Improving question asking in high-functioning adolescents with autism spectrum disorders: effectiveness of small-group training.

PubMed

Palmen, Annemiek; Didden, Robert; Arts, Marieke

2008-01-01

Small-group training consisting of feedback and self-management was effective in improving question-asking skills during tutorial conversations in nine high-functioning adolescents with autism spectrum disorder. Training was implemented in a therapy room and lasted 6 weeks. Sessions were conducted once a week and lasted about an hour. Experimenters collected data during tutorial conversations in a natural setting. Training of question-asking skills consisted of verbal feedback and role-play during short simulated conversations and a table game. A self-management strategy and common stimuli (e.g., flowchart) were included to promote generalization. Mean percentage of correct questions during tutorial conversations improved significantly after training. Response efficiency also increased. Participants and personal coaches evaluated the training as effective and acceptable.

Classification of breast MRI lesions using small-size training sets: comparison of deep learning approaches

NASA Astrophysics Data System (ADS)

Amit, Guy; Ben-Ari, Rami; Hadad, Omer; Monovich, Einat; Granot, Noa; Hashoul, Sharbell

2017-03-01

Diagnostic interpretation of breast MRI studies requires meticulous work and a high level of expertise. Computerized algorithms can assist radiologists by automatically characterizing the detected lesions. Deep learning approaches have shown promising results in natural image classification, but their applicability to medical imaging is limited by the shortage of large annotated training sets. In this work, we address automatic classification of breast MRI lesions using two different deep learning approaches. We propose a novel image representation for dynamic contrast enhanced (DCE) breast MRI lesions, which combines the morphological and kinetics information in a single multi-channel image. We compare two classification approaches for discriminating between benign and malignant lesions: training a designated convolutional neural network and using a pre-trained deep network to extract features for a shallow classifier. The domain-specific trained network provided higher classification accuracy, compared to the pre-trained model, with an area under the ROC curve of 0.91 versus 0.81, and an accuracy of 0.83 versus 0.71. Similar accuracy was achieved in classifying benign lesions, malignant lesions, and normal tissue images. The trained network was able to improve accuracy by using the multi-channel image representation, and was more robust to reductions in the size of the training set. A small-size convolutional neural network can learn to accurately classify findings in medical images using only a few hundred images from a few dozen patients. With sufficient data augmentation, such a network can be trained to outperform a pre-trained out-of-domain classifier. Developing domain-specific deep-learning models for medical imaging can facilitate technological advancements in computer-aided diagnosis.

A comparison of four strategies for teaching a small foreign-language vocabulary.

PubMed

Petursdottir, Anna Ingeborg; Haflidadóttir, Rakel Dögg

2009-01-01

We compared the effects of tact training, listener training, and two types of intraverbal training on 2 children's acquisition of foreign-language tact, listener, and intraverbal relations. The children received all four types of training simultaneously with different stimulus sets. Native-foreign intraverbal training presented the greatest difficulty with acquisition for both children. All types of training generated increases in correct responding on tests for emergent relations, and some emerged to criterion. However, no type of training resulted in criterion-level performance on all relations.

Food hygiene training in small to medium-sized care settings.

PubMed

Seaman, Phillip; Eves, Anita

2008-10-01

Adoption of safe food handling practices is essential to effectively manage food safety. This study explores the impact of basic or foundation level food hygiene training on the attitudes and intentions of food handlers in care settings, using questionnaires based on the Theory of Planned Behaviour. Interviews were also conducted with food handlers and their managers to ascertain beliefs about the efficacy of, perceived barriers to, and relevance of food hygiene training. Most food handlers had undertaken formal food hygiene training; however, many who had not yet received training were preparing food, including high risk foods. Appropriate pre-training support and on-going supervision appeared to be lacking, thus limiting the effectiveness of training. Findings showed Subjective Norm to be the most significant influence on food handlers' intention to perform safe food handling practices, irrespective of training status, emphasising the role of important others in determining desirable behaviours.

Image aesthetic quality evaluation using convolution neural network embedded learning

NASA Astrophysics Data System (ADS)

Li, Yu-xin; Pu, Yuan-yuan; Xu, Dan; Qian, Wen-hua; Wang, Li-peng

2017-11-01

A way of embedded learning convolution neural network (ELCNN) based on the image content is proposed to evaluate the image aesthetic quality in this paper. Our approach can not only solve the problem of small-scale data but also score the image aesthetic quality. First, we chose Alexnet and VGG_S to compare for confirming which is more suitable for this image aesthetic quality evaluation task. Second, to further boost the image aesthetic quality classification performance, we employ the image content to train aesthetic quality classification models. But the training samples become smaller and only using once fine-tuning cannot make full use of the small-scale data set. Third, to solve the problem in second step, a way of using twice fine-tuning continually based on the aesthetic quality label and content label respective is proposed, the classification probability of the trained CNN models is used to evaluate the image aesthetic quality. The experiments are carried on the small-scale data set of Photo Quality. The experiment results show that the classification accuracy rates of our approach are higher than the existing image aesthetic quality evaluation approaches.

"Learning to Work" in Small Businesses: Learning and Training for Young Adults with Learning Disabilities

ERIC Educational Resources Information Center

Ruggeri-Stevens, Geoff; Goodwin, Susan

2007-01-01

Purpose: The paper alerts small business employers to new dictates of the Disability Discrimination Act (2005) as it applies to learning disabilities. Then the "Learning to Work" project featured in the paper offers small business employers a set of approaches and methods for the identification of a learning-disabled young adult…

Assessment of the generalization of learned image reconstruction and the potential for transfer learning.

PubMed

Knoll, Florian; Hammernik, Kerstin; Kobler, Erich; Pock, Thomas; Recht, Michael P; Sodickson, Daniel K

2018-05-17

Although deep learning has shown great promise for MR image reconstruction, an open question regarding the success of this approach is the robustness in the case of deviations between training and test data. The goal of this study is to assess the influence of image contrast, SNR, and image content on the generalization of learned image reconstruction, and to demonstrate the potential for transfer learning. Reconstructions were trained from undersampled data using data sets with varying SNR, sampling pattern, image contrast, and synthetic data generated from a public image database. The performance of the trained reconstructions was evaluated on 10 in vivo patient knee MRI acquisitions from 2 different pulse sequences that were not used during training. Transfer learning was evaluated by fine-tuning baseline trainings from synthetic data with a small subset of in vivo MR training data. Deviations in SNR between training and testing led to substantial decreases in reconstruction image quality, whereas image contrast was less relevant. Trainings from heterogeneous training data generalized well toward the test data with a range of acquisition parameters. Trainings from synthetic, non-MR image data showed residual aliasing artifacts, which could be removed by transfer learning-inspired fine-tuning. This study presents insights into the generalization ability of learned image reconstruction with respect to deviations in the acquisition settings between training and testing. It also provides an outlook for the potential of transfer learning to fine-tune trainings to a particular target application using only a small number of training cases. © 2018 International Society for Magnetic Resonance in Medicine.

The Effects of Expert Systems Training versus Content-Based Training on the Troubleshooting Achievement of Onan Corporation Service Personnel. Training and Development Research Center, Project Number Forty-Eight.

ERIC Educational Resources Information Center

Westerdahl, Edward John

This study compared the effectiveness and efficiency of trainees in the Onan small products gasoline course under two training curricula: (1) the control group curriculum was the in-place course on the Emerald generator set; and (2) the experimental group curriculum was essentially the same with the addition of one lesson based on methods used by…

Training set size, scale, and features in Geographic Object-Based Image Analysis of very high resolution unmanned aerial vehicle imagery

NASA Astrophysics Data System (ADS)

Ma, Lei; Cheng, Liang; Li, Manchun; Liu, Yongxue; Ma, Xiaoxue

2015-04-01

Unmanned Aerial Vehicle (UAV) has been used increasingly for natural resource applications in recent years due to their greater availability and the miniaturization of sensors. In addition, Geographic Object-Based Image Analysis (GEOBIA) has received more attention as a novel paradigm for remote sensing earth observation data. However, GEOBIA generates some new problems compared with pixel-based methods. In this study, we developed a strategy for the semi-automatic optimization of object-based classification, which involves an area-based accuracy assessment that analyzes the relationship between scale and the training set size. We found that the Overall Accuracy (OA) increased as the training set ratio (proportion of the segmented objects used for training) increased when the Segmentation Scale Parameter (SSP) was fixed. The OA increased more slowly as the training set ratio became larger and a similar rule was obtained according to the pixel-based image analysis. The OA decreased as the SSP increased when the training set ratio was fixed. Consequently, the SSP should not be too large during classification using a small training set ratio. By contrast, a large training set ratio is required if classification is performed using a high SSP. In addition, we suggest that the optimal SSP for each class has a high positive correlation with the mean area obtained by manual interpretation, which can be summarized by a linear correlation equation. We expect that these results will be applicable to UAV imagery classification to determine the optimal SSP for each class.

ChoiceKey: a real-time speech recognition program for psychology experiments with a small response set.

PubMed

Donkin, Christopher; Brown, Scott D; Heathcote, Andrew

2009-02-01

Psychological experiments often collect choice responses using buttonpresses. However, spoken responses are useful in many cases-for example, when working with special clinical populations, or when a paradigm demands vocalization, or when accurate response time measurements are desired. In these cases, spoken responses are typically collected using a voice key, which usually involves manual coding by experimenters in a tedious and error-prone manner. We describe ChoiceKey, an open-source speech recognition package for MATLAB. It can be optimized by training for small response sets and different speakers. We show ChoiceKey to be reliable with minimal training for most participants in experiments with two different responses. Problems presented by individual differences, and occasional atypical responses, are examined, and extensions to larger response sets are explored. The ChoiceKey source files and instructions may be downloaded as supplemental materials for this article from brm.psychonomic-journals.org/content/supplemental.

The Effects of Interset Rest on Adaptation to 7 Weeks of Explosive Training in Young Soccer Players

PubMed Central

Ramirez-Campillo, Rodrigo; Andrade, David C.; Álvarez, Cristian; Henríquez-Olguín, Carlos; Martínez, Cristian; Báez-SanMartín, Eduardo; Silva-Urra, Juan; Burgos, Carlos; Izquierdo, Mikel

2014-01-01

The aim of the study was to compare the effects of plyometric training using 30, 60, or 120 s of rest between sets on explosive adaptations in young soccer players. Four groups of athletes (age 10.4 ± 2.3 y; soccer experience 3.3 ± 1.5 y) were randomly formed: control (CG; n = 15), plyometric training with 30 s (G30; n = 13), 60 s (G60; n = 14), and 120 s (G120; n = 12) of rest between training sets. Before and after intervention players were measured in jump ability, 20-m sprint time, change of direction speed (CODS), and kicking performance. The training program was applied during 7 weeks, 2 sessions per week, for a total of 840 jumps. After intervention the G30, G60 and G120 groups showed a significant (p = 0.0001 – 0.04) and small to moderate effect size (ES) improvement in the countermovement jump (ES = 0.49; 0.58; 0.55), 20 cm drop jump reactive strength index (ES = 0.81; 0.89; 0.86), CODS (ES = -1.03; -0.87; -1.04), and kicking performance (ES = 0.39; 0.49; 0.43), with no differences between treatments. The study shows that 30, 60, and 120 s of rest between sets ensure similar significant and small to moderate ES improvement in jump, CODS, and kicking performance during high-intensity short-term explosive training in young male soccer players. Key points Replacing some soccer drills by low volume high-intensity plyometric training would be beneficial in jumping, change of direction speed, and kicking ability in young soccer players. A rest period of 30, 60 or 120 seconds between low-volume high-intensity plyometric sets would induce significant and similar explosive adaptations during a short-term training period in young soccer players. Data from this research can be helpful for soccer trainers in choosing efficient drills and characteristics of between sets recovery programs to enhance performances in young male soccer players. PMID:24790481

Different Skills and Their Different Effects on Personal Development: An Investigation of European Social Fund Objective 4 Financed Training in SMEs in Britain

ERIC Educational Resources Information Center

Devins, David; Johnson, Steve; Sutherland, John

2004-01-01

This paper examines a data set that has its origins in European Social Fund Objective 4 financed training programmes in small- to medium-sized enterprises (SMEs) in Britain to examine the extent to which three different personal development outcomes are attributable to different types of skills acquired during the training process. The three…

Using automatic alignment to analyze endangered language data: Testing the viability of untrained alignment

PubMed Central

DiCanio, Christian; Nam, Hosung; Whalen, Douglas H.; Timothy Bunnell, H.; Amith, Jonathan D.; García, Rey Castillo

2013-01-01

While efforts to document endangered languages have steadily increased, the phonetic analysis of endangered language data remains a challenge. The transcription of large documentation corpora is, by itself, a tremendous feat. Yet, the process of segmentation remains a bottleneck for research with data of this kind. This paper examines whether a speech processing tool, forced alignment, can facilitate the segmentation task for small data sets, even when the target language differs from the training language. The authors also examined whether a phone set with contextualization outperforms a more general one. The accuracy of two forced aligners trained on English (hmalign and p2fa) was assessed using corpus data from Yoloxóchitl Mixtec. Overall, agreement performance was relatively good, with accuracy at 70.9% within 30 ms for hmalign and 65.7% within 30 ms for p2fa. Segmental and tonal categories influenced accuracy as well. For instance, additional stop allophones in hmalign's phone set aided alignment accuracy. Agreement differences between aligners also corresponded closely with the types of data on which the aligners were trained. Overall, using existing alignment systems was found to have potential for making phonetic analysis of small corpora more efficient, with more allophonic phone sets providing better agreement than general ones. PMID:23967953

Using automatic alignment to analyze endangered language data: testing the viability of untrained alignment.

PubMed

DiCanio, Christian; Nam, Hosung; Whalen, Douglas H; Bunnell, H Timothy; Amith, Jonathan D; García, Rey Castillo

2013-09-01

While efforts to document endangered languages have steadily increased, the phonetic analysis of endangered language data remains a challenge. The transcription of large documentation corpora is, by itself, a tremendous feat. Yet, the process of segmentation remains a bottleneck for research with data of this kind. This paper examines whether a speech processing tool, forced alignment, can facilitate the segmentation task for small data sets, even when the target language differs from the training language. The authors also examined whether a phone set with contextualization outperforms a more general one. The accuracy of two forced aligners trained on English (hmalign and p2fa) was assessed using corpus data from Yoloxóchitl Mixtec. Overall, agreement performance was relatively good, with accuracy at 70.9% within 30 ms for hmalign and 65.7% within 30 ms for p2fa. Segmental and tonal categories influenced accuracy as well. For instance, additional stop allophones in hmalign's phone set aided alignment accuracy. Agreement differences between aligners also corresponded closely with the types of data on which the aligners were trained. Overall, using existing alignment systems was found to have potential for making phonetic analysis of small corpora more efficient, with more allophonic phone sets providing better agreement than general ones.

Acute effects of verbal feedback on upper-body performance in elite athletes.

PubMed

Argus, Christos K; Gill, Nicholas D; Keogh, Justin Wl; Hopkins, Will G

2011-12-01

Argus, CK, Gill, ND, Keogh, JWL, and Hopkins, WG. Acute effects of verbal feedback on upper-body performance in elite athletes. J Strength Cond Res 25(12): 3282-3287, 2011-Improved training quality has the potential to enhance training adaptations. Previous research suggests that receiving feedback improves single-effort maximal strength and power tasks, but whether quality of a training session with repeated efforts can be improved remains unclear. The purpose of this investigation was to determine the effects of verbal feedback on upper-body performance in a resistance training session consisting of multiple sets and repetitions in well-trained athletes. Nine elite rugby union athletes were assessed using the bench throw exercise on 4 separate occasions each separated by 7 days. Each athlete completed 2 sessions consisting of 3 sets of 4 repetitions of the bench throw with feedback provided after each repetition and 2 identical sessions where no feedback was provided after each repetition. When feedback was received, there was a small increase of 1.8% (90% confidence limits, ±2.7%) and 1.3% (±0.7%) in mean peak power and velocity when averaged over the 3 sets. When individual sets were compared, there was a tendency toward the improvements in mean peak power being greater in the second and third sets. These results indicate that providing verbal feedback produced acute improvements in upper-body power output of well-trained athletes. The benefits of feedback may be greatest in the latter sets of training and could improve training quality and result in greater long-term adaptation.

Effects of Strength Training Sessions Performed with Different Exercise Orders and Intervals on Blood Pressure and Heart Rate Variability.

PubMed

Lemos, Sandro; Figueiredo, Tiago; Marques, Silvio; Leite, Thalita; Cardozo, Diogo; Willardson, Jeffrey M; Simão, Roberto

2018-01-01

This study compared the effect of a strength training session performed at different exercise orders and rest intervals on blood pressure and heart rate variability (HRV). Fifteen trained men performed different upper body exercise sequences [large to small muscle mass (SEQA) and small to large muscle mass (SEQB)] in randomized order with rest intervals between sets and exercises of 40 or 90 seconds. Fifteen repetition maximum loads were tested to control the training intensity and the total volume load. The results showed, significant reductions for systolic blood pressure (SBP) for all sequences compared to baseline and, post-exercise: SEQA90 at 20, 30, 40, 50 and 60 minutes; SEQA40 and SEQB40 at 20 minutes and SEQB90 at 10, 20, 30, 40, 50 and 60 minutes. For diastolic blood pressure (DBP), significant reductions were found for three sequences compared to baseline and, post-exercise: SEQA90 and SEQA40 at 50 and 60 minutes; SEQB40 at 10, 30 and 60 minutes. For HRV, there were significant differences in frequency domain for all sequences compared to baseline. In conclusion, when performing upper body strength training sessions, it is suggested that 90 second rest intervals between sets and exercises promotes a post-exercise hypotensive response in SBP. The 40 second rest interval between sets and exercises was associated with greater cardiac stress, and might be contraindicated when working with individuals that exhibit symptoms of cardiovascular disease.

Bassett healthcare rural surgery experience.

PubMed

Borgstrom, David C; Heneghan, Steven J

2009-12-01

The surgical training at Bassett is naturally broader than in many university settings, with a survey showing that nearly 70% of graduates who practice general surgery remain in a rurally designated area. Rural surgery experience falls into 3 categories: undergraduate, graduate, and postgraduate. The general surgery training program has no competing fellowships or subspecialty residencies; residents get significant experience with endoscopy; ear, nose, and throat; plastic and hand surgery; and obstetrics and gynecology. The rural setting lifestyle is valued by the students, residents, and fellows alike. It provides an ideal setting for recognizing the specific nuances of small-town American life, with a high-quality education and surgical experience.

Small and Home-Based Businesses: Measures of Success and the Contribution of Local Development Services

ERIC Educational Resources Information Center

Brooks, Lara; Whitacre, Brian; Shideler, Dave; Muske, Glenn; Woods, Mike

2012-01-01

Small and home-based businesses have long been identified by Extension educators as an important component of economic development, particularly in rural areas. The services available to these businesses can take many forms, including management training, accessibility of local funding, providing incubation facilities, or setting up mentoring…

Detection of prostate cancer on multiparametric MRI

NASA Astrophysics Data System (ADS)

Seah, Jarrel C. Y.; Tang, Jennifer S. N.; Kitchen, Andy

2017-03-01

In this manuscript, we describe our approach and methods to the ProstateX challenge, which achieved an overall AUC of 0.84 and the runner-up position. We train a deep convolutional neural network to classify lesions marked on multiparametric MRI of the prostate as clinically significant or not. We implement a novel addition to the standard convolutional architecture described as auto-windowing which is clinically inspired and designed to overcome some of the difficulties faced in MRI interpretation, where high dynamic ranges and low contrast edges may cause difficulty for traditional convolutional neural networks trained on high contrast natural imagery. We demonstrate that this system can be trained end to end and outperforms a similar architecture without such additions. Although a relatively small training set was provided, we use extensive data augmentation to prevent overfitting and transfer learning to improve convergence speed, showing that deep convolutional neural networks can be feasibly trained on small datasets.

Deep learning in the small sample size setting: cascaded feed forward neural networks for medical image segmentation

NASA Astrophysics Data System (ADS)

Gaonkar, Bilwaj; Hovda, David; Martin, Neil; Macyszyn, Luke

2016-03-01

Deep Learning, refers to large set of neural network based algorithms, have emerged as promising machine- learning tools in the general imaging and computer vision domains. Convolutional neural networks (CNNs), a specific class of deep learning algorithms, have been extremely effective in object recognition and localization in natural images. A characteristic feature of CNNs, is the use of a locally connected multi layer topology that is inspired by the animal visual cortex (the most powerful vision system in existence). While CNNs, perform admirably in object identification and localization tasks, typically require training on extremely large datasets. Unfortunately, in medical image analysis, large datasets are either unavailable or are extremely expensive to obtain. Further, the primary tasks in medical imaging are organ identification and segmentation from 3D scans, which are different from the standard computer vision tasks of object recognition. Thus, in order to translate the advantages of deep learning to medical image analysis, there is a need to develop deep network topologies and training methodologies, that are geared towards medical imaging related tasks and can work in a setting where dataset sizes are relatively small. In this paper, we present a technique for stacked supervised training of deep feed forward neural networks for segmenting organs from medical scans. Each `neural network layer' in the stack is trained to identify a sub region of the original image, that contains the organ of interest. By layering several such stacks together a very deep neural network is constructed. Such a network can be used to identify extremely small regions of interest in extremely large images, inspite of a lack of clear contrast in the signal or easily identifiable shape characteristics. What is even more intriguing is that the network stack achieves accurate segmentation even when it is trained on a single image with manually labelled ground truth. We validate this approach,using a publicly available head and neck CT dataset. We also show that a deep neural network of similar depth, if trained directly using backpropagation, cannot acheive the tasks achieved using our layer wise training paradigm.

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

NASA Astrophysics Data System (ADS)

León, Madeleine; Escalante-Ramirez, Boris

2013-11-01

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

Training of polyp staging systems using mixed imaging modalities.

PubMed

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

2018-05-04

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

Perspiring Capitalists: Latinos and the Henry Ford Service School, 1918-1928.

ERIC Educational Resources Information Center

Valdes, Dennis Nodin

1981-01-01

The Ford Service School was a program established by the Ford Motor Company to train a small core of highly educated, well-connected men to set up the most important automotive empire in Latin America. This core group was crucial in setting up sales branches and assembly plants in foreign markets. (NQA)

Effects of a Short Teacher Training Programme on the Management of Children's Sexual Behaviours: A Pilot Study

ERIC Educational Resources Information Center

Charnaud, Jean-Paul; Turner, William

2015-01-01

This small-scale quasi-experimental study set out to examine the effects of a brief training programme aiming to develop primary school teachers' knowledge, attitudes and confidence in recognising and responding to children who display sexual behaviours. Data on prevalence of sexual behaviours observed by teachers in the study, their level of…

Volunteer English as a Second Language Instructional Program for Non-English Speaking Adults. Final Report.

ERIC Educational Resources Information Center

Catholic Social Services, Harrisburg, PA.

The primary goal of a multi-purpose project was to utilize both Literacy Volunteers of America (LVA) and Laubach Literacy Action (LLA) in training volunteers to teach English to refugees. Catholic Social Services trained 163 volunteers who were placed in adult basic education (ABE) classes, small group instruction settings, and one-to-one tutoring…

Quantum annealing versus classical machine learning applied to a simplified computational biology problem

NASA Astrophysics Data System (ADS)

Li, Richard Y.; Di Felice, Rosa; Rohs, Remo; Lidar, Daniel A.

2018-03-01

Transcription factors regulate gene expression, but how these proteins recognize and specifically bind to their DNA targets is still debated. Machine learning models are effective means to reveal interaction mechanisms. Here we studied the ability of a quantum machine learning approach to classify and rank binding affinities. Using simplified data sets of a small number of DNA sequences derived from actual binding affinity experiments, we trained a commercially available quantum annealer to classify and rank transcription factor binding. The results were compared to state-of-the-art classical approaches for the same simplified data sets, including simulated annealing, simulated quantum annealing, multiple linear regression, LASSO, and extreme gradient boosting. Despite technological limitations, we find a slight advantage in classification performance and nearly equal ranking performance using the quantum annealer for these fairly small training data sets. Thus, we propose that quantum annealing might be an effective method to implement machine learning for certain computational biology problems.

A Component-Based Vocabulary-Extensible Sign Language Gesture Recognition Framework.

PubMed

Wei, Shengjing; Chen, Xiang; Yang, Xidong; Cao, Shuai; Zhang, Xu

2016-04-19

Sign language recognition (SLR) can provide a helpful tool for the communication between the deaf and the external world. This paper proposed a component-based vocabulary extensible SLR framework using data from surface electromyographic (sEMG) sensors, accelerometers (ACC), and gyroscopes (GYRO). In this framework, a sign word was considered to be a combination of five common sign components, including hand shape, axis, orientation, rotation, and trajectory, and sign classification was implemented based on the recognition of five components. Especially, the proposed SLR framework consisted of two major parts. The first part was to obtain the component-based form of sign gestures and establish the code table of target sign gesture set using data from a reference subject. In the second part, which was designed for new users, component classifiers were trained using a training set suggested by the reference subject and the classification of unknown gestures was performed with a code matching method. Five subjects participated in this study and recognition experiments under different size of training sets were implemented on a target gesture set consisting of 110 frequently-used Chinese Sign Language (CSL) sign words. The experimental results demonstrated that the proposed framework can realize large-scale gesture set recognition with a small-scale training set. With the smallest training sets (containing about one-third gestures of the target gesture set) suggested by two reference subjects, (82.6 ± 13.2)% and (79.7 ± 13.4)% average recognition accuracy were obtained for 110 words respectively, and the average recognition accuracy climbed up to (88 ± 13.7)% and (86.3 ± 13.7)% when the training set included 50~60 gestures (about half of the target gesture set). The proposed framework can significantly reduce the user's training burden in large-scale gesture recognition, which will facilitate the implementation of a practical SLR system.

Machine learning of molecular properties: Locality and active learning

NASA Astrophysics Data System (ADS)

Gubaev, Konstantin; Podryabinkin, Evgeny V.; Shapeev, Alexander V.

2018-06-01

In recent years, the machine learning techniques have shown great potent1ial in various problems from a multitude of disciplines, including materials design and drug discovery. The high computational speed on the one hand and the accuracy comparable to that of density functional theory on another hand make machine learning algorithms efficient for high-throughput screening through chemical and configurational space. However, the machine learning algorithms available in the literature require large training datasets to reach the chemical accuracy and also show large errors for the so-called outliers—the out-of-sample molecules, not well-represented in the training set. In the present paper, we propose a new machine learning algorithm for predicting molecular properties that addresses these two issues: it is based on a local model of interatomic interactions providing high accuracy when trained on relatively small training sets and an active learning algorithm of optimally choosing the training set that significantly reduces the errors for the outliers. We compare our model to the other state-of-the-art algorithms from the literature on the widely used benchmark tests.

Development and Validation of the Guided Group Discussion Self-Estimate Inventory (GGD-SEI).

ERIC Educational Resources Information Center

Martin, David; Campbell, Bill

1998-01-01

A 19-item self-report measure was designed to promote increased self-awareness of a group leader's perceived ability to facilitate small group discussion. Results of analysis show high reliability and validity. The instrument, developed for use within education and training settings, provides a useful measure of guided small-group discussion…

Corpus and Method for Identifying Citations in Non-Academic Text (Open Access, Publisher’s Version)

DTIC Science & Technology

2014-05-31

patents, train a CRF classifier to find new citations, and apply a reranker to incorporate non-local information. Our best system achieves 0.83 F -score on...report precision, recall, and F -scores on chunk level. CRF training and decoding is performed with the CRF++ package7 using its default setting. 5.1...only obtain a very small number of training examples for statistical rerankers. 7http://crfpp.sourceforge.net Precision Recall F -score TEXT 0.7997 0.7805

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.

High-Throughput Classification of Radiographs Using Deep Convolutional Neural Networks.

PubMed

Rajkomar, Alvin; Lingam, Sneha; Taylor, Andrew G; Blum, Michael; Mongan, John

2017-02-01

The study aimed to determine if computer vision techniques rooted in deep learning can use a small set of radiographs to perform clinically relevant image classification with high fidelity. One thousand eight hundred eighty-five chest radiographs on 909 patients obtained between January 2013 and July 2015 at our institution were retrieved and anonymized. The source images were manually annotated as frontal or lateral and randomly divided into training, validation, and test sets. Training and validation sets were augmented to over 150,000 images using standard image manipulations. We then pre-trained a series of deep convolutional networks based on the open-source GoogLeNet with various transformations of the open-source ImageNet (non-radiology) images. These trained networks were then fine-tuned using the original and augmented radiology images. The model with highest validation accuracy was applied to our institutional test set and a publicly available set. Accuracy was assessed by using the Youden Index to set a binary cutoff for frontal or lateral classification. This retrospective study was IRB approved prior to initiation. A network pre-trained on 1.2 million greyscale ImageNet images and fine-tuned on augmented radiographs was chosen. The binary classification method correctly classified 100 % (95 % CI 99.73-100 %) of both our test set and the publicly available images. Classification was rapid, at 38 images per second. A deep convolutional neural network created using non-radiological images, and an augmented set of radiographs is effective in highly accurate classification of chest radiograph view type and is a feasible, rapid method for high-throughput annotation.

Infrared small target detection with kernel Fukunaga Koontz transform

NASA Astrophysics Data System (ADS)

Liu, Rui-ming; Liu, Er-qi; Yang, Jie; Zhang, Tian-hao; Wang, Fang-lin

2007-09-01

The Fukunaga-Koontz transform (FKT) has been proposed for many years. It can be used to solve two-pattern classification problems successfully. However, there are few researchers who have definitely extended FKT to kernel FKT (KFKT). In this paper, we first complete this task. Then a method based on KFKT is developed to detect infrared small targets. KFKT is a supervised learning algorithm. How to construct training sets is very important. For automatically detecting targets, the synthetic target images and real background images are used to train KFKT. Because KFKT can represent the higher order statistical properties of images, we expect better detection performance of KFKT than that of FKT. The well-devised experiments verify that KFKT outperforms FKT in detecting infrared small targets.

Learning Data Set Influence on Identification Accuracy of Gas Turbine Neural Network Model

NASA Astrophysics Data System (ADS)

Kuznetsov, A. V.; Makaryants, G. M.

2018-01-01

There are many gas turbine engine identification researches via dynamic neural network models. It should minimize errors between model and real object during identification process. Questions about training data set processing of neural networks are usually missed. This article presents a study about influence of data set type on gas turbine neural network model accuracy. The identification object is thermodynamic model of micro gas turbine engine. The thermodynamic model input signal is the fuel consumption and output signal is the engine rotor rotation frequency. Four types input signals was used for creating training and testing data sets of dynamic neural network models - step, fast, slow and mixed. Four dynamic neural networks were created based on these types of training data sets. Each neural network was tested via four types test data sets. In the result 16 transition processes from four neural networks and four test data sets from analogous solving results of thermodynamic model were compared. The errors comparison was made between all neural network errors in each test data set. In the comparison result it was shown error value ranges of each test data set. It is shown that error values ranges is small therefore the influence of data set types on identification accuracy is low.

Building community resilience through mental health infrastructure and training in post-Katrina New Orleans.

PubMed

Springgate, Benjamin F; Wennerstrom, Ashley; Meyers, Diana; Allen, Charles E; Vannoy, Steven D; Bentham, Wayne; Wells, Kenneth B

2011-01-01

To describe a disaster recovery model focused on developing mental health services and capacity-building within a disparities-focused, community-academic participatory partnership framework. Community-based participatory, partnered training and services delivery intervention in a post-disaster setting. Post-Katrina Greater New Orleans community. More than 400 community providers from more than 70 health and social services agencies participated in the trainings. Partnered development of a training and services delivery program involving physicians, therapists, community health workers, and other clinical and non-clinical personnel to improve access and quality of care for mental health services in a post-disaster setting. Services delivery (outreach, education, screening, referral, direct treatment); training delivery; satisfaction and feedback related to training; partnered development of training products. Clinical services in the form of outreach, education, screening, referral and treatment were provided in excess of 110,000 service units. More than 400 trainees participated in training, and provided feedback that led to evolution of training curricula and training products, to meet evolving community needs over time. Participant satisfaction with training generally scored very highly. This paper describes a participatory, health-focused model of community recovery that began with addressing emerging, unmet mental health needs using a disparities-conscious partnership framework as one of the principle mechanisms for intervention. Population mental health needs were addressed by investment in infrastructure and services capacity among small and medium sized non-profit organizations working in disaster-impacted, low resource settings.

Neural network approach to proximity effect corrections in electron-beam lithography

NASA Astrophysics Data System (ADS)

Frye, Robert C.; Cummings, Kevin D.; Rietman, Edward A.

1990-05-01

The proximity effect, caused by electron beam backscattering during resist exposure, is an important concern in writing submicron features. It can be compensated by appropriate local changes in the incident beam dose, but computation of the optimal correction usually requires a prohibitively long time. We present an example of such a computation on a small test pattern, which we performed by an iterative method. We then used this solution as a training set for an adaptive neural network. After training, the network computed the same correction as the iterative method, but in a much shorter time. Correcting the image with a software based neural network resulted in a decrease in the computation time by a factor of 30, and a hardware based network enhanced the computation speed by more than a factor of 1000. Both methods had an acceptably small error of 0.5% compared to the results of the iterative computation. Additionally, we verified that the neural network correctly generalized the solution of the problem to include patterns not contained in its training set.

Beyond Fine Tuning: Adding capacity to leverage few labels

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

Hodas, Nathan O.; Shaffer, Kyle J.; Yankov, Artem

2017-12-09

In this paper we present a technique to train neural network models on small amounts of data. Current methods for training neural networks on small amounts of rich data typically rely on strategies such as fine-tuning a pre-trained neural networks or the use of domain-specific hand-engineered features. Here we take the approach of treating network layers, or entire networks, as modules and combine pre-trained modules with untrained modules, to learn the shift in distributions between data sets. The central impact of using a modular approach comes from adding new representations to a network, as opposed to replacing representations via fine-tuning.more » Using this technique, we are able surpass results using standard fine-tuning transfer learning approaches, and we are also able to significantly increase performance over such approaches when using smaller amounts of data.« less

Agile convolutional neural network for pulmonary nodule classification using CT images.

PubMed

Zhao, Xinzhuo; Liu, Liyao; Qi, Shouliang; Teng, Yueyang; Li, Jianhua; Qian, Wei

2018-04-01

To distinguish benign from malignant pulmonary nodules using CT images is critical for their precise diagnosis and treatment. A new Agile convolutional neural network (CNN) framework is proposed to conquer the challenges of a small-scale medical image database and the small size of the nodules, and it improves the performance of pulmonary nodule classification using CT images. A hybrid CNN of LeNet and AlexNet is constructed through combining the layer settings of LeNet and the parameter settings of AlexNet. A dataset with 743 CT image nodule samples is built up based on the 1018 CT scans of LIDC to train and evaluate the Agile CNN model. Through adjusting the parameters of the kernel size, learning rate, and other factors, the effect of these parameters on the performance of the CNN model is investigated, and an optimized setting of the CNN is obtained finally. After finely optimizing the settings of the CNN, the estimation accuracy and the area under the curve can reach 0.822 and 0.877, respectively. The accuracy of the CNN is significantly dependent on the kernel size, learning rate, training batch size, dropout, and weight initializations. The best performance is achieved when the kernel size is set to [Formula: see text], the learning rate is 0.005, the batch size is 32, and dropout and Gaussian initialization are used. This competitive performance demonstrates that our proposed CNN framework and the optimization strategy of the CNN parameters are suitable for pulmonary nodule classification characterized by small medical datasets and small targets. The classification model might help diagnose and treat pulmonary nodules effectively.

Genomic Prediction of Seed Quality Traits Using Advanced Barley Breeding Lines.

PubMed

Nielsen, Nanna Hellum; Jahoor, Ahmed; Jensen, Jens Due; Orabi, Jihad; Cericola, Fabio; Edriss, Vahid; Jensen, Just

2016-01-01

Genomic selection was recently introduced in plant breeding. The objective of this study was to develop genomic prediction for important seed quality parameters in spring barley. The aim was to predict breeding values without expensive phenotyping of large sets of lines. A total number of 309 advanced spring barley lines tested at two locations each with three replicates were phenotyped and each line was genotyped by Illumina iSelect 9Kbarley chip. The population originated from two different breeding sets, which were phenotyped in two different years. Phenotypic measurements considered were: seed size, protein content, protein yield, test weight and ergosterol content. A leave-one-out cross-validation strategy revealed high prediction accuracies ranging between 0.40 and 0.83. Prediction across breeding sets resulted in reduced accuracies compared to the leave-one-out strategy. Furthermore, predicting across full and half-sib-families resulted in reduced prediction accuracies. Additionally, predictions were performed using reduced marker sets and reduced training population sets. In conclusion, using less than 200 lines in the training set can result in low prediction accuracy, and the accuracy will then be highly dependent on the family structure of the selected training set. However, the results also indicate that relatively small training sets (200 lines) are sufficient for genomic prediction in commercial barley breeding. In addition, our results indicate a minimum marker set of 1,000 to decrease the risk of low prediction accuracy for some traits or some families.

Genomic Prediction of Seed Quality Traits Using Advanced Barley Breeding Lines

PubMed Central

Nielsen, Nanna Hellum; Jahoor, Ahmed; Jensen, Jens Due; Orabi, Jihad; Cericola, Fabio; Edriss, Vahid; Jensen, Just

2016-01-01

Genomic selection was recently introduced in plant breeding. The objective of this study was to develop genomic prediction for important seed quality parameters in spring barley. The aim was to predict breeding values without expensive phenotyping of large sets of lines. A total number of 309 advanced spring barley lines tested at two locations each with three replicates were phenotyped and each line was genotyped by Illumina iSelect 9Kbarley chip. The population originated from two different breeding sets, which were phenotyped in two different years. Phenotypic measurements considered were: seed size, protein content, protein yield, test weight and ergosterol content. A leave-one-out cross-validation strategy revealed high prediction accuracies ranging between 0.40 and 0.83. Prediction across breeding sets resulted in reduced accuracies compared to the leave-one-out strategy. Furthermore, predicting across full and half-sib-families resulted in reduced prediction accuracies. Additionally, predictions were performed using reduced marker sets and reduced training population sets. In conclusion, using less than 200 lines in the training set can result in low prediction accuracy, and the accuracy will then be highly dependent on the family structure of the selected training set. However, the results also indicate that relatively small training sets (200 lines) are sufficient for genomic prediction in commercial barley breeding. In addition, our results indicate a minimum marker set of 1,000 to decrease the risk of low prediction accuracy for some traits or some families. PMID:27783639

Mental skills training with basic combat training soldiers: A group-randomized trial.

PubMed

Adler, Amy B; Bliese, Paul D; Pickering, Michael A; Hammermeister, Jon; Williams, Jason; Harada, Coreen; Csoka, Louis; Holliday, Bernie; Ohlson, Carl

2015-11-01

Cognitive skills training has been linked to greater skills, self-efficacy, and performance. Although research in a variety of organizational settings has demonstrated training efficacy, few studies have assessed cognitive skills training using rigorous, longitudinal, randomized trials with active controls. The present study examined cognitive skills training in a high-risk occupation by randomizing 48 platoons (N = 2,432 soldiers) in basic combat training to either (a) mental skills training or (b) an active comparison condition (military history). Surveys were conducted at baseline and 3 times across the 10-week course. Multilevel mixed-effects models revealed that soldiers in the mental skills training condition reported greater use of a range of cognitive skills and increased confidence relative to those in the control condition. Soldiers in the mental skills training condition also performed better on obstacle course events, rappelling, physical fitness, and initial weapons qualification scores, although effects were generally moderated by gender and previous experience. Overall, effects were small; however, given the rigor of the design, the findings clearly contribute to the broader literature by providing supporting evidence that cognitive training skills can enhance performance in occupational and sports settings. Future research should address gender and experience to determine the need for targeting such training appropriately. (c) 2015 APA, all rights reserved).

Effects of a structured 20-session slow-cortical-potential-based neurofeedback program on attentional performance in children and adolescents with attention-deficit hyperactivity disorder: retrospective analysis of an open-label pilot-approach and 6-month follow-up.

PubMed

Albrecht, Johanna S; Bubenzer-Busch, Sarah; Gallien, Anne; Knospe, Eva Lotte; Gaber, Tilman J; Zepf, Florian D

2017-01-01

The aim of this approach was to conduct a structured electroencephalography-based neurofeedback training program for children and adolescents with attention-deficit hyperactivity disorder (ADHD) using slow cortical potentials with an intensive first (almost daily sessions) and second phase of training (two sessions per week) and to assess aspects of attentional performance. A total of 24 young patients with ADHD participated in the 20-session training program. During phase I of training (2 weeks, 10 sessions), participants were trained on weekdays. During phase II, neurofeedback training occurred twice per week (5 weeks). The patients' inattention problems were measured at three assessment time points before (pre, T0) and after (post, T1) the training and at a 6-month follow-up (T2); the assessments included neuropsychological tests (Alertness and Divided Attention subtests of the Test for Attentional Performance; Sustained Attention Dots and Shifting Attentional Set subtests of the Amsterdam Neuropsychological Test) and questionnaire data (inattention subscales of the so-called Fremdbeurteilungsbogen für Hyperkinetische Störungen and Child Behavior Checklist/4-18 [CBCL/4-18]). All data were analyzed retrospectively. The mean auditive reaction time in a Divided Attention task decreased significantly from T0 to T1 (medium effect), which was persistent over time and also found for a T0-T2 comparison (larger effects). In the Sustained Attention Dots task, the mean reaction time was reduced from T0-T1 and T1-T2 (small effects), whereas in the Shifting Attentional Set task, patients were able to increase the number of trials from T1-T2 and significantly diminished the number of errors (T1-T2 & T0-T2, large effects). First positive but very small effects and preliminary results regarding different parameters of attentional performance were detected in young individuals with ADHD. The limitations of the obtained preliminary data are the rather small sample size, the lack of a control group/a placebo condition and the open-label approach because of the clinical setting and retrospective analysis. The value of the current approach lies in providing pilot data for future studies involving larger samples.

Interdisciplinary problem-based learning as a method to prepare Micronesia for public health emergencies.

PubMed

Yamada, Seiji; Durand, A Mark; Chen, Tai-Ho; Maskarinec, Gregory G

2007-03-01

The University of Hawai'i Pacific Basin Bioterrorism Curriculum Development Project has developed a problem-based learning (PBL) curriculum for teaching health professionals and health professional students about bioterrorism and other public health emergencies. These PBL cases have been incorporated into interdisciplinary training settings in community-based settings, such as in the small island districts of the U.S.-Affiliated Pacific Islands. Quantitative and qualitative methods have been utilized in the evaluation of the PBL cases, PBL tutorials, and the accomplishment of learning objectives. Evaluation of the PBL tutorials demonstrates that PBL is an educational and training modality appropriate for such settings. Participants found it helpful to learn in interdisciplinary groups. The educational process was modified in accordance with local culture. PBL is a useful educational modality for settings where healthcare staffing and available resources are limited.

Effects and dose–response relationships of resistance training on physical performance in youth athletes: a systematic review and meta-analysis

PubMed Central

Lesinski, Melanie; Prieske, Olaf; Granacher, Urs

2016-01-01

Objectives To quantify age, sex, sport and training type-specific effects of resistance training on physical performance, and to characterise dose–response relationships of resistance training parameters that could maximise gains in physical performance in youth athletes. Design Systematic review and meta-analysis of intervention studies. Data sources Studies were identified by systematic literature search in the databases PubMed and Web of Science (1985–2015). Weighted mean standardised mean differences (SMDwm) were calculated using random-effects models. Eligibility criteria for selecting studies Only studies with an active control group were included if these investigated the effects of resistance training in youth athletes (6–18 years) and tested at least one physical performance measure. Results 43 studies met the inclusion criteria. Our analyses revealed moderate effects of resistance training on muscle strength and vertical jump performance (SMDwm 0.8–1.09), and small effects on linear sprint, agility and sport-specific performance (SMDwm 0.58–0.75). Effects were moderated by sex and resistance training type. Independently computed dose–response relationships for resistance training parameters revealed that a training period of >23 weeks, 5 sets/exercise, 6–8 repetitions/set, a training intensity of 80–89% of 1 repetition maximum (RM), and 3–4 min rest between sets were most effective to improve muscle strength (SMDwm 2.09–3.40). Summary/conclusions Resistance training is an effective method to enhance muscle strength and jump performance in youth athletes, moderated by sex and resistance training type. Dose–response relationships for key training parameters indicate that youth coaches should primarily implement resistance training programmes with fewer repetitions and higher intensities to improve physical performance measures of youth athletes. PMID:26851290

SU-E-E-05: Improving Contouring Precision and Consistency for Physicians-In-Training with Simple Lab Experiments

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

Ma, L; Larson, D A

2015-06-15

Purpose: Target contouring for high-dose treatments such as radiosurgery of brain metastases is highly critical in eliminating marginal failure and reducing complications as shown by recent clinical studies. In order to improve contouring accuracy and practice consistency for the procedure, we introduced a self-assessed physics lab practice for the physicians-in-training. Methods: A set of commercially acquired high-precision PMMA plastic spheres were randomly embedded in a Styrofoam block and then scanned with the CT/MR via the clinical procedural imaging protocol. A group of first-year physicians-in-training (n=6) from either neurosurgery or radiation oncology department were asked to contour the scanned objects (diametermore » ranged from 0.4 cm to 3.8 cm). These user-defined contours were then compared with the ideal contour sets of object shape for self assessments to determine the maximum areas of the observed discrepancies and method of improvements. Results: The largest discrepancies from initial practice were consistently found to be located near the extreme longitudinal portions of the target for all the residents. Discrepancy was especially prominent when contouring small objects < 1.0 cm in diameters. For example, the mean volumes rendered from the initial contour data set differed from the ideal data set by 7.7%±6.6% for the participants (p> 0.23 suggesting agreement cannot be established). However, when incorporating a secondary imaging scan such as reconstructed coronal or sagittal images in a repeat practice, the agreement was dramatically improved yielding p<0.02 in agreement with the reference data set for all the participants. Conclusion: A simple physics lab revealed a common pitfall in contouring small metastatic brain tumors for radiosurgical procedures and provided a systematic tool for physicians-in-training in improving their clinical contouring skills. Dr Ma is current a board member of international stereotactic radiosurgical society.« less

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.

Bumblebees (Bombus terrestris) and honeybees (Apis mellifera) prefer similar colours of higher spectral purity over trained colours.

PubMed

Rohde, Katja; Papiorek, Sarah; Lunau, Klaus

2013-03-01

Differences in the concentration of pigments as well as their composition and spatial arrangement cause intraspecific variation in the spectral signature of flowers. Known colour preferences and requirements for flower-constant foraging bees predict different responses to colour variability. In experimental settings, we simulated small variations of unicoloured petals and variations in the spatial arrangement of colours within tricoloured petals using artificial flowers and studied their impact on the colour choices of bumblebees and honeybees. Workers were trained to artificial flowers of a given colour and then given the simultaneous choice between three test colours: either the training colour, one colour of lower and one of higher spectral purity, or the training colour, one colour of lower and one of higher dominant wavelength; in all cases the perceptual contrast between the training colour and the additional test colours was similarly small. Bees preferred artificial test flowers which resembled the training colour with the exception that they preferred test colours with higher spectral purity over trained colours. Testing the behaviour of bees at artificial flowers displaying a centripetal or centrifugal arrangement of three equally sized colours with small differences in spectral purity, bees did not prefer any type of artificial flowers, but preferentially choose the most spectrally pure area for the first antenna contact at both types of artificial flowers. Our results indicate that innate preferences for flower colours of high spectral purity in pollinators might exert selective pressure on the evolution of flower colours.

Emergency obstetric simulation training: how do we know where we are going, if we don't know where we have been?

PubMed

Calvert, Katrina L; McGurgan, Paul M; Debenham, Edward M; Gratwick, Frances J; Maouris, Panos

2013-12-01

Obstetric emergencies contribute significantly to maternal morbidity and mortality. Current training in the management of obstetric emergencies in Australia and internationally focusses on utilising a multidisciplinary simulation-based model. Arguments for and against this type of training exist, using both economic and clinical reasoning. To identify the evidence base for the clinical impact of simulation training in obstetric emergencies and to address some of the concerns regarding appropriate delivery of obstetric emergency training in the Australian setting. A literature search was performed to identify research undertaken in the area of obstetric emergency training. The initial literature search using broad search terms identified 887 articles which were then reviewed and considered for inclusion if they provided original research with a specific emphasis on the impact of training on clinical outcomes. Ninety-two articles were identified, comprising evidence in the following clinical situations: eclampsia, shoulder dystocia, postpartum haemorrhage, maternal collapse, cord prolapse and teamwork training. Evidence exists for a benefit in knowledge or skills gained from simulation training and for the benefit of training in small units without access to high-fidelity equipment or facilities. Evidence exists for a positive impact of training in obstetric emergencies, although the majority of the available evidence applies to evaluation at the level of participants' confidence, knowledge or skills rather than at the level of impact on clinical outcomes. The model of simulation-based training is an appropriate one for the Australian setting and should be further utilised in rural and remote settings. © 2013 The Royal Australian and New Zealand College of Obstetricians and Gynaecologists.

PDA usage and training: targeting curriculum for residents and faculty.

PubMed

Morris, Carl G; Church, Lili; Vincent, Chris; Rao, Ashwin

2007-06-01

Utilization of personal digital assistants (PDAs) in residency education is common, but information about their use and how residents are trained to use them is limited. Better understanding of resident and faculty PDA use and training is needed. We used a cross-sectional survey of 598 residents and faculty from the WWAMI (Washington, Wyoming, Alaska, Montana, and Idaho) Family Medicine Residency Network regarding PDA usage and training. Use of PDAs is common among residents (94%) and faculty (79%). Ninety-six percent of faculty and residents report stable or increasing frequency of use over time. The common barriers to PDA use relate to lack of time, knowledge, and formal education. Approximately half of PDA users (52%) have received some formal training; however, the majority of users report being self-taught. Faculty and residents prefer either small-group or one-on-one settings with hands-on, self-directed, interactive formats for PDA training. Large-group settings in lecture, written, or computer program formats were considered less helpful or desirable. PDAs have become a commonly used clinical tool. Lack of time and adequate training present a barrier to optimal application of PDAs in family medicine residency education.

EFFECTS OF HIV/AIDS ON MATERNITY CARE PROVIDERS IN KENYA

PubMed Central

Turan, Janet M.; Bukusi, Elizabeth A.; Cohen, Craig R.; Sande, John; Miller, Suellen

2008-01-01

Objective To explore the impact of HIV/AIDS on maternity care providers (MCP) in labor and delivery in a high HIV prevalence setting in sub-Saharan Africa. Design Qualitative one-on-one in-depth interviews with MCPs. Setting Four health facilities providing labor and delivery services (2 public hospitals, a public health center, and a small private maternity hospital) in Kisumu, Nyanza Province, Kenya. Participants Eighteen (18) MCPs, including 14 nurse/midwives, 2 physician assistants, and 2 physicians (ob/gyn specialists). Results The HIV/AIDS epidemic has had numerous adverse effects and a few positive effects on MCPs in this setting. Adverse effects include reductions in the number of health care providers, increased workload, burnout, reduced availability of services in small health facilities when workers are absent due to attending HIV/AIDS training programs, difficulties with confidentiality and unwanted disclosure, and MCPs' fears of becoming HIV infected and the resulting stigma and discrimination. Positive effects include improved infection control procedures on maternity wards and enhanced MCP knowledge and skills. Conclusion A multi-faceted package including policy, infrastructure, and training interventions is needed to support MCPs in these settings and ensure that they are able to perform their critical roles in maternal healthcare and prevention of HIV/AIDS transmission. PMID:18811779

Doing the Job Better

ERIC Educational Resources Information Center

Amer Vocat J, 1970

1970-01-01

The topics of six speeches presented at the 1969 American Vocational Convention include a bilingual office occupations project, distributive education in a rural setting, vocational subject mix and on the job training curriculum, integrated shop programs for small high schools, and innovations in industrial arts and home economics. (BC)

Combining Crowd and Expert Labels using Decision Theoretic Active Learning

DTIC Science & Technology

2015-10-11

meta-data such as titles, author information and keywords. Motivating Application: Biomedical Systematic Reviews Evidence - based medicine (EBM) aims to...individuals trained in evidence - based medicine ; usually MDs) reading the entire set of citations retrieved via database search to identify the small

Active learning strategies for the deduplication of electronic patient data using classification trees.

PubMed

Sariyar, M; Borg, A; Pommerening, K

2012-10-01

Supervised record linkage methods often require a clerical review to gain informative training data. Active learning means to actively prompt the user to label data with special characteristics in order to minimise the review costs. We conducted an empirical evaluation to investigate whether a simple active learning strategy using binary comparison patterns is sufficient or if string metrics together with a more sophisticated algorithm are necessary to achieve high accuracies with a small training set. Based on medical registry data with different numbers of attributes, we used active learning to acquire training sets for classification trees, which were then used to classify the remaining data. Active learning for binary patterns means that every distinct comparison pattern represents a stratum from which one item is sampled. Active learning for patterns consisting of the Levenshtein string metric values uses an iterative process where the most informative and representative examples are added to the training set. In this context, we extended the active learning strategy by Sarawagi and Bhamidipaty (2002). On the original data set, active learning based on binary comparison patterns leads to the best results. When dropping four or six attributes, using string metrics leads to better results. In both cases, not more than 200 manually reviewed training examples are necessary. In record linkage applications where only forename, name and birthday are available as attributes, we suggest the sophisticated active learning strategy based on string metrics in order to achieve highly accurate results. We recommend the simple strategy if more attributes are available, as in our study. In both cases, active learning significantly reduces the amount of manual involvement in training data selection compared to usual record linkage settings. Copyright © 2012 Elsevier Inc. All rights reserved.

Short-term adaptations following Complex Training in team-sports: A meta-analysis

PubMed Central

Martinez-Rodriguez, Alejandro; Calleja-González, Julio; Alcaraz, Pedro E.

2017-01-01

Objective The purpose of this meta-analysis was to study the short-term adaptations on sprint and vertical jump (VJ) performance following Complex Training (CT) in team-sports. CT is a resistance training method aimed at developing both strength and power, which has a direct effect on sprint and VJ. It consists on alternating heavy resistance training exercises with plyometric/power ones, set for set, on the same workout. Methods A search of electronic databases up to July 2016 (PubMed-MEDLINE, SPORTDiscus, Web of Knowledge) was conducted. Inclusion criteria: 1) at least one CT intervention group; 2) training protocols ≥4-wks; 3) sample of team-sport players; 4) sprint or VJ as an outcome variable. Effect sizes (ES) of each intervention were calculated and subgroup analyses were performed. Results A total of 9 studies (13 CT groups) met the inclusion criteria. Medium effect sizes (ES) (ES = 0.73) were obtained for pre-post improvements in sprint, and small (ES = 0.41) in VJ, following CT. Experimental-groups presented better post-intervention sprint (ES = 1.01) and VJ (ES = 0.63) performance than control-groups. Sprint large ESs were exhibited in younger athletes (<20 years old; ES = 1.13); longer CT interventions (≥6 weeks; ES = 0.95); conditioning activities with intensities ≤85% 1RM (ES = 0.96) and protocols with frequencies of <3 sessions/week (ES = 0.84). Medium ESs were obtained in Division I players (ES = 0.76); training programs >12 total sessions (ES = 0.74). VJ Large ESs in programs with >12 total sessions (ES = 0.81). Medium ESs obtained for under-Division I individuals (ES = 0.56); protocols with intracomplex rest intervals ≥2 min (ES = 0.55); conditioning activities with intensities ≤85% 1RM (ES = 0.64); basketball/volleyball players (ES = 0.55). Small ESs were found for younger athletes (ES = 0.42); interventions ≥6 weeks (ES = 0.45). Conclusions CT interventions have positive medium effects on sprint performance and small effects on VJ in team-sport athletes. This training method is a suitable option to include in the season planning. PMID:28662108

Predicting Mouse Liver Microsomal Stability with “Pruned” Machine Learning Models and Public Data

PubMed Central

Perryman, Alexander L.; Stratton, Thomas P.; Ekins, Sean; Freundlich, Joel S.

2015-01-01

Purpose Mouse efficacy studies are a critical hurdle to advance translational research of potential therapeutic compounds for many diseases. Although mouse liver microsomal (MLM) stability studies are not a perfect surrogate for in vivo studies of metabolic clearance, they are the initial model system used to assess metabolic stability. Consequently, we explored the development of machine learning models that can enhance the probability of identifying compounds possessing MLM stability. Methods Published assays on MLM half-life values were identified in PubChem, reformatted, and curated to create a training set with 894 unique small molecules. These data were used to construct machine learning models assessed with internal cross-validation, external tests with a published set of antitubercular compounds, and independent validation with an additional diverse set of 571 compounds (PubChem data on percent metabolism). Results “Pruning” out the moderately unstable/moderately stable compounds from the training set produced models with superior predictive power. Bayesian models displayed the best predictive power for identifying compounds with a half-life ≥1 hour. Conclusions Our results suggest the pruning strategy may be of general benefit to improve test set enrichment and provide machine learning models with enhanced predictive value for the MLM stability of small organic molecules. This study represents the most exhaustive study to date of using machine learning approaches with MLM data from public sources. PMID:26415647

Predicting Mouse Liver Microsomal Stability with "Pruned" Machine Learning Models and Public Data.

PubMed

Perryman, Alexander L; Stratton, Thomas P; Ekins, Sean; Freundlich, Joel S

2016-02-01

Mouse efficacy studies are a critical hurdle to advance translational research of potential therapeutic compounds for many diseases. Although mouse liver microsomal (MLM) stability studies are not a perfect surrogate for in vivo studies of metabolic clearance, they are the initial model system used to assess metabolic stability. Consequently, we explored the development of machine learning models that can enhance the probability of identifying compounds possessing MLM stability. Published assays on MLM half-life values were identified in PubChem, reformatted, and curated to create a training set with 894 unique small molecules. These data were used to construct machine learning models assessed with internal cross-validation, external tests with a published set of antitubercular compounds, and independent validation with an additional diverse set of 571 compounds (PubChem data on percent metabolism). "Pruning" out the moderately unstable / moderately stable compounds from the training set produced models with superior predictive power. Bayesian models displayed the best predictive power for identifying compounds with a half-life ≥1 h. Our results suggest the pruning strategy may be of general benefit to improve test set enrichment and provide machine learning models with enhanced predictive value for the MLM stability of small organic molecules. This study represents the most exhaustive study to date of using machine learning approaches with MLM data from public sources.

Boosting productivity: a framework for professional/amateur collaborative teamwork

NASA Astrophysics Data System (ADS)

Al-Shedhani, Saleh S.

2002-11-01

As technology advances, remote operation of telescopes has paved the way for joint observational projects between Astronomy clubs. Equipped with a small telescope, a standard CCD, and a networked computer, the observatory can be set up to carry out several photometric studies. However, most club members lack the basic training and background required for such tasks. A collaborative network between professionals and amateurs is proposed to utilize professional know-how and amateurs' readiness for continuous observations. Working as a team, various long-term observational projects can be carried out using small telescopes. Professionals can play an important role in raising the standards of astronomy clubs via specialized training programs for members on how to use the available technology to search/observe certain events (e.g. supernovae, comets, etc.). Professionals in return can accumulate a research-relevant database and can set up an early notification scheme based on comparative analyses of the recently-added images in an online archive. Here we present a framework for the above collaborative teamwork that uses web-based communication tools to establish remote/robotic operation of the telescope, and an online archive and discussion forum, to maximize the interactions between professionals and amateurs and to boost the productivity of small telescope observatories.

Research on Daily Objects Detection Based on Deep Neural Network

NASA Astrophysics Data System (ADS)

Ding, Sheng; Zhao, Kun

2018-03-01

With the rapid development of deep learning, great breakthroughs have been made in the field of object detection. In this article, the deep learning algorithm is applied to the detection of daily objects, and some progress has been made in this direction. Compared with traditional object detection methods, the daily objects detection method based on deep learning is faster and more accurate. The main research work of this article: 1. collect a small data set of daily objects; 2. in the TensorFlow framework to build different models of object detection, and use this data set training model; 3. the training process and effect of the model are improved by fine-tuning the model parameters.

Acute neuromuscular and performance responses to Nordic hamstring exercises completed before or after football training.

PubMed

Lovell, Ric; Siegler, Jason C; Knox, Michael; Brennan, Scott; Marshall, Paul W M

2016-12-01

The optimal scheduling of Nordic Hamstring exercises (NHEs) relative to football training sessions is unknown. We examined the acute neuromuscular and performance responses to NHE undertaken either before (BT) or after (AT) simulated football training. Twelve amateur players performed six sets of five repetitions of the NHE either before or after 60 min of standardised football-specific exercise (SAFT 60 ). Surface electromyography signals (EMG) of the hamstring muscles were recorded during both the NHE, and maximum eccentric actions of the knee flexors (0.52 rad · s -1 ) performed before and after the NHE programme, and at 15 min intervals during SAFT 60 . Ten-metre sprint times were recorded on three occasions during each 15 min SAFT 60 segment. Greater eccentric hamstring fatigue following the NHE programme was observed in BT versus AT (19.8 %; very likely small effect), which was particularly apparent in the latter range of knee flexion (0-15°; 39.6%; likely moderate effect), and synonymous with hamstring EMG declines (likely small-likely moderate effects). Performing NHE BT attenuated sprint performance declines (2.0-3.2%; likely small effects), but decreased eccentric hamstring peak torque (-14.1 to -18.9%; likely small effects) during football-specific exercise. Performing NHE prior to football training reduces eccentric hamstring strength and may exacerbate hamstring injury risk.

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

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

Model-Based Learning of Local Image Features for Unsupervised Texture Segmentation

NASA Astrophysics Data System (ADS)

Kiechle, Martin; Storath, Martin; Weinmann, Andreas; Kleinsteuber, Martin

2018-04-01

Features that capture well the textural patterns of a certain class of images are crucial for the performance of texture segmentation methods. The manual selection of features or designing new ones can be a tedious task. Therefore, it is desirable to automatically adapt the features to a certain image or class of images. Typically, this requires a large set of training images with similar textures and ground truth segmentation. In this work, we propose a framework to learn features for texture segmentation when no such training data is available. The cost function for our learning process is constructed to match a commonly used segmentation model, the piecewise constant Mumford-Shah model. This means that the features are learned such that they provide an approximately piecewise constant feature image with a small jump set. Based on this idea, we develop a two-stage algorithm which first learns suitable convolutional features and then performs a segmentation. We note that the features can be learned from a small set of images, from a single image, or even from image patches. The proposed method achieves a competitive rank in the Prague texture segmentation benchmark, and it is effective for segmenting histological images.

Reflective teaching practices: an approach to teaching communication skills in a small-group setting.

PubMed

Fryer-Edwards, Kelly; Arnold, Robert M; Baile, Walter; Tulsky, James A; Petracca, Frances; Back, Anthony

2006-07-01

Small-group teaching is particularly suited for complex skills such as communication. Existing work has identified the basic elements of small-group teaching, but few descriptions of higher-order teaching practices exist in the medical literature. Thus the authors developed an empirically driven and theoretically grounded model for small-group communication-skills teaching. Between 2002 and 2005, teaching observations were collected over 100 hours of direct contact time between four expert facilitators and 120 medical oncology fellows participating in Oncotalk, a semiannual, four-day retreat focused on end-of-life communication skills. The authors conducted small-group teaching observations, semistructured interviews with faculty participants, video or audio recording with transcript review, and evaluation of results by faculty participants. Teaching skills observed during the retreats included a linked set of reflective, process-oriented teaching practices: identifying a learning edge, proposing and testing hypotheses, and calibrating learner self-assessments. Based on observations and debriefings with facilitators, the authors developed a conceptual model of teaching that illustrates an iterative loop of teaching practices aimed at enhancing learners' engagement and self-efficacy. Through longitudinal, empirical observations, this project identified a set of specific teaching skills for small-group settings with applicability to other clinical teaching settings. This study extends current theory and teaching practice prescriptions by describing specific teaching practices required for effective teaching. These reflective teaching practices, while developed for communication skills training, may be useful for teaching other challenging topics such as ethics and professionalism.

Ranking the whole MEDLINE database according to a large training set using text indexing.

PubMed

Suomela, Brian P; Andrade, Miguel A

2005-03-24

The MEDLINE database contains over 12 million references to scientific literature, with about 3/4 of recent articles including an abstract of the publication. Retrieval of entries using queries with keywords is useful for human users that need to obtain small selections. However, particular analyses of the literature or database developments may need the complete ranking of all the references in the MEDLINE database as to their relevance to a topic of interest. This report describes a method that does this ranking using the differences in word content between MEDLINE entries related to a topic and the whole of MEDLINE, in a computational time appropriate for an article search query engine. We tested the capabilities of our system to retrieve MEDLINE references which are relevant to the subject of stem cells. We took advantage of the existing annotation of references with terms from the MeSH hierarchical vocabulary (Medical Subject Headings, developed at the National Library of Medicine). A training set of 81,416 references was constructed by selecting entries annotated with the MeSH term stem cells or some child in its sub tree. Frequencies of all nouns, verbs, and adjectives in the training set were computed and the ratios of word frequencies in the training set to those in the entire MEDLINE were used to score references. Self-consistency of the algorithm, benchmarked with a test set containing the training set and an equal number of references randomly selected from MEDLINE was better using nouns (79%) than adjectives (73%) or verbs (70%). The evaluation of the system with 6,923 references not used for training, containing 204 articles relevant to stem cells according to a human expert, indicated a recall of 65% for a precision of 65%. This strategy appears to be useful for predicting the relevance of MEDLINE references to a given concept. The method is simple and can be used with any user-defined training set. Choice of the part of speech of the words used for classification has important effects on performance. Lists of words, scripts, and additional information are available from the web address http://www.ogic.ca/projects/ks2004/.

The Critical Role of Organic Chemistry in Drug Discovery.

PubMed

Rotella, David P

2016-10-19

Small molecules remain the backbone for modern drug discovery. They are conceived and synthesized by medicinal chemists, many of whom were originally trained as organic chemists. Support from government and industry to provide training and personnel for continued development of this critical skill set has been declining for many years. This Viewpoint highlights the value of organic chemistry and organic medicinal chemists in the complex journey of drug discovery as a reminder that basic science support must be restored.

Peer-Assisted Learning in the Athletic Training Clinical Setting

PubMed Central

Henning, Jolene M; Weidner, Thomas G; Jones, James

2006-01-01

Context: Athletic training educators often anecdotally suggest that athletic training students enhance their learning by teaching their peers. However, peer-assisted learning (PAL) has not been examined within athletic training education in order to provide evidence for its current use or as a pedagogic tool. Objective: To describe the prevalence of PAL in athletic training clinical education and to identify students' perceptions of PAL. Design: Descriptive. Setting: “The Athletic Training Student Seminar” at the National Athletic Trainers' Association 2002 Annual Meeting and Clinical Symposia. Patients or Other Participants: A convenience sample of 138 entry-level male and female athletic training students. Main Outcome Measure(s): Students' perceptions regarding the prevalence and benefits of and preferences for PAL were measured using the Athletic Training Peer-Assisted Learning Assessment Survey. The Survey is a self-report tool with 4 items regarding the prevalence of PAL and 7 items regarding perceived benefits and preferences. Results: A total of 66% of participants practiced a moderate to large amount of their clinical skills with other athletic training students. Sixty percent of students reported feeling less anxious when performing clinical skills on patients in front of other athletic training students than in front of their clinical instructors. Chi-square analysis revealed that 91% of students enrolled in Commission on Accreditation of Allied Health Education Programs–accredited athletic training education programs learned a minimal to small amount of clinical skills from their peers compared with 65% of students in Joint Review Committee on Educational Programs in Athletic Training–candidacy schools (χ2 3 = 14.57, P < .01). Multiple analysis of variance revealed significant interactions between sex and academic level on several items regarding benefits and preferences. Conclusions: According to athletic training students, PAL is occurring in the athletic training clinical setting. Entry-level students are utilizing their peers as resources for practicing clinical skills and report benefiting from the collaboration. Educators should consider deliberately integrating PAL into athletic training education programs to enhance student learning and collaboration. PMID:16619102

Effects and dose-response relationships of resistance training on physical performance in youth athletes: a systematic review and meta-analysis.

PubMed

Lesinski, Melanie; Prieske, Olaf; Granacher, Urs

2016-07-01

To quantify age, sex, sport and training type-specific effects of resistance training on physical performance, and to characterise dose-response relationships of resistance training parameters that could maximise gains in physical performance in youth athletes. Systematic review and meta-analysis of intervention studies. Studies were identified by systematic literature search in the databases PubMed and Web of Science (1985-2015). Weighted mean standardised mean differences (SMDwm) were calculated using random-effects models. Only studies with an active control group were included if these investigated the effects of resistance training in youth athletes (6-18 years) and tested at least one physical performance measure. 43 studies met the inclusion criteria. Our analyses revealed moderate effects of resistance training on muscle strength and vertical jump performance (SMDwm 0.8-1.09), and small effects on linear sprint, agility and sport-specific performance (SMDwm 0.58-0.75). Effects were moderated by sex and resistance training type. Independently computed dose-response relationships for resistance training parameters revealed that a training period of >23 weeks, 5 sets/exercise, 6-8 repetitions/set, a training intensity of 80-89% of 1 repetition maximum (RM), and 3-4 min rest between sets were most effective to improve muscle strength (SMDwm 2.09-3.40). Resistance training is an effective method to enhance muscle strength and jump performance in youth athletes, moderated by sex and resistance training type. Dose-response relationships for key training parameters indicate that youth coaches should primarily implement resistance training programmes with fewer repetitions and higher intensities to improve physical performance measures of youth athletes. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Tomography and generative training with quantum Boltzmann machines

NASA Astrophysics Data System (ADS)

Kieferová, Mária; Wiebe, Nathan

2017-12-01

The promise of quantum neural nets, which utilize quantum effects to model complex data sets, has made their development an aspirational goal for quantum machine learning and quantum computing in general. Here we provide methods of training quantum Boltzmann machines. Our work generalizes existing methods and provides additional approaches for training quantum neural networks that compare favorably to existing methods. We further demonstrate that quantum Boltzmann machines enable a form of partial quantum state tomography that further provides a generative model for the input quantum state. Classical Boltzmann machines are incapable of this. This verifies the long-conjectured connection between tomography and quantum machine learning. Finally, we prove that classical computers cannot simulate our training process in general unless BQP=BPP , provide lower bounds on the complexity of the training procedures and numerically investigate training for small nonstoquastic Hamiltonians.

Improved Classification of Mammograms Following Idealized Training

PubMed Central

Hornsby, Adam N.; Love, Bradley C.

2014-01-01

People often make decisions by stochastically retrieving a small set of relevant memories. This limited retrieval implies that human performance can be improved by training on idealized category distributions (Giguère & Love, 2013). Here, we evaluate whether the benefits of idealized training extend to categorization of real-world stimuli, namely classifying mammograms as normal or tumorous. Participants in the idealized condition were trained exclusively on items that, according to a norming study, were relatively unambiguous. Participants in the actual condition were trained on a representative range of items. Despite being exclusively trained on easy items, idealized-condition participants were more accurate than those in the actual condition when tested on a range of item types. However, idealized participants experienced difficulties when test items were very dissimilar from training cases. The benefits of idealization, attributable to reducing noise arising from cognitive limitations in memory retrieval, suggest ways to improve real-world decision making. PMID:24955325

Improved Classification of Mammograms Following Idealized Training.

PubMed

Hornsby, Adam N; Love, Bradley C

2014-06-01

People often make decisions by stochastically retrieving a small set of relevant memories. This limited retrieval implies that human performance can be improved by training on idealized category distributions (Giguère & Love, 2013). Here, we evaluate whether the benefits of idealized training extend to categorization of real-world stimuli, namely classifying mammograms as normal or tumorous. Participants in the idealized condition were trained exclusively on items that, according to a norming study, were relatively unambiguous. Participants in the actual condition were trained on a representative range of items. Despite being exclusively trained on easy items, idealized-condition participants were more accurate than those in the actual condition when tested on a range of item types. However, idealized participants experienced difficulties when test items were very dissimilar from training cases. The benefits of idealization, attributable to reducing noise arising from cognitive limitations in memory retrieval, suggest ways to improve real-world decision making.

Back-pressure Effect on Shock-Train Location in a Scramjet Engine Isolator

DTIC Science & Technology

2010-03-01

valves .......................................................................................... 57 Side project: making an actuator stand...21 Figure 8. Main manual shut off valve ...................................................................................22 Figure 9 . A small...characteristic about this wind tunnel. With Mach 1.8 nozzle, prior to test runs, the upstream regulator pressure valve (Figure 9 ) was set at

Automatic Earthquake Detection by Active Learning

NASA Astrophysics Data System (ADS)

Bergen, K.; Beroza, G. C.

2017-12-01

In recent years, advances in machine learning have transformed fields such as image recognition, natural language processing and recommender systems. Many of these performance gains have relied on the availability of large, labeled data sets to train high-accuracy models; labeled data sets are those for which each sample includes a target class label, such as waveforms tagged as either earthquakes or noise. Earthquake seismologists are increasingly leveraging machine learning and data mining techniques to detect and analyze weak earthquake signals in large seismic data sets. One of the challenges in applying machine learning to seismic data sets is the limited labeled data problem; learning algorithms need to be given examples of earthquake waveforms, but the number of known events, taken from earthquake catalogs, may be insufficient to build an accurate detector. Furthermore, earthquake catalogs are known to be incomplete, resulting in training data that may be biased towards larger events and contain inaccurate labels. This challenge is compounded by the class imbalance problem; the events of interest, earthquakes, are infrequent relative to noise in continuous data sets, and many learning algorithms perform poorly on rare classes. In this work, we investigate the use of active learning for automatic earthquake detection. Active learning is a type of semi-supervised machine learning that uses a human-in-the-loop approach to strategically supplement a small initial training set. The learning algorithm incorporates domain expertise through interaction between a human expert and the algorithm, with the algorithm actively posing queries to the user to improve detection performance. We demonstrate the potential of active machine learning to improve earthquake detection performance with limited available training data.

Development and validation of a radiomics nomogram for progression-free survival prediction in stage IV EGFR-mutant non-small cell lung cancer

NASA Astrophysics Data System (ADS)

Song, Jiangdian; Zang, Yali; Li, Weimin; Zhong, Wenzhao; Shi, Jingyun; Dong, Di; Fang, Mengjie; Liu, Zaiyi; Tian, Jie

2017-03-01

Accurately predict the risk of disease progression and benefit of tyrosine kinase inhibitors (TKIs) therapy for stage IV non-small cell lung cancer (NSCLC) patients with activing epidermal growth factor receptor (EGFR) mutations by current staging methods are challenge. We postulated that integrating a classifier consisted of multiple computed tomography (CT) phenotypic features, and other clinicopathological risk factors into a single model could improve risk stratification and prediction of progression-free survival (PFS) of EGFR TKIs for these patients. Patients confirmed as stage IV EGFR-mutant NSCLC received EGFR TKIs with no resection; pretreatment contrast enhanced CT performed at approximately 2 weeks before the treatment was enrolled. A six-CT-phenotypic-feature-based classifier constructed by the LASSO Cox regression model, and three clinicopathological factors: pathologic N category, performance status (PS) score, and intrapulmonary metastasis status were used to construct a nomogram in a training set of 115 patients. The prognostic and predictive accuracy of this nomogram was then subjected to an external independent validation of 107 patients. PFS between the training and independent validation set is no statistical difference by Mann-Whitney U test (P = 0.2670). PFS of the patients could be predicted with good consistency compared with the actual survival. C-index of the proposed individualized nomogram in the training set (0·707, 95%CI: 0·643, 0·771) and the independent validation set (0·715, 95%CI: 0·650, 0·780) showed the potential of clinical prognosis to predict PFS of stage IV EGFR-mutant NSCLC from EGFR TKIs. The individualized nomogram might facilitate patient counselling and individualise management of patients with this disease.

Effects of hydraulic resistance circuit training on physical fitness components of potential relevance to +Gz tolerance.

PubMed

Jacobs, I; Bell, D G; Pope, J; Lee, W

1987-08-01

Recent studies carried out in the United States and Sweden have demonstrated that strength training can improve +Gz acceleration tolerance. Based on these findings, the Canadian Forces have introduced a training program for aircrew of high performance aircraft. This report describes the changes in physical fitness components considered relevant to +Gz tolerance after 12 weeks of training with this program. Prior to beginning training, 45 military personnel were tested, but only 20 completed a minimum of 24 training sessions. The following variables were measured in these 20 subjects before and after training: maximal strength of several large muscle groups during isokinetic contractions, maximal aerobic power and an endurance fitness index, maximal anaerobic power, anthropometric characteristics, and maximal expiratory pressure generated during exhalation. Training involved hydraulic resistance circuit training 2-4 times/week. The circuit consisted of 3 consecutive sets at each of 8 stations using Hydra-Gym equipment. The exercise:rest ratio was 20:40 s for the initial 4 training weeks and was then changed to 30:50. After training the changes in anthropometric measurements suggested that lean body mass was increased. Small, but significant, increases were also measured in muscle strength during bench press, biceps curls, squats, knee extension, and knee flexion. Neither maximal anaerobic power (i.e. muscular endurance) nor maximal expiratory pressure were changed after the training. Indices of endurance fitness were also increased in the present study. The relatively small increases in strength are probably due to the design of the exercise:rest ratio which resulted in improved strength and aerobic fitness.(ABSTRACT TRUNCATED AT 250 WORDS)

Use of a dementia training designed for nurse aides to train other staff.

PubMed

Irvine, A Blair; Beaty, Jeff A; Seeley, John R; Bourgeois, Michelle

2013-12-01

Problematic resident behaviors may escalate in long-term care facilities (LTCs). If nurse aides (NAs) are not nearby, the nearest staff to intervene may be non-direct care workers (NDCWs), who have little or no dementia training. This pilot research tested Internet dementia-training program, designed for NAs, on NDCWs in a LTC setting. Sixty-eight NDCWs participated, filling out two baseline surveys at 1-month intervals and a posttest survey after training. The surveys included video-situation testing, items addressing psychosocial constructs associated with behavior change, and measures training-acceptance. Paired t tests showed significant positive effects on measures of knowledge, attitudes, self-efficacy, and behavioral intentions, with small-moderate effect sizes. Nursing staff as well as non-health care workers showed improved scores, and the web-site training program was well received by all participants. These results suggest that Internet training may allow staff development coordinators to conserve limited resources by cross-training of different job categories with the same program.

Gene expression analysis using a highly sensitive DNA microarray for colorectal cancer screening.

PubMed

Koga, Yoshikatsu; Yamazaki, Nobuyoshi; Takizawa, Satoko; Kawauchi, Junpei; Nomura, Osamu; Yamamoto, Seiichiro; Saito, Norio; Kakugawa, Yasuo; Otake, Yosuke; Matsumoto, Minori; Matsumura, Yasuhiro

2014-01-01

Half of all patients with small, right-sided, non-metastatic colorectal cancer (CRC) have negative results for the fecal occult blood test (FOBT). In the present study, the usefulness of CRC screening with a highly sensitive DNA microarray was evaluated in comparison with that by FOBT using fecal samples. A total of 53 patients with CRC and 61 healthy controls were divided into "training" and "validation sets". For the gene profiling, total RNA extracted from 0.5 g of feces was hybridized to a highly sensitive DNA chip. The expressions of 43 genes were significantly higher in the patients with CRC than in healthy controls (p<0.05). In the training set, the sensitivity and specificity of the DNA chip assay using six genes were 85.4% and 85.2%, respectively. On the other hand, in the validation set, the sensitivity and specificity of the DNA chip assay were 85.2% and 85.7%, respectively. The sensitivities of the DNA chip assay were higher than those of FOBT in cases of the small, right-sided, early-CRC, tumor invading up to the muscularis propria (i.e. surface tumor) subgroups. In particular, the sensitivities of the DNA chip assay in the surface tumor and early-CRC subgroups were significantly higher than those of FOBT (p=0.023 and 0.019, respectively.). Gene profiling assay using a highly sensitive DNA chip was more effective than FOBT at detecting patients with small, right-sided, surface tumor, and early-stage CRC.

Acute Effects of Back Squats on Countermovement Jump Performance Across Multiple Sets of A Contrast Training Protocol in Resistance-Trained Males.

PubMed

Bauer, Pascal; Sansone, Pierpaolo; Mitter, Benedikt; Makivic, Bojan; Seitz, Laurent B; Tschan, Harald

2018-01-03

The present study was designed to evaluate the voluntary post-activation potentiation (PAP) effects of moderate (MI) or high intensity (HI) back squat exercises on countermovement jump (CMJ) performance across multiple sets of a contrast training protocol. Sixty resistance-trained male subjects (age, 23.3 ± 3.3 y; body mass, 86.0 ± 13.9 kg; parallel back squat 1-repetition maximum [1-RM], 155.2 ± 30.0 kg) participated in a randomized, cross-over study. After familiarization, the subjects visited the laboratory on three separate occasions. They performed a contrast PAP protocol comprising three sets of either MI (6×60% of 1-RM) or HI back squats (4x90% of 1-RM) or 20 s of recovery (CTRL) alternated with seven CMJs that were performed at 15 s, and 1, 3, 5, 7, 9 and 11 min after the back squats or recovery. Jump height and relative peak power output recorded with a force platform during MI and HI conditions were compared to those recorded during control condition to calculate the voluntary PAP effect. CMJ performance was decreased immediately after the squats but increased across all three sets of MI and HI between 3 - 7 minutes post-recovery. However, voluntary PAP effects were small or trivial and no difference between the three sets could be found. These findings demonstrate that practitioners can use MI and HI back squats to potentiate CMJs across a contrast training protocol, but a minimum of 3 min of recovery after the squats is needed to benefit from voluntary PAP.

Training set expansion: an approach to improving the reconstruction of biological networks from limited and uneven reliable interactions

PubMed Central

Yip, Kevin Y.; Gerstein, Mark

2009-01-01

Motivation: An important problem in systems biology is reconstructing complete networks of interactions between biological objects by extrapolating from a few known interactions as examples. While there are many computational techniques proposed for this network reconstruction task, their accuracy is consistently limited by the small number of high-confidence examples, and the uneven distribution of these examples across the potential interaction space, with some objects having many known interactions and others few. Results: To address this issue, we propose two computational methods based on the concept of training set expansion. They work particularly effectively in conjunction with kernel approaches, which are a popular class of approaches for fusing together many disparate types of features. Both our methods are based on semi-supervised learning and involve augmenting the limited number of gold-standard training instances with carefully chosen and highly confident auxiliary examples. The first method, prediction propagation, propagates highly confident predictions of one local model to another as the auxiliary examples, thus learning from information-rich regions of the training network to help predict the information-poor regions. The second method, kernel initialization, takes the most similar and most dissimilar objects of each object in a global kernel as the auxiliary examples. Using several sets of experimentally verified protein–protein interactions from yeast, we show that training set expansion gives a measurable performance gain over a number of representative, state-of-the-art network reconstruction methods, and it can correctly identify some interactions that are ranked low by other methods due to the lack of training examples of the involved proteins. Contact: mark.gerstein@yale.edu Availability: The datasets and additional materials can be found at http://networks.gersteinlab.org/tse. PMID:19015141

Hyperspectral data discrimination methods

NASA Astrophysics Data System (ADS)

Casasent, David P.; Chen, Xuewen

2000-12-01

Hyperspectral data provides spectral response information that provides detailed chemical, moisture, and other description of constituent parts of an item. These new sensor data are useful in USDA product inspection. However, such data introduce problems such as the curse of dimensionality, the need to reduce the number of features used to accommodate realistic small training set sizes, and the need to employ discriminatory features and still achieve good generalization (comparable training and test set performance). Several two-step methods are compared to a new and preferable single-step spectral decomposition algorithm. Initial results on hyperspectral data for good/bad almonds and for good/bad (aflatoxin infested) corn kernels are presented. The hyperspectral application addressed differs greatly from prior USDA work (PLS) in which the level of a specific channel constituent in food was estimated. A validation set (separate from the test set) is used in selecting algorithm parameters. Threshold parameters are varied to select the best Pc operating point. Initial results show that nonlinear features yield improved performance.

Pilot Evaluation of a Communication Skills Training Program for Psychiatry Residents Using Standardized Patient Assessment.

PubMed

Ditton-Phare, Philippa; Sandhu, Harsimrat; Kelly, Brian; Kissane, David; Loughland, Carmel

2016-10-01

Mental health clinicians can experience difficulties communicating diagnostic information to patients and their families/carers, especially about distressing psychiatric disorders such as schizophrenia. There is evidence for the effectiveness of communication skills training (CST) for improving diagnostic discussions, particularly in specialties such as oncology, but only limited evidence exists about CST for psychiatry. This study evaluated a CST program specifically developed for psychiatry residents called ComPsych that focuses on conveying diagnostic and prognostic information about schizophrenia. The ComPsych program consists of an introductory lecture, module booklets for trainees, and exemplary skills videos, followed by small group role-plays with simulated patients (SPs) led by a trained facilitator. A standardized patient assessment (SPA) was digitally recorded pre- and post-training with a SP using a standardized scenario in a time-limited (15 min) period. Recorded SPAs were independently rated using a validated coding system (ComSkil) to identify frequency of skills used in five skills categories (agenda setting, checking, questioning, information organization, and empathic communication). Thirty trainees (15 males and 15 females; median age = 32) undertaking their vocational specialty training in psychiatry participated in ComPsych training and pre- and post-ComPsych SPAs. Skills increased post-training for agenda setting (d = -0.82), while questioning skills (d = 0.56) decreased. There were no significant differences in any other skills grouping, although checking, information organization, and empathic communication skills tended to increase post-training. A dose effect was observed for agenda setting, with trainees who attended more CST sessions outperforming those attending fewer. Findings support the generalization and translation of ComPsych CST to psychiatry.

Final-year veterinary students' perceptions of their communication competencies and a communication skills training program delivered in a primary care setting and based on Kolb's Experiential Learning Theory.

PubMed

Meehan, Michael P; Menniti, Marie F

2014-01-01

Veterinary graduates require effective communication skills training to successfully transition from university into practice. Although the literature has supported the need for veterinary student communication skills training programs, there is minimal research using learning theory to design programs and explore students' perceptions of such programs. This study investigated veterinary students' perceptions of (1) their communication skills and (2) the usefulness of a communication skills training program designed with Kolb's Experiential Learning Theory (ELT) as a framework and implemented in a primary care setting. Twenty-nine final-year veterinary students from the Ontario Veterinary College attended a 3-week communication skills training rotation. Pre- and post-training surveys explored their communication objectives, confidence in their communication skills, and the usefulness of specific communication training strategies. The results indicated that both before and after training, students were most confident in building rapport, displaying empathy, recognizing how bonded a client is with his or her pet, and listening. They were least confident in managing clients who were angry or not happy with the charges and who monopolized the appointment. Emotionally laden topics, such as breaking bad news and managing euthanasia discussions, were also identified as challenging and in need of improvement. Interactive small-group discussions and review of video-recorded authentic client appointments were most valuable for their learning and informed students' self-awareness of their non-verbal communication. These findings support the use of Kolb's ELT as a theoretical framework and of video review and reflection to guide veterinary students' learning of communication skills in a primary care setting.

Teacher's Guide to the Frontier Army Museum, Fort Leavenworth, Kansas.

ERIC Educational Resources Information Center

2001

After the Louisiana Purchase, Thomas Jefferson put together his own group to explore the new territory under the leadership of Captain Meriwether Lewis. After receiving training in how to make scientific observations and collect specimens, Lewis and Captain William Clark, and their small group of frontiersmen, set off in 1804 with Sacajawea as…

Evaluation of a Numeracy Intervention Program Focusing on Basic Numerical Knowledge and Conceptual Knowledge: A Pilot Study.

ERIC Educational Resources Information Center

Kaufmann, Liane; Handl, Pia; Thony, Brigitte

2003-01-01

In this study, six elementary grade children with developmental dyscalculia were trained individually and in small group settings with a one-semester program stressing basic numerical knowledge and conceptual knowledge. All the children showed considerable and partly significant performance increases on all calculation components. Results suggest…

Evidence Summary for New York City's Small Schools of Choice. Top Tier Evidence Initiative

ERIC Educational Resources Information Center

Coalition for Evidence-Based Policy, 2015

2015-01-01

U.S. social programs, set up to address important problems, often fall short by funding specific models/strategies ("interventions") that are not effective. When evaluated in scientifically-rigorous studies, social interventions in K-12 education, job training, crime prevention, and other areas are frequently found ineffective or…

The Impact of In-Service Training of Correctional Counselors.

ERIC Educational Resources Information Center

Smith, Thomas H.

An empirical study was made on treatment atmosphere and shifts in interpersonal behavior in a military correctional treatment setting. The program studied was a small rehabilitation unit housing 100 to 140 enlisted men convicted by special or general court martial of various offenses ranging from AWOL to manslaughter. The objective of the unit was…

Trainers for Operators of Guided Missiles

DTIC Science & Technology

1945-09-28

APPENDIX I SELECTION AND TRAINING IN THE PROCESS UF. REMOTE CUNTRÜL " . Treater: Henschke. One of the most difficult tasks in new hrms is...perimeter to the center. The carriage of arm No, 2 has a magnet upon which a target (a small iron automobile ) may be placed. Since the arms can be set

Fusion of footsteps and face biometrics on an unsupervised and uncontrolled environment

NASA Astrophysics Data System (ADS)

Vera-Rodriguez, Ruben; Tome, Pedro; Fierrez, Julian; Ortega-Garcia, Javier

2012-06-01

This paper reports for the first time experiments on the fusion of footsteps and face on an unsupervised and not controlled environment for person authentication. Footstep recognition is a relatively new biometric based on signals extracted from people walking over floor sensors. The idea of the fusion between footsteps and face starts from the premise that in an area where footstep sensors are installed it is very simple to place a camera to capture also the face of the person that walks over the sensors. This setup may find application in scenarios like ambient assisted living, smart homes, eldercare, or security access. The paper reports a comparative assessment of both biometrics using the same database and experimental protocols. In the experimental work we consider two different applications: smart homes (small group of users with a large set of training data) and security access (larger group of users with a small set of training data) obtaining results of 0.9% and 5.8% EER respectively for the fusion of both modalities. This is a significant performance improvement compared with the results obtained by the individual systems.

Efficiency of multi-breed genomic selection for dairy cattle breeds with different sizes of reference population.

PubMed

Hozé, C; Fritz, S; Phocas, F; Boichard, D; Ducrocq, V; Croiseau, P

2014-01-01

Single-breed genomic selection (GS) based on medium single nucleotide polymorphism (SNP) density (~50,000; 50K) is now routinely implemented in several large cattle breeds. However, building large enough reference populations remains a challenge for many medium or small breeds. The high-density BovineHD BeadChip (HD chip; Illumina Inc., San Diego, CA) containing 777,609 SNP developed in 2010 is characterized by short-distance linkage disequilibrium expected to be maintained across breeds. Therefore, combining reference populations can be envisioned. A population of 1,869 influential ancestors from 3 dairy breeds (Holstein, Montbéliarde, and Normande) was genotyped with the HD chip. Using this sample, 50K genotypes were imputed within breed to high-density genotypes, leading to a large HD reference population. This population was used to develop a multi-breed genomic evaluation. The goal of this paper was to investigate the gain of multi-breed genomic evaluation for a small breed. The advantage of using a large breed (Normande in the present study) to mimic a small breed is the large potential validation population to compare alternative genomic selection approaches more reliably. In the Normande breed, 3 training sets were defined with 1,597, 404, and 198 bulls, and a unique validation set included the 394 youngest bulls. For each training set, estimated breeding values (EBV) were computed using pedigree-based BLUP, single-breed BayesC, or multi-breed BayesC for which the reference population was formed by any of the Normande training data sets and 4,989 Holstein and 1,788 Montbéliarde bulls. Phenotypes were standardized by within-breed genetic standard deviation, the proportion of polygenic variance was set to 30%, and the estimated number of SNP with a nonzero effect was about 7,000. The 2 genomic selection (GS) approaches were performed using either the 50K or HD genotypes. The correlations between EBV and observed daughter yield deviations (DYD) were computed for 6 traits and using the different prediction approaches. Compared with pedigree-based BLUP, the average gain in accuracy with GS in small populations was 0.057 for the single-breed and 0.086 for multi-breed approach. This gain was up to 0.193 and 0.209, respectively, with the large reference population. Improvement of EBV prediction due to the multi-breed evaluation was higher for animals not closely related to the reference population. In the case of a breed with a small reference population size, the increase in correlation due to multi-breed GS was 0.141 for bulls without their sire in reference population compared with 0.016 for bulls with their sire in reference population. These results demonstrate that multi-breed GS can contribute to increase genomic evaluation accuracy in small breeds. Copyright © 2014 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

MABAL: a Novel Deep-Learning Architecture for Machine-Assisted Bone Age Labeling.

PubMed

Mutasa, Simukayi; Chang, Peter D; Ruzal-Shapiro, Carrie; Ayyala, Rama

2018-02-05

Bone age assessment (BAA) is a commonly performed diagnostic study in pediatric radiology to assess skeletal maturity. The most commonly utilized method for assessment of BAA is the Greulich and Pyle method (Pediatr Radiol 46.9:1269-1274, 2016; Arch Dis Child 81.2:172-173, 1999) atlas. The evaluation of BAA can be a tedious and time-consuming process for the radiologist. As such, several computer-assisted detection/diagnosis (CAD) methods have been proposed for automation of BAA. Classical CAD tools have traditionally relied on hard-coded algorithmic features for BAA which suffer from a variety of drawbacks. Recently, the advent and proliferation of convolutional neural networks (CNNs) has shown promise in a variety of medical imaging applications. There have been at least two published applications of using deep learning for evaluation of bone age (Med Image Anal 36:41-51, 2017; JDI 1-5, 2017). However, current implementations are limited by a combination of both architecture design and relatively small datasets. The purpose of this study is to demonstrate the benefits of a customized neural network algorithm carefully calibrated to the evaluation of bone age utilizing a relatively large institutional dataset. In doing so, this study will aim to show that advanced architectures can be successfully trained from scratch in the medical imaging domain and can generate results that outperform any existing proposed algorithm. The training data consisted of 10,289 images of different skeletal age examinations, 8909 from the hospital Picture Archiving and Communication System at our institution and 1383 from the public Digital Hand Atlas Database. The data was separated into four cohorts, one each for male and female children above the age of 8, and one each for male and female children below the age of 10. The testing set consisted of 20 radiographs of each 1-year-age cohort from 0 to 1 years to 14-15+ years, half male and half female. The testing set included left-hand radiographs done for bone age assessment, trauma evaluation without significant findings, and skeletal surveys. A 14 hidden layer-customized neural network was designed for this study. The network included several state of the art techniques including residual-style connections, inception layers, and spatial transformer layers. Data augmentation was applied to the network inputs to prevent overfitting. A linear regression output was utilized. Mean square error was used as the network loss function and mean absolute error (MAE) was utilized as the primary performance metric. MAE accuracies on the validation and test sets for young females were 0.654 and 0.561 respectively. For older females, validation and test accuracies were 0.662 and 0.497 respectively. For young males, validation and test accuracies were 0.649 and 0.585 respectively. Finally, for older males, validation and test set accuracies were 0.581 and 0.501 respectively. The female cohorts were trained for 900 epochs each and the male cohorts were trained for 600 epochs. An eightfold cross-validation set was employed for hyperparameter tuning. Test error was obtained after training on a full data set with the selected hyperparameters. Using our proposed customized neural network architecture on our large available data, we achieved an aggregate validation and test set mean absolute errors of 0.637 and 0.536 respectively. To date, this is the best published performance on utilizing deep learning for bone age assessment. Our results support our initial hypothesis that customized, purpose-built neural networks provide improved performance over networks derived from pre-trained imaging data sets. We build on that initial work by showing that the addition of state-of-the-art techniques such as residual connections and inception architecture further improves prediction accuracy. This is important because the current assumption for use of residual and/or inception architectures is that a large pre-trained network is required for successful implementation given the relatively small datasets in medical imaging. Instead we show that a small, customized architecture incorporating advanced CNN strategies can indeed be trained from scratch, yielding significant improvements in algorithm accuracy. It should be noted that for all four cohorts, testing error outperformed validation error. One reason for this is that our ground truth for our test set was obtained by averaging two pediatric radiologist reads compared to our training data for which only a single read was used. This suggests that despite relatively noisy training data, the algorithm could successfully model the variation between observers and generate estimates that are close to the expected ground truth.

Unsupervised progressive elastic band exercises for frail geriatric inpatients objectively monitored by new exercise-integrated technology-a feasibility trial with an embedded qualitative study.

PubMed

Rathleff, C R; Bandholm, T; Spaich, E G; Jorgensen, M; Andreasen, J

2017-01-01

Frailty is a serious condition frequently present in geriatric inpatients that potentially causes serious adverse events. Strength training is acknowledged as a means of preventing or delaying frailty and loss of function in these patients. However, limited hospital resources challenge the amount of supervised training, and unsupervised training could possibly supplement supervised training thereby increasing the total exercise dose during admission. A new valid and reliable technology, the BandCizer, objectively measures the exact training dosage performed. The purpose was to investigate feasibility and acceptability of an unsupervised progressive strength training intervention monitored by BandCizer for frail geriatric inpatients. This feasibility trial included 15 frail inpatients at a geriatric ward. At hospitalization, the patients were prescribed two elastic band exercises to be performed unsupervised once daily. A BandCizer Datalogger enabling measurement of the number of sets, repetitions, and time-under-tension was attached to the elastic band. The patients were instructed in performing strength training: 3 sets of 10 repetitions (10-12 repetition maximum (RM)) with a separation of 2-min pauses and a time-under-tension of 8 s. The feasibility criterion for the unsupervised progressive exercises was that 33% of the recommended number of sets would be performed by at least 30% of patients. In addition, patients and staff were interviewed about their experiences with the intervention. Four (27%) out of 15 patients completed 33% of the recommended number of sets. For the total sample, the average percent of performed sets was 23% and for those who actually trained ( n  = 12) 26%. Patients and staff expressed a general positive attitude towards the unsupervised training as an addition to the supervised training sessions. However, barriers were also described-especially constant interruptions. Based on the predefined criterion for feasibility, the unsupervised training was not feasible, although the criterion was almost met. The patients and staff mainly expressed positive attitudes towards the unsupervised training. As even a small training dosage has been shown to improve the physical performance of geriatric inpatients, the proposed intervention might be relevant if the interruptions are decreased in future large-scale trials and if the adherence is increased. ClinicalTrials.gov: NCT02702557, February 29, 2016. Data Protection Agency: 2016-42, February 25, 2016. Ethics Committee: No registration needed, December 8, 2015 (e-mail correspondence).

Effect of missing data on multitask prediction methods.

PubMed

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

2018-05-22

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

Testing of Haar-Like Feature in Region of Interest Detection for Automated Target Recognition (ATR) System

NASA Technical Reports Server (NTRS)

Zhang, Yuhan; Lu, Dr. Thomas

2010-01-01

The objectives of this project were to develop a ROI (Region of Interest) detector using Haar-like feature similar to the face detection in Intel's OpenCV library, implement it in Matlab code, and test the performance of the new ROI detector against the existing ROI detector that uses Optimal Trade-off Maximum Average Correlation Height filter (OTMACH). The ROI detector included 3 parts: 1, Automated Haar-like feature selection in finding a small set of the most relevant Haar-like features for detecting ROIs that contained a target. 2, Having the small set of Haar-like features from the last step, a neural network needed to be trained to recognize ROIs with targets by taking the Haar-like features as inputs. 3, using the trained neural network from the last step, a filtering method needed to be developed to process the neural network responses into a small set of regions of interests. This needed to be coded in Matlab. All the 3 parts needed to be coded in Matlab. The parameters in the detector needed to be trained by machine learning and tested with specific datasets. Since OpenCV library and Haar-like feature were not available in Matlab, the Haar-like feature calculation needed to be implemented in Matlab. The codes for Adaptive Boosting and max/min filters in Matlab could to be found from the Internet but needed to be integrated to serve the purpose of this project. The performance of the new detector was tested by comparing the accuracy and the speed of the new detector against the existing OTMACH detector. The speed was referred as the average speed to find the regions of interests in an image. The accuracy was measured by the number of false positives (false alarms) at the same detection rate between the two detectors.

Exploring the process of professional socialisation and development during pharmacy pre-registration training in England.

PubMed

Jee, Samuel D; Schafheutle, Ellen I; Noyce, Peter R

2016-08-01

To explore the process of professional socialisation in pharmacy trainees during pre-registration training. A prospective, longitudinal qualitative design was used. A purposive sample of 20 trainees from community and hospital pharmacy in North West England was recruited. A total of 79 semi-structured interviews were conducted with trainees on three occasions during training and once four months after training. Data were analysed thematically using template analysis. Early on in training, non-pharmacists played a significant role in socialising trainees into the work setting; pharmacists played the stronger role towards the end. Pre-registration tutors were strong role models throughout training. Training experiences differed between settings, where services provided and patient mix varied. Hospital trainees learnt about specialist medicines on ward rotations. Community trainees developed knowledge of over-the-counter, and less complex, medicines. In hospital, trainees were exposed to a range of role models in comparison to community where this was generally limited to a small pharmacy team. Newly qualified pharmacists were challenged by having full responsibility and accountability. This study showed the experiences encountered by trainees that affect their professional socialisation. More standardisation across training sites may reduce the variation in experiences and professional socialisation and development. Formal training for pre-registration tutors and support staff that play a key role in supporting trainees could be considered. Support for newly qualified pharmacists may allay the challenging transition they face when entering practice. © 2016 Royal Pharmaceutical Society.

How to successfully select and implement electronic health records (EHR) in small ambulatory practice settings.

PubMed

Lorenzi, Nancy M; Kouroubali, Angelina; Detmer, Don E; Bloomrosen, Meryl

2009-02-23

Adoption of EHRs by U.S. ambulatory practices has been slow despite the perceived benefits of their use. Most evaluations of EHR implementations in the literature apply to large practice settings. While there are similarities relating to EHR implementation in large and small practice settings, the authors argue that scale is an important differentiator. Focusing on small ambulatory practices, this paper outlines the benefits and barriers to EHR use in this setting, and provides a "field guide" for these practices to facilitate successful EHR implementation. The benefits of EHRs in ambulatory practices include improved patient care and office efficiency, and potential financial benefits. Barriers to EHRs include costs; lack of standardization of EHR products and the design of vendor systems for large practice environments; resistance to change; initial difficulty of system use leading to productivity reduction; and perceived accrual of benefits to society and payers rather than providers. The authors stress the need for developing a flexible change management strategy when introducing EHRs that is relevant to the small practice environment; the strategy should acknowledge the importance of relationship management and the role of individual staff members in helping the entire staff to manage change. Practice staff must create an actionable vision outlining realistic goals for the implementation, and all staff must buy into the project. The authors detail the process of implementing EHRs through several stages: decision, selection, pre-implementation, implementation, and post-implementation. They stress the importance of identifying a champion to serve as an advocate of the value of EHRs and provide direction and encouragement for the project. Other key activities include assessing and redesigning workflow; understanding financial issues; conducting training that is well-timed and meets the needs of practice staff; and evaluating the implementation process. The EHR implementation experience depends on a variety of factors including the technology, training, leadership, the change management process, and the individual character of each ambulatory practice environment. Sound processes must support both technical and personnel-related organizational components. Additional research is needed to further refine recommendations for the small physician practice and the nuances of specific medical specialties.

Optically-energized, emp-resistant, fast-acting, explosion initiating device

DOEpatents

Benson, David A.; Kuswa, Glenn W.

1987-01-01

Optical energy, provided from a remote user-operated source, is utilized to initially electrically charge a capacitor in a circuit that also contains an explosion initiating transducer in contact with a small explosive train contained in an attachable housing. Additional optical energy is subsequently supplied in a preferred embodiment to an optically responsive phototransistor acting in conjunction with a silicon controlled rectifer to release the stored electrical energy through the explosion initiating transducer to set off the explosive train. All energy transfers between the user and the explosive apparatus, either for charging it up or for setting it off, are conveyed optically and may be accomplished in a single optical fiber with coding to distinguish between specific optical energy transfers and between these and any extraneous signals.

In silico segmentations of lentivirus envelope sequences

PubMed Central

Boissin-Quillon, Aurélia; Piau, Didier; Leroux, Caroline

2007-01-01

Background The gene encoding the envelope of lentiviruses exhibits a considerable plasticity, particularly the region which encodes the surface (SU) glycoprotein. Interestingly, mutations do not appear uniformly along the sequence of SU, but they are clustered in restricted areas, called variable (V) regions, which are interspersed with relatively more stable regions, called constant (C) regions. We look for specific signatures of C/V regions, using hidden Markov models constructed with SU sequences of the equine, human, small ruminant and simian lentiviruses. Results Our models yield clear and accurate delimitations of the C/V regions, when the test set and the training set were made up of sequences of the same lentivirus, but also when they were made up of sequences of different lentiviruses. Interestingly, the models predicted the different regions of lentiviruses such as the bovine and feline lentiviruses, not used in the training set. Models based on composite training sets produce accurate segmentations of sequences of all these lentiviruses. Conclusion Our results suggest that each C/V region has a specific statistical oligonucleotide composition, and that the C (respectively V) regions of one of these lentiviruses are statistically more similar to the C (respectively V) regions of the other lentiviruses, than to the V (respectively C) regions of the same lentivirus. PMID:17376229

DEWS (DEep White matter hyperintensity Segmentation framework): A fully automated pipeline for detecting small deep white matter hyperintensities in migraineurs.

PubMed

Park, Bo-Yong; Lee, Mi Ji; Lee, Seung-Hak; Cha, Jihoon; Chung, Chin-Sang; Kim, Sung Tae; Park, Hyunjin

2018-01-01

Migraineurs show an increased load of white matter hyperintensities (WMHs) and more rapid deep WMH progression. Previous methods for WMH segmentation have limited efficacy to detect small deep WMHs. We developed a new fully automated detection pipeline, DEWS (DEep White matter hyperintensity Segmentation framework), for small and superficially-located deep WMHs. A total of 148 non-elderly subjects with migraine were included in this study. The pipeline consists of three components: 1) white matter (WM) extraction, 2) WMH detection, and 3) false positive reduction. In WM extraction, we adjusted the WM mask to re-assign misclassified WMHs back to WM using many sequential low-level image processing steps. In WMH detection, the potential WMH clusters were detected using an intensity based threshold and region growing approach. For false positive reduction, the detected WMH clusters were classified into final WMHs and non-WMHs using the random forest (RF) classifier. Size, texture, and multi-scale deep features were used to train the RF classifier. DEWS successfully detected small deep WMHs with a high positive predictive value (PPV) of 0.98 and true positive rate (TPR) of 0.70 in the training and test sets. Similar performance of PPV (0.96) and TPR (0.68) was attained in the validation set. DEWS showed a superior performance in comparison with other methods. Our proposed pipeline is freely available online to help the research community in quantifying deep WMHs in non-elderly adults.

Cognitive flexibility modulates maturation and music-training-related changes in neural sound discrimination.

PubMed

Saarikivi, Katri; Putkinen, Vesa; Tervaniemi, Mari; Huotilainen, Minna

2016-07-01

Previous research has demonstrated that musicians show superior neural sound discrimination when compared to non-musicians, and that these changes emerge with accumulation of training. Our aim was to investigate whether individual differences in executive functions predict training-related changes in neural sound discrimination. We measured event-related potentials induced by sound changes coupled with tests for executive functions in musically trained and non-trained children aged 9-11 years and 13-15 years. High performance in a set-shifting task, indexing cognitive flexibility, was linked to enhanced maturation of neural sound discrimination in both musically trained and non-trained children. Specifically, well-performing musically trained children already showed large mismatch negativity (MMN) responses at a young age as well as at an older age, indicating accurate sound discrimination. In contrast, the musically trained low-performing children still showed an increase in MMN amplitude with age, suggesting that they were behind their high-performing peers in the development of sound discrimination. In the non-trained group, in turn, only the high-performing children showed evidence of an age-related increase in MMN amplitude, and the low-performing children showed a small MMN with no age-related change. These latter results suggest an advantage in MMN development also for high-performing non-trained individuals. For the P3a amplitude, there was an age-related increase only in the children who performed well in the set-shifting task, irrespective of music training, indicating enhanced attention-related processes in these children. Thus, the current study provides the first evidence that, in children, cognitive flexibility may influence age-related and training-related plasticity of neural sound discrimination. © 2016 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

Feedback facilitates transfer of training with US Hispanic workers in a healthcare laundry linen facility.

PubMed

Lebbon, Angela R; Lee, Sin Chien; Johnson, Douglas A

2015-12-01

This study aimed to increase safety knowledge and behaviour of US Hispanic custodial workers in healthcare through a culturally appropriate training and monitoring process. A single-group, repeated-measures, pre-test and post-test design was used to examine training effectiveness across four sets of behaviours with 23 Spanish-speaking workers. Small group, lecture-style training in Spanish with pictures and video resulted in significant improvements in knowledge and behaviour. However, additional analyses show that behavioural feedback was the critical component in improving safety behaviour during transfer of training. Findings from reaction, knowledge, behaviour and results measures suggest that group training and graphic feedback is culturally appropriate and effective with Hispanic workers. Further investigation is needed to understand cultural factors that facilitate effective development and delivery of safety training and feedback to US Hispanic workers. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Electronic spectra from TDDFT and machine learning in chemical space

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

Ramakrishnan, Raghunathan; Hartmann, Mia; Tapavicza, Enrico

Due to its favorable computational efficiency, time-dependent (TD) density functional theory (DFT) enables the prediction of electronic spectra in a high-throughput manner across chemical space. Its predictions, however, can be quite inaccurate. We resolve this issue with machine learning models trained on deviations of reference second-order approximate coupled-cluster (CC2) singles and doubles spectra from TDDFT counterparts, or even from DFT gap. We applied this approach to low-lying singlet-singlet vertical electronic spectra of over 20 000 synthetically feasible small organic molecules with up to eight CONF atoms. The prediction errors decay monotonously as a function of training set size. For amore » training set of 10 000 molecules, CC2 excitation energies can be reproduced to within +/- 0.1 eV for the remaining molecules. Analysis of our spectral database via chromophore counting suggests that even higher accuracies can be achieved. Based on the evidence collected, we discuss open challenges associated with data-driven modeling of high-lying spectra and transition intensities.« less

Statistical learning in reading: variability in irrelevant letters helps children learn phonics skills.

PubMed

Apfelbaum, Keith S; Hazeltine, Eliot; McMurray, Bob

2013-07-01

Early reading abilities are widely considered to derive in part from statistical learning of regularities between letters and sounds. Although there is substantial evidence from laboratory work to support this, how it occurs in the classroom setting has not been extensively explored; there are few investigations of how statistics among letters and sounds influence how children actually learn to read or what principles of statistical learning may improve learning. We examined 2 conflicting principles that may apply to learning grapheme-phoneme-correspondence (GPC) regularities for vowels: (a) variability in irrelevant units may help children derive invariant relationships and (b) similarity between words may force children to use a deeper analysis of lexical structure. We trained 224 first-grade students on a small set of GPC regularities for vowels, embedded in words with either high or low consonant similarity, and tested their generalization to novel tasks and words. Variability offered a consistent benefit over similarity for trained and new words in both trained and new tasks.

Sleep restores loss of generalized but not rote learning of synthetic speech.

PubMed

Fenn, Kimberly M; Margoliash, Daniel; Nusbaum, Howard C

2013-09-01

Sleep-dependent consolidation has been demonstrated for declarative and procedural memory but few theories of consolidation distinguish between rote and generalized learning, suggesting similar consolidation should occur for both. However, studies using rote and generalized learning have suggested different patterns of consolidation may occur, although different tasks have been used across studies. Here we directly compared consolidation of rote and generalized learning using a single speech identification task. Training on a large set of novel stimuli resulted in substantial generalized learning, and sleep restored performance that had degraded after 12 waking hours. Training on a small set of repeated stimuli primarily resulted in rote learning and performance also degraded after 12 waking hours but was not restored by sleep. Moreover performance was significantly worse 24-h after rote training. Our results suggest a functional dissociation between the mechanisms of consolidation for rote and generalized learning which has broad implications for memory models. Copyright © 2013 Elsevier B.V. All rights reserved.

A generalized LSTM-like training algorithm for second-order recurrent neural networks

PubMed Central

Monner, Derek; Reggia, James A.

2011-01-01

The Long Short Term Memory (LSTM) is a second-order recurrent neural network architecture that excels at storing sequential short-term memories and retrieving them many time-steps later. LSTM’s original training algorithm provides the important properties of spatial and temporal locality, which are missing from other training approaches, at the cost of limiting it’s applicability to a small set of network architectures. Here we introduce the Generalized Long Short-Term Memory (LSTM-g) training algorithm, which provides LSTM-like locality while being applicable without modification to a much wider range of second-order network architectures. With LSTM-g, all units have an identical set of operating instructions for both activation and learning, subject only to the configuration of their local environment in the network; this is in contrast to the original LSTM training algorithm, where each type of unit has its own activation and training instructions. When applied to LSTM architectures with peephole connections, LSTM-g takes advantage of an additional source of back-propagated error which can enable better performance than the original algorithm. Enabled by the broad architectural applicability of LSTM-g, we demonstrate that training recurrent networks engineered for specific tasks can produce better results than single-layer networks. We conclude that LSTM-g has the potential to both improve the performance and broaden the applicability of spatially and temporally local gradient-based training algorithms for recurrent neural networks. PMID:21803542

ReactionPredictor: prediction of complex chemical reactions at the mechanistic level using machine learning.

PubMed

Kayala, Matthew A; Baldi, Pierre

2012-10-22

Proposing reasonable mechanisms and predicting the course of chemical reactions is important to the practice of organic chemistry. Approaches to reaction prediction have historically used obfuscating representations and manually encoded patterns or rules. Here we present ReactionPredictor, a machine learning approach to reaction prediction that models elementary, mechanistic reactions as interactions between approximate molecular orbitals (MOs). A training data set of productive reactions known to occur at reasonable rates and yields and verified by inclusion in the literature or textbooks is derived from an existing rule-based system and expanded upon with manual curation from graduate level textbooks. Using this training data set of complex polar, hypervalent, radical, and pericyclic reactions, a two-stage machine learning prediction framework is trained and validated. In the first stage, filtering models trained at the level of individual MOs are used to reduce the space of possible reactions to consider. In the second stage, ranking models over the filtered space of possible reactions are used to order the reactions such that the productive reactions are the top ranked. The resulting model, ReactionPredictor, perfectly ranks polar reactions 78.1% of the time and recovers all productive reactions 95.7% of the time when allowing for small numbers of errors. Pericyclic and radical reactions are perfectly ranked 85.8% and 77.0% of the time, respectively, rising to >93% recovery for both reaction types with a small number of allowed errors. Decisions about which of the polar, pericyclic, or radical reaction type ranking models to use can be made with >99% accuracy. Finally, for multistep reaction pathways, we implement the first mechanistic pathway predictor using constrained tree-search to discover a set of reasonable mechanistic steps from given reactants to given products. Webserver implementations of both the single step and pathway versions of ReactionPredictor are available via the chemoinformatics portal http://cdb.ics.uci.edu/.

Money for nothing? The net costs of medical training.

PubMed

Barros, Pedro P; Machado, Sara R

2010-09-01

One of the stages of medical training is the residency programme. Hosting institutions often claim compensation for the training provided. How much should this compensation be? According to our results, given the benefits arising from having residents among the house staff, no transfer (either tuition fee or subsidy) should be set to compensate the hosting institution for providing medical training. This paper quantifies the net costs of medical training, defined as the training costs over and above the wage paid. We jointly consider two effects. On the one hand, residents take extra time and resources from both the hosting institution and the supervisor. On the other hand, residents can be regarded as a less expensive substitute to nurses and/or graduate physicians, in the production of health care, both in primary care centres and hospitals. The net effect can be either positive or negative. We use the fact that residents, in Portugal, are centrally allocated to National Health Service hospitals to treat them as a fixed exogenous production factor. The data used comes from Portuguese hospitals and primary care centres. Cost function estimates point to a small negative marginal impact of residents on hospitals' (-0.02%) and primary care centres' (-0.9%) costs. Nonetheless, there is a positive relation between size and cost to the very large hospitals and primary care centres. Our approach to estimation of residents' costs controls for other teaching activities hospitals might have (namely undergraduate Medical Schools). Overall, the net costs of medical training appear to be quite small.

Optoelectronics applications in multimedia shooting training systems: SPARTAN

NASA Astrophysics Data System (ADS)

Glogowski, Tomasz; Hlosta, Pawel; Stepniak, Slawomir; Swiderski, Waldemar

2017-10-01

Multimedia shooting training systems are increasingly being used in the training of security staff and uniformed services. An advanced practicing-training system SPARTAN for simulation of small arms shooting has been designed and manufactured by Autocomp Management Ltd. and Military Institute of Armament Technology for the Polish Ministry of National Defence. SPARTAN is a stationary device designed to teach, monitor and evaluate the targeting of small arms and to prepare soldiers for: • firing the live ammunition at open ranges for combat targets and silhouettes • detection, classification and engagement of real targets upon different terrains, weather conditions and periods during the day • team work as a squad during the mission by using different types of arms • suitable reactions in untypical scenarios. Placed in any room the training set consists of: • the projection system that generates realistic 3D imaging of the battlefield (such as combat shooting range) in high-resolution • system that tracks weapons aiming points • sound system which delivers realistic mapping of acoustic surroundings • operator station with which the training is conducted and controlled • central processing unit based on PC computers equipped with specialist software realizing individual system functions • units of smart weapons equipped with radio communication modules, injection laser diodes and pneumatic reloading system. The system make possible training by firing in dynamic scenarios, using combat weapons and live ammunition against visible targets moving on a screen. The use of infrared camera for detecting the position of impact of a projectile.

Support and Strategies for Change Among Small Patient-Centered Medical Home Practices

PubMed Central

Scholle, Sarah Hudson; Asche, Stephen E.; Morton, Suzanne; Solberg, Leif I.; Tirodkar, Manasi A.; Jaén, Carlos Roberto

2013-01-01

PURPOSE We aimed to determine the motivations and barriers facing small practices that seek to adopt the patient-centered medical home (PCMH) model, as well as the type of help and strategies they use. METHODS We surveyed lead physicians at practices with fewer than 5 physicians, stratified by state and level of National Committee for Quality Assurance PCMH recognition, using a Web-based survey with telephone, fax, and mail follow-up. The response rate was 59%, yielding a total sample of 249 practices from 23 states. RESULTS Improving quality and patient experience were the strongest motivations for PCMH implementation; time and resources were the biggest barriers. Most practices participated in demonstration projects or received financial rewards for PCMH, and most received training or other kinds of help. Practices found training and help related to completing the PCMH application to be the most useful. Training for patients was both less common and less valued. The most commonly used strategies for practice transformation were staff training, systematizing processes of care, and quality measurement/goal setting. The least commonly endorsed strategy was involving patients in quality improvement. Practices with a higher level of PCMH recognition were more likely to have electronic health records, to report barriers, and to use measurement-based quality improvement strategies. CONCLUSIONS To spread the adoption of the PCMH model among small practices, financial support, practical training, and other help are likely to continue to be important. Few practices involved patients in their implementation, so it would be helpful to test the impact of greater patient involvement in the PCMH. PMID:23690387

Spectral imaging using consumer-level devices and kernel-based regression.

PubMed

Heikkinen, Ville; Cámara, Clara; Hirvonen, Tapani; Penttinen, Niko

2016-06-01

Hyperspectral reflectance factor image estimations were performed in the 400-700 nm wavelength range using a portable consumer-level laptop display as an adjustable light source for a trichromatic camera. Targets of interest were ColorChecker Classic samples, Munsell Matte samples, geometrically challenging tempera icon paintings from the turn of the 20th century, and human hands. Measurements and simulations were performed using Nikon D80 RGB camera and Dell Vostro 2520 laptop screen as a light source. Estimations were performed without spectral characteristics of the devices and by emphasizing simplicity for training sets and estimation model optimization. Spectral and color error images are shown for the estimations using line-scanned hyperspectral images as the ground truth. Estimations were performed using kernel-based regression models via a first-degree inhomogeneous polynomial kernel and a Matérn kernel, where in the latter case the median heuristic approach for model optimization and link function for bounded estimation were evaluated. Results suggest modest requirements for a training set and show that all estimation models have markedly improved accuracy with respect to the DE00 color distance (up to 99% for paintings and hands) and the Pearson distance (up to 98% for paintings and 99% for hands) from a weak training set (Digital ColorChecker SG) case when small representative training data were used in the estimation.

Learning in data-limited multimodal scenarios: Scandent decision forests and tree-based features.

PubMed

Hor, Soheil; Moradi, Mehdi

2016-12-01

Incomplete and inconsistent datasets often pose difficulties in multimodal studies. We introduce the concept of scandent decision trees to tackle these difficulties. Scandent trees are decision trees that optimally mimic the partitioning of the data determined by another decision tree, and crucially, use only a subset of the feature set. We show how scandent trees can be used to enhance the performance of decision forests trained on a small number of multimodal samples when we have access to larger datasets with vastly incomplete feature sets. Additionally, we introduce the concept of tree-based feature transforms in the decision forest paradigm. When combined with scandent trees, the tree-based feature transforms enable us to train a classifier on a rich multimodal dataset, and use it to classify samples with only a subset of features of the training data. Using this methodology, we build a model trained on MRI and PET images of the ADNI dataset, and then test it on cases with only MRI data. We show that this is significantly more effective in staging of cognitive impairments compared to a similar decision forest model trained and tested on MRI only, or one that uses other kinds of feature transform applied to the MRI data. Copyright © 2016. Published by Elsevier B.V.

The effects of low-volume resistance training with and without advanced techniques in trained subjects.

PubMed

Gieβsing, Jùrgen; Fisher, James; Steele, James; Rothe, Frank; Raubold, Kristin; Eichmann, Björn

2016-03-01

This study examined low-volume resistance training (RT) in trained participants with and without advanced training methods. Trained participants (RT experience 4±3 years) were randomised to groups performing single-set RT: ssRM (N.=21) performing repetitions to self-determined repetition maximum (RM), ssMMF (N.=30) performing repetitions to momentary muscular failure (MMF), and ssRP (N.=28) performing repetitions to self-determined RM using a rest pause (RP) method. Each performed supervised RT twice/week for 10 weeks. Outcomes included maximal isometric strength and body composition using bioelectrical impedance analysis. The ssRM group did not significantly improve in any outcome. The ssMMF and ssRP groups both significantly improved strength (p < 0.05). Magnitude of changes using effect size (ES) was examined between groups. Strength ES's were considered large for ssMMF (0.91 to 1.57) and ranging small to large for ssRP (0.42 to 1.06). Body composition data revealed significant improvements (P<0.05) in muscle and fat mass and percentages for whole body, upper limbs and trunk for ssMMF, but only upper limbs for ssRP. Body composition ES's ranged moderate to large for ssMMF (0.56 to 1.27) and ranged small to moderate for ssRP (0.28 to 0.52). ssMMF also significantly improved (P<0.05) total abdominal fat and increased intracellular water with moderate ES's (-0.62 and 0.56, respectively). Training to self-determined RM is not efficacious for trained participants. Training to MMF produces greatest improvements in strength and body composition, however, RP style training does offer some benefit.

Multi-Axis Test Facility

NASA Image and Video Library

1959-11-01

Multi-Axis Test Facility, Space Progress Report, November 1, 1959: The Multi Axis Space Test Inertia Facility [MASTIF], informally referred to as the Gimbal Rig, was installed inside the Altitude Wind Tunnel. The rig, which spun on three axis simultaneously, was used to train the Mercury astronauts on how to bring a spinning spacecraft under control and to determine the effects of rapid spinning on the astronaut's eyesight and psyche. Small gaseous nitrogen jets were operated by the pilot to gain control of the rig after it had been set in motion. Part 1 shows pilot Joe Algranti in the rig as it rotates over one, two, and three axis. It also has overall views of the test set-up with researchers and technicians on the test platform. Part 2 shows Algranti being secured in the rig prior to the test. The rig is set in motion and the pilot slowly brings it under control. The Mercury astronauts trained on the MASTIF in early spring of 1960.

Marginal ambulatory teaching cost under varying levels of service utilization.

PubMed

Panton, D M; Mushlin, A I; Gavett, J W

1980-06-01

The ambulatory component of residency training jointly produces two products, namely, training and patient services. In costing educational programs of this type, two approaches are frequently taken. The first considers the total costs of the educational program, including training and patient services. These costs are usually constructed from historical accounting records. The second approach attempts to cost the joint products separately, based upon estimates of future changes in program costs, if the product in question is added to or removed from the program. The second approach relates to typical decisions facing the managers of medical centers and practices used for teaching purposes. This article reports such a study of costs in a primary-care residency training program in a hospital outpatient setting. The costs of the product, i.e., on-the-job training, are evaluated using a replacement-cost concept under different levels of patient services. The results show that the cost of the product, training, is small at full clinical utilization and is sensitive to changes in the volume of services provided.

Deformable image registration using convolutional neural networks

NASA Astrophysics Data System (ADS)

Eppenhof, Koen A. J.; Lafarge, Maxime W.; Moeskops, Pim; Veta, Mitko; Pluim, Josien P. W.

2018-03-01

Deformable image registration can be time-consuming and often needs extensive parameterization to perform well on a specific application. We present a step towards a registration framework based on a three-dimensional convolutional neural network. The network directly learns transformations between pairs of three-dimensional images. The outputs of the network are three maps for the x, y, and z components of a thin plate spline transformation grid. The network is trained on synthetic random transformations, which are applied to a small set of representative images for the desired application. Training therefore does not require manually annotated ground truth deformation information. The methodology is demonstrated on public data sets of inspiration-expiration lung CT image pairs, which come with annotated corresponding landmarks for evaluation of the registration accuracy. Advantages of this methodology are its fast registration times and its minimal parameterization.

Small Enterprise Development Pre-Service Training Manual. Training for Development. Peace Corps Information Collection & Exchange Training Manual No. T-42.

ERIC Educational Resources Information Center

Pragma Corp., Falls Church, VA.

This manual is intended to assist Peace Corps trainers in providing preservice technical training in small enterprise development. The manual includes a total of 40 training sessions divided among 10 learning modules. The following topics are covered: the main principles of small enterprise development; Peace Corps volunteers as small enterprise…

The Influence of Art Expertise and Training on Emotion and Preference Ratings for Representational and Abstract Artworks

PubMed Central

van Paasschen, Jorien; Bacci, Francesca; Melcher, David P.

2015-01-01

Across cultures and throughout recorded history, humans have produced visual art. This raises the question of why people report such an emotional response to artworks and find some works more beautiful or compelling than others. In the current study we investigated the interplay between art expertise, and emotional and preference judgments. Sixty participants (40 novices, 20 art experts) rated a set of 150 abstract artworks and portraits during two occasions: in a laboratory setting and in a museum. Before commencing their second session, half of the art novices received a brief training on stylistic and art historical aspects of abstract art and portraiture. Results showed that art experts rated the artworks higher than novices on aesthetic facets (beauty and wanting), but no group differences were observed on affective evaluations (valence and arousal). The training session made a small effect on ratings of preference compared to the non-trained group of novices. Overall, these findings are consistent with the idea that affective components of art appreciation are less driven by expertise and largely consistent across observers, while more cognitive aspects of aesthetic viewing depend on viewer characteristics such as art expertise. PMID:26244368

Parsimonious kernel extreme learning machine in primal via Cholesky factorization.

PubMed

Zhao, Yong-Ping

2016-08-01

Recently, extreme learning machine (ELM) has become a popular topic in machine learning community. By replacing the so-called ELM feature mappings with the nonlinear mappings induced by kernel functions, two kernel ELMs, i.e., P-KELM and D-KELM, are obtained from primal and dual perspectives, respectively. Unfortunately, both P-KELM and D-KELM possess the dense solutions in direct proportion to the number of training data. To this end, a constructive algorithm for P-KELM (CCP-KELM) is first proposed by virtue of Cholesky factorization, in which the training data incurring the largest reductions on the objective function are recruited as significant vectors. To reduce its training cost further, PCCP-KELM is then obtained with the application of a probabilistic speedup scheme into CCP-KELM. Corresponding to CCP-KELM, a destructive P-KELM (CDP-KELM) is presented using a partial Cholesky factorization strategy, where the training data incurring the smallest reductions on the objective function after their removals are pruned from the current set of significant vectors. Finally, to verify the efficacy and feasibility of the proposed algorithms in this paper, experiments on both small and large benchmark data sets are investigated. Copyright © 2016 Elsevier Ltd. All rights reserved.

The Influence of Art Expertise and Training on Emotion and Preference Ratings for Representational and Abstract Artworks.

PubMed

van Paasschen, Jorien; Bacci, Francesca; Melcher, David P

2015-01-01

Across cultures and throughout recorded history, humans have produced visual art. This raises the question of why people report such an emotional response to artworks and find some works more beautiful or compelling than others. In the current study we investigated the interplay between art expertise, and emotional and preference judgments. Sixty participants (40 novices, 20 art experts) rated a set of 150 abstract artworks and portraits during two occasions: in a laboratory setting and in a museum. Before commencing their second session, half of the art novices received a brief training on stylistic and art historical aspects of abstract art and portraiture. Results showed that art experts rated the artworks higher than novices on aesthetic facets (beauty and wanting), but no group differences were observed on affective evaluations (valence and arousal). The training session made a small effect on ratings of preference compared to the non-trained group of novices. Overall, these findings are consistent with the idea that affective components of art appreciation are less driven by expertise and largely consistent across observers, while more cognitive aspects of aesthetic viewing depend on viewer characteristics such as art expertise.

Video Self-Modeling Applications With Students With Autism Spectrum Disorder in a Small Private School Setting

ERIC Educational Resources Information Center

Tom, Buggey

2005-01-01

Videotaped self-modeling (VSM) was developed as a means to allow participants to view themselves in situations where they are performing at a more advanced level than they typically function. VSM has been used effectively to train positive behaviors and reduce unwanted behaviors across a range of ages and behaviors; however, few studies of VSM…

Facilitating Facilitators to Facilitate, in Problem or Enquiry Based Learning Sessions

ERIC Educational Resources Information Center

Coelho, Catherine

2014-01-01

Problem based learning (PBL) has been used in dental education over the past 20 years and uses a patient case scenario to stimulate learning in a small group setting, where a trained facilitator does not teach but guides the group to bring about deep contextualized learning, to be empathetic to each other and to encourage fair and equitable…

Challenges of Citizenship Education in Small States--Issues of Context vs. Content. A Background Paper Prepared for the Pan-Commonwealth Project on Heritage, Multiculturalism and Citizenship.

ERIC Educational Resources Information Center

Degazon-Johnson, Roli

Ten years ago in the Harare Declaration, heads of government from across the British Commonwealth committed to upholding critical commonwealth-held values. While initially focusing on Human Rights Education, the Commonwealth Secretariat set about enabling member governments to promote greater awareness, education, and training supporting…

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.

Bees' subtle colour preferences: how bees respond to small changes in pigment concentration

NASA Astrophysics Data System (ADS)

Papiorek, Sarah; Rohde, Katja; Lunau, Klaus

2013-07-01

Variability in flower colour of animal-pollinated plants is common and caused, inter alia, by inter-individual differences in pigment concentrations. If and how pollinators, especially bees, respond to these small differences in pigment concentration is not known, but it is likely that flower colour variability impacts the choice behaviour of all flower visitors that exhibit innate and learned colour preferences. In behavioural experiments, we simulated varying pigment concentrations and studied its impact on the colour choices of bumblebees and honeybees. Individual bees were trained to artificial flowers having a specific concentration of a pigment, i.e. Acridine Orange or Aniline Blue, and then given the simultaneous choice between three test colours including the training colour, one colour of lower and one colour of higher pigment concentration. For each pigment, two set-ups were provided, covering the range of low to middle and the range of middle to high pigment concentrations. Despite the small bee-subjective perceptual contrasts between the tested stimuli and regardless of training towards medium concentrations, bees preferred neither the training stimuli nor the stimuli offering the highest pigment concentration but more often chose those stimuli offering the highest spectral purity and the highest chromatic contrast against the background. Overall, this study suggests that bees choose an intermediate pigment concentration due to its optimal conspicuousness. It is concluded that the spontaneous preferences of bees for flower colours of high spectral purity might exert selective pressure on the evolution of floral colours and of flower pigmentation.

Reliable gains? Evidence for substantially underpowered designs in studies of working memory training transfer to fluid intelligence.

PubMed

Bogg, Tim; Lasecki, Leanne

2014-01-01

In recent years, cognitive scientists and commercial interests (e.g., Fit Brains, Lumosity) have focused research attention and financial resources on cognitive tasks, especially working memory tasks, to explore and exploit possible transfer effects to general cognitive abilities, such as fluid intelligence. The increased research attention has produced mixed findings, as well as contention about the disposition of the evidence base. To address this contention, Au et al. (2014) recently conducted a meta-analysis of extant controlled experimental studies of n-back task training transfer effects on measures of fluid intelligence in healthy adults; the results of which showed a small training transfer effect. Using several approaches, the current review evaluated and re-analyzed the meta-analytic data for the presence of two different forms of small-study effects: (1) publication bias in the presence of low power and; (2) low power in the absence of publication bias. The results of these approaches showed no evidence of selection bias in the working memory training literature, but did show evidence of small-study effects related to low power in the absence of publication bias. While the effect size estimate identified by Au et al. (2014) provided the most precise estimate to date, it should be interpreted in the context of a uniformly low-powered base of evidence. The present work concludes with a brief set of considerations for assessing the adequacy of a body of research findings for the application of meta-analytic techniques.

Reinforcement Learning with Autonomous Small Unmanned Aerial Vehicles in Cluttered Environments

NASA Technical Reports Server (NTRS)

Tran, Loc; Cross, Charles; Montague, Gilbert; Motter, Mark; Neilan, James; Qualls, Garry; Rothhaar, Paul; Trujillo, Anna; Allen, B. Danette

2015-01-01

We present ongoing work in the Autonomy Incubator at NASA Langley Research Center (LaRC) exploring the efficacy of a data set aggregation approach to reinforcement learning for small unmanned aerial vehicle (sUAV) flight in dense and cluttered environments with reactive obstacle avoidance. The goal is to learn an autonomous flight model using training experiences from a human piloting a sUAV around static obstacles. The training approach uses video data from a forward-facing camera that records the human pilot's flight. Various computer vision based features are extracted from the video relating to edge and gradient information. The recorded human-controlled inputs are used to train an autonomous control model that correlates the extracted feature vector to a yaw command. As part of the reinforcement learning approach, the autonomous control model is iteratively updated with feedback from a human agent who corrects undesired model output. This data driven approach to autonomous obstacle avoidance is explored for simulated forest environments furthering autonomous flight under the tree canopy research. This enables flight in previously inaccessible environments which are of interest to NASA researchers in Earth and Atmospheric sciences.

Reduction in training time of a deep learning model in detection of lesions in CT

NASA Astrophysics Data System (ADS)

Makkinejad, Nazanin; Tajbakhsh, Nima; Zarshenas, Amin; Khokhar, Ashfaq; Suzuki, Kenji

2018-02-01

Deep learning (DL) emerged as a powerful tool for object detection and classification in medical images. Building a well-performing DL model, however, requires a huge number of images for training, and it takes days to train a DL model even on a cutting edge high-performance computing platform. This study is aimed at developing a method for selecting a "small" number of representative samples from a large collection of training samples to train a DL model for the could be used to detect polyps in CT colonography (CTC), without compromising the classification performance. Our proposed method for representative sample selection (RSS) consists of a K-means clustering algorithm. For the performance evaluation, we applied the proposed method to select samples for the training of a massive training artificial neural network based DL model, to be used for the classification of polyps and non-polyps in CTC. Our results show that the proposed method reduce the training time by a factor of 15, while maintaining the classification performance equivalent to the model trained using the full training set. We compare the performance using area under the receiveroperating- characteristic curve (AUC).

The self-care for people initiative: the outcome evaluation.

PubMed

White, Alan; South, Jane; Bagnall, Anne-Marie; Forshaw, Mark; Spoor, Chris; Marchant, Paul; Witty, Karl

2012-10-01

To determine the effects of a community-based training programme in self-care on the lay population. Self Care is recognised as being a cornerstone of the populations health, but to date there have been few large-scale studies of its effectiveness on the general public. This paper reports on an evaluation of a self-care skills training course delivered in small group sessions within workplace and parent and toddler group settings to a lay population. A quasi-experimental longitudinal study of 12-month duration was conducted in three intervention primary care trusts (PCTs) and two similar comparison PCTs in England. The sample comprised 1568 self-selecting participants: 868 received the intervention and 700 did not. No changes were seen in usage of General Practitioner services, the primary outcome, however, statistical analysis suggested that being in the intervention group may be associated with increased use of out-of-hours and secondary care services. At six months' follow-up small but statistically significant positive effects of being in the intervention group were seen on self-esteem, well-being and anxiety scores. At 12 months' follow-up small but statistically significant positive effects of being in the intervention group were also seen on recovery locus of control, health literacy and self-esteem scores, and on knowledge of adult cough. The clinical significance of these very small changes is unclear. The training programme had a small but positive effect, which was still evident at 12 months, on individuals' knowledge and confidence levels with regard to managing their own health, but did not lead to reductions in health service use.

An ensemble heterogeneous classification methodology for discovering health-related knowledge in social media messages.

PubMed

Tuarob, Suppawong; Tucker, Conrad S; Salathe, Marcel; Ram, Nilam

2014-06-01

The role of social media as a source of timely and massive information has become more apparent since the era of Web 2.0.Multiple studies illustrated the use of information in social media to discover biomedical and health-related knowledge.Most methods proposed in the literature employ traditional document classification techniques that represent a document as a bag of words.These techniques work well when documents are rich in text and conform to standard English; however, they are not optimal for social media data where sparsity and noise are norms.This paper aims to address the limitations posed by the traditional bag-of-word based methods and propose to use heterogeneous features in combination with ensemble machine learning techniques to discover health-related information, which could prove to be useful to multiple biomedical applications, especially those needing to discover health-related knowledge in large scale social media data.Furthermore, the proposed methodology could be generalized to discover different types of information in various kinds of textual data. Social media data is characterized by an abundance of short social-oriented messages that do not conform to standard languages, both grammatically and syntactically.The problem of discovering health-related knowledge in social media data streams is then transformed into a text classification problem, where a text is identified as positive if it is health-related and negative otherwise.We first identify the limitations of the traditional methods which train machines with N-gram word features, then propose to overcome such limitations by utilizing the collaboration of machine learning based classifiers, each of which is trained to learn a semantically different aspect of the data.The parameter analysis for tuning each classifier is also reported. Three data sets are used in this research.The first data set comprises of approximately 5000 hand-labeled tweets, and is used for cross validation of the classification models in the small scale experiment, and for training the classifiers in the real-world large scale experiment.The second data set is a random sample of real-world Twitter data in the US.The third data set is a random sample of real-world Facebook Timeline posts. Two sets of evaluations are conducted to investigate the proposed model's ability to discover health-related information in the social media domain: small scale and large scale evaluations.The small scale evaluation employs 10-fold cross validation on the labeled data, and aims to tune parameters of the proposed models, and to compare with the stage-of-the-art method.The large scale evaluation tests the trained classification models on the native, real-world data sets, and is needed to verify the ability of the proposed model to handle the massive heterogeneity in real-world social media. The small scale experiment reveals that the proposed method is able to mitigate the limitations in the well established techniques existing in the literature, resulting in performance improvement of 18.61% (F-measure).The large scale experiment further reveals that the baseline fails to perform well on larger data with higher degrees of heterogeneity, while the proposed method is able to yield reasonably good performance and outperform the baseline by 46.62% (F-Measure) on average. Copyright © 2014 Elsevier Inc. All rights reserved.

Decaying relevance of clinical data towards future decisions in data-driven inpatient clinical order sets.

PubMed

Chen, Jonathan H; Alagappan, Muthuraman; Goldstein, Mary K; Asch, Steven M; Altman, Russ B

2017-06-01

Determine how varying longitudinal historical training data can impact prediction of future clinical decisions. Estimate the "decay rate" of clinical data source relevance. We trained a clinical order recommender system, analogous to Netflix or Amazon's "Customers who bought A also bought B..." product recommenders, based on a tertiary academic hospital's structured electronic health record data. We used this system to predict future (2013) admission orders based on different subsets of historical training data (2009 through 2012), relative to existing human-authored order sets. Predicting future (2013) inpatient orders is more accurate with models trained on just one month of recent (2012) data than with 12 months of older (2009) data (ROC AUC 0.91 vs. 0.88, precision 27% vs. 22%, recall 52% vs. 43%, all P<10 -10 ). Algorithmically learned models from even the older (2009) data was still more effective than existing human-authored order sets (ROC AUC 0.81, precision 16% recall 35%). Training with more longitudinal data (2009-2012) was no better than using only the most recent (2012) data, unless applying a decaying weighting scheme with a "half-life" of data relevance about 4 months. Clinical practice patterns (automatically) learned from electronic health record data can vary substantially across years. Gold standards for clinical decision support are elusive moving targets, reinforcing the need for automated methods that can adapt to evolving information. Prioritizing small amounts of recent data is more effective than using larger amounts of older data towards future clinical predictions. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Quantum annealing versus classical machine learning applied to a simplified computational biology problem

PubMed Central

Li, Richard Y.; Di Felice, Rosa; Rohs, Remo; Lidar, Daniel A.

2018-01-01

Transcription factors regulate gene expression, but how these proteins recognize and specifically bind to their DNA targets is still debated. Machine learning models are effective means to reveal interaction mechanisms. Here we studied the ability of a quantum machine learning approach to predict binding specificity. Using simplified datasets of a small number of DNA sequences derived from actual binding affinity experiments, we trained a commercially available quantum annealer to classify and rank transcription factor binding. The results were compared to state-of-the-art classical approaches for the same simplified datasets, including simulated annealing, simulated quantum annealing, multiple linear regression, LASSO, and extreme gradient boosting. Despite technological limitations, we find a slight advantage in classification performance and nearly equal ranking performance using the quantum annealer for these fairly small training data sets. Thus, we propose that quantum annealing might be an effective method to implement machine learning for certain computational biology problems. PMID:29652405

Pollution Prevention through Peer Education: A Community Health Worker and Small and Home-Based Business Initiative on the Arizona-Sonora Border

PubMed Central

Moreno Ramírez, Denise; Ramírez-Andreotta, Mónica D.; Vea, Lourdes; Estrella-Sánchez, Rocío; Wolf, Ann Marie A.; Kilungo, Aminata; Spitz, Anna H.; Betterton, Eric A.

2015-01-01

Government-led pollution prevention programs tend to focus on large businesses due to their potential to pollute larger quantities, therefore leaving a gap in programs targeting small and home-based businesses. In light of this gap, we set out to determine if a voluntary, peer education approach led by female, Hispanic community health workers (promotoras) can influence small and home-based businesses to implement pollution prevention strategies on-site. This paper describes a partnership between promotoras from a non-profit organization and researchers from a university working together to reach these businesses in a predominately Hispanic area of Tucson, Arizona. From 2008 to 2011, the promotora-led pollution prevention program reached a total of 640 small and home-based businesses. Program activities include technical trainings for promotoras and businesses, generation of culturally and language appropriate educational materials, and face-to-face peer education via multiple on-site visits. To determine the overall effectiveness of the program, surveys were used to measure best practices implemented on-site, perceptions towards pollution prevention, and overall satisfaction with the industry-specific trainings. This paper demonstrates that promotoras can promote the implementation of pollution prevention best practices by Hispanic small and home-based businesses considered “hard-to-reach” by government-led programs. PMID:26371028

Pollution Prevention through Peer Education: A Community Health Worker and Small and Home-Based Business Initiative on the Arizona-Sonora Border.

PubMed

Ramírez, Denise Moreno; Ramírez-Andreotta, Mónica D; Vea, Lourdes; Estrella-Sánchez, Rocío; Wolf, Ann Marie A; Kilungo, Aminata; Spitz, Anna H; Betterton, Eric A

2015-09-09

Government-led pollution prevention programs tend to focus on large businesses due to their potential to pollute larger quantities, therefore leaving a gap in programs targeting small and home-based businesses. In light of this gap, we set out to determine if a voluntary, peer education approach led by female, Hispanic community health workers (promotoras) can influence small and home-based businesses to implement pollution prevention strategies on-site. This paper describes a partnership between promotoras from a non-profit organization and researchers from a university working together to reach these businesses in a predominately Hispanic area of Tucson, Arizona. From 2008 to 2011, the promotora-led pollution prevention program reached a total of 640 small and home-based businesses. Program activities include technical trainings for promotoras and businesses, generation of culturally and language appropriate educational materials, and face-to-face peer education via multiple on-site visits. To determine the overall effectiveness of the program, surveys were used to measure best practices implemented on-site, perceptions towards pollution prevention, and overall satisfaction with the industry-specific trainings. This paper demonstrates that promotoras can promote the implementation of pollution prevention best practices by Hispanic small and home-based businesses considered "hard-to-reach" by government-led programs.

NURSES’ PERCEPTIONS OF COMMUNICATION TRAINING IN THE ICU

PubMed Central

Radtke, Jill V.; Tate, Judith A.; Happ, Mary Beth

2011-01-01

Summary Objective To describe the experience and perceptions of nurse study participants regarding a communication intervention (training and communication tools) for use with nonspeaking, critically-ill patients. Research Methodology/Design Small focus groups and an individual interview were conducted with six critical care nurses. Transcripts were analysed using qualitative content analysis and constant comparison. Setting Two ICUs within a large, metropolitan medical centre in western Pennsylvania, United States of America. Main Outcome Measures Critical care nurses’ evaluations of (1) a basic communication skills training program (BCST) and (2) augmentative and alternative communication strategies (AAC) introduced during their study participation. Results Six main categories were identified in the data: 1) communication value/perceived competence; 2) communication intention; 3) benefits of training; 4) barriers to implementation; 5) preferences/utilization of strategies; and 6) leading-following. Perceived value of and individual competence in communication with nonspeaking patients varied. Nurses prioritized communication about physical needs, but recognized complexity of other intended patient messages. Nurses evaluated the BCST as helpful in reinforcing basic communication strategies and found several new strategies effective. Advanced strategies received mixed reviews. Primary barriers to practice integration included patients’ mental status, time constraints, and the small proportion of nurses trained or knowledgeable about best patient communication practices in the ICU. Conclusions The results suggest that the communication skills training program could be valuable in reinforcing basic/intuitive communication strategies, assisting in the acquisition of new skills, and ensuring communication supply availability. Practice integration will likely require unit-wide interdisciplinary dissemination, expert modelling and reinforcement. PMID:22172745

Joint genomic evaluation of French dairy cattle breeds using multiple-trait models.

PubMed

Karoui, Sofiene; Carabaño, María Jesús; Díaz, Clara; Legarra, Andrés

2012-12-07

Using a multi-breed reference population might be a way of increasing the accuracy of genomic breeding values in small breeds. Models involving mixed-breed data do not take into account the fact that marker effects may differ among breeds. This study was aimed at investigating the impact on accuracy of increasing the number of genotyped candidates in the training set by using a multi-breed reference population, in contrast to single-breed genomic evaluations. Three traits (milk production, fat content and female fertility) were analyzed by genomic mixed linear models and Bayesian methodology. Three breeds of French dairy cattle were used: Holstein, Montbéliarde and Normande with 2976, 950 and 970 bulls in the training population, respectively and 964, 222 and 248 bulls in the validation population, respectively. All animals were genotyped with the Illumina Bovine SNP50 array. Accuracy of genomic breeding values was evaluated under three scenarios for the correlation of genomic breeding values between breeds (r(g)): uncorrelated (1), r(g) = 0; estimated r(g) (2); high, r(g) = 0.95 (3). Accuracy and bias of predictions obtained in the validation population with the multi-breed training set were assessed by the coefficient of determination (R(2)) and by the regression coefficient of daughter yield deviations of validation bulls on their predicted genomic breeding values, respectively. The genetic variation captured by the markers for each trait was similar to that estimated for routine pedigree-based genetic evaluation. Posterior means for rg ranged from -0.01 for fertility between Montbéliarde and Normande to 0.79 for milk yield between Montbéliarde and Holstein. Differences in R(2) between the three scenarios were notable only for fat content in the Montbéliarde breed: from 0.27 in scenario (1) to 0.33 in scenarios (2) and (3). Accuracies for fertility were lower than for other traits. Using a multi-breed reference population resulted in small or no increases in accuracy. Only the breed with a small data set and large genetic correlation with the breed with a large data set showed increased accuracy for the traits with moderate (milk) to high (fat content) heritability. No benefit was observed for fertility, a lowly heritable trait.

Joint genomic evaluation of French dairy cattle breeds using multiple-trait models

PubMed Central

2012-01-01

Background Using a multi-breed reference population might be a way of increasing the accuracy of genomic breeding values in small breeds. Models involving mixed-breed data do not take into account the fact that marker effects may differ among breeds. This study was aimed at investigating the impact on accuracy of increasing the number of genotyped candidates in the training set by using a multi-breed reference population, in contrast to single-breed genomic evaluations. Methods Three traits (milk production, fat content and female fertility) were analyzed by genomic mixed linear models and Bayesian methodology. Three breeds of French dairy cattle were used: Holstein, Montbéliarde and Normande with 2976, 950 and 970 bulls in the training population, respectively and 964, 222 and 248 bulls in the validation population, respectively. All animals were genotyped with the Illumina Bovine SNP50 array. Accuracy of genomic breeding values was evaluated under three scenarios for the correlation of genomic breeding values between breeds (rg): uncorrelated (1), rg = 0; estimated rg (2); high, rg = 0.95 (3). Accuracy and bias of predictions obtained in the validation population with the multi-breed training set were assessed by the coefficient of determination (R2) and by the regression coefficient of daughter yield deviations of validation bulls on their predicted genomic breeding values, respectively. Results The genetic variation captured by the markers for each trait was similar to that estimated for routine pedigree-based genetic evaluation. Posterior means for rg ranged from −0.01 for fertility between Montbéliarde and Normande to 0.79 for milk yield between Montbéliarde and Holstein. Differences in R2 between the three scenarios were notable only for fat content in the Montbéliarde breed: from 0.27 in scenario (1) to 0.33 in scenarios (2) and (3). Accuracies for fertility were lower than for other traits. Conclusions Using a multi-breed reference population resulted in small or no increases in accuracy. Only the breed with a small data set and large genetic correlation with the breed with a large data set showed increased accuracy for the traits with moderate (milk) to high (fat content) heritability. No benefit was observed for fertility, a lowly heritable trait. PMID:23216664

Recovery of pectoralis major and triceps brachii after bench press exercise.

PubMed

Ferreira, Diogo V; Gentil, Paulo; Soares, Saulo Rodrigo Sampaio; Bottaro, Martim

2017-11-01

The present study evaluated and compared the recovery of pectoralis major (PM) and triceps brachii (TB) muscles of trained men after bench press exercise. Eighteen volunteers performed eight sets of bench press exercise to momentary muscle failure and were evaluated for TB and PM peak torque and total work on an isokinetic dynamometer. PM peak torque and total work remained lower than baseline for 72 and 96 h, respectively. TB peak torque was only different from baseline immediately post training, while total work was significantly lower than baseline immediately and 48 h after training. Normalized peak torque values were only different between TB and PM at 48 h after training. Considering the small and nonsignificant difference between the recovery of TB and PM muscles, the results suggest that bench press exercise may promote a similar stress on these muscles. Muscle Nerve 56: 963-967, 2017. © 2016 Wiley Periodicals, Inc.

Land of the thunder dragon is on the move. Bhutan.

PubMed

Molitor, C

1992-08-01

A small and landlocked country in the Himalayas, the size of Switzerland, Bhutan or Druk Yul, Land of the Thunder Dragon, had for centuries been isolated from the outside world. Finally, its tradition-bound people are beginning to pick up new trades and vocations. Penjore Timber Industries & Exports Ltd. is one of the 1st modern wood-processing complexes in Bhutan still with a predominantly subsistence and barter agriculture economy. The company, set up with the financial support of the Asian Development Bank (ADB), is producing broomsticks, handles for tools, wooden doors, and window frames mainly for export. The industrial sector is small and accounts for only 4% of GDP. Most of the 125 private enterprises in the country are small. A development bank, the Bhutan Development Finance Corporation (BDFC), was established in 1988 with ADB support for the development of private industry. A general education system was established and schools were opened only in the early 1960s. The government had given the development of trained manpower high priority in its 5th Economic and Social Development Plan (FY 1981/82-FY 1986/87). The Royal Institute of Management (RIM) was established in 1986. About 40 trainees each in secretarial, accounting, and basic management training programs and 150 managerial personnel from public and private agencies are trained each year by RIM which the ADB supports under the Second Multiproject Loan to Bhutan with cofinancing by the Norwegian Development Agency. So far RIM has designed 12 different training courses, 92 students graduated in 1989, and by 1995 about 30 training courses are envisioned. According to 1987 data in a recent UN report Bhutan is the only one of the world's 42 least-developed countries with a more than 10% agricultural production growth rate where real GDP growth has outspaced population growth.

Distributed Adaptive Binary Quantization for Fast Nearest Neighbor Search.

PubMed

Xianglong Liu; Zhujin Li; Cheng Deng; Dacheng Tao

2017-11-01

Hashing has been proved an attractive technique for fast nearest neighbor search over big data. Compared with the projection based hashing methods, prototype-based ones own stronger power to generate discriminative binary codes for the data with complex intrinsic structure. However, existing prototype-based methods, such as spherical hashing and K-means hashing, still suffer from the ineffective coding that utilizes the complete binary codes in a hypercube. To address this problem, we propose an adaptive binary quantization (ABQ) method that learns a discriminative hash function with prototypes associated with small unique binary codes. Our alternating optimization adaptively discovers the prototype set and the code set of a varying size in an efficient way, which together robustly approximate the data relations. Our method can be naturally generalized to the product space for long hash codes, and enjoys the fast training linear to the number of the training data. We further devise a distributed framework for the large-scale learning, which can significantly speed up the training of ABQ in the distributed environment that has been widely deployed in many areas nowadays. The extensive experiments on four large-scale (up to 80 million) data sets demonstrate that our method significantly outperforms state-of-the-art hashing methods, with up to 58.84% performance gains relatively.

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.

Effect of training on word-recognition performance in noise for young normal-hearing and older hearing-impaired listeners.

PubMed

Burk, Matthew H; Humes, Larry E; Amos, Nathan E; Strauser, Lauren E

2006-06-01

The objective of this study was to evaluate the effectiveness of a training program for hearing-impaired listeners to improve their speech-recognition performance within a background noise when listening to amplified speech. Both noise-masked young normal-hearing listeners, used to model the performance of elderly hearing-impaired listeners, and a group of elderly hearing-impaired listeners participated in the study. Of particular interest was whether training on an isolated word list presented by a standardized talker can generalize to everyday speech communication across novel talkers. Word-recognition performance was measured for both young normal-hearing (n = 16) and older hearing-impaired (n = 7) adults. Listeners were trained on a set of 75 monosyllabic words spoken by a single female talker over a 9- to 14-day period. Performance for the familiar (trained) talker was measured before and after training in both open-set and closed-set response conditions. Performance on the trained words of the familiar talker were then compared with those same words spoken by three novel talkers and to performance on a second set of untrained words presented by both the familiar and unfamiliar talkers. The hearing-impaired listeners returned 6 mo after their initial training to examine retention of the trained words as well as their ability to transfer any knowledge gained from word training to sentences containing both trained and untrained words. Both young normal-hearing and older hearing-impaired listeners performed significantly better on the word list in which they were trained versus a second untrained list presented by the same talker. Improvements on the untrained words were small but significant, indicating some generalization to novel words. The large increase in performance on the trained words, however, was maintained across novel talkers, pointing to the listener's greater focus on lexical memorization of the words rather than a focus on talker-specific acoustic characteristics. On return in 6 mo, listeners performed significantly better on the trained words relative to their initial baseline performance. Although the listeners performed significantly better on trained versus untrained words in isolation, once the trained words were embedded in sentences, no improvement in recognition over untrained words within the same sentences was shown. Older hearing-impaired listeners were able to significantly improve their word-recognition abilities through training with one talker and to the same degree as young normal-hearing listeners. The improved performance was maintained across talkers and across time. This might imply that training a listener using a standardized list and talker may still provide benefit when these same words are presented by novel talkers outside the clinic. However, training on isolated words was not sufficient to transfer to fluent speech for the specific sentence materials used within this study. Further investigation is needed regarding approaches to improve a hearing aid user's speech understanding in everyday communication situations.

Development of a Direct Spectrophotometric and Chemometric Method for Determining Food Dye Concentrations.

PubMed

Arroz, Erin; Jordan, Michael; Dumancas, Gerard G

2017-07-01

An ultraviolet visible (UV-Vis) spectrophotometric and partial least squares (PLS) chemometric method was developed for the simultaneous determination of erythrosine B (red), Brilliant Blue, and tartrazine (yellow) dyes. A training set (n = 64) was generated using a full factorial design and its accuracy was tested in a test set (n = 13) using a Box-Behnken design. The test set garnered a root mean square error (RMSE) of 1.79 × 10 -7 for blue, 4.59 × 10 -7 for red, and 1.13 × 10 -6 for yellow dyes. The relatively small RMSE suggests only a small difference between predicted versus measured concentrations, demonstrating the accuracy of our model. The relative error of prediction (REP) for the test set were 11.73%, 19.52%, 19.38%, for blue, red, and yellow dyes, respectively. A comparable overlay between the actual candy samples and their replicated synthetic spectra were also obtained indicating the model as a potentially accurate method for determining concentrations of dyes in food samples.

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

Aldegunde, Manuel, E-mail: M.A.Aldegunde-Rodriguez@warwick.ac.uk; Kermode, James R., E-mail: J.R.Kermode@warwick.ac.uk; Zabaras, Nicholas

This paper presents the development of a new exchange–correlation functional from the point of view of machine learning. Using atomization energies of solids and small molecules, we train a linear model for the exchange enhancement factor using a Bayesian approach which allows for the quantification of uncertainties in the predictions. A relevance vector machine is used to automatically select the most relevant terms of the model. We then test this model on atomization energies and also on bulk properties. The average model provides a mean absolute error of only 0.116 eV for the test points of the G2/97 set butmore » a larger 0.314 eV for the test solids. In terms of bulk properties, the prediction for transition metals and monovalent semiconductors has a very low test error. However, as expected, predictions for types of materials not represented in the training set such as ionic solids show much larger errors.« less

Elimination of echolalic responding to questions through the training of a generalized verbal response.

PubMed Central

Schreibman, L; Carr, E G

1978-01-01

Echolalia, the parroting of the speech of others, is a severe communication disorder frequently associated with childhood schizophrenia and mental retardation. Two echolalic children, one schizophrenic and one retarded, were treated in a multiple-baseline design across subjects. Each child was taught to make an appropriate, non-echolalic verbal response (i.e., "I don't know") to a small set of previously echoed questions. After such training, this response generalized across a broad set of untrained questions that had formerly been echoed. The results obtained were the same irrespective of the specific experimenter who presented the questions. Further, each child discriminated appropriately between those questions that had previously been echoed and those that had not. Followup probes showed that treatment gains were maintained one month later. The procedure is economical, in that it produces a rapid and widespread cessation of echolalic responding. PMID:730631

Detection of Cheating by Decimation Algorithm

NASA Astrophysics Data System (ADS)

Yamanaka, Shogo; Ohzeki, Masayuki; Decelle, Aurélien

2015-02-01

We expand the item response theory to study the case of "cheating students" for a set of exams, trying to detect them by applying a greedy algorithm of inference. This extended model is closely related to the Boltzmann machine learning. In this paper we aim to infer the correct biases and interactions of our model by considering a relatively small number of sets of training data. Nevertheless, the greedy algorithm that we employed in the present study exhibits good performance with a few number of training data. The key point is the sparseness of the interactions in our problem in the context of the Boltzmann machine learning: the existence of cheating students is expected to be very rare (possibly even in real world). We compare a standard approach to infer the sparse interactions in the Boltzmann machine learning to our greedy algorithm and we find the latter to be superior in several aspects.

One parameter binary black hole inverse problem using a sparse training set

NASA Astrophysics Data System (ADS)

Carrillo, M.; Gracia-Linares, M.; González, J. A.; Guzmán, F. S.

In this paper, we use Artificial Neural Networks (ANNs) to estimate the mass ratio q in a binary black hole collision out of the gravitational wave (GW) strain. We assume the strain is a time series (TS) that contains a part of the orbital phase and the ring-down of the final black hole. We apply the method to the strain itself in the time domain and also in the frequency domain. We present the accuracy in the prediction of the ANNs trained with various values of signal-to-noise ratio (SNR). The core of our results is that the estimate of the mass ratio is obtained with a small sample of training signals and resulting in predictions with errors of the order of 1% for our best ANN configurations.

An empirical study of race times in recreational endurance runners.

PubMed

Vickers, Andrew J; Vertosick, Emily A

2016-01-01

Studies of endurance running have typically involved elite athletes, small sample sizes and measures that require special expertise or equipment. We examined factors associated with race performance and explored methods for race time prediction using information routinely available to a recreational runner. An Internet survey was used to collect data from recreational endurance runners (N = 2303). The cohort was split 2:1 into a training set and validation set to create models to predict race time. Sex, age, BMI and race training were associated with mean race velocity for all race distances. The difference in velocity between males and females decreased with increasing distance. Tempo runs were more strongly associated with velocity for shorter distances, while typical weekly training mileage and interval training had similar associations with velocity for all race distances. The commonly used Riegel formula for race time prediction was well-calibrated for races up to a half-marathon, but dramatically underestimated marathon time, giving times at least 10 min too fast for half of runners. We built two models to predict marathon time. The mean squared error for Riegel was 381 compared to 228 (model based on one prior race) and 208 (model based on two prior races). Our findings can be used to inform race training and to provide more accurate race time predictions for better pacing.

Estimating Spectra from Photometry

NASA Astrophysics Data System (ADS)

Kalmbach, J. Bryce; Connolly, Andrew J.

2017-12-01

Measuring the physical properties of galaxies such as redshift frequently requires the use of spectral energy distributions (SEDs). SED template sets are, however, often small in number and cover limited portions of photometric color space. Here we present a new method to estimate SEDs as a function of color from a small training set of template SEDs. We first cover the mathematical background behind the technique before demonstrating our ability to reconstruct spectra based upon colors and then compare our results to other common interpolation and extrapolation methods. When the photometric filters and spectra overlap, we show that the error in the estimated spectra is reduced by more than 65% compared to the more commonly used techniques. We also show an expansion of the method to wavelengths beyond the range of the photometric filters. Finally, we demonstrate the usefulness of our technique by generating 50 additional SED templates from an original set of 10 and by applying the new set to photometric redshift estimation. We are able to reduce the photometric redshifts standard deviation by at least 22.0% and the outlier rejected bias by over 86.2% compared to original set for z ≤ 3.

Analysis of small-bowel capsule endoscopy reading by using Quickview mode: training assistants for reading may produce a high diagnostic yield and save time for physicians.

PubMed

Shiotani, Akiko; Honda, Keisuke; Kawakami, Makiko; Kimura, Yoshiki; Yamanaka, Yoshiyuki; Fujita, Minoru; Matsumoto, Hiroshi; Tarumi, Ken-ichi; Manabe, Noriaki; Haruma, Ken

2012-01-01

The aim was to investigate the clinical utility of RAPID Access 6.5 Quickview software and to evaluate whether preview of the capsule endoscopy video by a trained nurse could detect significant lesions accurately compared with endoscopists. As reading capsule endoscopy is time consuming, one possible cost-effective strategy could be the use of trained nonphysicians or newly available software to preread and identify potentially important capsule images. The 100 capsule images of a variety of significant lesions from 87 patients were investigated. The minimum percentages for settings of sensitivity that could pick up the selected images and the detection rate for significant lesions by a well-trained nurse, two endoscopists with limited experience in reading, and one well-trained physician were examined. The frequency of the selected lesions picked up by Quickview mode using percentages for sensitivity settings of 5%, 15%, 25%, and 35% were 61%, 74%, 93%, and 98%, respectively. The percentages for sensitivity significantly correlated (r=0.78, P<0.001) with the reading time. The detection rate by the nurse or the well-trained physician was significantly higher than that by the physician with limited capsule experience (87% and 84.1% vs. 62.7%; P<0.01). The clinical use of Quickview at 25% did not significantly improve the detection rate. Quickview mode can reduce reading time but has an unacceptably miss rate for potentially important lesions. Use of a trained nonphysician assistant can reduce physician's time and improve diagnostic yield.

Small Business Management Training Tools Directory.

ERIC Educational Resources Information Center

American Association of Community and Junior Colleges, Washington, DC. National Small Business Training Network.

This directory is designed to assist in the identification of supplementary materials to support program development for small businesses. Following introductory comments and an overview of small business management training, section I lists training tools available from the Small Business Administration (SBA). Section II provides descriptions and…

From genus to phylum: large-subunit and internal transcribed spacer rRNA operon regions show similar classification accuracies influenced by database composition.

PubMed

Porras-Alfaro, Andrea; Liu, Kuan-Liang; Kuske, Cheryl R; Xie, Gary

2014-02-01

We compared the classification accuracy of two sections of the fungal internal transcribed spacer (ITS) region, individually and combined, and the 5' section (about 600 bp) of the large-subunit rRNA (LSU), using a naive Bayesian classifier and BLASTN. A hand-curated ITS-LSU training set of 1,091 sequences and a larger training set of 8,967 ITS region sequences were used. Of the factors evaluated, database composition and quality had the largest effect on classification accuracy, followed by fragment size and use of a bootstrap cutoff to improve classification confidence. The naive Bayesian classifier and BLASTN gave similar results at higher taxonomic levels, but the classifier was faster and more accurate at the genus level when a bootstrap cutoff was used. All of the ITS and LSU sections performed well (>97.7% accuracy) at higher taxonomic ranks from kingdom to family, and differences between them were small at the genus level (within 0.66 to 1.23%). When full-length sequence sections were used, the LSU outperformed the ITS1 and ITS2 fragments at the genus level, but the ITS1 and ITS2 showed higher accuracy when smaller fragment sizes of the same length and a 50% bootstrap cutoff were used. In a comparison using the larger ITS training set, ITS1 and ITS2 had very similar accuracy classification for fragments between 100 and 200 bp. Collectively, the results show that any of the ITS or LSU sections we tested provided comparable classification accuracy to the genus level and underscore the need for larger and more diverse classification training sets.

From Genus to Phylum: Large-Subunit and Internal Transcribed Spacer rRNA Operon Regions Show Similar Classification Accuracies Influenced by Database Composition

PubMed Central

Liu, Kuan-Liang; Kuske, Cheryl R.

2014-01-01

We compared the classification accuracy of two sections of the fungal internal transcribed spacer (ITS) region, individually and combined, and the 5′ section (about 600 bp) of the large-subunit rRNA (LSU), using a naive Bayesian classifier and BLASTN. A hand-curated ITS-LSU training set of 1,091 sequences and a larger training set of 8,967 ITS region sequences were used. Of the factors evaluated, database composition and quality had the largest effect on classification accuracy, followed by fragment size and use of a bootstrap cutoff to improve classification confidence. The naive Bayesian classifier and BLASTN gave similar results at higher taxonomic levels, but the classifier was faster and more accurate at the genus level when a bootstrap cutoff was used. All of the ITS and LSU sections performed well (>97.7% accuracy) at higher taxonomic ranks from kingdom to family, and differences between them were small at the genus level (within 0.66 to 1.23%). When full-length sequence sections were used, the LSU outperformed the ITS1 and ITS2 fragments at the genus level, but the ITS1 and ITS2 showed higher accuracy when smaller fragment sizes of the same length and a 50% bootstrap cutoff were used. In a comparison using the larger ITS training set, ITS1 and ITS2 had very similar accuracy classification for fragments between 100 and 200 bp. Collectively, the results show that any of the ITS or LSU sections we tested provided comparable classification accuracy to the genus level and underscore the need for larger and more diverse classification training sets. PMID:24242255

A Semisupervised Support Vector Machines Algorithm for BCI Systems

PubMed Central

Qin, Jianzhao; Li, Yuanqing; Sun, Wei

2007-01-01

As an emerging technology, brain-computer interfaces (BCIs) bring us new communication interfaces which translate brain activities into control signals for devices like computers, robots, and so forth. In this study, we propose a semisupervised support vector machine (SVM) algorithm for brain-computer interface (BCI) systems, aiming at reducing the time-consuming training process. In this algorithm, we apply a semisupervised SVM for translating the features extracted from the electrical recordings of brain into control signals. This SVM classifier is built from a small labeled data set and a large unlabeled data set. Meanwhile, to reduce the time for training semisupervised SVM, we propose a batch-mode incremental learning method, which can also be easily applied to the online BCI systems. Additionally, it is suggested in many studies that common spatial pattern (CSP) is very effective in discriminating two different brain states. However, CSP needs a sufficient labeled data set. In order to overcome the drawback of CSP, we suggest a two-stage feature extraction method for the semisupervised learning algorithm. We apply our algorithm to two BCI experimental data sets. The offline data analysis results demonstrate the effectiveness of our algorithm. PMID:18368141

Odor Recognition vs. Classification in Artificial Olfaction

NASA Astrophysics Data System (ADS)

Raman, Baranidharan; Hertz, Joshua; Benkstein, Kurt; Semancik, Steve

2011-09-01

Most studies in chemical sensing have focused on the problem of precise identification of chemical species that were exposed during the training phase (the recognition problem). However, generalization of training to predict the chemical composition of untrained gases based on their similarity with analytes in the training set (the classification problem) has received very limited attention. These two analytical tasks pose conflicting constraints on the system. While correct recognition requires detection of molecular features that are unique to an analyte, generalization to untrained chemicals requires detection of features that are common across a desired class of analytes. A simple solution that addresses both issues simultaneously can be obtained from biological olfaction, where the odor class and identity information are decoupled and extracted individually over time. Mimicking this approach, we proposed a hierarchical scheme that allowed initial discrimination between broad chemical classes (e.g. contains oxygen) followed by finer refinements using additional data into sub-classes (e.g. ketones vs. alcohols) and, eventually, specific compositions (e.g. ethanol vs. methanol) [1]. We validated this approach using an array of temperature-controlled chemiresistors. We demonstrated that a small set of training analytes is sufficient to allow generalization to novel chemicals and that the scheme provides robust categorization despite aging. Here, we provide further characterization of this approach.

Strengthening training in rural practice in Germany: new approach for undergraduate medical curriculum towards sustaining rural health care.

PubMed

Holst, Jens; Normann, Oliver; Herrmann, Markus

2015-01-01

After decades of providing a dense network of quality medical care, Germany is facing an increasing shortage of medical doctors in rural areas. Current graduation rates of generalists do not counterbalance the loss due to retirement. Informed by international evidence, different strategies to ensure rural medical care are under debate, including innovative teaching approaches during undergraduate training. The University of Magdeburg in Saxony-Anhalt was the first medical school in Germany to offer a rural elective for graduate students. During the 2014 summer semester, 14 medical students attended a two-weekend program in a small village in Northern Saxony-Anhalt that allowed them to become more familiar with a rural community and rural health issues. The elective course raised a series of relevant topics for setting up rural practice and provided students with helpful insight into living and working conditions in rural practice. Preliminary evaluations indicate that the rural medicine course allowed medical students to reduce pre-existing concerns and had positive impact on their willingness to set up a rural medical office after graduation. Even short-term courses in rural practice can help reduce training-related barriers that prevent young physicians from working in rural areas. Undergraduate medical training is promising to attenuate the emerging undersupply in rural areas.

Decaying Relevance of Clinical Data Towards Future Decisions in Data-Driven Inpatient Clinical Order Sets

PubMed Central

Chen, Jonathan H; Alagappan, Muthuraman; Goldstein, Mary K; Asch, Steven M; Altman, Russ B

2017-01-01

Objective Determine how varying longitudinal historical training data can impact prediction of future clinical decisions. Estimate the “decay rate” of clinical data source relevance. Materials and Methods We trained a clinical order recommender system, analogous to Netflix or Amazon’s “Customers who bought A also bought B…” product recommenders, based on a tertiary academic hospital’s structured electronic health record data. We used this system to predict future (2013) admission orders based on different subsets of historical training data (2009 through 2012), relative to existing human-authored order sets. Results Predicting future (2013) inpatient orders is more accurate with models trained on just one month of recent (2012) data than with 12 months of older (2009) data (ROC AUC 0.91 vs. 0.88, precision 27% vs. 22%, recall 52% vs. 43%, all P<10−10). Algorithmically learned models from even the older (2009) data was still more effective than existing human-authored order sets (ROC AUC 0.81, precision 16% recall 35%). Training with more longitudinal data (2009–2012) was no better than using only the most recent (2012) data, unless applying a decaying weighting scheme with a “half-life” of data relevance about 4 months. Discussion Clinical practice patterns (automatically) learned from electronic health record data can vary substantially across years. Gold standards for clinical decision support are elusive moving targets, reinforcing the need for automated methods that can adapt to evolving information. Conclusions and Relevance Prioritizing small amounts of recent data is more effective than using larger amounts of older data towards future clinical predictions. PMID:28495350

NASA Software Engineering Benchmarking Study

NASA Technical Reports Server (NTRS)

Rarick, Heather L.; Godfrey, Sara H.; Kelly, John C.; Crumbley, Robert T.; Wifl, Joel M.

2013-01-01

To identify best practices for the improvement of software engineering on projects, NASA's Offices of Chief Engineer (OCE) and Safety and Mission Assurance (OSMA) formed a team led by Heather Rarick and Sally Godfrey to conduct this benchmarking study. The primary goals of the study are to identify best practices that: Improve the management and technical development of software intensive systems; Have a track record of successful deployment by aerospace industries, universities [including research and development (R&D) laboratories], and defense services, as well as NASA's own component Centers; and Identify candidate solutions for NASA's software issues. Beginning in the late fall of 2010, focus topics were chosen and interview questions were developed, based on the NASA top software challenges. Between February 2011 and November 2011, the Benchmark Team interviewed a total of 18 organizations, consisting of five NASA Centers, five industry organizations, four defense services organizations, and four university or university R and D laboratory organizations. A software assurance representative also participated in each of the interviews to focus on assurance and software safety best practices. Interviewees provided a wealth of information on each topic area that included: software policy, software acquisition, software assurance, testing, training, maintaining rigor in small projects, metrics, and use of the Capability Maturity Model Integration (CMMI) framework, as well as a number of special topics that came up in the discussions. NASA's software engineering practices compared favorably with the external organizations in most benchmark areas, but in every topic, there were ways in which NASA could improve its practices. Compared to defense services organizations and some of the industry organizations, one of NASA's notable weaknesses involved communication with contractors regarding its policies and requirements for acquired software. One of NASA's strengths was its software assurance practices, which seemed to rate well in comparison to the other organizational groups and also seemed to include a larger scope of activities. An unexpected benefit of the software benchmarking study was the identification of many opportunities for collaboration in areas including metrics, training, sharing of CMMI experiences and resources such as instructors and CMMI Lead Appraisers, and even sharing of assets such as documented processes. A further unexpected benefit of the study was the feedback on NASA practices that was received from some of the organizations interviewed. From that feedback, other potential areas where NASA could improve were highlighted, such as accuracy of software cost estimation and budgetary practices. The detailed report contains discussion of the practices noted in each of the topic areas, as well as a summary of observations and recommendations from each of the topic areas. The resulting 24 recommendations from the topic areas were then consolidated to eliminate duplication and culled into a set of 14 suggested actionable recommendations. This final set of actionable recommendations, listed below, are items that can be implemented to improve NASA's software engineering practices and to help address many of the items that were listed in the NASA top software engineering issues. 1. Develop and implement standard contract language for software procurements. 2. Advance accurate and trusted software cost estimates for both procured and in-house software and improve the capture of actual cost data to facilitate further improvements. 3. Establish a consistent set of objectives and expectations, specifically types of metrics at the Agency level, so key trends and models can be identified and used to continuously improve software processes and each software development effort. 4. Maintain the CMMI Maturity Level requirement for critical NASA projects and use CMMI to measure organizations developing software for NASA. 5.onsolidate, collect and, if needed, develop common processes principles and other assets across the Agency in order to provide more consistency in software development and acquisition practices and to reduce the overall cost of maintaining or increasing current NASA CMMI maturity levels. 6. Provide additional support for small projects that includes: (a) guidance for appropriate tailoring of requirements for small projects, (b) availability of suitable tools, including support tool set-up and training, and (c) training for small project personnel, assurance personnel and technical authorities on the acceptable options for tailoring requirements and performing assurance on small projects. 7. Develop software training classes for the more experienced software engineers using on-line training, videos, or small separate modules of training that can be accommodated as needed throughout a project. 8. Create guidelines to structure non-classroom training opportunities such as mentoring, peer reviews, lessons learned sessions, and on-the-job training. 9. Develop a set of predictive software defect data and a process for assessing software testing metric data against it. 10. Assess Agency-wide licenses for commonly used software tools. 11. Fill the knowledge gap in common software engineering practices for new hires and co-ops.12. Work through the Science, Technology, Engineering and Mathematics (STEM) program with universities in strengthening education in the use of common software engineering practices and standards. 13. Follow up this benchmark study with a deeper look into what both internal and external organizations perceive as the scope of software assurance, the value they expect to obtain from it, and the shortcomings they experience in the current practice. 14. Continue interactions with external software engineering environment through collaborations, knowledge sharing, and benchmarking.

Participatory Training to Improve Safety and Health in Small Construction Sites in Some Countries in Asia: Development and Application of the WISCON Training Program.

PubMed

Kawakami, Tsuyoshi

2016-08-01

A participatory training program, Work Improvement in Small Construction Sites, was developed to provide practical support measures to the small construction sector. Managers and workers from selected small sites were interviewed about their occupational safety and health risks. The Work Improvement in Small Construction Sites training program comprised a 45-item action checklist, photos, and illustrations showing local examples and group work methods. Pilot training workshops were carried out with workers and employers in Cambodia, Laos, Mongolia, Thailand, and Vietnam. Participants subsequently planned, and using locally available low-cost materials, implemented their own improvements such as hand-made hand trucks to carry heavy materials, removal of projecting nails from timber materials, and fences to protect roof workers from falling. Local Work Improvement in Small Construction Sites trainers consisting of government officials, workers, employers, and nongovernment organization representatives were then trained to implement the Work Improvement in Small Construction Sites training widely. Keys to success were easy-to-apply training tools aiming at immediate, low-cost improvements, and collaboration with various local people's networks. © The Author(s) 2016.

HOMER® Micropower Optimization Model

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

Lilienthal, P.

2005-01-01

NREL has developed the HOMER micropower optimization model. The model can analyze all of the available small power technologies individually and in hybrid configurations to identify least-cost solutions to energy requirements. This capability is valuable to a diverse set of energy professionals and applications. NREL has actively supported its growing user base and developed training programs around the model. These activities are helping to grow the global market for solar technologies.

Small Business Training: A Guide for Program Building.

ERIC Educational Resources Information Center

Jellison, Holly M., Ed.

Offering information for staff orientations at institutions launching new small business training programs, for newly assigned Small Business Administration (SBA) field personnel, and for annual program reviews and revisions, this guide explains how to organize and deliver quality small business training in a cost-effective manner. Section 1…

Limited Effects of Set Shifting Training in Healthy Older Adults

PubMed Central

Grönholm-Nyman, Petra; Soveri, Anna; Rinne, Juha O.; Ek, Emilia; Nyholm, Alexandra; Stigsdotter Neely, Anna; Laine, Matti

2017-01-01

Our ability to flexibly shift between tasks or task sets declines in older age. As this decline may have adverse effects on everyday life of elderly people, it is of interest to study whether set shifting ability can be trained, and if training effects generalize to other cognitive tasks. Here, we report a randomized controlled trial where healthy older adults trained set shifting with three different set shifting tasks. The training group (n = 17) performed adaptive set shifting training for 5 weeks with three training sessions a week (45 min/session), while the active control group (n = 16) played three different computer games for the same period. Both groups underwent extensive pre- and post-testing and a 1-year follow-up. Compared to the controls, the training group showed significant improvements on the trained tasks. Evidence for near transfer in the training group was very limited, as it was seen only on overall accuracy on an untrained computerized set shifting task. No far transfer to other cognitive functions was observed. One year later, the training group was still better on the trained tasks but the single near transfer effect had vanished. The results suggest that computerized set shifting training in the elderly shows long-lasting effects on the trained tasks but very little benefit in terms of generalization. PMID:28386226

Implementation of an "after hours" resident educational program in a general surgery residency: a paradigm for increasing formal didactic training outside of the hospital setting in the era of the 80-hour workweek.

PubMed

Fields, Ryan C; Bowman, Michele C; Freeman, Bradley D; Klingensmith, Mary E

2009-01-01

Residency programs have been forced to curtail many educational activities to comply with duty-hour restrictions. We describe an "after hours" educational program as a forum to provide small-group education customized for each training level to compliment our formal curriculum. Sessions within each general surgery specialty were organized such that 1 session each month was open to either junior (R1 and R2) or senior (R3-R5) trainees and hosted by surgical faculty. Attendance was optional and limited to 15 residents per session with the format determined by the hosting faculty. Participants completed a postsession survey. Fourteen sessions were held during the 2008-2009 academic year. All sessions were >90% subscribed within 1 week of announcement and attendance was 88%. The average session duration was 2.6 +/- 0.4 hours. Junior resident sessions focused on preparing R1 and R2 residents to handle common consult questions; senior resident sessions were modeled as "mock oral boards." Resident and faculty responses to the postsession questionnaire were similar and favorable with respect to the educational value of this format. There is enthusiasm among faculty and trainees to provide small-group, level-specific educational programs outside of the hospital setting and the 80-hour workweek. Such a program is easily implemented, highly effective, and well received. This format has the added benefit of improving interaction between faculty and residents and increasing the camaraderie of a surgical training program.

Effectiveness of cognitive remediation and emotion skills training (CREST) for anorexia nervosa in group format: a naturalistic pilot study.

PubMed

Tchanturia, Kate; Doris, Eli; Fleming, Caroline

2014-05-01

This study aims to evaluate a novel and brief skills-based therapy for inpatients with anorexia nervosa, which addressed 'cold' and 'hot' cognitions in group format. Adult inpatients with anorexia nervosa participated in the cognitive remediation and emotion skills training groups. Participants who attended all group sessions completed patient satisfaction and self-report questionnaires. Analysis of the data showed that social anhedonia (measured by the Revised Social Anhedonia Scale) decreased significantly between pre- and post-interventions, with small effect size (d=0.39). Motivation (perceived 'importance to change' and 'ability to change') was found to have increased with small effect sizes (d=0.23 and d=0.16), but these changes did not reach statistical significance. The cognitive remediation and emotion skills training group had positive feedback from both the patients and therapists delivering this structured intervention. Improved strategies are needed both in supporting inpatients to tolerate the group therapy setting and in helping them to develop the skills necessary for participation. Further larger-scale research in this area is needed to consolidate these findings. Copyright © 2014 John Wiley & Sons, Ltd and Eating Disorders Association.

The effects of a physical activity intervention on employees in small and medium enterprises: a mixed methods study.

PubMed

Edmunds, Sarah; Stephenson, Duncan; Clow, Angela

2013-01-01

Workplaces have potential as a setting for physical activity promotion but evidence of the effectiveness of intervention programmes in small and medium sized enterprises is limited. This paper reports the impact of an intervention which trained existing employees to promote physical activity to their colleagues. Eighty-nine previously low-active employees from 17 small and medium sized organisations participated. A mixed methods evaluation design was used. Quantitative data were collected at baseline and 6 months later using an online questionnaire. Qualitative data from a series of 6 focus groups were analysed. Repeated measures t-tests showed significant increases over time in physical activity, general health rating, satisfaction with life and positive mood states. There were significant decreases in body mass index (BMI), perceived stress, negative mood states and presenteeism. There was no change in absenteeism. Analysis of focus group data provided further insight into the impact of the intervention. Five major themes emerged: awareness of physical activity; sustaining physical activity behaviour change; improved health and well-being; enhanced social networks; and embedding physical activity in the workplace culture. This study shows it is feasible and effective to train employees in small and medium sized enterprises to support their colleagues in physical activity behaviour change.

Emotion-independent face recognition

NASA Astrophysics Data System (ADS)

De Silva, Liyanage C.; Esther, Kho G. P.

2000-12-01

Current face recognition techniques tend to work well when recognizing faces under small variations in lighting, facial expression and pose, but deteriorate under more extreme conditions. In this paper, a face recognition system to recognize faces of known individuals, despite variations in facial expression due to different emotions, is developed. The eigenface approach is used for feature extraction. Classification methods include Euclidean distance, back propagation neural network and generalized regression neural network. These methods yield 100% recognition accuracy when the training database is representative, containing one image representing the peak expression for each emotion of each person apart from the neutral expression. The feature vectors used for comparison in the Euclidean distance method and for training the neural network must be all the feature vectors of the training set. These results are obtained for a face database consisting of only four persons.

Retina verification system based on biometric graph matching.

PubMed

Lajevardi, Seyed Mehdi; Arakala, Arathi; Davis, Stephen A; Horadam, Kathy J

2013-09-01

This paper presents an automatic retina verification framework based on the biometric graph matching (BGM) algorithm. The retinal vasculature is extracted using a family of matched filters in the frequency domain and morphological operators. Then, retinal templates are defined as formal spatial graphs derived from the retinal vasculature. The BGM algorithm, a noisy graph matching algorithm, robust to translation, non-linear distortion, and small rotations, is used to compare retinal templates. The BGM algorithm uses graph topology to define three distance measures between a pair of graphs, two of which are new. A support vector machine (SVM) classifier is used to distinguish between genuine and imposter comparisons. Using single as well as multiple graph measures, the classifier achieves complete separation on a training set of images from the VARIA database (60% of the data), equaling the state-of-the-art for retina verification. Because the available data set is small, kernel density estimation (KDE) of the genuine and imposter score distributions of the training set are used to measure performance of the BGM algorithm. In the one dimensional case, the KDE model is validated with the testing set. A 0 EER on testing shows that the KDE model is a good fit for the empirical distribution. For the multiple graph measures, a novel combination of the SVM boundary and the KDE model is used to obtain a fair comparison with the KDE model for the single measure. A clear benefit in using multiple graph measures over a single measure to distinguish genuine and imposter comparisons is demonstrated by a drop in theoretical error of between 60% and more than two orders of magnitude.

Training for Creativity and Innovation in Small Enterprises in Ethiopia

ERIC Educational Resources Information Center

Mihret Dessie, Wondifraw; Shumetie Ademe, Arega

2017-01-01

Policy makers recognize the role of small businesses in bringing about economic growth and reducing or eliminating poverty, and training can contribute significantly to this process. The present study adds to the small firm literature by examining whether training encourages small firms to be more creative and innovative. It does so by…

Closing the Gap: Meeting the Small Business Training Challenge in Connecticut.

ERIC Educational Resources Information Center

Harwood, Richard C.

The training needs of Connecticut's small businesses and their employees are not being adequately met. Small businesses face an economy placing increasing demands on them: a worsening labor shortage, an aging work force, and changing skills in the workplace. Gaps in private and public sector training programs impede small businesses from meeting…

Improvements in Attention and Decision-Making Following Combined Behavioral Training and Brain Stimulation.

PubMed

Filmer, Hannah L; Varghese, Elizabeth; Hawkins, Guy E; Mattingley, Jason B; Dux, Paul E

2017-07-01

In recent years there has been a significant commercial interest in 'brain training' - massed or spaced practice on a small set of tasks to boost cognitive performance. Recently, researchers have combined cognitive training regimes with brain stimulation to try and maximize training benefits, leading to task-specific cognitive enhancement. It remains unclear, however, whether the performance gains afforded by such regimes can transfer to untrained tasks, or how training and stimulation affect the brain's latent information processing dynamics. To examine these issues, we applied transcranial direct current stimulation (tDCS) over the prefrontal cortex while participants undertook decision-making training over several days. Anodal, relative to cathodal/sham tDCS, increased performance gains from training. Critically, these gains were reliable for both trained and untrained tasks. The benefit of anodal tDCS occurred for left, but not right, prefrontal stimulation, and was absent for stimulation delivered without concurrent training. Modeling revealed left anodal stimulation combined with training caused an increase in the brain's rate of evidence accumulation for both tasks. Thus tDCS applied during training has the potential to modulate training gains and give rise to transferable performance benefits for distinct cognitive operations through an increase in the rate at which the brain acquires information. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Using Goal Achievement Training in juvenile justice settings to improve substance use services for youth on community supervision.

PubMed

Fisher, Jacqueline Horan; Becan, Jennifer E; Harris, Philip W; Nager, Alexis; Baird-Thomas, Connie; Hogue, Aaron; Bartkowski, John P; Wiley, Tisha

2018-04-30

The link between substance use and involvement in the juvenile justice system has been well established. Justice-involved youth tend to have higher rates of drug use than their non-offending peers. At the same time, continued use can contribute to an elevated risk of recidivism, which leads to further, and oftentimes more serious, involvement with the juvenile justice system. Because of these high rates of use, the juvenile justice system is well positioned to help identify youth with substance use problems and connect them to treatment. However, research has found that only about 60% of juvenile probation agencies screen all youth for substance involvement, and even fewer provide comprehensive assessment or help youth enroll in substance use treatment. This paper describes an integrated training curriculum that was developed to help juvenile justice agencies improve their continuum of care for youth probationers with substance use problems. Goal Achievement Training (GAT) provides a platform for continuous quality improvement via two sessions delivered onsite to small groups of staff from juvenile justice and behavioral health agencies. In the first session, participants are taught to identify goals and goal steps for addressing identified areas of unmet need (i.e., screening, assessment, and linkage to treatment services). In the second session, participants learn principles and strategies of data-driven decision-making for achieving these goals. This paper highlights GAT as a model for the effective implementation of cost-efficient training strategies designed to increase self-directed quality improvement activities that can be applied to any performance domain within juvenile justice settings. Efforts to monitor implementation fidelity of GAT within the specific context of the juvenile justice settings are highlighted. Challenges to setting the stage for process improvement generally, as well as specific hurdles within juvenile justice settings are discussed, as are next steps in disseminating findings regarding the fidelity to and effectiveness of GAT in this unique context. Clinical Trials Registration number - NCT02672150 .

Effects of Goal Setting on Performance and Job Satisfaction

ERIC Educational Resources Information Center

Ivancevich, John M.

1976-01-01

Studied the effect of goal-setting training on the performance and job satisfaction of sales personnel. One group was trained in participative goal setting; one group was trained in assigned goal setting; and one group received no training. Both trained groups showed temporary improvements in performance and job satisfaction. For availability see…

Planning and problem-solving training for patients with schizophrenia: a randomized controlled trial

PubMed Central

2011-01-01

Background The purpose of this study was to assess whether planning and problem-solving training is more effective in improving functional capacity in patients with schizophrenia than a training program addressing basic cognitive functions. Methods Eighty-nine patients with schizophrenia were randomly assigned either to a computer assisted training of planning and problem-solving or a training of basic cognition. Outcome variables included planning and problem-solving ability as well as functional capacity, which represents a proxy measure for functional outcome. Results Planning and problem-solving training improved one measure of planning and problem-solving more strongly than basic cognition training, while two other measures of planning did not show a differential effect. Participants in both groups improved over time in functional capacity. There was no differential effect of the interventions on functional capacity. Conclusion A differential effect of targeting specific cognitive functions on functional capacity could not be established. Small differences on cognitive outcome variables indicate a potential for differential effects. This will have to be addressed in further research including longer treatment programs and other settings. Trial registration ClinicalTrials.gov NCT00507988 PMID:21527028

Large scale analysis of protein-binding cavities using self-organizing maps and wavelet-based surface patches to describe functional properties, selectivity discrimination, and putative cross-reactivity.

PubMed

Kupas, Katrin; Ultsch, Alfred; Klebe, Gerhard

2008-05-15

A new method to discover similar substructures in protein binding pockets, independently of sequence and folding patterns or secondary structure elements, is introduced. The solvent-accessible surface of a binding pocket, automatically detected as a depression on the protein surface, is divided into a set of surface patches. Each surface patch is characterized by its shape as well as by its physicochemical characteristics. Wavelets defined on surfaces are used for the description of the shape, as they have the great advantage of allowing a comparison at different resolutions. The number of coefficients to describe the wavelets can be chosen with respect to the size of the considered data set. The physicochemical characteristics of the patches are described by the assignment of the exposed amino acid residues to one or more of five different properties determinant for molecular recognition. A self-organizing neural network is used to project the high-dimensional feature vectors onto a two-dimensional layer of neurons, called a map. To find similarities between the binding pockets, in both geometrical and physicochemical features, a clustering of the projected feature vector is performed using an automatic distance- and density-based clustering algorithm. The method was validated with a small training data set of 109 binding cavities originating from a set of enzymes covering 12 different EC numbers. A second test data set of 1378 binding cavities, extracted from enzymes of 13 different EC numbers, was then used to prove the discriminating power of the algorithm and to demonstrate its applicability to large scale analyses. In all cases, members of the data set with the same EC number were placed into coherent regions on the map, with small distances between them. Different EC numbers are separated by large distances between the feature vectors. A third data set comprising three subfamilies of endopeptidases is used to demonstrate the ability of the algorithm to detect similar substructures between functionally related active sites. The algorithm can also be used to predict the function of novel proteins not considered in training data set. 2007 Wiley-Liss, Inc.

A Simple Evaluation Tool (ET-CET) Indicates Increase of Diagnostic Skills From Small Bowel Capsule Endoscopy Training Courses: A Prospective Observational European Multicenter Study.

PubMed

Albert, J G; Humbla, O; McAlindon, M E; Davison, C; Seitz, U; Fraser, C; Hagenmüller, F; Noetzel, E; Spada, C; Riccioni, M E; Barnert, J; Filmann, N; Keuchel, M

2015-10-01

Small bowel capsule endoscopy (SBCE) has become a first line diagnostic tool. Several training courses with a similar format have been established in Europe; however, data on learning curve and training in SBCE remain sparse.Between 2008 and 2011, different basic SBCE training courses were organized internationally in UK (n = 2), Italy (n = 2), Germany (n = 2), Finland (n = 1), and nationally in Germany (n = 10), applying similar 8-hour curricula with 50% lectures and 50% hands-on training. The Given PillCam System was used in 12 courses, the Olympus EndoCapsule system in 5, respectively. A simple evaluation tool for capsule endoscopy training (ET-CET) was developed using 10 short SBCE videos including relevant lesions and normal or irrelevant findings. For each video, delegates were required to record a diagnosis (achievable total score from 0 to 10) and the clinical relevance (achievable total score 0 to 10). ET-CET was performed at baseline before the course and repeated, with videos in altered order, after the course.Two hundred ninety-four delegates (79.3% physicians, 16.3% nurses, 4.4% others) were included for baseline analysis, 268 completed the final evaluation. Forty percent had no previous experience in SBCE, 33% had performed 10 or less procedures. Median scores for correct diagnosis improved from 4.0 (IQR 3) to 7.0 (IQR 3) during the courses (P < 0.001, Wilcoxon), and for correct classification of relevance of the lesions from 5.0 (IQR 3) to 7.0 (IQR 3) (P < 0.001), respectively. Improvement was not dependent on experience, profession, SBCE system, or course setting. Previous experience in SBCE was associated with higher baseline scores for correct diagnosis (P < 0.001; Kruskal-Wallis). Additionally, independent nonparametric partial correlation with experience in gastroscopy (rho 0.33) and colonoscopy (rho 0.27) was observed (P < 0.001).A simple ET-CET demonstrated significant improvement of diagnostic skills on completion of formal basic SBCE courses with hands-on training, regardless of preexisting experience, profession, and course setting. Baseline scores for correct diagnoses show a plateau after interpretation of 25 SBCE before courses, supporting this number as a compromise for credentialing. Experience in flexible endoscopy may be useful before attending an SBCE course.

3D multimodal MRI brain glioma tumor and edema segmentation: a graph cut distribution matching approach.

PubMed

Njeh, Ines; Sallemi, Lamia; Ayed, Ismail Ben; Chtourou, Khalil; Lehericy, Stephane; Galanaud, Damien; Hamida, Ahmed Ben

2015-03-01

This study investigates a fast distribution-matching, data-driven algorithm for 3D multimodal MRI brain glioma tumor and edema segmentation in different modalities. We learn non-parametric model distributions which characterize the normal regions in the current data. Then, we state our segmentation problems as the optimization of several cost functions of the same form, each containing two terms: (i) a distribution matching prior, which evaluates a global similarity between distributions, and (ii) a smoothness prior to avoid the occurrence of small, isolated regions in the solution. Obtained following recent bound-relaxation results, the optima of the cost functions yield the complement of the tumor region or edema region in nearly real-time. Based on global rather than pixel wise information, the proposed algorithm does not require an external learning from a large, manually-segmented training set, as is the case of the existing methods. Therefore, the ensuing results are independent of the choice of a training set. Quantitative evaluations over the publicly available training and testing data set from the MICCAI multimodal brain tumor segmentation challenge (BraTS 2012) demonstrated that our algorithm yields a highly competitive performance for complete edema and tumor segmentation, among nine existing competing methods, with an interesting computing execution time (less than 0.5s per image). Copyright © 2014 Elsevier Ltd. All rights reserved.

Any Two Learning Algorithms Are (Almost) Exactly Identical

NASA Technical Reports Server (NTRS)

Wolpert, David H.

2000-01-01

This paper shows that if one is provided with a loss function, it can be used in a natural way to specify a distance measure quantifying the similarity of any two supervised learning algorithms, even non-parametric algorithms. Intuitively, this measure gives the fraction of targets and training sets for which the expected performance of the two algorithms differs significantly. Bounds on the value of this distance are calculated for the case of binary outputs and 0-1 loss, indicating that any two learning algorithms are almost exactly identical for such scenarios. As an example, for any two algorithms A and B, even for small input spaces and training sets, for less than 2e(-50) of all targets will the difference between A's and B's generalization performance of exceed 1%. In particular, this is true if B is bagging applied to A, or boosting applied to A. These bounds can be viewed alternatively as telling us, for example, that the simple English phrase 'I expect that algorithm A will generalize from the training set with an accuracy of at least 75% on the rest of the target' conveys 20,000 bytes of information concerning the target. The paper ends by discussing some of the subtleties of extending the distance measure to give a full (non-parametric) differential geometry of the manifold of learning algorithms.

Invitation choice structure has no impact on attendance in a female business training program in Kenya.

PubMed

Diwan, Faizan; Makana, Grace; McKenzie, David; Paruzzolo, Silvia

2014-01-01

Business training programs are a common form of support to small businesses, but organizations providing this training often struggle to get business owners to attend. We evaluate the role of invitation choice structure in determining agreement to participate and actual attendance. A field experiment randomly assigned female small business owners in Kenya (N = 1172) to one of three invitation types: a standard opt-in invitation; an active choice invitation where business owners had to explicitly say yes or no to the invitation; and an enhanced active choice invitation which highlighted the costs of saying no. We find no statistically significant effect of these alternative choice structures on willingness to participate in training, attending at least one day, and completing the course. The 95 percent confidence interval for the active treatment effect on attendance is [-1.9%, +9.5%], while for the enhanced active choice treatment it is [-4.1%, +7.7%]. The effect sizes consistent with our data are smaller than impacts measured in health and retirement savings studies in the United States. We examine several potential explanations for the lack of effect in a developing country setting. We find evidence consistent with two potential reasons being limited decision-making power amongst some women, and lower levels of cognition making the enhanced active choice wording less effective.

Invitation Choice Structure Has No Impact on Attendance in a Female Business Training Program in Kenya

PubMed Central

Diwan, Faizan; Makana, Grace; McKenzie, David; Paruzzolo, Silvia

2014-01-01

Business training programs are a common form of support to small businesses, but organizations providing this training often struggle to get business owners to attend. We evaluate the role of invitation choice structure in determining agreement to participate and actual attendance. A field experiment randomly assigned female small business owners in Kenya (N = 1172) to one of three invitation types: a standard opt-in invitation; an active choice invitation where business owners had to explicitly say yes or no to the invitation; and an enhanced active choice invitation which highlighted the costs of saying no. We find no statistically significant effect of these alternative choice structures on willingness to participate in training, attending at least one day, and completing the course. The 95 percent confidence interval for the active treatment effect on attendance is [−1.9%, +9.5%], while for the enhanced active choice treatment it is [−4.1%, +7.7%]. The effect sizes consistent with our data are smaller than impacts measured in health and retirement savings studies in the United States. We examine several potential explanations for the lack of effect in a developing country setting. We find evidence consistent with two potential reasons being limited decision-making power amongst some women, and lower levels of cognition making the enhanced active choice wording less effective. PMID:25299647

Neuro-Triggered Training.

DTIC Science & Technology

1992-04-30

Avnalabiliy Co~e ’Dst Special The views and conclusions in this document am those of the aunots and should not be intepreted as necesnuily repesenmg the...accuracy and the adaptive error tolerance. We have set up a small new recording room for this project and cleared it of 60-Hz electromagnetic noise...stimuli sounded alike. In the semantic condition, subjects were required to decide if the high frequency, open-class monosyllabic words were opposite or not

TeamSTEPPS for health care risk managers: Improving teamwork and communication.

PubMed

Cooke, Marcia

2016-07-01

Ineffective communication among the health care team is a leading cause of errors in the patient care setting. Studies assessing training related to communication and teamwork in the clinical team are prevalent, however, teamwork training at the administrative level is lacking. This includes individuals in leadership positions such as health care risk managers. The purpose was to determine the impact of an educational intervention on the knowledge and attitudes related to communication and teamwork in the health care risk management population. The educational intervention was an adaptation of a national teamwork training program and incorporated didactic content as well as video vignettes and small group activities. Measurement of knowledge and attitudes were used to determine the impact of the education program. Knowledge and attitudes were assessed pre- and postcourse. Findings indicate that teamwork education tailored to the needs of the specific audience resulted in knowledge gained and improved attitudes toward the components of teamwork. The attitudes that most significantly improved were related to team structure and situation monitoring. There was no improvement in participants' attitudes toward leadership, mutual support, and communication. Team training has been shown to improve safety culture, patient satisfaction, and clinical outcomes. Including risk managers in training on teamwork, communication, and collaboration can serve to foster a common language among clinicians and management. In addition, a measurement related to implementation in the health care setting may yield insight into the impact of training. Qualitative measurement may allow the researcher to delve deeper into how these health care facilities are using team training interventions. © 2016 American Society for Healthcare Risk Management of the American Hospital Association.

Pneumothorax detection in chest radiographs using convolutional neural networks

NASA Astrophysics Data System (ADS)

Blumenfeld, Aviel; Konen, Eli; Greenspan, Hayit

2018-02-01

This study presents a computer assisted diagnosis system for the detection of pneumothorax (PTX) in chest radiographs based on a convolutional neural network (CNN) for pixel classification. Using a pixel classification approach allows utilization of the texture information in the local environment of each pixel while training a CNN model on millions of training patches extracted from a relatively small dataset. The proposed system uses a pre-processing step of lung field segmentation to overcome the large variability in the input images coming from a variety of imaging sources and protocols. Using a CNN classification, suspected pixel candidates are extracted within each lung segment. A postprocessing step follows to remove non-physiological suspected regions and noisy connected components. The overall percentage of suspected PTX area was used as a robust global decision for the presence of PTX in each lung. The system was trained on a set of 117 chest x-ray images with ground truth segmentations of the PTX regions. The system was tested on a set of 86 images and reached diagnosis accuracy of AUC=0.95. Overall preliminary results are promising and indicate the growing ability of CAD based systems to detect findings in medical imaging on a clinical level accuracy.

Variable practice with lenses improves visuo-motor plasticity

NASA Technical Reports Server (NTRS)

Roller, C. A.; Cohen, H. S.; Kimball, K. T.; Bloomberg, J. J.

2001-01-01

Novel sensorimotor situations present a unique challenge to an individual's adaptive ability. Using the simple and easily measured paradigm of visual-motor rearrangement created by the use of visual displacement lenses, we sought to determine whether an individual's ability to adapt to visuo-motor discordance could be improved through training. Subjects threw small balls at a stationary target during a 3-week practice regimen involving repeated exposure to one set of lenses in block practice (x 2.0 magnifying lenses), multiple sets of lenses in variable practice (x 2.0 magnifying, x 0.5 minifying and up-down reversing lenses) or sham lenses. At the end of training, adaptation to a novel visuo-motor situation (20-degree right shift lenses) was tested. We found that (1) training with variable practice can increase adaptability to a novel visuo-motor situation, (2) increased adaptability is retained for at least 1 month and is transferable to further novel visuo-motor permutations and (3) variable practice improves performance of a simple motor task even in the undisturbed state. These results have implications for the design of clinical rehabilitation programs and countermeasures to enhance astronaut adaptability, facilitating adaptive transitions between gravitational environments.

Using transfer learning to detect galaxy mergers

NASA Astrophysics Data System (ADS)

Ackermann, Sandro; Schawinksi, Kevin; Zhang, Ce; Weigel, Anna K.; Turp, M. Dennis

2018-05-01

We investigate the use of deep convolutional neural networks (deep CNNs) for automatic visual detection of galaxy mergers. Moreover, we investigate the use of transfer learning in conjunction with CNNs, by retraining networks first trained on pictures of everyday objects. We test the hypothesis that transfer learning is useful for improving classification performance for small training sets. This would make transfer learning useful for finding rare objects in astronomical imaging datasets. We find that these deep learning methods perform significantly better than current state-of-the-art merger detection methods based on nonparametric systems like CAS and GM20. Our method is end-to-end and robust to image noise and distortions; it can be applied directly without image preprocessing. We also find that transfer learning can act as a regulariser in some cases, leading to better overall classification accuracy (p = 0.02). Transfer learning on our full training set leads to a lowered error rate from 0.0381 down to 0.0321, a relative improvement of 15%. Finally, we perform a basic sanity-check by creating a merger sample with our method, and comparing with an already existing, manually created merger catalogue in terms of colour-mass distribution and stellar mass function.

Fourier spatial frequency analysis for image classification: training the training set

NASA Astrophysics Data System (ADS)

Johnson, Timothy H.; Lhamo, Yigah; Shi, Lingyan; Alfano, Robert R.; Russell, Stewart

2016-04-01

The Directional Fourier Spatial Frequencies (DFSF) of a 2D image can identify similarity in spatial patterns within groups of related images. A Support Vector Machine (SVM) can then be used to classify images if the inter-image variance of the FSF in the training set is bounded. However, if variation in FSF increases with training set size, accuracy may decrease as the size of the training set increases. This calls for a method to identify a set of training images from among the originals that can form a vector basis for the entire class. Applying the Cauchy product method we extract the DFSF spectrum from radiographs of osteoporotic bone, and use it as a matched filter set to eliminate noise and image specific frequencies, and demonstrate that selection of a subset of superclassifiers from within a set of training images improves SVM accuracy. Central to this challenge is that the size of the search space can become computationally prohibitive for all but the smallest training sets. We are investigating methods to reduce the search space to identify an optimal subset of basis training images.

SkData: data sets and algorithm evaluation protocols in Python

NASA Astrophysics Data System (ADS)

Bergstra, James; Pinto, Nicolas; Cox, David D.

2015-01-01

Machine learning benchmark data sets come in all shapes and sizes, whereas classification algorithms assume sanitized input, such as (x, y) pairs with vector-valued input x and integer class label y. Researchers and practitioners know all too well how tedious it can be to get from the URL of a new data set to a NumPy ndarray suitable for e.g. pandas or sklearn. The SkData library handles that work for a growing number of benchmark data sets (small and large) so that one-off in-house scripts for downloading and parsing data sets can be replaced with library code that is reliable, community-tested, and documented. The SkData library also introduces an open-ended formalization of training and testing protocols that facilitates direct comparison with published research. This paper describes the usage and architecture of the SkData library.

Multi-year encoding of daily rainfall and streamflow via the fractal-multifractal method

NASA Astrophysics Data System (ADS)

Puente, C. E.; Maskey, M.; Sivakumar, B.

2017-12-01

A deterministic geometric approach, the fractal-multifractal (FM) method, which has been proven to be faithful in encoding daily geophysical sets over a year, is used to describe records over multiple years at a time. Looking for FM parameter trends over longer periods, the present study shows FM descriptions of daily rainfall and streamflow gathered over five consecutive years optimizing deviations on accumulated sets. The results for 100 and 60 sets of five years for rainfall streamflow, respectively, near Sacramento, California illustrate that: (a) encoding of both types of data sets may be accomplished with relatively small errors; and (b) predicting the geometry of both variables appears to be possible, even five years ahead, training neural networks on the respective FM parameters. It is emphasized that the FM approach not only captures the accumulated sets over successive pentades but also preserves other statistical attributes including the overall "texture" of the records.

Results of a NASA Kennedy Space Center Earned Value Management Pilot Project

NASA Technical Reports Server (NTRS)

Delgado, Hector N.; Rhodeside, Glenn R.

2004-01-01

The Earn Value Management Pilot provided a tremendous amount of data on the strengths and weaknesses of the new financial system, the ability to support EVM from many viewpoints, the lack of tools for small to medium projects implementing EVM, and the training and environment necessary to successfully deploy EVM to all projects. This data along with other pilots will prove invaluable. Deploying EVM should not be taken lightly - a full assessment of capabilities and supporting infrastructure should be done prior to any deployment, and some very basic questions should be asked. For instance, will sufficient training be provided? Can the project managers readily and easily obtain all the necessary data? If EVM is to thrive in all projects regardless of cost, the transition should be as seamless as possible, minimizing cost and effort, and with the end user in mind. In setting up an EVM implementation, the question, "How does the project manager benefit from this process?" must remain at the forefront. Further research in this area is needed to answer the question,"Is EVM cost effective in small projects?" The authors welcome knowledge sharing with other organizations that are striving to gain the benefits of EVM on small projects.

Convolutional networks for vehicle track segmentation

NASA Astrophysics Data System (ADS)

Quach, Tu-Thach

2017-10-01

Existing methods to detect vehicle tracks in coherent change detection images, a product of combining two synthetic aperture radar images taken at different times of the same scene, rely on simple and fast models to label track pixels. These models, however, are unable to capture natural track features, such as continuity and parallelism. More powerful but computationally expensive models can be used in offline settings. We present an approach that uses dilated convolutional networks consisting of a series of 3×3 convolutions to segment vehicle tracks. The design of our networks considers the fact that remote sensing applications tend to operate in low power and have limited training data. As a result, we aim for small and efficient networks that can be trained end-to-end to learn natural track features entirely from limited training data. We demonstrate that our six-layer network, trained on just 90 images, is computationally efficient and improves the F-score on a standard dataset to 0.992, up from 0.959 obtained by the current state-of-the-art method.

Association of high proliferation marker Ki-67 expression with DCEMR imaging features of breast: a large scale evaluation

NASA Astrophysics Data System (ADS)

Saha, Ashirbani; Harowicz, Michael R.; Grimm, Lars J.; Kim, Connie E.; Ghate, Sujata V.; Walsh, Ruth; Mazurowski, Maciej A.

2018-02-01

One of the methods widely used to measure the proliferative activity of cells in breast cancer patients is the immunohistochemical (IHC) measurement of the percentage of cells stained for nuclear antigen Ki-67. Use of Ki-67 expression as a prognostic marker is still under investigation. However, numerous clinical studies have reported an association between a high Ki-67 and overall survival (OS) and disease free survival (DFS). On the other hand, to offer non-invasive alternative in determining Ki-67 expression, researchers have made recent attempts to study the association of Ki-67 expression with magnetic resonance (MR) imaging features of breast cancer in small cohorts (<30). Here, we present a large scale evaluation of the relationship between imaging features and Ki-67 score as: (a) we used a set of 450 invasive breast cancer patients, (b) we extracted a set of 529 imaging features of shape and enhancement from breast, tumor and fibroglandular tissue of the patients, (c) used a subset of patients as the training set to select features and trained a multivariate logistic regression model to predict high versus low Ki-67 values, and (d) we validated the performance of the trained model in an independent test set using the area-under the receiver operating characteristics (ROC) curve (AUC) of the values predicted. Our model was able to predict high versus low Ki-67 in the test set with an AUC of 0.67 (95% CI: 0.58-0.75, p<1.1e-04). Thus, a moderate strength of association of Ki-67 values and MRextracted imaging features was demonstrated in our experiments.

Supervisory needs of research doctoral students in a university teaching hospital setting.

PubMed

Caldwell, Patrina Hy; Oldmeadow, Wendy; Jones, Cheryl A

2012-10-01

Teaching hospitals affiliated with universities are now common sites for research higher degree supervision. We hypothesised that the hospital environment poses unique challenges to supervision compared with the traditional university research institute setting. This study aimed to identify and rank important supervision issues in a clinical setting from the students' perspective. Using the Delphi method to explore issues and facilitate consensus, small group discussions were conducted with 10 research doctoral students from a tertiary teaching hospital. We identified supervision issues that are unique to the hospital-based context. These include the demands placed on supervisors combining clinical and supervisory roles, the challenges of academic medical/scientific writing and career issues for students who are already established in their professions. Other issues identified, common to all doctoral students, include differing expectations between students and supervisors (with students wanting support for their career plans, training in research skills and increasing autonomy and responsibility), supervisor access, quality and frequency of meetings, lack of training in writing and dealing with conflicts. Our research identified that postgraduate students of supervisors who combine clinical and supervisory roles report significant issues with supervision, some of which are unique to the clinical setting. Clinician researchers who supervise postgraduate students need to balance clinical and supervisory responsibilities, identify and negotiate student expectations early in candidature and provide career counselling to students who are already highly experienced. Furthermore, clinician supervisors should undertake postgraduate supervisor training programme tailored to the hospital setting to better support their students. © 2012 The Authors. Journal of Paediatrics and Child Health © 2012 Paediatrics and Child Health Division (Royal Australasian College of Physicians).

Predicting binding poses and affinities for protein - ligand complexes in the 2015 D3R Grand Challenge using a physical model with a statistical parameter estimation

NASA Astrophysics Data System (ADS)

Grudinin, Sergei; Kadukova, Maria; Eisenbarth, Andreas; Marillet, Simon; Cazals, Frédéric

2016-09-01

The 2015 D3R Grand Challenge provided an opportunity to test our new model for the binding free energy of small molecules, as well as to assess our protocol to predict binding poses for protein-ligand complexes. Our pose predictions were ranked 3-9 for the HSP90 dataset, depending on the assessment metric. For the MAP4K dataset the ranks are very dispersed and equal to 2-35, depending on the assessment metric, which does not provide any insight into the accuracy of the method. The main success of our pose prediction protocol was the re-scoring stage using the recently developed Convex-PL potential. We make a thorough analysis of our docking predictions made with AutoDock Vina and discuss the effect of the choice of rigid receptor templates, the number of flexible residues in the binding pocket, the binding pocket size, and the benefits of re-scoring. However, the main challenge was to predict experimentally determined binding affinities for two blind test sets. Our affinity prediction model consisted of two terms, a pairwise-additive enthalpy, and a non pairwise-additive entropy. We trained the free parameters of the model with a regularized regression using affinity and structural data from the PDBBind database. Our model performed very well on the training set, however, failed on the two test sets. We explain the drawback and pitfalls of our model, in particular in terms of relative coverage of the test set by the training set and missed dynamical properties from crystal structures, and discuss different routes to improve it.

Putting the MeaT into TeaM Training: Development, Delivery, and Evaluation of a Surgical Team-Training Workshop.

PubMed

Seymour, Neal E; Paige, John T; Arora, Sonal; Fernandez, Gladys L; Aggarwal, Rajesh; Tsuda, Shawn T; Powers, Kinga A; Langlois, Gerard; Stefanidis, Dimitrios

2016-01-01

Despite importance to patient care, team training is infrequently used in surgical education. To address this, a workshop was developed by the Association for Surgical Education Simulation Committee to teach team training using high-fidelity patient simulators and the American College of Surgeons-Association of Program Directors in Surgery team-training curriculum. Workshops were conducted at 3 national meetings. Participants completed preworkshop and postworkshop questionnaires to define experience, confidence in using simulation, intention to implement, as well as workshop content quality. The course consisted of (A) a didactic review of Preparation, Implementation, and Debriefing and (B) facilitated small group simulation sessions followed by debriefings. Of 78 participants, 51 completed the workshops. Overall, 65% indicated that residents at their institutions used patient simulation, but only 33% used the American College of Surgeons-the Association of Program Directors in Surgery team-training modules. The workshop increased confidence to implement simulation team training (3.4 ± 1.3 vs 4.5 ± 0.9). Quality and importance were rated highly (5.4 ± 00.6, highest score = 6). Preparation for simulation-based team training is possible in this workshop setting, although the effect on actual implementation remains to be determined. Copyright © 2015 Association of Program Directors in Surgery. Published by Elsevier Inc. All rights reserved.

High-Intensity Small-Sided Games versus Repeated Sprint Training in Junior Soccer Players.

PubMed

Eniseler, Niyazi; Şahan, Çağatay; Özcan, Ilker; Dinler, Kıvanç

2017-12-01

The aim of this study was to compare the effects of high-intensity small-sided games training (SSGT) versus repeated-sprint training (RST) on repeated-sprint ability (RSA), soccer specific endurance performance and short passing ability among junior soccer players. The junior soccer players were recruited from of a professional team (age 16.9 ± 1.1 years). The tests included the repeated-shuttle-sprint ability test (RSSAT), Yo-Yo Intermittent Recovery Test level 1 (Yo-Yo IR1) and Loughborough Soccer Passing Test (LSPT). Nineteen participants were randomly assigned to either the small-sided games training (SSGTG) (n = 10) or repeated-sprint training group (RSTG) (n = 9). Small-sided games or repeated-sprint training were added to the regular training sessions for two days of the regular practice week. The Wilcoxon signed-rank and Mann-Whitney U tests were used to examine differences in groups and training effects. A time x training group effect was found in the improvement of short-passing ability for the smallsided games training group which showed significantly better scores than the repeated-sprint training group (p ≤ 0.05). Both groups showed similar improvements in RSAdecrement (p < 0.05). Only the repeated-sprint training group improved in the Yo-Yo IR1 (p < 0.05). This study clearly shows that high-intensity small-sided games training can be used as an effective training mode to enhance both repeated sprint ability and short-passing ability.

High-Intensity Small-Sided Games versus Repeated Sprint Training in Junior Soccer Players

PubMed Central

Şahan, Çağatay; Özcan, Ilker; Dinler, Kıvanç

2017-01-01

Abstract The aim of this study was to compare the effects of high-intensity small-sided games training (SSGT) versus repeated-sprint training (RST) on repeated-sprint ability (RSA), soccer specific endurance performance and short passing ability among junior soccer players. The junior soccer players were recruited from of a professional team (age 16.9 ± 1.1 years). The tests included the repeated-shuttle-sprint ability test (RSSAT), Yo-Yo Intermittent Recovery Test level 1 (Yo-Yo IR1) and Loughborough Soccer Passing Test (LSPT). Nineteen participants were randomly assigned to either the small-sided games training (SSGTG) (n = 10) or repeated-sprint training group (RSTG) (n = 9). Small-sided games or repeated-sprint training were added to the regular training sessions for two days of the regular practice week. The Wilcoxon signed-rank and Mann-Whitney U tests were used to examine differences in groups and training effects. A time x training group effect was found in the improvement of short-passing ability for the smallsided games training group which showed significantly better scores than the repeated-sprint training group (p ≤ 0.05). Both groups showed similar improvements in RSAdecrement (p < 0.05). Only the repeated-sprint training group improved in the Yo-Yo IR1 (p < 0.05). This study clearly shows that high-intensity small-sided games training can be used as an effective training mode to enhance both repeated sprint ability and short-passing ability. PMID:29339990

Small Scale Irrigation Systems: A Training Manual. Planning--Construction--Operation and Maintenance of Small Scale Irrigation Systems. A Two-Week In-Service Training Program for Peace Corps Volunteers. Training for Development. Peace Corps Information Collection & Exchange Training Manual No. T-13.

ERIC Educational Resources Information Center

Development Planning and Research Associates, Inc., Manhattan, KS.

This manual provides materials for a two-week inservice training program for Peace Corps volunteers on the planning, construction, and operation and maintenance of small-scale irrigation systems. The workshop is designed to be given by two experienced professionals: one with practical knowledge of irrigation system design, operation, and…

Mentoring portfolio use in undergraduate and postgraduate medical education.

PubMed

Dekker, Hanke; Driessen, Erik; Ter Braak, Edith; Scheele, Fedde; Slaets, Joris; Van Der Molen, Thys; Cohen-Schotanus, Janke

2009-10-01

Mentoring is widely acknowledged as being crucial for portfolio learning. The aim of this study is to examine how mentoring portfolio use has been implemented in undergraduate and postgraduate settings. The results of interviews with six key persons involved in setting up portfolio use in medical education programmes were used to develop a questionnaire, which was administered to 30 coordinators of undergraduate and postgraduate portfolio programmes in the Netherlands and Flanders. The interviews yielded four main aspects of the portfolio mentoring process--educational aims, individual meetings, small group sessions and mentor characteristics. Based on the questionnaire data, 16 undergraduate and 14 postgraduate programmes were described. Providing feedback and stimulating reflection were the main objectives of the mentoring process. Individual meetings were the favourite method for mentoring (26 programmes). Small group sessions to support the use of portfolios were held in 16 programmes, mostly in the undergraduate setting. In general, portfolio mentors were clinically qualified academic staff trained for their mentoring tasks. This study provides a variety of practical insights into implementing mentoring processes in portfolio programmes.

Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks

PubMed Central

2017-01-01

In de novo drug design, computational strategies are used to generate novel molecules with good affinity to the desired biological target. In this work, we show that recurrent neural networks can be trained as generative models for molecular structures, similar to statistical language models in natural language processing. We demonstrate that the properties of the generated molecules correlate very well with the properties of the molecules used to train the model. In order to enrich libraries with molecules active toward a given biological target, we propose to fine-tune the model with small sets of molecules, which are known to be active against that target. Against Staphylococcus aureus, the model reproduced 14% of 6051 hold-out test molecules that medicinal chemists designed, whereas against Plasmodium falciparum (Malaria), it reproduced 28% of 1240 test molecules. When coupled with a scoring function, our model can perform the complete de novo drug design cycle to generate large sets of novel molecules for drug discovery. PMID:29392184

Comparison of the applicability domain of a quantitative structure-activity relationship for estrogenicity with a large chemical inventory.

PubMed

Netzeva, Tatiana I; Gallegos Saliner, Ana; Worth, Andrew P

2006-05-01

The aim of the present study was to illustrate that it is possible and relatively straightforward to compare the domain of applicability of a quantitative structure-activity relationship (QSAR) model in terms of its physicochemical descriptors with a large inventory of chemicals. A training set of 105 chemicals with data for relative estrogenic gene activation, obtained in a recombinant yeast assay, was used to develop the QSAR. A binary classification model for predicting active versus inactive chemicals was developed using classification tree analysis and two descriptors with a clear physicochemical meaning (octanol-water partition coefficient, or log Kow, and the number of hydrogen bond donors, or n(Hdon)). The model demonstrated a high overall accuracy (90.5%), with a sensitivity of 95.9% and a specificity of 78.1%. The robustness of the model was evaluated using the leave-many-out cross-validation technique, whereas the predictivity was assessed using an artificial external test set composed of 12 compounds. The domain of the QSAR training set was compared with the chemical space covered by the European Inventory of Existing Commercial Chemical Substances (EINECS), as incorporated in the CDB-EC software, in the log Kow / n(Hdon) plane. The results showed that the training set and, therefore, the applicability domain of the QSAR model covers a small part of the physicochemical domain of the inventory, even though a simple method for defining the applicability domain (ranges in the descriptor space) was used. However, a large number of compounds are located within the narrow descriptor window.

An application of deep learning in the analysis of stellar spectra

NASA Astrophysics Data System (ADS)

Fabbro, S.; Venn, K. A.; O'Briain, T.; Bialek, S.; Kielty, C. L.; Jahandar, F.; Monty, S.

2018-04-01

Spectroscopic surveys require fast and efficient analysis methods to maximize their scientific impact. Here, we apply a deep neural network architecture to analyse both SDSS-III APOGEE DR13 and synthetic stellar spectra. When our convolutional neural network model (StarNet) is trained on APOGEE spectra, we show that the stellar parameters (temperature, gravity, and metallicity) are determined with similar precision and accuracy as the APOGEE pipeline. StarNet can also predict stellar parameters when trained on synthetic data, with excellent precision and accuracy for both APOGEE data and synthetic data, over a wide range of signal-to-noise ratios. In addition, the statistical uncertainties in the stellar parameter determinations are comparable to the differences between the APOGEE pipeline results and those determined independently from optical spectra. We compare StarNet to other data-driven methods; for example, StarNet and the Cannon 2 show similar behaviour when trained with the same data sets; however, StarNet performs poorly on small training sets like those used by the original Cannon. The influence of the spectral features on the stellar parameters is examined via partial derivatives of the StarNet model results with respect to the input spectra. While StarNet was developed using the APOGEE observed spectra and corresponding ASSET synthetic data, we suggest that this technique is applicable to other wavelength ranges and other spectral surveys.

Fully automated, real-time 3D ultrasound segmentation to estimate first trimester placental volume using deep learning.

PubMed

Looney, Pádraig; Stevenson, Gordon N; Nicolaides, Kypros H; Plasencia, Walter; Molloholli, Malid; Natsis, Stavros; Collins, Sally L

2018-06-07

We present a new technique to fully automate the segmentation of an organ from 3D ultrasound (3D-US) volumes, using the placenta as the target organ. Image analysis tools to estimate organ volume do exist but are too time consuming and operator dependant. Fully automating the segmentation process would potentially allow the use of placental volume to screen for increased risk of pregnancy complications. The placenta was segmented from 2,393 first trimester 3D-US volumes using a semiautomated technique. This was quality controlled by three operators to produce the "ground-truth" data set. A fully convolutional neural network (OxNNet) was trained using this ground-truth data set to automatically segment the placenta. OxNNet delivered state-of-the-art automatic segmentation. The effect of training set size on the performance of OxNNet demonstrated the need for large data sets. The clinical utility of placental volume was tested by looking at predictions of small-for-gestational-age babies at term. The receiver-operating characteristics curves demonstrated almost identical results between OxNNet and the ground-truth). Our results demonstrated good similarity to the ground-truth and almost identical clinical results for the prediction of SGA.

Toxicity challenges in environmental chemicals: Prediction of ...

EPA Pesticide Factsheets

Physiologically based pharmacokinetic (PBPK) models bridge the gap between in vitro assays and in vivo effects by accounting for the adsorption, distribution, metabolism, and excretion of xenobiotics, which is especially useful in the assessment of human toxicity. Quantitative structure-activity relationships (QSAR) serve as a vital tool for the high-throughput prediction of chemical-specific PBPK parameters, such as the fraction of a chemical unbound by plasma protein (Fub). The presented work explores the merit of utilizing experimental pharmaceutical Fub data for the construction of a universal QSAR model, in order to compensate for the limited range of high-quality experimental Fub data for environmentally relevant chemicals, such as pollutants, pesticides, and consumer products. Independent QSAR models were constructed with three machine-learning algorithms, k nearest neighbors (kNN), random forest (RF), and support vector machine (SVM) regression, from a large pharmaceutical training set (~1000) and assessed with independent test sets of pharmaceuticals (~200) and environmentally relevant chemicals in the ToxCast program (~400). Small descriptor sets yielded the optimal balance of model complexity and performance, providing insight into the biochemical factors of plasma protein binding, while preventing over fitting to the training set. Overlaps in chemical space between pharmaceutical and environmental compounds were considered through applicability of do

Size-independent neural networks based first-principles method for accurate prediction of heat of formation of fuels

NASA Astrophysics Data System (ADS)

Yang, GuanYa; Wu, Jiang; Chen, ShuGuang; Zhou, WeiJun; Sun, Jian; Chen, GuanHua

2018-06-01

Neural network-based first-principles method for predicting heat of formation (HOF) was previously demonstrated to be able to achieve chemical accuracy in a broad spectrum of target molecules [L. H. Hu et al., J. Chem. Phys. 119, 11501 (2003)]. However, its accuracy deteriorates with the increase in molecular size. A closer inspection reveals a systematic correlation between the prediction error and the molecular size, which appears correctable by further statistical analysis, calling for a more sophisticated machine learning algorithm. Despite the apparent difference between simple and complex molecules, all the essential physical information is already present in a carefully selected set of small molecule representatives. A model that can capture the fundamental physics would be able to predict large and complex molecules from information extracted only from a small molecules database. To this end, a size-independent, multi-step multi-variable linear regression-neural network-B3LYP method is developed in this work, which successfully improves the overall prediction accuracy by training with smaller molecules only. And in particular, the calculation errors for larger molecules are drastically reduced to the same magnitudes as those of the smaller molecules. Specifically, the method is based on a 164-molecule database that consists of molecules made of hydrogen and carbon elements. 4 molecular descriptors were selected to encode molecule's characteristics, among which raw HOF calculated from B3LYP and the molecular size are also included. Upon the size-independent machine learning correction, the mean absolute deviation (MAD) of the B3LYP/6-311+G(3df,2p)-calculated HOF is reduced from 16.58 to 1.43 kcal/mol and from 17.33 to 1.69 kcal/mol for the training and testing sets (small molecules), respectively. Furthermore, the MAD of the testing set (large molecules) is reduced from 28.75 to 1.67 kcal/mol.

Enhancing deep convolutional neural network scheme for breast cancer diagnosis with unlabeled data.

PubMed

Sun, Wenqing; Tseng, Tzu-Liang Bill; Zhang, Jianying; Qian, Wei

2017-04-01

In this study we developed a graph based semi-supervised learning (SSL) scheme using deep convolutional neural network (CNN) for breast cancer diagnosis. CNN usually needs a large amount of labeled data for training and fine tuning the parameters, and our proposed scheme only requires a small portion of labeled data in training set. Four modules were included in the diagnosis system: data weighing, feature selection, dividing co-training data labeling, and CNN. 3158 region of interests (ROIs) with each containing a mass extracted from 1874 pairs of mammogram images were used for this study. Among them 100 ROIs were treated as labeled data while the rest were treated as unlabeled. The area under the curve (AUC) observed in our study was 0.8818, and the accuracy of CNN is 0.8243 using the mixed labeled and unlabeled data. Copyright © 2016. Published by Elsevier Ltd.

Training for an effective health and safety committee in a small business setting.

PubMed

Crollard, Allison; Neitzel, Richard L; Dominguez, Carlos F; Seixas, Noah S

2013-01-01

Health and safety committees are often heralded as a key element of successful health and safety programs, and are thought to represent a means of engaging workers in health and safety efforts. While the understanding of the factors that make these committees effective is growing, there are few resources for how to assist committees in developing these characteristics. This paper describes one approach to creating and implementing a training intervention aimed at improving health and safety committee function at one multilingual worksite. Short-term impacts were evaluated via questionnaire and qualitative observations of committee function. Results indicated high satisfaction with the training as well as modest increases in participation, cooperation, role clarity, and comfort with health and safety skills among committee members. The committee also made considerable achievements in establishing new processes for effective function. Similar interventions may be useful in other workplaces to increase health and safety committee success.

Improving the Accuracy and Training Speed of Motor Imagery Brain-Computer Interfaces Using Wavelet-Based Combined Feature Vectors and Gaussian Mixture Model-Supervectors.

PubMed

Lee, David; Park, Sang-Hoon; Lee, Sang-Goog

2017-10-07

In this paper, we propose a set of wavelet-based combined feature vectors and a Gaussian mixture model (GMM)-supervector to enhance training speed and classification accuracy in motor imagery brain-computer interfaces. The proposed method is configured as follows: first, wavelet transforms are applied to extract the feature vectors for identification of motor imagery electroencephalography (EEG) and principal component analyses are used to reduce the dimensionality of the feature vectors and linearly combine them. Subsequently, the GMM universal background model is trained by the expectation-maximization (EM) algorithm to purify the training data and reduce its size. Finally, a purified and reduced GMM-supervector is used to train the support vector machine classifier. The performance of the proposed method was evaluated for three different motor imagery datasets in terms of accuracy, kappa, mutual information, and computation time, and compared with the state-of-the-art algorithms. The results from the study indicate that the proposed method achieves high accuracy with a small amount of training data compared with the state-of-the-art algorithms in motor imagery EEG classification.

QSAR as a random event: modeling of nanoparticles uptake in PaCa2 cancer cells.

PubMed

Toropov, Andrey A; Toropova, Alla P; Puzyn, Tomasz; Benfenati, Emilio; Gini, Giuseppina; Leszczynska, Danuta; Leszczynski, Jerzy

2013-06-01

Quantitative structure-property/activity relationships (QSPRs/QSARs) are a tool to predict various endpoints for various substances. The "classic" QSPR/QSAR analysis is based on the representation of the molecular structure by the molecular graph. However, simplified molecular input-line entry system (SMILES) gradually becomes most popular representation of the molecular structure in the databases available on the Internet. Under such circumstances, the development of molecular descriptors calculated directly from SMILES becomes attractive alternative to "classic" descriptors. The CORAL software (http://www.insilico.eu/coral) is provider of SMILES-based optimal molecular descriptors which are aimed to correlate with various endpoints. We analyzed data set on nanoparticles uptake in PaCa2 pancreatic cancer cells. The data set includes 109 nanoparticles with the same core but different surface modifiers (small organic molecules). The concept of a QSAR as a random event is suggested in opposition to "classic" QSARs which are based on the only one distribution of available data into the training and the validation sets. In other words, five random splits into the "visible" training set and the "invisible" validation set were examined. The SMILES-based optimal descriptors (obtained by the Monte Carlo technique) for these splits are calculated with the CORAL software. The statistical quality of all these models is good. Copyright © 2013 Elsevier Ltd. All rights reserved.

Predicting tumor hypoxia in non-small cell lung cancer by combining CT, FDG PET and dynamic contrast-enhanced CT.

PubMed

Even, Aniek J G; Reymen, Bart; La Fontaine, Matthew D; Das, Marco; Jochems, Arthur; Mottaghy, Felix M; Belderbos, José S A; De Ruysscher, Dirk; Lambin, Philippe; van Elmpt, Wouter

2017-11-01

Most solid tumors contain inadequately oxygenated (i.e., hypoxic) regions, which tend to be more aggressive and treatment resistant. Hypoxia PET allows visualization of hypoxia and may enable treatment adaptation. However, hypoxia PET imaging is expensive, time-consuming and not widely available. We aimed to predict hypoxia levels in non-small cell lung cancer (NSCLC) using more easily available imaging modalities: FDG-PET/CT and dynamic contrast-enhanced CT (DCE-CT). For 34 NSCLC patients, included in two clinical trials, hypoxia HX4-PET/CT, planning FDG-PET/CT and DCE-CT scans were acquired before radiotherapy. Scans were non-rigidly registered to the planning CT. Tumor blood flow (BF) and blood volume (BV) were calculated by kinetic analysis of DCE-CT images. Within the gross tumor volume, independent clusters, i.e., supervoxels, were created based on FDG-PET/CT. For each supervoxel, tumor-to-background ratios (TBR) were calculated (median SUV/aorta SUV mean ) for HX4-PET/CT and supervoxel features (median, SD, entropy) for the other modalities. Two random forest models (cross-validated: 10 folds, five repeats) were trained to predict the hypoxia TBR; one based on CT, FDG, BF and BV, and one with only CT and FDG features. Patients were split in a training (trial NCT01024829) and independent test set (trial NCT01210378). For each patient, predicted, and observed hypoxic volumes (HV) (TBR > 1.2) were compared. Fifteen patients (3291 supervoxels) were used for training and 19 patients (1502 supervoxels) for testing. The model with all features (RMSE training: 0.19 ± 0.01, test: 0.27) outperformed the model with only CT and FDG-PET features (RMSE training: 0.20 ± 0.01, test: 0.29). All tumors of the test set were correctly classified as normoxic or hypoxic (HV > 1 cm 3 ) by the best performing model. We created a data-driven methodology to predict hypoxia levels and hypoxia spatial patterns using CT, FDG-PET and DCE-CT features in NSCLC. The model correctly classifies all tumors, and could therefore, aid tumor hypoxia classification and patient stratification.

Objective and subjective methods for quantifying training load in wheelchair basketball small-sided games.

PubMed

Iturricastillo, Aitor; Granados, Cristina; Los Arcos, Asier; Yanci, Javier

2017-04-01

The aim of the present study was to analyse the training load in wheelchair basketball small-sided games and determine the relationship between heart rate (HR)-based training load and perceived exertion (RPE)-based training load methods among small-sided games bouts. HR-based measurements of training load included Edwards' training load and Stagno's training impulses (TRIMP MOD ) while RPE-based training load measurements included cardiopulmonary (session RPEres) and muscular (session RPEmus) values. Data were collected from 12 wheelchair basketball players during five consecutive weeks. The total load for the small-sided games sessions was 67.5 ± 6.7 and 55.3 ± 12.5 AU in HR-based training load (Edwards' training load and TRIMP MOD ), while the RPE-based training loads were 99.3 ± 26.9 (session RPEres) and 100.8 ± 31.2 AU (session RPEmus). Bout-to-bout analysis identified greater session RPEmus in the third [P < 0.05; effect size (ES) = 0.66, moderate] and fourth bouts (P < 0.05; ES = 0.64, moderate) than in the first bout, but other measures did not differ. Mean correlations indicated a trivial and small relationship among HR-based and RPE-based training loads. It is suggested that HR-based and RPE-based training loads provide different information, but these two methods could be complementary because one method could help us to understand the limitations of the other.

Transfer and Use of Training Technology: A Model for Matching Training Approaches with Training Settings. Technical Report No. 74-24.

ERIC Educational Resources Information Center

Haverland, Edgar M.

The report describes a project designed to facilitate the transfer and utilization of training technology by developing a model for evaluating training approaches or innovtions in relation to the requirements, resources, and constraints of specific training settings. The model consists of two parallel sets of open-ended questions--one set…

The International Relations Committee of the Association of Anaesthetists of Great Britain and Ireland.

PubMed

Aitken, H; O'Sullivan, E

2007-12-01

The International Relations Committee of the Association of Anaesthetists of Great Britain and Ireland was established over 30 years ago to assist anaesthetists working in developing countries. The committee has attempted to make an impact through distribution of educational materials, supporting training courses and investing in a number of small equipment projects. In 2005, the Overseas Anaesthesia Fund was set up to allow members to donate directly to support our work.

Implementation of customized health information technology in diabetes self management programs.

PubMed

Alexander, Susan; Frith, Karen H; O'Keefe, Louise; Hennigan, Michael A

2011-01-01

The project was a nurse-led implementation of a software application, designed to combine clinical and demographic records for a diabetes education program, which would result in secure, long-term record storage. Clinical information systems may be prohibitively expensive for small practices and require extensive training for implementation. A review of the literature suggests that the use of simple, practice-based registries offer an economical method of monitoring the outcomes of diabetic patients. The database was designed using a common software application, Microsoft Access. The theory used to guide implementation and staff training was Rogers' Diffusion of Innovations theory (1995). Outcomes after a 3-month period included incorporation of 100% of new clinical and demographic patient records into the database and positive changes in staff attitudes regarding software applications used in diabetes self-management training. These objectives were met while keeping project costs under budgeted amounts. As a function of the clinical nurse specialist (CNS) researcher role, there is a need for CNSs to identify innovative and economical methods of data collection. The success of this nurse-led project reinforces suggestions in the literature for less costly methods of data maintenance in small practice settings. Ongoing utilization and enhancement have resulted in the creation of a robust database that could aid in the research of multiple clinical issues. Clinical nurse specialists can use existing evidence to guide and improve both their own practice and outcomes for patients and organizations. Further research regarding specific factors that predict efficient transition of informatics applications, how these factors vary according to practice settings, and the role of the CNS in implementation of such applications is needed.

Two microRNA panels to discriminate three subtypes of lung carcinoma in bronchial brushing specimens.

PubMed

Huang, Wei; Hu, Jie; Yang, Da-wei; Fan, Xin-ting; Jin, Yi; Hou, Ying-yong; Wang, Ji-ping; Yuan, Yun-feng; Tan, Yun-shan; Zhu, Xiong-Zeng; Bai, Chun-xue; Wu, Ying; Zhu, Hong-guang; Lu, Shao-hua

2012-12-01

Effective treatment for lung cancer requires accuracy in subclassification of carcinoma subtypes. To identify microRNAs in bronchial brushing specimens for discriminating small cell lung cancer (SCLC) from non-small cell lung cancer (NSCLC) and for further differentiating squamous cell carcinoma (SQ) from adenocarcinoma (AC). Microarrays were used to screen 723 microRNAs in laser-captured, microdissected cancer cells from 82 snap-frozen surgical lung specimens. Quantitative reverse-transcriptase polymerase chain reaction was performed on 153 macrodissected formalin-fixed, paraffin-embedded (FFPE) surgical lung specimens to evaluate seven microRNA candidates discovered from microarrays. Two microRNA panels were constructed on the basis of a training cohort (n = 85) and validated using an independent cohort (n = 68). The microRNA panels were applied as differentiators of SCLC from NSCLC and of SQ from AC in 207 bronchial brushing specimens. Two microRNA panels yielded high diagnostic accuracy in discriminating SCLC from NSCLC (miR-29a and miR-375; area under the curve [AUC], 0.991 and 0.982 for training and validation data set, respectively) and in differentiating SQ from AC (miR-205 and miR-34a; AUC, 0.977 and 0.982 for training and validation data set, respectively) in FFPE surgical lung specimens. Moreover, the microRNA panels accurately differentiated SCLC from NSCLC (AUC, 0.947) and SQ from AC (AUC, 0.962) in bronchial brushing specimens. We found two microRNA panels that accurately discriminated between the three subtypes of lung carcinoma in bronchial brushing specimens. The identified microRNA panels may have considerable clinical value in differential diagnosis and optimizing treatment strategies based on lung cancer subtypes.

The Impact of Trained Volunteer Mealtime Assistants on Dietary Intake and Satisfaction with Mealtime Care in Adult Hospital Inpatients: A Systematic Review.

PubMed

Howson, F F A; Sayer, A A; Roberts, H C

2017-01-01

Malnutrition is common in hospital inpatients and is associated with increased morbidity and mortality. Insufficient assistance at mealtimes can contribute to this and therefore trained volunteer mealtime assistants may be of benefit. To identify and review the current evidence for the impact of trained volunteer mealtime assistants on dietary intake and satisfaction with mealtime care in adult hospital inpatients. A systematic search of Medline, Embase and CINAHL was conducted to identify relevant articles. Articles of any methodology were considered. Quality assessment and data extraction were carried out by two reviewers independently. Participants were inpatients in a hospital setting, including rehabilitation units. Participants in long term care facilities were excluded. Articles that examined the effect of trained volunteer mealtime assistants on nutritional outcomes or satisfaction with mealtime care were included. 5576 articles were identified, of which 14 were included in the review. Nine were small research studies and five were quality improvement initiatives. The quality of eight studies was moderate, with one study being of lower quality. Eight articles reported dietary intake and seven demonstrated an improvement, with protein intakes at volunteer mealtimes increasing by 4.3g-10.1g and energy intakes by 44-105kcal. Ten articles reported positive staff, patient and volunteer feedback. No adverse events were reported. There is evidence from small studies and improvement projects that trained volunteer mealtime assistants are safe and improve satisfaction with mealtime care in hospital inpatients, although evidence for an effect on dietary intake was less consistent. Larger studies with robust methodology are required to confirm this.

Engineering youth service system infrastructure: Hawaii's continued efforts at large-scale implementation through knowledge management strategies.

PubMed

Nakamura, Brad J; Mueller, Charles W; Higa-McMillan, Charmaine; Okamura, Kelsie H; Chang, Jaime P; Slavin, Lesley; Shimabukuro, Scott

2014-01-01

Hawaii's Child and Adolescent Mental Health Division provides a unique illustration of a youth public mental health system with a long and successful history of large-scale quality improvement initiatives. Many advances are linked to flexibly organizing and applying knowledge gained from the scientific literature and move beyond installing a limited number of brand-named treatment approaches that might be directly relevant only to a small handful of system youth. This article takes a knowledge-to-action perspective and outlines five knowledge management strategies currently under way in Hawaii. Each strategy represents one component of a larger coordinated effort at engineering a service system focused on delivering both brand-named treatment approaches and complimentary strategies informed by the evidence base. The five knowledge management examples are (a) a set of modular-based professional training activities for currently practicing therapists, (b) an outreach initiative for supporting youth evidence-based practices training at Hawaii's mental health-related professional programs, (c) an effort to increase consumer knowledge of and demand for youth evidence-based practices, (d) a practice and progress agency performance feedback system, and (e) a sampling of system-level research studies focused on understanding treatment as usual. We end by outlining a small set of lessons learned and a longer term vision for embedding these efforts into the system's infrastructure.

Australian Small Business Participation in Training Activities

ERIC Educational Resources Information Center

Webster, Beverley; Walker, Elizabeth; Brown, Alan

2005-01-01

Purpose: This purpose of this paper is to investigate the use of on-line training by small businesses in Australia. It explores the relationship between the owners acceptance and use of the Internet, and their current participation in training opportunities. Design/Methodology/Approach: A sample of small businesses which had participated in an…

Military Training Lands Historic Context: Small Arms Ranges

DTIC Science & Technology

2010-03-01

ER D C/ CE R L TR -1 0 -1 1 Military Training Lands Historic Context Small Arms Ranges Dan Archibald, Adam Smith, Sunny Adams, and...unlimited. ERDC/CERL TR-10-11 March 2010 Military Training Lands Historic Context Small Arms Ranges Dan Archibald, Adam Smith, Sunny Adams...context for military training lands, written to satisfy a part of Section 110 of the National Historic Preservation Act (NHPA) of 1966 as amended

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.

The effect of open kinetic chain knee extensor resistance training at different training loads on anterior knee laxity in the uninjured.

PubMed

Barcellona, Massimo G; Morrissey, Matthew C

2016-04-01

The commonly used open kinetic chain knee extensor (OKCKE) exercise loads the sagittal restraints to knee anterior tibial translation. To investigate the effect of different loads of OKCKE resistance training on anterior knee laxity (AKL) in the uninjured knee. non-clinical trial. Randomization into one of three supervised training groups occurred with training 3 times per week for 12 weeks. Subjects in the LOW and HIGH groups performed OKCKE resistance training at loads of 2 sets of 20 repetition maximum (RM) and 20 sets of 2RM, respectively. Subjects in the isokinetic training group (ISOK) performed isokinetic OKCKE resistance training using 2 sets of 20 maximal efforts. AKL was measured using the KT2000 arthrometer with concurrent measurement of lateral hamstrings muscle activity at baseline, 6 weeks and 12 weeks. Twenty six subjects participated (LOW n = 9, HIGH n = 10, ISOK n = 7). The main finding from this study is that a 12-week OKCKE resistance training programme at loads of 20 sets of 2RM, leads to an increase in manual maximal AKL. OKCKE resistance training at high loads (20 sets of 2RM) increases AKL while low load OKCKE resistance training (2 sets of 20RM) and isokinetic OKCKE resistance training at 2 sets of 20RM does not. Copyright © 2015 Elsevier Ltd. All rights reserved.

Music acquisition: effects of enculturation and formal training on development.

PubMed

Hannon, Erin E; Trainor, Laurel J

2007-11-01

Musical structure is complex, consisting of a small set of elements that combine to form hierarchical levels of pitch and temporal structure according to grammatical rules. As with language, different systems use different elements and rules for combination. Drawing on recent findings, we propose that music acquisition begins with basic features, such as peripheral frequency-coding mechanisms and multisensory timing connections, and proceeds through enculturation, whereby everyday exposure to a particular music system creates, in a systematic order of acquisition, culture-specific brain structures and representations. Finally, we propose that formal musical training invokes domain-specific processes that affect salience of musical input and the amount of cortical tissue devoted to its processing, as well as domain-general processes of attention and executive functioning.

The implementation and evaluation of a communication skills training program for oncology nurses.

PubMed

Banerjee, Smita C; Manna, Ruth; Coyle, Nessa; Penn, Stacey; Gallegos, Tess E; Zaider, Talia; Krueger, Carol A; Bialer, Philip A; Bylund, Carma L; Parker, Patricia A

2017-09-01

Many nurses express difficulty in communicating with their patients, especially in oncology settings where there are numerous challenges and high-stake decisions during the course of diagnosis and treatment. Providing specific training in communication skills is one way to enhance the communication between nurses and their patients. We developed and implemented a communication skills training program for nurses, consisting of three teaching modules: responding empathically to patients; discussing death, dying, and end-of-life goals of care; and responding to challenging interactions with families. Training included didactic and experiential small group role plays. This paper presents results on program evaluation, self-efficacy, and behavioral demonstration of learned communication skills. Three hundred forty-two inpatient oncology nurses participated in a 1-day communication skills training program and completed course evaluations, self-reports, and pre- and post-standardized patient assessments. Participants rated the training favorably, and they reported significant gains in self-efficacy in their ability to communicate with patients in various contexts. Participants also demonstrated significant improvement in several empathic skills, as well as in clarifying skill. Our work demonstrates that implementation of a nurse communication skills training program at a major cancer center is feasible and acceptable and has a significant impact on participants' self-efficacy and uptake of communication skills.

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

PubMed Central

2018-01-01

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

Cultural and communication awareness for general practice registrars who are international medical graduates: a project of CoastCityCountry Training.

PubMed

Duncan, Geraldine F; Gilbey, David

2007-02-01

(1) To generate discussion about Australian culture and language with GP registrars using the medium of poetry; and (2) to introduce discussion about language and communication skills in a role-play format that GP registrars would embrace as part of their clinical training. (1) A variety of Australian poems was selected to reflect six themes: men, women, the Bush, ANZACS, Aboriginal Australia and migrants, which would provide a basis for discussion on a range of cultural issues to aid a medical professional trained overseas in developing further understanding of aspects of Australian culture. (2) A series of role plays was developed to reflect the clinical themes of each Day Release education program. These were enacted in a small group setting by preselected GP registrars with feedback from a medical educator and an English as a Second Language teacher. The Riverina/Murrumbidgee area of New South Wales, one of the three local training groups of CoastCityCountryTraining. GP registrars attached to the Riverina/Murrumbidgee Local Training Group. To show that discussion of poetry and participation in role plays are active language- and cultural-learning environments capable of enhancing understanding of a range of issues about Australia that are relevant to a GP registrar. There was increased participation by GP registrars in accessing the set material prior to each session. It was noted that there was also increased active involvement of all registrars in discussion within the group throughout the year. Discussion allowed clarification of aspects of Australian culture to participants with different international medical backgrounds, as well as providing an opportunity for GP registrars to share their own experiences.

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

PubMed

Brock, Cara M; Herndon, Christopher M

2017-06-01

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

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

Quantitative analysis of single- vs. multiple-set programs in resistance training.

PubMed

Wolfe, Brian L; LeMura, Linda M; Cole, Phillip J

2004-02-01

The purpose of this study was to examine the existing research on single-set vs. multiple-set resistance training programs. Using the meta-analytic approach, we included studies that met the following criteria in our analysis: (a) at least 6 subjects per group; (b) subject groups consisting of single-set vs. multiple-set resistance training programs; (c) pretest and posttest strength measures; (d) training programs of 6 weeks or more; (e) apparently "healthy" individuals free from orthopedic limitations; and (f) published studies in English-language journals only. Sixteen studies generated 103 effect sizes (ESs) based on a total of 621 subjects, ranging in age from 15-71 years. Across all designs, intervention strategies, and categories, the pretest to posttest ES in muscular strength was (chi = 1.4 +/- 1.4; 95% confidence interval, 0.41-3.8; p < 0.001). The results of 2 x 2 analysis of variance revealed simple main effects for age, training status (trained vs. untrained), and research design (p < 0.001). No significant main effects were found for sex, program duration, and set end point. Significant interactions were found for training status and program duration (6-16 weeks vs. 17-40 weeks) and number of sets performed (single vs. multiple). The data indicated that trained individuals performing multiple sets generated significantly greater increases in strength (p < 0.001). For programs with an extended duration, multiple sets were superior to single sets (p < 0.05). This quantitative review indicates that single-set programs for an initial short training period in untrained individuals result in similar strength gains as multiple-set programs. However, as progression occurs and higher gains are desired, multiple-set programs are more effective.

One program, multiple training sites: does site of family medicine training influence professional practice location?

PubMed

Jamieson, Jean L; Kernahan, Jill; Calam, Betty; Sivertz, Kristin S

2013-01-01

Numerous strategies have been suggested to increase recruitment of family physicians to rural communities and smaller regional centers. One approach has been to implement distributed postgraduate education programs where trainees spend substantial time in such communities. The purpose of the current study was to compare the eventual practice location of family physicians who undertook their postgraduate training through a single university but who were based in either metropolitan or distributed, non-metropolitan communities. Since 1998, the Department of Family Practice at the University of British Columbia in Canada has conducted an annual survey of its residents at 2, 5, and 10 years after completion of training. The authors received Ethics Board approval to use this anonymized data to identify personal and educational factors that predict future practice location. The overall response rate was 45%. At 2 years (N=222), residents trained in distributed sites were 15 times more likely to enter practice in rural communities, small towns and regional centers than those who trained in metropolitan teaching centers. This was even more predictive for retention in non-urban practice sites. Among the subgroup of physicians who remained in a single practice location for more than a year preceding the survey, those who trained in smaller sites were 36 times more likely to choose a rural or regional practice setting. While the vast majority of those trained in metropolitan sites chose an urban practice location, a subgroup of those with some rural upbringing were more likely to practice in rural or regional settings. Trainees from distributed sites considered themselves more prepared for practice regardless of ultimate practice location. Participation in a distributed postgraduate family medicine training site is an important predictor of a non-urban practice location. This effect persists for 10 years after completion of training and is independent of other predictors of non-urban practice including gender, rural upbringing, and rural undergraduate training. It is hypothesized that this is due not only to a curriculum that supports preparedness for this type of practice but also to opportunities to develop personal and professional roots in these communities.

Moderate Load Eccentric Exercise; A Distinct Novel Training Modality

PubMed Central

Hoppeler, Hans

2016-01-01

Over the last 20 years a number of studies have been published using progressive eccentric exercise protocols on motorized ergometers or similar devices that allow for controlled application of eccentric loads. Exercise protocols ramp eccentric loads over an initial 3 weeks period in order to prevent muscle damage and delayed onset muscle soreness. Final training loads reach 400–500 W in rehabilitative settings and over 1200 W in elite athletes. Training is typically carried out three times per week for durations of 20–30 min. This type of training has been characterizes as moderate load eccentric exercise. It has also been denoted RENEW (Resistance Exercise via Negative Eccentric Work by LaStayo et al., 2014). It is distinct from plyometric exercises (i.e., drop jumps) that impose muscle loads of several thousand Watts on muscles and tendons. It is also distinct from eccentric overload training whereby loads in a conventional strength training setting are increased in the eccentric phase of the movement to match concentric loads. Moderate load eccentric exercise (or RENEW) has been shown to be similarly effective as conventional strength training in increasing muscle strength and muscle volume. However, as carried out at higher angular velocities of joint movement, it reduces joint loads. A hallmark of moderate load eccentric exercise is the fact that the energy requirements are typically 4-fold smaller than in concentric exercise of the same load. This makes moderate load eccentric exercise training the tool of choice in medical conditions with limitations in muscle energy supply. The use and effectiveness of moderate load eccentric exercise has been demonstrated mostly in small scale studies for cardiorespiratory conditions, sarcopenia of old age, cancer, diabetes type 2, and neurological conditions. It has also been used effectively in the prevention and rehabilitation of injuries of the locomotor system in particular the rehabilitation after anterior cruciate ligament surgery. PMID:27899894

Influence relevance voting: an accurate and interpretable virtual high throughput screening method.

PubMed

Swamidass, S Joshua; Azencott, Chloé-Agathe; Lin, Ting-Wan; Gramajo, Hugo; Tsai, Shiou-Chuan; Baldi, Pierre

2009-04-01

Given activity training data from high-throughput screening (HTS) experiments, virtual high-throughput screening (vHTS) methods aim to predict in silico the activity of untested chemicals. We present a novel method, the Influence Relevance Voter (IRV), specifically tailored for the vHTS task. The IRV is a low-parameter neural network which refines a k-nearest neighbor classifier by nonlinearly combining the influences of a chemical's neighbors in the training set. Influences are decomposed, also nonlinearly, into a relevance component and a vote component. The IRV is benchmarked using the data and rules of two large, open, competitions, and its performance compared to the performance of other participating methods, as well as of an in-house support vector machine (SVM) method. On these benchmark data sets, IRV achieves state-of-the-art results, comparable to the SVM in one case, and significantly better than the SVM in the other, retrieving three times as many actives in the top 1% of its prediction-sorted list. The IRV presents several other important advantages over SVMs and other methods: (1) the output predictions have a probabilistic semantic; (2) the underlying inferences are interpretable; (3) the training time is very short, on the order of minutes even for very large data sets; (4) the risk of overfitting is minimal, due to the small number of free parameters; and (5) additional information can easily be incorporated into the IRV architecture. Combined with its performance, these qualities make the IRV particularly well suited for vHTS.

Developing a Suitable Model for Water Uptake for Biodegradable Polymers Using Small Training Sets.

PubMed

Valenzuela, Loreto M; Knight, Doyle D; Kohn, Joachim

2016-01-01

Prediction of the dynamic properties of water uptake across polymer libraries can accelerate polymer selection for a specific application. We first built semiempirical models using Artificial Neural Networks and all water uptake data, as individual input. These models give very good correlations (R (2) > 0.78 for test set) but very low accuracy on cross-validation sets (less than 19% of experimental points within experimental error). Instead, using consolidated parameters like equilibrium water uptake a good model is obtained (R (2) = 0.78 for test set), with accurate predictions for 50% of tested polymers. The semiempirical model was applied to the 56-polymer library of L-tyrosine-derived polyarylates, identifying groups of polymers that are likely to satisfy design criteria for water uptake. This research demonstrates that a surrogate modeling effort can reduce the number of polymers that must be synthesized and characterized to identify an appropriate polymer that meets certain performance criteria.

The Importance of Being an Insider: How Networks Influence the Small Firm's Engagement with Formal Training

ERIC Educational Resources Information Center

Bishop, Dan

2011-01-01

Purpose: The purpose of this paper is to examine the ways in which the small firm's external relationships influence its approach to formal training and training providers. Design/methodology/approach: A qualitative approach was adopted, involving semi-structured interviews with senior managers, in 25 small firms in South Wales. These interviews…

Computer aided detection of clusters of microcalcifications on full field digital mammograms

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

Ge Jun; Sahiner, Berkman; Hadjiiski, Lubomir M.

2006-08-15

We are developing a computer-aided detection (CAD) system to identify microcalcification clusters (MCCs) automatically on full field digital mammograms (FFDMs). The CAD system includes six stages: preprocessing; image enhancement; segmentation of microcalcification candidates; false positive (FP) reduction for individual microcalcifications; regional clustering; and FP reduction for clustered microcalcifications. At the stage of FP reduction for individual microcalcifications, a truncated sum-of-squares error function was used to improve the efficiency and robustness of the training of an artificial neural network in our CAD system for FFDMs. At the stage of FP reduction for clustered microcalcifications, morphological features and features derived from themore » artificial neural network outputs were extracted from each cluster. Stepwise linear discriminant analysis (LDA) was used to select the features. An LDA classifier was then used to differentiate clustered microcalcifications from FPs. A data set of 96 cases with 192 images was collected at the University of Michigan. This data set contained 96 MCCs, of which 28 clusters were proven by biopsy to be malignant and 68 were proven to be benign. The data set was separated into two independent data sets for training and testing of the CAD system in a cross-validation scheme. When one data set was used to train and validate the convolution neural network (CNN) in our CAD system, the other data set was used to evaluate the detection performance. With the use of a truncated error metric, the training of CNN could be accelerated and the classification performance was improved. The CNN in combination with an LDA classifier could substantially reduce FPs with a small tradeoff in sensitivity. By using the free-response receiver operating characteristic methodology, it was found that our CAD system can achieve a cluster-based sensitivity of 70, 80, and 90 % at 0.21, 0.61, and 1.49 FPs/image, respectively. For case-based performance evaluation, a sensitivity of 70, 80, and 90 % can be achieved at 0.07, 0.17, and 0.65 FPs/image, respectively. We also used a data set of 216 mammograms negative for clustered microcalcifications to further estimate the FP rate of our CAD system. The corresponding FP rates were 0.15, 0.31, and 0.86 FPs/image for cluster-based detection when negative mammograms were used for estimation of FP rates.« less

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.

Synergies Between Quantum Mechanics and Machine Learning in Reaction Prediction.

PubMed

Sadowski, Peter; Fooshee, David; Subrahmanya, Niranjan; Baldi, Pierre

2016-11-28

Machine learning (ML) and quantum mechanical (QM) methods can be used in two-way synergy to build chemical reaction expert systems. The proposed ML approach identifies electron sources and sinks among reactants and then ranks all source-sink pairs. This addresses a bottleneck of QM calculations by providing a prioritized list of mechanistic reaction steps. QM modeling can then be used to compute the transition states and activation energies of the top-ranked reactions, providing additional or improved examples of ranked source-sink pairs. Retraining the ML model closes the loop, producing more accurate predictions from a larger training set. The approach is demonstrated in detail using a small set of organic radical reactions.

Evaluation of CNN as anthropomorphic model observer

NASA Astrophysics Data System (ADS)

Massanes, Francesc; Brankov, Jovan G.

2017-03-01

Model observers (MO) are widely used in medical imaging to act as surrogates of human observers in task-based image quality evaluation, frequently towards optimization of reconstruction algorithms. In this paper, we explore the use of convolutional neural networks (CNN) to be used as MO. We will compare CNN MO to alternative MO currently being proposed and used such as the relevance vector machine based MO and channelized Hotelling observer (CHO). As the success of the CNN, and other deep learning approaches, is rooted in large data sets availability, which is rarely the case in medical imaging systems task-performance evaluation, we will evaluate CNN performance on both large and small training data sets.

Impact of a Small Cell on the RF-EMF Exposure in a Train

PubMed Central

Aerts, Sam; Plets, David; Thielens, Arno; Martens, Luc; Joseph, Wout

2015-01-01

The deployment of a miniature mobile-phone base station or small cell in a train car significantly improves the coverage and the capacity of a mobile network service on the train. However, the impact of the small cell on the passengers’ exposure to radio-frequency electromagnetic fields (RF-EMF) is unknown. In this study, we assessed experimentally the RF-EMF exposure of a mobile-phone user who is either connected to the outdoor macrocell network or to an in-train small cell, while traveling on the train, by means of the absorbed-dose concept, which combines the base station downlink exposure with the mobile-phone uplink exposure. For Global System for Mobile Communications (GSM) technology at 1800 MHz, we found that by connecting to a small cell, the brain exposure of the user could realistically be reduced by a factor 35 and the whole-body exposure by a factor 11. PMID:25734793

A comparative study of two hazard handling training methods for novice drivers.

PubMed

Wang, Y B; Zhang, W; Salvendy, G

2010-10-01

The effectiveness of two hazard perception training methods, simulation-based error training (SET) and video-based guided error training (VGET), for novice drivers' hazard handling performance was tested, compared, and analyzed. Thirty-two novice drivers participated in the hazard perception training. Half of the participants were trained using SET by making errors and/or experiencing accidents while driving with a desktop simulator. The other half were trained using VGET by watching prerecorded video clips of errors and accidents that were made by other people. The two groups had exposure to equal numbers of errors for each training scenario. All the participants were tested and evaluated for hazard handling on a full cockpit driving simulator one week after training. Hazard handling performance and hazard response were measured in this transfer test. Both hazard handling performance scores and hazard response distances were significantly better for the SET group than the VGET group. Furthermore, the SET group had more metacognitive activities and intrinsic motivation. SET also seemed more effective in changing participants' confidence, but the result did not reach the significance level. SET exhibited a higher training effectiveness of hazard response and handling than VGET in the simulated transfer test. The superiority of SET might benefit from the higher levels of metacognition and intrinsic motivation during training, which was observed in the experiment. Future research should be conducted to assess whether the advantages of error training are still effective under real road conditions.

Set Shifting Training with Categorization Tasks

PubMed Central

Soveri, Anna; Waris, Otto; Laine, Matti

2013-01-01

The very few cognitive training studies targeting an important executive function, set shifting, have reported performance improvements that also generalized to untrained tasks. The present randomized controlled trial extends set shifting training research by comparing previously used cued training with uncued training. A computerized adaptation of the Wisconsin Card Sorting Test was utilized as the training task in a pretest-posttest experimental design involving three groups of university students. One group received uncued training (n = 14), another received cued training (n = 14) and the control group (n = 14) only participated in pre- and posttests. The uncued training group showed posttraining performance increases on their training task, but neither training group showed statistically significant transfer effects. Nevertheless, comparison of effect sizes for transfer effects indicated that our results did not differ significantly from the previous studies. Our results suggest that the cognitive effects of computerized set shifting training are mostly task-specific, and would preclude any robust generalization effects with this training. PMID:24324717

Teaching NMR spectra analysis with nmr.cheminfo.org.

PubMed

Patiny, Luc; Bolaños, Alejandro; Castillo, Andrés M; Bernal, Andrés; Wist, Julien

2018-06-01

Teaching spectra analysis and structure elucidation requires students to get trained on real problems. This involves solving exercises of increasing complexity and when necessary using computational tools. Although desktop software packages exist for this purpose, nmr.cheminfo.org platform offers students an online alternative. It provides a set of exercises and tools to help solving them. Only a small number of exercises are currently available, but contributors are invited to submit new ones and suggest new types of problems. Copyright © 2018 John Wiley & Sons, Ltd.

Simulators Sustainment Management: Advanced Planning Information

DTIC Science & Technology

2006-05-23

1M per year Competition Type: TSA II Small Business Set-A-Side, FFP Program Mgr : James H. Burks,507 ACSS/GFLC, (801) 586-1859; jim.burks...ACRONYM(S) 11. SPONSOR/MONITOR’S REPORT NUMBER(S) 12 . DISTRIBUTION/AVAILABILITY STATEMENT Approved for public release; distribution unlimited 13...BEST O G D E N A I R L O G I S T I C S C E N T E R Training Systems Business Opportunities FY06-07 HC-130P Weapon System Trainer T-38C Aircrew

Irrigation Training Manual. Planning, Design, Operation, and Management of Small-Scale Irrigation Systems [and] Irrigation Reference Manual. A Technical Reference to Be Used with the Peace Corps Irrigation Training Manual T0076 in the Selection, Planning, Design, Operation, and Management of Small-Scale Irrigation Systems.

ERIC Educational Resources Information Center

Salazar, LeRoy; And Others

This resource for trainers involved in irrigated agriculture training for Peace Corps volunteers consists of two parts: irrigation training manual and irrigation reference manual. The complete course should fully prepare volunteers serving as irrigation, specialists to plan, implement, evaluate and manage small-scale irrigation projects in arid,…

The efficacy of a multifactorial memory training in older adults living in residential care settings.

PubMed

Vranić, Andrea; Španić, Ana Marija; Carretti, Barbara; Borella, Erika

2013-11-01

Several studies have shown an increase in memory performance after teaching mnemonic techniques to older participants. However, transfer effects to non-trained tasks are generally either very small, or not found. The present study investigates the efficacy of a multifactorial memory training program for older adults living in a residential care center. The program combines teaching of memory strategies with activities based on metacognitive (metamemory) and motivational aspects. Specific training-related gains in the Immediate list recall task (criterion task), as well as transfer effects on measures of short-term memory, long-term memory, working memory, motivational (need for cognition), and metacognitive aspects (subjective measure of one's memory) were examined. Maintenance of training benefits was assessed after seven months. Fifty-one older adults living in a residential care center, with no cognitive impairments, participated in the study. Participants were randomly assigned to two programs: the experimental group attended the training program, while the active control group was involved in a program in which different psychological issues were discussed. A benefit in the criterion task and substantial general transfer effects were found for the trained group, but not for the active control, and they were maintained at the seven months follow-up. Our results suggest that training procedures, which combine teaching of strategies with metacognitive-motivational aspects, can improve cognitive functioning and attitude toward cognitive activities in older adults.

A time-series method for automated measurement of changes in mitotic and interphase duration from time-lapse movies.

PubMed

Sigoillot, Frederic D; Huckins, Jeremy F; Li, Fuhai; Zhou, Xiaobo; Wong, Stephen T C; King, Randall W

2011-01-01

Automated time-lapse microscopy can visualize proliferation of large numbers of individual cells, enabling accurate measurement of the frequency of cell division and the duration of interphase and mitosis. However, extraction of quantitative information by manual inspection of time-lapse movies is too time-consuming to be useful for analysis of large experiments. Here we present an automated time-series approach that can measure changes in the duration of mitosis and interphase in individual cells expressing fluorescent histone 2B. The approach requires analysis of only 2 features, nuclear area and average intensity. Compared to supervised learning approaches, this method reduces processing time and does not require generation of training data sets. We demonstrate that this method is as sensitive as manual analysis in identifying small changes in interphase or mitotic duration induced by drug or siRNA treatment. This approach should facilitate automated analysis of high-throughput time-lapse data sets to identify small molecules or gene products that influence timing of cell division.

A neural network for noise correlation classification

NASA Astrophysics Data System (ADS)

Paitz, Patrick; Gokhberg, Alexey; Fichtner, Andreas

2018-02-01

We present an artificial neural network (ANN) for the classification of ambient seismic noise correlations into two categories, suitable and unsuitable for noise tomography. By using only a small manually classified data subset for network training, the ANN allows us to classify large data volumes with low human effort and to encode the valuable subjective experience of data analysts that cannot be captured by a deterministic algorithm. Based on a new feature extraction procedure that exploits the wavelet-like nature of seismic time-series, we efficiently reduce the dimensionality of noise correlation data, still keeping relevant features needed for automated classification. Using global- and regional-scale data sets, we show that classification errors of 20 per cent or less can be achieved when the network training is performed with as little as 3.5 per cent and 16 per cent of the data sets, respectively. Furthermore, the ANN trained on the regional data can be applied to the global data, and vice versa, without a significant increase of the classification error. An experiment where four students manually classified the data, revealed that the classification error they would assign to each other is substantially larger than the classification error of the ANN (>35 per cent). This indicates that reproducibility would be hampered more by human subjectivity than by imperfections of the ANN.

Model-based segmentation of abdominal aortic aneurysms in CTA images

NASA Astrophysics Data System (ADS)

de Bruijne, Marleen; van Ginneken, Bram; Niessen, Wiro J.; Loog, Marco; Viergever, Max A.

2003-05-01

Segmentation of thrombus in abdominal aortic aneurysms is complicated by regions of low boundary contrast and by the presence of many neighboring structures in close proximity to the aneurysm wall. We present an automated method that is similar to the well known Active Shape Models (ASM), combining a three-dimensional shape model with a one-dimensional boundary appearance model. Our contribution is twofold: we developed a non-parametric appearance modeling scheme that effectively deals with a highly varying background, and we propose a way of generalizing models of curvilinear structures from small training sets. In contrast with the conventional ASM approach, the new appearance model trains on both true and false examples of boundary profiles. The probability that a given image profile belongs to the boundary is obtained using k nearest neighbor (kNN) probability density estimation. The performance of this scheme is compared to that of original ASMs, which minimize the Mahalanobis distance to the average true profile in the training set. The generalizability of the shape model is improved by modeling the objects axis deformation independent of its cross-sectional deformation. A leave-one-out experiment was performed on 23 datasets. Segmentation using the kNN appearance model significantly outperformed the original ASM scheme; average volume errors were 5.9% and 46% respectively.

Consistent free energy landscapes and thermodynamic properties of small proteins based on a single all-atom force field employing an implicit solvation.

PubMed

Kim, Eunae; Jang, Soonmin; Pak, Youngshang

2007-10-14

We have attempted to improve the PARAM99 force field in conjunction with the generalized Born (GB) solvation model with a surface area correction for more consistent protein folding simulations. For this purpose, using an extended alphabeta training set of five well-studied molecules with various folds (alpha, beta, and betabetaalpha), a previously modified version of PARAM99/GBSA is further refined, such that all native states of the five training species correspond to their lowest free energy minimum states. The resulting modified force field (PARAM99MOD5/GBSA) clearly produces reasonably acceptable conformational free energy surfaces of the training set with correct identifications of their native states in the free energy minimum states. Moreover, due to its well-balanced nature, this new force field is expected to describe secondary structure propensities of diverse folds in a more consistent manner. Remarkably, temperature dependent behaviors simulated with the current force field are in good agreement with the experiment. This agreement is a significant improvement over the existing standard all-atom force fields. In addition, fundamentally important thermodynamic quantities, such as folding enthalpy (DeltaH) and entropy (DeltaS), agree reasonably well with the experimental data.

Hypoxic Repeat Sprint Training Improves Rugby Player's Repeated Sprint but Not Endurance Performance

PubMed Central

Hamlin, Michael J.; Olsen, Peter D.; Marshall, Helen C.; Lizamore, Catherine A.; Elliot, Catherine A.

2017-01-01

This study aims to investigate the performance changes in 19 well-trained male rugby players after repeat-sprint training (six sessions of four sets of 5 × 5 s sprints with 25 s and 5 min of active recovery between reps and sets, respectively) in either normobaric hypoxia (HYP; n = 9; FIO2 = 14.5%) or normobaric normoxia (NORM; n = 10; FIO2 = 20.9%). Three weeks after the intervention, 2 additional repeat-sprint training sessions in hypoxia (FIO2 = 14.5%) was investigated in both groups to gauge the efficacy of using “top-up” sessions for previously hypoxic-trained subjects and whether a small hypoxic dose would be beneficial for the previously normoxic-trained group. Repeated sprint (8 × 20 m) and Yo-Yo Intermittent Recovery Level 1 (YYIR1) performances were tested twice at baseline (Pre 1 and Pre 2) and weekly after (Post 1–3) the initial intervention (intervention 1) and again weekly after the second “top-up” intervention (Post 4–5). After each training set, heart rate, oxygen saturation, and rate of perceived exertion were recorded. Compared to baseline (mean of Pre 1 and Pre 2), both the hypoxic and normoxic groups similarly lowered fatigue over the 8 sprints 1 week after the intervention (Post 1: −1.8 ± 1.6%, −1.5 ± 1.4%, mean change ± 90% CI in HYP and NORM groups, respectively). However, from Post 2 onwards, only the hypoxic group maintained the performance improvement compared to baseline (Post 2: −2.1 ± 1.8%, Post 3: −2.3 ± 1.7%, Post 4: −1.9 ± 1.8%, and Post 5: −1.2 ± 1.7%). Compared to the normoxic group, the hypoxic group was likely to have substantially less fatigue at Post 3–5 (−2.0 ± 2.4%, −2.2 ± 2.4%, −1.6 ± 2.4% Post 3, Post 4, Post 5, respectively). YYIR1 performances improved throughout the recovery period in both groups (13–37% compared to baseline) with unclear differences found between groups. The addition of two sessions of “top-up” training after intervention 1, had little effect on either group. Repeat-sprint training in hypoxia for six sessions increases repeat sprint ability but not YYIR1 performance in well-trained rugby players. PMID:28223938

Comparison of projection skills of deterministic ensemble methods using pseudo-simulation data generated from multivariate Gaussian distribution

NASA Astrophysics Data System (ADS)

Oh, Seok-Geun; Suh, Myoung-Seok

2017-07-01

The projection skills of five ensemble methods were analyzed according to simulation skills, training period, and ensemble members, using 198 sets of pseudo-simulation data (PSD) produced by random number generation assuming the simulated temperature of regional climate models. The PSD sets were classified into 18 categories according to the relative magnitude of bias, variance ratio, and correlation coefficient, where each category had 11 sets (including 1 truth set) with 50 samples. The ensemble methods used were as follows: equal weighted averaging without bias correction (EWA_NBC), EWA with bias correction (EWA_WBC), weighted ensemble averaging based on root mean square errors and correlation (WEA_RAC), WEA based on the Taylor score (WEA_Tay), and multivariate linear regression (Mul_Reg). The projection skills of the ensemble methods improved generally as compared with the best member for each category. However, their projection skills are significantly affected by the simulation skills of the ensemble member. The weighted ensemble methods showed better projection skills than non-weighted methods, in particular, for the PSD categories having systematic biases and various correlation coefficients. The EWA_NBC showed considerably lower projection skills than the other methods, in particular, for the PSD categories with systematic biases. Although Mul_Reg showed relatively good skills, it showed strong sensitivity to the PSD categories, training periods, and number of members. On the other hand, the WEA_Tay and WEA_RAC showed relatively superior skills in both the accuracy and reliability for all the sensitivity experiments. This indicates that WEA_Tay and WEA_RAC are applicable even for simulation data with systematic biases, a short training period, and a small number of ensemble members.

Assessing the Value of Workforce Training. A Guide for Small and Mid-sized Companies and the Providers that Serve Them.

ERIC Educational Resources Information Center

Askov, Eunice N.; Hoops, John; Alamprese, Judith

This booklet provides an introduction to evaluating a work force training program, both to assess its impact and to improve its effectiveness. The guide provides instructions for assessing a single training program, rather than a training department in a company or a training provider. It is targeted at small and midsized companies and the…

sw-SVM: sensor weighting support vector machines for EEG-based brain-computer interfaces.

PubMed

Jrad, N; Congedo, M; Phlypo, R; Rousseau, S; Flamary, R; Yger, F; Rakotomamonjy, A

2011-10-01

In many machine learning applications, like brain-computer interfaces (BCI), high-dimensional sensor array data are available. Sensor measurements are often highly correlated and signal-to-noise ratio is not homogeneously spread across sensors. Thus, collected data are highly variable and discrimination tasks are challenging. In this work, we focus on sensor weighting as an efficient tool to improve the classification procedure. We present an approach integrating sensor weighting in the classification framework. Sensor weights are considered as hyper-parameters to be learned by a support vector machine (SVM). The resulting sensor weighting SVM (sw-SVM) is designed to satisfy a margin criterion, that is, the generalization error. Experimental studies on two data sets are presented, a P300 data set and an error-related potential (ErrP) data set. For the P300 data set (BCI competition III), for which a large number of trials is available, the sw-SVM proves to perform equivalently with respect to the ensemble SVM strategy that won the competition. For the ErrP data set, for which a small number of trials are available, the sw-SVM shows superior performances as compared to three state-of-the art approaches. Results suggest that the sw-SVM promises to be useful in event-related potentials classification, even with a small number of training trials.

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.

Evaluation of a low density DNA microarray for small B-cell non-Hodgkin lymphoma differential diagnosis.

PubMed

Gillet, Jean-Pierre; Molina, Thierry Jo; Jamart, Jacques; Gaulard, Philippe; Leroy, Karen; Briere, Josette; Theate, Ivan; Thieblemont, Catherine; Bosly, Andre; Herin, Michel; Hamels, Jacques; Remacle, Jose

2009-03-01

Lymphomas are classified according to the World Health Organisation (WHO) classification which defines subtypes on the basis of clinical, morphological, immunophenotypic, molecular and cytogenetic criteria. Differential diagnosis of the subtypes is sometimes difficult, especially for small B-cell lymphoma (SBCL). Standardisation of molecular genetic assays using multiple gene expression analysis by microarrays could be a useful complement to the current diagnosis. The aim of the present study was to develop a low density DNA microarray for the analysis of 107 genes associated with B-cell non-Hodgkin lymphoma and to evaluate its performance in the diagnosis of SBCL. A predictive tool based on Fisher discriminant analysis using a training set of 40 patients including four different subtypes (follicular lymphoma n = 15, mantle cell lymphoma n = 7, B-cell chronic lymphocytic leukemia n = 6 and splenic marginal zone lymphoma n = 12) was designed. A short additional preliminary analysis to gauge the accuracy of this signature was then performed on an external set of nine patients. Using this model, eight of nine of those samples were classified successfully. This pilot study demonstrates that such a microarray tool may be a promising diagnostic approach for small B-cell non-Hodgkin lymphoma.

The West Virginia Occupational Safety and Health Initiative: practicum training for a new marketplace.

PubMed

Meyer, J D; Becker, P E; Stockdale, T; Ducatman, A M

1999-05-01

Occupational medicine practice has experienced a shift from larger corporate medical departments to organizations providing services for a variety of industries. Specific training needs will accompany this shift in practice patterns; these may differ from those developed in the traditional industrial or corporate medical department setting. The West Virginia Occupational Health and Safety Initiative involves occupational medicine residents in consultation to a variety of small industries and businesses. It uses the expertise of occupational physicians, health and safety extension faculty, and faculty in engineering and industrial hygiene. Residents participate in multidisciplinary evaluations of worksites, and develop competencies in team-building, workplace health and safety evaluation, and occupational medical consulting. Specific competencies that address requirements for practicum training are used to measure the trainee's acquisition of knowledge and skills. Particular attention is paid to the acquisition of group problem-solving expertise, skills relevant to the current market in practice opportunities, and the specific career interests of the resident physician. Preliminary evaluation indicates the usefulness of training in evaluation of diverse industries and worksites. We offer this program as a training model that can prepare residents for the challenges of a changing marketplace for occupational health and safety services.

Training the Trainers for Small Business.

ERIC Educational Resources Information Center

Gibb, Allan A.

1990-01-01

Training for small businesses requires an entrepreneurial rather than a conventional approach. Critical trainer competencies include profiling the business, segmenting the market, understanding the business development process, introducing the relevant environment, delivering enterprise skills training, and teaching across the board. (SK)

High-Impact, Self-Motivated Training Within an Enriched Environment With Single Animal Tracking Dose-Dependently Promotes Motor Skill Acquisition and Functional Recovery.

PubMed

Starkey, Michelle L; Bleul, Christiane; Kasper, Hansjörg; Mosberger, Alice C; Zörner, Björn; Giger, Stefan; Gullo, Miriam; Buschmann, Frank; Schwab, Martin E

2014-07-01

Functional recovery following central nervous system injuries is strongly influenced by rehabilitative training. In the clinical setting, the intensity of training and the level of motivation for a particular task are known to play important roles. With increasing neuroscience studies investigating the effects of training and rehabilitation, it is important to understand how the amount and type of training of individuals influences outcome. However, little is known about the influence of spontaneous "self-training" during daily life as it is often uncontrolled, not recorded, and mostly disregarded. Here, we investigated the effects of the intensity of self-training on motor skill acquisition in normal, intact rats and on the recovery of functional motor behavior following spinal cord injury in adult rats. We used a custom-designed small animal tracking system, "RatTrack," to continuously record the activity of multiple rats, simultaneously in a complex Natural Habitat-enriched environment. Naïve, adult rats performed high-intensity, self-motivated motor training, which resulted in them out-performing rats that were conventionally housed and trained on skilled movement tasks, for example, skilled prehension (grasping) and ladder walking. Following spinal cord injury the amount of self-training was correlated with improved functional recovery. These data suggest that high-impact, self-motivated training leads to superior skill acquisition and functional recovery than conventional training paradigms. These findings have important implications for the design of animal studies investigating rehabilitation and for the planning of human rehabilitation programs. © The Author(s) 2014.

Training Requirements and Curriculum Content for Primary Care Providers Delivering Preventive Oral Health Services to Children Enrolled in Medicaid.

PubMed

Sams, Lattice D; Rozier, R Gary; Quinonez, Rocio B

2016-07-01

Despite the emphasis on delivery of preventive oral health services in non-dental settings, limited information exists about state Medicaid policies and strategies to educate practicing physicians in the delivery of these services. This study aims to determine: (1) training requirements and policies for reimbursement of oral health services, (2) teaching delivery methods used to train physicians, and (3) curricula content available to providers among states that reimburse non-dental providers for oral health services. Using Web-based Internet searches as the primary data source, and a supplemental e-mail survey of all states offering in-person training, we assessed training requirements, methods of delivery for training, and curriculum content for states with Medicaid reimbursement to primary care providers delivering preventive oral health services. RESULTS of descriptive analyses are presented for information collected and updated in 2014. Forty-two states provide training sessions or resources to providers, 34 requiring provider training before reimbursement for oral health services. Web-based training is the most common CME delivery method. Only small differences in curricular content were reported by the 11 states that use in-person didactic sessions as the delivery method. Although we found that most states require training and curricular content is similar, training was most often delivered using Web-based courses without any additional delivery methods. Research is needed to evaluate the impact of a mixture of training methods and other quality improvement methods on increased adoption and implementation of preventive oral health services in medical practices.

Convolutional networks for vehicle track segmentation

DOE PAGES

Quach, Tu-Thach

2017-08-19

Existing methods to detect vehicle tracks in coherent change detection images, a product of combining two synthetic aperture radar images taken at different times of the same scene, rely on simple, fast models to label track pixels. These models, however, are unable to capture natural track features such as continuity and parallelism. More powerful, but computationally expensive models can be used in offline settings. We present an approach that uses dilated convolutional networks consisting of a series of 3-by-3 convolutions to segment vehicle tracks. The design of our networks considers the fact that remote sensing applications tend to operate inmore » low power and have limited training data. As a result, we aim for small, efficient networks that can be trained end-to-end to learn natural track features entirely from limited training data. We demonstrate that our 6-layer network, trained on just 90 images, is computationally efficient and improves the F-score on a standard dataset to 0.992, up from 0.959 obtained by the current state-of-the-art method.« less

Convolutional networks for vehicle track segmentation

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

Quach, Tu-Thach

Existing methods to detect vehicle tracks in coherent change detection images, a product of combining two synthetic aperture radar images taken at different times of the same scene, rely on simple, fast models to label track pixels. These models, however, are unable to capture natural track features such as continuity and parallelism. More powerful, but computationally expensive models can be used in offline settings. We present an approach that uses dilated convolutional networks consisting of a series of 3-by-3 convolutions to segment vehicle tracks. The design of our networks considers the fact that remote sensing applications tend to operate inmore » low power and have limited training data. As a result, we aim for small, efficient networks that can be trained end-to-end to learn natural track features entirely from limited training data. We demonstrate that our 6-layer network, trained on just 90 images, is computationally efficient and improves the F-score on a standard dataset to 0.992, up from 0.959 obtained by the current state-of-the-art method.« less

Resolving Transition Metal Chemical Space: Feature Selection for Machine Learning and Structure-Property Relationships.

PubMed

Janet, Jon Paul; Kulik, Heather J

2017-11-22

Machine learning (ML) of quantum mechanical properties shows promise for accelerating chemical discovery. For transition metal chemistry where accurate calculations are computationally costly and available training data sets are small, the molecular representation becomes a critical ingredient in ML model predictive accuracy. We introduce a series of revised autocorrelation functions (RACs) that encode relationships of the heuristic atomic properties (e.g., size, connectivity, and electronegativity) on a molecular graph. We alter the starting point, scope, and nature of the quantities evaluated in standard ACs to make these RACs amenable to inorganic chemistry. On an organic molecule set, we first demonstrate superior standard AC performance to other presently available topological descriptors for ML model training, with mean unsigned errors (MUEs) for atomization energies on set-aside test molecules as low as 6 kcal/mol. For inorganic chemistry, our RACs yield 1 kcal/mol ML MUEs on set-aside test molecules in spin-state splitting in comparison to 15-20× higher errors for feature sets that encode whole-molecule structural information. Systematic feature selection methods including univariate filtering, recursive feature elimination, and direct optimization (e.g., random forest and LASSO) are compared. Random-forest- or LASSO-selected subsets 4-5× smaller than the full RAC set produce sub- to 1 kcal/mol spin-splitting MUEs, with good transferability to metal-ligand bond length prediction (0.004-5 Å MUE) and redox potential on a smaller data set (0.2-0.3 eV MUE). Evaluation of feature selection results across property sets reveals the relative importance of local, electronic descriptors (e.g., electronegativity, atomic number) in spin-splitting and distal, steric effects in redox potential and bond lengths.

A short, structured skills training course for critical care physiotherapists in a lower-middle income country.

PubMed

Tunpattu, Sanjeewa; Newey, Victoria; Sigera, Chathurani; De Silva, Pubudu; Goonarathna, Amal; Aluthge, Iranga; Thambavita, Pasan; Perera, Rohan; Meegahawatte, Amila; Isaam, Ilhaam; Dondorp, Arjen M; Haniffa, Rashan

2018-01-10

The aim of this article is to describe the delivery and acceptability of a short, structured training course for critical care physiotherapy and its effects on the knowledge and skills of the participants in Sri Lanka, a lower-middle income country. The two-day program combining short didactic sessions with small group workshops and skills stations was developed and delivered by local facilitators in partnership with an overseas specialist physiotherapist trainer. The impact was assessed using pre/post-course self-assessment, pre/post-course multiple-choice-question (MCQ) papers, and an end-of-course feedback questionnaire. Fifty-six physiotherapists (26% of critical care physiotherapists in Sri Lanka) participated. Overall confidence in common critical care physiotherapy skills improved from 11.6% to 59.2% in pre/post-training self-assessments, respectively. Post-course MCQ scores (mean score = 63.2) and percentage of passes (87.5%) were higher than pre-course scores (mean score = 36.6; percentage of passes = 12.5%). Overall feedback was very positive as 75% of the participants were highly satisfied with the course's contribution to improved critical care knowledge. This short, structured, critical care focused physiotherapy training has potential benefit to participating physiotherapists. Further, it provides an evidence that collaborative program can be planned and conducted successfully in a resource poor setting. This sustainable short course model may be adaptable to other resource-limited settings.

Single- vs. Multiple-Set Strength Training in Women.

ERIC Educational Resources Information Center

Schlumberger, Andreas; Stec, Justyna; Schmidtbleicher, Dietmar

2001-01-01

Compared the effects of single- and multiple-set strength training in women with basic experience in resistance training. Both training groups had significant strength improvements in leg extension. In the seated bench press, only the three-set group showed a significant increase in maximal strength. There were higher strength gains overall in the…

Optimizing support vector machine learning for semi-arid vegetation mapping by using clustering analysis

NASA Astrophysics Data System (ADS)

Su, Lihong

In remote sensing communities, support vector machine (SVM) learning has recently received increasing attention. SVM learning usually requires large memory and enormous amounts of computation time on large training sets. According to SVM algorithms, the SVM classification decision function is fully determined by support vectors, which compose a subset of the training sets. In this regard, a solution to optimize SVM learning is to efficiently reduce training sets. In this paper, a data reduction method based on agglomerative hierarchical clustering is proposed to obtain smaller training sets for SVM learning. Using a multiple angle remote sensing dataset of a semi-arid region, the effectiveness of the proposed method is evaluated by classification experiments with a series of reduced training sets. The experiments show that there is no loss of SVM accuracy when the original training set is reduced to 34% using the proposed approach. Maximum likelihood classification (MLC) also is applied on the reduced training sets. The results show that MLC can also maintain the classification accuracy. This implies that the most informative data instances can be retained by this approach.

Handwritten word preprocessing for database adaptation

NASA Astrophysics Data System (ADS)

Oprean, Cristina; Likforman-Sulem, Laurence; Mokbel, Chafic

2013-01-01

Handwriting recognition systems are typically trained using publicly available databases, where data have been collected in controlled conditions (image resolution, paper background, noise level,...). Since this is not often the case in real-world scenarios, classification performance can be affected when novel data is presented to the word recognition system. To overcome this problem, we present in this paper a new approach called database adaptation. It consists of processing one set (training or test) in order to adapt it to the other set (test or training, respectively). Specifically, two kinds of preprocessing, namely stroke thickness normalization and pixel intensity normalization are considered. The advantage of such approach is that we can re-use the existing recognition system trained on controlled data. We conduct several experiments with the Rimes 2011 word database and with a real-world database. We adapt either the test set or the training set. Results show that training set adaptation achieves better results than test set adaptation, at the cost of a second training stage on the adapted data. Accuracy of data set adaptation is increased by 2% to 3% in absolute value over no adaptation.

Health promotion and empowerment in Henganofi District, Papua New Guinea.

PubMed

Barcham, Richard; Silas, Esther; Irie, Jesse

2016-01-01

Evidence shows that the government of Papua New Guinea is failing to provide basic services in health to the majority of its people. Local non-government organisations (NGOs), partnered with international NGOs, are attempting to fill this gap. With limited resources, these small Indigenous organisations must focus much of their effort on training that supports self-reliance as the main strategy for communities to improve their quality of life. This project explored the training content and methodology of Touching The Untouchables (TTU), a small Indigenous NGO based in Goroka, Eastern Highlands Province, that has trained a network of village volunteers in health promotion and safe motherhood.
Village life imposes multiple demands, from self-sufficiency in food to maintaining law and order. There are established attitudes about power and dependence, referred to as 'cargo thinking'. Cargo thinking stands as a barrier to the necessity of self-reliance, and requires training strategies that seek to empower participants to create change from their own initiative. Empowerment is understood as oriented towards individual people taking collective action to improve their circumstances by rectifying disparities in social power and control. To achieve self-reliance, empowerment is necessarily operational on the levels of person, community and society.
In addition to being operational on all three levels of empowerment, the training content and methodology adopted and developed by TTU demonstrate that empowering practice in training employs approaches to knowledge that are evidence-based, reflexive, contextual and skill-based. Creating knowledge that is reflexive and exploring knowledge about the broader context uses special kinds of communicative tools that facilitate discussion on history, society and political economy. Furthermore, training methodologies that are oriented to empowerment create settings that require the use of all three types of communication required for cooperative action: dramaturgical, normative and teleological communication.
The success of TTU's training content and methodology demonstrates that creating the conditions for achieving collective self-reliance through empowerment is a necessary part of primary health promotion in Papua New Guinea, and that underlying the success of empowerment oriented training are definable types of knowledge and communication.

Performance of machine-learning scoring functions in structure-based virtual screening.

PubMed

Wójcikowski, Maciej; Ballester, Pedro J; Siedlecki, Pawel

2017-04-25

Classical scoring functions have reached a plateau in their performance in virtual screening and binding affinity prediction. Recently, machine-learning scoring functions trained on protein-ligand complexes have shown great promise in small tailored studies. They have also raised controversy, specifically concerning model overfitting and applicability to novel targets. Here we provide a new ready-to-use scoring function (RF-Score-VS) trained on 15 426 active and 893 897 inactive molecules docked to a set of 102 targets. We use the full DUD-E data sets along with three docking tools, five classical and three machine-learning scoring functions for model building and performance assessment. Our results show RF-Score-VS can substantially improve virtual screening performance: RF-Score-VS top 1% provides 55.6% hit rate, whereas that of Vina only 16.2% (for smaller percent the difference is even more encouraging: RF-Score-VS top 0.1% achieves 88.6% hit rate for 27.5% using Vina). In addition, RF-Score-VS provides much better prediction of measured binding affinity than Vina (Pearson correlation of 0.56 and -0.18, respectively). Lastly, we test RF-Score-VS on an independent test set from the DEKOIS benchmark and observed comparable results. We provide full data sets to facilitate further research in this area (http://github.com/oddt/rfscorevs) as well as ready-to-use RF-Score-VS (http://github.com/oddt/rfscorevs_binary).

Random Access Memories: A New Paradigm for Target Detection in High Resolution Aerial Remote Sensing Images.

PubMed

Zou, Zhengxia; Shi, Zhenwei

2018-03-01

We propose a new paradigm for target detection in high resolution aerial remote sensing images under small target priors. Previous remote sensing target detection methods frame the detection as learning of detection model + inference of class-label and bounding-box coordinates. Instead, we formulate it from a Bayesian view that at inference stage, the detection model is adaptively updated to maximize its posterior that is determined by both training and observation. We call this paradigm "random access memories (RAM)." In this paradigm, "Memories" can be interpreted as any model distribution learned from training data and "random access" means accessing memories and randomly adjusting the model at detection phase to obtain better adaptivity to any unseen distribution of test data. By leveraging some latest detection techniques e.g., deep Convolutional Neural Networks and multi-scale anchors, experimental results on a public remote sensing target detection data set show our method outperforms several other state of the art methods. We also introduce a new data set "LEarning, VIsion and Remote sensing laboratory (LEVIR)", which is one order of magnitude larger than other data sets of this field. LEVIR consists of a large set of Google Earth images, with over 22 k images and 10 k independently labeled targets. RAM gives noticeable upgrade of accuracy (an mean average precision improvement of 1% ~ 4%) of our baseline detectors with acceptable computational overhead.

33 CFR 334.830 - Lake Michigan; small-arms range adjacent to U.S. Naval Training Center, Great Lakes, Ill.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 33 Navigation and Navigable Waters 3 2014-07-01 2014-07-01 false Lake Michigan; small-arms range adjacent to U.S. Naval Training Center, Great Lakes, Ill. 334.830 Section 334.830 Navigation and Navigable... REGULATIONS § 334.830 Lake Michigan; small-arms range adjacent to U.S. Naval Training Center, Great Lakes, Ill...

33 CFR 334.830 - Lake Michigan; small-arms range adjacent to U.S. Naval Training Center, Great Lakes, Ill.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 33 Navigation and Navigable Waters 3 2012-07-01 2012-07-01 false Lake Michigan; small-arms range adjacent to U.S. Naval Training Center, Great Lakes, Ill. 334.830 Section 334.830 Navigation and Navigable... REGULATIONS § 334.830 Lake Michigan; small-arms range adjacent to U.S. Naval Training Center, Great Lakes, Ill...

33 CFR 334.830 - Lake Michigan; small-arms range adjacent to U.S. Naval Training Center, Great Lakes, Ill.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 33 Navigation and Navigable Waters 3 2010-07-01 2010-07-01 false Lake Michigan; small-arms range adjacent to U.S. Naval Training Center, Great Lakes, Ill. 334.830 Section 334.830 Navigation and Navigable... REGULATIONS § 334.830 Lake Michigan; small-arms range adjacent to U.S. Naval Training Center, Great Lakes, Ill...

33 CFR 334.830 - Lake Michigan; small-arms range adjacent to U.S. Naval Training Center, Great Lakes, Ill.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 33 Navigation and Navigable Waters 3 2011-07-01 2011-07-01 false Lake Michigan; small-arms range adjacent to U.S. Naval Training Center, Great Lakes, Ill. 334.830 Section 334.830 Navigation and Navigable... REGULATIONS § 334.830 Lake Michigan; small-arms range adjacent to U.S. Naval Training Center, Great Lakes, Ill...

33 CFR 334.830 - Lake Michigan; small-arms range adjacent to U.S. Naval Training Center, Great Lakes, Ill.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 33 Navigation and Navigable Waters 3 2013-07-01 2013-07-01 false Lake Michigan; small-arms range adjacent to U.S. Naval Training Center, Great Lakes, Ill. 334.830 Section 334.830 Navigation and Navigable... REGULATIONS § 334.830 Lake Michigan; small-arms range adjacent to U.S. Naval Training Center, Great Lakes, Ill...

The utility and feasibility of business training for neurosurgeons.

PubMed

Giller, Cole A

2008-04-01

Socioeconomic changes have imposed many administrative demands on neurosurgeons, including managing facilities such as the intensive care unit without absolute authority and maintaining referrals, marketing in an increasingly competitive environment, effecting change within stubborn hospital systems, negotiating fair contracts with insurance companies, using financial statements to make financial decisions, managing small groups under new rules of human resources, navigating a Byzantine system of reimbursement, and assessing entrepreneurial opportunities. A set of new tools and skills has been developed by the business community in response to similar problems that may be of use to neurosurgeons. These advances are reviewed in a neurosurgical context, and routes to business training for the neurosurgeon are discussed. Recent advances in business are discussed with a focus on their relevance to neurosurgical practice. Current neurosurgical interest in business training and training opportunities for neurosurgeons are presented. Interest in business training within the neurosurgery community is keen, and advances in the field of business may be helpful in addressing the new tasks faced by neurosurgeons. New tools from advances in business are available which have been invaluable to corporations and may be helpful to neurosurgeons wanting to improve efficiency and maintain competitive advantage. Business training is available to neurosurgeons through a variety of routes.

An empirical study of ensemble-based semi-supervised learning approaches for imbalanced splice site datasets.

PubMed

Stanescu, Ana; Caragea, Doina

2015-01-01

Recent biochemical advances have led to inexpensive, time-efficient production of massive volumes of raw genomic data. Traditional machine learning approaches to genome annotation typically rely on large amounts of labeled data. The process of labeling data can be expensive, as it requires domain knowledge and expert involvement. Semi-supervised learning approaches that can make use of unlabeled data, in addition to small amounts of labeled data, can help reduce the costs associated with labeling. In this context, we focus on the problem of predicting splice sites in a genome using semi-supervised learning approaches. This is a challenging problem, due to the highly imbalanced distribution of the data, i.e., small number of splice sites as compared to the number of non-splice sites. To address this challenge, we propose to use ensembles of semi-supervised classifiers, specifically self-training and co-training classifiers. Our experiments on five highly imbalanced splice site datasets, with positive to negative ratios of 1-to-99, showed that the ensemble-based semi-supervised approaches represent a good choice, even when the amount of labeled data consists of less than 1% of all training data. In particular, we found that ensembles of co-training and self-training classifiers that dynamically balance the set of labeled instances during the semi-supervised iterations show improvements over the corresponding supervised ensemble baselines. In the presence of limited amounts of labeled data, ensemble-based semi-supervised approaches can successfully leverage the unlabeled data to enhance supervised ensembles learned from highly imbalanced data distributions. Given that such distributions are common for many biological sequence classification problems, our work can be seen as a stepping stone towards more sophisticated ensemble-based approaches to biological sequence annotation in a semi-supervised framework.

An empirical study of ensemble-based semi-supervised learning approaches for imbalanced splice site datasets

PubMed Central

2015-01-01

Background Recent biochemical advances have led to inexpensive, time-efficient production of massive volumes of raw genomic data. Traditional machine learning approaches to genome annotation typically rely on large amounts of labeled data. The process of labeling data can be expensive, as it requires domain knowledge and expert involvement. Semi-supervised learning approaches that can make use of unlabeled data, in addition to small amounts of labeled data, can help reduce the costs associated with labeling. In this context, we focus on the problem of predicting splice sites in a genome using semi-supervised learning approaches. This is a challenging problem, due to the highly imbalanced distribution of the data, i.e., small number of splice sites as compared to the number of non-splice sites. To address this challenge, we propose to use ensembles of semi-supervised classifiers, specifically self-training and co-training classifiers. Results Our experiments on five highly imbalanced splice site datasets, with positive to negative ratios of 1-to-99, showed that the ensemble-based semi-supervised approaches represent a good choice, even when the amount of labeled data consists of less than 1% of all training data. In particular, we found that ensembles of co-training and self-training classifiers that dynamically balance the set of labeled instances during the semi-supervised iterations show improvements over the corresponding supervised ensemble baselines. Conclusions In the presence of limited amounts of labeled data, ensemble-based semi-supervised approaches can successfully leverage the unlabeled data to enhance supervised ensembles learned from highly imbalanced data distributions. Given that such distributions are common for many biological sequence classification problems, our work can be seen as a stepping stone towards more sophisticated ensemble-based approaches to biological sequence annotation in a semi-supervised framework. PMID:26356316

Feature extraction using convolutional neural network for classifying breast density in mammographic images

NASA Astrophysics Data System (ADS)

Thomaz, Ricardo L.; Carneiro, Pedro C.; Patrocinio, Ana C.

2017-03-01

Breast cancer is the leading cause of death for women in most countries. The high levels of mortality relate mostly to late diagnosis and to the direct proportionally relationship between breast density and breast cancer development. Therefore, the correct assessment of breast density is important to provide better screening for higher risk patients. However, in modern digital mammography the discrimination among breast densities is highly complex due to increased contrast and visual information for all densities. Thus, a computational system for classifying breast density might be a useful tool for aiding medical staff. Several machine-learning algorithms are already capable of classifying small number of classes with good accuracy. However, machinelearning algorithms main constraint relates to the set of features extracted and used for classification. Although well-known feature extraction techniques might provide a good set of features, it is a complex task to select an initial set during design of a classifier. Thus, we propose feature extraction using a Convolutional Neural Network (CNN) for classifying breast density by a usual machine-learning classifier. We used 307 mammographic images downsampled to 260x200 pixels to train a CNN and extract features from a deep layer. After training, the activation of 8 neurons from a deep fully connected layer are extracted and used as features. Then, these features are feedforward to a single hidden layer neural network that is cross-validated using 10-folds to classify among four classes of breast density. The global accuracy of this method is 98.4%, presenting only 1.6% of misclassification. However, the small set of samples and memory constraints required the reuse of data in both CNN and MLP-NN, therefore overfitting might have influenced the results even though we cross-validated the network. Thus, although we presented a promising method for extracting features and classifying breast density, a greater database is still required for evaluating the results.

Thinking Outside of Outpatient: Underutilized Settings for Psychotherapy Education.

PubMed

Blumenshine, Philip; Lenet, Alison E; Havel, Lauren K; Arbuckle, Melissa R; Cabaniss, Deborah L

2017-02-01

Although psychiatry residents are expected to achieve competency in conducting psychotherapy during their training, it is unclear how psychotherapy teaching is integrated across diverse clinical settings. Between January and March 2015, 177 psychiatry residency training directors were sent a survey asking about psychotherapy training practices in their programs, as well as perceived barriers to psychotherapy teaching. Eighty-two training directors (44%) completed the survey. While 95% indicated that psychotherapy was a formal learning objective for outpatient clinic rotations, fifty percent or fewer noted psychotherapy was a learning objective in other settings. Most program directors would like to see psychotherapy training included (particularly supportive psychotherapy and cognitive behavioral therapy) on inpatient (82%) and consultation-liaison settings (57%). The most common barriers identified to teaching psychotherapy in these settings were time and perceived inadequate staff training and interest. Non-outpatient rotations appear to be an underutilized setting for psychotherapy teaching.

Development of new business opportunities for minorities in nuclear energy. Final report

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

Spight, C.

1980-12-15

In Part I of this report the basis for the optimal development of new business opportunities for minorities in nuclear energy programs is defined within the successful completion of all contract tasks. The basis presented consists of an identification of a set of qualified minority-owned small businesses, a defined reservoir of highly trained minorities with applicable expertise, a policy context for the development of opportunities, and a proposed networking structure for information transfer/professional development. In Part II a contractor-focused analysis of the structure of the nuclear industry, a breakdown of the DOE nuclear program by region and functional area, andmore » a directory of minority-owned small businesses by region are presented.« less

Deep learning based state recognition of substation switches

NASA Astrophysics Data System (ADS)

Wang, Jin

2018-06-01

Different from the traditional method which recognize the state of substation switches based on the running rules of electrical power system, this work proposes a novel convolutional neuron network-based state recognition approach of substation switches. Inspired by the theory of transfer learning, we first establish a convolutional neuron network model trained on the large-scale image set ILSVRC2012, then the restricted Boltzmann machine is employed to replace the full connected layer of the convolutional neuron network and trained on our small image dataset of 110kV substation switches to get a stronger model. Experiments conducted on our image dataset of 110kV substation switches show that, the proposed approach can be applicable to the substation to reduce the running cost and implement the real unattended operation.

Establishing Fire Safety Skills Using Behavioral Skills Training

ERIC Educational Resources Information Center

Houvouras, Andrew J., IV; Harvey, Mark T.

2014-01-01

The use of behavioral skills training (BST) to educate 3 adolescent boys on the risks of lighters and fire setting was evaluated using in situ assessment in a school setting. Two participants had a history of fire setting. After training, all participants adhered to established rules: (a) avoid a deactivated lighter, (b) leave the training area,…

A practical model for the train-set utilization: The case of Beijing-Tianjin passenger dedicated line in China

PubMed Central

Li, Xiaomeng; Yang, Zhuo

2017-01-01

As a sustainable transportation mode, high-speed railway (HSR) has become an efficient way to meet the huge travel demand. However, due to the high acquisition and maintenance cost, it is impossible to build enough infrastructure and purchase enough train-sets. Great efforts are required to improve the transport capability of HSR. The utilization efficiency of train-sets (carrying tools of HSR) is one of the most important factors of the transport capacity of HSR. In order to enhance the utilization efficiency of the train-sets, this paper proposed a train-set circulation optimization model to minimize the total connection time. An innovative two-stage approach which contains segments generation and segments combination was designed to solve this model. In order to verify the feasibility of the proposed approach, an experiment was carried out in the Beijing-Tianjin passenger dedicated line, to fulfill a 174 trips train diagram. The model results showed that compared with the traditional Ant Colony Algorithm (ACA), the utilization efficiency of train-sets can be increased from 43.4% (ACA) to 46.9% (Two-Stage), and 1 train-set can be saved up to fulfill the same transportation tasks. The approach proposed in the study is faster and more stable than the traditional ones, by using which, the HSR staff can draw up the train-sets circulation plan more quickly and the utilization efficiency of the HSR system is also improved. PMID:28489933

Comprehensive simulation-enhanced training curriculum for an advanced minimally invasive procedure: a randomized controlled trial.

PubMed

Zevin, Boris; Dedy, Nicolas J; Bonrath, Esther M; Grantcharov, Teodor P

2017-05-01

There is no comprehensive simulation-enhanced training curriculum to address cognitive, psychomotor, and nontechnical skills for an advanced minimally invasive procedure. 1) To develop and provide evidence of validity for a comprehensive simulation-enhanced training (SET) curriculum for an advanced minimally invasive procedure; (2) to demonstrate transfer of acquired psychomotor skills from a simulation laboratory to live porcine model; and (3) to compare training outcomes of SET curriculum group and chief resident group. University. This prospective single-blinded, randomized, controlled trial allocated 20 intermediate-level surgery residents to receive either conventional training (control) or SET curriculum training (intervention). The SET curriculum consisted of cognitive, psychomotor, and nontechnical training modules. Psychomotor skills in a live anesthetized porcine model in the OR was the primary outcome. Knowledge of advanced minimally invasive and bariatric surgery and nontechnical skills in a simulated OR crisis scenario were the secondary outcomes. Residents in the SET curriculum group went on to perform a laparoscopic jejunojejunostomy in the OR. Cognitive, psychomotor, and nontechnical skills of SET curriculum group were also compared to a group of 12 chief surgery residents. SET curriculum group demonstrated superior psychomotor skills in a live porcine model (56 [47-62] versus 44 [38-53], P<.05) and superior nontechnical skills (41 [38-45] versus 31 [24-40], P<.01) compared with conventional training group. SET curriculum group and conventional training group demonstrated equivalent knowledge (14 [12-15] versus 13 [11-15], P = 0.47). SET curriculum group demonstrated equivalent psychomotor skills in the live porcine model and in the OR in a human patient (56 [47-62] versus 63 [61-68]; P = .21). SET curriculum group demonstrated inferior knowledge (13 [11-15] versus 16 [14-16]; P<.05), equivalent psychomotor skill (63 [61-68] versus 68 [62-74]; P = .50), and superior nontechnical skills (41 [38-45] versus 34 [27-35], P<.01) compared with chief resident group. Completion of the SET curriculum resulted in superior training outcomes, compared with conventional surgery training. Implementation of the SET curriculum can standardize training for an advanced minimally invasive procedure and can ensure that comprehensive proficiency milestones are met before exposure to patient care. Copyright © 2017 American Society for Bariatric Surgery. Published by Elsevier Inc. All rights reserved.

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

PubMed

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

2010-02-01

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

Neural control of magnetic suspension systems

NASA Technical Reports Server (NTRS)

Gray, W. Steven

1993-01-01

The purpose of this research program is to design, build and test (in cooperation with NASA personnel from the NASA Langley Research Center) neural controllers for two different small air-gap magnetic suspension systems. The general objective of the program is to study neural network architectures for the purpose of control in an experimental setting and to demonstrate the feasibility of the concept. The specific objectives of the research program are: (1) to demonstrate through simulation and experimentation the feasibility of using neural controllers to stabilize a nonlinear magnetic suspension system; (2) to investigate through simulation and experimentation the performance of neural controllers designs under various types of parametric and nonparametric uncertainty; (3) to investigate through simulation and experimentation various types of neural architectures for real-time control with respect to performance and complexity; and (4) to benchmark in an experimental setting the performance of neural controllers against other types of existing linear and nonlinear compensator designs. To date, the first one-dimensional, small air-gap magnetic suspension system has been built, tested and delivered to the NASA Langley Research Center. The device is currently being stabilized with a digital linear phase-lead controller. The neural controller hardware is under construction. Two different neural network paradigms are under consideration, one based on hidden layer feedforward networks trained via back propagation and one based on using Gaussian radial basis functions trained by analytical methods related to stability conditions. Some advanced nonlinear control algorithms using feedback linearization and sliding mode control are in simulation studies.

Beyond the Rose-Colored Binoculars: How to Launch a Successful Physics Career in the 21st Century

NASA Astrophysics Data System (ADS)

Bailey, Crystal

Physics degree holders are among the most employable in the world, often doing everything from managing a research lab at a multi-million dollar corporation, to developing solutions to global problems in their own small startups. Employers know that with a physics training, a potential hire has acquired a broad problem-solving skill set that translates to almost any environment, as well as an ability to be self- guided and -motivated so that they can learn whatever skills are needed to successfully achieve their goals. Therefore it's no surprise that the majority of physics graduates find employment in private sector, industrial settings. Yet at the same time, only about 25 graduating PhDs will take a permanent faculty position- yet academic careers are usually the only track to which students are exposed while earning their degrees. In this talk, I will explore less-familiar (but more common!) career paths for physics graduates, and provide resources to help faculty mentors give their students better information and training for a broader scope of career possibilities.

Multicategory nets of single-layer perceptrons: complexity and sample-size issues.

PubMed

Raudys, Sarunas; Kybartas, Rimantas; Zavadskas, Edmundas Kazimieras

2010-05-01

The standard cost function of multicategory single-layer perceptrons (SLPs) does not minimize the classification error rate. In order to reduce classification error, it is necessary to: 1) refuse the traditional cost function, 2) obtain near to optimal pairwise linear classifiers by specially organized SLP training and optimal stopping, and 3) fuse their decisions properly. To obtain better classification in unbalanced training set situations, we introduce the unbalance correcting term. It was found that fusion based on the Kulback-Leibler (K-L) distance and the Wu-Lin-Weng (WLW) method result in approximately the same performance in situations where sample sizes are relatively small. The explanation for this observation is by theoretically known verity that an excessive minimization of inexact criteria becomes harmful at times. Comprehensive comparative investigations of six real-world pattern recognition (PR) problems demonstrated that employment of SLP-based pairwise classifiers is comparable and as often as not outperforming the linear support vector (SV) classifiers in moderate dimensional situations. The colored noise injection used to design pseudovalidation sets proves to be a powerful tool for facilitating finite sample problems in moderate-dimensional PR tasks.

Serendipitous Offline Learning in a Neuromorphic Robot.

PubMed

Stewart, Terrence C; Kleinhans, Ashley; Mundy, Andrew; Conradt, Jörg

2016-01-01

We demonstrate a hybrid neuromorphic learning paradigm that learns complex sensorimotor mappings based on a small set of hard-coded reflex behaviors. A mobile robot is first controlled by a basic set of reflexive hand-designed behaviors. All sensor data is provided via a spike-based silicon retina camera (eDVS), and all control is implemented via spiking neurons simulated on neuromorphic hardware (SpiNNaker). Given this control system, the robot is capable of simple obstacle avoidance and random exploration. To train the robot to perform more complex tasks, we observe the robot and find instances where the robot accidentally performs the desired action. Data recorded from the robot during these times is then used to update the neural control system, increasing the likelihood of the robot performing that task in the future, given a similar sensor state. As an example application of this general-purpose method of training, we demonstrate the robot learning to respond to novel sensory stimuli (a mirror) by turning right if it is present at an intersection, and otherwise turning left. In general, this system can learn arbitrary relations between sensory input and motor behavior.

Correcting Evaluation Bias of Relational Classifiers with Network Cross Validation

DTIC Science & Technology

2010-01-01

classi- fication algorithms: simple random resampling (RRS), equal-instance random resampling (ERS), and network cross-validation ( NCV ). The first two... NCV procedure that eliminates overlap between test sets altogether. The procedure samples for k disjoint test sets that will be used for evaluation...propLabeled ∗ S) nodes from train Pool in f erenceSet =network − trainSet F = F ∪ < trainSet, test Set, in f erenceSet > end for output: F NCV addresses

Multiple-point statistical simulation for hydrogeological models: 3-D training image development and conditioning strategies

NASA Astrophysics Data System (ADS)

Høyer, Anne-Sophie; Vignoli, Giulio; Mejer Hansen, Thomas; Thanh Vu, Le; Keefer, Donald A.; Jørgensen, Flemming

2017-12-01

Most studies on the application of geostatistical simulations based on multiple-point statistics (MPS) to hydrogeological modelling focus on relatively fine-scale models and concentrate on the estimation of facies-level structural uncertainty. Much less attention is paid to the use of input data and optimal construction of training images. For instance, even though the training image should capture a set of spatial geological characteristics to guide the simulations, the majority of the research still relies on 2-D or quasi-3-D training images. In the present study, we demonstrate a novel strategy for 3-D MPS modelling characterized by (i) realistic 3-D training images and (ii) an effective workflow for incorporating a diverse group of geological and geophysical data sets. The study covers an area of 2810 km2 in the southern part of Denmark. MPS simulations are performed on a subset of the geological succession (the lower to middle Miocene sediments) which is characterized by relatively uniform structures and dominated by sand and clay. The simulated domain is large and each of the geostatistical realizations contains approximately 45 million voxels with size 100 m × 100 m × 5 m. Data used for the modelling include water well logs, high-resolution seismic data, and a previously published 3-D geological model. We apply a series of different strategies for the simulations based on data quality, and develop a novel method to effectively create observed spatial trends. The training image is constructed as a relatively small 3-D voxel model covering an area of 90 km2. We use an iterative training image development strategy and find that even slight modifications in the training image create significant changes in simulations. Thus, this study shows how to include both the geological environment and the type and quality of input information in order to achieve optimal results from MPS modelling. We present a practical workflow to build the training image and effectively handle different types of input information to perform large-scale geostatistical modelling.

78 FR 41812 - Office of the Secretary of Transportation

Federal Register 2010, 2011, 2012, 2013, 2014

2013-07-11

... portfolio. 3. Provide a minimum of 20 hours of individual or group counseling sessions to small businesses... technical assistance, business training programs, business assessment, management training, counseling... activities such as information dissemination, small business counseling, and technical assistance with small...

Semi-supervised learning for genomic prediction of novel traits with small reference populations: an application to residual feed intake in dairy cattle.

PubMed

Yao, Chen; Zhu, Xiaojin; Weigel, Kent A

2016-11-07

Genomic prediction for novel traits, which can be costly and labor-intensive to measure, is often hampered by low accuracy due to the limited size of the reference population. As an option to improve prediction accuracy, we introduced a semi-supervised learning strategy known as the self-training model, and applied this method to genomic prediction of residual feed intake (RFI) in dairy cattle. We describe a self-training model that is wrapped around a support vector machine (SVM) algorithm, which enables it to use data from animals with and without measured phenotypes. Initially, a SVM model was trained using data from 792 animals with measured RFI phenotypes. Then, the resulting SVM was used to generate self-trained phenotypes for 3000 animals for which RFI measurements were not available. Finally, the SVM model was re-trained using data from up to 3792 animals, including those with measured and self-trained RFI phenotypes. Incorporation of additional animals with self-trained phenotypes enhanced the accuracy of genomic predictions compared to that of predictions that were derived from the subset of animals with measured phenotypes. The optimal ratio of animals with self-trained phenotypes to animals with measured phenotypes (2.5, 2.0, and 1.8) and the maximum increase achieved in prediction accuracy measured as the correlation between predicted and actual RFI phenotypes (5.9, 4.1, and 2.4%) decreased as the size of the initial training set (300, 400, and 500 animals with measured phenotypes) increased. The optimal number of animals with self-trained phenotypes may be smaller when prediction accuracy is measured as the mean squared error rather than the correlation between predicted and actual RFI phenotypes. Our results demonstrate that semi-supervised learning models that incorporate self-trained phenotypes can achieve genomic prediction accuracies that are comparable to those obtained with models using larger training sets that include only animals with measured phenotypes. Semi-supervised learning can be helpful for genomic prediction of novel traits, such as RFI, for which the size of reference population is limited, in particular, when the animals to be predicted and the animals in the reference population originate from the same herd-environment.

Automated Essay Grading using Machine Learning Algorithm

NASA Astrophysics Data System (ADS)

Ramalingam, V. V.; Pandian, A.; Chetry, Prateek; Nigam, Himanshu

2018-04-01

Essays are paramount for of assessing the academic excellence along with linking the different ideas with the ability to recall but are notably time consuming when they are assessed manually. Manual grading takes significant amount of evaluator’s time and hence it is an expensive process. Automated grading if proven effective will not only reduce the time for assessment but comparing it with human scores will also make the score realistic. The project aims to develop an automated essay assessment system by use of machine learning techniques by classifying a corpus of textual entities into small number of discrete categories, corresponding to possible grades. Linear regression technique will be utilized for training the model along with making the use of various other classifications and clustering techniques. We intend to train classifiers on the training set, make it go through the downloaded dataset, and then measure performance our dataset by comparing the obtained values with the dataset values. We have implemented our model using java.

Fine-tuning convolutional deep features for MRI based brain tumor classification

NASA Astrophysics Data System (ADS)

Ahmed, Kaoutar B.; Hall, Lawrence O.; Goldgof, Dmitry B.; Liu, Renhao; Gatenby, Robert A.

2017-03-01

Prediction of survival time from brain tumor magnetic resonance images (MRI) is not commonly performed and would ordinarily be a time consuming process. However, current cross-sectional imaging techniques, particularly MRI, can be used to generate many features that may provide information on the patient's prognosis, including survival. This information can potentially be used to identify individuals who would benefit from more aggressive therapy. Rather than using pre-defined and hand-engineered features as with current radiomics methods, we investigated the use of deep features extracted from pre-trained convolutional neural networks (CNNs) in predicting survival time. We also provide evidence for the power of domain specific fine-tuning in improving the performance of a pre-trained CNN's, even though our data set is small. We fine-tuned a CNN initially trained on a large natural image recognition dataset (Imagenet ILSVRC) and transferred the learned feature representations to the survival time prediction task, obtaining over 81% accuracy in a leave one out cross validation.

Smartphone-Based System for Learning and Inferring Hearing Aid Settings.

PubMed

Aldaz, Gabriel; Puria, Sunil; Leifer, Larry J

2016-10-01

Previous research has shown that hearing aid wearers can successfully self-train their instruments' gain-frequency response and compression parameters in everyday situations. Combining hearing aids with a smartphone introduces additional computing power, memory, and a graphical user interface that may enable greater setting personalization. To explore the benefits of self-training with a smartphone-based hearing system, a parameter space was chosen with four possible combinations of microphone mode (omnidirectional and directional) and noise reduction state (active and off). The baseline for comparison was the "untrained system," that is, the manufacturer's algorithm for automatically selecting microphone mode and noise reduction state based on acoustic environment. The "trained system" first learned each individual's preferences, self-entered via a smartphone in real-world situations, to build a trained model. The system then predicted the optimal setting (among available choices) using an inference engine, which considered the trained model and current context (e.g., sound environment, location, and time). To develop a smartphone-based prototype hearing system that can be trained to learn preferred user settings. Determine whether user study participants showed a preference for trained over untrained system settings. An experimental within-participants study. Participants used a prototype hearing system-comprising two hearing aids, Android smartphone, and body-worn gateway device-for ∼6 weeks. Sixteen adults with mild-to-moderate sensorineural hearing loss (HL) (ten males, six females; mean age = 55.5 yr). Fifteen had ≥6 mo of experience wearing hearing aids, and 14 had previous experience using smartphones. Participants were fitted and instructed to perform daily comparisons of settings ("listening evaluations") through a smartphone-based software application called Hearing Aid Learning and Inference Controller (HALIC). In the four-week-long training phase, HALIC recorded individual listening preferences along with sensor data from the smartphone-including environmental sound classification, sound level, and location-to build trained models. In the subsequent two-week-long validation phase, participants performed blinded listening evaluations comparing settings predicted by the trained system ("trained settings") to those suggested by the hearing aids' untrained system ("untrained settings"). We analyzed data collected on the smartphone and hearing aids during the study. We also obtained audiometric and demographic information. Overall, the 15 participants with valid data significantly preferred trained settings to untrained settings (paired-samples t test). Seven participants had a significant preference for trained settings, while one had a significant preference for untrained settings (binomial test). The remaining seven participants had nonsignificant preferences. Pooling data across participants, the proportion of times that each setting was chosen in a given environmental sound class was on average very similar. However, breaking down the data by participant revealed strong and idiosyncratic individual preferences. Fourteen participants reported positive feelings of clarity, competence, and mastery when training via HALIC. The obtained data, as well as subjective participant feedback, indicate that smartphones could become viable tools to train hearing aids. Individuals who are tech savvy and have milder HL seem well suited to take advantages of the benefits offered by training with a smartphone. American Academy of Audiology

How well does multiple OCR error correction generalize?

NASA Astrophysics Data System (ADS)

Lund, William B.; Ringger, Eric K.; Walker, Daniel D.

2013-12-01

As the digitization of historical documents, such as newspapers, becomes more common, the need of the archive patron for accurate digital text from those documents increases. Building on our earlier work, the contributions of this paper are: 1. in demonstrating the applicability of novel methods for correcting optical character recognition (OCR) on disparate data sets, including a new synthetic training set, 2. enhancing the correction algorithm with novel features, and 3. assessing the data requirements of the correction learning method. First, we correct errors using conditional random fields (CRF) trained on synthetic training data sets in order to demonstrate the applicability of the methodology to unrelated test sets. Second, we show the strength of lexical features from the training sets on two unrelated test sets, yielding a relative reduction in word error rate on the test sets of 6.52%. New features capture the recurrence of hypothesis tokens and yield an additional relative reduction in WER of 2.30%. Further, we show that only 2.0% of the full training corpus of over 500,000 feature cases is needed to achieve correction results comparable to those using the entire training corpus, effectively reducing both the complexity of the training process and the learned correction model.

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].

Domain Adaptation for Alzheimer’s Disease Diagnostics

PubMed Central

Wachinger, Christian; Reuter, Martin

2016-01-01

With the increasing prevalence of Alzheimer’s disease, research focuses on the early computer-aided diagnosis of dementia with the goal to understand the disease process, determine risk and preserving factors, and explore preventive therapies. By now, large amounts of data from multi-site studies have been made available for developing, training, and evaluating automated classifiers. Yet, their translation to the clinic remains challenging, in part due to their limited generalizability across different datasets. In this work, we describe a compact classification approach that mitigates overfitting by regularizing the multinomial regression with the mixed ℓ1/ℓ2 norm. We combine volume, thickness, and anatomical shape features from MRI scans to characterize neuroanatomy for the three-class classification of Alzheimer’s disease, mild cognitive impairment and healthy controls. We demonstrate high classification accuracy via independent evaluation within the scope of the CADDementia challenge. We, furthermore, demonstrate that variations between source and target datasets can substantially influence classification accuracy. The main contribution of this work addresses this problem by proposing an approach for supervised domain adaptation based on instance weighting. Integration of this method into our classifier allows us to assess different strategies for domain adaptation. Our results demonstrate (i) that training on only the target training set yields better results than the naïve combination (union) of source and target training sets, and (ii) that domain adaptation with instance weighting yields the best classification results, especially if only a small training component of the target dataset is available. These insights imply that successful deployment of systems for computer-aided diagnostics to the clinic depends not only on accurate classifiers that avoid overfitting, but also on a dedicated domain adaptation strategy. PMID:27262241

Using statistical and machine learning to help institutions detect suspicious access to electronic health records.

PubMed

Boxwala, Aziz A; Kim, Jihoon; Grillo, Janice M; Ohno-Machado, Lucila

2011-01-01

To determine whether statistical and machine-learning methods, when applied to electronic health record (EHR) access data, could help identify suspicious (ie, potentially inappropriate) access to EHRs. From EHR access logs and other organizational data collected over a 2-month period, the authors extracted 26 features likely to be useful in detecting suspicious accesses. Selected events were marked as either suspicious or appropriate by privacy officers, and served as the gold standard set for model evaluation. The authors trained logistic regression (LR) and support vector machine (SVM) models on 10-fold cross-validation sets of 1291 labeled events. The authors evaluated the sensitivity of final models on an external set of 58 events that were identified as truly inappropriate and investigated independently from this study using standard operating procedures. The area under the receiver operating characteristic curve of the models on the whole data set of 1291 events was 0.91 for LR, and 0.95 for SVM. The sensitivity of the baseline model on this set was 0.8. When the final models were evaluated on the set of 58 investigated events, all of which were determined as truly inappropriate, the sensitivity was 0 for the baseline method, 0.76 for LR, and 0.79 for SVM. The LR and SVM models may not generalize because of interinstitutional differences in organizational structures, applications, and workflows. Nevertheless, our approach for constructing the models using statistical and machine-learning techniques can be generalized. An important limitation is the relatively small sample used for the training set due to the effort required for its construction. The results suggest that statistical and machine-learning methods can play an important role in helping privacy officers detect suspicious accesses to EHRs.

Using statistical and machine learning to help institutions detect suspicious access to electronic health records

PubMed Central

Kim, Jihoon; Grillo, Janice M; Ohno-Machado, Lucila

2011-01-01

Objective To determine whether statistical and machine-learning methods, when applied to electronic health record (EHR) access data, could help identify suspicious (ie, potentially inappropriate) access to EHRs. Methods From EHR access logs and other organizational data collected over a 2-month period, the authors extracted 26 features likely to be useful in detecting suspicious accesses. Selected events were marked as either suspicious or appropriate by privacy officers, and served as the gold standard set for model evaluation. The authors trained logistic regression (LR) and support vector machine (SVM) models on 10-fold cross-validation sets of 1291 labeled events. The authors evaluated the sensitivity of final models on an external set of 58 events that were identified as truly inappropriate and investigated independently from this study using standard operating procedures. Results The area under the receiver operating characteristic curve of the models on the whole data set of 1291 events was 0.91 for LR, and 0.95 for SVM. The sensitivity of the baseline model on this set was 0.8. When the final models were evaluated on the set of 58 investigated events, all of which were determined as truly inappropriate, the sensitivity was 0 for the baseline method, 0.76 for LR, and 0.79 for SVM. Limitations The LR and SVM models may not generalize because of interinstitutional differences in organizational structures, applications, and workflows. Nevertheless, our approach for constructing the models using statistical and machine-learning techniques can be generalized. An important limitation is the relatively small sample used for the training set due to the effort required for its construction. Conclusion The results suggest that statistical and machine-learning methods can play an important role in helping privacy officers detect suspicious accesses to EHRs. PMID:21672912

Dissociable effects of game elements on motivation and cognition in a task-switching training in middle childhood

PubMed Central

Dörrenbächer, Sandra; Müller, Philipp M.; Tröger, Johannes; Kray, Jutta

2014-01-01

Although motivational reinforcers are often used to enhance the attractiveness of trainings of cognitive control in children, little is known about how such motivational manipulations of the setting contribute to separate gains in motivation and cognitive-control performance. Here we provide a framework for systematically investigating the impact of a motivational video-game setting on the training motivation, the task performance, and the transfer success in a task-switching training in middle-aged children (8–11 years of age). We manipulated both the type of training (low-demanding/single-task training vs. high-demanding/task-switching training) as well as the motivational setting (low-motivational/without video-game elements vs. high-motivational/with video-game elements) separately from another. The results indicated that the addition of game elements to a training setting enhanced the intrinsic interest in task practice, independently of the cognitive demands placed by the training type. In the task-switching group, the high-motivational training setting led to an additional enhancement of task and switching performance during the training phase right from the outset. These motivation-induced benefits projected onto the switching performance in a switching situation different from the trained one (near-transfer measurement). However, in structurally dissimilar cognitive tasks (far-transfer measurement), the motivational gains only transferred to the response dynamics (speed of processing). Hence, the motivational setting clearly had a positive impact on the training motivation and on the paradigm-specific task-switching abilities; it did not, however, consistently generalize on broad cognitive processes. These findings shed new light on the conflation of motivation and cognition in childhood and may help to refine guidelines for designing adequate training interventions. PMID:25431564

LVQ and backpropagation neural networks applied to NASA SSME data

NASA Technical Reports Server (NTRS)

Doniere, Timothy F.; Dhawan, Atam P.

1993-01-01

Feedfoward neural networks with backpropagation learning have been used as function approximators for modeling the space shuttle main engine (SSME) sensor signals. The modeling of these sensor signals is aimed at the development of a sensor fault detection system that can be used during ground test firings. The generalization capability of a neural network based function approximator depends on the training vectors which in this application may be derived from a number of SSME ground test-firings. This yields a large number of training vectors. Large training sets can cause the time required to train the network to be very large. Also, the network may not be able to generalize for large training sets. To reduce the size of the training sets, the SSME test-firing data is reduced using the learning vector quantization (LVQ) based technique. Different compression ratios were used to obtain compressed data in training the neural network model. The performance of the neural model trained using reduced sets of training patterns is presented and compared with the performance of the model trained using complete data. The LVQ can also be used as a function approximator. The performance of the LVQ as a function approximator using reduced training sets is presented and compared with the performance of the backpropagation network.

Maximizing lipocalin prediction through balanced and diversified training set and decision fusion.

PubMed

Nath, Abhigyan; Subbiah, Karthikeyan

2015-12-01

Lipocalins are short in sequence length and perform several important biological functions. These proteins are having less than 20% sequence similarity among paralogs. Experimentally identifying them is an expensive and time consuming process. The computational methods based on the sequence similarity for allocating putative members to this family are also far elusive due to the low sequence similarity existing among the members of this family. Consequently, the machine learning methods become a viable alternative for their prediction by using the underlying sequence/structurally derived features as the input. Ideally, any machine learning based prediction method must be trained with all possible variations in the input feature vector (all the sub-class input patterns) to achieve perfect learning. A near perfect learning can be achieved by training the model with diverse types of input instances belonging to the different regions of the entire input space. Furthermore, the prediction performance can be improved through balancing the training set as the imbalanced data sets will tend to produce the prediction bias towards majority class and its sub-classes. This paper is aimed to achieve (i) the high generalization ability without any classification bias through the diversified and balanced training sets as well as (ii) enhanced the prediction accuracy by combining the results of individual classifiers with an appropriate fusion scheme. Instead of creating the training set randomly, we have first used the unsupervised Kmeans clustering algorithm to create diversified clusters of input patterns and created the diversified and balanced training set by selecting an equal number of patterns from each of these clusters. Finally, probability based classifier fusion scheme was applied on boosted random forest algorithm (which produced greater sensitivity) and K nearest neighbour algorithm (which produced greater specificity) to achieve the enhanced predictive performance than that of individual base classifiers. The performance of the learned models trained on Kmeans preprocessed training set is far better than the randomly generated training sets. The proposed method achieved a sensitivity of 90.6%, specificity of 91.4% and accuracy of 91.0% on the first test set and sensitivity of 92.9%, specificity of 96.2% and accuracy of 94.7% on the second blind test set. These results have established that diversifying training set improves the performance of predictive models through superior generalization ability and balancing the training set improves prediction accuracy. For smaller data sets, unsupervised Kmeans based sampling can be an effective technique to increase generalization than that of the usual random splitting method. Copyright © 2015 Elsevier Ltd. All rights reserved.

Global Maps of Temporal Streamflow Characteristics Based on Observations from Many Small Catchments

NASA Astrophysics Data System (ADS)

Beck, H.; van Dijk, A.; de Roo, A.

2014-12-01

Streamflow (Q) estimation in ungauged catchments is one of the greatest challenges facing hydrologists. We used observed Q from approximately 7500 small catchments (<10,000 km2) around the globe to train neural network ensembles to estimate temporal Q distribution characteristics from climate and physiographic characteristics of the catchments. In total 17 Q characteristics were selected, including mean annual Q, baseflow index, and a number of flow percentiles. Training coefficients of determination for the estimation of the Q characteristics ranged from 0.56 for the baseflow recession constant to 0.93 for the Q timing. Overall, climate indices dominated among the predictors. Predictors related to soils and geology were the least important, perhaps due to data quality. The trained neural network ensembles were subsequently applied spatially over the ice-free land surface including ungauged regions, resulting in global maps of the Q characteristics (0.125° spatial resolution). These maps possess several unique features: 1) they represent purely observation-driven estimates; 2) are based on an unprecedentedly large set of catchments; and 3) have associated uncertainty estimates. The maps can be used for various hydrological applications, including the diagnosis of macro-scale hydrological models. To demonstrate this, the produced maps were compared to equivalent maps derived from the simulated daily Q of five macro-scale hydrological models, highlighting various opportunities for improvement in model Q behavior. The produced dataset is available for download.

UArizona at the CLEF eRisk 2017 Pilot Task: Linear and Recurrent Models for Early Depression Detection

PubMed Central

Sadeque, Farig; Xu, Dongfang; Bethard, Steven

2017-01-01

The 2017 CLEF eRisk pilot task focuses on automatically detecting depression as early as possible from a users’ posts to Reddit. In this paper we present the techniques employed for the University of Arizona team’s participation in this early risk detection shared task. We leveraged external information beyond the small training set, including a preexisting depression lexicon and concepts from the Unified Medical Language System as features. For prediction, we used both sequential (recurrent neural network) and non-sequential (support vector machine) models. Our models perform decently on the test data, and the recurrent neural models perform better than the non-sequential support vector machines while using the same feature sets. PMID:29075167

Wind tunnel analysis of the aerodynamic loads on rolling stock over railway embankments: the effect of shelter windbreaks.

PubMed

Avila-Sanchez, Sergio; Pindado, Santiago; Lopez-Garcia, Oscar; Sanz-Andres, Angel

2014-01-01

Wind-flow pattern over embankments involves an overexposure of the rolling stock travelling on them to wind loads. Windbreaks are a common solution for changing the flow characteristic in order to decrease unwanted effects induced by the presence of cross-wind. The shelter effectiveness of a set of windbreaks placed over a railway twin-track embankment is experimentally analysed. A set of two-dimensional wind tunnel tests are undertaken and results corresponding to pressure tap measurements over a section of a typical high-speed train are herein presented. The results indicate that even small-height windbreaks provide sheltering effects to the vehicles. Also, eaves located at the windbreak tips seem to improve their sheltering effect.

Wind Tunnel Analysis of the Aerodynamic Loads on Rolling Stock over Railway Embankments: The Effect of Shelter Windbreaks

PubMed Central

Avila-Sanchez, Sergio; Lopez-Garcia, Oscar; Sanz-Andres, Angel

2014-01-01

Wind-flow pattern over embankments involves an overexposure of the rolling stock travelling on them to wind loads. Windbreaks are a common solution for changing the flow characteristic in order to decrease unwanted effects induced by the presence of cross-wind. The shelter effectiveness of a set of windbreaks placed over a railway twin-track embankment is experimentally analysed. A set of two-dimensional wind tunnel tests are undertaken and results corresponding to pressure tap measurements over a section of a typical high-speed train are herein presented. The results indicate that even small-height windbreaks provide sheltering effects to the vehicles. Also, eaves located at the windbreak tips seem to improve their sheltering effect. PMID:25544954

Use of Chemical Pesticides in Ethiopia: A Cross-Sectional Comparative Study on Knowledge, Attitude and Practice of Farmers and Farm Workers in Three Farming Systems.

PubMed

Negatu, Beyene; Kromhout, Hans; Mekonnen, Yalemtshay; Vermeulen, Roel

2016-06-01

Chemical pesticides, regardless of their inherent hazard, are used intensively in the fast changing agricultural sector of Ethiopia. We conducted a cross-sectional pesticide Knowledge, Attitude and Practice (KAP) survey among 601 farmers and farm workers (applicators and re-entry workers) in three farming systems [large-scale closed greenhouses (LSGH), large-scale open farms (LSOF), and small-scale irrigated farms (SSIF)]. Main observations were that 85% of workers did not attain any pesticide-related training, 81% were not aware of modern alternatives for chemical pesticides, 10% used a full set of personal protective equipment, and 62% did not usually bath or shower after work. Among applicators pesticide training attendance was highest in LSGH (35%) and was lowest in SSIF (4%). None of the female re-entry farm workers had received pesticide-related training. Personal protective equipment use was twice as high among pesticide applicators as among re-entry workers (13 versus 7%), while none of the small-scale farm workers used personal protection equipment. Stockpiling and burial of empty pesticide containers and discarding empty pesticide containers in farming fields were reported in both LSOF and by 75% of the farm workers in SSIF. Considerable increment in chemical pesticide usage intensity, illegitimate usages of DDT and Endosulfan on food crops and direct import of pesticides without the formal Ethiopian registration process were also indicated. These results point out a general lack of training and knowledge regarding the safe use of pesticides in all farming systems but especially among small-scale farmers. This in combination with the increase in chemical pesticide usage in the past decade likely results in occupational and environmental health risks. Improved KAP that account for institutional difference among various farming systems and enforcement of regulatory measures including the available occupational and environmental proclamations in Ethiopia are urgently needed. © The Author 2016. Published by Oxford University Press on behalf of the British Occupational Hygiene Society.

The UXO Classification Demonstration at San Luis Obispo, CA

DTIC Science & Technology

2010-09-01

Set ................................45  2.17.2  Active Learning Training and Test Set ..........................................47  2.17.3  Extended...optimized algorithm by applying it to only the unlabeled data in the test set. 2.17.2 Active Learning Training and Test Set SIG also used active ... learning [12]. Active learning , an alternative approach for constructing a training set, is used in conjunction with either supervised or semi

Does rational selection of training and test sets improve the outcome of QSAR modeling?

PubMed

Martin, Todd M; Harten, Paul; Young, Douglas M; Muratov, Eugene N; Golbraikh, Alexander; Zhu, Hao; Tropsha, Alexander

2012-10-22

Prior to using a quantitative structure activity relationship (QSAR) model for external predictions, its predictive power should be established and validated. In the absence of a true external data set, the best way to validate the predictive ability of a model is to perform its statistical external validation. In statistical external validation, the overall data set is divided into training and test sets. Commonly, this splitting is performed using random division. Rational splitting methods can divide data sets into training and test sets in an intelligent fashion. The purpose of this study was to determine whether rational division methods lead to more predictive models compared to random division. A special data splitting procedure was used to facilitate the comparison between random and rational division methods. For each toxicity end point, the overall data set was divided into a modeling set (80% of the overall set) and an external evaluation set (20% of the overall set) using random division. The modeling set was then subdivided into a training set (80% of the modeling set) and a test set (20% of the modeling set) using rational division methods and by using random division. The Kennard-Stone, minimal test set dissimilarity, and sphere exclusion algorithms were used as the rational division methods. The hierarchical clustering, random forest, and k-nearest neighbor (kNN) methods were used to develop QSAR models based on the training sets. For kNN QSAR, multiple training and test sets were generated, and multiple QSAR models were built. The results of this study indicate that models based on rational division methods generate better statistical results for the test sets than models based on random division, but the predictive power of both types of models are comparable.

Teaching Health Center Graduate Medical Education Locations Predominantly Located in Federally Designated Underserved Areas.

PubMed

Barclift, Songhai C; Brown, Elizabeth J; Finnegan, Sean C; Cohen, Elena R; Klink, Kathleen

2016-05-01

Background The Teaching Health Center Graduate Medical Education (THCGME) program is an Affordable Care Act funding initiative designed to expand primary care residency training in community-based ambulatory settings. Statute suggests, but does not require, training in underserved settings. Residents who train in underserved settings are more likely to go on to practice in similar settings, and graduates more often than not practice near where they have trained. Objective The objective of this study was to describe and quantify federally designated clinical continuity training sites of the THCGME program. Methods Geographic locations of the training sites were collected and characterized as Health Professional Shortage Area, Medically Underserved Area, Population, or rural areas, and were compared with the distribution of Centers for Medicare and Medicaid Services (CMS)-funded training positions. Results More than half of the teaching health centers (57%) are located in states that are in the 4 quintiles with the lowest CMS-funded resident-to-population ratio. Of the 109 training sites identified, more than 70% are located in federally designated high-need areas. Conclusions The THCGME program is a model that funds residency training in community-based ambulatory settings. Statute suggests, but does not explicitly require, that training take place in underserved settings. Because the majority of the 109 clinical training sites of the 60 funded programs in 2014-2015 are located in federally designated underserved locations, the THCGME program deserves further study as a model to improve primary care distribution into high-need communities.

76 FR 12395 - Small Business Jobs Act Implementation

Federal Register 2010, 2011, 2012, 2013, 2014

2011-03-07

... 1, Continued. Room 2 Lender Roundtable, Continued. Room 3 Investing in Counseling and Training... Counseling and Training Services to Support the Growth of Small Business. Please note that the SBA will also... business contracting. Expanding Resources for Counseling and Training SBA has at least one District Office...

Using Local Resources for Small-Business Training.

ERIC Educational Resources Information Center

Kelly, Leslie; Thompson, Phillip L.

1988-01-01

A training program for members of the Indianapolis Chamber of Commerce Training Advisory Committee, which is 80 percent small business, incorporated the following features: (1) four hours in the morning; (2) Friday morning programs; (3) $35 per program; (4) series; and (5) low fees to trainers. (JOW)

Simulation improves procedural protocol adherence during central venous catheter placement: a randomized-controlled trial

PubMed Central

Peltan, Ithan D.; Shiga, Takashi; Gordon, James A.; Currier, Paul F.

2015-01-01

Background Simulation training may improve proficiency at and reduces complications from central venous catheter (CVC) placement, but the scope of simulation’s effect remains unclear. This randomized controlled trial evaluated the effects of a pragmatic CVC simulation program on procedural protocol adherence, technical skill, and patient outcomes. Methods Internal medicine interns were randomized to standard training for CVC insertion or standard training plus simulation-based mastery training. Standard training involved a lecture, a video-based online module, and instruction by the supervising physician during actual CVC insertions. Intervention-group subjects additionally underwent supervised training on a venous access simulator until they demonstrated procedural competence. Raters evaluated interns’ performance during internal jugular CVC placement on actual patients in the medical intensive care unit. Generalized estimating equations were used to account for outcome clustering within trainees. Results We observed 52 interns place 87 CVCs. Simulation-trained interns exhibited better adherence to prescribed procedural technique than interns who received only standard training (p=0.024). There were no significant differences detected in first-attempt or overall cannulation success rates, mean needle passes, global assessment scores or complication rates. Conclusions Simulation training added to standard training improved protocol adherence during CVC insertion by novice practitioners. This study may have been too small to detect meaningful differences in venous cannulation proficiency and other clinical outcomes, highlighting the difficulty of patient-centered simulation research in settings where poor outcomes are rare. For high-performing systems, where protocol deviations may provide an important proxy for rare procedural complications, simulation may improve CVC insertion quality and safety. PMID:26154250

Cost-effectiveness of essential newborn care training in urban first-level facilities.

PubMed

Manasyan, Albert; Chomba, Elwyn; McClure, Elizabeth M; Wright, Linda L; Krzywanski, Sara; Carlo, Waldemar A

2011-05-01

To determine the cost-effectiveness of the World Health Organization (WHO) Essential Newborn Care (ENC) training of health care providers in first-level facilities in the 2 largest cities in Zambia. Data were extracted from a study in which the effectiveness of the ENC training was evaluated (including universal precautions and cleanliness, routine neonatal care, resuscitation, thermoregulation, breastfeeding, skin-to-skin care, care of the small infant, danger signs, and common illnesses). The costs to train an ENC instructor for each first-level delivery facility and the costs of salary/benefits for 2 coordinators responsible for maintenance of the program were recorded in 2005 US dollars. The incremental costs per life gained and per disability-adjusted life-year averted were calculated. A 5-day ENC training-of-trainers was conducted in Lusaka, Zambia, to certify 18 college-trained midwives as ENC instructors. The instructors trained all clinic midwives working in their first-level facilities as part of a before-and-after study of the effect of ENC training on early neonatal mortality conducted from Oct 2004 to Nov 2006. All-cause 7-day (early) neonatal mortality decreased from 11.5 per 1000 to 6.8 per 1000 live births after ENC training of the clinic midwives (relative risk: 0.59; 95% confidence interval: 0.48-0.77; P < .001; 40 615 births). The intervention costs were $208 per life saved and $5.24 per disability-adjusted life-year averted. ENC training of clinic midwives who provide care in low-risk facilities is a low-cost intervention that can reduce early neonatal mortality in these settings.

Downlink Training Techniques for FDD Massive MIMO Systems: Open-Loop and Closed-Loop Training With Memory

NASA Astrophysics Data System (ADS)

Choi, Junil; Love, David J.; Bidigare, Patrick

2014-10-01

The concept of deploying a large number of antennas at the base station, often called massive multiple-input multiple-output (MIMO), has drawn considerable interest because of its potential ability to revolutionize current wireless communication systems. Most literature on massive MIMO systems assumes time division duplexing (TDD), although frequency division duplexing (FDD) dominates current cellular systems. Due to the large number of transmit antennas at the base station, currently standardized approaches would require a large percentage of the precious downlink and uplink resources in FDD massive MIMO be used for training signal transmissions and channel state information (CSI) feedback. To reduce the overhead of the downlink training phase, we propose practical open-loop and closed-loop training frameworks in this paper. We assume the base station and the user share a common set of training signals in advance. In open-loop training, the base station transmits training signals in a round-robin manner, and the user successively estimates the current channel using long-term channel statistics such as temporal and spatial correlations and previous channel estimates. In closed-loop training, the user feeds back the best training signal to be sent in the future based on channel prediction and the previously received training signals. With a small amount of feedback from the user to the base station, closed-loop training offers better performance in the data communication phase, especially when the signal-to-noise ratio is low, the number of transmit antennas is large, or prior channel estimates are not accurate at the beginning of the communication setup, all of which would be mostly beneficial for massive MIMO systems.

Creating Opportunities: Good Practice in Small Business Training for Australian Rural Women.

ERIC Educational Resources Information Center

Simpson, Lyn; Daws, Leonie; Wood, Leanne

2002-01-01

To overcome barriers to participation in small business training faced by rural Australian women, training needs and delivery issues were identified and a good practice matrix was developed with the following components: marketing, content, delivery, support, impact, and innovation. Underlying principles included unique needs, diversity, use of…

Detection of explosive cough events in audio recordings by internal sound analysis.

PubMed

Rocha, B M; Mendes, L; Couceiro, R; Henriques, J; Carvalho, P; Paiva, R P

2017-07-01

We present a new method for the discrimination of explosive cough events, which is based on a combination of spectral content descriptors and pitch-related features. After the removal of near-silent segments, a vector of event boundaries is obtained and a proposed set of 9 features is extracted for each event. Two data sets, recorded using electronic stethoscopes and comprising a total of 46 healthy subjects and 13 patients, were employed to evaluate the method. The proposed feature set is compared to three other sets of descriptors: a baseline, a combination of both sets, and an automatic selection of the best 10 features from both sets. The combined feature set yields good results on the cross-validated database, attaining a sensitivity of 92.3±2.3% and a specificity of 84.7±3.3%. Besides, this feature set seems to generalize well when it is trained on a small data set of patients, with a variety of respiratory and cardiovascular diseases, and tested on a bigger data set of mostly healthy subjects: a sensitivity of 93.4% and a specificity of 83.4% are achieved in those conditions. These results demonstrate that complementing the proposed feature set with a baseline set is a promising approach.

Semi-supervised morphosyntactic classification of Old Icelandic.

PubMed

Urban, Kryztof; Tangherlini, Timothy R; Vijūnas, Aurelijus; Broadwell, Peter M

2014-01-01

We present IceMorph, a semi-supervised morphosyntactic analyzer of Old Icelandic. In addition to machine-read corpora and dictionaries, it applies a small set of declension prototypes to map corpus words to dictionary entries. A web-based GUI allows expert users to modify and augment data through an online process. A machine learning module incorporates prototype data, edit-distance metrics, and expert feedback to continuously update part-of-speech and morphosyntactic classification. An advantage of the analyzer is its ability to achieve competitive classification accuracy with minimum training data.

49 CFR 232.213 - Extended haul trains.

Code of Federal Regulations, 2010 CFR

2010-10-01

..., DEPARTMENT OF TRANSPORTATION BRAKE SYSTEM SAFETY STANDARDS FOR FREIGHT AND OTHER NON-PASSENGER TRAINS AND... extended haul trains will originate and a description of the trains that will be operated as extended haul.... (5) The train shall have no more than one pick-up and one set-out en route, except for the set-out of...

75 FR 42181 - Notice of Funding Availability for the Small Business Transportation Resource Center Program

Federal Register 2010, 2011, 2012, 2013, 2014

2010-07-20

... of 20 hours of individual or group counseling sessions to small businesses per month. (D) Planning... training programs, such as, business assessment, management training, counseling, technical assistance... business counseling, and technical assistance with small businesses currently doing business with public...

76 FR 30990 - Notice of Funding Availability for the Small Business Transportation Resource Center Program

Federal Register 2010, 2011, 2012, 2013, 2014

2011-05-27

... contracts and subcontracts portfolio. 3. Provide a minimum of 20 hours of individual or group counseling... training programs, such as, business assessment, management training, counseling, technical assistance... information dissemination, small business counseling, and technical assistance with small businesses currently...

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.

Child health in low-resource settings: pathways through UK paediatric training.

PubMed

Goenka, Anu; Magnus, Dan; Rehman, Tanya; Williams, Bhanu; Long, Andrew; Allen, Steve J

2013-11-01

UK doctors training in paediatrics benefit from experience of child health in low-resource settings. Institutions in low-resource settings reciprocally benefit from hosting UK trainees. A wide variety of opportunities exist for trainees working in low-resource settings including clinical work, research and the development of transferable skills in management, education and training. This article explores a range of pathways for UK trainees to develop experience in low-resource settings. It is important for trainees to start planning a robust rationale early for global child health activities via established pathways, in the interests of their own professional development as well as UK service provision. In the future, run-through paediatric training may include core elements of global child health, as well as designated 'tracks' for those wishing to develop their career in global child health further. Hands-on experience in low-resource settings is a critical component of these training initiatives.

Relearning and Retaining Personally-Relevant Words using Computer-Based Flashcard Software in Primary Progressive Aphasia.

PubMed

Evans, William S; Quimby, Megan; Dickey, Michael Walsh; Dickerson, Bradford C

2016-01-01

Although anomia treatments have often focused on training small sets of words in the hopes of promoting generalization to untrained items, an alternative is to directly train a larger set of words more efficiently. The current case study reports on a novel treatment for a patient with semantic variant Primary Progressive Aphasia (svPPA), in which the patient was taught to make and practice flashcards for personally-relevant words using an open-source computer program (Anki). Results show that the patient was able to relearn and retain a large subset of her studied words for up to 20 months, the full duration of the study period. At the end of treatment, she showed good retention for 139 words. While only a subset of the 591 studied overall, this is still far more words than is typically targeted in svPPA interventions. Furthermore, she showed evidence of generalization to perceptually distinct stimuli during confrontation naming and temporary gains in semantic fluency, suggesting limited gains in semantic knowledge as a result of training. This case represents a successful example of patient-centered treatment, where the patient used a computer-based intervention independently at home. It also illustrates how data captured from computer-based treatments during routine clinical care can provide valuable "practice-based evidence" for motivating further treatment research.

Relearning and Retaining Personally-Relevant Words using Computer-Based Flashcard Software in Primary Progressive Aphasia

PubMed Central

Evans, William S.; Quimby, Megan; Dickey, Michael Walsh; Dickerson, Bradford C.

2016-01-01

Although anomia treatments have often focused on training small sets of words in the hopes of promoting generalization to untrained items, an alternative is to directly train a larger set of words more efficiently. The current case study reports on a novel treatment for a patient with semantic variant Primary Progressive Aphasia (svPPA), in which the patient was taught to make and practice flashcards for personally-relevant words using an open-source computer program (Anki). Results show that the patient was able to relearn and retain a large subset of her studied words for up to 20 months, the full duration of the study period. At the end of treatment, she showed good retention for 139 words. While only a subset of the 591 studied overall, this is still far more words than is typically targeted in svPPA interventions. Furthermore, she showed evidence of generalization to perceptually distinct stimuli during confrontation naming and temporary gains in semantic fluency, suggesting limited gains in semantic knowledge as a result of training. This case represents a successful example of patient-centered treatment, where the patient used a computer-based intervention independently at home. It also illustrates how data captured from computer-based treatments during routine clinical care can provide valuable “practice-based evidence” for motivating further treatment research. PMID:27899886

Use of qualitative and quantitative information in neural networks for assessing agricultural chemical contamination of domestic wells

USGS Publications Warehouse

Mishra, A.; Ray, C.; Kolpin, D.W.

2004-01-01

A neural network analysis of agrichemical occurrence in groundwater was conducted using data from a pilot study of 192 small-diameter drilled and driven wells and 115 dug and bored wells in Illinois, a regional reconnaissance network of 303 wells across 12 Midwestern states, and a study of 687 domestic wells across Iowa. Potential factors contributing to well contamination (e.g., depth to aquifer material, well depth, and distance to cropland) were investigated. These contributing factors were available in either numeric (actual or categorical) or descriptive (yes or no) format. A method was devised to use the numeric and descriptive values simultaneously. Training of the network was conducted using a standard backpropagation algorithm. Approximately 15% of the data was used for testing. Analysis indicated that training error was quite low for most data. Testing results indicated that it was possible to predict the contamination potential of a well with pesticides. However, predicting the actual level of contamination was more difficult. For pesticide occurrence in drilled and driven wells, the network predictions were good. The performance of the network was poorer for predicting nitrate occurrence in dug and bored wells. Although the data set for Iowa was large, the prediction ability of the trained network was poor, due to descriptive or categorical input parameters, compared with smaller data sets such as that for Illinois, which contained more numeric information.

Gene function prediction based on the Gene Ontology hierarchical structure.

PubMed

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

2014-01-01

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

Online learning from input versus offline memory evolution in adult word learning: effects of neighborhood density and phonologically related practice.

PubMed

Storkel, Holly L; Bontempo, Daniel E; Pak, Natalie S

2014-10-01

In this study, the authors investigated adult word learning to determine how neighborhood density and practice across phonologically related training sets influence online learning from input during training versus offline memory evolution during no-training gaps. Sixty-one adults were randomly assigned to learn low- or high-density nonwords. Within each density condition, participants were trained on one set of words and then were trained on a second set of words, consisting of phonological neighbors of the first set. Learning was measured in a picture-naming test. Data were analyzed using multilevel modeling and spline regression. Steep learning during input was observed, with new words from dense neighborhoods and new words that were neighbors of recently learned words (i.e., second-set words) being learned better than other words. In terms of memory evolution, large and significant forgetting was observed during 1-week gaps in training. Effects of density and practice during memory evolution were opposite of those during input. Specifically, forgetting was greater for high-density and second-set words than for low-density and first-set words. High phonological similarity, regardless of source (i.e., known words or recent training), appears to facilitate online learning from input but seems to impede offline memory evolution.

The problems of professional training of practice-oriented specialists for small enterprises of footwear and leather production industries in Uzbekistan

NASA Astrophysics Data System (ADS)

Ilkhamova, M. U.; Gafurov, J. K.; Maksudova, U. M.; Vassiliadis, S.

2017-10-01

At the present, the State authorities of the Republic of Uzbekistan pay special attention to the development of small and medium businesses and, in particular, to the enterprises oriented on manufacturing products with high added value. The leather and footwear industry of Uzbekistan is one of the dynamically developing sectors of economy. However, the study of the situation demonstrates that the increase in number of small and medium footwear and leather enterprises that have taken place in recent years, is not accompanied by a formation of corresponding professional training system for the enterprises, especially for associate specialists. The analysis of the legal base disclosed that the professional training level in footwear industry enterprises does not meet the up-to-date manufacturing requirements. The study is devoted to the issues of professional training of practice-oriented staff - the specialists for small enterprises of footwear and leather industry. The main task is the development of new vocational courses and programs for the training and professional development of personnel at all levels. The basic stages of complete staff training cycle for footwear sector have been determined based on the practical experience of staff training for small footwear enterprises in Greece. The 3-6 months duration short-term courses recommended for associate and medium level specialists have been developed and evaluated.

Machine Tool Technology. Automatic Screw Machine Troubleshooting & Set-Up Training Outlines [and] Basic Operator's Skills Set List.

ERIC Educational Resources Information Center

Anoka-Hennepin Technical Coll., Minneapolis, MN.

This set of two training outlines and one basic skills set list are designed for a machine tool technology program developed during a project to retrain defense industry workers at risk of job loss or dislocation because of conversion of the defense industry. The first troubleshooting training outline lists the categories of problems that develop…

Three learning phases for radial-basis-function networks.

PubMed

Schwenker, F; Kestler, H A; Palm, G

2001-05-01

In this paper, learning algorithms for radial basis function (RBF) networks are discussed. Whereas multilayer perceptrons (MLP) are typically trained with backpropagation algorithms, starting the training procedure with a random initialization of the MLP's parameters, an RBF network may be trained in many different ways. We categorize these RBF training methods into one-, two-, and three-phase learning schemes. Two-phase RBF learning is a very common learning scheme. The two layers of an RBF network are learnt separately; first the RBF layer is trained, including the adaptation of centers and scaling parameters, and then the weights of the output layer are adapted. RBF centers may be trained by clustering, vector quantization and classification tree algorithms, and the output layer by supervised learning (through gradient descent or pseudo inverse solution). Results from numerical experiments of RBF classifiers trained by two-phase learning are presented in three completely different pattern recognition applications: (a) the classification of 3D visual objects; (b) the recognition hand-written digits (2D objects); and (c) the categorization of high-resolution electrocardiograms given as a time series (ID objects) and as a set of features extracted from these time series. In these applications, it can be observed that the performance of RBF classifiers trained with two-phase learning can be improved through a third backpropagation-like training phase of the RBF network, adapting the whole set of parameters (RBF centers, scaling parameters, and output layer weights) simultaneously. This, we call three-phase learning in RBF networks. A practical advantage of two- and three-phase learning in RBF networks is the possibility to use unlabeled training data for the first training phase. Support vector (SV) learning in RBF networks is a different learning approach. SV learning can be considered, in this context of learning, as a special type of one-phase learning, where only the output layer weights of the RBF network are calculated, and the RBF centers are restricted to be a subset of the training data. Numerical experiments with several classifier schemes including k-nearest-neighbor, learning vector quantization and RBF classifiers trained through two-phase, three-phase and support vector learning are given. The performance of the RBF classifiers trained through SV learning and three-phase learning are superior to the results of two-phase learning, but SV learning often leads to complex network structures, since the number of support vectors is not a small fraction of the total number of data points.

Which peer teaching methods do medical students prefer?

PubMed

Jayakumar, Nithish; Srirathan, Danushan; Shah, Rishita; Jakubowska, Agnieszka; Clarke, Andrew; Annan, David; Albasha, Dekan

2016-01-01

The beneficial effects of peer teaching in medical education have been well-described in the literature. However, it is unclear whether students prefer to be taught by peers in small or large group settings. This study's aim was to identify differences in medical students' preferences and perceptions of small-group versus large-group peer teaching. Questionnaires were administered to medical students in Year 3 and Year 4 (first 2 years of clinical training) at one institution in the United Kingdom to identify their experiences and perceptions of small-and large-group peer teaching. For this study, small-group peer teaching was defined as a tutorial, or similar, taught by peer tutor to a group of 5 students or less. Large-group peer teaching was defined as a lecture, or similar, taught by peer tutors to a group of more than 20 students. Seventy-three students (81% response rate) completed the questionnaires (54% males; median age of 23). Nearly 55% of respondents reported prior exposure to small-group peer teaching but a larger proportion of respondents (86%) had previously attended large-group peer teaching. Of all valid responses, 49% did not have a preference of peer teaching method while 47% preferred small-group peer teaching. The majority of Year 3 students preferred small-group peer teaching to no preference (62.5% vs 37.5%, Fisher's exact test; P = 0.035) whereas most Year 4 students did not report a particular preference. Likert-scale responses showed that the majority of students held negative perceptions about large-group peer teaching, in comparison with small-group peer teaching, with respect to (1) interactivity, (2) a comfortable environment to ask questions, and (3) feedback received. Most respondents in this study did not report a preference for small-versus large-group settings when taught by peers. More Year 3 respondents were likely to prefer small-group peer teaching as opposed to Year 4 respondents.

Training the elderly in pedestrian safety: Transfer effect between two virtual reality simulation devices.

PubMed

Maillot, Pauline; Dommes, Aurélie; Dang, Nguyen-Thong; Vienne, Fabrice

2017-02-01

A virtual-reality training program has been developed to help older pedestrians make safer street-crossing decisions in two-way traffic situations. The aim was to develop a small-scale affordable and transportable simulation device that allowed transferring effects to a full-scale device involving actual walking. 20 younger adults and 40 older participants first participated in a pre-test phase to assess their street crossings using both full-scale and small-scale simulation devices. Then, a trained older group (20 participants) completed two 1.5-h training sessions with the small-scale device, whereas an older control group received no training (19 participants). Thereafter, the 39 older trained and untrained participants took part in a 1.5-h post-test phase again with both devices. Pre-test phase results suggested significant differences between both devices in the group of older participants only. Unlike younger participants, older participants accepted more often to cross and had more collisions on the small-scale simulation device than on the full-scale one. Post-test phase results showed that training older participants on the small-scale device allowed a significant global decrease in the percentage of accepted crossings and collisions on both simulation devices. But specific improvements regarding the way participants took into account the speed of approaching cars and vehicles in the far lane were notable only on the full-scale simulation device. The findings suggest that the small-scale simulation device triggers a greater number of unsafe decisions compared to a full-scale one that allows actual crossings. But findings reveal that such a small-scale simulation device could be a good means to improve the safety of street-crossing decisions and behaviors among older pedestrians, suggesting a transfer of learning effect between the two simulation devices, from training people with a miniature device to measuring their specific progress with a full-scale one. Copyright © 2016 Elsevier Ltd. All rights reserved.

Recognition Using Hybrid Classifiers.

PubMed

Osadchy, Margarita; Keren, Daniel; Raviv, Dolev

2016-04-01

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

Rocket University at KSC

NASA Technical Reports Server (NTRS)

Sullivan, Steven J.

2014-01-01

"Rocket University" is an exciting new initiative at Kennedy Space Center led by NASA's Engineering and Technology Directorate. This hands-on experience has been established to develop, refine & maintain targeted flight engineering skills to enable the Agency and KSC strategic goals. Through "RocketU", KSC is developing a nimble, rapid flight engineering life cycle systems knowledge base. Ongoing activities in RocketU develop and test new technologies and potential customer systems through small scale vehicles, build and maintain flight experience through balloon and small-scale rocket missions, and enable a revolving fresh perspective of engineers with hands on expertise back into the large scale NASA programs, providing a more experienced multi-disciplined set of systems engineers. This overview will define the Program, highlight aspects of the training curriculum, and identify recent accomplishments and activities.

Effect of Sling Exercise Training on Balance in Patients with Stroke: A Meta-Analysis

PubMed Central

Peng, Qiyuan; Chen, Jingjie; Zou, Yucong; Liu, Gang

2016-01-01

Objective This study aims to evaluate the effect of sling exercise training (SET) on balance in patients with stroke. Methods PubMed, Cochrane Library, Ovid LWW, CBM, CNKI, WanFang, and VIP databases were searched for randomized controlled trials of the effect of SET on balance in patients with stroke. The study design and participants were subjected to metrological analysis. Berg balance Scale (BBS), Barthel index score (BI), and Fugl-Meyer Assessment (FMA) were used as independent parameters for evaluating balance function, activities of daily living(ADL) and motor function after stroke respectively, and were subjected to meta-analysis by RevMan5.3 software. Results Nine studies with 460 participants were analyzed. Results of meta-analysis showed that the SET treatment combined with conventional rehabilitation was superior to conventional rehabilitation treatments, with increased degrees of BBS (WMD = 3.81, 95% CI [0.15, 7.48], P = 0.04), BI (WMD = 12.98, 95% CI [8.39, 17.56], P < 0.00001), and FMA (SMD = 0.76, 95% CI [0.41, 1.11], P < 0.0001). Conclusion Based on limited evidence from 9 trials, the SET treatment combined with conventional rehabilitation was superior to conventional rehabilitation treatments, with increased degrees of BBS, BI and FMA, So the SET treatment can improvement of balance function after stroke, but the interpretation of our findings is required to be made with caution due to limitations in included trials such as small sample sizes and the risk of bias. Therefore, more multi-center and large-sampled randomized controlled trials are needed to confirm its clinical applications. PMID:27727288

Smartphone-Based System for Learning and Inferring Hearing Aid Settings

PubMed Central

Aldaz, Gabriel; Puria, Sunil; Leifer, Larry J.

2017-01-01

Background Previous research has shown that hearing aid wearers can successfully self-train their instruments’ gain-frequency response and compression parameters in everyday situations. Combining hearing aids with a smartphone introduces additional computing power, memory, and a graphical user interface that may enable greater setting personalization. To explore the benefits of self-training with a smartphone-based hearing system, a parameter space was chosen with four possible combinations of microphone mode (omnidirectional and directional) and noise reduction state (active and off). The baseline for comparison was the “untrained system,” that is, the manufacturer’s algorithm for automatically selecting microphone mode and noise reduction state based on acoustic environment. The “trained system” first learned each individual’s preferences, self-entered via a smartphone in real-world situations, to build a trained model. The system then predicted the optimal setting (among available choices) using an inference engine, which considered the trained model and current context (e.g., sound environment, location, and time). Purpose To develop a smartphone-based prototype hearing system that can be trained to learn preferred user settings. Determine whether user study participants showed a preference for trained over untrained system settings. Research Design An experimental within-participants study. Participants used a prototype hearing system—comprising two hearing aids, Android smartphone, and body-worn gateway device—for ~6 weeks. Study Sample Sixteen adults with mild-to-moderate sensorineural hearing loss (HL) (ten males, six females; mean age = 55.5 yr). Fifteen had ≥6 mo of experience wearing hearing aids, and 14 had previous experience using smartphones. Intervention Participants were fitted and instructed to perform daily comparisons of settings (“listening evaluations”) through a smartphone-based software application called Hearing Aid Learning and Inference Controller (HALIC). In the four-week-long training phase, HALIC recorded individual listening preferences along with sensor data from the smartphone—including environmental sound classification, sound level, and location—to build trained models. In the subsequent two-week-long validation phase, participants performed blinded listening evaluations comparing settings predicted by the trained system (“trained settings”) to those suggested by the hearing aids’ untrained system (“untrained settings”). Data Collection and Analysis We analyzed data collected on the smartphone and hearing aids during the study. We also obtained audiometric and demographic information. Results Overall, the 15 participants with valid data significantly preferred trained settings to untrained settings (paired-samples t test). Seven participants had a significant preference for trained settings, while one had a significant preference for untrained settings (binomial test). The remaining seven participants had nonsignificant preferences. Pooling data across participants, the proportion of times that each setting was chosen in a given environmental sound class was on average very similar. However, breaking down the data by participant revealed strong and idiosyncratic individual preferences. Fourteen participants reported positive feelings of clarity, competence, and mastery when training via HALIC. Conclusions The obtained data, as well as subjective participant feedback, indicate that smartphones could become viable tools to train hearing aids. Individuals who are tech savvy and have milder HL seem well suited to take advantages of the benefits offered by training with a smartphone. PMID:27718350

Effects of resistance training on running economy and cross-country performance.

PubMed

Barnes, Kyle R; Hopkins, Will G; McGuigan, Michael R; Northuis, Mark E; Kilding, Andrew E

2013-12-01

Heavy-resistance training and plyometric training offer distinct physiological and neuromuscular adaptations that could enhance running economy and, consequently, distance-running performance. To date, no studies have examined the effect of combining the two modes of training on running economy or performance. Fifty collegiate male and female cross-country runners performed a 5-km time trial and a series of laboratory-based tests to determine aerobic, anthropometric, biomechanical, and neuromuscular characteristics. Thereafter, each athlete participated in a season of six to eight collegiate cross-country races for 13 wk. After the first 4 wk, athletes were randomly assigned to either heavy-resistance or plyometric plus heavy-resistance training. Five days after completing their final competition, runners repeated the same set of laboratory tests. We also estimated the effects of the intervention on competition performance throughout the season using athletes of other teams as controls. Heavy-resistance training produced small-moderate improvements in peak speed, running economy, and neuromuscular characteristics relative to plyometric resistance training, whereas changes in biomechanical measures favored plyometric resistance training. Men made less gains than women in most tests. Both treatments had possibly harmful effects on competition times in men (mean = 0.5%; 90% confidence interval = ±1.2%), but there may have been benefit for some individuals. Both treatments were likely beneficial for all women (-1.2%; ±1.3%), but heavy-resistance training was possibly better than plyometric resistance training. The changes in laboratory-based parameters related to distance-running performance were consistent with the changes in competition times for women but only partly for men. Our data indicate that women should include heavy-resistance training in their programs, but men should be cautious about using it in season until more research establishes whether certain men are positive or negative responders.

Pediatric advanced life support training of pediatricians in New Jersey: cause for concern?

PubMed

van Amerongen, R; Klig, S; Cunningham, F; Sylvester, L; Silber, S

2000-02-01

The Pediatric Advanced Life Support (PALS) course teaches the fundamental basics for pediatric emergency care, and it is recommended that all physicians, nurses, and paramedics who care for children complete training and refresher courses on a regular basis. The purpose of this study was to determine how many pediatricians in general practice participated in PALS courses in the first 3 years since its introduction in New Jersey. A questionnaire was sent to all PALS training centers in New Jersey that administered the course from 1990 through 1993. The questionnaire was designed to determine the number of physicians trained; their specialty, and their practice setting. The questionnaire and follow-up telephone interviews focused on the perceptions of course coordinators as to why primary care pediatricians did or did not take PALS courses, and their recommendations for improving pediatrician participation. Two PALS training centers provided courses for only 1 year and did not maintain records of their students. A total of 3652 individuals completed training in the remaining 11 centers. Only 649 of these students were physicians. The largest groups of physicians who completed training were Emergency Medicine physicians (248) and Pediatric residents (175). Forty-two students were pediatricians in general office-based practice, which represents a crude rate of only 0.81% of New Jersey American Academy of Pediatrics (AAP) members. Training center coordinators offered several opinions for these findings. The majority of those students who participated in PALS training were not physicians. Pediatricians in general office practice accounted for a small percentage of those who could have participated. Further research should be conducted to determine attitudes toward PALS training and the barriers that exist to the office-based pediatrician participating in PALS training.

Evaluating the Impact of Electronic Training on Organizational Performance in an SME Food Manufacturing Environment

ERIC Educational Resources Information Center

Fry, Richard C.

2011-01-01

Many small to medium sized manufacturing organizations do not have adequate resources to conduct formalized workplace training or properly evaluate its results. The purpose of this study was to examine the effectiveness of electronic training on workplace behavior and small business organizational performance in the manufacturing environment using…

Small Scale Marine Fisheries: An Extension Training Manual. TR-30.

ERIC Educational Resources Information Center

Martinson, Steven; And Others

This manual is designed for use in a preservice training program for prospective volunteers whose Peace Corps service will be spent working with small-scale artisanal fishing communities in developing nations. The program consists of 8 weeks of intensive training to develop competencies in marine fisheries technology and fisheries extension work…

[Evaluation of the learning curve of residents in localizing a phantom target with ultrasonography].

PubMed

Dessieux, T; Estebe, J-P; Bloc, S; Mercadal, L; Ecoffey, C

2008-10-01

Few information are available regarding the learning curve in ultrasonography and even less for ultrasound-guided regional anesthesia. This study aimed to evaluate in a training program the learning curve on a phantom of 12 residents novice in ultrasonography. Twelve trainees inexperienced in ultrasonography were given introductory training consisting of didactic formation on the various components of the portable ultrasound machine (i.e. on/off button, gain, depth, resolution, and image storage). Then, students performed three trials, in two sets of increased difficulty, at executing these predefined tasks: adjustments of the machine, then localization of a small plastic piece introduced into roasting pork (3 cm below the surface). At the end of the evaluation, the residents were asked to insert a 22 G needle into an exact predetermined target (i.e. point of fascia intersection). The progression of the needle was continuously controlled by ultrasound visualization using injection of a small volume of water (needle perpendicular to the longitudinal plane of the ultrasound beam). Two groups of two different examiners evaluated for each three trials the skill of the residents (quality, time to perform the machine adjustments, to localize the plastic target, and to hydrolocalize, and volume used for hydrolocalization). After each trial, residents evaluated their performance using a difficulty scale (0: easy to 10: difficult). All residents performed the adjustments from the last trial of each set, with a learning curve observed in terms of duration. Localization of the plastic piece was achieved by all residents at the 6th trial, with a shorter duration of localization. Hydrolocalization was achieved after the 4th trial by all subjects. Difficulty scale was correlated to the number of trials. All these results were independent of the experience of residents in regional anesthesia. Four trials were necessary to adjust correctly the machine, to localize a target, and to complete hydrolocalization. Ultrasonography in regional anesthesia seems to be a fast-learning technique, using this kind of practical training.

21st Century extravehicular activities: Synergizing past and present training methods for future spacewalking success

NASA Astrophysics Data System (ADS)

Moore, Sandra K.; Gast, Matthew A.

2010-10-01

Neil Armstrong's understated words, "That's one small step for man, one giant leap for mankind" were spoken from Tranquility Base forty years ago. Even today, those words resonate in the ears of millions, including many who had yet to be born when man first landed on the surface of the moon. By their very nature, and in the true spirit of exploration, extravehicular activities (EVAs) have generated much excitement throughout the history of manned spaceflight. From Ed White's first spacewalk in the June of 1965, to the first steps on the moon in 1969, to the expected completion of the International Space Station (ISS), the ability to exist, live and work in the vacuum of space has stood as a beacon of what is possible. It was NASA's first spacewalk that taught engineers on the ground the valuable lesson that successful spacewalking requires a unique set of learned skills. That lesson sparked extensive efforts to develop and define the training requirements necessary to ensure success. As focus shifted from orbital activities to lunar surface activities, the required skill set and subsequently the training methods changed. The requirements duly changed again when NASA left the moon for the last time in 1972 and have continued to evolve through the SkyLab, Space Shuttle, and ISS eras. Yet because the visits to the moon were so long ago, NASA's expertise in the realm of extra-terrestrial EVAs has diminished. As manned spaceflight again shifts its focus beyond low earth orbit, EVA's success will depend on the ability to synergize the knowledge gained over 40+ years of spacewalking to create a training method that allows a single crewmember to perform equally well, whether performing an EVA on the surface of the Moon, while in the vacuum of space, or heading for a rendezvous with Mars. This paper reviews NASA's past and present EVA training methods and extrapolates techniques from both to construct the basis for future EVA astronaut training.

21st Century Extravehicular Activities: Synergizing Past and Present Training Methods for Future Spacewalking Success

NASA Technical Reports Server (NTRS)

Moore, Sandra K.; Gast, Matthew A.

2009-01-01

Neil Armstrong's understated words, "That's one small step for man, one giant leap for mankind." were spoken from Tranquility Base forty years ago. Even today, those words resonate in the ears of millions, including many who had yet to be born when man first landed on the surface of the moon. By their very nature, and in the the spirit of exploration, extravehicular activities (EVAs) have generated much excitement throughout the history of manned spaceflight. From Ed White's first space walk in June of 1965, to the first steps on the moon in 1969, to the expected completion of the International Space Station (ISS), the ability to exist, live and work in the vacuum of space has stood as a beacon of what is possible. It was NASA's first spacewalk that taught engineers on the ground the valuable lesson that successful spacewalking requires a unique set of learned skills. That lesson sparked extensive efforts to develop and define the training requirements necessary to ensure success. As focus shifted from orbital activities to lunar surface activities, the required skill-set and subsequently the training methods, changed. The requirements duly changed again when NASA left the moon for the last time in 1972 and have continued to evolve through the Skylab, Space Shuttle; and ISS eras. Yet because the visits to the moon were so long ago, NASA's expertise in the realm of extra-terrestrial EVAs has diminished. As manned spaceflight again shifts its focus beyond low earth orbit, EVA success will depend on the ability to synergize the knowledge gained over 40+ years of spacewalking to create a training method that allows a single crewmember to perform equally well, whether performing an EVA on the surface of the Moon, while in the vacuum of space, or heading for a rendezvous with Mars. This paper reviews NASA's past and present EVA training methods and extrapolates techniques from both to construct the basis for future EVA astronaut training.

The pipeline training program in maternal and child health: interdisciplinary preparation of undergraduate students from underrepresented groups.

PubMed

Pizur-Barnekow, Kris; Rhyner, Paula M; Lund, Shelley

2010-05-01

The Preparing Academically Successful Students in Maternal and Child Health (MCH PASS) training program provided financial support and specialized training to occupational therapy (OT) and speech-language pathology (SLP) undergraduate students from underrepresented groups in maternal and child health. The project assisted undergraduate trainees to matriculate into graduate programs in their respective fields and facilitated application into long-term maternal and child health training programs. Sixteen trainees (8 OT and 8 SLP) participated in an undergraduate training program with an emphasis on interdisciplinary teaming, family mentoring, leadership development, public health and population-based research. Instruction occurred in community and classroom settings through didactic instruction and small group discussions. Fifteen of the trainees applied to and were accepted in graduate programs in their respective fields. Two trainees applied to a long-term MCH training program. Students reported increased knowledge about programs that serve women and children, the effects of poverty on health, interdisciplinary teaming and the daily routines of families who have a child with a special health care need. The MCH PASS program provided a unique opportunity for undergraduate students in OT and SLP to learn about public health with an emphasis on maternal and child health. The specialized preparation enabled students to understand better the health concerns of underserved families whose children have special health care needs.

Effectuality of Cleaning Workers' Training and Cleaning Enterprises' Chemical Health Hazard Risk Profiling.

PubMed

Suleiman, Abdulqadir M; Svendsen, Kristin V H

2015-12-01

Goal-oriented communication of risk of hazards is necessary in order to reduce risk of workers' exposure to chemicals. Adequate training of workers and enterprise priority setting are essential elements. Cleaning enterprises have many challenges and the existing paradigms influence the risk levels of these enterprises. Information on organization and enterprises' prioritization in training programs was gathered from cleaning enterprises. A measure of enterprises' conceptual level of importance of chemical health hazards and a model for working out the risk index (RI) indicating enterprises' conceptual risk level was established and used to categorize the enterprises. In 72.3% of cases, training takes place concurrently with task performances and in 67.4% experienced workers conduct the trainings. There is disparity between employers' opinion on competence level of the workers and reality. Lower conceptual level of importance was observed for cleaning enterprises of different sizes compared with regional safety delegates and occupational hygienists. Risk index values show no difference in risk level between small and large enterprises. Training of cleaning workers lacks the prerequisite for suitability and effectiveness to counter risks of chemical health hazards. There is dereliction of duty by management in the sector resulting in a lack of competence among the cleaning workers. Instituting acceptable easily attainable safety competence level for cleaners will conduce to risk reduction, and enforcement of attainment of the competence level would be a positive step.

A glimpse from the inside of a space suit: What is it really like to train for an EVA?

NASA Astrophysics Data System (ADS)

Gast, Matthew A.; Moore, Sandra K.

2011-01-01

The beauty of the view from the office of a spacewalking astronaut gives the impression of simplicity, but few beyond the astronauts, and those who train them, know what it really takes to get there. Extravehicular Activity (EVA) training is an intense process that utilizes NASA's Neutral Buoyancy Laboratory (NBL) to develop a very specific skill set needed to safely construct and maintain the orbiting International Space Station. To qualify for flight assignments, astronauts must demonstrate the ability to work safely and efficiently in the physically demanding environment of the space suit, possess an acute ability to resolve unforeseen problems, and implement proper tool protocols to ensure no tools will be lost in space. Through the insights and the lessons learned by actual EVA astronauts and EVA instructors, this paper will take you on a journey through an astronaut's earliest experiences working in the space suit, termed the Extravehicular Mobility Unit (EMU), in the underwater training environment of the NBL. This work details an actual Suit Qualification NBL training event, outlines the numerous challenges the astronauts face throughout their initial training, and the various ways they adapt their own abilities to overcome them. The goal of this paper is to give everyone a small glimpse into what it is really like to work in a space suit.

A Glimpse from the Inside of a Space Suit: What Is It Really Like to Train for an EVA?

NASA Technical Reports Server (NTRS)

Gast, Matthew A.; Moore, Sandra K.

2009-01-01

The beauty of the view from the office of a spacewalking astronaut gives the impression of simplicity, but few beyond the astronauts, and those who train them, know what it really takes to get there. Extravehicular Activity (EVA) training is an intense process that utilizes NASA's Neutral Buoyancy Laboratory (NBL) to develop a very specific skill set needed to safely construct and maintain the orbiting International Space Station. To qualify for flight assignments, astronauts must demonstrate the ability to work safely and efficiently in the physically demanding environment of the spacesuit, possess an acute ability to resolve unforeseen problems, and implement proper tool protocols to ensure no tools will be lost in space. Through the insights and the lessons learned by actual EVA astronauts and EVA instructors, this paper twill take you on a journey through an astronaut's earliest experiences working in the spacesuit. termed the Extravehicular Mobility Unit (EMU), in the underwater training environment of the NBL. This work details an actual Suit Qualification NBL training event, outlines the numerous challenges the astronauts face throughout their initial training, and the various ways they adapt their own abilities to overcome them. The goal of this paper is to give everyone a small glimpse into what it is really like to work in a spacesuit.

Sequential Classifier Training for Rice Mapping with Multitemporal Remote Sensing Imagery

NASA Astrophysics Data System (ADS)

Guo, Y.; Jia, X.; Paull, D.

2017-10-01

Most traditional methods for rice mapping with remote sensing data are effective when they are applied to the initial growing stage of rice, as the practice of flooding during this period makes the spectral characteristics of rice fields more distinguishable. In this study, we propose a sequential classifier training approach for rice mapping that can be used over the whole growing period of rice for monitoring various growth stages. Rice fields are firstly identified during the initial flooding period. The identified rice fields are used as training data to train a classifier that separates rice and non-rice pixels. The classifier is then used as a priori knowledge to assist the training of classifiers for later rice growing stages. This approach can be applied progressively to sequential image data, with only a small amount of training samples being required from each image. In order to demonstrate the effectiveness of the proposed approach, experiments were conducted at one of the major rice-growing areas in Australia. The proposed approach was applied to a set of multitemporal remote sensing images acquired by the Sentinel-2A satellite. Experimental results show that, compared with traditional spectral-indexbased algorithms, the proposed method is able to achieve more stable and consistent rice mapping accuracies and it reaches higher than 80% during the whole rice growing period.

Prescription Writing in Small Groups as a Clinical Pharmacology Educational Intervention: Perceptions of Preclerkship Medical Students.

PubMed

James, Henry; Tayem, Yasin I Y; Al Khaja, K A J; Veeramuthu, Sindhan; Sequeira, Reginald P

2016-08-01

Medical students do not perform well in writing prescriptions, and the 3 variables-learner, teacher, and instructional method-are held responsible to various degrees. The objective of this clinical pharmacology educational intervention was to improve medical students' perceptions, motivation, and participation in prescription-writing sessions. The study participants were second-year medical students of the College of Medicine and Medical Sciences of the Arabian Gulf University, Bahrain. Two prescription-writing sessions were conducted using clinical case scenarios based on problems the students had studied as part of the problem-based learning curriculum. At the end of the respiratory system subunit, the training was conducted in small groups, each facilitated by a tutor. At the end of the cardiovascular system subunit, the training was conducted in a traditional large-group classroom setting. Data were collected with the help of a questionnaire at the end of each session and a focus group discussion. A majority of the students (95.3% ± 2.4%) perceived the small-group method better for teaching and learning of all aspects of prescription writing: analyzing the clinical case scenario, applying clinical pharmacology knowledge for therapeutic reasoning, using a formulary for searching relevant prescribing information, and in writing a complete prescription. Students also endorsed the small-group method for better interaction among themselves and with the tutor and for the ease of asking questions and clarifying doubts. In view of the principles of adult learning, where motivation and interaction are important, teaching and learning prescription writing in small groups deserve a serious consideration in medical curricula. © 2015, The American College of Clinical Pharmacology.

Patterns of Relating Between Physicians and Medical Assistants in Small Family Medicine Offices

PubMed Central

Elder, Nancy C.; Jacobson, C. Jeffrey; Bolon, Shannon K.; Fixler, Joseph; Pallerla, Harini; Busick, Christina; Gerrety, Erica; Kinney, Dee; Regan, Saundra; Pugnale, Michael

2014-01-01

PURPOSE The clinician-colleague relationship is a cornerstone of relationship-centered care (RCC); in small family medicine offices, the clinician–medical assistant (MA) relationship is especially important. We sought to better understand the relationship between MA roles and the clinician-MA relationship within the RCC framework. METHODS We conducted an ethnographic study of 5 small family medicine offices (having <5 clinicians) in the Cincinnati Area Research and Improvement Group (CARInG) Network using interviews, surveys, and observations. We interviewed 19 MAs and supervisors and 11 clinicians (9 family physicians and 2 nurse practitioners) and observed 15 MAs in practice. Qualitative analysis used the editing style. RESULTS MAs’ roles in small family medicine offices were determined by MA career motivations and clinician-MA relationships. MA career motivations comprised interest in health care, easy training/workload, and customer service orientation. Clinician-MA relationships were influenced by how MAs and clinicians respond to their perceptions of MA clinical competence (illustrated predominantly by comparing MAs with nurses) and organizational structure. We propose a model, trust and verify, to describe the structure of the clinician-MA relationship. This model is informed by clinicians’ roles in hiring and managing MAs and the social familiarity of MAs and clinicians. Within the RCC framework, these findings can be seen as previously undefined constraints and freedoms in what is known as the Complex Responsive Process of Relating between clinicians and MAs. CONCLUSIONS Improved understanding of clinician-MA relationships will allow a better appreciation of how clinicians and MAs function in family medicine teams. Our findings may assist small offices undergoing practice transformation and guide future research to improve the education, training, and use of MAs in the family medicine setting. PMID:24615311

Issues in the Development and Evaluation of Cross-Cultural Training in a Business Setting.

ERIC Educational Resources Information Center

Broadbooks, Wendy J.

Issues in the development and evaluation of cross-cultural training in a business setting were investigated. Cross-cultural training and cross-cultural evaluation were defined as training and evaluation of training that involve the interaction of participants from two or more different countries. Two evaluations of a management development-type…

SIBSHIP SIZE AND YOUNG WOMEN'S TRANSITIONS TO ADULTHOOD IN INDIA.

PubMed

Santhya, K G; Zavier, A J Francis

2017-11-01

In India, a substantial proportion of young people are growing up in smaller families with fewer siblings than earlier generations of young people. Studies exploring the associations between declines in sibship size and young people's life experiences are limited. Drawing on data from a sub-nationally representative study conducted in 2006-08 of over 50,000 youths in India, this paper examines the associations between surviving sibship size and young women's (age 20-24) transitions to adulthood. Young women who reported no or a single surviving sibling were categorized as those with a small surviving sibship size, and those who reported two or more surviving siblings as those with a large surviving sibship size. Bivariate and multivariate regression analyses were conducted to ascertain the relationship between sibship size and outcome indicators. Analysis was also done separately for low- and high-fertility settings. Small sibship size tended to have a positive influence in many ways on young women's chances of making successful transitions to adulthood. Young women with fewer siblings were more likely than others to report secondary school completion, participation in vocational skills training programmes, experience of gender egalitarian socialization practices, adherence to gender egalitarian norms, exercise of pre-marital agency and small family size preferences. These associations were more apparent in low- than high-fertility settings.

Comparative differential proteomic analysis of minimal change disease and focal segmental glomerulosclerosis.

PubMed

Pérez, Vanessa; López, Dolores; Boixadera, Ester; Ibernón, Meritxell; Espinal, Anna; Bonet, Josep; Romero, Ramón

2017-02-03

Minimal change disease (MCD) and primary focal segmental glomerulosclerosis (FSGS) are glomerular diseases characterized by nephrotic syndrome. Their diagnosis requires a renal biopsy, but it is an invasive procedure with potential complications. In a small biopsy sample, where only normal glomeruli are observed, FSGS cannot be differentiated from MCD. The correct diagnosis is crucial to an effective treatment, as MCD is normally responsive to steroid therapy, whereas FSGS is usually resistant. The purpose of our study was to discover and validate novel early urinary biomarkers capable to differentiate between MCD and FSGS. Forty-nine patients biopsy-diagnosed of MCD and primary FSGS were randomly subdivided into a training set (10 MCD, 11 FSGS) and a validation set (14 MCD, 14 FSGS). The urinary proteome of the training set was analyzed by two-dimensional differential gel electrophoresis coupled with mass spectrometry. The proteins identified were quantified by enzyme-linked immunosorbent assay in urine samples from the validation set. Urinary concentration of alpha-1 antitrypsin, transferrin, histatin-3 and 39S ribosomal protein L17 was decreased and calretinin was increased in FSGS compared to MCD. These proteins were used to build a decision tree capable to predict patient's pathology. This preliminary study suggests a group of urinary proteins as possible non-invasive biomarkers with potential value in the differential diagnosis of MCD and FSGS. These biomarkers would reduce the number of misdiagnoses, avoiding unnecessary or inadequate treatments.

Performance of machine-learning scoring functions in structure-based virtual screening

PubMed Central

Wójcikowski, Maciej; Ballester, Pedro J.; Siedlecki, Pawel

2017-01-01

Classical scoring functions have reached a plateau in their performance in virtual screening and binding affinity prediction. Recently, machine-learning scoring functions trained on protein-ligand complexes have shown great promise in small tailored studies. They have also raised controversy, specifically concerning model overfitting and applicability to novel targets. Here we provide a new ready-to-use scoring function (RF-Score-VS) trained on 15 426 active and 893 897 inactive molecules docked to a set of 102 targets. We use the full DUD-E data sets along with three docking tools, five classical and three machine-learning scoring functions for model building and performance assessment. Our results show RF-Score-VS can substantially improve virtual screening performance: RF-Score-VS top 1% provides 55.6% hit rate, whereas that of Vina only 16.2% (for smaller percent the difference is even more encouraging: RF-Score-VS top 0.1% achieves 88.6% hit rate for 27.5% using Vina). In addition, RF-Score-VS provides much better prediction of measured binding affinity than Vina (Pearson correlation of 0.56 and −0.18, respectively). Lastly, we test RF-Score-VS on an independent test set from the DEKOIS benchmark and observed comparable results. We provide full data sets to facilitate further research in this area (http://github.com/oddt/rfscorevs) as well as ready-to-use RF-Score-VS (http://github.com/oddt/rfscorevs_binary). PMID:28440302

Optimal Sparse Upstream Sensor Placement for Hydrokinetic Turbines

NASA Astrophysics Data System (ADS)

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

2016-11-01

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

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

Potash, Peter J.; Bell, Eric B.; Harrison, Joshua J.

Predictive models for tweet deletion have been a relatively unexplored area of Twitter-related computational research. We first approach the deletion of tweets as a spam detection problem, applying a small set of handcrafted features to improve upon the current state-of-the- art in predicting deleted tweets. Next, we apply our approach to a dataset of deleted tweets that better reflects the current deletion rate. Since tweets are deleted for reasons beyond just the presence of spam, we apply topic modeling and text embeddings in order to capture the semantic content of tweets that can lead to tweet deletion. Our goal ismore » to create an effective model that has a low-dimensional feature space and is also language-independent. A lean model would be computationally advantageous processing high-volumes of Twitter data, which can reach 9,885 tweets per second. Our results show that a small set of spam-related features combined with word topics and character-level text embeddings provide the best f1 when trained with a random forest model. The highest precision of the deleted tweet class is achieved by a modification of paragraph2vec to capture author identity.« less

Optimization of genomic selection training populations with a genetic algorithm

USDA-ARS?s Scientific Manuscript database

In this article, we derive a computationally efficient statistic to measure the reliability of estimates of genetic breeding values for a fixed set of genotypes based on a given training set of genotypes and phenotypes. We adopt a genetic algorithm scheme to find a training set of certain size from ...

An assisted navigation training framework based on judgment theory using sparse and discrete human-machine interfaces.

PubMed

Lopes, Ana C; Nunes, Urbano

2009-01-01

This paper aims to present a new framework to train people with severe motor disabilities steering an assisted mobile robot (AMR), such as a powered wheelchair. Users with high level of motor disabilities are not able to use standard HMIs, which provide a continuous command signal (e. g. standard joystick). For this reason HMIs providing a small set of simple commands, which are sparse and discrete in time must be used (e. g. scanning interface, or brain computer interface), making very difficult to steer the AMR. In this sense, the assisted navigation training framework (ANTF) is designed to train users driving the AMR, in indoor structured environments, using this type of HMIs. Additionally it provides user characterization on steering the robot, which will later be used to adapt the AMR navigation system to human competence steering the AMR. A rule-based lens (RBL) model is used to characterize users on driving the AMR. Individual judgment performance choosing the best manoeuvres is modeled using a genetic-based policy capturing (GBPC) technique characterized to infer non-compensatory judgment strategies from human decision data. Three user models, at three different learning stages, using the RBL paradigm, are presented.

Relative optical navigation around small bodies via Extreme Learning Machine

NASA Astrophysics Data System (ADS)

Law, Andrew M.

To perform close proximity operations under a low-gravity environment, relative and absolute positions are vital information to the maneuver. Hence navigation is inseparably integrated in space travel. Extreme Learning Machine (ELM) is presented as an optical navigation method around small celestial bodies. Optical Navigation uses visual observation instruments such as a camera to acquire useful data and determine spacecraft position. The required input data for operation is merely a single image strip and a nadir image. ELM is a machine learning Single Layer feed-Forward Network (SLFN), a type of neural network (NN). The algorithm is developed on the predicate that input weights and biases can be randomly assigned and does not require back-propagation. The learned model is the output layer weights which are used to calculate a prediction. Together, Extreme Learning Machine Optical Navigation (ELM OpNav) utilizes optical images and ELM algorithm to train the machine to navigate around a target body. In this thesis the asteroid, Vesta, is the designated celestial body. The trained ELMs estimate the position of the spacecraft during operation with a single data set. The results show the approach is promising and potentially suitable for on-board navigation.

The Effects of High Intensity Interval Training vs Steady State Training on Aerobic and Anaerobic Capacity

PubMed Central

Foster, Carl; Farland, Courtney V.; Guidotti, Flavia; Harbin, Michelle; Roberts, Brianna; Schuette, Jeff; Tuuri, Andrew; Doberstein, Scott T.; Porcari, John P.

2015-01-01

High intensity interval training (HIIT) has become an increasingly popular form of exercise due to its potentially large effects on exercise capacity and small time requirement. This study compared the effects of two HIIT protocols vs steady-state training on aerobic and anaerobic capacity following 8-weeks of training. Fifty-five untrained college-aged subjects were randomly assigned to three training groups (3x weekly). Steady-state (n = 19) exercised (cycle ergometer) 20 minutes at 90% of ventilatory threshold (VT). Tabata (n = 21) completed eight intervals of 20s at 170% VO2max/10s rest. Meyer (n = 15) completed 13 sets of 30s (20 min) @ 100% PVO2 max/ 60s recovery, average PO = 90% VT. Each subject did 24 training sessions during 8 weeks. Results: There were significant (p < 0.05) increases in VO2max (+19, +18 and +18%) and PPO (+17, +24 and +14%) for each training group, as well as significant increases in peak (+8, + 9 and +5%) & mean (+4, +7 and +6%) power during Wingate testing, but no significant differences between groups. Measures of the enjoyment of the training program indicated that the Tabata protocol was significantly less enjoyable (p < 0.05) than the steady state and Meyer protocols, and that the enjoyment of all protocols declined (p < 0.05) across the duration of the study. The results suggest that although HIIT protocols are time efficient, they are not superior to conventional exercise training in sedentary young adults. Key points Steady state training equivalent to HIIT in untrained students Mild interval training presents very similar physiologic challenge compared to steady state training HIIT (particularly very high intensity variants were less enjoyable than steady state or mild interval training Enjoyment of training decreases across the course of an 8 week experimental training program PMID:26664271

The Effects of High Intensity Interval Training vs Steady State Training on Aerobic and Anaerobic Capacity.

PubMed

Foster, Carl; Farland, Courtney V; Guidotti, Flavia; Harbin, Michelle; Roberts, Brianna; Schuette, Jeff; Tuuri, Andrew; Doberstein, Scott T; Porcari, John P

2015-12-01

High intensity interval training (HIIT) has become an increasingly popular form of exercise due to its potentially large effects on exercise capacity and small time requirement. This study compared the effects of two HIIT protocols vs steady-state training on aerobic and anaerobic capacity following 8-weeks of training. Fifty-five untrained college-aged subjects were randomly assigned to three training groups (3x weekly). Steady-state (n = 19) exercised (cycle ergometer) 20 minutes at 90% of ventilatory threshold (VT). Tabata (n = 21) completed eight intervals of 20s at 170% VO2max/10s rest. Meyer (n = 15) completed 13 sets of 30s (20 min) @ 100% PVO2 max/ 60s recovery, average PO = 90% VT. Each subject did 24 training sessions during 8 weeks. There were significant (p < 0.05) increases in VO2max (+19, +18 and +18%) and PPO (+17, +24 and +14%) for each training group, as well as significant increases in peak (+8, + 9 and +5%) & mean (+4, +7 and +6%) power during Wingate testing, but no significant differences between groups. Measures of the enjoyment of the training program indicated that the Tabata protocol was significantly less enjoyable (p < 0.05) than the steady state and Meyer protocols, and that the enjoyment of all protocols declined (p < 0.05) across the duration of the study. The results suggest that although HIIT protocols are time efficient, they are not superior to conventional exercise training in sedentary young adults. Key pointsSteady state training equivalent to HIIT in untrained studentsMild interval training presents very similar physiologic challenge compared to steady state trainingHIIT (particularly very high intensity variants were less enjoyable than steady state or mild interval trainingEnjoyment of training decreases across the course of an 8 week experimental training program.

Training a whole-book LSTM-based recognizer with an optimal training set

NASA Astrophysics Data System (ADS)

Soheili, Mohammad Reza; Yousefi, Mohammad Reza; Kabir, Ehsanollah; Stricker, Didier

2018-04-01

Despite the recent progress in OCR technologies, whole-book recognition, is still a challenging task, in particular in case of old and historical books, that the unknown font faces or low quality of paper and print contributes to the challenge. Therefore, pre-trained recognizers and generic methods do not usually perform up to required standards, and usually the performance degrades for larger scale recognition tasks, such as of a book. Such reportedly low error-rate methods turn out to require a great deal of manual correction. Generally, such methodologies do not make effective use of concepts such redundancy in whole-book recognition. In this work, we propose to train Long Short Term Memory (LSTM) networks on a minimal training set obtained from the book to be recognized. We show that clustering all the sub-words in the book, and using the sub-word cluster centers as the training set for the LSTM network, we can train models that outperform any identical network that is trained with randomly selected pages of the book. In our experiments, we also show that although the sub-word cluster centers are equivalent to about 8 pages of text for a 101- page book, a LSTM network trained on such a set performs competitively compared to an identical network that is trained on a set of 60 randomly selected pages of the book.

THE EFFECTS OF COMPUTER-BASED FIRE SAFETY TRAINING ON THE KNOWLEDGE, ATTITUDES, AND PRACTICES OF CAREGIVERS

PubMed Central

Harrington, Susan S.; Walker, Bonnie L.

2010-01-01

Background Older adults in small residential board and care facilities are at a particularly high risk of fire death and injury because of their characteristics and environment. Methods The authors investigated computer-based instruction as a way to teach fire emergency planning to owners, operators, and staff of small residential board and care facilities. Participants (N = 59) were randomly assigned to a treatment or control group. Results Study participants who completed the training significantly improved their scores from pre- to posttest when compared to a control group. Participants indicated on the course evaluation that the computers were easy to use for training (97%) and that they would like to use computers for future training courses (97%). Conclusions This study demonstrates the potential for using interactive computer-based training as a viable alternative to instructor-led training to meet the fire safety training needs of owners, operators, and staff of small board and care facilities for the elderly. PMID:19263929

Effects of interset whole-body vibration on bench press resistance training in trained and untrained individuals.

PubMed

Timon, Rafael; Collado-Mateo, Daniel; Olcina, Guillermo; Gusi, Narcis

2016-03-01

Previous studies have demonstrated positive effects of acute vibration exercise on concentric strength and power, but few have observed the effects of vibration exposure on resistance training. The aim of this study was to verify the effects of whole body vibration applied to the chest via hands on bench press resistance training in trained and untrained individuals. Nineteen participants (10 recreationally trained bodybuilders and 9 untrained students) performed two randomized sessions of resistance training on separate days. Each strength session consisted of 3 bench press sets with a load of 75% 1RM to failure in each set, with 2 minutes' rest between sets. All subjects performed the same strength training with either, vibration exposure (12 Hz, 4 mm) of 30 seconds immediately before each bench press set or without vibration. Number of total repetitions, kinematic parameters, blood lactate and perceived exertion were analyzed. In the untrained group, vibration exposure caused a significant increase in the mean velocity (from 0.36±0.02 to 0.39±0.03 m/s) and acceleration (from 0.75±0.10 to 0.86±0.09 m/s2), as well as a decrease in perceived effort (from 8±0.57 to 7.35±0.47) in the first bench press set, but no change was observed in the third bench press set. In the recreationally trained bodybuilders, vibration exposure did not cause any improvement on the performance of bench press resistance training. These results suggest that vibration exposure applied just before the bench press exercise could be a good practice to be implemented by untrained individuals in resistance training.

The effectiveness of three sets of school-based instructional materials and community training on the acquisition and generalization of community laundry skills by students with severe handicaps.

PubMed

Morrow, S A; Bates, P E

1987-01-01

This study examined the effectiveness of three sets of school-based instructional materials and community training on acquisition and generalization of a community laundry skill by nine students with severe handicaps. School-based instruction involved artificial materials (pictures), simulated materials (cardboard replica of a community washing machine), and natural materials (modified home model washing machine). Generalization assessments were conducted at two different community laundromats, on two machines represented fully by the school-based instructional materials and two machines not represented fully by these materials. After three phases of school-based instruction, the students were provided ten community training trials in one laundromat setting and a final assessment was conducted in both the trained and untrained community settings. A multiple probe design across students was used to evaluate the effectiveness of the three types of school instruction and community training. After systematic training, most of the students increased their laundry performance with all three sets of school-based materials; however, generalization of these acquired skills was limited in the two community settings. Direct training in one of the community settings resulted in more efficient acquisition of the laundry skills and enhanced generalization to the untrained laundromat setting for most of the students. Results of this study are discussed in regard to the issue of school versus community-based instruction and recommendations are made for future research in this area.

Physicians in rural West Virginia emergency departments: residency training and board certification status.

PubMed

McGirr, J; Williams, J M; Prescott, J E

1998-04-01

To describe the training and certification of physicians who staff small EDs in rural West Virginia. A survey of rural hospital-based EDs was performed. The authors chose to study all hospitals in counties with populations of <30,000 and in which the hospital was the only one in the county. Interviews were conducted with the medical director of the ED or the hospital administrator, depending on who was available at the time of interview. Data collected describing the emergency physicians (EPs) employed at each facility included: medical school and residency training, specialty board certification, and certification in a variety of life support courses. General information about each ED, such as census and hospital resources, was also obtained. Interview data were collected on a survey form and subsequently entered into a database. Descriptive analyses were performed. 20 hospitals met rural criteria for inclusion in the study and all were included. The median number of full-time physicians per ED was 2 (IQR 2-4). 98 part-time doctors were identified; 28 (29%) of these were residents in training. 13/40 (33%) of full-time and 37/98 (38%) of part-time physicians were foreign medical graduates. Only 3/40 (7.5%) of full-time EPs completed residency training in emergency medicine (EM). Only 4/98 (4%) of part-time EPs were residency-trained in EM. 50% of full-time EPs were board-certified in a primary care specialty. Only 5/42 (12%) of full-time EPs were board-certified in EM. One third of full-time and the majority of part-time EPs were not board-certified in any specialty whatsoever. The majority of EPs had been certified in Advanced Cardiac Life Support, but fewer had been certified in Advanced Trauma Life Support and/or Pediatric Advanced Life Support/Advanced Pediatric Life Support. The majority of physicians staffing small rural EDs in West Virginia are neither residency-trained nor board-certified in EM. Further studies are warranted to determine the most efficient and effective way to maximize the skills and availability of emergency care providers in rural settings.

13 CFR 121.406 - How does a small business concern qualify to provide manufactured products or other supply items...

Code of Federal Regulations, 2013 CFR

2013-01-01

...-disabled veteran-owned small business set-aside, WOSB or EDWOSB set-aside, or 8(a) contract? 121.406... items under a small business set-aside, service-disabled veteran-owned small business set-aside, WOSB or... small business set-aside, service-disabled veteran-owned small business set-aside, WOSB or EDWOSB set...

13 CFR 121.406 - How does a small business concern qualify to provide manufactured products or other supply items...

Code of Federal Regulations, 2014 CFR

2014-01-01

...-disabled veteran-owned small business set-aside, WOSB or EDWOSB set-aside, or 8(a) contract? 121.406... items under a small business set-aside, service-disabled veteran-owned small business set-aside, WOSB or... small business set-aside, service-disabled veteran-owned small business set-aside, WOSB or EDWOSB set...

13 CFR 121.406 - How does a small business concern qualify to provide manufactured products or other supply items...

Code of Federal Regulations, 2012 CFR

2012-01-01

...-disabled veteran-owned small business set-aside, WOSB or EDWOSB set-aside, or 8(a) contract? 121.406... items under a small business set-aside, service-disabled veteran-owned small business set-aside, WOSB or... small business set-aside, service-disabled veteran-owned small business set-aside, WOSB or EDWOSB set...

The Effects of Transfer in Teaching Vocabulary to School Children: An Analysis of the Dependencies between Lists of Trained and Non-Trained Words

ERIC Educational Resources Information Center

Frost, Jørgen; Ottem, Ernst; Hagtvet, Bente E.; Snow, Catherine E.

2016-01-01

In the present study, 81 Norwegian students were taught the meaning of words by the Word Generation (WG) method and 51 Norwegian students were taught by an approach inspired by the Thinking Schools (TS) concept. Two sets of words were used: a set of words to be trained and a set of non-trained control words. The two teaching methods yielded no…

Education, Retraining and Workplaces. Final Report.

ERIC Educational Resources Information Center

Williams, Kristen M.; Felker, Daniel B.

A project was undertaken to develop a guide to help small business programs select good education and training programs for their staff. An initial step was a review of literature related to adult education and training in small business and training evaluation. The initial plan was to have the guidebook be specific to a given industry or other…

Implementing an Audience-Specific Small-Group Gatekeeper Training Program to Respond to Suicide Risk among College Students: A Case Study

ERIC Educational Resources Information Center

Cimini, M. Dolores; Rivero, Estela M.; Bernier, Joseph E.; Stanley, Judith A.; Murray, Andrea D.; Anderson, Drew A.; Wright, Heidi R.; Bapat, Mona

2014-01-01

Objective: This case study evaluated the effectiveness of an audience-specific, single-session, small-group interactive gatekeeper training program conducted at a large northeastern public university. Participants: Participants were 335 faculty, staff, and students completing gatekeeper training programs tailored to their group needs. Methods:…

Training Rural Special Educators to Transition to the Workplace: Lessons for Small Teacher Education Programs.

ERIC Educational Resources Information Center

Fallon, Moira A.; Hammons, Jo-Ann

There are many important workplace issues that must be considered when training rural special educators, particularly those who come from small rural environments with limited diversity. Teacher education programs and rural educators view practicum experiences as integral in transitioning from the training program to the diverse challenges of the…

Preventing a Relapse or Setting Goals? Elucidating the Impact of Post-Training Transfer Interventions on Training Transfer Performance

ERIC Educational Resources Information Center

Rahyuda, Agoes Ganesha; Soltani, Ebrahim; Syed, Jawad

2018-01-01

Based on a review of the literature on post-training transfer interventions, this paper offers a conceptual model that elucidates potential mechanisms through which two types of post-training transfer intervention (relapse prevention and proximal plus distal goal setting) influence the transfer of training. We explain how the application of…

Family Hotel Businesses: Strategic Planning and the Need for Education and Training

ERIC Educational Resources Information Center

Peters, Mike; Buhalis, Dimitrios

2004-01-01

Small businesses dominate the tourism and hospitality industry worldwide and are of critical importance for the competitiveness of destinations. Small family hotel businesses are characterised by a number of specific business processes which generate particular training and educational needs. It is increasingly clear that small businesses are not…

a Fully Automated Pipeline for Classification Tasks with AN Application to Remote Sensing

NASA Astrophysics Data System (ADS)

Suzuki, K.; Claesen, M.; Takeda, H.; De Moor, B.

2016-06-01

Nowadays deep learning has been intensively in spotlight owing to its great victories at major competitions, which undeservedly pushed `shallow' machine learning methods, relatively naive/handy algorithms commonly used by industrial engineers, to the background in spite of their facilities such as small requisite amount of time/dataset for training. We, with a practical point of view, utilized shallow learning algorithms to construct a learning pipeline such that operators can utilize machine learning without any special knowledge, expensive computation environment, and a large amount of labelled data. The proposed pipeline automates a whole classification process, namely feature-selection, weighting features and the selection of the most suitable classifier with optimized hyperparameters. The configuration facilitates particle swarm optimization, one of well-known metaheuristic algorithms for the sake of generally fast and fine optimization, which enables us not only to optimize (hyper)parameters but also to determine appropriate features/classifier to the problem, which has conventionally been a priori based on domain knowledge and remained untouched or dealt with naïve algorithms such as grid search. Through experiments with the MNIST and CIFAR-10 datasets, common datasets in computer vision field for character recognition and object recognition problems respectively, our automated learning approach provides high performance considering its simple setting (i.e. non-specialized setting depending on dataset), small amount of training data, and practical learning time. Moreover, compared to deep learning the performance stays robust without almost any modification even with a remote sensing object recognition problem, which in turn indicates that there is a high possibility that our approach contributes to general classification problems.

Knowledge-Based Methods To Train and Optimize Virtual Screening Ensembles

PubMed Central

2016-01-01

Ensemble docking can be a successful virtual screening technique that addresses the innate conformational heterogeneity of macromolecular drug targets. Yet, lacking a method to identify a subset of conformational states that effectively segregates active and inactive small molecules, ensemble docking may result in the recommendation of a large number of false positives. Here, three knowledge-based methods that construct structural ensembles for virtual screening are presented. Each method selects ensembles by optimizing an objective function calculated using the receiver operating characteristic (ROC) curve: either the area under the ROC curve (AUC) or a ROC enrichment factor (EF). As the number of receptor conformations, N, becomes large, the methods differ in their asymptotic scaling. Given a set of small molecules with known activities and a collection of target conformations, the most resource intense method is guaranteed to find the optimal ensemble but scales as O(2N). A recursive approximation to the optimal solution scales as O(N2), and a more severe approximation leads to a faster method that scales linearly, O(N). The techniques are generally applicable to any system, and we demonstrate their effectiveness on the androgen nuclear hormone receptor (AR), cyclin-dependent kinase 2 (CDK2), and the peroxisome proliferator-activated receptor δ (PPAR-δ) drug targets. Conformations that consisted of a crystal structure and molecular dynamics simulation cluster centroids were used to form AR and CDK2 ensembles. Multiple available crystal structures were used to form PPAR-δ ensembles. For each target, we show that the three methods perform similarly to one another on both the training and test sets. PMID:27097522

Bayesian Estimation of Small Effects in Exercise and Sports Science.

PubMed

Mengersen, Kerrie L; Drovandi, Christopher C; Robert, Christian P; Pyne, David B; Gore, Christopher J

2016-01-01

The aim of this paper is to provide a Bayesian formulation of the so-called magnitude-based inference approach to quantifying and interpreting effects, and in a case study example provide accurate probabilistic statements that correspond to the intended magnitude-based inferences. The model is described in the context of a published small-scale athlete study which employed a magnitude-based inference approach to compare the effect of two altitude training regimens (live high-train low (LHTL), and intermittent hypoxic exposure (IHE)) on running performance and blood measurements of elite triathletes. The posterior distributions, and corresponding point and interval estimates, for the parameters and associated effects and comparisons of interest, were estimated using Markov chain Monte Carlo simulations. The Bayesian analysis was shown to provide more direct probabilistic comparisons of treatments and able to identify small effects of interest. The approach avoided asymptotic assumptions and overcame issues such as multiple testing. Bayesian analysis of unscaled effects showed a probability of 0.96 that LHTL yields a substantially greater increase in hemoglobin mass than IHE, a 0.93 probability of a substantially greater improvement in running economy and a greater than 0.96 probability that both IHE and LHTL yield a substantially greater improvement in maximum blood lactate concentration compared to a Placebo. The conclusions are consistent with those obtained using a 'magnitude-based inference' approach that has been promoted in the field. The paper demonstrates that a fully Bayesian analysis is a simple and effective way of analysing small effects, providing a rich set of results that are straightforward to interpret in terms of probabilistic statements.

Comparison of molecular breeding values based on within- and across-breed training in beef cattle.

PubMed

Kachman, Stephen D; Spangler, Matthew L; Bennett, Gary L; Hanford, Kathryn J; Kuehn, Larry A; Snelling, Warren M; Thallman, R Mark; Saatchi, Mahdi; Garrick, Dorian J; Schnabel, Robert D; Taylor, Jeremy F; Pollak, E John

2013-08-16

Although the efficacy of genomic predictors based on within-breed training looks promising, it is necessary to develop and evaluate across-breed predictors for the technology to be fully applied in the beef industry. The efficacies of genomic predictors trained in one breed and utilized to predict genetic merit in differing breeds based on simulation studies have been reported, as have the efficacies of predictors trained using data from multiple breeds to predict the genetic merit of purebreds. However, comparable studies using beef cattle field data have not been reported. Molecular breeding values for weaning and yearling weight were derived and evaluated using a database containing BovineSNP50 genotypes for 7294 animals from 13 breeds in the training set and 2277 animals from seven breeds (Angus, Red Angus, Hereford, Charolais, Gelbvieh, Limousin, and Simmental) in the evaluation set. Six single-breed and four across-breed genomic predictors were trained using pooled data from purebred animals. Molecular breeding values were evaluated using field data, including genotypes for 2227 animals and phenotypic records of animals born in 2008 or later. Accuracies of molecular breeding values were estimated based on the genetic correlation between the molecular breeding value and trait phenotype. With one exception, the estimated genetic correlations of within-breed molecular breeding values with trait phenotype were greater than 0.28 when evaluated in the breed used for training. Most estimated genetic correlations for the across-breed trained molecular breeding values were moderate (> 0.30). When molecular breeding values were evaluated in breeds that were not in the training set, estimated genetic correlations clustered around zero. Even for closely related breeds, within- or across-breed trained molecular breeding values have limited prediction accuracy for breeds that were not in the training set. For breeds in the training set, across- and within-breed trained molecular breeding values had similar accuracies. The benefit of adding data from other breeds to a within-breed training population is the ability to produce molecular breeding values that are more robust across breeds and these can be utilized until enough training data has been accumulated to allow for a within-breed training set.

[Development of a New Scholastic Program for Medication Counseling Practice in Preclinical Training, Constructed for Junior Students by Senior Students Based on Their Experiences of On-site Practice].

PubMed

Suzuki, Sayo; Aono, Izumi; Imai, Natsumi; Kuwabara, Aki; Kenda, Yuki; Matsumoto, Minako; Yoshida, Aya; Watanabe, Asuka; Takagi, Akinori; Kobayashi, Noriko; Saeki, Haruko; Ohtani, Hisakazu; Nakamura, Tomonori; Kizu, Junko

2017-01-01

 Long-term practical on-site training, based on the Model Core Curriculum for Pharmaceutical Education, is a core program of the 6-year course of pharmaceutical education, introduced in Japan in 2010. In particular, medication counseling in practical training in 5th-year provides valuable opportunities for communication with real patients rather than simulated patients (SPs). However, it can also cause anxiety in 4th-year students before practical training. To address such concerns, upperclassmen (5th- and 6th-year students), who have already completed practical training, constructed and conducted a new educational program for medication counseling practice in preclinical training based on their experiences. They also developed case scenarios and played the role of patients themselves to create more realistic clinical settings. Advice from professional SPs was also provided. The 5-step program is composed of 1st counseling, 1st small group discussion (SGD) for improving counseling, 2nd revised counseling based on the 1st SGD, 2nd SGD, and development of a counseling plan and presentation. Educational effects of the program were evaluated by questionnaire survey after preclinical training in 4th-year students and after their practical training in 5th-year students. This new program, the Advanced Medication Counseling Practice, was found to be useful to reduce anxiety about communication with patients among 4th-year students (about 90%). Even after their practical training in 5th-year, they still appreciated usefulness of this program (about 80%). This program is still valued 4 years after its development. We developed the Advanced Medication Counseling Practice in preclinical training for junior students by senior students.

Training improves interobserver reliability for the diagnosis of scaphoid fracture displacement.

PubMed

Buijze, Geert A; Guitton, Thierry G; van Dijk, C Niek; Ring, David

2012-07-01

The diagnosis of displacement in scaphoid fractures is notorious for poor interobserver reliability. We tested whether training can improve interobserver reliability and sensitivity, specificity, and accuracy for the diagnosis of scaphoid fracture displacement on radiographs and CT scans. Sixty-four orthopaedic surgeons rated a set of radiographs and CT scans of 10 displaced and 10 nondisplaced scaphoid fractures for the presence of displacement, using a web-based rating application. Before rating, observers were randomized to a training group (34 observers) and a nontraining group (30 observers). The training group received an online training module before the rating session, and the nontraining group did not. Interobserver reliability for training and nontraining was assessed by Siegel's multirater kappa and the Z-test was used to test for significance. There was a small, but significant difference in the interobserver reliability for displacement ratings in favor of the training group compared with the nontraining group. Ratings of radiographs and CT scans combined resulted in moderate agreement for both groups. The average sensitivity, specificity, and accuracy of diagnosing displacement of scaphoid fractures were, respectively, 83%, 85%, and 84% for the nontraining group and 87%, 86%, and 87% for the training group. Assuming a 5% prevalence of fracture displacement, the positive predictive value was 0.23 in the nontraining group and 0.25 in the training group. The negative predictive value was 0.99 in both groups. Our results suggest training can improve interobserver reliability and sensitivity, specificity and accuracy for the diagnosis of scaphoid fracture displacement, but the improvements are slight. These findings are encouraging for future research regarding interobserver variation and how to reduce it further.

48 CFR 19.502-2 - Total small business set-asides.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 48 Federal Acquisition Regulations System 1 2014-10-01 2014-10-01 false Total small business set... SOCIOECONOMIC PROGRAMS SMALL BUSINESS PROGRAMS Set-Asides for Small Business 19.502-2 Total small business set... exclusively for small business concerns and shall be set aside for small business unless the contracting...

Effects of draught load exercise and training on calcium homeostasis in horses.

PubMed

Vervuert, I; Coenen, M; Zamhöfer, J

2005-01-01

This study was conducted to investigate the effects of draught load exercise on calcium (Ca) homeostasis in young horses. Five 2-year-old untrained Standardbred horses were studied in a 4-month training programme. All exercise workouts were performed on a treadmill at a 6% incline and with a constant draught load of 40 kg (0.44 kN). The training programme started with a standardized exercise test (SET 1; six incremental steps of 5 min duration each, first step 1.38 m/s, stepwise increase by 0.56 m/s). A training programme was then initiated which consisted of low-speed exercise sessions (LSE; constant velocity at 1.67 m/s for 60 min, 48 training sessions in total). After the 16th and 48th LSE sessions, SETs (SET 2: middle of training period, SET 3: finishing training period) were performed again under the identical test protocol of SET 1. Blood samples for blood lactate, plasma total Ca, blood ionized calcium (Ca(2+)), blood pH, plasma inorganic phosphorus (P(i)) and plasma intact parathyroid hormone (PTH) were collected before, during and after SETs, and before and after the first, 16th, 32nd and 48th LSE sessions. During SETs there was a decrease in ionized Ca(2+) and a rise in lactate, P(i) and intact PTH. The LSEs resulted in an increase in pH and P(i), whereas lactate, ionized Ca(2+), total Ca and intact PTH were not affected. No changes in Ca metabolism were detected in the course of training. Results of this study suggest that the type of exercise influences Ca homeostasis and intact PTH response, but that these effects are not influenced in the course of the training period.

Machine learning approaches to diagnosis and laterality effects in semantic dementia discourse.

PubMed

Garrard, Peter; Rentoumi, Vassiliki; Gesierich, Benno; Miller, Bruce; Gorno-Tempini, Maria Luisa

2014-06-01

Advances in automatic text classification have been necessitated by the rapid increase in the availability of digital documents. Machine learning (ML) algorithms can 'learn' from data: for instance a ML system can be trained on a set of features derived from written texts belonging to known categories, and learn to distinguish between them. Such a trained system can then be used to classify unseen texts. In this paper, we explore the potential of the technique to classify transcribed speech samples along clinical dimensions, using vocabulary data alone. We report the accuracy with which two related ML algorithms [naive Bayes Gaussian (NBG) and naive Bayes multinomial (NBM)] categorized picture descriptions produced by: 32 semantic dementia (SD) patients versus 10 healthy, age-matched controls; and SD patients with left- (n = 21) versus right-predominant (n = 11) patterns of temporal lobe atrophy. We used information gain (IG) to identify the vocabulary features that were most informative to each of these two distinctions. In the SD versus control classification task, both algorithms achieved accuracies of greater than 90%. In the right- versus left-temporal lobe predominant classification, NBM achieved a high level of accuracy (88%), but this was achieved by both NBM and NBG when the features used in the training set were restricted to those with high values of IG. The most informative features for the patient versus control task were low frequency content words, generic terms and components of metanarrative statements. For the right versus left task the number of informative lexical features was too small to support any specific inferences. An enriched feature set, including values derived from Quantitative Production Analysis (QPA) may shed further light on this little understood distinction. Copyright © 2013 Elsevier Ltd. All rights reserved.

Parametric motion control of robotic arms: A biologically based approach using neural networks

NASA Technical Reports Server (NTRS)

Bock, O.; D'Eleuterio, G. M. T.; Lipitkas, J.; Grodski, J. J.

1993-01-01

A neural network based system is presented which is able to generate point-to-point movements of robotic manipulators. The foundation of this approach is the use of prototypical control torque signals which are defined by a set of parameters. The parameter set is used for scaling and shaping of these prototypical torque signals to effect a desired outcome of the system. This approach is based on neurophysiological findings that the central nervous system stores generalized cognitive representations of movements called synergies, schemas, or motor programs. It has been proposed that these motor programs may be stored as torque-time functions in central pattern generators which can be scaled with appropriate time and magnitude parameters. The central pattern generators use these parameters to generate stereotypical torque-time profiles, which are then sent to the joint actuators. Hence, only a small number of parameters need to be determined for each point-to-point movement instead of the entire torque-time trajectory. This same principle is implemented for controlling the joint torques of robotic manipulators where a neural network is used to identify the relationship between the task requirements and the torque parameters. Movements are specified by the initial robot position in joint coordinates and the desired final end-effector position in Cartesian coordinates. This information is provided to the neural network which calculates six torque parameters for a two-link system. The prototypical torque profiles (one per joint) are then scaled by those parameters. After appropriate training of the network, our parametric control design allowed the reproduction of a trained set of movements with relatively high accuracy, and the production of previously untrained movements with comparable accuracy. We conclude that our approach was successful in discriminating between trained movements and in generalizing to untrained movements.

Fast and accurate semantic annotation of bioassays exploiting a hybrid of machine learning and user confirmation.

PubMed

Clark, Alex M; Bunin, Barry A; Litterman, Nadia K; Schürer, Stephan C; Visser, Ubbo

2014-01-01

Bioinformatics and computer aided drug design rely on the curation of a large number of protocols for biological assays that measure the ability of potential drugs to achieve a therapeutic effect. These assay protocols are generally published by scientists in the form of plain text, which needs to be more precisely annotated in order to be useful to software methods. We have developed a pragmatic approach to describing assays according to the semantic definitions of the BioAssay Ontology (BAO) project, using a hybrid of machine learning based on natural language processing, and a simplified user interface designed to help scientists curate their data with minimum effort. We have carried out this work based on the premise that pure machine learning is insufficiently accurate, and that expecting scientists to find the time to annotate their protocols manually is unrealistic. By combining these approaches, we have created an effective prototype for which annotation of bioassay text within the domain of the training set can be accomplished very quickly. Well-trained annotations require single-click user approval, while annotations from outside the training set domain can be identified using the search feature of a well-designed user interface, and subsequently used to improve the underlying models. By drastically reducing the time required for scientists to annotate their assays, we can realistically advocate for semantic annotation to become a standard part of the publication process. Once even a small proportion of the public body of bioassay data is marked up, bioinformatics researchers can begin to construct sophisticated and useful searching and analysis algorithms that will provide a diverse and powerful set of tools for drug discovery researchers.

THE SYNTHETIC-OVERSAMPLING METHOD: USING PHOTOMETRIC COLORS TO DISCOVER EXTREMELY METAL-POOR STARS

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

Miller, A. A., E-mail: amiller@astro.caltech.edu

2015-09-20

Extremely metal-poor (EMP) stars ([Fe/H] ≤ −3.0 dex) provide a unique window into understanding the first generation of stars and early chemical enrichment of the universe. EMP stars are exceptionally rare, however, and the relatively small number of confirmed discoveries limits our ability to exploit these near-field probes of the first ∼500 Myr after the Big Bang. Here, a new method to photometrically estimate [Fe/H] from only broadband photometric colors is presented. I show that the method, which utilizes machine-learning algorithms and a training set of ∼170,000 stars with spectroscopically measured [Fe/H], produces a typical scatter of ∼0.29 dex. Thismore » performance is similar to what is achievable via low-resolution spectroscopy, and outperforms other photometric techniques, while also being more general. I further show that a slight alteration to the model, wherein synthetic EMP stars are added to the training set, yields the robust identification of EMP candidates. In particular, this synthetic-oversampling method recovers ∼20% of the EMP stars in the training set, at a precision of ∼0.05. Furthermore, ∼65% of the false positives from the model are very metal-poor stars ([Fe/H] ≤ −2.0 dex). The synthetic-oversampling method is biased toward the discovery of warm (∼F-type) stars, a consequence of the targeting bias from the Sloan Digital Sky Survey/Sloan Extension for Galactic Understanding survey. This EMP selection method represents a significant improvement over alternative broadband optical selection techniques. The models are applied to >12 million stars, with an expected yield of ∼600 new EMP stars, which promises to open new avenues for exploring the early universe.« less

Fast and accurate semantic annotation of bioassays exploiting a hybrid of machine learning and user confirmation

PubMed Central

Bunin, Barry A.; Litterman, Nadia K.; Schürer, Stephan C.; Visser, Ubbo

2014-01-01

Bioinformatics and computer aided drug design rely on the curation of a large number of protocols for biological assays that measure the ability of potential drugs to achieve a therapeutic effect. These assay protocols are generally published by scientists in the form of plain text, which needs to be more precisely annotated in order to be useful to software methods. We have developed a pragmatic approach to describing assays according to the semantic definitions of the BioAssay Ontology (BAO) project, using a hybrid of machine learning based on natural language processing, and a simplified user interface designed to help scientists curate their data with minimum effort. We have carried out this work based on the premise that pure machine learning is insufficiently accurate, and that expecting scientists to find the time to annotate their protocols manually is unrealistic. By combining these approaches, we have created an effective prototype for which annotation of bioassay text within the domain of the training set can be accomplished very quickly. Well-trained annotations require single-click user approval, while annotations from outside the training set domain can be identified using the search feature of a well-designed user interface, and subsequently used to improve the underlying models. By drastically reducing the time required for scientists to annotate their assays, we can realistically advocate for semantic annotation to become a standard part of the publication process. Once even a small proportion of the public body of bioassay data is marked up, bioinformatics researchers can begin to construct sophisticated and useful searching and analysis algorithms that will provide a diverse and powerful set of tools for drug discovery researchers. PMID:25165633

STS-60 Cosmonauts in Weightless Environment Training Facility (WETF) training

NASA Image and Video Library

1993-01-07

Russian Cosmonaut Vladimir Titov maneuvers a small life raft during bailout training at JSC's Weightless Environment Training Facility (WETF). Two SCUBA-equipped divers assisted Titov in the STS-60 training exercise.

Restaurant supervisor safety training: evaluating a small business training intervention.

PubMed

Bush, Diane; Paleo, Lyn; Baker, Robin; Dewey, Robin; Toktogonova, Nurgul; Cornelio, Deogracia

2009-01-01

We developed and assessed a program designed to help small business owners/managers conduct short training sessions with their employees, involve employees in identifying and addressing workplace hazards, and make workplace changes (including physical and work practice changes) to improve workplace safety. During 2006, in partnership with a major workers' compensation insurance carrier and a restaurant trade association, university-based trainers conducted workshops for more than 200 restaurant and food service owners/managers. Workshop participants completed posttests to assess their knowledge, attitudes, and intentions to implement health and safety changes. On-site follow-up interviews with 10 participants were conducted three to six months after the training to assess the extent to which program components were used and worksite changes were made. Post-training assessments demonstrated that attendees increased their understanding and commitment to health and safety, and felt prepared to provide health and safety training to their employees. Follow-up interviews indicated that participants incorporated core program concepts into their training and supervision practices. Participants conducted training, discussed workplace hazards and solutions with employees, and made changes in the workplace and work practices to improve workers' health and safety. This program demonstrated that owners of small businesses can adopt a philosophy of employee involvement in their health and safety programs if provided with simple, easy-to-use materials and a training demonstration. Attending a workshop where they can interact with other owners/ managers of small restaurants was also a key to the program's success.

National Wind Distance Learning Collaborative

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

Dr. James B. Beddow

2013-03-29

Executive Summary The energy development assumptions identified in the Department of Energy's position paper, 20% Wind Energy by 2030, projected an exploding demand for wind energy-related workforce development. These primary assumptions drove a secondary set of assumptions that early stage wind industry workforce development and training paradigms would need to undergo significant change if the workforce needs were to be met. The current training practice and culture within the wind industry is driven by a relatively small number of experts with deep field experience and knowledge. The current training methodology is dominated by face-to-face, classroom based, instructor present training. Givenmore » these assumptions and learning paradigms, the purpose of the National Wind Distance Learning Collaborative was to determine the feasibility of developing online learning strategies and products focused on training wind technicians. The initial project scope centered on (1) identifying resources that would be needed for development of subject matter and course design/delivery strategies for industry-based (non-academic) training, and (2) development of an appropriate Learning Management System (LMS). As the project unfolded, the initial scope was expanded to include development of learning products and the addition of an academic-based training partner. The core partners included two training entities, industry-based Airstreams Renewables and academic-based Lake Area Technical Institute. A third partner, Vision Video Interactive, Inc. provided technology-based learning platforms (hardware and software). The revised scope yielded an expanded set of results beyond the initial expectation. Eight learning modules were developed for the industry-based Electrical Safety course. These modules were subsequently redesigned and repurposed for test application in an academic setting. Software and hardware developments during the project's timeframe enabled redesign providing for student access through the use of tablet devices such as iPads. Early prototype Learning Management Systems (LMS) featuring more student-centric access and interfaces with emerging social media were developed and utilized during the testing applications. The project also produced soft results involving cross learning between and among the partners regarding subject matter expertise, online learning pedagogy, and eLearning technology-based platforms. The partners believe that the most significant, overarching accomplishment of the project was the development and implementation of goals, activities, and outcomes that significantly exceeded those proposed in the initial grant application submitted in 2009. Key specific accomplishments include: (1) development of a set of 8 online learning modules addressing electrical safety as it relates to the work of wind technicians; (3) development of a flexible, open-ended Learning Management System (LMS): (3) creation of a robust body of learning (knowledge, experience, skills, and relationships). Project leaders have concluded that there is substantial resource equity that could be leverage and recommend that it be carried forward to pursue a Next Stage Opportunity relating to development of an online core curriculum for institute and community college energy workforce development programs.« less

Interprofessional Communication Skills Training for Serious Illness: Evaluation of a Small-Group, Simulated Patient Intervention

PubMed Central

Bays, Alison M.; Engelberg, Ruth A.; Back, Anthony L.; Ford, Dee W.; Downey, Lois; Shannon, Sarah E.; Doorenbos, Ardith Z.; Edlund, Barbara; Christianson, Phyllis; Arnold, Richard W.; O'Connor, Kim; Kross, Erin K.; Reinke, Lynn F.; Cecere Feemster, Laura; Fryer-Edwards, Kelly; Alexander, Stewart C.; Tulsky, James A.

2014-01-01

Abstract Background: Communication with patients and families is an essential component of high-quality care in serious illness. Small-group skills training can result in new communication behaviors, but past studies have used facilitators with extensive experience, raising concerns this is not scalable. Objective: The objective was to investigate the effect of an experiential communication skills building workshop (Codetalk), led by newly trained facilitators, on internal medicine trainees' and nurse practitioner students' ability to communicate bad news and express empathy. Design: Trainees participated in Codetalk; skill improvement was evaluated through pre- and post- standardized patient (SP) encounters. Setting and subjects: The subjects were internal medicine residents and nurse practitioner students at two universities. Intervention and measurements: The study was carried out in anywhere from five to eight half-day sessions over a month. The first and last sessions included audiotaped trainee SP encounters coded for effective communication behaviors. The primary outcome was change in communication scores from pre-intervention to post-intervention. We also measured trainee characteristics to identify predictors of performance and change in performance over time. Results: We enrolled 145 trainees who completed pre- and post-intervention SP interviews—with participation rates of 52% for physicians and 14% for nurse practitioners. Trainees' scores improved in 8 of 11 coded behaviors (p<0.05). The only significant predictors of performance were having participated in the intervention (p<0.001) and study site (p<0.003). The only predictor of improvement in performance over time was participating in the intervention (p<0.001). Conclusions: A communication skills intervention using newly trained facilitators was associated with improvement in trainees' skills in giving bad news and expressing empathy. Improvement in communication skills did not vary by trainee characteristics. PMID:24180700

Deep multi-scale location-aware 3D convolutional neural networks for automated detection of lacunes of presumed vascular origin.

PubMed

Ghafoorian, Mohsen; Karssemeijer, Nico; Heskes, Tom; Bergkamp, Mayra; Wissink, Joost; Obels, Jiri; Keizer, Karlijn; de Leeuw, Frank-Erik; Ginneken, Bram van; Marchiori, Elena; Platel, Bram

2017-01-01

Lacunes of presumed vascular origin (lacunes) are associated with an increased risk of stroke, gait impairment, and dementia and are a primary imaging feature of the small vessel disease. Quantification of lacunes may be of great importance to elucidate the mechanisms behind neuro-degenerative disorders and is recommended as part of study standards for small vessel disease research. However, due to the different appearance of lacunes in various brain regions and the existence of other similar-looking structures, such as perivascular spaces, manual annotation is a difficult, elaborative and subjective task, which can potentially be greatly improved by reliable and consistent computer-aided detection (CAD) routines. In this paper, we propose an automated two-stage method using deep convolutional neural networks (CNN). We show that this method has good performance and can considerably benefit readers. We first use a fully convolutional neural network to detect initial candidates. In the second step, we employ a 3D CNN as a false positive reduction tool. As the location information is important to the analysis of candidate structures, we further equip the network with contextual information using multi-scale analysis and integration of explicit location features. We trained, validated and tested our networks on a large dataset of 1075 cases obtained from two different studies. Subsequently, we conducted an observer study with four trained observers and compared our method with them using a free-response operating characteristic analysis. Shown on a test set of 111 cases, the resulting CAD system exhibits performance similar to the trained human observers and achieves a sensitivity of 0.974 with 0.13 false positives per slice. A feasibility study also showed that a trained human observer would considerably benefit once aided by the CAD system.

Effective Sequential Classifier Training for SVM-Based Multitemporal Remote Sensing Image Classification

NASA Astrophysics Data System (ADS)

Guo, Yiqing; Jia, Xiuping; Paull, David

2018-06-01

The explosive availability of remote sensing images has challenged supervised classification algorithms such as Support Vector Machines (SVM), as training samples tend to be highly limited due to the expensive and laborious task of ground truthing. The temporal correlation and spectral similarity between multitemporal images have opened up an opportunity to alleviate this problem. In this study, a SVM-based Sequential Classifier Training (SCT-SVM) approach is proposed for multitemporal remote sensing image classification. The approach leverages the classifiers of previous images to reduce the required number of training samples for the classifier training of an incoming image. For each incoming image, a rough classifier is firstly predicted based on the temporal trend of a set of previous classifiers. The predicted classifier is then fine-tuned into a more accurate position with current training samples. This approach can be applied progressively to sequential image data, with only a small number of training samples being required from each image. Experiments were conducted with Sentinel-2A multitemporal data over an agricultural area in Australia. Results showed that the proposed SCT-SVM achieved better classification accuracies compared with two state-of-the-art model transfer algorithms. When training data are insufficient, the overall classification accuracy of the incoming image was improved from 76.18% to 94.02% with the proposed SCT-SVM, compared with those obtained without the assistance from previous images. These results demonstrate that the leverage of a priori information from previous images can provide advantageous assistance for later images in multitemporal image classification.

Sharing a Personal Trainer: Personal and Social Benefits of Individualized, Small-Group Training.

PubMed

Wayment, Heidi A; McDonald, Rachael L

2017-11-01

Wayment, HA and McDonald, RL. Sharing a personal trainer: personal and social benefits of individualized, small-group training. J Strength Cond Res 31(11): 3137-3145, 2017-We examined a novel personal fitness training program that combines personal training principles in a small-group training environment. In a typical training session, exercisers warm-up together but receive individualized training for 50 minutes with 1-5 other adults who range in age, exercise experience, and goals for participation. Study participants were 98 regularly exercising adult members of a fitness studio in the southwestern United States (64 women and 32 men), aged 19-78 years (mean, 46.52 years; SD = 14.15). Average membership time was 2 years (range, 1-75 months; mean, 23.54 months; SD = 20.10). In collaboration with the program directors, we developed a scale to assess satisfaction with key features of this unique training program. Participants completed an online survey in Fall 2015. Hypotheses were tested with a serial mediator model (model 6) using the SPSS PROCESS module. In support of the basic tenets of self-determination theory, satisfaction with small-group, individualized training supported basic psychological needs, which in turn were associated with greater autonomous exercise motivation and life satisfaction. Satisfaction with this unique training method was also associated with greater exercise self-efficacy. Autonomous exercise motivation was associated with both exercise self-efficacy and greater self-reported health and energy. Discussion focuses on why exercise programs that foster a sense of social belonging (in addition to motivation and efficacy) may be helpful for successful adherence to an exercise program.

Team Training and Retention of Skills Acquired Above Real Time Training on a Flight Simulator

NASA Technical Reports Server (NTRS)

Ali, Syed Friasat; Guckenberger, Dutch; Crane, Peter; Rossi, Marcia; Williams, Mayard; Williams, Jason; Archer, Matt

2000-01-01

Above Real-Time Training (ARTT) is the training acquired on a real time simulator when it is modified to present events at a faster pace than normal. The experiments related to training of pilots performed by NASA engineers (Kolf in 1973, Hoey in 1976) and others (Guckenberger, Crane and their associates in the nineties) have shown that in comparison with the real time training (RTT), ARTT provides the following benefits: increased rate of skill acquisition, reduced simulator and aircraft training time, and more effective training for emergency procedures. Two sets of experiments have been performed; they are reported in professional conferences and the respective papers are included in this report. The retention of effects of ARTT has been studied in the first set of experiments and the use of ARTT as top-off training has been examined in the second set of experiments. In ARTT, the pace of events was 1.5 times the pace in RTT. In both sets of experiments, university students were trained to perform an aerial gunnery task. The training unit was equipped with a joystick and a throttle. The student acted as a nose gunner in a hypothetical two place attack aircraft. The flight simulation software was installed on a Universal Distributed Interactive Simulator platform supplied by ECC International of Orlando, Florida. In the first set of experiments, two training programs RTT or ART7 were used. Students were then tested in real time on more demanding scenarios: either immediately after training or two days later. The effects of ARTT did not decrease over a two day retention interval and ARTT was more time efficient than real time training. Therefore, equal test performance could be achieved with less clock-time spent in the simulator. In the second set of experiments three training programs RTT or ARTT or RARTT, were used. In RTT, students received 36 minutes of real time training. In ARTT, students received 36 minutes of above real time training. In RARTT, students received 18 minutes of real time training and 18 minutes of above real time training as top-off training. Students were then tested in real time on more demanding scenarios. The use of ARTT as top-off training after RTT offered better training than RTT alone or ARTT alone. It is, however, suggested that a similar experiment be conducted on a relatively more complex task with a larger sample of participants. Within the proposed duration of the research effort, the setting up of experiments and trial runs on using ARTT for team training were also scheduled but they could not be accomplished due to extra ordinary challenges faced in developing the required software configuration. Team training is, however, scheduled in a future study sponsored by NASA at Tuskegee University.

Budget Online Learning Algorithm for Least Squares SVM.

PubMed

Jian, Ling; Shen, Shuqian; Li, Jundong; Liang, Xijun; Li, Lei

2017-09-01

Batch-mode least squares support vector machine (LSSVM) is often associated with unbounded number of support vectors (SVs'), making it unsuitable for applications involving large-scale streaming data. Limited-scale LSSVM, which allows efficient updating, seems to be a good solution to tackle this issue. In this paper, to train the limited-scale LSSVM dynamically, we present a budget online LSSVM (BOLSSVM) algorithm. Methodologically, by setting a fixed budget for SVs', we are able to update the LSSVM model according to the updated SVs' set dynamically without retraining from scratch. In particular, when a new small chunk of SVs' substitute for the old ones, the proposed algorithm employs a low rank correction technology and the Sherman-Morrison-Woodbury formula to compute the inverse of saddle point matrix derived from the LSSVM's Karush-Kuhn-Tucker (KKT) system, which, in turn, updates the LSSVM model efficiently. In this way, the proposed BOLSSVM algorithm is especially useful for online prediction tasks. Another merit of the proposed BOLSSVM is that it can be used for k -fold cross validation. Specifically, compared with batch-mode learning methods, the computational complexity of the proposed BOLSSVM method is significantly reduced from O(n 4 ) to O(n 3 ) for leave-one-out cross validation with n training samples. The experimental results of classification and regression on benchmark data sets and real-world applications show the validity and effectiveness of the proposed BOLSSVM algorithm.

The dynamics of copper intercalated molybdenum ditelluride

NASA Astrophysics Data System (ADS)

Onofrio, Nicolas; Guzman, David; Strachan, Alejandro

2016-11-01

Layered transition metal dichalcogenides are emerging as key materials in nanoelectronics and energy applications. Predictive models to understand their growth, thermomechanical properties, and interaction with metals are needed in order to accelerate their incorporation into commercial products. Interatomic potentials enable large-scale atomistic simulations connecting first principle methods and devices. We present a ReaxFF reactive force field to describe molybdenum ditelluride and its interactions with copper. We optimized the force field parameters to describe the energetics, atomic charges, and mechanical properties of (i) layered MoTe2, Mo, and Cu in various phases, (ii) the intercalation of Cu atoms and small clusters within the van der Waals gap of MoTe2, and (iii) bond dissociation curves. The training set consists of an extensive set of first principles calculations computed using density functional theory (DFT). We validate the force field via the prediction of the adhesion of a single layer MoTe2 on a Cu(111) surface and find good agreement with DFT results not used in the training set. We characterized the mobility of the Cu ions intercalated into MoTe2 under the presence of an external electric field via finite temperature molecular dynamics simulations. The results show a significant increase in drift velocity for electric fields of approximately 0.4 V/Å and that mobility increases with Cu ion concentration.

Quantifying Neuromuscular Fatigue Induced by an Intense Training Session in Rugby Sevens.

PubMed

Marrier, Bruno; Le Meur, Yann; Robineau, Julien; Lacome, Mathieu; Couderc, Anthony; Hausswirth, Christophe; Piscione, Julien; Morin, Jean-Benoît

2017-02-01

To compare the sensitivity of a sprint vs a countermovement-jump (CMJ) test after an intense training session in international rugby sevens players, as well as analyze the effects of fatigue on sprint acceleration. Thirteen international rugby sevens players completed two 30-m sprints and a set of 4 repetitions of CMJ before and after a highly demanding rugby sevens training session. Change in CMJ height was unclear (-3.6%; ±90% confidence limits 11.9%. Chances of a true positive/trivial/negative change: 24/10/66%), while a very likely small increase in 30-m sprint time was observed (1.0%; ±0.7%, 96/3/1%). A very likely small decrease in the maximum horizontal theoretical velocity (V 0 ) (-2.4; ±1.8%, 1/4/95%) was observed. A very large correlation (r = -.79 ± .23) between the variations of V 0 and 30-m-sprint performance was also observed. Changes in 30-m sprint time were negatively and very largely correlated with the distance covered above the maximal aerobic speed (r = -.71 ± .32). The CMJ test appears to be less sensitive than the sprint test, which casts doubts on the usefulness of a vertical-jump test in sports such as rugby that mainly involve horizontal motions. The decline in sprint performance relates more to a decrease in velocity than in force capability and is correlated with the distance covered at high intensity.

Deciding for themselves.

PubMed

Mccormack, J; Nelson, C

1985-11-01

World Education, in a collaboration with PfP/International and with funding from US AID, has begun comprehensive program in Kenya that offers non-governmental organizations non-formal training, technical assistance in organization and business management, and financial assistance in the form of loans for revolving credit funds. The approach emphasizes Kenyans deciding for themselves about the directions projects should take. This article discusses the Tototo Home Industries' rural economic development program. After receiving a loan from Tototo, the women of Bofu village planned to stock their small village shop with matches, kerosene, soap, salt, and cooking oil. The remainder of the loan was saved to purchase future stock. For this project, bookkeeping and management skills were necessary. To meet this need, the Tototo small business advisor designed a simple cash boom system to be used by all the groups. Sessions in accounting were included in the annual training of trainers workshop. Currently, accounts advisor visit the groups monthly to provide follow-up training and assistance to ensure the women understand how to record project transactions accurately. The lesson to be drawn from these projects is simple. It is not unrealistic to set high expectations for project participants, but it is important to remain aware of the difficulty of the new concepts presented to these groups. In order for them to adequately master both concept and practice, the participants must be given sufficient time and support. In fact, consistent follow-up and close contact with the villages is the key to Tototo's success.

Supervised neural network classification of pre-sliced cooked pork ham images using quaternionic singular values.

PubMed

Valous, Nektarios A; Mendoza, Fernando; Sun, Da-Wen; Allen, Paul

2010-03-01

The quaternionic singular value decomposition is a technique to decompose a quaternion matrix (representation of a colour image) into quaternion singular vector and singular value component matrices exposing useful properties. The objective of this study was to use a small portion of uncorrelated singular values, as robust features for the classification of sliced pork ham images, using a supervised artificial neural network classifier. Images were acquired from four qualities of sliced cooked pork ham typically consumed in Ireland (90 slices per quality), having similar appearances. Mahalanobis distances and Pearson product moment correlations were used for feature selection. Six highly discriminating features were used as input to train the neural network. An adaptive feedforward multilayer perceptron classifier was employed to obtain a suitable mapping from the input dataset. The overall correct classification performance for the training, validation and test set were 90.3%, 94.4%, and 86.1%, respectively. The results confirm that the classification performance was satisfactory. Extracting the most informative features led to the recognition of a set of different but visually quite similar textural patterns based on quaternionic singular values. Copyright 2009 Elsevier Ltd. All rights reserved.

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.

Systematic review of skills transfer after surgical simulation-based training.

PubMed

Dawe, S R; Pena, G N; Windsor, J A; Broeders, J A J L; Cregan, P C; Hewett, P J; Maddern, G J

2014-08-01

Simulation-based training assumes that skills are directly transferable to the patient-based setting, but few studies have correlated simulated performance with surgical performance. A systematic search strategy was undertaken to find studies published since the last systematic review, published in 2007. Inclusion of articles was determined using a predetermined protocol, independent assessment by two reviewers and a final consensus decision. Studies that reported on the use of surgical simulation-based training and assessed the transferability of the acquired skills to a patient-based setting were included. Twenty-seven randomized clinical trials and seven non-randomized comparative studies were included. Fourteen studies investigated laparoscopic procedures, 13 endoscopic procedures and seven other procedures. These studies provided strong evidence that participants who reached proficiency in simulation-based training performed better in the patient-based setting than their counterparts who did not have simulation-based training. Simulation-based training was equally as effective as patient-based training for colonoscopy, laparoscopic camera navigation and endoscopic sinus surgery in the patient-based setting. These studies strengthen the evidence that simulation-based training, as part of a structured programme and incorporating predetermined proficiency levels, results in skills transfer to the operative setting. © 2014 BJS Society Ltd. Published by John Wiley & Sons Ltd.

33 CFR 334.280 - James River between the entrance to Skiffes Creek and Mulberry Point, Va.; army training and...

Code of Federal Regulations, 2010 CFR

2010-07-01

... to Skiffes Creek and Mulberry Point, Va.; army training and small craft testing area. 334.280 Section... DEFENSE DANGER ZONE AND RESTRICTED AREA REGULATIONS § 334.280 James River between the entrance to Skiffes Creek and Mulberry Point, Va.; army training and small craft testing area. (a) The restricted area...

Differences in safety training among smaller and larger construction firms with non-native workers: Evidence of overlapping vulnerabilities

PubMed Central

Guerin, Rebecca J.; Keller, Brenna M.; Flynn, Michael A.; Salgado, Cathy; Hudson, Dennis

2017-01-01

Collaborative efforts between the National Institute for Occupational Safety and Health (NIOSH) and the American Society of Safety Engineers (ASSE) led to a report focusing on overlapping occupational vulnerabilities, specifically small construction businesses employing young, non-native workers. Following the report, an online survey was conducted by ASSE with construction business representatives focusing on training experiences of non-native workers. Results were grouped by business size (50 or fewer employees or more than 50 employees). Smaller businesses were less likely to employ a supervisor who speaks the same language as immigrant workers (p < .001). Non-native workers in small businesses received fewer hours of both initial safety training (p = .005) and monthly ongoing safety training (p = .042). Immigrant workers in smaller businesses were less likely to receive every type of safety training identified in the survey (including pre-work safety orientation [p < .001], job-specific training [p < .001], OSHA 10-hour training [p = .001], and federal/state required training [p < .001]). The results highlight some of the challenges a vulnerable worker population faces in a small business, and can be used to better focus intervention efforts. Among businesses represented in this sample, there are deflcits in the amount, frequency, and format of workplace safety and health training provided to non-native workers in smaller construction businesses compared to those in larger businesses. The types of training conducted for non-native workers in small business were less likely to take into account the language and literacy issues faced by these workers. The findings suggest the need for a targeted approach in providing occupational safety and health training to non-native workers employed by smaller construction businesses. PMID:29375194

Vocational Education and Training in Small Ethnic Minority Businesses in the UK

ERIC Educational Resources Information Center

Hussain, Javed; Matlay, Harry

2007-01-01

Purpose: This paper seeks to investigate the provision of Vocational Education and Training (VET) in Small Ethnic Minority Businesses (SEMBs) operating in the West Midlands region of the UK. Design/methodology/approach: A qualitative approach is employed, involving in-depth, face-to-face interviews with 66 owner/managers of small ethnic minority…

Differences in Physiological Responses to Interval Training in Cyclists With and Without Interval Training Experience

PubMed Central

Hebisz, Rafal; Borkowski, Jacek; Zatoń, Marek

2016-01-01

Abstract The aim of this study was to determine differences in glycolytic metabolite concentrations and work output in response to an all-out interval training session in 23 cyclists with at least 2 years of interval training experience (E) and those inexperienced (IE) in this form of training. The intervention involved subsequent sets of maximal intensity exercise on a cycle ergometer. Each set comprised four 30 s repetitions interspersed with 90 s recovery periods; sets were repeated when blood pH returned to 7.3. Measurements of post-exercise hydrogen (H+) and lactate ion (LA-) concentrations and work output were taken. The experienced cyclists performed significantly more sets of maximal efforts than the inexperienced athletes (5.8 ± 1.2 vs. 4.3 ± 0.9 sets, respectively). Work output decreased in each subsequent set in the IE group and only in the last set in the E group. Distribution of power output changed only in the E group; power decreased in the initial repetitions of set only to increase in the final repetitions. H+ concentration decreased in the third, penultimate, and last sets in the E group and in each subsequent set in the IE group. LA- decreased in the last set in both groups. In conclusion, the experienced cyclists were able to repeatedly induce elevated levels of lactic acidosis. Power output distribution changed with decreased acid–base imbalance. In this way, this group could compensate for a decreased anaerobic metabolism. The above factors allowed cyclists experienced in interval training to perform more sets of maximal exercise without a decrease in power output compared with inexperienced cyclists. PMID:28149346

An accelerated training method for back propagation networks

NASA Technical Reports Server (NTRS)

Shelton, Robert O. (Inventor)

1993-01-01

The principal objective is to provide a training procedure for a feed forward, back propagation neural network which greatly accelerates the training process. A set of orthogonal singular vectors are determined from the input matrix such that the standard deviations of the projections of the input vectors along these singular vectors, as a set, are substantially maximized, thus providing an optimal means of presenting the input data. Novelty exists in the method of extracting from the set of input data, a set of features which can serve to represent the input data in a simplified manner, thus greatly reducing the time/expense to training the system.

Effects and Dose-Response Relationships of Motor Imagery Practice on Strength Development in Healthy Adult Populations: a Systematic Review and Meta-analysis.

PubMed

Paravlic, Armin H; Slimani, Maamer; Tod, David; Marusic, Uros; Milanovic, Zoran; Pisot, Rado

2018-05-01

Motor imagery (MI), a mental simulation of a movement without overt muscle contraction, has been largely used to improve general motor tasks. However, the effects of MI practice on maximal voluntary strength (MVS) remain equivocal. The aims of this meta-analysis were to (1) estimate whether MI practice intervention can meaningfully improve MVS in healthy adults; (2) compare the effects of MI practice on MVS with its combination with physical practice (MI-C), and with physical practice (PP) training alone; and (3) investigate the dose-response relationships of MI practice. Seven electronic databases were searched up to April 2017. Initially 717 studies were identified; however, after evaluation of the study characteristics, data from 13 articles involving 370 participants were extracted. The meta-analysis was completed on MVS as the primary parameter. In addition, parameters associated with training volume, training intensity, and time spent training were used to investigate dose-response relationships. MI practice moderately improved MVS. When compared to conventional PP, effects were of small benefit in favour of PP. MI-C when compared to PP showed unclear effects. MI practice produced moderate effects in both upper and lower extremities on MVS. The cortical representation area of the involved muscles did not modify the effects. Meta-regression analysis revealed that (a) a training period of 4 weeks, (b) a frequency of three times per week, (c) two to three sets per single session, (d) 25 repetitions per single set, and (e) single session duration of 15 min were associated with enhanced improvements in muscle strength following MI practice. Similar dose-response relationships were observed following MI and PP. The present meta-analysis demonstrates that compared to a no-exercise control group of healthy adults, MI practice increases MVS, but less than PP. These findings suggest that MI practice could be considered as a substitute or additional training tool to preserve muscle function when athletes are not exposed to maximal training intensities.

Pilot test of a peer-led small-group video intervention to promote mammography screening among Chinese American immigrants

PubMed Central

Maxwell, Annette E.; Wang, Judy H.; Young, Lucy; Crespi, Catherine M.; Mistry, Ritesh; Sudan, Madhuri; Bastani, Roshan

2010-01-01

This study evaluated the feasibility, acceptability and potential effect of a small-group video intervention led by trained Chinese American lay educators who recruited Chinese American women not up to date on mammography screening. Nine lay educators conducted 14 “breast health tea time workshops” in community settings and private homes that started with watching a culturally tailored video promoting screening followed by a question and answer session and distribution of print materials. Many group attendees did not have health insurance or a regular doctor, had low levels of income and were not proficient in English. Forty-four percent of the attendees reported receipt of a mammogram within 6 months after the small-group session with higher odds of screening among women who had lived in the U.S. less than 10% of their lifetime. Four of the educators were very interested in conducting another group session in the next 6 months. PMID:20720095

Cross-domain and multi-task transfer learning of deep convolutional neural network for breast cancer diagnosis in digital breast tomosynthesis

NASA Astrophysics Data System (ADS)

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

2018-02-01

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

The Potential of Distance Education and Training for Small and Medium-Sized Enterprises in the Mediterranean Countries of the European Community. A Report for the Commission of the European Communities--Task Force Human Resources, Education, Training, and Youth.

ERIC Educational Resources Information Center

Quintino, Luisa

An evaluation was made of the training needs of the small and medium-sized enterprises (SMEs) in Portugal, Spain, Greece, and Italy and the potential of open, distance, flexible, and multimedia learning to meet those needs. The methodology included contacts with training providers, governmental institutions, and SMEs and circulation of…

Detection of Thermal Erosion Gullies from High-Resolution Images Using Deep Learning

NASA Astrophysics Data System (ADS)

Huang, L.; Liu, L.; Jiang, L.; Zhang, T.; Sun, Y.

2017-12-01

Thermal erosion gullies, one type of thermokarst landforms, develop due to thawing of ice-rich permafrost. Mapping the location and extent of thermal erosion gullies can help understand the spatial distribution of thermokarst landforms and their temporal evolution. Remote sensing images provide an effective way for mapping thermokarst landforms, especially thermokarst lakes. However, thermal erosion gullies are challenging to map from remote sensing images due to their small sizes and significant variations in geometric/radiometric properties. It is feasible to manually identify these features, as a few previous studies have carried out. However manual methods are labor-intensive, therefore, cannot be used for a large study area. In this work, we conduct automatic mapping of thermal erosion gullies from high-resolution images by using Deep Learning. Our study area is located in Eboling Mountain (Qinghai, China). Within a 6 km2 peatland area underlain by ice-rich permafrost, at least 20 thermal erosional gullies are well developed. The image used is a 15-cm-resolution Digital Orthophoto Map (DOM) generated in July 2016. First, we extracted 14 gully patches and ten non-gully patches as training data. And we performed image augmentation. Next, we fine-tuned the pre-trained model of DeepLab, a deep-learning algorithm for semantic image segmentation based on Deep Convolutional Neural Networks. Then, we performed inference on the whole DOM and obtained intermediate results in forms of polygons for all identified gullies. At last, we removed misidentified polygons based on a few pre-set criteria on the size and shape of each polygon. Our final results include 42 polygons. Validated against field measurements using GPS, most of the gullies are detected correctly. There are 20 false detections due to the small number and low quality of training images. We also found three new gullies that missed in the field observations. This study shows that (1) despite a challenging mapping task, DeepLab can detect small, irregular-shaped thermal erosion gullies with high accuracy. (2) Automatic detection is critical for mapping thermal erosion gully since manual mapping or field work may miss some targets even in a relatively small region. (3) The quantity and quality of training data are crucial for detection accuracy.

Pattern classifier for health monitoring of helicopter gearboxes

NASA Technical Reports Server (NTRS)

Chin, Hsinyung; Danai, Kourosh; Lewicki, David G.

1993-01-01

The application of a newly developed diagnostic method to a helicopter gearbox is demonstrated. This method is a pattern classifier which uses a multi-valued influence matrix (MVIM) as its diagnostic model. The method benefits from a fast learning algorithm, based on error feedback, that enables it to estimate gearbox health from a small set of measurement-fault data. The MVIM method can also assess the diagnosability of the system and variability of the fault signatures as the basis to improve fault signatures. This method was tested on vibration signals reflecting various faults in an OH-58A main rotor transmission gearbox. The vibration signals were then digitized and processed by a vibration signal analyzer to enhance and extract various features of the vibration data. The parameters obtained from this analyzer were utilized to train and test the performance of the MVIM method in both detection and diagnosis. The results indicate that the MVIM method provided excellent detection results when the full range of faults effects on the measurements were included in training, and it had a correct diagnostic rate of 95 percent when the faults were included in training.

A Generic Deep-Learning-Based Approach for Automated Surface Inspection.

PubMed

Ren, Ruoxu; Hung, Terence; Tan, Kay Chen

2018-03-01

Automated surface inspection (ASI) is a challenging task in industry, as collecting training dataset is usually costly and related methods are highly dataset-dependent. In this paper, a generic approach that requires small training data for ASI is proposed. First, this approach builds classifier on the features of image patches, where the features are transferred from a pretrained deep learning network. Next, pixel-wise prediction is obtained by convolving the trained classifier over input image. An experiment on three public and one industrial data set is carried out. The experiment involves two tasks: 1) image classification and 2) defect segmentation. The results of proposed algorithm are compared against several best benchmarks in literature. In the classification tasks, the proposed method improves accuracy by 0.66%-25.50%. In the segmentation tasks, the proposed method reduces error escape rates by 6.00%-19.00% in three defect types and improves accuracies by 2.29%-9.86% in all seven defect types. In addition, the proposed method achieves 0.0% error escape rate in the segmentation task of industrial data.

Data-driven simultaneous fault diagnosis for solid oxide fuel cell system using multi-label pattern identification

NASA Astrophysics Data System (ADS)

Li, Shuanghong; Cao, Hongliang; Yang, Yupu

2018-02-01

Fault diagnosis is a key process for the reliability and safety of solid oxide fuel cell (SOFC) systems. However, it is difficult to rapidly and accurately identify faults for complicated SOFC systems, especially when simultaneous faults appear. In this research, a data-driven Multi-Label (ML) pattern identification approach is proposed to address the simultaneous fault diagnosis of SOFC systems. The framework of the simultaneous-fault diagnosis primarily includes two components: feature extraction and ML-SVM classifier. The simultaneous-fault diagnosis approach can be trained to diagnose simultaneous SOFC faults, such as fuel leakage, air leakage in different positions in the SOFC system, by just using simple training data sets consisting only single fault and not demanding simultaneous faults data. The experimental result shows the proposed framework can diagnose the simultaneous SOFC system faults with high accuracy requiring small number training data and low computational burden. In addition, Fault Inference Tree Analysis (FITA) is employed to identify the correlations among possible faults and their corresponding symptoms at the system component level.

Internal medicine residency redesign: proposal of the Internal Medicine Working Group.

PubMed

Horwitz, Ralph I; Kassirer, Jerome P; Holmboe, Eric S; Humphrey, Holly J; Verghese, Abraham; Croft, Carol; Kwok, Minjung; Loscalzo, Joseph

2011-09-01

Concerned with the quality of internal medicine training, many leaders in the field assembled to assess the state of the residency, evaluate the decline in interest in the specialty, and create a framework for invigorating the discipline. Although many external factors are responsible, we also found ourselves culpable: allowing senior role models to opt out of important training activities, ignoring a progressive atrophy of bedside skills, and focusing on lock-step curricula, lectures, and compiled diagnostic and therapeutic strategies. The group affirmed its commitment to a vision of internal medicine rooted in science and learned with mentors at the bedside. Key factors for new emphasis include patient-centered small group teaching, greater incorporation of clinical epidemiology and health services research, and better schedule control for trainees. Because previous proposals were weakened by lack of evidence, we propose to organize the Cooperative Educational Studies Group, a pool of training programs that will collect a common data set describing their programs, design interventions to be tested rigorously in multi-methodological approaches, and at the same time produce knowledge about high-quality practice. Copyright © 2011 Elsevier Inc. All rights reserved.

Young workers in the construction industry and initial OSH-training when entering work life.

PubMed

Holte, Kari Anne; Kjestveit, Kari

2012-01-01

Studies have found that young workers are at risk for injuries. The risk for accidents is high within construction, indicating that young workers may be especially vulnerable in this industry. In Norway, it is possible to enter the construction industry as a full time worker at the age of 18. The aim of this paper was to explore how young construction workers are received at their workplace with regards to OHS-training. The study was designed as a qualitative case study. Each case consisted of a young worker or apprentice (< 25 years), a colleague, the immediate superior, the OHS manager, and a safety representative in the company. The interviews were recorded and analyzed through content analysis. The results showed that there were differences between large and small companies, where large companies had more formalized routines and systems for receiving and training young workers. These routines were however more dependent on requirements set by legislators and contractors more than by company size, since the legislation has different requirements with impact on OHS.

Automated Identification of Northern Leaf Blight-Infected Maize Plants from Field Imagery Using Deep Learning.

PubMed

DeChant, Chad; Wiesner-Hanks, Tyr; Chen, Siyuan; Stewart, Ethan L; Yosinski, Jason; Gore, Michael A; Nelson, Rebecca J; Lipson, Hod

2017-11-01

Northern leaf blight (NLB) can cause severe yield loss in maize; however, scouting large areas to accurately diagnose the disease is time consuming and difficult. We demonstrate a system capable of automatically identifying NLB lesions in field-acquired images of maize plants with high reliability. This approach uses a computational pipeline of convolutional neural networks (CNNs) that addresses the challenges of limited data and the myriad irregularities that appear in images of field-grown plants. Several CNNs were trained to classify small regions of images as containing NLB lesions or not; their predictions were combined into separate heat maps, then fed into a final CNN trained to classify the entire image as containing diseased plants or not. The system achieved 96.7% accuracy on test set images not used in training. We suggest that such systems mounted on aerial- or ground-based vehicles can help in automated high-throughput plant phenotyping, precision breeding for disease resistance, and reduced pesticide use through targeted application across a variety of plant and disease categories.

48 CFR 819.502-2 - Total small business set-asides.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 48 Federal Acquisition Regulations System 5 2010-10-01 2010-10-01 false Total small business set... SOCIOECONOMIC PROGRAMS SMALL BUSINESS PROGRAMS Set-Asides for Small Business 819.502-2 Total small business set-asides. (a) When a total small business set-aside is made, one of the following statements, as applicable...

48 CFR 819.502-2 - Total small business set-asides.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 48 Federal Acquisition Regulations System 5 2011-10-01 2011-10-01 false Total small business set... SOCIOECONOMIC PROGRAMS SMALL BUSINESS PROGRAMS Set-Asides for Small Business 819.502-2 Total small business set-asides. (a) When a total small business set-aside is made, one of the following statements, as applicable...

48 CFR 819.502-2 - Total small business set-asides.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 48 Federal Acquisition Regulations System 5 2013-10-01 2013-10-01 false Total small business set... SOCIOECONOMIC PROGRAMS SMALL BUSINESS PROGRAMS Set-Asides for Small Business 819.502-2 Total small business set-asides. (a) When a total small business set-aside is made, one of the following statements, as applicable...

48 CFR 19.502-2 - Total small business set-asides.

Code of Federal Regulations, 2012 CFR

2012-10-01

... 48 Federal Acquisition Regulations System 1 2012-10-01 2012-10-01 false Total small business set... SOCIOECONOMIC PROGRAMS SMALL BUSINESS PROGRAMS Set-Asides for Small Business 19.502-2 Total small business set... contracting officer does not proceed with the small business set-aside and purchases on an unrestricted basis...

48 CFR 19.502-2 - Total small business set-asides.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 48 Federal Acquisition Regulations System 1 2011-10-01 2011-10-01 false Total small business set... SOCIOECONOMIC PROGRAMS SMALL BUSINESS PROGRAMS Set-Asides for Small Business 19.502-2 Total small business set... contracting officer does not proceed with the small business set-aside and purchases on an unrestricted basis...

48 CFR 19.502-2 - Total small business set-asides.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 48 Federal Acquisition Regulations System 1 2013-10-01 2013-10-01 false Total small business set... SOCIOECONOMIC PROGRAMS SMALL BUSINESS PROGRAMS Set-Asides for Small Business 19.502-2 Total small business set... contracting officer does not proceed with the small business set-aside and purchases on an unrestricted basis...

48 CFR 819.502-2 - Total small business set-asides.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 48 Federal Acquisition Regulations System 5 2014-10-01 2014-10-01 false Total small business set... SOCIOECONOMIC PROGRAMS SMALL BUSINESS PROGRAMS Set-Asides for Small Business 819.502-2 Total small business set-asides. (a) When a total small business set-aside is made, one of the following statements, as applicable...

48 CFR 819.502-2 - Total small business set-asides.

Code of Federal Regulations, 2012 CFR

2012-10-01

... 48 Federal Acquisition Regulations System 5 2012-10-01 2012-10-01 false Total small business set... SOCIOECONOMIC PROGRAMS SMALL BUSINESS PROGRAMS Set-Asides for Small Business 819.502-2 Total small business set-asides. (a) When a total small business set-aside is made, one of the following statements, as applicable...

An ecological evaluation of the metabolic benefits due to robot-assisted gait training.

PubMed

Peri, E; Biffi, E; Maghini, C; Marzorati, M; Diella, E; Pedrocchi, A; Turconi, A C; Reni, G

2015-08-01

Cerebral palsy (CP), one of the most common neurological disorders in childhood, features affected individual's motor skills and muscle actions. This results in elevated heart rate and rate of oxygen uptake during sub-maximal exercise, thus indicating a mean energy expenditure higher than healthy subjects. Rehabilitation, currently involving also robot-based devices, may have an impact also on these aspects. In this study, an ecological setting has been proposed to evaluate the energy expenditure of 4 children with CP before and after a robot-assisted gait training. Even if the small sample size makes it difficult to give general indications, results presented here are promising. Indeed, children showed an increasing trend of the energy expenditure per minute and a decreasing trend of the energy expenditure per step, in accordance to the control group. These data suggest a metabolic benefit of the treatment that may increase the locomotion efficiency of disabled children.

Degradation analysis in the estimation of photometric redshifts from non-representative training sets

NASA Astrophysics Data System (ADS)

Rivera, J. D.; Moraes, B.; Merson, A. I.; Jouvel, S.; Abdalla, F. B.; Abdalla, M. C. B.

2018-07-01

We perform an analysis of photometric redshifts estimated by using a non-representative training sets in magnitude space. We use the ANNz2 and GPz algorithms to estimate the photometric redshift both in simulations and in real data from the Sloan Digital Sky Survey (DR12). We show that for the representative case, the results obtained by using both algorithms have the same quality, using either magnitudes or colours as input. In order to reduce the errors when estimating the redshifts with a non-representative training set, we perform the training in colour space. We estimate the quality of our results by using a mock catalogue which is split samples cuts in the r band between 19.4 < r < 20.8. We obtain slightly better results with GPz on single point z-phot estimates in the complete training set case, however the photometric redshifts estimated with ANNz2 algorithm allows us to obtain mildly better results in deeper r-band cuts when estimating the full redshift distribution of the sample in the incomplete training set case. By using a cumulative distribution function and a Monte Carlo process, we manage to define a photometric estimator which fits well the spectroscopic distribution of galaxies in the mock testing set, but with a larger scatter. To complete this work, we perform an analysis of the impact on the detection of clusters via density of galaxies in a field by using the photometric redshifts obtained with a non-representative training set.

Degradation analysis in the estimation of photometric redshifts from non-representative training sets

NASA Astrophysics Data System (ADS)

Rivera, J. D.; Moraes, B.; Merson, A. I.; Jouvel, S.; Abdalla, F. B.; Abdalla, M. C. B.

2018-04-01

We perform an analysis of photometric redshifts estimated by using a non-representative training sets in magnitude space. We use the ANNz2 and GPz algorithms to estimate the photometric redshift both in simulations as well as in real data from the Sloan Digital Sky Survey (DR12). We show that for the representative case, the results obtained by using both algorithms have the same quality, either using magnitudes or colours as input. In order to reduce the errors when estimating the redshifts with a non-representative training set, we perform the training in colour space. We estimate the quality of our results by using a mock catalogue which is split samples cuts in the r-band between 19.4 < r < 20.8. We obtain slightly better results with GPz on single point z-phot estimates in the complete training set case, however the photometric redshifts estimated with ANNz2 algorithm allows us to obtain mildly better results in deeper r-band cuts when estimating the full redshift distribution of the sample in the incomplete training set case. By using a cumulative distribution function and a Monte-Carlo process, we manage to define a photometric estimator which fits well the spectroscopic distribution of galaxies in the mock testing set, but with a larger scatter. To complete this work, we perform an analysis of the impact on the detection of clusters via density of galaxies in a field by using the photometric redshifts obtained with a non-representative training set.

Data Programming: Creating Large Training Sets, Quickly.

PubMed

Ratner, Alexander; De Sa, Christopher; Wu, Sen; Selsam, Daniel; Ré, Christopher

2016-12-01

Large labeled training sets are the critical building blocks of supervised learning methods and are key enablers of deep learning techniques. For some applications, creating labeled training sets is the most time-consuming and expensive part of applying machine learning. We therefore propose a paradigm for the programmatic creation of training sets called data programming in which users express weak supervision strategies or domain heuristics as labeling functions , which are programs that label subsets of the data, but that are noisy and may conflict. We show that by explicitly representing this training set labeling process as a generative model, we can "denoise" the generated training set, and establish theoretically that we can recover the parameters of these generative models in a handful of settings. We then show how to modify a discriminative loss function to make it noise-aware, and demonstrate our method over a range of discriminative models including logistic regression and LSTMs. Experimentally, on the 2014 TAC-KBP Slot Filling challenge, we show that data programming would have led to a new winning score, and also show that applying data programming to an LSTM model leads to a TAC-KBP score almost 6 F1 points over a state-of-the-art LSTM baseline (and into second place in the competition). Additionally, in initial user studies we observed that data programming may be an easier way for non-experts to create machine learning models when training data is limited or unavailable.

Data Programming: Creating Large Training Sets, Quickly

PubMed Central

Ratner, Alexander; De Sa, Christopher; Wu, Sen; Selsam, Daniel; Ré, Christopher

2018-01-01

Large labeled training sets are the critical building blocks of supervised learning methods and are key enablers of deep learning techniques. For some applications, creating labeled training sets is the most time-consuming and expensive part of applying machine learning. We therefore propose a paradigm for the programmatic creation of training sets called data programming in which users express weak supervision strategies or domain heuristics as labeling functions, which are programs that label subsets of the data, but that are noisy and may conflict. We show that by explicitly representing this training set labeling process as a generative model, we can “denoise” the generated training set, and establish theoretically that we can recover the parameters of these generative models in a handful of settings. We then show how to modify a discriminative loss function to make it noise-aware, and demonstrate our method over a range of discriminative models including logistic regression and LSTMs. Experimentally, on the 2014 TAC-KBP Slot Filling challenge, we show that data programming would have led to a new winning score, and also show that applying data programming to an LSTM model leads to a TAC-KBP score almost 6 F1 points over a state-of-the-art LSTM baseline (and into second place in the competition). Additionally, in initial user studies we observed that data programming may be an easier way for non-experts to create machine learning models when training data is limited or unavailable. PMID:29872252

Task Analysis of Tactical Leadership Skills for Bradley Infantry Fighting Vehicle Leaders

DTIC Science & Technology

1986-10-01

The Bradley Leader Trainer is conceptualized as a device or set of de - vices that can be used to teach Bradley leaders to perform their full set of...experts. The task list was examined to de - termine critical training requirements, requirements for training device sup- port of this training, and...Functions/ j ITask | |Task | |Task | [Training j , To Further De - | ;Critical Train- | iTninir

Chinese National Optical Education Small Private Online Course system

NASA Astrophysics Data System (ADS)

Zhang, XiaoJie; Lin, YuanFang; Liu, Xu; Liu, XiangDong; Cen, ZhaoFeng; Li, XiaoTong; Zheng, XiaoDong; Wang, XiaoPing

2017-08-01

In order to realize the sharing of high quality course resources and promote the deep integration of `Internet+' higher education and talent training, a new on-line to off-line specialized courses teaching mode was explored in Chinese colleges and universities, which emphasized different teaching places, being organized asynchronously and localized. The latest progress of the Chinese National Optical Education Small Private On-line Course (CNOESPOC) system set up by Zhejiang University and other colleges and universities having disciplines in the field of optics and photonics under the guidance of the Chinese National Steering Committee of Optics and Photonics (CNSCOP) was introduced in this paper. The On-line to Off-line (O2O) optical education teaching resource sharing practice offers a new good example for higher education in China under the background of Internet +.

Guided training relative to direct skill training for individuals with cognitive impairments after stroke: a pilot randomized trial

PubMed Central

Skidmore, Elizabeth R.; Butters, Meryl; Whyte, Ellen; Grattan, Emily; Shen, Jennifer; Terhorst, Lauren

2016-01-01

Objective To examine the effects of direct skill training and guided training for promoting independence after stroke. Design Single-blind randomized pilot study. Setting Inpatient rehabilitation facility. Participants Forty-three participants in inpatient rehabilitation with acute stroke and cognitive impairments. Interventions Participants were randomized to receive direct skill training (n=22, 10 sessions as adjunct to usual inpatient rehabilitation) or guided training (n=21, same dose). Main Outcome Measure The Functional Independence Measure assessed independence at baseline, rehabilitation discharge, and months 3, 6, and 12. Results Linear mixed models (random intercept, other effects fixed) revealed a significant intervention by time interaction (F4,150=5.11, p<0.001), a significant main effect of time (F4,150=49.25, p<0.001), and a significant effect of stroke severity (F1,150=34.46, p<.001). There was no main effect of intervention (F1,150=0.07, p=0.79). Change in Functional Independence Measures scores was greater for the DIRECT group at rehabilitation discharge (effect size of between group differences, d=0.28) and greater for the GUIDE group at months 3 (d=0.16), 6 (d=0.39), and 12 (d=0.53). The difference between groups in mean 12 month change scores was 10.57 points. Conclusions Guided training, provided in addition to usual care, offered a small advantage in the recovery of independence, relative to direct skill training. Future studies examining guided training in combination with other potentially potent intervention elements may further advise best practices in rehabilitation for individuals with cognitive impairments after acute stroke. PMID:27794487

Principles to Consider in Defining New Directions in Internal Medicine Training and Certification

PubMed Central

Turner, Barbara J; Centor, Robert M; Rosenthal, Gary E

2006-01-01

SGIM endoreses seven principles related to current thinking about internal medicine training: 1) internal medicine requires a full three years of residency training before subspecialization; 2) internal medicine residency programs must dramatically increase support for training in the ambulatory setting and offer equivalent opportunities for training in both inpatient and outpatient medicine; 3) in settings where adequate support and time are devoted to ambulatory training, the third year of residency could offer an opportunity to develop further expertise or mastery in a specific type or setting of care; 4) further certification in specific specialties within internal medicine requires the completion of an approved fellowship program; 5) areas of mastery in internal medicine can be demonstrated through modified board certification and recertification examinations; 6) certification processes throughout internal medicine should focus increasingly on demonstration of clinical competence through adherence to validated standards of care within and across practice settings; and 7) regardless of the setting in which General Internists practice, we should unite to promote the critical role that this specialty serves in patient care. PMID:16637826

Understanding the impact of interprofessional collaboration on the quality of care: a case report from a small-scale resource limited health care environment.

PubMed

Busari, Jamiu O; Moll, Franka M; Duits, Ashley J

2017-01-01

A critical assessment of current health care practices, as well as the training needs of various health care providers, is crucial for improving patient care. Several approaches have been proposed for defining these needs with attention on communication as a key competency for effective collaboration. Taking our cultural context, resource limitations, and small-scale setting into account, we researched the applicability of a mixed focus group approach for analysis of the communication between doctors and nurses, as well as the measures for improvement. Assessment of nurse-physician communication perception in patient care in a Caribbean setting. Focus group sessions consisting of nurses, interns, and medical specialists were conducted using an ethnographic approach, paying attention to existing communication, risk evaluation, and recommendations for improvement. Data derived from the focus group sessions were analyzed by thematic synthesis method with descriptive themes and development of analytic themes. The initial focus group sessions produced an extensive list of key recommendations which could be clustered into three domains (standardization, sustainment, and collaboration). Further discussion of these domains in focus groups showed nurses' and physicians' domain perspectives and effects on patient care to be broadly similar. Risks related to lack of information, knowledge sharing, and professional respect were clearly described by the participants. The described mixed focus group session approach for effectively determining current interprofessional communication and key improvement areas seems suitable for our small-scale, limited resource setting. The impact of the cultural context should be further evaluated by a similar study in a different cultural context.

Robust Statistical Fusion of Image Labels

PubMed Central

Landman, Bennett A.; Asman, Andrew J.; Scoggins, Andrew G.; Bogovic, John A.; Xing, Fangxu; Prince, Jerry L.

2011-01-01

Image labeling and parcellation (i.e. assigning structure to a collection of voxels) are critical tasks for the assessment of volumetric and morphometric features in medical imaging data. The process of image labeling is inherently error prone as images are corrupted by noise and artifacts. Even expert interpretations are subject to subjectivity and the precision of the individual raters. Hence, all labels must be considered imperfect with some degree of inherent variability. One may seek multiple independent assessments to both reduce this variability and quantify the degree of uncertainty. Existing techniques have exploited maximum a posteriori statistics to combine data from multiple raters and simultaneously estimate rater reliabilities. Although quite successful, wide-scale application has been hampered by unstable estimation with practical datasets, for example, with label sets with small or thin objects to be labeled or with partial or limited datasets. As well, these approaches have required each rater to generate a complete dataset, which is often impossible given both human foibles and the typical turnover rate of raters in a research or clinical environment. Herein, we propose a robust approach to improve estimation performance with small anatomical structures, allow for missing data, account for repeated label sets, and utilize training/catch trial data. With this approach, numerous raters can label small, overlapping portions of a large dataset, and rater heterogeneity can be robustly controlled while simultaneously estimating a single, reliable label set and characterizing uncertainty. The proposed approach enables many individuals to collaborate in the construction of large datasets for labeling tasks (e.g., human parallel processing) and reduces the otherwise detrimental impact of rater unavailability. PMID:22010145

Solid harmonic wavelet scattering for predictions of molecule properties

NASA Astrophysics Data System (ADS)

Eickenberg, Michael; Exarchakis, Georgios; Hirn, Matthew; Mallat, Stéphane; Thiry, Louis

2018-06-01

We present a machine learning algorithm for the prediction of molecule properties inspired by ideas from density functional theory (DFT). Using Gaussian-type orbital functions, we create surrogate electronic densities of the molecule from which we compute invariant "solid harmonic scattering coefficients" that account for different types of interactions at different scales. Multilinear regressions of various physical properties of molecules are computed from these invariant coefficients. Numerical experiments show that these regressions have near state-of-the-art performance, even with relatively few training examples. Predictions over small sets of scattering coefficients can reach a DFT precision while being interpretable.

What shape do UK trainees want their training to be? Results of a cross-sectional study

PubMed Central

Harries, Rhiannon L; Rashid, Mustafa; Smitham, Peter; Vesey, Alex; McGregor, Richard; Scheeres, Karl; Bailey, Jon; Sohaib, Syed Mohammed Afzal; Prior, Matthew; Frost, Jonathan; Al-Deeb, Walid; Kugathasan, Gana; Gokani, Vimal J

2016-01-01

Objectives The British Government is acting on recommendations to overhaul postgraduate training to meet the needs of the changing population, to produce generalist doctors undergoing shorter broad-based training (Greenaway Review). Only 45 doctors in training were involved in the consultation process. This study aims to obtain a focused perspective on the proposed reforms by doctors in training from across specialities. Design Prospective, questionnaire-based cross-sectional study. Setting/participants Following validation, a 31-item electronic questionnaire was distributed via trainee organisations and Postgraduate Local Education and Training Board (LETB) mailing lists. Throughout the 10-week study period, the survey was publicised on several social media platforms. Results Of the 3603 demographically representative respondents, 69% knew about proposed changes. Of the respondents, 73% expressed a desire to specialise, with 54% keen to provide general emergency cover. A small proportion (12%) stated that current training pathway length is too long, although 86% felt that it is impossible to achieve independent practitioner-level proficiency in a shorter period of time than is currently required. Opinions regarding credentialing were mixed, but tended towards disagreement. The vast majority (97%) felt credentialing should not be funded by doctors in training. Respondents preferred longer placement lengths with increasing career progression. Doctors in training value early generalised training (65%), with suggestions for further improvement. Conclusions This is the first large-scale cross-specialty study regarding the Shape of Training Review. Although there are recommendations which trainees support, it is clear that one size does not fit all. Most trainees are keen to provide a specialist service on an emergency generalist background. Credentialing is a contentious issue; however, we believe removing aspects from curricula into post-Certificate of Completion of Training (CCT) credentialing programmes with shortened specialty training routes only degrades the current consultant expertise, and does not serve the population. Educational needs, not political winds, should drive changes in postgraduate medical education and all stakeholders should be involved. PMID:27855084

Working with Men Who Batter.

ERIC Educational Resources Information Center

Edleson, Jeffrey L.

1984-01-01

Examines factors associated with wife abuse and describes major components of a small group program designed to help men who batter. These include self-observation, cognitive restructuring, interpersonal skills training, relaxation training, and establishing a small group environment for intervention. (JAC)

A self-trained classification technique for producing 30 m percent-water maps from Landsat data

USGS Publications Warehouse

Rover, Jennifer R.; Wylie, Bruce K.; Ji, Lei

2010-01-01

Small bodies of water can be mapped with moderate-resolution satellite data using methods where water is mapped as subpixel fractions using field measurements or high-resolution images as training datasets. A new method, developed from a regression-tree technique, uses a 30 m Landsat image for training the regression tree that, in turn, is applied to the same image to map subpixel water. The self-trained method was evaluated by comparing the percent-water map with three other maps generated from established percent-water mapping methods: (1) a regression-tree model trained with a 5 m SPOT 5 image, (2) a regression-tree model based on endmembers and (3) a linear unmixing classification technique. The results suggest that subpixel water fractions can be accurately estimated when high-resolution satellite data or intensively interpreted training datasets are not available, which increases our ability to map small water bodies or small changes in lake size at a regional scale.

Dose-volume histogram prediction using density estimation.

PubMed

Skarpman Munter, Johanna; Sjölund, Jens

2015-09-07

Knowledge of what dose-volume histograms can be expected for a previously unseen patient could increase consistency and quality in radiotherapy treatment planning. We propose a machine learning method that uses previous treatment plans to predict such dose-volume histograms. The key to the approach is the framing of dose-volume histograms in a probabilistic setting.The training consists of estimating, from the patients in the training set, the joint probability distribution of some predictive features and the dose. The joint distribution immediately provides an estimate of the conditional probability of the dose given the values of the predictive features. The prediction consists of estimating, from the new patient, the distribution of the predictive features and marginalizing the conditional probability from the training over this. Integrating the resulting probability distribution for the dose yields an estimate of the dose-volume histogram.To illustrate how the proposed method relates to previously proposed methods, we use the signed distance to the target boundary as a single predictive feature. As a proof-of-concept, we predicted dose-volume histograms for the brainstems of 22 acoustic schwannoma patients treated with stereotactic radiosurgery, and for the lungs of 9 lung cancer patients treated with stereotactic body radiation therapy. Comparing with two previous attempts at dose-volume histogram prediction we find that, given the same input data, the predictions are similar.In summary, we propose a method for dose-volume histogram prediction that exploits the intrinsic probabilistic properties of dose-volume histograms. We argue that the proposed method makes up for some deficiencies in previously proposed methods, thereby potentially increasing ease of use, flexibility and ability to perform well with small amounts of training data.

A Neural Network Aero Design System for Advanced Turbo-Engines

NASA Technical Reports Server (NTRS)

Sanz, Jose M.

1999-01-01

An inverse design method calculates the blade shape that produces a prescribed input pressure distribution. By controlling this input pressure distribution the aerodynamic design objectives can easily be met. Because of the intrinsic relationship between pressure distribution and airfoil physical properties, a Neural Network can be trained to choose the optimal pressure distribution that would meet a set of physical requirements. Neural network systems have been attempted in the context of direct design methods. From properties ascribed to a set of blades the neural network is trained to infer the properties of an 'interpolated' blade shape. The problem is that, especially in transonic regimes where we deal with intrinsically non linear and ill posed problems, small perturbations of the blade shape can produce very large variations of the flow parameters. It is very unlikely that, under these circumstances, a neural network will be able to find the proper solution. The unique situation in the present method is that the neural network can be trained to extract the required input pressure distribution from a database of pressure distributions while the inverse method will still compute the exact blade shape that corresponds to this 'interpolated' input pressure distribution. In other words, the interpolation process is transferred to a smoother problem, namely, finding what pressure distribution would produce the required flow conditions and, once this is done, the inverse method will compute the exact solution for this problem. The use of neural network is, in this context, highly related to the use of proper optimization techniques. The optimization is used essentially as an automation procedure to force the input pressure distributions to achieve the required aero and structural design parameters. A multilayered feed forward network with back-propagation is used to train the system for pattern association and classification.

Using an interactive DVD about type 2 diabetes and insulin therapy in a UK South Asian community and in patient education and healthcare provider training.

PubMed

Patel, Naina; Stone, Margaret A; Hadjiconstantinou, Michelle; Hiles, Steve; Troughton, Jacqui; Martin-Stacey, Lorraine; Daly, Heather; Carey, Marian; Khulpateea, Anita; Davies, Melanie J; Khunti, Kamlesh

2015-09-01

To develop and pilot-test the feasibility and effectiveness of an interactive DVD about misconceptions within South Asian communities regarding insulin treatment in type 2 diabetes, for educating patients and community members and training healthcare providers. The project setting was a South Asian (mainly Indian) community in Leicester, UK. Qualitative evidence from our previous studies was used to inform the content of the DVD script and accompanying resources. The intervention involved three components: facilitating DVD viewings for people with/without diabetes in community settings; training healthcare providers involved in managing South Asian patients with diabetes in primary care; and using the DVD and resources in primary care patient consultations. Evaluation involved a range of approaches including face-to-face interviews, telephone feedback and questionnaires. Analysis of questionnaires and qualitative feedback from community participants showed some significant changes in attitudes and understanding about insulin and high acceptability of the DVD. Healthcare providers who attended the training found it informative and perceived the DVD and visual resources as potentially useful for facilitating acceptance of insulin. Primary care patient recruitment was challenging, but participants described the DVD as an acceptable and informative way of learning about insulin therapy. The DVD intervention was effective and feasible at community and healthcare provider levels. Although based on a small sample, at patient level our findings suggested that the DVD worked at different levels helping some to accept the need for insulin and others to consolidate a decision to commence this treatment. Consideration needs to be given to patient engagement strategies for implementation in primary care consultations. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

BUMPER v1.0: a Bayesian user-friendly model for palaeo-environmental reconstruction

NASA Astrophysics Data System (ADS)

Holden, Philip B.; Birks, H. John B.; Brooks, Stephen J.; Bush, Mark B.; Hwang, Grace M.; Matthews-Bird, Frazer; Valencia, Bryan G.; van Woesik, Robert

2017-02-01

We describe the Bayesian user-friendly model for palaeo-environmental reconstruction (BUMPER), a Bayesian transfer function for inferring past climate and other environmental variables from microfossil assemblages. BUMPER is fully self-calibrating, straightforward to apply, and computationally fast, requiring ˜ 2 s to build a 100-taxon model from a 100-site training set on a standard personal computer. We apply the model's probabilistic framework to generate thousands of artificial training sets under ideal assumptions. We then use these to demonstrate the sensitivity of reconstructions to the characteristics of the training set, considering assemblage richness, taxon tolerances, and the number of training sites. We find that a useful guideline for the size of a training set is to provide, on average, at least 10 samples of each taxon. We demonstrate general applicability to real data, considering three different organism types (chironomids, diatoms, pollen) and different reconstructed variables. An identically configured model is used in each application, the only change being the input files that provide the training-set environment and taxon-count data. The performance of BUMPER is shown to be comparable with weighted average partial least squares (WAPLS) in each case. Additional artificial datasets are constructed with similar characteristics to the real data, and these are used to explore the reasons for the differing performances of the different training sets.

Funding Continuing Training in Small and Medium-Sized Enterprises: Discussion and Case Studies from across the EU. CEDEFOP Panorama Series.

ERIC Educational Resources Information Center

Pukkinen, Tommi; Romijn, Clemens; Elson-Rogers, Sarah

There are three main parts to this report of a study that used case studies to showcase the different approaches used to encourage more continuing training within small and medium-sized enterprises (SMEs) across the European Union (EU). Section 1 discusses the importance of funding training in SMEs and highlights the various types of funding…

Education and Training that Meets the Needs of Small Business: List of 198 Studies with Abstracts and Reasons for Exclusion. Support Document

ERIC Educational Resources Information Center

Dawe, Susan; Naidu, Radhika; Harris, Lee-Ann

2007-01-01

This document lists 198 studies with abstracts and reasons for exclusion in support of the main report, "Education and Training that Meets the Needs of Small Business: A Systematic Review of Research" (ED499699). [This work has been produced with funding provided through the Australian Department of Education, Science and Training. For a…

Community Capacity Building in Regional VET: Small Business and Developing an Integrated Lifelong Learning Community.

ERIC Educational Resources Information Center

Plane, Karen

In a competitive market training economy, vocational education and training (VET) and small business in Australia face a number of challenges. They need to qualify the extent of lifelong learning skills being used in the small firm workplace, define the range of learning partnerships both within VET and the wider informal learning community in…

Learning to change taxonomies

NASA Astrophysics Data System (ADS)

Eneva, Elena; Petrushin, Valery A.

2002-03-01

Taxonomies are valuable tools for structuring and representing our knowledge about the world. They are widely used in many domains, where information about species, products, customers, publications, etc. needs to be organized. In the absence of standards, many taxonomies of the same entities can co-exist. A problem arises when data categorized in a particular taxonomy needs to be used by a procedure (methodology or algorithm) that uses a different taxonomy. Usually, a labor-intensive manual approach is used to solve this problem. This paper describes a machine learning approach which aids domain experts in changing taxonomies. It allows learning relationships between two taxonomies and mapping the data from one taxonomy into another. The proposed approach uses decision trees and bootstrapping for learning mappings of instances from the source to the target taxonomies. A C4.5 decision tree classifier is trained on a small manually labeled training set and applied to a randomly selected sample from the unlabeled data. The classification results are analyzed and the misclassified items are corrected and all items are added to the training set. This procedure is iterated until unlabeled data is available or an acceptable error rate is reached. In the latter case the last classifier is used to label all the remaining data. We test our approach on a database of products obtained from as grocery store chain and find that it performs well, reaching 92.6% accuracy while requiring the human expert to explicitly label only 18% of the entire data.

Effect of confinement in small space flight size cages on insulin sensitivity of exercise-trained rats

NASA Technical Reports Server (NTRS)

Mondon, C. E.; Dolkas, C. B.; Reaven, G. M.

1983-01-01

The effect of confinement in small cages (simulating the size to be used in future space Shuttle missions) on insulin sensitivity was studied in rats having an increased insulin sensitivity due to exercise training prior to confinement. Oral glucose tolerance tests (OGTT) were given to both control and exercise-trained rats before and after placement in the small cages for 7 days. The insulin resistance was assessed by the product of the area of the insulin and glucose curves of the OGTT (IG index). Results show that the values obtained before confinement were one-half as high in exercise-trained rats as those in control rats, reflecting an increased sensitivity to insulin with exercise training. After 7 days confinement, the IG index was found to be not significantly different from initial values for both control and exercise-trained rats. These findings suggest that increased insulin sensitivity in exercise-trained rats persists 7 days after cessation of running activity. The data also indicate that exercise training, before flight, may be beneficial in minimizing the loss of insulin sensitivity expected with decreased use of gravity dependent muscles during exposure to hypogravity in space flight.

Comparison of molecular breeding values based on within- and across-breed training in beef cattle

PubMed Central

2013-01-01

Background Although the efficacy of genomic predictors based on within-breed training looks promising, it is necessary to develop and evaluate across-breed predictors for the technology to be fully applied in the beef industry. The efficacies of genomic predictors trained in one breed and utilized to predict genetic merit in differing breeds based on simulation studies have been reported, as have the efficacies of predictors trained using data from multiple breeds to predict the genetic merit of purebreds. However, comparable studies using beef cattle field data have not been reported. Methods Molecular breeding values for weaning and yearling weight were derived and evaluated using a database containing BovineSNP50 genotypes for 7294 animals from 13 breeds in the training set and 2277 animals from seven breeds (Angus, Red Angus, Hereford, Charolais, Gelbvieh, Limousin, and Simmental) in the evaluation set. Six single-breed and four across-breed genomic predictors were trained using pooled data from purebred animals. Molecular breeding values were evaluated using field data, including genotypes for 2227 animals and phenotypic records of animals born in 2008 or later. Accuracies of molecular breeding values were estimated based on the genetic correlation between the molecular breeding value and trait phenotype. Results With one exception, the estimated genetic correlations of within-breed molecular breeding values with trait phenotype were greater than 0.28 when evaluated in the breed used for training. Most estimated genetic correlations for the across-breed trained molecular breeding values were moderate (> 0.30). When molecular breeding values were evaluated in breeds that were not in the training set, estimated genetic correlations clustered around zero. Conclusions Even for closely related breeds, within- or across-breed trained molecular breeding values have limited prediction accuracy for breeds that were not in the training set. For breeds in the training set, across- and within-breed trained molecular breeding values had similar accuracies. The benefit of adding data from other breeds to a within-breed training population is the ability to produce molecular breeding values that are more robust across breeds and these can be utilized until enough training data has been accumulated to allow for a within-breed training set. PMID:23953034

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.

Sample Selection for Training Cascade Detectors.

PubMed

Vállez, Noelia; Deniz, Oscar; Bueno, Gloria

2015-01-01

Automatic detection systems usually require large and representative training datasets in order to obtain good detection and false positive rates. Training datasets are such that the positive set has few samples and/or the negative set should represent anything except the object of interest. In this respect, the negative set typically contains orders of magnitude more images than the positive set. However, imbalanced training databases lead to biased classifiers. In this paper, we focus our attention on a negative sample selection method to properly balance the training data for cascade detectors. The method is based on the selection of the most informative false positive samples generated in one stage to feed the next stage. The results show that the proposed cascade detector with sample selection obtains on average better partial AUC and smaller standard deviation than the other compared cascade detectors.

The Small Business Challenge to Management Education.

ERIC Educational Resources Information Center

Gibb, Allan A.

1983-01-01

Reviews the needs of the owner/manager for training and development and indicates how they might best be met. Discusses the nature of management in small business and explores implications for training methods. Also discusses program possibilities and marketing strategies. (JOW)

The Impact of Protein Structure and Sequence Similarity on the Accuracy of Machine-Learning Scoring Functions for Binding Affinity Prediction

PubMed Central

Peng, Jiangjun; Leung, Yee; Leung, Kwong-Sak; Wong, Man-Hon; Lu, Gang; Ballester, Pedro J.

2018-01-01

It has recently been claimed that the outstanding performance of machine-learning scoring functions (SFs) is exclusively due to the presence of training complexes with highly similar proteins to those in the test set. Here, we revisit this question using 24 similarity-based training sets, a widely used test set, and four SFs. Three of these SFs employ machine learning instead of the classical linear regression approach of the fourth SF (X-Score which has the best test set performance out of 16 classical SFs). We have found that random forest (RF)-based RF-Score-v3 outperforms X-Score even when 68% of the most similar proteins are removed from the training set. In addition, unlike X-Score, RF-Score-v3 is able to keep learning with an increasing training set size, becoming substantially more predictive than X-Score when the full 1105 complexes are used for training. These results show that machine-learning SFs owe a substantial part of their performance to training on complexes with dissimilar proteins to those in the test set, against what has been previously concluded using the same data. Given that a growing amount of structural and interaction data will be available from academic and industrial sources, this performance gap between machine-learning SFs and classical SFs is expected to enlarge in the future. PMID:29538331

The Impact of Protein Structure and Sequence Similarity on the Accuracy of Machine-Learning Scoring Functions for Binding Affinity Prediction.

PubMed

Li, Hongjian; Peng, Jiangjun; Leung, Yee; Leung, Kwong-Sak; Wong, Man-Hon; Lu, Gang; Ballester, Pedro J

2018-03-14

It has recently been claimed that the outstanding performance of machine-learning scoring functions (SFs) is exclusively due to the presence of training complexes with highly similar proteins to those in the test set. Here, we revisit this question using 24 similarity-based training sets, a widely used test set, and four SFs. Three of these SFs employ machine learning instead of the classical linear regression approach of the fourth SF (X-Score which has the best test set performance out of 16 classical SFs). We have found that random forest (RF)-based RF-Score-v3 outperforms X-Score even when 68% of the most similar proteins are removed from the training set. In addition, unlike X-Score, RF-Score-v3 is able to keep learning with an increasing training set size, becoming substantially more predictive than X-Score when the full 1105 complexes are used for training. These results show that machine-learning SFs owe a substantial part of their performance to training on complexes with dissimilar proteins to those in the test set, against what has been previously concluded using the same data. Given that a growing amount of structural and interaction data will be available from academic and industrial sources, this performance gap between machine-learning SFs and classical SFs is expected to enlarge in the future.

The effectiveness and cost-effectiveness of parent training/education programmes for the treatment of conduct disorder, including oppositional defiant disorder, in children.

PubMed

Dretzke, J; Frew, E; Davenport, C; Barlow, J; Stewart-Brown, S; Sandercock, J; Bayliss, S; Raftery, J; Hyde, C; Taylor, R

2005-12-01

To assess the clinical and cost-effectiveness of parent training programmes for the treatment of children with conduct disorder (CD) up to the age of 18 years. Electronic databases. For the effectiveness review, relevant studies were identified and evaluated. A quantitative synthesis of behavioural outcomes across trials was also undertaken using two approaches: vote counting and meta-analysis. The economic analysis consisted of reviewing previous economic/cost evaluations of parent training/education programmes and the economic information within sponsor's submissions; carrying out a detailed exploration of costs of parent training/education programmes; and a de novo modelling assessment of the cost-effectiveness of parent training/education programmes. The potential budget impact to the health service of implementing such programmes was also considered. Many of the 37 randomised controlled trials that met the review inclusion and exclusion criteria were assessed as being of poor methodological quality. Studies were clinically heterogeneous in terms of the population, type of parent training/education programme and content, setting, delivery, length and child behaviour outcomes used. Both vote counting and meta-analysis revealed a consistent trend across all studies towards short-term effectiveness (up to 4 months) of parent training/education programmes (compared with control) as measured by a change in child behaviour. Pooled estimates showed a statistically significant improvement on the Eyberg Child Behaviour Inventory frequency and intensity scales, the Dyadic Parent-Child Interaction Coding System and the Child Behaviour Checklist. No studies reported a statistically significant result favouring control over parent training/education programmes. There were few statistically significant differences between different parent training/education programmes, although there was a trend towards more intensive interventions (e.g. longer contact hours, additional child involvement) being more effective. The cost of treating CD is high, with costs incurred by many agencies. A recent study suggested that by age 28, costs for individuals with CD were around 10 times higher than for those with no problems, with a mean cost of 70,019 pounds sterling. Criminality incurs the greatest cost, followed by educational provision, foster and residential care and state benefits. Only a small proportion of these costs fall on health services. Using a 'bottom-up' costing approach, the costs per family of providing parent training/education programmes range from 629 pounds sterling to 3839 pounds sterling depending on the type and style of delivery. Using the conservative assumption that there are no cost savings from treatment, a total lifetime quality of life gain of 0.1 would give a cost per quality-adjusted life-year of between 38,393 pounds sterling and 6288 pounds sterling depending on the type of programme delivery and setting. Parent training/education programmes appear to be an effective and potentially cost-effective therapy for children with CD. However, the relative effectiveness and cost-effectiveness of different models (such as therapy intensity and setting) require further investigation. Further research is required on the impact of parent training/education programmes on the quality of life of children with CD and their parents/carers, as well as on longer term child outcomes.

Estimation of the chemical-induced eye injury using a weight-of-evidence (WoE) battery of 21 artificial neural network (ANN) c-QSAR models (QSAR-21): part I: irritation potential.

PubMed

Verma, Rajeshwar P; Matthews, Edwin J

2015-03-01

Evaluation of potential chemical-induced eye injury through irritation and corrosion is required to ensure occupational and consumer safety for industrial, household and cosmetic ingredient chemicals. The historical method for evaluating eye irritant and corrosion potential of chemicals is the rabbit Draize test. However, the Draize test is controversial and its use is diminishing - the EU 7th Amendment to the Cosmetic Directive (76/768/EEC) and recast Regulation now bans marketing of new cosmetics having animal testing of their ingredients and requires non-animal alternative tests for safety assessments. Thus, in silico and/or in vitro tests are advocated. QSAR models for eye irritation have been reported for several small (congeneric) data sets; however, large global models have not been described. This report describes FDA/CFSAN's development of 21 ANN c-QSAR models (QSAR-21) to predict eye irritation using the ADMET Predictor program and a diverse training data set of 2928 chemicals. The 21 models had external (20% test set) and internal validation and average training/verification/test set statistics were: 88/88/85(%) sensitivity and 82/82/82(%) specificity, respectively. The new method utilized multiple artificial neural network (ANN) molecular descriptor selection functionalities to maximize the applicability domain of the battery. The eye irritation models will be used to provide information to fill the critical data gaps for the safety assessment of cosmetic ingredient chemicals. Copyright © 2014 Elsevier Inc. All rights reserved.

Coping with challenging behaviours of children with autism: effectiveness of brief training workshop for frontline staff in special education settings.

PubMed

Ling, C Y M; Mak, W W S

2012-03-01

The present study examined the effectiveness of three staff training elements: psychoeducation (PE) on autism, introduction of functional behavioural analysis (FBA) and emotional management (EM), on the reaction of challenging behaviours for frontline staff towards children with autism in Hong Kong special education settings. A sample of 311 frontline staff in educational settings was recruited to one of the three conditions: control, PE-FBA and PE-FBA-EM groups. A total of 175 participants completed all three sets of questionnaires during pre-training, immediate post-training and 1-month follow-up. Findings showed that the one-session staff training workshop increased staff knowledge of autism and perceived efficacy but decrease helping behavioural intention. In spite of the limited effectiveness of a one-session staff training workshop, continued staff training is still necessary for the improvement of service quality. Further exploration on how to change emotion response of staff is important. © 2011 The Authors. Journal of Intellectual Disability Research © 2011 Blackwell Publishing Ltd.

Splendidly blended: a machine learning set up for CDU control

NASA Astrophysics Data System (ADS)

Utzny, Clemens

2017-06-01

As the concepts of machine learning and artificial intelligence continue to grow in importance in the context of internet related applications it is still in its infancy when it comes to process control within the semiconductor industry. Especially the branch of mask manufacturing presents a challenge to the concepts of machine learning since the business process intrinsically induces pronounced product variability on the background of small plate numbers. In this paper we present the architectural set up of a machine learning algorithm which successfully deals with the demands and pitfalls of mask manufacturing. A detailed motivation of this basic set up followed by an analysis of its statistical properties is given. The machine learning set up for mask manufacturing involves two learning steps: an initial step which identifies and classifies the basic global CD patterns of a process. These results form the basis for the extraction of an optimized training set via balanced sampling. A second learning step uses this training set to obtain the local as well as global CD relationships induced by the manufacturing process. Using two production motivated examples we show how this approach is flexible and powerful enough to deal with the exacting demands of mask manufacturing. In one example we show how dedicated covariates can be used in conjunction with increased spatial resolution of the CD map model in order to deal with pathological CD effects at the mask boundary. The other example shows how the model set up enables strategies for dealing tool specific CD signature differences. In this case the balanced sampling enables a process control scheme which allows usage of the full tool park within the specified tight tolerance budget. Overall, this paper shows that the current rapid developments off the machine learning algorithms can be successfully used within the context of semiconductor manufacturing.

The time course of short-term hypertrophy in the absence of eccentric muscle damage.

PubMed

Stock, Matt S; Mota, Jacob A; DeFranco, Ryan N; Grue, Katherine A; Jacobo, A Unique; Chung, Eunhee; Moon, Jordan R; DeFreitas, Jason M; Beck, Travis W

2017-05-01

It has been proposed that the increase in skeletal muscle mass observed during the initial weeks of initiating a resistance training program is concomitant with eccentric muscle damage and edema. We examined the time course of muscle hypertrophy during 4 weeks of concentric-only resistance training. Thirteen untrained men performed unilateral concentric-only dumbbell curls and shoulder presses twice per week for 4 weeks. Sets of 8-12 repetitions were performed to failure, and training loads were increased during each session. Subjects consumed 500 ml of whole milk during training. Assessments of soreness, lean mass, echo intensity, muscle thickness, relaxed and flexed arm circumference, and isokinetic strength were performed every 72 or 96 h. Soreness, echo intensity, relaxed circumference, and peak torque data did not significantly change. Significant increases in lean mass, muscle thickness, and flexed circumference were observed within seven training sessions. Lean mass was elevated at tests #7 (+109.3 g, p = .002) and #8 (+116.1 g, p = .035), with eight different subjects showing changes above the minimal difference of 139.1 g. Muscle thickness was elevated at tests #6 (+0.23 cm, p = .004), #7 (+0.31 cm, p < .001), and #8 (+0.27 cm, p < .001), with ten subjects exceeding the minimal difference of 0.24 cm. There were no changes for the control arm. In individuals beginning a resistance training program, small but detectable increases in hypertrophy may occur in the absence of eccentric muscle damage within seven training sessions.

Training artificial neural networks directly on the concordance index for censored data using genetic algorithms.

PubMed

Kalderstam, Jonas; Edén, Patrik; Bendahl, Pär-Ola; Strand, Carina; Fernö, Mårten; Ohlsson, Mattias

2013-06-01

The concordance index (c-index) is the standard way of evaluating the performance of prognostic models in the presence of censored data. Constructing prognostic models using artificial neural networks (ANNs) is commonly done by training on error functions which are modified versions of the c-index. Our objective was to demonstrate the capability of training directly on the c-index and to evaluate our approach compared to the Cox proportional hazards model. We constructed a prognostic model using an ensemble of ANNs which were trained using a genetic algorithm. The individual networks were trained on a non-linear artificial data set divided into a training and test set both of size 2000, where 50% of the data was censored. The ANNs were also trained on a data set consisting of 4042 patients treated for breast cancer spread over five different medical studies, 2/3 used for training and 1/3 used as a test set. A Cox model was also constructed on the same data in both cases. The two models' c-indices on the test sets were then compared. The ranking performance of the models is additionally presented visually using modified scatter plots. Cross validation on the cancer training set did not indicate any non-linear effects between the covariates. An ensemble of 30 ANNs with one hidden neuron was therefore used. The ANN model had almost the same c-index score as the Cox model (c-index=0.70 and 0.71, respectively) on the cancer test set. Both models identified similarly sized low risk groups with at most 10% false positives, 49 for the ANN model and 60 for the Cox model, but repeated bootstrap runs indicate that the difference was not significant. A significant difference could however be seen when applied on the non-linear synthetic data set. In that case the ANN ensemble managed to achieve a c-index score of 0.90 whereas the Cox model failed to distinguish itself from the random case (c-index=0.49). We have found empirical evidence that ensembles of ANN models can be optimized directly on the c-index. Comparison with a Cox model indicates that near identical performance is achieved on a real cancer data set while on a non-linear data set the ANN model is clearly superior. Copyright © 2013 Elsevier B.V. All rights reserved.

Effects of training set selection on pain recognition via facial expressions

NASA Astrophysics Data System (ADS)

Shier, Warren A.; Yanushkevich, Svetlana N.

2016-07-01

This paper presents an approach to pain expression classification based on Gabor energy filters with Support Vector Machines (SVMs), followed by analyzing the effects of training set variations on the systems classification rate. This approach is tested on the UNBC-McMaster Shoulder Pain Archive, which consists of spontaneous pain images, hand labelled using the Prkachin and Solomon Pain Intensity scale. In this paper, the subjects pain intensity level has been quantized into three disjoint groups: no pain, weak pain and strong pain. The results of experiments show that Gabor energy filters with SVMs provide comparable or better results to previous filter- based pain recognition methods, with precision rates of 74%, 30% and 78% for no pain, weak pain and strong pain, respectively. The study of effects of intra-class skew, or changing the number of images per subject, show that both completely removing and over-representing poor quality subjects in the training set has little effect on the overall accuracy of the system. This result suggests that poor quality subjects could be removed from the training set to save offline training time and that SVM is robust not only to outliers in training data, but also to significant amounts of poor quality data mixed into the training sets.

International standards for programmes of training in intensive care medicine in Europe.

PubMed

2011-03-01

To develop internationally harmonised standards for programmes of training in intensive care medicine (ICM). Standards were developed by using consensus techniques. A nine-member nominal group of European intensive care experts developed a preliminary set of standards. These were revised and refined through a modified Delphi process involving 28 European national coordinators representing national training organisations using a combination of moderated discussion meetings, email, and a Web-based tool for determining the level of agreement with each proposed standard, and whether the standard could be achieved in the respondent's country. The nominal group developed an initial set of 52 possible standards which underwent four iterations to achieve maximal consensus. All national coordinators approved a final set of 29 standards in four domains: training centres, training programmes, selection of trainees, and trainers' profiles. Only three standards were considered immediately achievable by all countries, demonstrating a willingness to aspire to quality rather than merely setting a minimum level. Nine proposed standards which did not achieve full consensus were identified as potential candidates for future review. This preliminary set of clearly defined and agreed standards provides a transparent framework for assuring the quality of training programmes, and a foundation for international harmonisation and quality improvement of training in ICM.

48 CFR 19.506 - Withdrawing or modifying small business set-asides.

Code of Federal Regulations, 2010 CFR

2010-10-01

... a withdrawal of an individual small business set-aside by giving written notice to the agency small... small business set-asides. 19.506 Section 19.506 Federal Acquisition Regulations System FEDERAL ACQUISITION REGULATION SOCIOECONOMIC PROGRAMS SMALL BUSINESS PROGRAMS Set-Asides for Small Business 19.506...

48 CFR 19.506 - Withdrawing or modifying small business set-asides.

Code of Federal Regulations, 2014 CFR

2014-10-01

... small business set-asides. 19.506 Section 19.506 Federal Acquisition Regulations System FEDERAL ACQUISITION REGULATION SOCIOECONOMIC PROGRAMS SMALL BUSINESS PROGRAMS Set-Asides for Small Business 19.506 Withdrawing or modifying small business set-asides. (a) If, before award of a contract involving a small...

48 CFR 19.506 - Withdrawing or modifying small business set-asides.

Code of Federal Regulations, 2013 CFR

2013-10-01

... small business set-asides. 19.506 Section 19.506 Federal Acquisition Regulations System FEDERAL ACQUISITION REGULATION SOCIOECONOMIC PROGRAMS SMALL BUSINESS PROGRAMS Set-Asides for Small Business 19.506 Withdrawing or modifying small business set-asides. (a) If, before award of a contract involving a small...

48 CFR 19.506 - Withdrawing or modifying small business set-asides.

Code of Federal Regulations, 2011 CFR

2011-10-01

... small business set-asides. 19.506 Section 19.506 Federal Acquisition Regulations System FEDERAL ACQUISITION REGULATION SOCIOECONOMIC PROGRAMS SMALL BUSINESS PROGRAMS Set-Asides for Small Business 19.506 Withdrawing or modifying small business set-asides. (a) If, before award of a contract involving a small...

48 CFR 19.506 - Withdrawing or modifying small business set-asides.

Code of Federal Regulations, 2012 CFR

2012-10-01

... small business set-asides. 19.506 Section 19.506 Federal Acquisition Regulations System FEDERAL ACQUISITION REGULATION SOCIOECONOMIC PROGRAMS SMALL BUSINESS PROGRAMS Set-Asides for Small Business 19.506 Withdrawing or modifying small business set-asides. (a) If, before award of a contract involving a small...

Factors influencing research productivity among health sciences librarians.

PubMed Central

Fenske, R E; Dalrymple, P W

1992-01-01

Secondary analysis was performed of data collected in 1989 from a random sample of members of the Medical Library Association. Results show that about half the sample had at least one publication; academic health sciences librarians were much more likely than hospital librarians to have published. Almost half the sample had taken formal courses in research, but only a small percentage had taken continuing education (CE) courses in research. Institutional support services for research were most available in academic settings. The combination of institutional support, CE training, and research courses explained 31.1% of the variation in research productivity among academic librarians; these factors were less important in hospitals and other institutional settings. The authors suggest that health sciences librarians working outside academia should seek support for research from sources outside the employing institution. PMID:1422506

Humanoids in Support of Lunar and Planetary Surface Operations

NASA Technical Reports Server (NTRS)

Stoica, Adrian; Keymeulen, Didier

2006-01-01

This paper presents a vision of humanoid robots as human's key partners in future space exploration, in particular for construction, maintenance/repair and operation of lunar/planetary habitats, bases and settlements. It integrates this vision with the recent plans for human and robotic exploration, aligning a set of milestones for operational capability of humanoids with the schedule for the next decades and development spirals in the Project Constellation. These milestones relate to a set of incremental challenges, for the solving of which new humanoid technologies are needed. A system of systems integrative approach that would lead to readiness of cooperating humanoid crews is sketched. Robot fostering, training/education techniques, and improved cognitive/sensory/motor development techniques are considered essential elements for achieving intelligent humanoids. A pilot project using small-scale Fujitsu HOAP-2 humanoid is outlined.

Effect of creatine supplementation and drop-set resistance training in untrained aging adults.

PubMed

Johannsmeyer, Sarah; Candow, Darren G; Brahms, C Markus; Michel, Deborah; Zello, Gordon A

2016-10-01

To investigate the effects of creatine supplementation and drop-set resistance training in untrained aging adults. Participants were randomized to one of two groups: Creatine (CR: n=14, 7 females, 7 males; 58.0±3.0yrs, 0.1g/kg/day of creatine+0.1g/kg/day of maltodextrin) or Placebo (PLA: n=17, 7 females, 10 males; age: 57.6±5.0yrs, 0.2g/kg/day of maltodextrin) during 12weeks of drop-set resistance training (3days/week; 2 sets of leg press, chest press, hack squat and lat pull-down exercises performed to muscle fatigue at 80% baseline 1-repetition maximum [1-RM] immediately followed by repetitions to muscle fatigue at 30% baseline 1-RM). Prior to and following training and supplementation, assessments were made for body composition, muscle strength, muscle endurance, tasks of functionality, muscle protein catabolism and diet. Drop-set resistance training improved muscle mass, muscle strength, muscle endurance and tasks of functionality (p<0.05). The addition of creatine to drop-set resistance training significantly increased body mass (p=0.002) and muscle mass (p=0.007) compared to placebo. Males on creatine increased muscle strength (lat pull-down only) to a greater extent than females on creatine (p=0.005). Creatine enabled males to resistance train at a greater capacity over time compared to males on placebo (p=0.049) and females on creatine (p=0.012). Males on creatine (p=0.019) and females on placebo (p=0.014) decreased 3-MH compared to females on creatine. The addition of creatine to drop-set resistance training augments the gains in muscle mass from resistance training alone. Creatine is more effective in untrained aging males compared to untrained aging females. Copyright © 2016 Elsevier Inc. All rights reserved.

Linear Vector Quantisation and Uniform Circular Arrays based decoupled two-dimensional angle of arrival estimation

NASA Astrophysics Data System (ADS)

Ndaw, Joseph D.; Faye, Andre; Maïga, Amadou S.

2017-05-01

Artificial neural networks (ANN)-based models are efficient ways of source localisation. However very large training sets are needed to precisely estimate two-dimensional Direction of arrival (2D-DOA) with ANN models. In this paper we present a fast artificial neural network approach for 2D-DOA estimation with reduced training sets sizes. We exploit the symmetry properties of Uniform Circular Arrays (UCA) to build two different datasets for elevation and azimuth angles. Linear Vector Quantisation (LVQ) neural networks are then sequentially trained on each dataset to separately estimate elevation and azimuth angles. A multilevel training process is applied to further reduce the training sets sizes.

Automated detection of pulmonary nodules in CT images with support vector machines

NASA Astrophysics Data System (ADS)

Liu, Lu; Liu, Wanyu; Sun, Xiaoming

2008-10-01

Many methods have been proposed to avoid radiologists fail to diagnose small pulmonary nodules. Recently, support vector machines (SVMs) had received an increasing attention for pattern recognition. In this paper, we present a computerized system aimed at pulmonary nodules detection; it identifies the lung field, extracts a set of candidate regions with a high sensitivity ratio and then classifies candidates by the use of SVMs. The Computer Aided Diagnosis (CAD) system presented in this paper supports the diagnosis of pulmonary nodules from Computed Tomography (CT) images as inflammation, tuberculoma, granuloma..sclerosing hemangioma, and malignant tumor. Five texture feature sets were extracted for each lesion, while a genetic algorithm based feature selection method was applied to identify the most robust features. The selected feature set was fed into an ensemble of SVMs classifiers. The achieved classification performance was 100%, 92.75% and 90.23% in the training, validation and testing set, respectively. It is concluded that computerized analysis of medical images in combination with artificial intelligence can be used in clinical practice and may contribute to more efficient diagnosis.

A High-Resolution Tile-Based Approach for Classifying Biological Regions in Whole-Slide Histopathological Images

PubMed Central

Hoffman, R.A.; Kothari, S.; Phan, J.H.; Wang, M.D.

2016-01-01

Computational analysis of histopathological whole slide images (WSIs) has emerged as a potential means for improving cancer diagnosis and prognosis. However, an open issue relating to the automated processing of WSIs is the identification of biological regions such as tumor, stroma, and necrotic tissue on the slide. We develop a method for classifying WSI portions (512x512-pixel tiles) into biological regions by (1) extracting a set of 461 image features from each WSI tile, (2) optimizing tile-level prediction models using nested cross-validation on a small (600 tile) manually annotated tile-level training set, and (3) validating the models against a much larger (1.7x106 tile) data set for which ground truth was available on the whole-slide level. We calculated the predicted prevalence of each tissue region and compared this prevalence to the ground truth prevalence for each image in an independent validation set. Results show significant correlation between the predicted (using automated system) and reported biological region prevalences with p < 0.001 for eight of nine cases considered. PMID:27532012

A High-Resolution Tile-Based Approach for Classifying Biological Regions in Whole-Slide Histopathological Images.

PubMed

Hoffman, R A; Kothari, S; Phan, J H; Wang, M D

Computational analysis of histopathological whole slide images (WSIs) has emerged as a potential means for improving cancer diagnosis and prognosis. However, an open issue relating to the automated processing of WSIs is the identification of biological regions such as tumor, stroma, and necrotic tissue on the slide. We develop a method for classifying WSI portions (512x512-pixel tiles) into biological regions by (1) extracting a set of 461 image features from each WSI tile, (2) optimizing tile-level prediction models using nested cross-validation on a small (600 tile) manually annotated tile-level training set, and (3) validating the models against a much larger (1.7x10 6 tile) data set for which ground truth was available on the whole-slide level. We calculated the predicted prevalence of each tissue region and compared this prevalence to the ground truth prevalence for each image in an independent validation set. Results show significant correlation between the predicted (using automated system) and reported biological region prevalences with p < 0.001 for eight of nine cases considered.

Higher Skills. A Case Study of the Role of Further Education Colleges in Meeting the Training Needs of the Small Plant and Tool Hire Industry.

ERIC Educational Resources Information Center

Further Education Unit, London (England).

A British project explored ways further education colleges could help meet training needs of small businesses, specifically the small plant and tool hire industry. The industry's leading organization, the Hire Association of Europe (HAE), provided a list of members; responsibility for making contact rested with the colleges. The most effective…

Margined winner-take-all: New learning rule for pattern recognition.

PubMed

Fukushima, Kunihiko

2018-01-01

The neocognitron is a deep (multi-layered) convolutional neural network that can be trained to recognize visual patterns robustly. In the intermediate layers of the neocognitron, local features are extracted from input patterns. In the deepest layer, based on the features extracted in the intermediate layers, input patterns are classified into classes. A method called IntVec (interpolating-vector) is used for this purpose. This paper proposes a new learning rule called margined Winner-Take-All (mWTA) for training the deepest layer. Every time when a training pattern is presented during the learning, if the result of recognition by WTA (Winner-Take-All) is an error, a new cell is generated in the deepest layer. Here we put a certain amount of margin to the WTA. In other words, only during the learning, a certain amount of handicap is given to cells of classes other than that of the training vector, and the winner is chosen under this handicap. By introducing the margin to the WTA, we can generate a compact set of cells, with which a high recognition rate can be obtained with a small computational cost. The ability of this mWTA is demonstrated by computer simulation. Copyright © 2017 Elsevier Ltd. All rights reserved.

Ensembles of novelty detection classifiers for structural health monitoring using guided waves

NASA Astrophysics Data System (ADS)

Dib, Gerges; Karpenko, Oleksii; Koricho, Ermias; Khomenko, Anton; Haq, Mahmoodul; Udpa, Lalita

2018-01-01

Guided wave structural health monitoring uses sparse sensor networks embedded in sophisticated structures for defect detection and characterization. The biggest challenge of those sensor networks is developing robust techniques for reliable damage detection under changing environmental and operating conditions (EOC). To address this challenge, we develop a novelty classifier for damage detection based on one class support vector machines. We identify appropriate features for damage detection and introduce a feature aggregation method which quadratically increases the number of available training observations. We adopt a two-level voting scheme by using an ensemble of classifiers and predictions. Each classifier is trained on a different segment of the guided wave signal, and each classifier makes an ensemble of predictions based on a single observation. Using this approach, the classifier can be trained using a small number of baseline signals. We study the performance using Monte-Carlo simulations of an analytical model and data from impact damage experiments on a glass fiber composite plate. We also demonstrate the classifier performance using two types of baseline signals: fixed and rolling baseline training set. The former requires prior knowledge of baseline signals from all EOC, while the latter does not and leverages the fact that EOC vary slowly over time and can be modeled as a Gaussian process.

Building machine learning force fields for nanoclusters

NASA Astrophysics Data System (ADS)

Zeni, Claudio; Rossi, Kevin; Glielmo, Aldo; Fekete, Ádám; Gaston, Nicola; Baletto, Francesca; De Vita, Alessandro

2018-06-01

We assess Gaussian process (GP) regression as a technique to model interatomic forces in metal nanoclusters by analyzing the performance of 2-body, 3-body, and many-body kernel functions on a set of 19-atom Ni cluster structures. We find that 2-body GP kernels fail to provide faithful force estimates, despite succeeding in bulk Ni systems. However, both 3- and many-body kernels predict forces within an ˜0.1 eV/Å average error even for small training datasets and achieve high accuracy even on out-of-sample, high temperature structures. While training and testing on the same structure always provide satisfactory accuracy, cross-testing on dissimilar structures leads to higher prediction errors, posing an extrapolation problem. This can be cured using heterogeneous training on databases that contain more than one structure, which results in a good trade-off between versatility and overall accuracy. Starting from a 3-body kernel trained this way, we build an efficient non-parametric 3-body force field that allows accurate prediction of structural properties at finite temperatures, following a newly developed scheme [A. Glielmo et al., Phys. Rev. B 95, 214302 (2017)]. We use this to assess the thermal stability of Ni19 nanoclusters at a fractional cost of full ab initio calculations.

Replacing Lectures with Small Groups: The Impact of Flipping the Residency Conference Day

PubMed Central

King, Andrew M.; Mayer, Chad; Barrie, Michael; Greenberger, Sarah; Way, David P.

2018-01-01

The flipped classroom, an educational alternative to the traditional lecture, has been widely adopted by educators at all levels of education and across many disciplines. In the flipped classroom, learners prepare in advance of the face-to-face meeting by learning content material on their own. Classroom time is reserved for application of the learned content to solving problems or discussing cases. Over the past year, we replaced most residency program lectures with small-group discussions using the flipped-classroom model, case-based learning, simulation and procedure labs. In the new model, residents prepared for conference by reviewing a patient case and studying suggested learning materials. Conference day was set aside for facilitated small-group discussions about the case. This is a cross-cohort study of emergency medicine residents who experienced the lecture-based curriculum to residents in the new flipped-classroom curriculum using paired comparisons (independent t-tests) on in-training exam scores while controlling for program year level. We also compared results of the evaluation of various program components. We observed no differences between cohorts on in-training examination scores. Small-group methods were rated the same across program years. Two program components in the new curriculum, an updated format of both adult and pediatric case conferences, were rated significantly higher on program quality. In preparation for didactics, residents in the new curriculum report spending more time on average with outside learning materials, including almost twice as much time reviewing textbooks. Residents found the new format of the case conferences to be of higher quality because of the inclusion of rapid-fire case discussions with targeted learning points. PMID:29383050

Long-Term Abstract Learning of Attentional Set

ERIC Educational Resources Information Center

Leber, Andrew B.; Kawahara, Jun-Ichiro; Gabari, Yuji

2009-01-01

How does past experience influence visual search strategy (i.e., attentional set)? Recent reports have shown that, when given the option to use 1 of 2 attentional sets, observers persist with the set previously required in a training phase. Here, 2 related questions are addressed. First, does the training effect result only from perseveration with…

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