three months (change of 0.14 m/s vs. 0.08 m/s, respectively; p = 0.029) after training. Only the overground walking training group significantly improved comfortable walking speed (p = 0.001), aspects of gait symmetry (p = 0.032), and activity (p = 0.003) immediately after training. Gains were maintained at the three-month follow-up (p < 0.05) for all measures except activity. Improvements in participation were not demonstrated. Overgound walking training was more beneficial than body weight-supported treadmill training at improving self-selected walking speed for the participants in this study. © The Author(s) 2014.

Active Planning, Sensing and Recognition Using a Resource-Constrained Discriminant POMDP

DTIC Science & Technology

2014-06-28

classes of military vehicles, with sample images shown in Fig. 1. The vehicles were captured from various angles. 4785 images with depression angles 17...and 30◦ are used for training, and 4351 images with depression angles 15◦ and 45◦ are used for testing. The azimuth angles are quantized into 12...selection by collecting the engine sounds for the 8 vehicle classes from the Youtube . The sounds are attenuated differently in 6 view directions

Robust kernel collaborative representation for face recognition

NASA Astrophysics Data System (ADS)

Huang, Wei; Wang, Xiaohui; Ma, Yanbo; Jiang, Yuzheng; Zhu, Yinghui; Jin, Zhong

2015-05-01

One of the greatest challenges of representation-based face recognition is that the training samples are usually insufficient. In other words, the training set usually does not include enough samples to show varieties of high-dimensional face images caused by illuminations, facial expressions, and postures. When the test sample is significantly different from the training samples of the same subject, the recognition performance will be sharply reduced. We propose a robust kernel collaborative representation based on virtual samples for face recognition. We think that the virtual training set conveys some reasonable and possible variations of the original training samples. Hence, we design a new object function to more closely match the representation coefficients generated from the original and virtual training sets. In order to further improve the robustness, we implement the corresponding representation-based face recognition in kernel space. It is noteworthy that any kind of virtual training samples can be used in our method. We use noised face images to obtain virtual face samples. The noise can be approximately viewed as a reflection of the varieties of illuminations, facial expressions, and postures. Our work is a simple and feasible way to obtain virtual face samples to impose Gaussian noise (and other types of noise) specifically to the original training samples to obtain possible variations of the original samples. Experimental results on the FERET, Georgia Tech, and ORL face databases show that the proposed method is more robust than two state-of-the-art face recognition methods, such as CRC and Kernel CRC.

Semi-supervised classification tool for DubaiSat-2 multispectral imagery

NASA Astrophysics Data System (ADS)

Al-Mansoori, Saeed

2015-10-01

This paper addresses a semi-supervised classification tool based on a pixel-based approach of the multi-spectral satellite imagery. There are not many studies demonstrating such algorithm for the multispectral images, especially when the image consists of 4 bands (Red, Green, Blue and Near Infrared) as in DubaiSat-2 satellite images. The proposed approach utilizes both unsupervised and supervised classification schemes sequentially to identify four classes in the image, namely, water bodies, vegetation, land (developed and undeveloped areas) and paved areas (i.e. roads). The unsupervised classification concept is applied to identify two classes; water bodies and vegetation, based on a well-known index that uses the distinct wavelengths of visible and near-infrared sunlight that is absorbed and reflected by the plants to identify the classes; this index parameter is called "Normalized Difference Vegetation Index (NDVI)". Afterward, the supervised classification is performed by selecting training homogenous samples for roads and land areas. Here, a precise selection of training samples plays a vital role in the classification accuracy. Post classification is finally performed to enhance the classification accuracy, where the classified image is sieved, clumped and filtered before producing final output. Overall, the supervised classification approach produced higher accuracy than the unsupervised method. This paper shows some current preliminary research results which point out the effectiveness of the proposed technique in a virtual perspective.

Nationally Certified School Psychologists' use and reported barriers to using evidence-based interventions in schools: the influence of graduate program training and education.

PubMed

Hicks, Taylor B; Shahidullah, Jeffrey D; Carlson, John S; Palejwala, Mohammed H

2014-12-01

The purpose of this study was to empirically investigate Nationally Certified School Psychologists' (NCSP) training in and use of evidence-based interventions (EBIs) for child behavior concerns as well as their reported implementation barriers. A modified Tailored Design Method (TDM; Dillman, Smyth, & Christian, 2009) using up to four mail-based participant contacts was used to obtain survey data (72% usable response rate; n = 392) from a randomly selected national sample of 548 currently practicing NCSPs. Lack of time was rated as the most serious barrier to behavioral EBI implementation, followed by a lack of necessary resources, and financial constraints. Nearly three-quarters (71%) of respondents reported a perceived inadequacy of graduate program training in behavioral EBIs, with a statistically significant difference found between respondents who attended American Psychological Association (APA)-accredited/National Association of School Psychologists (NASP)-approved programs and those who did not. These findings highlight the significant barriers school psychologists encounter when attempting to implement behavioral EBIs within applied practice, as well as the importance of graduate program training in implementation science. Implications for training, practice, and research are discussed. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Canadian residents' perceived manager training needs.

PubMed

Stergiopoulos, Vicky; Lieff, Susan; Razack, Saleem; Lee, A Curtis; Maniate, Jerry M; Hyde, Stacey; Taber, Sarah; Frank, Jason R

2010-01-01

Despite widespread endorsement for administrative training during residency, teaching and learning in this area remains intermittent and limited in most programmes. To inform the development of a Manager Train-the-Trainer program for faculty, the Royal College of Physicians and Surgeons of Canada undertook a survey of perceived Manager training needs among postgraduate trainees. A representative sample of Canadian specialty residents received a web-based questionnaire in 2009 assessing their perceived deficiencies in 13 Manager knowledge and 11 Manager skill domains, as determined by gap scores (GSs). GSs were defined as the difference between residents' perceived current and desired level of knowledge or skill in selected Manager domains. Residents' educational preferences for furthering their Manager knowledge and skills were also elicited. Among the 549 residents who were emailed the survey, 199 (36.2%) responded. Residents reported significant gaps in most knowledge and skills domains examined. Residents' preferred educational methods for learning Manager knowledge and skills included workshops, web-based formats and interactive small groups. The results of this national survey, highlighting significant perceived gaps in multiple Manager knowledge and skills domains, may inform the development of Manager curricula and faculty development activities to address deficiencies in training in this important area.

25 CFR 26.10 - When will I find out if I have been selected for Job Placement and Training assistance?

Code of Federal Regulations, 2010 CFR

2010-04-01

... INTERIOR HUMAN SERVICES JOB PLACEMENT AND TRAINING PROGRAM General Applicability § 26.10 When will I find out if I have been selected for Job Placement and Training assistance? (a) Your servicing office will notify you in writing within 30 calendar days once it receives a completed job training application...

Improved training for target detection using Fukunaga-Koontz transform and distance classifier correlation filter

NASA Astrophysics Data System (ADS)

Elbakary, M. I.; Alam, M. S.; Aslan, M. S.

2008-03-01

In a FLIR image sequence, a target may disappear permanently or may reappear after some frames and crucial information such as direction, position and size related to the target are lost. If the target reappears at a later frame, it may not be tracked again because the 3D orientation, size and location of the target might be changed. To obtain information about the target before disappearing and to detect the target after reappearing, distance classifier correlation filter (DCCF) is trained manualy by selecting a number of chips randomly. This paper introduces a novel idea to eliminates the manual intervention in training phase of DCCF. Instead of selecting the training chips manually and selecting the number of the training chips randomly, we adopted the K-means algorithm to cluster the training frames and based on the number of clusters we select the training chips such that a training chip for each cluster. To detect and track the target after reappearing in the field-ofview ,TBF and DCCF are employed. The contduced experiemnts using real FLIR sequences show results similar to the traditional agorithm but eleminating the manual intervention is the advantage of the proposed algorithm.

Domain Adaptation for Pedestrian Detection Based on Prediction Consistency

PubMed Central

Huan-ling, Tang; Zhi-yong, An

2014-01-01

Pedestrian detection is an active area of research in computer vision. It remains a quite challenging problem in many applications where many factors cause a mismatch between source dataset used to train the pedestrian detector and samples in the target scene. In this paper, we propose a novel domain adaptation model for merging plentiful source domain samples with scared target domain samples to create a scene-specific pedestrian detector that performs as well as rich target domain simples are present. Our approach combines the boosting-based learning algorithm with an entropy-based transferability, which is derived from the prediction consistency with the source classifications, to selectively choose the samples showing positive transferability in source domains to the target domain. Experimental results show that our approach can improve the detection rate, especially with the insufficient labeled data in target scene. PMID:25013850

The role of selective attention on academic foundations: a cognitive neuroscience perspective.

PubMed

Stevens, Courtney; Bavelier, Daphne

2012-02-15

To the extent that selective attention skills are relevant for academic foundations and amenable to training, they represent an important focus for the field of education. Here, drawing on research on the neurobiology of attention, we review hypothesized links between selective attention and processing across three domains important to early academic skills. First, we provide a brief review of the neural bases of selective attention, emphasizing the effects of selective attention on neural processing, as well as the neural systems important to deploying selective attention and managing response conflict. Second, we examine the developmental time course of selective attention. It is argued that developmental differences in selective attention are related to the neural systems important for deploying selective attention and managing response conflict. In contrast, once effectively deployed, selective attention acts through very similar neural mechanisms across ages. In the third section, we relate the processes of selective attention to three domains important to academic foundations: language, literacy, and mathematics. Fourth, drawing on recent literatures on the effects of video-game play and mind-brain training on selective attention, we discuss the possibility of training selective attention. The final section examines the application of these principles to educationally-focused attention-training programs for children. Copyright © 2011 Elsevier Ltd. All rights reserved.

The role of selective attention on academic foundations: A cognitive neuroscience perspective

PubMed Central

Stevens, Courtney; Bavelier, Daphne

2011-01-01

To the extent that selective attention skills are relevant for academic foundations and amenable to training, they represent an important focus for the field of education. Here, drawing on research on the neurobiology of attention, we review hypothesized links between selective attention and processing across three domains important to early academic skills. First, we provide a brief review of the neural bases of selective attention, emphasizing the effects of selective attention on neural processing, as well as the neural systems important to deploying selective attention and managing response conflict. Second, we examine the developmental time course of selective attention. It is argued that developmental differences in selective attention are related to the neural systems important for deploying selective attention and managing response conflict. In contrast, once effectively deployed, selective attention acts through very similar neural mechanisms across ages. In the third section, we relate the processes of selective attention to three domains important to academic foundations: language, literacy, and mathematics. Fourth, drawing on recent literatures on the effects of video-game play and mind-brain training on selective attention, we discuss the possibility of training selective attention. The final section examines the application of these principles to educationally-focused attention-training programs for children. PMID:22682909

Attrition Bias in Panel Data: A Sheep in Wolf's Clothing? A Case Study Based on the Mabel Survey.

PubMed

Cheng, Terence C; Trivedi, Pravin K

2015-09-01

This paper investigates the nature and consequences of sample attrition in a unique longitudinal survey of medical doctors. We describe the patterns of non-response and examine if attrition affects the econometric analysis of medical labour market outcomes using the estimation of physician earnings equations as a case study. We compare the econometric gestimates obtained from a number of different modelling strategies, which are as follows: balanced versus unbalanced samples; an attrition model for panel data based on the classic sample selection model; and a recently developed copula-based selection model. Descriptive evidence shows that doctors who work longer hours, have lower years of experience, are overseas trained and have changed their work location are more likely to drop out. Our analysis suggests that the impact of attrition on inference about the earnings of general practitioners is small. For specialists, there appears to be some evidence for an economically significant bias. Finally, we discuss how the top-up samples in the Medicine in Australia: Balancing Employment and Life survey can be used to address the problem of panel attrition. Copyright © 2015 John Wiley & Sons, Ltd.

Acute physiological responses to different circuit training protocols.

PubMed

Monteiro, A G; Alveno, D A; Prado, M; Monteiro, G A; Ugrinowitsch, C; Aoki, M S; Piçarro, I C

2008-12-01

The purpose of present study was to compare the acute physiological responses to a circuit weight training with the responses to a combined circuit training (weight training and treadmill run). The sample consisted of 25 individuals at an average state of training, 10 men and 15 female, between 18 and 35 year old. There were selected 60 second sets of resistance exercises to the circuit weight training (CWT). Whereas in the combined circuit training (CCT), the subjects spent 30 seconds on the same resistance exercises and 30 seconds running on the treadmill. The rest intervals between the sets lasted 15 seconds. The analysis of variance (ANOVA) with 5% significance level was utilized to the statistical analysis of the results. Comparing circuit training protocols, it was noted that CCT elicits a higher relative and absolute VO2 and energy expenditure values than CWT for both genders (P<0.05). Regarding inter-gender comparison, males showed higher absolute and relative VO2 and absolute energy expenditure values for both CWT and CCT than females (P<0.05). Females showed a significant greater %VO2max value for both CWT and CCT. Due to the experimental conditions used to state both circuit training bouts (CWT and CCT), the VO2 rate found was higher than the values reported by previous studies which used heavier weight lift. CCT seems adequate to produce cardiovascular improvements and greater energy expenditure for both men and women, while CWT group classes are sufficient only for unfit women.

22 CFR 62.4 - Categories of participant eligibility.

Code of Federal Regulations, 2013 CFR

2013-04-01

... participating in a structured training program conducted by the selecting sponsor. (d) Teacher. An individual..., selected by the Department of State for consultation, observation, research, training, or demonstration of..., observation, training, or demonstration of special skills in the United States. (3) Camp counselor. An...

22 CFR 62.4 - Categories of participant eligibility.

Code of Federal Regulations, 2014 CFR

2014-04-01

... participating in a structured training program conducted by the selecting sponsor. (d) Teacher. An individual..., selected by the Department of State for consultation, observation, research, training, or demonstration of..., observation, training, or demonstration of special skills in the United States. (3) Camp counselor. An...

22 CFR 62.4 - Categories of participant eligibility.

Code of Federal Regulations, 2010 CFR

2010-04-01

... participating in a structured training program conducted by the selecting sponsor. (d) Teacher. An individual..., selected by the Department of State for consultation, observation, research, training, or demonstration of..., observation, training, or demonstration of special skills in the United States. (3) Camp counselor. An...

22 CFR 62.4 - Categories of participant eligibility.

Code of Federal Regulations, 2011 CFR

2011-04-01

... participating in a structured training program conducted by the selecting sponsor. (d) Teacher. An individual..., selected by the Department of State for consultation, observation, research, training, or demonstration of..., observation, training, or demonstration of special skills in the United States. (3) Camp counselor. An...

22 CFR 62.4 - Categories of participant eligibility.

Code of Federal Regulations, 2012 CFR

2012-04-01

... participating in a structured training program conducted by the selecting sponsor. (d) Teacher. An individual..., selected by the Department of State for consultation, observation, research, training, or demonstration of..., observation, training, or demonstration of special skills in the United States. (3) Camp counselor. An...

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

Chile: Acceptability of a Training Program for Depression Management in Primary Care

PubMed Central

Marín, Rigoberto; Martínez, Pablo; Cornejo, Juan P.; Díaz, Berta; Peralta, José; Tala, Álvaro; Rojas, Graciela

2016-01-01

Background: In Chile, there are inconsistencies in the management of depression in primary care settings, and the National Depression Program, currently in effect, was implemented without a standardized training program. The objective of this study is to evaluate the acceptability of a training program on the management of depression for primary care health teams. Methods: The study was a randomized controlled trial, and two primary centers from the Metropolitan Region of Santiago were randomly selected to carry out the intervention training program. Pre-post surveys were applied, to evaluate expectations and satisfaction with the intervention, respectively. Descriptive and content analysis was carried out. Result: The sample consisted of 41 health professionals, 56.1% of who reported that their expectations for the intervention were met. All of the training activities were evaluated with scores higher than 6.4 (on a 1–7 scale). The trainers, the methodology, and the learning environment were considered strengths and facilitators of the program, while the limited duration of the training, the logistical problems faced during part of the program, and the lack of educational material were viewed as weaknesses. Conclusion: The intervention was well accepted by primary health care teams. However, the clinical impact in patients still has to be evaluated. PMID:27375531

Do increases in selected fitness parameters affect the aesthetic aspects of classical ballet performance?

PubMed

Twitchett, Emily A; Angioi, Manuela; Koutedakis, Yiannis; Wyon, Matthew

2011-03-01

Research has indicated that classical ballet dancers tend to have lower fitness levels and increased injury rates compared to other athletes with similar workloads. The aim of the current study was to examine the effects of a specifically tailored fitness training programme on the incidence of injury and the aesthetic quality of performance of classical ballet dancers compared to a control group. Proficiency in performance was evaluated at the beginning and end of the intervention period for both groups through a 4-min dance sequence using previously ratified marking criteria. The intervention group (n = 8) partook in a weekly 1-hr training session that included aerobic interval training, circuit training, and whole body vibration. All dancers' performance proficiency scores increased from pre-intervention testing to post-intervention. The intervention group's overall performance scores demonstrated a significantly greater increase (p = 0.03) than the equivalent for the control group. It was concluded that supplementary fitness training has a positive effect on aspects related to aesthetic dance performance as studied herein; further research is recommended on a larger and more varied sample. Practical applications from this study suggest that supplemental training should be part of a ballet dancer's regime, and minimal intervention time is required to have observable effects.

Sea Training at Maritime Academies Oversight. Hearings Before the Ad Hoc Select Subcommittee on Maritime Education and Training of the Committee on Merchant Marine and Fisheries, House of Representatives, Ninety-Sixth Congress, Second Session on Sea Training of United States Merchant Marine Officers and Different Ways of Satisfying This Requirement at the Various Maritime Academies.

ERIC Educational Resources Information Center

Congress of the U.S., Washington, DC. House Committee on Merchant Marine and Fisheries.

Recorded are minutes of hearings before the House Ad Hoc Select Subcommittee on Maritime Education and Training regarding the sea training of United States Merchant Marine officers. Examined are various approaches to meeting the sea training requirement, especially the options of maritime academy training vessels, sailing on U.S.-flag merchant…

The Effect of Approach/Avoidance Training on Alcohol Consumption Is Mediated by Change in Alcohol Action Tendency

PubMed Central

Sharbanee, Jason M.; Hu, Litje; Stritzke, Werner G. K.; Wiers, Reinout W.; Rinck, Mike; MacLeod, Colin

2014-01-01

Training people to respond to alcohol images by making avoidance joystick movements can affect subsequent alcohol consumption, and has shown initial efficacy as a treatment adjunct. However, the mechanisms that underlie the training’s efficacy are unknown. The present study aimed to determine 1) whether the training’s effect is mediated by a change in action tendency or a change in selective attention, and 2) whether the training’s effect is moderated by individual differences in working memory capacity (WMC). Three groups of social drinkers (total N = 74) completed either approach-alcohol training, avoid-alcohol training or a sham-training on the Approach-Avoidance Task (AAT). Participants’ WMC was assessed prior to training, while their alcohol-related action tendency and selective attention were assessed before and after the training on the recently developed Selective-Attention/Action Tendency Task (SA/ATT), before finally completing an alcohol taste-test. There was no significant main effect of approach/avoidance training on alcohol consumption during the taste-test. However, there was a significant indirect effect of training on alcohol consumption mediated by a change in action tendency, but no indirect effect mediated by a change in selective attention. There was inconsistent evidence of WMC moderating training efficacy, with moderation found only for the effect of approach-alcohol training on the AAT but not on the SA/ATT. Thus approach/avoidance training affects alcohol consumption specifically by changing the underlying action tendency. Multiple training sessions may be required in order to observe more substantive changes in drinking behaviour. PMID:24465750

A machine learning approach for classification of anatomical coverage in CT

NASA Astrophysics Data System (ADS)

Wang, Xiaoyong; Lo, Pechin; Ramakrishna, Bharath; Goldin, Johnathan; Brown, Matthew

2016-03-01

Automatic classification of anatomical coverage of medical images is critical for big data mining and as a pre-processing step to automatically trigger specific computer aided diagnosis systems. The traditional way to identify scans through DICOM headers has various limitations due to manual entry of series descriptions and non-standardized naming conventions. In this study, we present a machine learning approach where multiple binary classifiers were used to classify different anatomical coverages of CT scans. A one-vs-rest strategy was applied. For a given training set, a template scan was selected from the positive samples and all other scans were registered to it. Each registered scan was then evenly split into k × k × k non-overlapping blocks and for each block the mean intensity was computed. This resulted in a 1 × k3 feature vector for each scan. The feature vectors were then used to train a SVM based classifier. In this feasibility study, four classifiers were built to identify anatomic coverages of brain, chest, abdomen-pelvis, and chest-abdomen-pelvis CT scans. Each classifier was trained and tested using a set of 300 scans from different subjects, composed of 150 positive samples and 150 negative samples. Area under the ROC curve (AUC) of the testing set was measured to evaluate the performance in a two-fold cross validation setting. Our results showed good classification performance with an average AUC of 0.96.

Comparing Help-Seeking Behavior of Male and Female Survivors of Sexual Assault: A Content Analysis of a Hotline.

PubMed

Young, Stephen M; Pruett, Jana A; Colvin, Marianna L

2018-06-01

This content analysis examines written documentation of telephone calls to a regional sexual assault hotline over a 5-year period. All male callers identified as primary victims were selected for analysis ( n = 58) and a corresponding sample of female primary victims ( n = 58) were randomly selected for comparison to better understand the help-seeking behavior of sexual assault survivors and inform services accordingly. A summative content analysis revealed significant contrasting themes between male and female victims, including females significantly receiving more referrals and males accessing the hotline to tell their experience of being sexually assaulted due to perceived limited support. Implications for training, practice, and future research are discussed.

Cognitive training: How can it be adapted for surgical education?

PubMed

Wallace, Lauren; Raison, Nicholas; Ghumman, Faisal; Moran, Aidan; Dasgupta, Prokar; Ahmed, Kamran

2017-08-01

There is a need for new approaches to surgical training in order to cope with the increasing time pressures, ethical constraints, and legal limitations being placed on trainees. One of the most interesting of these new approaches is "cognitive training" or the use of psychological processes to enhance performance of skilled behaviour. Its ability to effectively improve motor skills in sport has raised the question as to whether it could also be used to improve surgical performance. The aim of this review is to provide an overview of the current evidence on the use of cognitive training within surgery, and evaluate the potential role it can play in surgical education. Scientific database searches were conducted to identify studies that investigated the use of cognitive training in surgery. The key studies were selected and grouped according to the type of cognitive training they examined. Available research demonstrated that cognitive training interventions resulted in greater performance benefits when compared to control training. In particular, cognitive training was found to improve surgical motor skills, as well as a number of non-technical outcomes. Unfortunately, key limitations restricting the generalizability of these findings include small sample size and conceptual issues arising from differing definitions of the term 'cognitive training'. When used appropriately, cognitive training can be a highly effective supplementary training tool in the development of technical skills in surgery. Although further studies are needed to refine our understanding, cognitive training should certainly play an important role in future surgical education. Copyright © 2016 Royal College of Surgeons of Edinburgh (Scottish charity number SC005317) and Royal College of Surgeons in Ireland. Published by Elsevier Ltd. All rights reserved.

Analyzing Kernel Matrices for the Identification of Differentially Expressed Genes

PubMed Central

Xia, Xiao-Lei; Xing, Huanlai; Liu, Xueqin

2013-01-01

One of the most important applications of microarray data is the class prediction of biological samples. For this purpose, statistical tests have often been applied to identify the differentially expressed genes (DEGs), followed by the employment of the state-of-the-art learning machines including the Support Vector Machines (SVM) in particular. The SVM is a typical sample-based classifier whose performance comes down to how discriminant samples are. However, DEGs identified by statistical tests are not guaranteed to result in a training dataset composed of discriminant samples. To tackle this problem, a novel gene ranking method namely the Kernel Matrix Gene Selection (KMGS) is proposed. The rationale of the method, which roots in the fundamental ideas of the SVM algorithm, is described. The notion of ''the separability of a sample'' which is estimated by performing -like statistics on each column of the kernel matrix, is first introduced. The separability of a classification problem is then measured, from which the significance of a specific gene is deduced. Also described is a method of Kernel Matrix Sequential Forward Selection (KMSFS) which shares the KMGS method's essential ideas but proceeds in a greedy manner. On three public microarray datasets, our proposed algorithms achieved noticeably competitive performance in terms of the B.632+ error rate. PMID:24349110

A multi-model fusion strategy for multivariate calibration using near and mid-infrared spectra of samples from brewing industry

NASA Astrophysics Data System (ADS)

Tan, Chao; Chen, Hui; Wang, Chao; Zhu, Wanping; Wu, Tong; Diao, Yuanbo

2013-03-01

Near and mid-infrared (NIR/MIR) spectroscopy techniques have gained great acceptance in the industry due to their multiple applications and versatility. However, a success of application often depends heavily on the construction of accurate and stable calibration models. For this purpose, a simple multi-model fusion strategy is proposed. It is actually the combination of Kohonen self-organizing map (KSOM), mutual information (MI) and partial least squares (PLSs) and therefore named as KMICPLS. It works as follows: First, the original training set is fed into a KSOM for unsupervised clustering of samples, on which a series of training subsets are constructed. Thereafter, on each of the training subsets, a MI spectrum is calculated and only the variables with higher MI values than the mean value are retained, based on which a candidate PLS model is constructed. Finally, a fixed number of PLS models are selected to produce a consensus model. Two NIR/MIR spectral datasets from brewing industry are used for experiments. The results confirms its superior performance to two reference algorithms, i.e., the conventional PLS and genetic algorithm-PLS (GAPLS). It can build more accurate and stable calibration models without increasing the complexity, and can be generalized to other NIR/MIR applications.

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

The effect of self-disclosure skill training on communication patterns of referred couples to counseling clinics.

PubMed

Zarei, Eghbal; Sanaeimanesh, Mehri

2014-01-01

This study aimed to examine the effect of self-disclosure skill training on communication patterns of referred couples to counseling clinics in Bandar Abbas. The applied research design was an experimental study using pre-test and post-test, which was performed on a population of all referred couples to counseling clinics in Bandar Abbas who were interested to participate in a self-disclosure training workshop in response to the announcement. This study was performed on 26 couples who were selected by simple, convenient sampling method; however, they were randomly assigned to the control and experiment groups. A pre-test was administrated before self-disclosure training. The applied instrument includes Christensen and Salavy's scale of communication patterns. Participants in the experiment group had six sessions of training workshop, each lasted for 90 min. After the intervention, both groups answered the questionnaire again. The collected data were analyzed with paired t-test and covariance statistics. The results showed that the intervention led to significant (p < 0.05) increase in mutual constructive communication pattern and a reduction in mutual avoidance, demand/withdraw, demanding man/withdrawing woman communication patterns. It was also found that the training was not effective on the communication pattern of demanding woman/withdrawing man. The training of simple, but important skills of self-disclosure can help couples to improve their communication and consequently improve their marital satisfaction.

Effects of aerobic and anaerobic training programs together with omega-3 supplement on interleukin-17 and CRP plasma levels in male mice.

PubMed

Alizadeh, Hamid; Daryanoosh, Farhad; Moatari, Maryam; Hoseinzadeh, Khadijeh

2015-01-01

Herein, we studied the effects of two different exercise protocols on IL-17 and CRP plasma levels along with the anti-inflammatory effects of fish oil. The purpose of the present study was to investigate the effect of Eicosapentaenoic Acid (EPA) and Docosahexaenoic Acid (DHA) consumption along with two different types of physical activities on IL-17 and CRP plasma levels in trained male mice. A total of 130 adult male mice of Syrian race with the age of 2 months and the weight of 35±1 grams were selected. At the beginning, 10 mice were killed in order to determine the amounts of pre-test variables. The rest of the mice were randomly divided into 6 groups including control group (n=20), supplement (n=20), aerobic exercise (n=20), anaerobic exercise (n=20), supplementaerobic exercise (n=20), and supplement-anaerobic exercise (n=20). Blood samples were withdrawn from the tail under intraperitoneal ketamine and xylasine anaesthesia. The anaerobic training program included 8 weeks of running on treadmill, 3 sessions per week; the aerobic training program included 8 weeks of running on treadmill, 5 sessions per week. At the end of the training program, the blood sample from each group was taken in order to measure the CRP and IL-17 levels. The analysis of variance (ANOVA) was used to determine the differences among the groups. The results showed that there was a significant difference in IL-17 and CRP plasma levels between the groups after 8 weeks (P<0.05). Following the two different training programs, both IL-17 and CRP plasma levels increased, although these observed increases were not same for two measured variables. The results might also show that the effect of the supplement depends on the type of training.

Effects of aerobic and anaerobic training programs together with omega-3 supplement on interleukin-17 and CRP plasma levels in male mice

PubMed Central

Alizadeh, Hamid; Daryanoosh, Farhad; Moatari, Maryam; Hoseinzadeh, Khadijeh

2015-01-01

Background: Herein, we studied the effects of two different exercise protocols on IL-17 and CRP plasma levels along with the anti-inflammatory effects of fish oil. The purpose of the present study was to investigate the effect of Eicosapentaenoic Acid (EPA) and Docosahexaenoic Acid (DHA) consumption along with two different types of physical activities on IL-17 and CRP plasma levels in trained male mice. Methods: A total of 130 adult male mice of Syrian race with the age of 2 months and the weight of 35±1 grams were selected. At the beginning, 10 mice were killed in order to determine the amounts of pre-test variables. The rest of the mice were randomly divided into 6 groups including control group (n=20), supplement (n=20), aerobic exercise (n=20), anaerobic exercise (n=20), supplementaerobic exercise (n=20), and supplement-anaerobic exercise (n=20). Blood samples were withdrawn from the tail under intraperitoneal ketamine and xylasine anaesthesia. The anaerobic training program included 8 weeks of running on treadmill, 3 sessions per week; the aerobic training program included 8 weeks of running on treadmill, 5 sessions per week. At the end of the training program, the blood sample from each group was taken in order to measure the CRP and IL-17 levels. The analysis of variance (ANOVA) was used to determine the differences among the groups. Results: The results showed that there was a significant difference in IL-17 and CRP plasma levels between the groups after 8 weeks (P<0.05). Conclusion: Following the two different training programs, both IL-17 and CRP plasma levels increased, although these observed increases were not same for two measured variables. The results might also show that the effect of the supplement depends on the type of training. PMID:26793627

Antenna analysis using neural networks

NASA Technical Reports Server (NTRS)

Smith, William T.

1992-01-01

Conventional computing schemes have long been used to analyze problems in electromagnetics (EM). The vast majority of EM applications require computationally intensive algorithms involving numerical integration and solutions to large systems of equations. The feasibility of using neural network computing algorithms for antenna analysis is investigated. The ultimate goal is to use a trained neural network algorithm to reduce the computational demands of existing reflector surface error compensation techniques. Neural networks are computational algorithms based on neurobiological systems. Neural nets consist of massively parallel interconnected nonlinear computational elements. They are often employed in pattern recognition and image processing problems. Recently, neural network analysis has been applied in the electromagnetics area for the design of frequency selective surfaces and beam forming networks. The backpropagation training algorithm was employed to simulate classical antenna array synthesis techniques. The Woodward-Lawson (W-L) and Dolph-Chebyshev (D-C) array pattern synthesis techniques were used to train the neural network. The inputs to the network were samples of the desired synthesis pattern. The outputs are the array element excitations required to synthesize the desired pattern. Once trained, the network is used to simulate the W-L or D-C techniques. Various sector patterns and cosecant-type patterns (27 total) generated using W-L synthesis were used to train the network. Desired pattern samples were then fed to the neural network. The outputs of the network were the simulated W-L excitations. A 20 element linear array was used. There were 41 input pattern samples with 40 output excitations (20 real parts, 20 imaginary). A comparison between the simulated and actual W-L techniques is shown for a triangular-shaped pattern. Dolph-Chebyshev is a different class of synthesis technique in that D-C is used for side lobe control as opposed to pattern shaping. The interesting thing about D-C synthesis is that the side lobes have the same amplitude. Five-element arrays were used. Again, 41 pattern samples were used for the input. Nine actual D-C patterns ranging from -10 dB to -30 dB side lobe levels were used to train the network. A comparison between simulated and actual D-C techniques for a pattern with -22 dB side lobe level is shown. The goal for this research was to evaluate the performance of neural network computing with antennas. Future applications will employ the backpropagation training algorithm to drastically reduce the computational complexity involved in performing EM compensation for surface errors in large space reflector antennas.

Antenna analysis using neural networks

NASA Astrophysics Data System (ADS)

Smith, William T.

1992-09-01

Conventional computing schemes have long been used to analyze problems in electromagnetics (EM). The vast majority of EM applications require computationally intensive algorithms involving numerical integration and solutions to large systems of equations. The feasibility of using neural network computing algorithms for antenna analysis is investigated. The ultimate goal is to use a trained neural network algorithm to reduce the computational demands of existing reflector surface error compensation techniques. Neural networks are computational algorithms based on neurobiological systems. Neural nets consist of massively parallel interconnected nonlinear computational elements. They are often employed in pattern recognition and image processing problems. Recently, neural network analysis has been applied in the electromagnetics area for the design of frequency selective surfaces and beam forming networks. The backpropagation training algorithm was employed to simulate classical antenna array synthesis techniques. The Woodward-Lawson (W-L) and Dolph-Chebyshev (D-C) array pattern synthesis techniques were used to train the neural network. The inputs to the network were samples of the desired synthesis pattern. The outputs are the array element excitations required to synthesize the desired pattern. Once trained, the network is used to simulate the W-L or D-C techniques. Various sector patterns and cosecant-type patterns (27 total) generated using W-L synthesis were used to train the network. Desired pattern samples were then fed to the neural network. The outputs of the network were the simulated W-L excitations. A 20 element linear array was used. There were 41 input pattern samples with 40 output excitations (20 real parts, 20 imaginary).

45 CFR 2522.210 - How are AmeriCorps participants recruited and selected?

Code of Federal Regulations, 2010 CFR

2010-10-01

... leadership pool—(1) Selection and training. From among individuals recruited under paragraph (b) of this... leadership potential, as determined by the Corporation, to receive special training to enhance their leadership ability. The leadership training will be provided by the Corporation directly or through a grant...

34 CFR 366.15 - What selection criteria does the Secretary use?

Code of Federal Regulations, 2010 CFR

2010-07-01

... Training and Technical Assistance § 366.15 What selection criteria does the Secretary use? The Secretary uses the following criteria to evaluate applications for new awards for training and technical... project; and (2) How the objectives further training and technical assistance with respect to planning...

Effective Chaperone Selection and Training for Enhanced Youth Experiences

ERIC Educational Resources Information Center

Anderson, Emily J.; Roop, Kelsey; MacArthur, Stacey

2017-01-01

This article identifies key strategies for selecting and training chaperones for youth programs. Although substantial research on volunteer core competencies and training exists, very little has been written to specifically address volunteers who serve in a chaperone capacity. We surveyed chaperones who had participated in an international youth…

45 CFR 2522.210 - How are AmeriCorps participants recruited and selected?

Code of Federal Regulations, 2011 CFR

2011-10-01

... leadership pool—(1) Selection and training. From among individuals recruited under paragraph (b) of this... leadership potential, as determined by the Corporation, to receive special training to enhance their leadership ability. The leadership training will be provided by the Corporation directly or through a grant...

45 CFR 2522.210 - How are AmeriCorps participants recruited and selected?

Code of Federal Regulations, 2012 CFR

2012-10-01

... leadership pool—(1) Selection and training. From among individuals recruited under paragraph (b) of this... leadership potential, as determined by the Corporation, to receive special training to enhance their leadership ability. The leadership training will be provided by the Corporation directly or through a grant...

45 CFR 2522.210 - How are AmeriCorps participants recruited and selected?

Code of Federal Regulations, 2014 CFR

2014-10-01

... leadership pool—(1) Selection and training. From among individuals recruited under paragraph (b) of this... leadership potential, as determined by the Corporation, to receive special training to enhance their leadership ability. The leadership training will be provided by the Corporation directly or through a grant...

45 CFR 2522.210 - How are AmeriCorps participants recruited and selected?

Code of Federal Regulations, 2013 CFR

2013-10-01

... leadership pool—(1) Selection and training. From among individuals recruited under paragraph (b) of this... leadership potential, as determined by the Corporation, to receive special training to enhance their leadership ability. The leadership training will be provided by the Corporation directly or through a grant...

An Inventory of Aquatic Macroinvertebrates and Calculation of Selected Biotic Indices for the U.S. Army Atterbury Reserve Forces Training Area near Edinburgh, Indiana, September 2000 - August 2002

USGS Publications Warehouse

Robinson, Bret A.

2004-01-01

Biotic indices (indicators of water-quality conditions) were calculated from the macroinvertebrate data. Ephemeroptera, Plecoptera, Trichoptera Richness Index values calculated for 23 samples collected from 16 sites ranged from 5 to 15, with more than 75 percent of the values falling within the range of 7 to 11. Hilsenhoff Biotic Index scores and Invertebrate Community Index scores calculated for samples collected at three sites indicate that water quality at these sites ranged from good to poor. The one site with a poor water-quality index score had a small drainage area. The small drainage area and dry conditions during the sampling period may have contributed to the poor scores calculated for this site.

Selection for Surgical Training: An Evidence-Based Review.

PubMed

Schaverien, Mark V

2016-01-01

The predictive relationship between candidate selection criteria for surgical training programs and future performance during and at the completion of training has been investigated for several surgical specialties, however there is no interspecialty agreement regarding which selection criteria should be used. Better understanding the predictive reliability between factors at selection and future performance may help to optimize the process and lead to greater standardization of the surgical selection process. PubMed and Ovid MEDLINE databases were searched. Over 560 potentially relevant publications were identified using the search strategy and screened using the Cochrane Collaboration Data Extraction and Assessment Template. 57 studies met the inclusion criteria. Several selection criteria used in the traditional selection demonstrated inconsistent correlation with subsequent performance during and at the end of surgical training. The following selection criteria, however, demonstrated good predictive relationships with subsequent resident performance: USMLE examination scores, Letters of Recommendation (LOR) including the Medical Student Performance Evaluation (MSPE), academic performance during clinical clerkships, the interview process, displaying excellence in extracurricular activities, and the use of unadjusted rank lists. This systematic review supports that the current selection process needs to be further evaluated and improved. Multicenter studies using standardized outcome measures of success are now required to improve the reliability of the selection process to select the best trainees. Published by Elsevier Inc.

Attentional Filter Training but Not Memory Training Improves Decision-Making.

PubMed

Schmicker, Marlen; Müller, Patrick; Schwefel, Melanie; Müller, Notger G

2017-01-01

Decision-making has a high practical relevance for daily performance. Its relation to other cognitive abilities such as executive control and memory is not fully understood. Here we asked whether training of either attentional filtering or memory storage would influence decision-making as indexed by repetitive assessments of the Iowa Gambling Task (IGT). The IGT was developed to assess and simulate real-life decision-making (Bechara et al., 2005). In this task, participants gain or lose money by developing advantageous or disadvantageous decision strategies. On five consecutive days we trained 29 healthy young adults (20-30 years) either in working memory (WM) storage or attentional filtering and measured their IGT scores after each training session. During memory training (MT) subjects performed a computerized delayed match-to-sample task where two displays of bars were presented in succession. During filter training (FT) participants had to indicate whether two simultaneously presented displays of bars matched or not. Whereas in MT the relevant target stimuli stood alone, in FT the targets were embedded within irrelevant distractors (bars in a different color). All subjects within each group improved their performance in the trained cognitive task. For the IGT, we observed an increase over time in the amount of money gained in the FT group only. Decision-making seems to be influenced more by training to filter out irrelevant distractors than by training to store items in WM. Selective attention could be responsible for the previously noted relationship between IGT performance and WM and is therefore more important for enhancing efficiency in decision-making.

Balanced VS Imbalanced Training Data: Classifying Rapideye Data with Support Vector Machines

NASA Astrophysics Data System (ADS)

Ustuner, M.; Sanli, F. B.; Abdikan, S.

2016-06-01

The accuracy of supervised image classification is highly dependent upon several factors such as the design of training set (sample selection, composition, purity and size), resolution of input imagery and landscape heterogeneity. The design of training set is still a challenging issue since the sensitivity of classifier algorithm at learning stage is different for the same dataset. In this paper, the classification of RapidEye imagery with balanced and imbalanced training data for mapping the crop types was addressed. Classification with imbalanced training data may result in low accuracy in some scenarios. Support Vector Machines (SVM), Maximum Likelihood (ML) and Artificial Neural Network (ANN) classifications were implemented here to classify the data. For evaluating the influence of the balanced and imbalanced training data on image classification algorithms, three different training datasets were created. Two different balanced datasets which have 70 and 100 pixels for each class of interest and one imbalanced dataset in which each class has different number of pixels were used in classification stage. Results demonstrate that ML and NN classifications are affected by imbalanced training data in resulting a reduction in accuracy (from 90.94% to 85.94% for ML and from 91.56% to 88.44% for NN) while SVM is not affected significantly (from 94.38% to 94.69%) and slightly improved. Our results highlighted that SVM is proven to be a very robust, consistent and effective classifier as it can perform very well under balanced and imbalanced training data situations. Furthermore, the training stage should be precisely and carefully designed for the need of adopted classifier.

How learning might strengthen existing visual object representations in human object-selective cortex.

PubMed

Brants, Marijke; Bulthé, Jessica; Daniels, Nicky; Wagemans, Johan; Op de Beeck, Hans P

2016-02-15

Visual object perception is an important function in primates which can be fine-tuned by experience, even in adults. Which factors determine the regions and the neurons that are modified by learning is still unclear. Recently, it was proposed that the exact cortical focus and distribution of learning effects might depend upon the pre-learning mapping of relevant functional properties and how this mapping determines the informativeness of neural units for the stimuli and the task to be learned. From this hypothesis we would expect that visual experience would strengthen the pre-learning distributed functional map of the relevant distinctive object properties. Here we present a first test of this prediction in twelve human subjects who were trained in object categorization and differentiation, preceded and followed by a functional magnetic resonance imaging session. Specifically, training increased the distributed multi-voxel pattern information for trained object distinctions in object-selective cortex, resulting in a generalization from pre-training multi-voxel activity patterns to after-training activity patterns. Simulations show that the increased selectivity combined with the inter-session generalization is consistent with a training-induced strengthening of a pre-existing selectivity map. No training-related neural changes were detected in other regions. In sum, training to categorize or individuate objects strengthened pre-existing representations in human object-selective cortex, providing a first indication that the neuroanatomical distribution of learning effects depends upon the pre-learning mapping of visual object properties. Copyright © 2015 Elsevier Inc. All rights reserved.

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.

The Collision Auto Repair Safety Study (CARSS): a health and safety intervention.

PubMed

Parker, David L; Bejan, Anca; Brosseau, Lisa M; Skan, Maryellen; Xi, Min

2015-01-01

Collision repair employs approximately 205,500 people in 33,400 shops. Workers are exposed to a diverse array of chemical, physical, and ergonomic hazards. CARSS was based on a random and purposeful sample. Baseline and one baseline and one-year evaluations consisted of 92 questions addressing issues, such as Right-to-Know, fire protection, painting-related hazards, ergonomics, electrical safety, and personal protective equipment. Owners received a report and selected at least 30% of items found deficient for remediation. In-person and web-based services were provided. Forty-nine shops were evaluated at baseline and 45 at follow-up. At baseline, 54% of items were present. This improved to 71% at follow-up (P < 0.0001). Respiratory protection improved 37% (P < 0.0001) and Right-to-Know training increased 30% (P < 0.0001). Owners completed 61% of items they selected for remediation. Small businesses' interventions should address the lack of personnel and administrative infrastructure. Tailored information regarding hazards and easy-to-use training and administrative programs overcome many barriers to improvement. © 2014 Wiley Periodicals, Inc.

Effect of anger management education on mental health and aggression of prisoner women.

PubMed

Bahrami, Elaheh; Mazaheri, Maryam Amidi; Hasanzadeh, Akbar

2016-01-01

"Uncontrolled anger" threats the compatible and health of people as serious risk. The effects of weaknesses and shortcomings in the management of anger, from personal distress and destruction interpersonal relationships beyond and linked to the public health problems, lack of compromises, and aggressive behavior adverse outcomes. This study investigates the effects of anger management education on mental health and aggression of prisoner women in Isfahan. The single-group quasi-experimental (pretest, posttest) by prisoner women in the central prison of Isfahan was done. Multi-stage random sampling method was used. Initially, 165 women were selected randomly and completed the Buss and Perry Aggression Questionnaire and the General Health Questionnaire-28, and among these, those with scores >78 (the cut point) in aggression scale was selected and among them 70 were randomly selected. In the next step, interventions in four 90 min training sessions were conducted. Posttest was performed within 1-month after the intervention. Data were analyzed using SPSS-20 software. Data analysis showed that anger management training was effective in reducing aggression (P < 0.001) and also had a positive effect on mental health (P < 0.001). According to the importance of aggression in consistency and individual and collective health and according to findings, presented educational programs on anger management is essential for female prisoners.

Limited-memory adaptive snapshot selection for proper orthogonal decomposition

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

Oxberry, Geoffrey M.; Kostova-Vassilevska, Tanya; Arrighi, Bill

2015-04-02

Reduced order models are useful for accelerating simulations in many-query contexts, such as optimization, uncertainty quantification, and sensitivity analysis. However, offline training of reduced order models can have prohibitively expensive memory and floating-point operation costs in high-performance computing applications, where memory per core is limited. To overcome this limitation for proper orthogonal decomposition, we propose a novel adaptive selection method for snapshots in time that limits offline training costs by selecting snapshots according an error control mechanism similar to that found in adaptive time-stepping ordinary differential equation solvers. The error estimator used in this work is related to theory boundingmore » the approximation error in time of proper orthogonal decomposition-based reduced order models, and memory usage is minimized by computing the singular value decomposition using a single-pass incremental algorithm. Results for a viscous Burgers’ test problem demonstrate convergence in the limit as the algorithm error tolerances go to zero; in this limit, the full order model is recovered to within discretization error. The resulting method can be used on supercomputers to generate proper orthogonal decomposition-based reduced order models, or as a subroutine within hyperreduction algorithms that require taking snapshots in time, or within greedy algorithms for sampling parameter space.« less

The Selection and Training of School Bus Drivers.

ERIC Educational Resources Information Center

McKnight, A. James; And Others

This report describes procedures used in developing a set of selection requirements and training objectives for operators of school buses. The selection requirements include collection and interpretation of personal history and other background information, physical examination covering vision, hearing, handicaps, and general health, written…

Drop-off Detection with the Long Cane: Effects of Different Cane Techniques on Performance

PubMed Central

Kim, Dae Shik; Emerson, Robert Wall; Curtis, Amy

2010-01-01

This study compared the drop-off detection performance with the two-point touch and constant contact cane techniques using a repeated-measures design with a convenience sample of 15 cane users with visual impairments. The constant contact technique was superior to the two-point touch technique in the drop-off detection rate and the 50% detection threshold. The findings may help an orientation and mobility instructor select an appropriate technique for a particular client or training situation. PMID:21209791

Effects of Simulated Surface Effect Ship Motions on Crew Habitability. Phase II. Volume 3. Visual-Motor Tasks and Subjective Evaluations

DTIC Science & Technology

1977-05-01

simulated rmotions ; and detaiJl.s on the daily work/rest schedule, as well as the overall run ,schedule (Ref.20). * Volume 4, "Crew Cognitive Functions...the outset: 1) the very small sampling of well- motivated crewmen made it difficult to generalize the results to a wider population; and 2) the...a:; backups. Selection of primary crewmen was based on satisfactory task learning and motivation demonstrated during the training period, any minor

Pharmacokinetics and selected pharmacodynamics of trazodone following intravenous and oral administration to horses undergoing fitness training.

PubMed

Knych, Heather K; Mama, Khursheed R; Steffey, Eugene P; Stanley, Scott D; Kass, Philip H

2017-10-01

OBJECTIVE To measure concentrations of trazodone and its major metabolite in plasma and urine after administration to healthy horses and concurrently assess selected physiologic and behavioral effects of the drug. ANIMALS 11 Thoroughbred horses enrolled in a fitness training program. PROCEDURES In a pilot investigation, 4 horses received trazodone IV (n = 2) or orally (2) to select a dose for the full study; 1 horse received a vehicle control treatment IV. For the full study, trazodone was initially administered IV (1.5 mg/kg) to 6 horses and subsequently given orally (4 mg/kg), with a 5-week washout period between treatments. Blood and urine samples were collected prior to drug administration and at multiple time points up to 48 hours afterward. Samples were analyzed for trazodone and metabolite concentrations, and pharmacokinetic parameters were determined; plasma drug concentrations following IV administration best fit a 3-compartment model. Behavioral and physiologic effects were assessed. RESULTS After IV administration, total clearance of trazodone was 6.85 ± 2.80 mL/min/kg, volume of distribution at steady state was 1.06 ± 0.07 L/kg, and elimination half-life was 8.58 ± 1.88 hours. Terminal phase half-life was 7.11 ± 1.70 hours after oral administration. Horses had signs of aggression and excitation, tremors, and ataxia at the highest IV dose (2 mg/kg) in the pilot investigation. After IV drug administration in the full study (1.5 mg/kg), horses were ataxic and had tremors; sedation was evident after oral administration. CONCLUSIONS AND CLINICAL RELEVANCE Administration of trazodone to horses elicited a wide range of effects. Additional study is warranted before clinical use of trazodone in horses can be recommended.

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

Validation of a Multimarker Model for Assessing Risk of Type 2 Diabetes from a Five-Year Prospective Study of 6784 Danish People (Inter99)

PubMed Central

Urdea, Mickey; Kolberg, Janice; Wilber, Judith; Gerwien, Robert; Moler, Edward; Rowe, Michael; Jorgensen, Paul; Hansen, Torben; Pedersen, Oluf; Jørgensen, Torben; Borch-Johnsen, Knut

2009-01-01

Background Improved identification of subjects at high risk for development of type 2 diabetes would allow preventive interventions to be targeted toward individuals most likely to benefit. In previous research, predictive biomarkers were identified and used to develop multivariate models to assess an individual's risk of developing diabetes. Here we describe the training and validation of the PreDx™ Diabetes Risk Score (DRS) model in a clinical laboratory setting using baseline serum samples from subjects in the Inter99 cohort, a population-based primary prevention study of cardiovascular disease. Methods Among 6784 subjects free of diabetes at baseline, 215 subjects progressed to diabetes (converters) during five years of follow-up. A nested case-control study was performed using serum samples from 202 converters and 597 randomly selected nonconverters. Samples were randomly assigned to equally sized training and validation sets. Seven biomarkers were measured using assays developed for use in a clinical reference laboratory. Results The PreDx DRS model performed better on the training set (area under the curve [AUC] = 0.837) than fasting plasma glucose alone (AUC = 0.779). When applied to the sequestered validation set, the PreDx DRS showed the same performance (AUC = 0.838), thus validating the model. This model had a better AUC than any other single measure from a fasting sample. Moreover, the model provided further risk stratification among high-risk subpopulations with impaired fasting glucose or metabolic syndrome. Conclusions The PreDx DRS provides the absolute risk of diabetes conversion in five years for subjects identified to be “at risk” using the clinical factors. PMID:20144324

Validation of a multimarker model for assessing risk of type 2 diabetes from a five-year prospective study of 6784 Danish people (Inter99).

PubMed

Urdea, Mickey; Kolberg, Janice; Wilber, Judith; Gerwien, Robert; Moler, Edward; Rowe, Michael; Jorgensen, Paul; Hansen, Torben; Pedersen, Oluf; Jørgensen, Torben; Borch-Johnsen, Knut

2009-07-01

Improved identification of subjects at high risk for development of type 2 diabetes would allow preventive interventions to be targeted toward individuals most likely to benefit. In previous research, predictive biomarkers were identified and used to develop multivariate models to assess an individual's risk of developing diabetes. Here we describe the training and validation of the PreDx Diabetes Risk Score (DRS) model in a clinical laboratory setting using baseline serum samples from subjects in the Inter99 cohort, a population-based primary prevention study of cardiovascular disease. Among 6784 subjects free of diabetes at baseline, 215 subjects progressed to diabetes (converters) during five years of follow-up. A nested case-control study was performed using serum samples from 202 converters and 597 randomly selected nonconverters. Samples were randomly assigned to equally sized training and validation sets. Seven biomarkers were measured using assays developed for use in a clinical reference laboratory. The PreDx DRS model performed better on the training set (area under the curve [AUC] = 0.837) than fasting plasma glucose alone (AUC = 0.779). When applied to the sequestered validation set, the PreDx DRS showed the same performance (AUC = 0.838), thus validating the model. This model had a better AUC than any other single measure from a fasting sample. Moreover, the model provided further risk stratification among high-risk subpopulations with impaired fasting glucose or metabolic syndrome. The PreDx DRS provides the absolute risk of diabetes conversion in five years for subjects identified to be "at risk" using the clinical factors. Copyright 2009 Diabetes Technology Society.

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…

Radiographer-led plan selection for bladder cancer radiotherapy: initiating a training programme and maintaining competency.

PubMed

McNair, H A; Hafeez, S; Taylor, H; Lalondrelle, S; McDonald, F; Hansen, V N; Huddart, R

2015-04-01

The implementation of plan of the day selection for patients receiving radiotherapy (RT) for bladder cancer requires efficient and confident decision-making. This article describes the development of a training programme and maintenance of competency. Cone beam CT (CBCT) images acquired on patients receiving RT for bladder cancer were assessed to establish baseline competency and training needs. A training programme was implemented, and observers were asked to select planning target volumes (PTVs) on two groups of 20 patients' images. After clinical implementation, the PTVs chosen were reviewed offline, and an audit performed after 3 years. A mean of 73% (range, 53-93%) concordance rate was achieved prior to training. Subsequent to training, the mean score decreased to 66% (Round 1), then increased to 76% (Round 2). Six radiographers and two clinicians successfully completed the training programme. An independent observer reviewed the images offline after clinical implementation, and a 91% (126/139) concordance rate was achieved. During the audit, 125 CBCT images from 13 patients were reviewed by a single observer and concordance was 92%. Radiographer-led selection of plan of the day was implemented successfully with the use of a training programme and continual assessment. Quality has been maintained over a period of 3 years. The training programme was successful in achieving and maintaining competency for a plan of the day technique.

Factors Influencing Residency Program Selection by Medical Students Pursuing Obstetrics and Gynecology.

PubMed

Alston, Meredith J; Metz, Torri D; Fothergill, Russell; Meg Autry, Amy; Wagner, Sarah A; Allshouse, Amanda A; Stephenson-Famy, Alyssa

2017-02-01

Little is known about the factors that influence medical student selection of obstetrics and gynecology (ob-gyn) residency programs. We assessed the factors influencing residency program selection by fourth-year medical students pursuing ob-gyn training. A voluntary, anonymous, 19-question survey of residency selection factors was distributed to all fourth-year medical students interviewing at 1 of 5 academic ob-gyn departments for a residency position during the 2013-2014 interview season. Participants were surveyed about the relative importance (not important, somewhat important, important) of various residency selection factors, including operative experience, exposure to subspecialties, curricular experience, access to fellowships, and administrative aspects of residency, including adherence to duty hour restrictions. Of 322 potential respondents, 262 (81%) completed the survey. Surgical training and training in laparoscopic surgery were deemed "important" by nearly all respondents (98%, 258 of 262, and 97%, 253 of 262, respectively). Factors that were considered "not important" by a significant group of respondents included maternity/paternity leave policies (22%, 58 of 259); opportunity for international rotations/electives (20%, 51 of 259); exposure to quality and safety initiatives (13%, 34 of 259); and training in abortion (13%, 34 of 262). Fourth-year medical students identified surgical training as the most important factor in selecting an ob-gyn residency, a finding that is particularly relevant as decreasing and changing surgical volumes affect residency training in this specialty.

The use of single-date MODIS imagery for estimating large-scale urban impervious surface fraction with spectral mixture analysis and machine learning techniques

NASA Astrophysics Data System (ADS)

Deng, Chengbin; Wu, Changshan

2013-12-01

Urban impervious surface information is essential for urban and environmental applications at the regional/national scales. As a popular image processing technique, spectral mixture analysis (SMA) has rarely been applied to coarse-resolution imagery due to the difficulty of deriving endmember spectra using traditional endmember selection methods, particularly within heterogeneous urban environments. To address this problem, we derived endmember signatures through a least squares solution (LSS) technique with known abundances of sample pixels, and integrated these endmember signatures into SMA for mapping large-scale impervious surface fraction. In addition, with the same sample set, we carried out objective comparative analyses among SMA (i.e. fully constrained and unconstrained SMA) and machine learning (i.e. Cubist regression tree and Random Forests) techniques. Analysis of results suggests three major conclusions. First, with the extrapolated endmember spectra from stratified random training samples, the SMA approaches performed relatively well, as indicated by small MAE values. Second, Random Forests yields more reliable results than Cubist regression tree, and its accuracy is improved with increased sample sizes. Finally, comparative analyses suggest a tentative guide for selecting an optimal approach for large-scale fractional imperviousness estimation: unconstrained SMA might be a favorable option with a small number of samples, while Random Forests might be preferred if a large number of samples are available.

Action video game training reduces the Simon Effect.

PubMed

Hutchinson, Claire V; Barrett, Doug J K; Nitka, Aleksander; Raynes, Kerry

2016-04-01

A number of studies have shown that training on action video games improves various aspects of visual cognition including selective attention and inhibitory control. Here, we demonstrate that action video game play can also reduce the Simon Effect, and, hence, may have the potential to improve response selection during the planning and execution of goal-directed action. Non-game-players were randomly assigned to one of four groups; two trained on a first-person-shooter game (Call of Duty) on either Microsoft Xbox or Nintendo DS, one trained on a visual training game for Nintendo DS, and a control group who received no training. Response times were used to contrast performance before and after training on a behavioral assay designed to manipulate stimulus-response compatibility (the Simon Task). The results revealed significantly faster response times and a reduced cost of stimulus-response incompatibility in the groups trained on the first-person-shooter game. No benefit of training was observed in the control group or the group trained on the visual training game. These findings are consistent with previous evidence that action game play elicits plastic changes in the neural circuits that serve attentional control, and suggest training may facilitate goal-directed action by improving players' ability to resolve conflict during response selection and execution.

Dropouts in Swiss Vocational Education and the Effect of Training Companies' Trainee Selection Methods

ERIC Educational Resources Information Center

Forsblom, Lara; Negrini, Lucio; Gurtner, Jean-Luc; Schumann, Stephan

2016-01-01

In the Swiss vocational education system, which is often called a "Dual System", trainees enter into an apprenticeship contract with a training company. On average, 25% of those contracts are terminated prematurely (PCT). This article examines the relationship between training companies' selection methods and PCTs. The investigation is…

34 CFR 388.20 - What additional selection criterion is used under this program?

Code of Federal Regulations, 2013 CFR

2013-07-01

... State unit in-service training plan responds to needs identified in their training needs assessment and... employment outcomes; and (iv) The State has conducted a needs assessment of the in-service training needs for... Secretary uses the following additional selection criteria to evaluate an application: (a) Evidence of need...

34 CFR 388.20 - What additional selection criterion is used under this program?

Code of Federal Regulations, 2011 CFR

2011-07-01

... State unit in-service training plan responds to needs identified in their training needs assessment and... employment outcomes; and (iv) The State has conducted a needs assessment of the in-service training needs for... Secretary uses the following additional selection criteria to evaluate an application: (a) Evidence of need...

34 CFR 388.20 - What additional selection criterion is used under this program?

Code of Federal Regulations, 2014 CFR

2014-07-01

... State unit in-service training plan responds to needs identified in their training needs assessment and... employment outcomes; and (iv) The State has conducted a needs assessment of the in-service training needs for... Secretary uses the following additional selection criteria to evaluate an application: (a) Evidence of need...

34 CFR 388.20 - What additional selection criterion is used under this program?

Code of Federal Regulations, 2010 CFR

2010-07-01

... State unit in-service training plan responds to needs identified in their training needs assessment and... employment outcomes; and (iv) The State has conducted a needs assessment of the in-service training needs for... Secretary uses the following additional selection criteria to evaluate an application: (a) Evidence of need...

34 CFR 388.20 - What additional selection criterion is used under this program?

Code of Federal Regulations, 2012 CFR

2012-07-01

... State unit in-service training plan responds to needs identified in their training needs assessment and... employment outcomes; and (iv) The State has conducted a needs assessment of the in-service training needs for... Secretary uses the following additional selection criteria to evaluate an application: (a) Evidence of need...

Target discrimination method for SAR images based on semisupervised co-training

NASA Astrophysics Data System (ADS)

Wang, Yan; Du, Lan; Dai, Hui

2018-01-01

Synthetic aperture radar (SAR) target discrimination is usually performed in a supervised manner. However, supervised methods for SAR target discrimination may need lots of labeled training samples, whose acquirement is costly, time consuming, and sometimes impossible. This paper proposes an SAR target discrimination method based on semisupervised co-training, which utilizes a limited number of labeled samples and an abundant number of unlabeled samples. First, Lincoln features, widely used in SAR target discrimination, are extracted from the training samples and partitioned into two sets according to their physical meanings. Second, two support vector machine classifiers are iteratively co-trained with the extracted two feature sets based on the co-training algorithm. Finally, the trained classifiers are exploited to classify the test data. The experimental results on real SAR images data not only validate the effectiveness of the proposed method compared with the traditional supervised methods, but also demonstrate the superiority of co-training over self-training, which only uses one feature set.

Educational technology in care management: technological profile of nurses in Portuguese hospitals.

PubMed

Landeiro, Maria José Lumini; Freire, Rosa Maria Albuquerque; Martins, Maria Manuela; Martins, Teresa Vieira; Peres, Heloísa Helena Ciqueto

2015-12-01

Objective To identify the technological profile of nurses in Portuguese hospitals. Method A quantitative exploratory study conducted in two hospitals in the northern region and one in the central region of Portugal. The sample was randomly selected and included 960 nurses. Results Of the participants, 420 (46.1%) used computers, 196 (23.4%) reported having knowledge about using computers for teaching, 174 (21.1%) used computers to teach, 112 (15.1%) recognized that using computers can be a technological means to supplement classroom training, 477 (61.6%) would like to receive training on using computers, and 382 (40.9%) reported self-learning of information technology. In relation to distance education, 706 (74.9%) reported they were familiar with it and 752 (76.4%) indicated an interest in participating in training using this modality. Conclusion Organizations should be mindful of the technological profile shown by this group of nurses and look for ways to introduce educational technologies in the management of care.

[The attitudes and behavior of the general primary-care physician towards the neurological patient].

PubMed

Casabella Abril, B; Pérez Sánchez, J

1995-04-15

1) To find the opinion of general practitioners working in primary care (GP in PC) regarding how they deal with neurological patients. 2) To find the effect on this question of intern training in family and community medicine (FCM). A survey filled out by a representative sample of GP in PC working at PC public clinics in 1991 in a health region in Catalonia. 56 GP in PC. A self-administered selection questionnaire (multiple choice and scale of 5 points). MEASUREMENTS, MAIN RESULTS AND CONCLUSIONS: Less confidence handling neurological patients than patients with other common medical conditions. Greater need for recycling in neurology than in other basic areas of medicine. Positive impact of FCM intern training on doctors' approach to the examination of neurological patients and application of basic exploratory techniques (ophthalmoscope, reflex hammer, diapason and phonendoscope). The GP intern-trained in FCM lacks confidence in present out-patient specialised support (the area neuropsychiatrist).

A Multi-Session Attribution Modification Program for Children with Aggressive Behaviour: Changes in Attributions, Emotional Reaction Estimates, and Self-Reported Aggression.

PubMed

Vassilopoulos, Stephanos P; Brouzos, Andreas; Andreou, Eleni

2015-09-01

Research suggests that aggressive children are prone to over-attribute hostile intentions to peers. The current study investigated whether this attributional style can be altered using a Cognitive Bias Modification of Interpretations (CBM-I) procedure. A sample of 10-12-year-olds selected for displaying aggressive behaviours was trained over three sessions to endorse benign rather than hostile attributions in response to ambiguous social scenarios. Compared to a test-retest control group (n = 18), children receiving CBM-I (n = 16) were less likely to endorse hostile attributions and more likely to endorse benign attributions in response to a new set of ambiguous social situations. Furthermore, aggressive behaviour scores reduced more in the trained group than in the untrained controls. Children who received attribution training also reported less perceived anger and showed a trend to report more self-control than those in the control group. Implications of these findings are discussed.

Use of medicines and adherence to standard treatment guidelines in rural community health centers, Timor-Leste.

PubMed

Higuchi, Michiyo; Okumura, Junko; Aoyama, Atsuko; Suryawati, Sri; Porter, John

2015-03-01

The use of medicines and nurses'/midwives' adherence to standard treatment guidelines (STGs) were examined in Timor-Leste during the early stage of the nation's new health system development. A cross-sectional study was conducted as the quantitative element of mixed methods research. Retrospective samples from patient registration books and prospective observations were obtained in 20 randomly selected rural community health centers. The medicines use indicators, in particular the level of injection use, in Timor-Leste did not suggest overprescription. Prescribers with clinical nurse training prescribed significantly fewer antibiotics than those without such training (P < .01). The adjusted odds ratio of prescribing adherence for clinical nurse training, after accounting for confounders and prescriber clustering, was 6.6 (P < .01). STGs for nonphysician health professionals at the primary health care level have potential value in basic health care delivery, including appropriate use of medicines, in resource-limited communities when strategically developed and introduced. © 2012 APJPH.

Impact of special aviation gymnastics instruments training on selected hormones in cadets' blood serum and plasma.

PubMed

Wochyński, Zbigniew; Sobiech, Krzysztof

2017-06-19

This study has aimed at investigating the impact of the Special Aviation Gymnastics Instruments (SAGI) training scheme on the blood serum cortisol, testosterone, insulin, and plasma adrenaline, noradrenaline, and dopamine in comparison with a control group. Fifty-five cadets, aged 20 years old, participated in the study. Cadets were divided into 2 groups: A (N = 41) - the SAGI-trained, and B (N = 14) - the control group. In both groups, blood was the examined material, sampled twice: before the training session (BT) and after the training session (AT), at the beginning (training session I), during (training session II), and after completion of the SAGI training session (training session III). Commercially available kits were used for assaying serum cortisol, testosterone, and insulin as well as plasma adrenaline, noradrenaline, and dopamine. Cadets' physical fitness was assessed by means of Aero-Synthetic Efficiency Tests. In group A, a significant decrease in serum cortisol (training session III) and insulin in three training sessions AT in comparison with the values BT was seen. A statistically significant increase in testosterone and catecholamines was noted in all 3 training sessions AT in comparison with the values BT. In group B, a statistically significant increase in cortisol (training session II), testosterone, and catecholamines was observed in all 3 training sessions AT vs. the values in training session BT. In group B, serum levels of all assayed hormones were higher in training session III than those in group A. In the examined group, the SAGI training produced fewer hormonal changes dependent on the intensity and exercise type and physical efficiency improvement than in the control group. Int J Occup Med Environ Health 2017;30(4):655-664. This work is available in Open Access model and licensed under a CC BY-NC 3.0 PL license.

Targeted training of the decision rule benefits rule-guided behavior in Parkinson's disease.

PubMed

Ell, Shawn W

2013-12-01

The impact of Parkinson's disease (PD) on rule-guided behavior has received considerable attention in cognitive neuroscience. The majority of research has used PD as a model of dysfunction in frontostriatal networks, but very few attempts have been made to investigate the possibility of adapting common experimental techniques in an effort to identify the conditions that are most likely to facilitate successful performance. The present study investigated a targeted training paradigm designed to facilitate rule learning and application using rule-based categorization as a model task. Participants received targeted training in which there was no selective-attention demand (i.e., stimuli varied along a single, relevant dimension) or nontargeted training in which there was selective-attention demand (i.e., stimuli varied along a relevant dimension as well as an irrelevant dimension). Following training, all participants were tested on a rule-based task with selective-attention demand. During the test phase, PD patients who received targeted training performed similarly to control participants and outperformed patients who did not receive targeted training. As a preliminary test of the generalizability of the benefit of targeted training, a subset of the PD patients were tested on the Wisconsin card sorting task (WCST). PD patients who received targeted training outperformed PD patients who did not receive targeted training on several WCST performance measures. These data further characterize the contribution of frontostriatal circuitry to rule-guided behavior. Importantly, these data also suggest that PD patient impairment, on selective-attention-demanding tasks of rule-guided behavior, is not inevitable and highlight the potential benefit of targeted training.

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.

Two different mechanisms support selective attention at different phases of training.

PubMed

Itthipuripat, Sirawaj; Cha, Kexin; Byers, Anna; Serences, John T

2017-06-01

Selective attention supports the prioritized processing of relevant sensory information to facilitate goal-directed behavior. Studies in human subjects demonstrate that attentional gain of cortical responses can sufficiently account for attention-related improvements in behavior. On the other hand, studies using highly trained nonhuman primates suggest that reductions in neural noise can better explain attentional facilitation of behavior. Given the importance of selective information processing in nearly all domains of cognition, we sought to reconcile these competing accounts by testing the hypothesis that extensive behavioral training alters the neural mechanisms that support selective attention. We tested this hypothesis using electroencephalography (EEG) to measure stimulus-evoked visual responses from human subjects while they performed a selective spatial attention task over the course of ~1 month. Early in training, spatial attention led to an increase in the gain of stimulus-evoked visual responses. Gain was apparent within ~100 ms of stimulus onset, and a quantitative model based on signal detection theory (SDT) successfully linked the magnitude of this gain modulation to attention-related improvements in behavior. However, after extensive training, this early attentional gain was eliminated even though there were still substantial attention-related improvements in behavior. Accordingly, the SDT-based model required noise reduction to account for the link between the stimulus-evoked visual responses and attentional modulations of behavior. These findings suggest that training can lead to fundamental changes in the way attention alters the early cortical responses that support selective information processing. Moreover, these data facilitate the translation of results across different species and across experimental procedures that employ different behavioral training regimes.

Two different mechanisms support selective attention at different phases of training

PubMed Central

Cha, Kexin; Byers, Anna; Serences, John T.

2017-01-01

Selective attention supports the prioritized processing of relevant sensory information to facilitate goal-directed behavior. Studies in human subjects demonstrate that attentional gain of cortical responses can sufficiently account for attention-related improvements in behavior. On the other hand, studies using highly trained nonhuman primates suggest that reductions in neural noise can better explain attentional facilitation of behavior. Given the importance of selective information processing in nearly all domains of cognition, we sought to reconcile these competing accounts by testing the hypothesis that extensive behavioral training alters the neural mechanisms that support selective attention. We tested this hypothesis using electroencephalography (EEG) to measure stimulus-evoked visual responses from human subjects while they performed a selective spatial attention task over the course of ~1 month. Early in training, spatial attention led to an increase in the gain of stimulus-evoked visual responses. Gain was apparent within ~100 ms of stimulus onset, and a quantitative model based on signal detection theory (SDT) successfully linked the magnitude of this gain modulation to attention-related improvements in behavior. However, after extensive training, this early attentional gain was eliminated even though there were still substantial attention-related improvements in behavior. Accordingly, the SDT-based model required noise reduction to account for the link between the stimulus-evoked visual responses and attentional modulations of behavior. These findings suggest that training can lead to fundamental changes in the way attention alters the early cortical responses that support selective information processing. Moreover, these data facilitate the translation of results across different species and across experimental procedures that employ different behavioral training regimes. PMID:28654635

Success in the TACP Training Program An Objective Method for Selecting Battlefield Airmen

DTIC Science & Technology

2009-12-23

rationale and evaluation of the physical training doctrine. J Strength Cond Res. 2009 Jul;23(4):1353-62. Interventions used to improve retention...week training programs on military physical performance. J Strength Cond Res. 2008 Mar;22(2):524-34. Muza SR. Military applications of hypoxic...1    Success in the TACP Training Program An Objective Method for Selecting Battlefield Airmen FINAL REPORT December 23, 2009

Increasing Complexity of Clinical Research in Gastroenterology: Implications for Training Clinician-Scientists

PubMed Central

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

2014-01-01

Background 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 research in gastroenterology from 1980 to 2010. Methods Three journals (Gastroenterology, Gut, and American Journal of Gastroenterology) were selected for evaluation given their continuous publication during the study period. Twenty original clinical articles were randomly selected from each journal from 1980, 1990, 2000, and 2010. Each article was assessed for topic studied, whether the outcome was clinical or physiologic, study design, sample size, number of authors and centers collaborating, and reporting of statistical methods such as sample size calculations, p-values, confidence intervals, and advanced techniques such as bioinformatics or multivariate modeling. Research support with external funding was also recorded. Results A total of 240 articles were included in the study. From 1980 to 2010, there was a significant increase in analytic studies (p<0.001), clinical outcomes (p=0.003), median number of authors per article (p<0.001), multicenter collaboration (p<0.001), sample size (p<0.001), and external funding (p<0.001)). There was significantly increased reporting of p-values (p=0.01), confidence intervals (p<0.001), and power calculations (p<0.001). There was also increased utilization of large multicenter databases (p=0.001), multivariate analyses (p<0.001), and bioinformatics techniques (p=0.001). Conclusions There has been a dramatic increase in complexity in clinical research related to gastroenterology and hepatology over the last three decades. This increase highlights the need for advanced training of clinical investigators to conduct future research. PMID:22475957

Development of an objective gene expression panel as an alternative to self-reported symptom scores in human influenza challenge trials.

PubMed

Muller, Julius; Parizotto, Eneida; Antrobus, Richard; Francis, James; Bunce, Campbell; Stranks, Amanda; Nichols, Marshall; McClain, Micah; Hill, Adrian V S; Ramasamy, Adaikalavan; Gilbert, Sarah C

2017-06-08

Influenza challenge trials are important for vaccine efficacy testing. Currently, disease severity is determined by self-reported scores to a list of symptoms which can be highly subjective. A more objective measure would allow for improved data analysis. Twenty-one volunteers participated in an influenza challenge trial. We calculated the daily sum of scores (DSS) for a list of 16 influenza symptoms. Whole blood collected at baseline and 24, 48, 72 and 96 h post challenge was profiled on Illumina HT12v4 microarrays. Changes in gene expression most strongly correlated with DSS were selected to train a Random Forest model and tested on two independent test sets consisting of 41 individuals profiled on a different microarray platform and 33 volunteers assayed by qRT-PCR. 1456 probes are significantly associated with DSS at 1% false discovery rate. We selected 19 genes with the largest fold change to train a random forest model. We observed good concordance between predicted and actual scores in the first test set (r = 0.57; RMSE = -16.1%) with the greatest agreement achieved on samples collected approximately 72 h post challenge. Therefore, we assayed samples collected at baseline and 72 h post challenge in the second test set by qRT-PCR and observed good concordance (r = 0.81; RMSE = -36.1%). We developed a 19-gene qRT-PCR panel to predict DSS, validated on two independent datasets. A transcriptomics based panel could provide a more objective measure of symptom scoring in future influenza challenge studies. Trial registration Samples were obtained from a clinical trial with the ClinicalTrials.gov Identifier: NCT02014870, first registered on December 5, 2013.

Training Select-in Interviewers for Astronaut Selection: A Program Evaluation

NASA Technical Reports Server (NTRS)

Hysong, S.; Galarza, L.; Holland, A.; Billica, Roger (Technical Monitor)

2000-01-01

Psychological factors critical to the success of short and long-duration missions have been identified in previous research; however, evaluation for such critical factors in astronaut applicants leaves much room for human interpretation. Thus, an evaluator training session was designed to standardize the interpretation of critical factors, as well as the structure of the select-in interview across evaluators. The purpose of this evaluative study was to determine the effectiveness of the evaluator training sessions and their potential impact on evaluator ratings.

Selecting, training and assessing new general practice community teachers in UK medical schools.

PubMed

Hydes, Ciaran; Ajjawi, Rola

2015-09-01

Standards for undergraduate medical education in the UK, published in Tomorrow's Doctors, include the criterion 'everyone involved in educating medical students will be appropriately selected, trained, supported and appraised'. To establish how new general practice (GP) community teachers of medical students are selected, initially trained and assessed by UK medical schools and establish the extent to which Tomorrow's Doctors standards are being met. A mixed-methods study with questionnaire data collected from 24 lead GPs at UK medical schools, 23 new GP teachers from two medical schools plus a semi-structured telephone interview with two GP leads. Quantitative data were analysed descriptively and qualitative data were analysed informed by framework analysis. GP teachers' selection is non-standardised. One hundred per cent of GP leads provide initial training courses for new GP teachers; 50% are mandatory. The content and length of courses varies. All GP leads use student feedback to assess teaching, but other required methods (peer review and patient feedback) are not universally used. To meet General Medical Council standards, medical schools need to include equality and diversity in initial training and use more than one method to assess new GP teachers. Wider debate about the selection, training and assessment of new GP teachers is needed to agree minimum standards.

Fully automatic time-window selection using machine learning for global adjoint tomography

NASA Astrophysics Data System (ADS)

Chen, Y.; Hill, J.; Lei, W.; Lefebvre, M. P.; Bozdag, E.; Komatitsch, D.; Tromp, J.

2017-12-01

Selecting time windows from seismograms such that the synthetic measurements (from simulations) and measured observations are sufficiently close is indispensable in a global adjoint tomography framework. The increasing amount of seismic data collected everyday around the world demands "intelligent" algorithms for seismic window selection. While the traditional FLEXWIN algorithm can be "automatic" to some extent, it still requires both human input and human knowledge or experience, and thus is not deemed to be fully automatic. The goal of intelligent window selection is to automatically select windows based on a learnt engine that is built upon a huge number of existing windows generated through the adjoint tomography project. We have formulated the automatic window selection problem as a classification problem. All possible misfit calculation windows are classified as either usable or unusable. Given a large number of windows with a known selection mode (select or not select), we train a neural network to predict the selection mode of an arbitrary input window. Currently, the five features we extract from the windows are its cross-correlation value, cross-correlation time lag, amplitude ratio between observed and synthetic data, window length, and minimum STA/LTA value. More features can be included in the future. We use these features to characterize each window for training a multilayer perceptron neural network (MPNN). Training the MPNN is equivalent to solve a non-linear optimization problem. We use backward propagation to derive the gradient of the loss function with respect to the weighting matrices and bias vectors and use the mini-batch stochastic gradient method to iteratively optimize the MPNN. Numerical tests show that with a careful selection of the training data and a sufficient amount of training data, we are able to train a robust neural network that is capable of detecting the waveforms in an arbitrary earthquake data with negligible detection error compared to existing selection methods (e.g. FLEXWIN). We will introduce in detail the mathematical formulation of the window-selection-oriented MPNN and show very encouraging results when applying the new algorithm to real earthquake data.

Finding strong lenses in CFHTLS using convolutional neural networks

NASA Astrophysics Data System (ADS)

Jacobs, C.; Glazebrook, K.; Collett, T.; More, A.; McCarthy, C.

2017-10-01

We train and apply convolutional neural networks, a machine learning technique developed to learn from and classify image data, to Canada-France-Hawaii Telescope Legacy Survey (CFHTLS) imaging for the identification of potential strong lensing systems. An ensemble of four convolutional neural networks was trained on images of simulated galaxy-galaxy lenses. The training sets consisted of a total of 62 406 simulated lenses and 64 673 non-lens negative examples generated with two different methodologies. An ensemble of trained networks was applied to all of the 171 deg2 of the CFHTLS wide field image data, identifying 18 861 candidates including 63 known and 139 other potential lens candidates. A second search of 1.4 million early-type galaxies selected from the survey catalogue as potential deflectors, identified 2465 candidates including 117 previously known lens candidates, 29 confirmed lenses/high-quality lens candidates, 266 novel probable or potential lenses and 2097 candidates we classify as false positives. For the catalogue-based search we estimate a completeness of 21-28 per cent with respect to detectable lenses and a purity of 15 per cent, with a false-positive rate of 1 in 671 images tested. We predict a human astronomer reviewing candidates produced by the system would identify 20 probable lenses and 100 possible lenses per hour in a sample selected by the robot. Convolutional neural networks are therefore a promising tool for use in the search for lenses in current and forthcoming surveys such as the Dark Energy Survey and the Large Synoptic Survey Telescope.

On the value-dependence of value-driven attentional capture.

PubMed

Anderson, Brian A; Halpern, Madeline

2017-05-01

Findings from an increasingly large number of studies have been used to argue that attentional capture can be dependent on the learned value of a stimulus, or value-driven. However, under certain circumstances attention can be biased to select stimuli that previously served as targets, independent of reward history. Value-driven attentional capture, as studied using the training phase-test phase design introduced by Anderson and colleagues, is widely presumed to reflect the combined influence of learned value and selection history. However, the degree to which attentional capture is at all dependent on value learning in this paradigm has recently been questioned. Support for value-dependence can be provided through one of two means: (1) greater attentional capture by prior targets following rewarded training than following unrewarded training, and (2) greater attentional capture by prior targets previously associated with high compared to low value. Using a variant of the original value-driven attentional capture paradigm, Sha and Jiang (Attention, Perception, and Psychophysics, 78, 403-414, 2016) failed to find evidence of either, and raised criticisms regarding the adequacy of evidence provided by prior studies using this particular paradigm. To address this disparity, here we provided a stringent test of the value-dependence hypothesis using the traditional value-driven attentional capture paradigm. With a sufficiently large sample size, value-dependence was observed based on both criteria, with no evidence of attentional capture without rewards during training. Our findings support the validity of the traditional value-driven attentional capture paradigm in measuring what its name purports to measure.

Poor retention does not have to be the rule: retention of volunteer community health workers in Uganda

PubMed Central

Ludwick, Teralynn; Brenner, Jennifer L; Kyomuhangi, Teddy; Wotton, Kathryn A; Kabakyenga, Jerome Kahuma

2014-01-01

Globally, health worker shortages continue to plague developing countries. Community health workers are increasingly being promoted to extend primary health care to underserved populations. Since 2004, Healthy Child Uganda (HCU) has trained volunteer community health workers in child health promotion in rural southwest Uganda. This study analyses the retention and motivation of volunteer community health workers trained by HCU. It presents retention rates over a 5-year period and provides insight into volunteer motivation. The findings are based on a 2010 retrospective review of the community health worker registry and the results of a survey on selection and motivation. The survey was comprised of qualitative and quantitative questions and verbally administered to a convenience sample of project participants. Between February 2004 and July 2009, HCU trained 404 community health workers (69% female) in 175 villages. Volunteers had an average age of 36.7 years, 4.9 children and some primary school education. Ninety-six per cent of volunteer community health workers were retained after 1 year (389/404), 91% after 2 years (386/404) and 86% after 5 years (101/117). Of the 54 ‘dropouts’, main reasons cited for discontinuation included ‘too busy’ (12), moved (11), business/employment (8), death (6) and separation/divorce (6). Of 58 questionnaire respondents, most (87%) reported having been selected at an inclusive community meeting. Pair-wise ranking was used to assess the importance of seven ‘motivational factors’ among respondents. Those highest ranked were ‘improved child health’, ‘education/training’ and ‘being asked for advice/assistance by peers’, while the modest ‘transport allowance’ ranked lowest. Our findings suggest that in our rural, African setting, volunteer community health workers can be retained over the medium term. Community health worker programmes should invest in community involvement in selection, quality training, supportive supervision and incentives, which may promote improved retention. PMID:23650334

Selecting, Training, and Supervising Office Personnel.

ERIC Educational Resources Information Center

Johnson, H. Webster

Designed to be of value to supervisors, office managers, and executives, this book gives a broad introductory background to the functions of selection, training, and supervision of office personnel. Under recruitment and selection, it covers sources of future employees, use of the application blank, testing, checking references, and interviewing.…

Towards evidence-based practice in medical training: making evaluations more meaningful.

PubMed

Drescher, Uta; Warren, Fiona; Norton, Kingsley

2004-12-01

The evaluation of training is problematic and the evidence base inconclusive. This situation may arise for 2 main reasons: training is not understood as a complex intervention and, related to this, the evaluation methods applied are often overly simplistic. This paper makes the case for construing training, especially in the field of specialist medical education, as a complex intervention. It also selectively reviews the available literature in order to match evaluative techniques with the demonstrated complexity. Construing training as a complex intervention can provide a framework for selecting the most appropriate methodology to evaluate a given training intervention and to appraise the evidence base for training fairly, choosing from among both quantitative and qualitative approaches and applying measurement at multiple levels of training impact.

Effect of self-care training program based on Orem's model on the behaviors leading to sexually transmitted disease in vulnerable women.

PubMed

Baghersad, Zahra; Alimohammadi, Nasrollah; Boroumandfar, Zahra; Golshiri, Parastoo

2016-01-01

Vulnerable women are prone to sexually transmitted diseases due to their high-risk behaviors. The present study aimed to investigate the effect of self-care training program based on Orem's model on the behaviors leading to sexually transmitted diseases in vulnerable women. This field trial was initially conducted on 100 women covered under health services and welfare organization in Isfahan city, who were selected by rationing ssampling. For needs assessment, they filled the self-care needs assessment questionnaire in three domains of knowledge, attitude, and practice. Then, at the stage of intervention (self-care training), 64 subjects were selected through convenient sampling and were assigned to experimental and control groups by random allocation. Data were analyzed by descriptive and analytical statistical tests through SPSS 18. Results showed that mean scores of knowledge ( P < 0.001), attitude ( P < 0.001), practice ( P = 0.04), and behavior change ( P = 0.01) were significantly higher immediately after and 3 months after intervention, compared to before intervention, but there was no significant difference in mean scores between immediately after and 3 months after intervention. With regard to these results, it can be concluded that if the educational programs are planned based on clients' real needs assessment, the learners follow the educational materials, related to their problems, more seriously and it results in a notable behavior change in them.

Detection of pathogenic Leptospira from selected environment in Kelantan and Terengganu, Malaysia.

PubMed

Ridzlan, F R; Bahaman, A R; Khairani-Bejo, S; Mutalib, A R

2010-12-01

Leptospirosis is recognized as one of the important zoonotic diseases in the world including Malaysia. A total of 145 soil and water samples were collected from selected National Service Training Centres (NSTC) in Kelantan and Terengganu. The samples were inoculated into modified semisolid Ellinghausen McCullough Johnson Harris (EMJH) medium, incubated at room temperature for 1 month and examined under the dark-field microscope. Positive growth of the leptospiral isolates were then confirmed with 8-Azaguanine Test, Polymerase Chain Reaction (PCR) assay and Microscopic Agglutination Test (MAT). Fifteen cultures (10.34%) exhibited positive growths which were seen under dark field microscope whilst only 20% (3/15) were confirmed as pathogenic species. based on 8-Azaguanine Test and PCR. Serological identification of the isolates with MAT showed that hebdomadis was the dominant serovar in Terengganu. Pathogenic leptospires can be detected in Malaysian environment and this has the potential to cause an outbreak. Therefore, precautionary steps against leptospirosis should be taken by camp authorities to ensure the safety of trainees.

Autonomous learning in gesture recognition by using lobe component analysis

NASA Astrophysics Data System (ADS)

Lu, Jian; Weng, Juyang

2007-02-01

Gesture recognition is a new human-machine interface method implemented by pattern recognition(PR).In order to assure robot safety when gesture is used in robot control, it is required to implement the interface reliably and accurately. Similar with other PR applications, 1) feature selection (or model establishment) and 2) training from samples, affect the performance of gesture recognition largely. For 1), a simple model with 6 feature points at shoulders, elbows, and hands, is established. The gestures to be recognized are restricted to still arm gestures, and the movement of arms is not considered. These restrictions are to reduce the misrecognition, but are not so unreasonable. For 2), a new biological network method, called lobe component analysis(LCA), is used in unsupervised learning. Lobe components, corresponding to high-concentrations in probability of the neuronal input, are orientation selective cells follow Hebbian rule and lateral inhibition. Due to the advantage of LCA method for balanced learning between global and local features, large amount of samples can be used in learning efficiently.

On-line Tool Wear Detection on DCMT070204 Carbide Tool Tip Based on Noise Cutting Audio Signal using Artificial Neural Network

NASA Astrophysics Data System (ADS)

Prasetyo, T.; Amar, S.; Arendra, A.; Zam Zami, M. K.

2018-01-01

This study develops an on-line detection system to predict the wear of DCMT070204 tool tip during the cutting process of the workpiece. The machine used in this research is CNC ProTurn 9000 to cut ST42 steel cylinder. The audio signal has been captured using the microphone placed in the tool post and recorded in Matlab. The signal is recorded at the sampling rate of 44.1 kHz, and the sampling size of 1024. The recorded signal is 110 data derived from the audio signal while cutting using a normal chisel and a worn chisel. And then perform signal feature extraction in the frequency domain using Fast Fourier Transform. Feature selection is done based on correlation analysis. And tool wear classification was performed using artificial neural networks with 33 input features selected. This artificial neural network is trained with back propagation method. Classification performance testing yields an accuracy of 74%.

Robust and Rapid Air-Borne Odor Tracking without Casting1,2,3

PubMed Central

Bhattacharyya, Urvashi

2015-01-01

Abstract Casting behavior (zigzagging across an odor stream) is common in air/liquid-borne odor tracking in open fields; however, terrestrial odor localization often involves path selection in a familiar environment. To study this, we trained rats to run toward an odor source in a multi-choice olfactory arena with near-laminar airflow. We find that rather than casting, rats run directly toward an odor port, and if this is incorrect, they serially sample other sources. This behavior is consistent and accurate in the presence of perturbations, such as novel odors, background odor, unilateral nostril stitching, and turbulence. We developed a model that predicts that this run-and-scan tracking of air-borne odors is faster than casting, provided there are a small number of targets at known locations. Thus, the combination of best-guess target selection with fallback serial sampling provides a rapid and robust strategy for finding odor sources in familiar surroundings. PMID:26665165

Sampling plans, selective insecticides and sustainability: the case for IPM as 'informed pest management'.

PubMed

Castle, Steven; Naranjo, Steven E

2009-12-01

Integrated Pest Management (IPM) is considered the central paradigm of insect pest management and is often characterized as a comprehensive use of multiple control tactics to reduce pest status while minimizing economic and environmental costs. As the principal precursor of IPM, the integrated control concept formulated the economic theory behind pest management decisions and specified an applied methodology for carrying out pest control. Sampling, economic thresholds and selective insecticides were three of the critical elements of that methodology and are now considered indispensable to the goals of IPM. We examine each of these elements in the context of contemporaneous information as well as accumulated experience and knowledge required for their skillful implementation in an IPM program. We conclude that while IPM is principally about integrating control tactics into an effective and sustainable approach to pest control, this overarching goal can only be achieved through well-trained practitioners, knowledgeable of the tenets conceived in the integrated control concept that ultimately yield informed pest management. (c) 2009 Society of Chemical Industry.

Evaluating the effectiveness of a training program that builds teachers’ capability to identify and appropriately refer middle and high school students with mental health problems in Brazil: an exploratory study

PubMed Central

2014-01-01

Background In Brazil, like many countries, there has been a failure to identify mental health problems (MHP) in young people and refer them to appropriate care and support. The school environment provides an ideal setting to do this. Therefore, effective programs need to be developed to train teachers to identify and appropriately refer children with possible MHP. We aimed to evaluate teachers’ ability to identify and appropriately refer students with possible MHP, and the effectiveness of a psychoeducational strategy to build teachers’ capability in this area. Methods To meet the first objective, we conducted a case-control study using a student sample. To meet the second, we employed longitudinal design with repeated measures before and after introducing the psychoeducational strategy using a teacher sample. In the case control study, the Youth Self-Report was used to investigate internalizing and externalizing problems. Before training, teachers selected 26 students who they thought were likely to have MHP. Twenty-six non-selected students acted as controls and were matched by gender, age and grade. The underlying principle was that if teachers could identify abnormal behaviors among their actual students, those with some MHP would likely be among the case group and those without among the control group. In the longitudinal study, 32 teachers were asked to evaluate six vignettes that highlighted behaviors indicating a high risk for psychosis, depression, conduct disorder, hyperactivity, mania, and normal adolescent behavior. We calculated the rates of correct answers for identifying the existence of some MHP and the need for referral before and after training; teachers were not asked to identify the individual conditions. Results Teachers were already able to identify the most symptomatic students, who had both internalizing and externalizing problems, as possibly having MHP, but teachers had difficulty in identifying students with internalizing problems alone. At least 50.0% of teachers learned to identify hypothetical cases as problematic and to make the appropriate referral, and 60.0% of teachers who before training could not identify normal adolescence learned to do so. Conclusions The strategy was partially effective but could be improved mainly by extending its duration, and including discussion of actual cases. PMID:24580750

Evaluating the effectiveness of a training program that builds teachers' capability to identify and appropriately refer middle and high school students with mental health problems in Brazil: an exploratory study.

PubMed

Vieira, Marlene A; Gadelha, Ary A; Moriyama, Taís S; Bressan, Rodrigo A; Bordin, Isabel A

2014-02-28

In Brazil, like many countries, there has been a failure to identify mental health problems (MHP) in young people and refer them to appropriate care and support. The school environment provides an ideal setting to do this. Therefore, effective programs need to be developed to train teachers to identify and appropriately refer children with possible MHP. We aimed to evaluate teachers' ability to identify and appropriately refer students with possible MHP, and the effectiveness of a psychoeducational strategy to build teachers' capability in this area. To meet the first objective, we conducted a case-control study using a student sample. To meet the second, we employed longitudinal design with repeated measures before and after introducing the psychoeducational strategy using a teacher sample. In the case control study, the Youth Self-Report was used to investigate internalizing and externalizing problems. Before training, teachers selected 26 students who they thought were likely to have MHP. Twenty-six non-selected students acted as controls and were matched by gender, age and grade. The underlying principle was that if teachers could identify abnormal behaviors among their actual students, those with some MHP would likely be among the case group and those without among the control group. In the longitudinal study, 32 teachers were asked to evaluate six vignettes that highlighted behaviors indicating a high risk for psychosis, depression, conduct disorder, hyperactivity, mania, and normal adolescent behavior. We calculated the rates of correct answers for identifying the existence of some MHP and the need for referral before and after training; teachers were not asked to identify the individual conditions. Teachers were already able to identify the most symptomatic students, who had both internalizing and externalizing problems, as possibly having MHP, but teachers had difficulty in identifying students with internalizing problems alone. At least 50.0% of teachers learned to identify hypothetical cases as problematic and to make the appropriate referral, and 60.0% of teachers who before training could not identify normal adolescence learned to do so. The strategy was partially effective but could be improved mainly by extending its duration, and including discussion of actual cases.

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.

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

An optimal sample data usage strategy to minimize overfitting and underfitting effects in regression tree models based on remotely-sensed data

USGS Publications Warehouse

Gu, Yingxin; Wylie, Bruce K.; Boyte, Stephen; Picotte, Joshua J.; Howard, Danny; Smith, Kelcy; Nelson, Kurtis

2016-01-01

Regression tree models have been widely used for remote sensing-based ecosystem mapping. Improper use of the sample data (model training and testing data) may cause overfitting and underfitting effects in the model. The goal of this study is to develop an optimal sampling data usage strategy for any dataset and identify an appropriate number of rules in the regression tree model that will improve its accuracy and robustness. Landsat 8 data and Moderate-Resolution Imaging Spectroradiometer-scaled Normalized Difference Vegetation Index (NDVI) were used to develop regression tree models. A Python procedure was designed to generate random replications of model parameter options across a range of model development data sizes and rule number constraints. The mean absolute difference (MAD) between the predicted and actual NDVI (scaled NDVI, value from 0–200) and its variability across the different randomized replications were calculated to assess the accuracy and stability of the models. In our case study, a six-rule regression tree model developed from 80% of the sample data had the lowest MAD (MADtraining = 2.5 and MADtesting = 2.4), which was suggested as the optimal model. This study demonstrates how the training data and rule number selections impact model accuracy and provides important guidance for future remote-sensing-based ecosystem modeling.

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

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.

Metric Sex Determination of the Human Coxal Bone on a Virtual Sample using Decision Trees.

PubMed

Savall, Frédéric; Faruch-Bilfeld, Marie; Dedouit, Fabrice; Sans, Nicolas; Rousseau, Hervé; Rougé, Daniel; Telmon, Norbert

2015-11-01

Decision trees provide an alternative to multivariate discriminant analysis, which is still the most commonly used in anthropometric studies. Our study analyzed the metric characterization of a recent virtual sample of 113 coxal bones using decision trees for sex determination. From 17 osteometric type I landmarks, a dataset was built with five classic distances traditionally reported in the literature and six new distances selected using the two-step ratio method. A ten-fold cross-validation was performed, and a decision tree was established on two subsamples (training and test sets). The decision tree established on the training set included three nodes and its application to the test set correctly classified 92% of individuals. This percentage was similar to the data of the literature. The usefulness of decision trees has been demonstrated in numerous fields. They have been already used in sex determination, body mass prediction, and ancestry estimation. This study shows another use of decision trees enabling simple and accurate sex determination. © 2015 American Academy of Forensic Sciences.

Basic Navigator Battery: An Experimental Selection Composite for Undergraduate Navigator Training.

ERIC Educational Resources Information Center

Shanahan, Frank M.; Kantor, Jeffrey E.

High rates of attrition among students in Undergraduate Navigator Training (UNT) is a major concern for Air Training Command. The main objective of this research was to evaluate the Basic Navigator Battery (BNB), a multi-test experimental selection instrument, for its potential to increase the validity of the Air Force Officer Qualifying Test…

Applicant Reactions to a Situational Judgment Test Used for Selection into Initial Teacher Training

ERIC Educational Resources Information Center

Klassen, Robert M.; Durksen, Tracy L.; Rowett, Emma; Patterson, Fiona

2014-01-01

We considered applicants' perceptions of the use of a pilot situational judgment test (SJT) designed for selection into primary and secondary teacher training programs in the UK. Quantitative and qualitative data were collected from 304 applicants (73% female) to two postgraduate (PGCE) training programs in the 2013-2014 application cycle.…

Analysis of Parametric Adaptive Signal Detection with Applications to Radars and Hyperspectral Imaging

DTIC Science & Technology

2010-02-01

98 8.4.5 Training Screening ............................. .................................................................99 8.5 Experimental...associated with the proposed parametric model. Several im- portant issues are discussed, including model order selection, training screening , and time...parameters associated with the NS-AR model. In addition, we develop model order selection, training screening , and time-series based whitening and

Performance-Based Services Acquisition

DTIC Science & Technology

2011-02-01

47  DoD’s acquisition workforce lacks training and experience in services contracting ... 47  Selecting correct metrics...services more effectively; vii (2) the DoD’s acquisition workforce lacks training and experience in services contracting; (3) selecting correct...private sector; (2) improve the training of government services acquisition personnel; and (3) the USD(AT&L) should incentivize the existing workforce

Effects of Behavioral Skills Training on Parental Treatment of Children's Food Selectivity

ERIC Educational Resources Information Center

Seiverling, Laura; Williams, Keith; Sturmey, Peter; Hart, Sadie

2012-01-01

We used behavioral skills training to teach parents of 3 children with autism spectrum disorder and food selectivity to conduct a home-based treatment package that consisted of taste exposure, escape extinction, and fading. Parent performance following training improved during both taste sessions and probe meals and was reflected in increases in…

34 CFR 361.32 - Use of profitmaking organizations for on-the-job training in connection with selected projects.

Code of Federal Regulations, 2010 CFR

2010-07-01

... on-the-job training in connection with selected projects. The State plan must assure that the... purpose of providing, as vocational rehabilitation services, on-the-job training and related programs for... 34 Education 2 2010-07-01 2010-07-01 false Use of profitmaking organizations for on-the-job...

Personnel Selection Influences on Remotely Piloted Aircraft Human-System Integration.

PubMed

Carretta, Thomas R; King, Raymond E

2015-08-01

Human-system integration (HSI) is a complex process used to design and develop systems that integrate human capabilities and limitations in an effective and affordable manner. Effective HSI incorporates several domains, including manpower, personnel and training, human factors, environment, safety, occupational health, habitability, survivability, logistics, intelligence, mobility, and command and control. To achieve effective HSI, the relationships among these domains must be considered. Although this integrated approach is well documented, there are many instances where it is not followed. Human factors engineers typically focus on system design with little attention to the skills, abilities, and other characteristics needed by human operators. When problems with fielded systems occur, additional training of personnel is developed and conducted. Personnel selection is seldom considered during the HSI process. Complex systems such as aviation require careful selection of the individuals who will interact with the system. Personnel selection is a two-stage process involving select-in and select-out procedures. Select-in procedures determine which candidates have the aptitude to profit from training and represent the best investment. Select-out procedures focus on medical qualification and determine who should not enter training for medical reasons. The current paper discusses the role of personnel selection in the HSI process in the context of remotely piloted aircraft systems.

OSIRIS-REx Flight Dynamics and Navigation Design

NASA Astrophysics Data System (ADS)

Williams, B.; Antreasian, P.; Carranza, E.; Jackman, C.; Leonard, J.; Nelson, D.; Page, B.; Stanbridge, D.; Wibben, D.; Williams, K.; Moreau, M.; Berry, K.; Getzandanner, K.; Liounis, A.; Mashiku, A.; Highsmith, D.; Sutter, B.; Lauretta, D. S.

2018-06-01

OSIRIS-REx is the first NASA mission to return a sample of an asteroid to Earth. Navigation and flight dynamics for the mission to acquire and return a sample of asteroid 101955 Bennu establish many firsts for space exploration. These include relatively small orbital maneuvers that are precise to ˜1 mm/s, close-up operations in a captured orbit about an asteroid that is small in size and mass, and planning and orbit phasing to revisit the same spot on Bennu in similar lighting conditions. After preliminary surveys and close approach flyovers of Bennu, the sample site will be scientifically characterized and selected. A robotic shock-absorbing arm with an attached sample collection head mounted on the main spacecraft bus acquires the sample, requiring navigation to Bennu's surface. A touch-and-go sample acquisition maneuver will result in the retrieval of at least 60 grams of regolith, and up to several kilograms. The flight activity concludes with a return cruise to Earth and delivery of the sample return capsule (SRC) for landing and sample recovery at the Utah Test and Training Range (UTTR).

Relationship between Air Traffic Selection and Training (AT-SAT)) Battery Test Scores and Composite Scores in the Initial en Route Air Traffic Control Qualification Training Course at the Federal Aviation Administration (FAA) Academy

ERIC Educational Resources Information Center

Kelley, Ronald Scott

2012-01-01

Scope and Method of Study: This study focused on the development and use of the AT-SAT test battery and the Initial En Route Qualification training course for the selection, training, and evaluation of air traffic controller candidates. The Pearson product moment correlation coefficient was used to measure the linear relationship between the…

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 effects of virtual reality-based balance exercises and conventional exercises on balance and fall risk in older adults living in nursing homes in Turkey.

PubMed

Yeşilyaprak, Sevgi Sevi; Yıldırım, Meriç Şenduran; Tomruk, Murat; Ertekin, Özge; Algun, Z Candan

2016-01-01

There is limited information on effective balance training techniques including virtual reality (VR)-based balance exercises in residential settings and no studies have been designed to compare the effects of VR-based balance exercises with conventional balance exercises in older adults living in nursing homes in Turkey. The objective of our study was to investigate the effects of VR-based balance exercises on balance and fall risk in comparison to conventional balance exercises in older adults living in nursing homes. A total sample of 18 subjects (65-82 years of age) with fall history who were randomly assigned to either the VR group (Group 1, n = 7) or the conventional exercise group (Group 2, n = 11) completed the exercise training. In both groups, Berg balance score (BBS), timed up & go duration, and left leg stance and tandem stance duration with eyes closed significantly improved with time (p < 0.05), but changes were similar in both groups (p > 0.05) after training, indicating that neither the exercise method was superior. Similar improvements were found in balance and fall risk with VR-based balance training and conventional balance training in older adults living in the nursing home. Both exercise trainings can be preferable by health care professionals considering fall prevention. Appropriate patient selection is essential.

Outcomes from two forms of training for first-responder competency in cholinergic crisis management.

PubMed

Andreatta, Pamela; Klotz, Jessica J; Madsen, James M; Hurst, Charles G; Talbot, Thomas B

2015-04-01

Military and civilian first responders must be able to recognize and effectively manage mass disaster casualties. Clinical management of injuries resulting from nerve agents provides different challenges for first responders than those of conventional weapons. We evaluated the impact of a mixed-methods training program on competency acquisition in cholinergic crisis clinical management using multimedia with either live animal or patient actor examples, and hands-on practice using SimMan3G mannequin simulators. A purposively selected sample of 204 civilian and military first responders who had not previously completed nerve agent training were assessed pre- and post-training for knowledge, performance, self-efficacy, and affective state. We conducted analysis of variance with repeated measures; statistical significance p < 0.05. Both groups had significant performance improvement across all assessment dimensions: knowledge > 20%, performance > 50%, self-efficacy > 34%, and affective state > 15%. There were no significant differences between the live animal and patient actor groups. These findings could aid in the specification of training for first-responder personnel in military and civilian service. Although less comprehensive than U.S. Army Medical Research Institute of Chemical Defense courses, the training outcomes associated with this easily distributed program demonstrate its value in increasing the competency of first responders in recognizing and managing a mass casualty cholinergic event. Reprint & Copyright © 2015 Association of Military Surgeons of the U.S.

Effects of training on short- and long-term skill retention in a complex multiple-task environment.

PubMed

Sauer, J; Hockey, G R; Wastell, D G

2000-12-01

The paper reports the results of an experiment on the performance and retention of a complex task. This was a computer-based simulation of the essential elements of a spacecraft's life support system. It allowed the authors to take a range of measures, including primary and secondary task performance, system intervention and information sampling strategies, mental model structure, and subjective operator state. The study compared the effectiveness of two methods of training, based on low level (procedure-based) and high level (system-based) understanding. Twenty-five participants were trained extensively on the task, then given a 1-h testing session. A second testing session was carried out 8 months after the first (with no intervening practice) with 17 of the original participants. While training had little effect on control performance, there were considerable effects on system management strategies, as well as in structure of operator's mental model. In the second testing session, the anticipated general performance decrement did not occur, though for complex faults there was an increase in selectivity towards the primary control task. The relevance of the findings for training and skill retention in real work environments is discussed in the context of a model of compensatory control.

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.

Predictors of performance improvements within a cognitive remediation program for schizophrenia.

PubMed

Scheu, Florian; Aghotor, Julia; Pfueller, Ute; Moritz, Steffen; Bohn, Francesca; Weisbrod, Matthias; Roesch-Ely, Daniela

2013-10-30

Cognitive impairment is regarded a core feature of schizophrenia and is associated with low psychosocial functioning. There is rich evidence that cognitive remediation can improve cognitive functions in patients with schizophrenia. However, little is known about what predicts individual remediation success. Some studies suggest that baseline cognitive impairment might be a limiting factor for training response. Aim of the current study was to further examine the role of cognitive and symptom variables as predictors of remediation success. We studied a total sample of 32 patients with schizophrenia and schizoaffective disorder who were engaged in a computer-based cognitive training program (CogPack). A pre-training test battery provided cognitive measures of selective attention, executive functioning, processing speed, verbal memory, and verbal intelligence along with measures for positive and negative symptoms. Training response was defined as improvement on training tasks. Correlation analyses revealed no significant relationship between any of the baseline cognitive or symptom measures and improvement rates. However, better baseline cognition was associated with a higher percentage of tasks with initial ceiling effects. We conclude that not carefully tailoring task difficulty to patients' cognitive abilities constitutes a much more severe threat to cognitive remediation success than cognitive impairment itself. © 2013 Elsevier Ireland Ltd. All rights reserved.

Vector excitation speech or audio coder for transmission or storage

NASA Technical Reports Server (NTRS)

Davidson, Grant (Inventor); Gersho, Allen (Inventor)

1989-01-01

A vector excitation coder compresses vectors by using an optimum codebook designed off line, using an initial arbitrary codebook and a set of speech training vectors exploiting codevector sparsity (i.e., by making zero all but a selected number of samples of lowest amplitude in each of N codebook vectors). A fast-search method selects a number N.sub.c of good excitation vectors from the codebook, where N.sub.c is much smaller tha ORIGIN OF INVENTION The invention described herein was made in the performance of work under a NASA contract, and is subject to the provisions of Public Law 96-517 (35 USC 202) under which the inventors were granted a request to retain title.

Dynamic, continuous multitasking training leads to task-specific improvements but does not transfer across action selection tasks

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

Spectral Classification of Galaxies at 0.5 <= z <= 1 in the CDFS: The Artificial Neural Network Approach

NASA Astrophysics Data System (ADS)

Teimoorinia, H.

2012-12-01

The aim of this work is to combine spectral energy distribution (SED) fitting with artificial neural network techniques to assign spectral characteristics to a sample of galaxies at 0.5 < z < 1. The sample is selected from the spectroscopic campaign of the ESO/GOODS-South field, with 142 sources having photometric data from the GOODS-MUSIC catalog covering bands between ~0.4 and 24 μm in 10-13 filters. We use the CIGALE code to fit photometric data to Maraston's synthesis spectra to derive mass, specific star formation rate, and age, as well as the best SED of the galaxies. We use the spectral models presented by Kinney et al. as targets in the wavelength interval ~1200-7500 Å. Then a series of neural networks are trained, with average performance ~90%, to classify the best SED in a supervised manner. We consider the effects of the prominent features of the best SED on the performance of the trained networks and also test networks on the galaxy spectra of Coleman et al., which have a lower resolution than the target models. In this way, we conclude that the trained networks take into account all the features of the spectra simultaneously. Using the method, 105 out of 142 galaxies of the sample are classified with high significance. The locus of the classified galaxies in the three graphs of the physical parameters of mass, age, and specific star formation rate appears consistent with the morphological characteristics of the galaxies.

SPECTRAL CLASSIFICATION OF GALAXIES AT 0.5 {<=} z {<=} 1 IN THE CDFS: THE ARTIFICIAL NEURAL NETWORK APPROACH

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

Teimoorinia, H., E-mail: hteimoo@uvic.ca

2012-12-01

The aim of this work is to combine spectral energy distribution (SED) fitting with artificial neural network techniques to assign spectral characteristics to a sample of galaxies at 0.5 < z < 1. The sample is selected from the spectroscopic campaign of the ESO/GOODS-South field, with 142 sources having photometric data from the GOODS-MUSIC catalog covering bands between {approx}0.4 and 24 {mu}m in 10-13 filters. We use the CIGALE code to fit photometric data to Maraston's synthesis spectra to derive mass, specific star formation rate, and age, as well as the best SED of the galaxies. We use the spectralmore » models presented by Kinney et al. as targets in the wavelength interval {approx}1200-7500 A. Then a series of neural networks are trained, with average performance {approx}90%, to classify the best SED in a supervised manner. We consider the effects of the prominent features of the best SED on the performance of the trained networks and also test networks on the galaxy spectra of Coleman et al., which have a lower resolution than the target models. In this way, we conclude that the trained networks take into account all the features of the spectra simultaneously. Using the method, 105 out of 142 galaxies of the sample are classified with high significance. The locus of the classified galaxies in the three graphs of the physical parameters of mass, age, and specific star formation rate appears consistent with the morphological characteristics of the galaxies.« less

MSEBAG: a dynamic classifier ensemble generation based on `minimum-sufficient ensemble' and bagging

NASA Astrophysics Data System (ADS)

Chen, Lei; Kamel, Mohamed S.

2016-01-01

In this paper, we propose a dynamic classifier system, MSEBAG, which is characterised by searching for the 'minimum-sufficient ensemble' and bagging at the ensemble level. It adopts an 'over-generation and selection' strategy and aims to achieve a good bias-variance trade-off. In the training phase, MSEBAG first searches for the 'minimum-sufficient ensemble', which maximises the in-sample fitness with the minimal number of base classifiers. Then, starting from the 'minimum-sufficient ensemble', a backward stepwise algorithm is employed to generate a collection of ensembles. The objective is to create a collection of ensembles with a descending fitness on the data, as well as a descending complexity in the structure. MSEBAG dynamically selects the ensembles from the collection for the decision aggregation. The extended adaptive aggregation (EAA) approach, a bagging-style algorithm performed at the ensemble level, is employed for this task. EAA searches for the competent ensembles using a score function, which takes into consideration both the in-sample fitness and the confidence of the statistical inference, and averages the decisions of the selected ensembles to label the test pattern. The experimental results show that the proposed MSEBAG outperforms the benchmarks on average.

Neural and Behavioral Correlates of Attentional Inhibition Training and Perceptual Discrimination Training in a Visual Flanker Task

PubMed Central

Melara, Robert D.; Singh, Shalini; Hien, Denise A.

2018-01-01

Two groups of healthy young adults were exposed to 3 weeks of cognitive training in a modified version of the visual flanker task, one group trained to discriminate the target (discrimination training) and the other group to ignore the flankers (inhibition training). Inhibition training, but not discrimination training, led to significant reductions in both Garner interference, indicating improved selective attention, and in Stroop interference, indicating more efficient resolution of stimulus conflict. The behavioral gains from training were greatest in participants who showed the poorest selective attention at pretest. Electrophysiological recordings revealed that inhibition training increased the magnitude of Rejection Positivity (RP) to incongruent distractors, an event-related potential (ERP) component associated with inhibitory control. Source modeling of RP uncovered a dipole in the medial frontal gyrus for those participants receiving inhibition training, but in the cingulate gyrus for those participants receiving discrimination training. Results suggest that inhibitory control is plastic; inhibition training improves conflict resolution, particularly in individuals with poor attention skills. PMID:29875644

Neural and Behavioral Correlates of Attentional Inhibition Training and Perceptual Discrimination Training in a Visual Flanker Task.

PubMed

Melara, Robert D; Singh, Shalini; Hien, Denise A

2018-01-01

Two groups of healthy young adults were exposed to 3 weeks of cognitive training in a modified version of the visual flanker task, one group trained to discriminate the target (discrimination training) and the other group to ignore the flankers (inhibition training). Inhibition training, but not discrimination training, led to significant reductions in both Garner interference, indicating improved selective attention, and in Stroop interference, indicating more efficient resolution of stimulus conflict. The behavioral gains from training were greatest in participants who showed the poorest selective attention at pretest. Electrophysiological recordings revealed that inhibition training increased the magnitude of Rejection Positivity (RP) to incongruent distractors, an event-related potential (ERP) component associated with inhibitory control. Source modeling of RP uncovered a dipole in the medial frontal gyrus for those participants receiving inhibition training, but in the cingulate gyrus for those participants receiving discrimination training. Results suggest that inhibitory control is plastic; inhibition training improves conflict resolution, particularly in individuals with poor attention skills.

Efficient use of historical data for genomic selection: a case study of rust resistance in wheat

USDA-ARS?s Scientific Manuscript database

Genomic selection (GS) is a new methodology that can improve wheat breeding efficiency. To implement GS, a training population (TP) with both phenotypic and genotypic data is required to train a statistical model used to predict genotyped selection candidates (SCs). Several factors impact prediction...

Occupational Training in Selected Metalworking Industries, 1974. A Report on a Survey of Selected Occupations.

ERIC Educational Resources Information Center

Bureau of Labor Statistics (DOL), New York, NY.

A survey was conducted regarding the occupational training provided by employers for fourteen occupations in four metalworking industries. The fourteen occupations selected for study included crane operator, electrician, layout worker, machine tool setter, machinist, mechanic, sheet metal worker, and tool and die maker. The four industries…

Adding words to the brain's visual dictionary: novel word learning selectively sharpens orthographic representations in the VWFA.

PubMed

Glezer, Laurie S; Kim, Judy; Rule, Josh; Jiang, Xiong; Riesenhuber, Maximilian

2015-03-25

The nature of orthographic representations in the human brain is still subject of much debate. Recent reports have claimed that the visual word form area (VWFA) in left occipitotemporal cortex contains an orthographic lexicon based on neuronal representations highly selective for individual written real words (RWs). This theory predicts that learning novel words should selectively increase neural specificity for these words in the VWFA. We trained subjects to recognize novel pseudowords (PWs) and used fMRI rapid adaptation to compare neural selectivity with RWs, untrained PWs (UTPWs), and trained PWs (TPWs). Before training, PWs elicited broadly tuned responses, whereas responses to RWs indicated tight tuning. After training, TPW responses resembled those of RWs, whereas UTPWs continued to show broad tuning. This change in selectivity was specific to the VWFA. Therefore, word learning appears to selectively increase neuronal specificity for the new words in the VWFA, thereby adding these words to the brain's visual dictionary. Copyright © 2015 the authors 0270-6474/15/354965-08$15.00/0.

Quantification of whey in fluid milk using confocal Raman microscopy and artificial neural network.

PubMed

Alves da Rocha, Roney; Paiva, Igor Moura; Anjos, Virgílio; Furtado, Marco Antônio Moreira; Bell, Maria José Valenzuela

2015-06-01

In this work, we assessed the use of confocal Raman microscopy and artificial neural network as a practical method to assess and quantify adulteration of fluid milk by addition of whey. Milk samples with added whey (from 0 to 100%) were prepared, simulating different levels of fraudulent adulteration. All analyses were carried out by direct inspection at the light microscope after depositing drops from each sample on a microscope slide and drying them at room temperature. No pre- or posttreatment (e.g., sample preparation or spectral correction) was required in the analyses. Quantitative determination of adulteration was performed through a feed-forward artificial neural network (ANN). Different ANN configurations were evaluated based on their coefficient of determination (R2) and root mean square error values, which were criteria for selecting the best predictor model. In the selected model, we observed that data from both training and validation subsets presented R2>99.99%, indicating that the combination of confocal Raman microscopy and ANN is a rapid, simple, and efficient method to quantify milk adulteration by whey. Because sample preparation and postprocessing of spectra were not required, the method has potential applications in health surveillance and food quality monitoring. Copyright © 2015 American Dairy Science Association. Published by Elsevier Inc. 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

Effectiveness of Gross Model-Based Emotion Regulation Strategies Training on Anger Reduction in Drug-Dependent Individuals and its Sustainability in Follow-up.

PubMed

Massah, Omid; Sohrabi, Faramarz; A'azami, Yousef; Doostian, Younes; Farhoudian, Ali; Daneshmand, Reza

2016-03-01

Emotion plays an important role in adapting to life changes and stressful events. Difficulty regulating emotions is one of the problems drug abusers often face, and teaching these individuals to express and manage their emotions can be effective on improving their difficult circumstances. The present study aimed to determine the effectiveness of the Gross model-based emotion regulation strategies training on anger reduction in drug-dependent individuals. The present study had a quasi-experimental design wherein pretest-posttest evaluations were applied using a control group. The population under study included addicts attending Marivan's methadone maintenance therapy centers in 2012 - 2013. Convenience sampling was used to select 30 substance-dependent individuals undergoing maintenance treatment who were then randomly assigned to the experiment and control groups. The experiment group received its training in eight two-hour sessions. Data were analyzed using analysis of co-variance and paired t-test. There was significant reduction in anger symptoms of drug-dependent individuals after gross model based emotion regulation training (ERT) (P < 0.001). Moreover, the effectiveness of the training on anger was persistent in the follow-up period. Symptoms of anger in drug-dependent individuals of this study were reduced by gross model-based emotion regulation strategies training. Based on the results of this study, we may conclude that the gross model based emotion regulation strategies training can be applied alongside other therapies to treat drug abusers undergoing rehabilitation.

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.

A technique to train new oculomotor behavior in patients with central macular scotomas during reading related tasks using scanning laser ophthalmoscopy: immediate functional benefits and gains retention.

PubMed

Déruaz, Anouk; Goldschmidt, Mira; Whatham, Andrew R; Mermoud, Christophe; Lorincz, Erika N; Schnider, Armin; Safran, Avinoam B

2006-11-23

Reading with a central scotoma involves the use of preferred retinal loci (PRLs) that enable both letter resolution and global viewing of word. Spontaneously developed PRLs however often privilege spatial resolution and, as a result, visual span is commonly limited by the position of the scotoma. In this study we designed and performed the pilot trial of a training procedure aimed at modifying oculomotor behavior in subjects with central field loss. We use an additional fixation point which, when combined with the initial PRL, allows the fulfillment of both letter resolution and global viewing of words. The training procedure comprises ten training sessions conducted with the scanning laser ophthalmoscope (SLO). Subjects have to read single letters and isolated words varying in length, by combining the use of their initial PRL with the one of an examiner's selected trained retinal locus (TRL). We enrolled five subjects to test for the feasibility of the training technique. They showed stable maculopathy and persisting major reading difficulties despite previous orthoptic rehabilitation. We evaluated ETDRS visual acuity, threshold character size for single letters and isolated words, accuracy for paragraphed text reading and reading strategies before, immediately after SLO training, and three months later. Training the use of multiple PRLs in patients with central field loss is feasible and contributes to adapt oculomotor strategies during reading related tasks. Immediately after SLO training subjects used in combination with their initial PRL the examiner's selected TRL and other newly self-selected PRLs. Training gains were also reflected in ETDRS acuity, threshold character size for words of different lengths and in paragraphed text reading. Interestingly, subjects benefited variously from the training procedure and gains were retained differently as a function of word length. We designed a new procedure for training patients with central field loss using scanning laser ophthalmoscopy. Our initial results on the acquisition of newly self-selected PRLs and the development of new oculomotor behaviors suggest that the procedure aiming primarily at developing an examiner's selected TRL might have initiated a more global functional adaptation process.

Using athletic training clinical education standards in radiography.

PubMed

Giordano, Shelley; Harris, Katherine

2012-01-01

The selection of clinical education sites for radiography students is based on availability, access to radiographic examinations, and appropriate student-to-technologist ratio. Radiography program directors are not required to evaluate sites based on their educational validity (eg, the clinical instructor's knowledge of basic teaching and learning principles, how well the site communicates with the program, or the clinical instructor's involvement in professional organizations). The purpose of this study was to determine if a set of 12 clinical education standards used in athletic training would be applicable and beneficial to radiography program directors when selecting clinical sites for students. A survey concerning the applicability of the athletic training standards to radiography site selection was completed by 270 directors of radiography programs accredited by the Joint Review Committee on Education in Radiologic Technology. The survey results indicated the athletic training clinical education standards were considered applicable to the selection of clinical sites for radiography students and would be beneficial to radiography program directors when selecting sites.

Automated detection of masses on whole breast volume ultrasound scanner: false positive reduction using deep convolutional neural network

NASA Astrophysics Data System (ADS)

Hiramatsu, Yuya; Muramatsu, Chisako; Kobayashi, Hironobu; Hara, Takeshi; Fujita, Hiroshi

2017-03-01

Breast cancer screening with mammography and ultrasonography is expected to improve sensitivity compared with mammography alone, especially for women with dense breast. An automated breast volume scanner (ABVS) provides the operator-independent whole breast data which facilitate double reading and comparison with past exams, contralateral breast, and multimodality images. However, large volumetric data in screening practice increase radiologists' workload. Therefore, our goal is to develop a computer-aided detection scheme of breast masses in ABVS data for assisting radiologists' diagnosis and comparison with mammographic findings. In this study, false positive (FP) reduction scheme using deep convolutional neural network (DCNN) was investigated. For training DCNN, true positive and FP samples were obtained from the result of our initial mass detection scheme using the vector convergence filter. Regions of interest including the detected regions were extracted from the multiplanar reconstraction slices. We investigated methods to select effective FP samples for training the DCNN. Based on the free response receiver operating characteristic analysis, simple random sampling from the entire candidates was most effective in this study. Using DCNN, the number of FPs could be reduced by 60%, while retaining 90% of true masses. The result indicates the potential usefulness of DCNN for FP reduction in automated mass detection on ABVS images.

Identifying Factors Associated with Risk Assessment Competencies of Public Health Emergency Responders.

PubMed

Hao, Jiejing; Ren, Jiaojiao; Wu, Qunhong; Hao, Yanhua; Sun, Hong; Ning, Ning; Ding, Ding

2017-06-04

This study aimed to better understand the current situation of risk assessment and identify the factors associated with competence of emergency responders in public health risk assessment. The participants were selected by a multi-stage, stratified cluster sampling method in Heilongjiang Centers for Disease Control and Prevention (CDC). The questionnaires that measured their perceptions on risk assessment competences were administered through the face-to-face survey. A final sample of 1889 staff was obtained. Of this sample, 78.6% of respondents rated their own risk assessment competences as "relatively low", contrasting with 21.4% rated as "relatively high". Most of the respondents (62.7%) did not participate in any risk assessment work. Only 13.7% and 42.7% of respondents reported participating in risk assessment training and were familiar with risk assessment tools. There existed statistical significance between risk assessment-related characteristics of respondents and their self-rated competences scores. Financial support from the government and administrative attention were regarded as the important factors contributing to risk assessment competences of CDC responders. Higher attention should be given to risk assessment training and enhancing the availability of surveillance data. Continuous efforts should be made to remove the financial and technical obstacles to improve the competences of risk assessment for public health emergency responders.

Identifying Factors Associated with Risk Assessment Competencies of Public Health Emergency Responders

PubMed Central

Hao, Jiejing; Ren, Jiaojiao; Wu, Qunhong; Hao, Yanhua; Sun, Hong; Ning, Ning; Ding, Ding

2017-01-01

This study aimed to better understand the current situation of risk assessment and identify the factors associated with competence of emergency responders in public health risk assessment. The participants were selected by a multi-stage, stratified cluster sampling method in Heilongjiang Centers for Disease Control and Prevention (CDC). The questionnaires that measured their perceptions on risk assessment competences were administered through the face-to-face survey. A final sample of 1889 staff was obtained. Of this sample, 78.6% of respondents rated their own risk assessment competences as “relatively low”, contrasting with 21.4% rated as “relatively high”. Most of the respondents (62.7%) did not participate in any risk assessment work. Only 13.7% and 42.7% of respondents reported participating in risk assessment training and were familiar with risk assessment tools. There existed statistical significance between risk assessment-related characteristics of respondents and their self-rated competences scores. Financial support from the government and administrative attention were regarded as the important factors contributing to risk assessment competences of CDC responders. Higher attention should be given to risk assessment training and enhancing the availability of surveillance data. Continuous efforts should be made to remove the financial and technical obstacles to improve the competences of risk assessment for public health emergency responders. PMID:28587226

EPA Selects Lawrence, Mass. Group for Brownfields Job Training Grant

EPA Pesticide Factsheets

Today, EPA announced that the Merrimack Valley Workforce Investment Board, of Lawrence, Mass., was one of 14 organizations nationwide selected to receive funding to operate environmental job training programs for local unemployed residents.

Initial identification & selection bias versus the eventual confirmation of talent: evidence for the benefits of a rocky road?

PubMed

McCarthy, Neil; Collins, Dave

2014-01-01

The relative age effect (RAE), whereby earlier birthdate children within a selection year are more commonly selected as talented, has been highlighted in the literature. As a consequence, these young athletes get into specialised training earlier and in greater numbers, leading (it is suggested) to a disproportionate opportunity for success. However, this disproportionality seems not to be manifest in senior teams. Accordingly, we examine the identification and conversion rates for academy rugby players, examining a sample of all players passing into and either graduating, or being dismissed from, a major English rugby academy. Data demonstrated a reversal of the RAE "benefit", whereby late-birth players were less likely to be selected, but more likely to achieve senior professional status. Possible reasons are explored and, on the basis of our data, we propose a psychologically based explanation of greater "growth" due to additional challenge experienced by these initially disadvantaged younger players.

Mental Effort and Performance as Determinants for the Dynamic Selection of Learning Tasks in Air Traffic Control Training

ERIC Educational Resources Information Center

Salden, Ron J.C.M.; Paas, Fred; Broers, Nick J.; van Merrienboer, Jeroen J. G.

2004-01-01

The differential effects of four task selection methods on training efficiency and transfer in computer-based training for Air Traffic Control were investigated. A non-dynamic condition, in which the learning tasks were presented to the participants in a fixed, predetermined sequence, was compared to three dynamic conditions, in which learning…

The Effect of Empathy Training on the Empathic Skills of Nurses.

PubMed

Kahriman, Ilknur; Nural, Nesrin; Arslan, Umit; Topbas, Murat; Can, Gamze; Kasim, Suheyla

2016-06-01

The profound impact of empathy training on quality nursing care has been recognized. Studies have shown that there has been little improvement in nurses' communication skills, and that they should work to enhance this area. Relevant training will lead to an improvement in nurses' empathic skills, which in turn, will enable them to understand their patients better, establish positive interpersonal relationships with them, and boost their professional satisfaction. To reveal the effect of empathy training on the empathic skills of nurses. This study was conducted as an experimental design. The research sample consisted of 48 nurses working at the pediatric clinics of Farabi hospital of Karadeniz Technical University in Turkey (N = 83). Two groups, an experimental group (group 1) and a control group (group 2) were determined after questionnaires were supplied to all nurses in the study sample. At first, it was intended to select these groups using a random method. However, since this may have meant that the experimental and control groups were formed from nurses working in the same service, the two groups were selected from different services to avoid possible interaction between them. The nurses in the Group 1 were provided with empathy training through group and creative drama techniques. Pre-tests and post-tests were conducted on both groups. Data was collected via a questionnaire designed around the topic "empathic skill scale-ESS", developed by Dokmen. The Kolmogorov Smirnov test was employed to assess whether the measurable data was suitable for normal distribution. Data was presented as numbers and percentage distributions, as mean ± standard deviation and Chi-square, and as student t tests and paired t tests. The level of significance was accepted as P < 0.05. The nurses in the experimental group had a mean score of 146.7 ± 38.8 and 169.5 ± 22.1 in the ESS pre-test and post-test, respectively. Although the nurses in the control group had a pre-test mean score of 133.7 ± 37.1, which increased to 135.1 ± 51.7 after the training, no statistically significant difference was found (P = 0.886). A comparison of the groups indicated that they scored similarly in the pre-test. However, the experimental group scored significantly higher than the control group in the post-test (P = 0.270 and P = 0.015, respectively). In the light of these findings, it is recommended that communication skills should be widely included in in-service training programs; similar studies should be conducted on broader control groups formed through randomization; and a comparison should be made between the findings.

The Effect of Empathy Training on the Empathic Skills of Nurses

PubMed Central

Kahriman, Ilknur; Nural, Nesrin; Arslan, Umit; Topbas, Murat; Can, Gamze; Kasim, Suheyla

2016-01-01

Background The profound impact of empathy training on quality nursing care has been recognized. Studies have shown that there has been little improvement in nurses’ communication skills, and that they should work to enhance this area. Relevant training will lead to an improvement in nurses’ empathic skills, which in turn, will enable them to understand their patients better, establish positive interpersonal relationships with them, and boost their professional satisfaction. Objectives To reveal the effect of empathy training on the empathic skills of nurses. Patients and Methods This study was conducted as an experimental design. The research sample consisted of 48 nurses working at the pediatric clinics of Farabi hospital of Karadeniz Technical University in Turkey (N = 83). Two groups, an experimental group (group 1) and a control group (group 2) were determined after questionnaires were supplied to all nurses in the study sample. At first, it was intended to select these groups using a random method. However, since this may have meant that the experimental and control groups were formed from nurses working in the same service, the two groups were selected from different services to avoid possible interaction between them. The nurses in the Group 1 were provided with empathy training through group and creative drama techniques. Pre-tests and post-tests were conducted on both groups. Data was collected via a questionnaire designed around the topic “empathic skill scale-ESS”, developed by Dokmen. The Kolmogorov Smirnov test was employed to assess whether the measurable data was suitable for normal distribution. Data was presented as numbers and percentage distributions, as mean ± standard deviation and Chi-square, and as student t tests and paired t tests. The level of significance was accepted as P < 0.05. Results The nurses in the experimental group had a mean score of 146.7 ± 38.8 and 169.5 ± 22.1 in the ESS pre-test and post-test, respectively. Although the nurses in the control group had a pre-test mean score of 133.7 ± 37.1, which increased to 135.1 ± 51.7 after the training, no statistically significant difference was found (P = 0.886). A comparison of the groups indicated that they scored similarly in the pre-test. However, the experimental group scored significantly higher than the control group in the post-test (P = 0.270 and P = 0.015, respectively). Conclusions In the light of these findings, it is recommended that communication skills should be widely included in in-service training programs; similar studies should be conducted on broader control groups formed through randomization; and a comparison should be made between the findings. PMID:27621922

A systematic review of interventions conducted in clinical or community settings to improve dual-task postural control in older adults

PubMed Central

Agmon, Maayan; Belza, Basia; Nguyen, Huong Q; Logsdon, Rebecca G; Kelly, Valerie E

2014-01-01

Background Injury due to falls is a major problem among older adults. Decrements in dual-task postural control performance (simultaneously performing two tasks, at least one of which requires postural control) have been associated with an increased risk of falling. Evidence-based interventions that can be used in clinical or community settings to improve dual-task postural control may help to reduce this risk. Purpose The aims of this systematic review are: 1) to identify clinical or community-based interventions that improved dual-task postural control among older adults; and 2) to identify the key elements of those interventions. Data sources Studies were obtained from a search conducted through October 2013 of the following electronic databases: PubMed, CINAHL, PsycINFO, and Web of Science. Study selection Randomized and nonrandomized controlled studies examining the effects of interventions aimed at improving dual-task postural control among community-dwelling older adults were selected. Data extraction All studies were evaluated based on methodological quality. Intervention characteristics including study purpose, study design, and sample size were identified, and effects of dual-task interventions on various postural control and cognitive outcomes were noted. Data synthesis Twenty-two studies fulfilled the selection criteria and were summarized in this review to identify characteristics of successful interventions. Limitations The ability to synthesize data was limited by the heterogeneity in participant characteristics, study designs, and outcome measures. Conclusion Dual-task postural control can be modified by specific training. There was little evidence that single-task training transferred to dual-task postural control performance. Further investigation of dual-task training using standardized outcome measurements is needed. PMID:24741296

CASPer, an online pre-interview screen for personal/professional characteristics: prediction of national licensure scores.

PubMed

Dore, Kelly L; Reiter, Harold I; Kreuger, Sharyn; Norman, Geoffrey R

2017-05-01

Typically, only a minority of applicants to health professional training are invited to interview. However, pre-interview measures of cognitive skills predict for national licensure scores (Gauer et al. in Med Educ Online 21 2016) and subsequently licensure scores predict for performance in practice (Tamblyn et al. in JAMA 288(23): 3019-3026, 2002; Tamblyn et al. in JAMA 298(9):993-1001, 2007). Assessment of personal and professional characteristics, with the same psychometric rigour of measures of cognitive abilities, are needed upstream in the selection to health profession training programs. To fill that need, Computer-based Assessment for Sampling Personal characteristics (CASPer)-an on-line, video-based screening test-was created. In this paper, we examine the correlation between CASPer and Canadian national licensure examination outcomes in 109 doctors who took CASPer at the time of selection to medical school. Specifically, CASPer scores were correlated against performance on cognitive and 'non-cognitive' subsections of both the Medical Council of Canada Qualifying Examination (MCCQE) Parts I (end of medical school) and Part II (18 months into specialty training). Unlike most national licensure exams, MCCQE has specific subcomponents examining personal/professional qualities, providing a unique opportunity for comparison. The results demonstrated moderate predictive validity of CASPer to national licensure outcomes of personal/professional characteristics three to six years after admission to medical school. These types of disattenuated correlations (r = 0.3-0.5) are not otherwise predicted by traditional screening measures. These data support the ability of a computer-based strategy to screen applicants in a feasible, reliable test, which has now demonstrated predictive validity, lending evidence of its validation for medical school applicant selection.

Using artificial intelligence to predict the risk for posterior capsule opacification after phacoemulsification.

PubMed

Mohammadi, Seyed-Farzad; Sabbaghi, Mostafa; Z-Mehrjardi, Hadi; Hashemi, Hassan; Alizadeh, Somayeh; Majdi, Mercede; Taee, Farough

2012-03-01

To apply artificial intelligence models to predict the occurrence of posterior capsule opacification (PCO) after phacoemulsification. Farabi Eye Hospital, Tehran, Iran. Clinical-based cross-sectional study. The posterior capsule status of eyes operated on for age-related cataract and the need for laser capsulotomy were determined. After a literature review, data polishing, and expert consultation, 10 input variables were selected. The QUEST algorithm was used to develop a decision tree. Three back-propagation artificial neural networks were constructed with 4, 20, and 40 neurons in 2 hidden layers and trained with the same transfer functions (log-sigmoid and linear transfer) and training protocol with randomly selected eyes. They were then tested on the remaining eyes and the networks compared for their performance. Performance indices were used to compare resultant models with the results of logistic regression analysis. The models were trained using 282 randomly selected eyes and then tested using 70 eyes. Laser capsulotomy for clinically significant PCO was indicated or had been performed 2 years postoperatively in 40 eyes. A sample decision tree was produced with accuracy of 50% (likelihood ratio 0.8). The best artificial neural network, which showed 87% accuracy and a positive likelihood ratio of 8, was achieved with 40 neurons. The area under the receiver-operating-characteristic curve was 0.71. In comparison, logistic regression reached accuracy of 80%; however, the likelihood ratio was not measurable because the sensitivity was zero. A prototype artificial neural network was developed that predicted posterior capsule status (requiring capsulotomy) with reasonable accuracy. No author has a financial or proprietary interest in any material or method mentioned. Copyright © 2012 ASCRS and ESCRS. Published by Elsevier Inc. All rights reserved.

Source Selection Simulation: Intact Team Training on Picking a Provider

DTIC Science & Technology

2015-06-01

seat of a new $100 million stealth fighter before giving her flight simulation time. The ar- gument for source-selection simulation ( SSS ) training is...dynamic is the creation of the SSS Tool. Drawing on his success in using a similar tool in contingency contracting, Long decided we should use a Web...of SSS intact team training. On Sept. 30–Oct. 3, 2014, Professors Long and Elsesser de- livered DAU’s first-ever Intact Team SSS Training to Eglin’s

Increasing contraceptive acceptance through empowerment of female community health volunteers in rural Nepal.

PubMed

Shrestha, Sarala

2002-06-01

The purpose of the study was to enhance contraceptive acceptance among currently-married women of reproductive age (CMWRA) through empowerment training of female community health volunteers (FCHVs). Seventeen FCHVs, who were working in Kakani Village Development Committee in the hills of central Nepal, attended an empowerment training that used participatory action research and reinforcement mechanisms. Following the training, the FCHVs were expected to empower the CMWRA to increase their contraceptive use. The impact of the intervention was assessed in a sample of 241 CMWRA, who were neither pregnant nor using contraceptives at the time of selection, by interviewing them before and six months after the intervention. The implementation of the intervention significantly increased the proportion of CMWRA knowing at least one contraceptive method (chi2(ldr)=71 .7, p=0.001). The use of modern contraceptives among the CMWRA from none before the intervention increased to 52.3% six months following the intervention. Satisfaction of the CMWRA with services provided by the FCHVs also significantly increased. The study concludes that empowerment training of FCHVs using participatory action research and peer reinforcement help increase the acceptance of contraceptives among CMWRA.

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.

Evaluation of an elderly care training programme for women.

PubMed

Bayik, T A; Uysal, A

2010-06-01

Caregiving across different cultures has been perceived conventionally as a private or family responsibility, predominantly performed by women who accept their caregiving as part of their gender role. This study aimed to design, deliver, and evaluate an elderly training programme for women by assessing their knowledge, attitudes and skills as a lay caregiver. Encouraging the women to find suitable positions for employment in private or governmental institutions was the further objective of the study. The study was a quasi-experimental one-group pre-test post-test design. The study was conducted in a solidarity centre for women and in a nursing home for the elderly. The sample covered 120 women selected from the community by convenience sampling. Data were gathered through pre- and post-test evaluation and observation forms in 2 May-22 December 2005. The training programme consisted of 230 h of didactic sessions, demonstrations and clinical practices. The mean change in the participants' knowledge score (pre-test: 41.44 +/- 0.92; post-test: 71.16 +/- 1.34) demonstrated a statistically significant improvement in their knowledge. According to clinical observations, most of them displayed satisfactory caring and communication skills towards the elderly. Virtually all participants reported increased skill, knowledge and confidence. The developed training programme was effective, resulting in an increased knowledge, the acquisition of good attitudes towards the elderly, and performing satisfactory caring and communication skills. Similar community-based programmes managed by nurses are recommended to support non-professional caregivers. The research is not only an innovative but also a revolutionary model to promote women.

What are the barriers to implementation of cardiopulmonary resuscitation training in secondary schools? A qualitative study

PubMed Central

Malta Hansen, Carolina; Rod, Morten Hulvej; Folke, Fredrik; Torp-Pedersen, Christian; Tjørnhøj-Thomsen, Tine

2016-01-01

Objective Cardiopulmonary resuscitation (CPR) training in schools is recommended to increase bystander CPR and thereby survival of out-of-hospital cardiac arrest, but despite mandating legislation, low rates of implementation have been observed in several countries, including Denmark. The purpose of the study was to explore barriers to implementation of CPR training in Danish secondary schools. Design A qualitative study based on individual interviews and focus groups with school leadership and teachers. Thematic analysis was used to identify regular patterns of meaning both within and across the interviews. Setting 8 secondary schools in Denmark. Schools were selected using strategic sampling to reach maximum variation, including schools with/without recent experience in CPR training of students, public/private schools and schools near to and far from hospitals. Participants The study population comprised 25 participants, 9 school leadership members and 16 teachers. Results School leadership and teachers considered it important for implementation and sustainability of CPR training that teachers conduct CPR training of students. However, they preferred external instructors to train students, unless teachers acquired the CPR skills which they considered were needed. They considered CPR training to differ substantially from other teaching subjects because it is a matter of life and death, and they therefore believed extraordinary skills were required for conducting the training. This was mainly rooted in their insecurity about their own CPR skills. CPR training kits seemed to lower expectations of skill requirements to conduct CPR training, but only among those who were familiar with such kits. Conclusions To facilitate implementation of CPR training in schools, it is necessary to have clear guidelines regarding the required proficiency level to train students in CPR, to provide teachers with these skills, and to underscore that extensive skills are not required to provide CPR. Further, it is important to familiarise teachers with CPR training kits. PMID:27113236

Sampling Methods and the Accredited Population in Athletic Training Education Research

ERIC Educational Resources Information Center

Carr, W. David; Volberding, Jennifer

2009-01-01

Context: We describe methods of sampling the widely-studied, yet poorly defined, population of accredited athletic training education programs (ATEPs). Objective: There are two purposes to this study; first to describe the incidence and types of sampling methods used in athletic training education research, and second to clearly define the…

A multi-model fusion strategy for multivariate calibration using near and mid-infrared spectra of samples from brewing industry.

PubMed

Tan, Chao; Chen, Hui; Wang, Chao; Zhu, Wanping; Wu, Tong; Diao, Yuanbo

2013-03-15

Near and mid-infrared (NIR/MIR) spectroscopy techniques have gained great acceptance in the industry due to their multiple applications and versatility. However, a success of application often depends heavily on the construction of accurate and stable calibration models. For this purpose, a simple multi-model fusion strategy is proposed. It is actually the combination of Kohonen self-organizing map (KSOM), mutual information (MI) and partial least squares (PLSs) and therefore named as KMICPLS. It works as follows: First, the original training set is fed into a KSOM for unsupervised clustering of samples, on which a series of training subsets are constructed. Thereafter, on each of the training subsets, a MI spectrum is calculated and only the variables with higher MI values than the mean value are retained, based on which a candidate PLS model is constructed. Finally, a fixed number of PLS models are selected to produce a consensus model. Two NIR/MIR spectral datasets from brewing industry are used for experiments. The results confirms its superior performance to two reference algorithms, i.e., the conventional PLS and genetic algorithm-PLS (GAPLS). It can build more accurate and stable calibration models without increasing the complexity, and can be generalized to other NIR/MIR applications. Copyright © 2012 Elsevier B.V. All rights reserved.

Technical player profiles related to the physical fitness of young female volleyball players predict team performance.

PubMed

Dávila-Romero, C; Hernández-Mocholí, M A; García-Hermoso, A

2015-03-01

This study is divided into three sequential stages: identification of fitness and game performance profiles (individual player performance), an assessment of the relationship between these profiles, and an assessment of the relationship between individual player profiles and team performance during play (in championship performance). The overall study sample comprised 525 (19 teams) female volleyball players aged 12-16 years and a subsample (N.=43) used to examine study aims one and two was selected from overall sample. Anthropometric, fitness and individual player performance (actual game) data were collected in the subsample. These data were analyzed through clustering methods, ANOVA and independence chi-square test. Then, we investigated whether the proportion of players with the highest individual player performance profile might predict a team's results in the championship. Cluster analysis identified three volleyball fitness profiles (high, medium, and low) and two individual player performance profiles (high and low). The results showed a relationship between both types of profile (fitness and individual player performance). Then, linear regression revealed a moderate relationship between the number of players with a high volleyball fitness profile and a team's results in the championship (R2=0.23). The current study findings may enable coaches and trainers to manage training programs more efficiently in order to obtain tailor-made training, identify volleyball-specific physical fitness training requirements and reach better results during competitions.

Improving labeling efficiency in automatic quality control of MRSI data.

PubMed

Pedrosa de Barros, Nuno; McKinley, Richard; Wiest, Roland; Slotboom, Johannes

2017-12-01

To improve the efficiency of the labeling task in automatic quality control of MR spectroscopy imaging data. 28'432 short and long echo time (TE) spectra (1.5 tesla; point resolved spectroscopy (PRESS); repetition time (TR)= 1,500 ms) from 18 different brain tumor patients were labeled by two experts as either accept or reject, depending on their quality. For each spectrum, 47 signal features were extracted. The data was then used to run several simulations and test an active learning approach using uncertainty sampling. The performance of the classifiers was evaluated as a function of the number of patients in the training set, number of spectra in the training set, and a parameter α used to control the level of classification uncertainty required for a new spectrum to be selected for labeling. The results showed that the proposed strategy allows reductions of up to 72.97% for short TE and 62.09% for long TE in the amount of data that needs to be labeled, without significant impact in classification accuracy. Further reductions are possible with significant but minimal impact in performance. Active learning using uncertainty sampling is an effective way to increase the labeling efficiency for training automatic quality control classifiers. Magn Reson Med 78:2399-2405, 2017. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

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.

Effectiveness of Positive Thinking Training Program on Nurses' Quality of Work Life through Smartphone Applications

PubMed Central

Dehghan, Azizallah

2017-01-01

Aim Job stress is a part of nurses' professional life that causes the decrease of the nurses' job satisfaction and quality of work life. This study aimed to determine the effect of positive thinking via social media applications on the nurses' quality of work life. Methods This was a pretest-posttest quasi-experimental study design with a control group. The samples were selected among the nurses in two hospitals in Fasa University of Medical Sciences and divided randomly into two interventional (n = 50) and control (n = 50) groups. Positive thinking training through telegrams was sent to the intervention group during a period of 3 months. Data were collected by using Brooks and Anderson's questionnaire of work life quality and analyzed by SPSS 18. Results The mean total scores of pretest and posttest in the intervention group improved noticeably and there were significant differences between mean scores of quality of work life in pretest and posttest scores in interventional groups (p < 0.001) and in dimensions of work life quality, home life (p < 0.001), work design (p < 0.001), work context (p < 0.001), and work world (p = 0.003). Conclusion This study concluded that positive thinking training via social media application enhanced nurses' quality of work life. This study is necessary to carry out on a larger sample size for generalizing findings better. PMID:28589174

Effectiveness of Positive Thinking Training Program on Nurses' Quality of Work Life through Smartphone Applications.

PubMed

Motamed-Jahromi, Mohadeseh; Fereidouni, Zhila; Dehghan, Azizallah

2017-01-01

Job stress is a part of nurses' professional life that causes the decrease of the nurses' job satisfaction and quality of work life. This study aimed to determine the effect of positive thinking via social media applications on the nurses' quality of work life. This was a pretest-posttest quasi-experimental study design with a control group. The samples were selected among the nurses in two hospitals in Fasa University of Medical Sciences and divided randomly into two interventional ( n = 50) and control ( n = 50) groups. Positive thinking training through telegrams was sent to the intervention group during a period of 3 months. Data were collected by using Brooks and Anderson's questionnaire of work life quality and analyzed by SPSS 18. The mean total scores of pretest and posttest in the intervention group improved noticeably and there were significant differences between mean scores of quality of work life in pretest and posttest scores in interventional groups ( p < 0.001) and in dimensions of work life quality, home life ( p < 0.001), work design ( p < 0.001), work context ( p < 0.001), and work world ( p = 0.003). This study concluded that positive thinking training via social media application enhanced nurses' quality of work life. This study is necessary to carry out on a larger sample size for generalizing findings better.

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.

Recall initiation strategies must be controlled in training studies that use immediate free recall tasks to measure the components of working memory capacity across time.

PubMed

Gibson, Bradley S; Gondoli, Dawn M; Johnson, Ann C; Robison, Matthew K

2014-01-01

There has been great interest in using working memory (WM) training regimens as an alternative treatment for ADHD, but it has recently been concluded that existing training regimens may not be optimally designed because they target the primary memory component but not the secondary component of WM capacity. This conclusion requires the ability to accurately measure changes in primary and secondary memory abilities over time. The immediate free recall task has been used in previous studies to measure these changes; however, one concern with these tasks is that the recall order required on training exercises may influence the recall strategy used during free recall, which may in turn influence the relative number of items recalled from primary and secondary memory. To address this issue, previous training studies have explicitly controlled recall strategy before and after training. However, the necessity of controlling for recall strategies has not been explicitly tested. The present study investigated the effects of forward-serial-order training on free recall performance under conditions in which recall strategy was not controlled using a sample of adolescents with ADHD. Unlike when recall order was controlled, the main findings showed selective improvement of the secondary memory component (as opposed to the primary memory component) when recall order was uncontrolled. This finding advances our understanding of WM training by highlighting the importance of controlling for recall strategies when free recall tasks are used to measure changes in the primary and secondary components of WM across time.