Semi-Supervised Recurrent Neural Network for Adverse Drug Reaction mention extraction.

PubMed

Gupta, Shashank; Pawar, Sachin; Ramrakhiyani, Nitin; Palshikar, Girish Keshav; Varma, Vasudeva

2018-06-13

Social media is a useful platform to share health-related information due to its vast reach. This makes it a good candidate for public-health monitoring tasks, specifically for pharmacovigilance. We study the problem of extraction of Adverse-Drug-Reaction (ADR) mentions from social media, particularly from Twitter. Medical information extraction from social media is challenging, mainly due to short and highly informal nature of text, as compared to more technical and formal medical reports. Current methods in ADR mention extraction rely on supervised learning methods, which suffer from labeled data scarcity problem. The state-of-the-art method uses deep neural networks, specifically a class of Recurrent Neural Network (RNN) which is Long-Short-Term-Memory network (LSTM). Deep neural networks, due to their large number of free parameters rely heavily on large annotated corpora for learning the end task. But in the real-world, it is hard to get large labeled data, mainly due to the heavy cost associated with the manual annotation. To this end, we propose a novel semi-supervised learning based RNN model, which can leverage unlabeled data also present in abundance on social media. Through experiments we demonstrate the effectiveness of our method, achieving state-of-the-art performance in ADR mention extraction. In this study, we tackle the problem of labeled data scarcity for Adverse Drug Reaction mention extraction from social media and propose a novel semi-supervised learning based method which can leverage large unlabeled corpus available in abundance on the web. Through empirical study, we demonstrate that our proposed method outperforms fully supervised learning based baseline which relies on large manually annotated corpus for a good performance.

Semi-supervised learning via regularized boosting working on multiple semi-supervised assumptions.

PubMed

Chen, Ke; Wang, Shihai

2011-01-01

Semi-supervised learning concerns the problem of learning in the presence of labeled and unlabeled data. Several boosting algorithms have been extended to semi-supervised learning with various strategies. To our knowledge, however, none of them takes all three semi-supervised assumptions, i.e., smoothness, cluster, and manifold assumptions, together into account during boosting learning. In this paper, we propose a novel cost functional consisting of the margin cost on labeled data and the regularization penalty on unlabeled data based on three fundamental semi-supervised assumptions. Thus, minimizing our proposed cost functional with a greedy yet stagewise functional optimization procedure leads to a generic boosting framework for semi-supervised learning. Extensive experiments demonstrate that our algorithm yields favorite results for benchmark and real-world classification tasks in comparison to state-of-the-art semi-supervised learning algorithms, including newly developed boosting algorithms. Finally, we discuss relevant issues and relate our algorithm to the previous work.

A Hybrid Semi-supervised Classification Scheme for Mining Multisource Geospatial Data

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

Vatsavai, Raju; Bhaduri, Budhendra L

2011-01-01

Supervised learning methods such as Maximum Likelihood (ML) are often used in land cover (thematic) classification of remote sensing imagery. ML classifier relies exclusively on spectral characteristics of thematic classes whose statistical distributions (class conditional probability densities) are often overlapping. The spectral response distributions of thematic classes are dependent on many factors including elevation, soil types, and ecological zones. A second problem with statistical classifiers is the requirement of large number of accurate training samples (10 to 30 |dimensions|), which are often costly and time consuming to acquire over large geographic regions. With the increasing availability of geospatial databases, itmore » is possible to exploit the knowledge derived from these ancillary datasets to improve classification accuracies even when the class distributions are highly overlapping. Likewise newer semi-supervised techniques can be adopted to improve the parameter estimates of statistical model by utilizing a large number of easily available unlabeled training samples. Unfortunately there is no convenient multivariate statistical model that can be employed for mulitsource geospatial databases. In this paper we present a hybrid semi-supervised learning algorithm that effectively exploits freely available unlabeled training samples from multispectral remote sensing images and also incorporates ancillary geospatial databases. We have conducted several experiments on real datasets, and our new hybrid approach shows over 25 to 35% improvement in overall classification accuracy over conventional classification schemes.« less

Deep Learning for Extreme Weather Detection

NASA Astrophysics Data System (ADS)

Prabhat, M.; Racah, E.; Biard, J.; Liu, Y.; Mudigonda, M.; Kashinath, K.; Beckham, C.; Maharaj, T.; Kahou, S.; Pal, C.; O'Brien, T. A.; Wehner, M. F.; Kunkel, K.; Collins, W. D.

2017-12-01

We will present our latest results from the application of Deep Learning methods for detecting, localizing and segmenting extreme weather patterns in climate data. We have successfully applied supervised convolutional architectures for the binary classification tasks of detecting tropical cyclones and atmospheric rivers in centered, cropped patches. We have subsequently extended our architecture to a semi-supervised formulation, which is capable of learning a unified representation of multiple weather patterns, predicting bounding boxes and object categories, and has the capability to detect novel patterns (w/ few, or no labels). We will briefly present our efforts in scaling the semi-supervised architecture to 9600 nodes of the Cori supercomputer, obtaining 15PF performance. Time permitting, we will highlight our efforts in pixel-level segmentation of weather patterns.

Determining Effects of Non-synonymous SNPs on Protein-Protein Interactions using Supervised and Semi-supervised Learning

PubMed Central

Zhao, Nan; Han, Jing Ginger; Shyu, Chi-Ren; Korkin, Dmitry

2014-01-01

Single nucleotide polymorphisms (SNPs) are among the most common types of genetic variation in complex genetic disorders. A growing number of studies link the functional role of SNPs with the networks and pathways mediated by the disease-associated genes. For example, many non-synonymous missense SNPs (nsSNPs) have been found near or inside the protein-protein interaction (PPI) interfaces. Determining whether such nsSNP will disrupt or preserve a PPI is a challenging task to address, both experimentally and computationally. Here, we present this task as three related classification problems, and develop a new computational method, called the SNP-IN tool (non-synonymous SNP INteraction effect predictor). Our method predicts the effects of nsSNPs on PPIs, given the interaction's structure. It leverages supervised and semi-supervised feature-based classifiers, including our new Random Forest self-learning protocol. The classifiers are trained based on a dataset of comprehensive mutagenesis studies for 151 PPI complexes, with experimentally determined binding affinities of the mutant and wild-type interactions. Three classification problems were considered: (1) a 2-class problem (strengthening/weakening PPI mutations), (2) another 2-class problem (mutations that disrupt/preserve a PPI), and (3) a 3-class classification (detrimental/neutral/beneficial mutation effects). In total, 11 different supervised and semi-supervised classifiers were trained and assessed resulting in a promising performance, with the weighted f-measure ranging from 0.87 for Problem 1 to 0.70 for the most challenging Problem 3. By integrating prediction results of the 2-class classifiers into the 3-class classifier, we further improved its performance for Problem 3. To demonstrate the utility of SNP-IN tool, it was applied to study the nsSNP-induced rewiring of two disease-centered networks. The accurate and balanced performance of SNP-IN tool makes it readily available to study the rewiring of large-scale protein-protein interaction networks, and can be useful for functional annotation of disease-associated SNPs. SNIP-IN tool is freely accessible as a web-server at http://korkinlab.org/snpintool/. PMID:24784581

Semi-Supervised Geographical Feature Detection

NASA Astrophysics Data System (ADS)

Yu, H.; Yu, L.; Kuo, K. S.

2016-12-01

Extraction and tracking geographical features is a fundamental requirement in many geoscience fields. However, this operation has become an increasingly challenging task for domain scientists when tackling a large amount of geoscience data. Although domain scientists may have a relatively clear definition of features, it is difficult to capture the presence of features in an accurate and efficient fashion. We propose a semi-supervised approach to address large geographical feature detection. Our approach has two main components. First, we represent a heterogeneous geoscience data in a unified high-dimensional space, which can facilitate us to evaluate the similarity of data points with respect to geolocation, time, and variable values. We characterize the data from these measures, and use a set of hash functions to parameterize the initial knowledge of the data. Second, for any user query, our approach can automatically extract the initial results based on the hash functions. To improve the accuracy of querying, our approach provides a visualization interface to display the querying results and allow users to interactively explore and refine them. The user feedback will be used to enhance our knowledge base in an iterative manner. In our implementation, we use high-performance computing techniques to accelerate the construction of hash functions. Our design facilitates a parallelization scheme for feature detection and extraction, which is a traditionally challenging problem for large-scale data. We evaluate our approach and demonstrate the effectiveness using both synthetic and real world datasets.

Coupled Semi-Supervised Learning

DTIC Science & Technology

2010-05-01

later in the thesis, in Chapter 5. CPL as a Case Study of Coupled Semi-Supervised Learning The results presented above demonstrate that coupling...EXTRACTION PATTERNS Our answer to the question posed above, then, is that our results with CPL serve as a case study of coupled semi-supervised learning of...that are incompatible with the coupling constraints. Thus, we argue that our results with CPL serve as a case study of coupled semi-supervised

A Novel Interdisciplinary Approach to Socio-Technical Complexity

NASA Astrophysics Data System (ADS)

Bassetti, Chiara

The chapter presents a novel interdisciplinary approach that integrates micro-sociological analysis into computer-vision and pattern-recognition modeling and algorithms, the purpose being to tackle socio-technical complexity at a systemic yet micro-grounded level. The approach is empirically-grounded and both theoretically- and analytically-driven, yet systemic and multidimensional, semi-supervised and computable, and oriented towards large scale applications. The chapter describes the proposed approach especially as for its sociological foundations, and as applied to the analysis of a particular setting --i.e. sport-spectator crowds. Crowds, better defined as large gatherings, are almost ever-present in our societies, and capturing their dynamics is crucial. From social sciences to public safety management and emergency response, modeling and predicting large gatherings' presence and dynamics, thus possibly preventing critical situations and being able to properly react to them, is fundamental. This is where semi/automated technologies can make the difference. The work presented in this chapter is intended as a scientific step towards such an objective.

Quality-Related Monitoring and Grading of Granulated Products by Weibull-Distribution Modeling of Visual Images with Semi-Supervised Learning.

PubMed

Liu, Jinping; Tang, Zhaohui; Xu, Pengfei; Liu, Wenzhong; Zhang, Jin; Zhu, Jianyong

2016-06-29

The topic of online product quality inspection (OPQI) with smart visual sensors is attracting increasing interest in both the academic and industrial communities on account of the natural connection between the visual appearance of products with their underlying qualities. Visual images captured from granulated products (GPs), e.g., cereal products, fabric textiles, are comprised of a large number of independent particles or stochastically stacking locally homogeneous fragments, whose analysis and understanding remains challenging. A method of image statistical modeling-based OPQI for GP quality grading and monitoring by a Weibull distribution(WD) model with a semi-supervised learning classifier is presented. WD-model parameters (WD-MPs) of GP images' spatial structures, obtained with omnidirectional Gaussian derivative filtering (OGDF), which were demonstrated theoretically to obey a specific WD model of integral form, were extracted as the visual features. Then, a co-training-style semi-supervised classifier algorithm, named COSC-Boosting, was exploited for semi-supervised GP quality grading, by integrating two independent classifiers with complementary nature in the face of scarce labeled samples. Effectiveness of the proposed OPQI method was verified and compared in the field of automated rice quality grading with commonly-used methods and showed superior performance, which lays a foundation for the quality control of GP on assembly lines.

Evaluation of Semi-supervised Learning for Classification of Protein Crystallization Imagery.

PubMed

Sigdel, Madhav; Dinç, İmren; Dinç, Semih; Sigdel, Madhu S; Pusey, Marc L; Aygün, Ramazan S

2014-03-01

In this paper, we investigate the performance of two wrapper methods for semi-supervised learning algorithms for classification of protein crystallization images with limited labeled images. Firstly, we evaluate the performance of semi-supervised approach using self-training with naïve Bayesian (NB) and sequential minimum optimization (SMO) as the base classifiers. The confidence values returned by these classifiers are used to select high confident predictions to be used for self-training. Secondly, we analyze the performance of Yet Another Two Stage Idea (YATSI) semi-supervised learning using NB, SMO, multilayer perceptron (MLP), J48 and random forest (RF) classifiers. These results are compared with the basic supervised learning using the same training sets. We perform our experiments on a dataset consisting of 2250 protein crystallization images for different proportions of training and test data. Our results indicate that NB and SMO using both self-training and YATSI semi-supervised approaches improve accuracies with respect to supervised learning. On the other hand, MLP, J48 and RF perform better using basic supervised learning. Overall, random forest classifier yields the best accuracy with supervised learning for our dataset.

Cross-Domain Semi-Supervised Learning Using Feature Formulation.

PubMed

Xingquan Zhu

2011-12-01

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

Semi-Supervised Marginal Fisher Analysis for Hyperspectral Image Classification

NASA Astrophysics Data System (ADS)

Huang, H.; Liu, J.; Pan, Y.

2012-07-01

The problem of learning with both labeled and unlabeled examples arises frequently in Hyperspectral image (HSI) classification. While marginal Fisher analysis is a supervised method, which cannot be directly applied for Semi-supervised classification. In this paper, we proposed a novel method, called semi-supervised marginal Fisher analysis (SSMFA), to process HSI of natural scenes, which uses a combination of semi-supervised learning and manifold learning. In SSMFA, a new difference-based optimization objective function with unlabeled samples has been designed. SSMFA preserves the manifold structure of labeled and unlabeled samples in addition to separating labeled samples in different classes from each other. The semi-supervised method has an analytic form of the globally optimal solution, and it can be computed based on eigen decomposition. Classification experiments with a challenging HSI task demonstrate that this method outperforms current state-of-the-art HSI-classification methods.

Evaluation of Semi-supervised Learning for Classification of Protein Crystallization Imagery

PubMed Central

Sigdel, Madhav; Dinç, İmren; Dinç, Semih; Sigdel, Madhu S.; Pusey, Marc L.; Aygün, Ramazan S.

2015-01-01

In this paper, we investigate the performance of two wrapper methods for semi-supervised learning algorithms for classification of protein crystallization images with limited labeled images. Firstly, we evaluate the performance of semi-supervised approach using self-training with naïve Bayesian (NB) and sequential minimum optimization (SMO) as the base classifiers. The confidence values returned by these classifiers are used to select high confident predictions to be used for self-training. Secondly, we analyze the performance of Yet Another Two Stage Idea (YATSI) semi-supervised learning using NB, SMO, multilayer perceptron (MLP), J48 and random forest (RF) classifiers. These results are compared with the basic supervised learning using the same training sets. We perform our experiments on a dataset consisting of 2250 protein crystallization images for different proportions of training and test data. Our results indicate that NB and SMO using both self-training and YATSI semi-supervised approaches improve accuracies with respect to supervised learning. On the other hand, MLP, J48 and RF perform better using basic supervised learning. Overall, random forest classifier yields the best accuracy with supervised learning for our dataset. PMID:25914518

Semi-Supervised Projective Non-Negative Matrix Factorization for Cancer Classification.

PubMed

Zhang, Xiang; Guan, Naiyang; Jia, Zhilong; Qiu, Xiaogang; Luo, Zhigang

2015-01-01

Advances in DNA microarray technologies have made gene expression profiles a significant candidate in identifying different types of cancers. Traditional learning-based cancer identification methods utilize labeled samples to train a classifier, but they are inconvenient for practical application because labels are quite expensive in the clinical cancer research community. This paper proposes a semi-supervised projective non-negative matrix factorization method (Semi-PNMF) to learn an effective classifier from both labeled and unlabeled samples, thus boosting subsequent cancer classification performance. In particular, Semi-PNMF jointly learns a non-negative subspace from concatenated labeled and unlabeled samples and indicates classes by the positions of the maximum entries of their coefficients. Because Semi-PNMF incorporates statistical information from the large volume of unlabeled samples in the learned subspace, it can learn more representative subspaces and boost classification performance. We developed a multiplicative update rule (MUR) to optimize Semi-PNMF and proved its convergence. The experimental results of cancer classification for two multiclass cancer gene expression profile datasets show that Semi-PNMF outperforms the representative methods.

Large-scale weakly supervised object localization via latent category learning.

PubMed

Chong Wang; Kaiqi Huang; Weiqiang Ren; Junge Zhang; Maybank, Steve

2015-04-01

Localizing objects in cluttered backgrounds is challenging under large-scale weakly supervised conditions. Due to the cluttered image condition, objects usually have large ambiguity with backgrounds. Besides, there is also a lack of effective algorithm for large-scale weakly supervised localization in cluttered backgrounds. However, backgrounds contain useful latent information, e.g., the sky in the aeroplane class. If this latent information can be learned, object-background ambiguity can be largely reduced and background can be suppressed effectively. In this paper, we propose the latent category learning (LCL) in large-scale cluttered conditions. LCL is an unsupervised learning method which requires only image-level class labels. First, we use the latent semantic analysis with semantic object representation to learn the latent categories, which represent objects, object parts or backgrounds. Second, to determine which category contains the target object, we propose a category selection strategy by evaluating each category's discrimination. Finally, we propose the online LCL for use in large-scale conditions. Evaluation on the challenging PASCAL Visual Object Class (VOC) 2007 and the large-scale imagenet large-scale visual recognition challenge 2013 detection data sets shows that the method can improve the annotation precision by 10% over previous methods. More importantly, we achieve the detection precision which outperforms previous results by a large margin and can be competitive to the supervised deformable part model 5.0 baseline on both data sets.

Quality-Related Monitoring and Grading of Granulated Products by Weibull-Distribution Modeling of Visual Images with Semi-Supervised Learning

PubMed Central

Liu, Jinping; Tang, Zhaohui; Xu, Pengfei; Liu, Wenzhong; Zhang, Jin; Zhu, Jianyong

2016-01-01

The topic of online product quality inspection (OPQI) with smart visual sensors is attracting increasing interest in both the academic and industrial communities on account of the natural connection between the visual appearance of products with their underlying qualities. Visual images captured from granulated products (GPs), e.g., cereal products, fabric textiles, are comprised of a large number of independent particles or stochastically stacking locally homogeneous fragments, whose analysis and understanding remains challenging. A method of image statistical modeling-based OPQI for GP quality grading and monitoring by a Weibull distribution(WD) model with a semi-supervised learning classifier is presented. WD-model parameters (WD-MPs) of GP images’ spatial structures, obtained with omnidirectional Gaussian derivative filtering (OGDF), which were demonstrated theoretically to obey a specific WD model of integral form, were extracted as the visual features. Then, a co-training-style semi-supervised classifier algorithm, named COSC-Boosting, was exploited for semi-supervised GP quality grading, by integrating two independent classifiers with complementary nature in the face of scarce labeled samples. Effectiveness of the proposed OPQI method was verified and compared in the field of automated rice quality grading with commonly-used methods and showed superior performance, which lays a foundation for the quality control of GP on assembly lines. PMID:27367703

Semi-supervised and unsupervised extreme learning machines.

PubMed

Huang, Gao; Song, Shiji; Gupta, Jatinder N D; Wu, Cheng

2014-12-01

Extreme learning machines (ELMs) have proven to be efficient and effective learning mechanisms for pattern classification and regression. However, ELMs are primarily applied to supervised learning problems. Only a few existing research papers have used ELMs to explore unlabeled data. In this paper, we extend ELMs for both semi-supervised and unsupervised tasks based on the manifold regularization, thus greatly expanding the applicability of ELMs. The key advantages of the proposed algorithms are as follows: 1) both the semi-supervised ELM (SS-ELM) and the unsupervised ELM (US-ELM) exhibit learning capability and computational efficiency of ELMs; 2) both algorithms naturally handle multiclass classification or multicluster clustering; and 3) both algorithms are inductive and can handle unseen data at test time directly. Moreover, it is shown in this paper that all the supervised, semi-supervised, and unsupervised ELMs can actually be put into a unified framework. This provides new perspectives for understanding the mechanism of random feature mapping, which is the key concept in ELM theory. Empirical study on a wide range of data sets demonstrates that the proposed algorithms are competitive with the state-of-the-art semi-supervised or unsupervised learning algorithms in terms of accuracy and efficiency.

Semi-supervised prediction of gene regulatory networks using machine learning algorithms.

PubMed

Patel, Nihir; Wang, Jason T L

2015-10-01

Use of computational methods to predict gene regulatory networks (GRNs) from gene expression data is a challenging task. Many studies have been conducted using unsupervised methods to fulfill the task; however, such methods usually yield low prediction accuracies due to the lack of training data. In this article, we propose semi-supervised methods for GRN prediction by utilizing two machine learning algorithms, namely, support vector machines (SVM) and random forests (RF). The semi-supervised methods make use of unlabelled data for training. We investigated inductive and transductive learning approaches, both of which adopt an iterative procedure to obtain reliable negative training data from the unlabelled data. We then applied our semi-supervised methods to gene expression data of Escherichia coli and Saccharomyces cerevisiae, and evaluated the performance of our methods using the expression data. Our analysis indicated that the transductive learning approach outperformed the inductive learning approach for both organisms. However, there was no conclusive difference identified in the performance of SVM and RF. Experimental results also showed that the proposed semi-supervised methods performed better than existing supervised methods for both organisms.

Safe semi-supervised learning based on weighted likelihood.

PubMed

Kawakita, Masanori; Takeuchi, Jun'ichi

2014-05-01

We are interested in developing a safe semi-supervised learning that works in any situation. Semi-supervised learning postulates that n(') unlabeled data are available in addition to n labeled data. However, almost all of the previous semi-supervised methods require additional assumptions (not only unlabeled data) to make improvements on supervised learning. If such assumptions are not met, then the methods possibly perform worse than supervised learning. Sokolovska, Cappé, and Yvon (2008) proposed a semi-supervised method based on a weighted likelihood approach. They proved that this method asymptotically never performs worse than supervised learning (i.e., it is safe) without any assumption. Their method is attractive because it is easy to implement and is potentially general. Moreover, it is deeply related to a certain statistical paradox. However, the method of Sokolovska et al. (2008) assumes a very limited situation, i.e., classification, discrete covariates, n(')→∞ and a maximum likelihood estimator. In this paper, we extend their method by modifying the weight. We prove that our proposal is safe in a significantly wide range of situations as long as n≤n('). Further, we give a geometrical interpretation of the proof of safety through the relationship with the above-mentioned statistical paradox. Finally, we show that the above proposal is asymptotically safe even when n(')

Fully Decentralized Semi-supervised Learning via Privacy-preserving Matrix Completion.

PubMed

Fierimonte, Roberto; Scardapane, Simone; Uncini, Aurelio; Panella, Massimo

2016-08-26

Distributed learning refers to the problem of inferring a function when the training data are distributed among different nodes. While significant work has been done in the contexts of supervised and unsupervised learning, the intermediate case of Semi-supervised learning in the distributed setting has received less attention. In this paper, we propose an algorithm for this class of problems, by extending the framework of manifold regularization. The main component of the proposed algorithm consists of a fully distributed computation of the adjacency matrix of the training patterns. To this end, we propose a novel algorithm for low-rank distributed matrix completion, based on the framework of diffusion adaptation. Overall, the distributed Semi-supervised algorithm is efficient and scalable, and it can preserve privacy by the inclusion of flexible privacy-preserving mechanisms for similarity computation. The experimental results and comparison on a wide range of standard Semi-supervised benchmarks validate our proposal.

Active semi-supervised learning method with hybrid deep belief networks.

PubMed

Zhou, Shusen; Chen, Qingcai; Wang, Xiaolong

2014-01-01

In this paper, we develop a novel semi-supervised learning algorithm called active hybrid deep belief networks (AHD), to address the semi-supervised sentiment classification problem with deep learning. First, we construct the previous several hidden layers using restricted Boltzmann machines (RBM), which can reduce the dimension and abstract the information of the reviews quickly. Second, we construct the following hidden layers using convolutional restricted Boltzmann machines (CRBM), which can abstract the information of reviews effectively. Third, the constructed deep architecture is fine-tuned by gradient-descent based supervised learning with an exponential loss function. Finally, active learning method is combined based on the proposed deep architecture. We did several experiments on five sentiment classification datasets, and show that AHD is competitive with previous semi-supervised learning algorithm. Experiments are also conducted to verify the effectiveness of our proposed method with different number of labeled reviews and unlabeled reviews respectively.

In-situ trainable intrusion detection system

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

Symons, Christopher T.; Beaver, Justin M.; Gillen, Rob

A computer implemented method detects intrusions using a computer by analyzing network traffic. The method includes a semi-supervised learning module connected to a network node. The learning module uses labeled and unlabeled data to train a semi-supervised machine learning sensor. The method records events that include a feature set made up of unauthorized intrusions and benign computer requests. The method identifies at least some of the benign computer requests that occur during the recording of the events while treating the remainder of the data as unlabeled. The method trains the semi-supervised learning module at the network node in-situ, such thatmore » the semi-supervised learning modules may identify malicious traffic without relying on specific rules, signatures, or anomaly detection.« less

A semi-supervised learning framework for biomedical event extraction based on hidden topics.

PubMed

Zhou, Deyu; Zhong, Dayou

2015-05-01

Scientists have devoted decades of efforts to understanding the interaction between proteins or RNA production. The information might empower the current knowledge on drug reactions or the development of certain diseases. Nevertheless, due to the lack of explicit structure, literature in life science, one of the most important sources of this information, prevents computer-based systems from accessing. Therefore, biomedical event extraction, automatically acquiring knowledge of molecular events in research articles, has attracted community-wide efforts recently. Most approaches are based on statistical models, requiring large-scale annotated corpora to precisely estimate models' parameters. However, it is usually difficult to obtain in practice. Therefore, employing un-annotated data based on semi-supervised learning for biomedical event extraction is a feasible solution and attracts more interests. In this paper, a semi-supervised learning framework based on hidden topics for biomedical event extraction is presented. In this framework, sentences in the un-annotated corpus are elaborately and automatically assigned with event annotations based on their distances to these sentences in the annotated corpus. More specifically, not only the structures of the sentences, but also the hidden topics embedded in the sentences are used for describing the distance. The sentences and newly assigned event annotations, together with the annotated corpus, are employed for training. Experiments were conducted on the multi-level event extraction corpus, a golden standard corpus. Experimental results show that more than 2.2% improvement on F-score on biomedical event extraction is achieved by the proposed framework when compared to the state-of-the-art approach. The results suggest that by incorporating un-annotated data, the proposed framework indeed improves the performance of the state-of-the-art event extraction system and the similarity between sentences might be precisely described by hidden topics and structures of the sentences. Copyright © 2015 Elsevier B.V. All rights reserved.

Label Information Guided Graph Construction for Semi-Supervised Learning.

PubMed

Zhuang, Liansheng; Zhou, Zihan; Gao, Shenghua; Yin, Jingwen; Lin, Zhouchen; Ma, Yi

2017-09-01

In the literature, most existing graph-based semi-supervised learning methods only use the label information of observed samples in the label propagation stage, while ignoring such valuable information when learning the graph. In this paper, we argue that it is beneficial to consider the label information in the graph learning stage. Specifically, by enforcing the weight of edges between labeled samples of different classes to be zero, we explicitly incorporate the label information into the state-of-the-art graph learning methods, such as the low-rank representation (LRR), and propose a novel semi-supervised graph learning method called semi-supervised low-rank representation. This results in a convex optimization problem with linear constraints, which can be solved by the linearized alternating direction method. Though we take LRR as an example, our proposed method is in fact very general and can be applied to any self-representation graph learning methods. Experiment results on both synthetic and real data sets demonstrate that the proposed graph learning method can better capture the global geometric structure of the data, and therefore is more effective for semi-supervised learning tasks.

Application of semi-supervised deep learning to lung sound analysis.

PubMed

Chamberlain, Daniel; Kodgule, Rahul; Ganelin, Daniela; Miglani, Vivek; Fletcher, Richard Ribon

2016-08-01

The analysis of lung sounds, collected through auscultation, is a fundamental component of pulmonary disease diagnostics for primary care and general patient monitoring for telemedicine. Despite advances in computation and algorithms, the goal of automated lung sound identification and classification has remained elusive. Over the past 40 years, published work in this field has demonstrated only limited success in identifying lung sounds, with most published studies using only a small numbers of patients (typically N<;20) and usually limited to a single type of lung sound. Larger research studies have also been impeded by the challenge of labeling large volumes of data, which is extremely labor-intensive. In this paper, we present the development of a semi-supervised deep learning algorithm for automatically classify lung sounds from a relatively large number of patients (N=284). Focusing on the two most common lung sounds, wheeze and crackle, we present results from 11,627 sound files recorded from 11 different auscultation locations on these 284 patients with pulmonary disease. 890 of these sound files were labeled to evaluate the model, which is significantly larger than previously published studies. Data was collected with a custom mobile phone application and a low-cost (US$30) electronic stethoscope. On this data set, our algorithm achieves ROC curves with AUCs of 0.86 for wheeze and 0.74 for crackle. Most importantly, this study demonstrates how semi-supervised deep learning can be used with larger data sets without requiring extensive labeling of data.

Patient-specific semi-supervised learning for postoperative brain tumor segmentation.

PubMed

Meier, Raphael; Bauer, Stefan; Slotboom, Johannes; Wiest, Roland; Reyes, Mauricio

2014-01-01

In contrast to preoperative brain tumor segmentation, the problem of postoperative brain tumor segmentation has been rarely approached so far. We present a fully-automatic segmentation method using multimodal magnetic resonance image data and patient-specific semi-supervised learning. The idea behind our semi-supervised approach is to effectively fuse information from both pre- and postoperative image data of the same patient to improve segmentation of the postoperative image. We pose image segmentation as a classification problem and solve it by adopting a semi-supervised decision forest. The method is evaluated on a cohort of 10 high-grade glioma patients, with segmentation performance and computation time comparable or superior to a state-of-the-art brain tumor segmentation method. Moreover, our results confirm that the inclusion of preoperative MR images lead to a better performance regarding postoperative brain tumor segmentation.

Semi-supervised manifold learning with affinity regularization for Alzheimer's disease identification using positron emission tomography imaging.

PubMed

Lu, Shen; Xia, Yong; Cai, Tom Weidong; Feng, David Dagan

2015-01-01

Dementia, Alzheimer's disease (AD) in particular is a global problem and big threat to the aging population. An image based computer-aided dementia diagnosis method is needed to providing doctors help during medical image examination. Many machine learning based dementia classification methods using medical imaging have been proposed and most of them achieve accurate results. However, most of these methods make use of supervised learning requiring fully labeled image dataset, which usually is not practical in real clinical environment. Using large amount of unlabeled images can improve the dementia classification performance. In this study we propose a new semi-supervised dementia classification method based on random manifold learning with affinity regularization. Three groups of spatial features are extracted from positron emission tomography (PET) images to construct an unsupervised random forest which is then used to regularize the manifold learning objective function. The proposed method, stat-of-the-art Laplacian support vector machine (LapSVM) and supervised SVM are applied to classify AD and normal controls (NC). The experiment results show that learning with unlabeled images indeed improves the classification performance. And our method outperforms LapSVM on the same dataset.

Improved semi-supervised online boosting for object tracking

NASA Astrophysics Data System (ADS)

Li, Yicui; Qi, Lin; Tan, Shukun

2016-10-01

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

Integrative gene network construction to analyze cancer recurrence using semi-supervised learning.

PubMed

Park, Chihyun; Ahn, Jaegyoon; Kim, Hyunjin; Park, Sanghyun

2014-01-01

The prognosis of cancer recurrence is an important research area in bioinformatics and is challenging due to the small sample sizes compared to the vast number of genes. There have been several attempts to predict cancer recurrence. Most studies employed a supervised approach, which uses only a few labeled samples. Semi-supervised learning can be a great alternative to solve this problem. There have been few attempts based on manifold assumptions to reveal the detailed roles of identified cancer genes in recurrence. In order to predict cancer recurrence, we proposed a novel semi-supervised learning algorithm based on a graph regularization approach. We transformed the gene expression data into a graph structure for semi-supervised learning and integrated protein interaction data with the gene expression data to select functionally-related gene pairs. Then, we predicted the recurrence of cancer by applying a regularization approach to the constructed graph containing both labeled and unlabeled nodes. The average improvement rate of accuracy for three different cancer datasets was 24.9% compared to existing supervised and semi-supervised methods. We performed functional enrichment on the gene networks used for learning. We identified that those gene networks are significantly associated with cancer-recurrence-related biological functions. Our algorithm was developed with standard C++ and is available in Linux and MS Windows formats in the STL library. The executable program is freely available at: http://embio.yonsei.ac.kr/~Park/ssl.php.

A Saliency Guided Semi-Supervised Building Change Detection Method for High Resolution Remote Sensing Images

PubMed Central

Hou, Bin; Wang, Yunhong; Liu, Qingjie

2016-01-01

Characterizations of up to date information of the Earth’s surface are an important application providing insights to urban planning, resources monitoring and environmental studies. A large number of change detection (CD) methods have been developed to solve them by utilizing remote sensing (RS) images. The advent of high resolution (HR) remote sensing images further provides challenges to traditional CD methods and opportunities to object-based CD methods. While several kinds of geospatial objects are recognized, this manuscript mainly focuses on buildings. Specifically, we propose a novel automatic approach combining pixel-based strategies with object-based ones for detecting building changes with HR remote sensing images. A multiresolution contextual morphological transformation called extended morphological attribute profiles (EMAPs) allows the extraction of geometrical features related to the structures within the scene at different scales. Pixel-based post-classification is executed on EMAPs using hierarchical fuzzy clustering. Subsequently, the hierarchical fuzzy frequency vector histograms are formed based on the image-objects acquired by simple linear iterative clustering (SLIC) segmentation. Then, saliency and morphological building index (MBI) extracted on difference images are used to generate a pseudo training set. Ultimately, object-based semi-supervised classification is implemented on this training set by applying random forest (RF). Most of the important changes are detected by the proposed method in our experiments. This study was checked for effectiveness using visual evaluation and numerical evaluation. PMID:27618903

A Saliency Guided Semi-Supervised Building Change Detection Method for High Resolution Remote Sensing Images.

PubMed

Hou, Bin; Wang, Yunhong; Liu, Qingjie

2016-08-27

Characterizations of up to date information of the Earth's surface are an important application providing insights to urban planning, resources monitoring and environmental studies. A large number of change detection (CD) methods have been developed to solve them by utilizing remote sensing (RS) images. The advent of high resolution (HR) remote sensing images further provides challenges to traditional CD methods and opportunities to object-based CD methods. While several kinds of geospatial objects are recognized, this manuscript mainly focuses on buildings. Specifically, we propose a novel automatic approach combining pixel-based strategies with object-based ones for detecting building changes with HR remote sensing images. A multiresolution contextual morphological transformation called extended morphological attribute profiles (EMAPs) allows the extraction of geometrical features related to the structures within the scene at different scales. Pixel-based post-classification is executed on EMAPs using hierarchical fuzzy clustering. Subsequently, the hierarchical fuzzy frequency vector histograms are formed based on the image-objects acquired by simple linear iterative clustering (SLIC) segmentation. Then, saliency and morphological building index (MBI) extracted on difference images are used to generate a pseudo training set. Ultimately, object-based semi-supervised classification is implemented on this training set by applying random forest (RF). Most of the important changes are detected by the proposed method in our experiments. This study was checked for effectiveness using visual evaluation and numerical evaluation.

Joint learning of labels and distance metric.

PubMed

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

2010-06-01

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

Marginal semi-supervised sub-manifold projections with informative constraints for dimensionality reduction and recognition.

PubMed

Zhang, Zhao; Zhao, Mingbo; Chow, Tommy W S

2012-12-01

In this work, sub-manifold projections based semi-supervised dimensionality reduction (DR) problem learning from partial constrained data is discussed. Two semi-supervised DR algorithms termed Marginal Semi-Supervised Sub-Manifold Projections (MS³MP) and orthogonal MS³MP (OMS³MP) are proposed. MS³MP in the singular case is also discussed. We also present the weighted least squares view of MS³MP. Based on specifying the types of neighborhoods with pairwise constraints (PC) and the defined manifold scatters, our methods can preserve the local properties of all points and discriminant structures embedded in the localized PC. The sub-manifolds of different classes can also be separated. In PC guided methods, exploring and selecting the informative constraints is challenging and random constraint subsets significantly affect the performance of algorithms. This paper also introduces an effective technique to select the informative constraints for DR with consistent constraints. The analytic form of the projection axes can be obtained by eigen-decomposition. The connections between this work and other related work are also elaborated. The validity of the proposed constraint selection approach and DR algorithms are evaluated by benchmark problems. Extensive simulations show that our algorithms can deliver promising results over some widely used state-of-the-art semi-supervised DR techniques. Copyright © 2012 Elsevier Ltd. All rights reserved.

Semi-automatic ground truth generation using unsupervised clustering and limited manual labeling: Application to handwritten character recognition

PubMed Central

Vajda, Szilárd; Rangoni, Yves; Cecotti, Hubert

2015-01-01

For training supervised classifiers to recognize different patterns, large data collections with accurate labels are necessary. In this paper, we propose a generic, semi-automatic labeling technique for large handwritten character collections. In order to speed up the creation of a large scale ground truth, the method combines unsupervised clustering and minimal expert knowledge. To exploit the potential discriminant complementarities across features, each character is projected into five different feature spaces. After clustering the images in each feature space, the human expert labels the cluster centers. Each data point inherits the label of its cluster’s center. A majority (or unanimity) vote decides the label of each character image. The amount of human involvement (labeling) is strictly controlled by the number of clusters – produced by the chosen clustering approach. To test the efficiency of the proposed approach, we have compared, and evaluated three state-of-the art clustering methods (k-means, self-organizing maps, and growing neural gas) on the MNIST digit data set, and a Lampung Indonesian character data set, respectively. Considering a k-nn classifier, we show that labeling manually only 1.3% (MNIST), and 3.2% (Lampung) of the training data, provides the same range of performance than a completely labeled data set would. PMID:25870463

Coupled dimensionality reduction and classification for supervised and semi-supervised multilabel learning

PubMed Central

Gönen, Mehmet

2014-01-01

Coupled training of dimensionality reduction and classification is proposed previously to improve the prediction performance for single-label problems. Following this line of research, in this paper, we first introduce a novel Bayesian method that combines linear dimensionality reduction with linear binary classification for supervised multilabel learning and present a deterministic variational approximation algorithm to learn the proposed probabilistic model. We then extend the proposed method to find intrinsic dimensionality of the projected subspace using automatic relevance determination and to handle semi-supervised learning using a low-density assumption. We perform supervised learning experiments on four benchmark multilabel learning data sets by comparing our method with baseline linear dimensionality reduction algorithms. These experiments show that the proposed approach achieves good performance values in terms of hamming loss, average AUC, macro F1, and micro F1 on held-out test data. The low-dimensional embeddings obtained by our method are also very useful for exploratory data analysis. We also show the effectiveness of our approach in finding intrinsic subspace dimensionality and semi-supervised learning tasks. PMID:24532862

Coupled dimensionality reduction and classification for supervised and semi-supervised multilabel learning.

PubMed

Gönen, Mehmet

2014-03-01

Coupled training of dimensionality reduction and classification is proposed previously to improve the prediction performance for single-label problems. Following this line of research, in this paper, we first introduce a novel Bayesian method that combines linear dimensionality reduction with linear binary classification for supervised multilabel learning and present a deterministic variational approximation algorithm to learn the proposed probabilistic model. We then extend the proposed method to find intrinsic dimensionality of the projected subspace using automatic relevance determination and to handle semi-supervised learning using a low-density assumption. We perform supervised learning experiments on four benchmark multilabel learning data sets by comparing our method with baseline linear dimensionality reduction algorithms. These experiments show that the proposed approach achieves good performance values in terms of hamming loss, average AUC, macro F 1 , and micro F 1 on held-out test data. The low-dimensional embeddings obtained by our method are also very useful for exploratory data analysis. We also show the effectiveness of our approach in finding intrinsic subspace dimensionality and semi-supervised learning tasks.

An iterated Laplacian based semi-supervised dimensionality reduction for classification of breast cancer on ultrasound images.

PubMed

Liu, Xiao; Shi, Jun; Zhou, Shichong; Lu, Minhua

2014-01-01

The dimensionality reduction is an important step in ultrasound image based computer-aided diagnosis (CAD) for breast cancer. A newly proposed l2,1 regularized correntropy algorithm for robust feature selection (CRFS) has achieved good performance for noise corrupted data. Therefore, it has the potential to reduce the dimensions of ultrasound image features. However, in clinical practice, the collection of labeled instances is usually expensive and time costing, while it is relatively easy to acquire the unlabeled or undetermined instances. Therefore, the semi-supervised learning is very suitable for clinical CAD. The iterated Laplacian regularization (Iter-LR) is a new regularization method, which has been proved to outperform the traditional graph Laplacian regularization in semi-supervised classification and ranking. In this study, to augment the classification accuracy of the breast ultrasound CAD based on texture feature, we propose an Iter-LR-based semi-supervised CRFS (Iter-LR-CRFS) algorithm, and then apply it to reduce the feature dimensions of ultrasound images for breast CAD. We compared the Iter-LR-CRFS with LR-CRFS, original supervised CRFS, and principal component analysis. The experimental results indicate that the proposed Iter-LR-CRFS significantly outperforms all other algorithms.

Self-supervised ARTMAP.

PubMed

Amis, Gregory P; Carpenter, Gail A

2010-03-01

Computational models of learning typically train on labeled input patterns (supervised learning), unlabeled input patterns (unsupervised learning), or a combination of the two (semi-supervised learning). In each case input patterns have a fixed number of features throughout training and testing. Human and machine learning contexts present additional opportunities for expanding incomplete knowledge from formal training, via self-directed learning that incorporates features not previously experienced. This article defines a new self-supervised learning paradigm to address these richer learning contexts, introducing a neural network called self-supervised ARTMAP. Self-supervised learning integrates knowledge from a teacher (labeled patterns with some features), knowledge from the environment (unlabeled patterns with more features), and knowledge from internal model activation (self-labeled patterns). Self-supervised ARTMAP learns about novel features from unlabeled patterns without destroying partial knowledge previously acquired from labeled patterns. A category selection function bases system predictions on known features, and distributed network activation scales unlabeled learning to prediction confidence. Slow distributed learning on unlabeled patterns focuses on novel features and confident predictions, defining classification boundaries that were ambiguous in the labeled patterns. Self-supervised ARTMAP improves test accuracy on illustrative low-dimensional problems and on high-dimensional benchmarks. Model code and benchmark data are available from: http://techlab.eu.edu/SSART/. Copyright 2009 Elsevier Ltd. All rights reserved.

Multi-Modal Curriculum Learning for Semi-Supervised Image Classification.

PubMed

Gong, Chen; Tao, Dacheng; Maybank, Stephen J; Liu, Wei; Kang, Guoliang; Yang, Jie

2016-07-01

Semi-supervised image classification aims to classify a large quantity of unlabeled images by typically harnessing scarce labeled images. Existing semi-supervised methods often suffer from inadequate classification accuracy when encountering difficult yet critical images, such as outliers, because they treat all unlabeled images equally and conduct classifications in an imperfectly ordered sequence. In this paper, we employ the curriculum learning methodology by investigating the difficulty of classifying every unlabeled image. The reliability and the discriminability of these unlabeled images are particularly investigated for evaluating their difficulty. As a result, an optimized image sequence is generated during the iterative propagations, and the unlabeled images are logically classified from simple to difficult. Furthermore, since images are usually characterized by multiple visual feature descriptors, we associate each kind of features with a teacher, and design a multi-modal curriculum learning (MMCL) strategy to integrate the information from different feature modalities. In each propagation, each teacher analyzes the difficulties of the currently unlabeled images from its own modality viewpoint. A consensus is subsequently reached among all the teachers, determining the currently simplest images (i.e., a curriculum), which are to be reliably classified by the multi-modal learner. This well-organized propagation process leveraging multiple teachers and one learner enables our MMCL to outperform five state-of-the-art methods on eight popular image data sets.

On supervised graph Laplacian embedding CA model & kernel construction and its application

NASA Astrophysics Data System (ADS)

Zeng, Junwei; Qian, Yongsheng; Wang, Min; Yang, Yongzhong

2017-01-01

There are many methods to construct kernel with given data attribute information. Gaussian radial basis function (RBF) kernel is one of the most popular ways to construct a kernel. The key observation is that in real-world data, besides the data attribute information, data label information also exists, which indicates the data class. In order to make use of both data attribute information and data label information, in this work, we propose a supervised kernel construction method. Supervised information from training data is integrated into standard kernel construction process to improve the discriminative property of resulting kernel. A supervised Laplacian embedding cellular automaton model is another key application developed for two-lane heterogeneous traffic flow with the safe distance and large-scale truck. Based on the properties of traffic flow in China, we re-calibrate the cell length, velocity, random slowing mechanism and lane-change conditions and use simulation tests to study the relationships among the speed, density and flux. The numerical results show that the large-scale trucks will have great effects on the traffic flow, which are relevant to the proportion of the large-scale trucks, random slowing rate and the times of the lane space change.

Improved regional-scale Brazilian cropping systems' mapping based on a semi-automatic object-based clustering approach

NASA Astrophysics Data System (ADS)

Bellón, Beatriz; Bégué, Agnès; Lo Seen, Danny; Lebourgeois, Valentine; Evangelista, Balbino Antônio; Simões, Margareth; Demonte Ferraz, Rodrigo Peçanha

2018-06-01

Cropping systems' maps at fine scale over large areas provide key information for further agricultural production and environmental impact assessments, and thus represent a valuable tool for effective land-use planning. There is, therefore, a growing interest in mapping cropping systems in an operational manner over large areas, and remote sensing approaches based on vegetation index time series analysis have proven to be an efficient tool. However, supervised pixel-based approaches are commonly adopted, requiring resource consuming field campaigns to gather training data. In this paper, we present a new object-based unsupervised classification approach tested on an annual MODIS 16-day composite Normalized Difference Vegetation Index time series and a Landsat 8 mosaic of the State of Tocantins, Brazil, for the 2014-2015 growing season. Two variants of the approach are compared: an hyperclustering approach, and a landscape-clustering approach involving a previous stratification of the study area into landscape units on which the clustering is then performed. The main cropping systems of Tocantins, characterized by the crop types and cropping patterns, were efficiently mapped with the landscape-clustering approach. Results show that stratification prior to clustering significantly improves the classification accuracies for underrepresented and sparsely distributed cropping systems. This study illustrates the potential of unsupervised classification for large area cropping systems' mapping and contributes to the development of generic tools for supporting large-scale agricultural monitoring across regions.

A semi-supervised classification algorithm using the TAD-derived background as training data

NASA Astrophysics Data System (ADS)

Fan, Lei; Ambeau, Brittany; Messinger, David W.

2013-05-01

In general, spectral image classification algorithms fall into one of two categories: supervised and unsupervised. In unsupervised approaches, the algorithm automatically identifies clusters in the data without a priori information about those clusters (except perhaps the expected number of them). Supervised approaches require an analyst to identify training data to learn the characteristics of the clusters such that they can then classify all other pixels into one of the pre-defined groups. The classification algorithm presented here is a semi-supervised approach based on the Topological Anomaly Detection (TAD) algorithm. The TAD algorithm defines background components based on a mutual k-Nearest Neighbor graph model of the data, along with a spectral connected components analysis. Here, the largest components produced by TAD are used as regions of interest (ROI's),or training data for a supervised classification scheme. By combining those ROI's with a Gaussian Maximum Likelihood (GML) or a Minimum Distance to the Mean (MDM) algorithm, we are able to achieve a semi supervised classification method. We test this classification algorithm against data collected by the HyMAP sensor over the Cooke City, MT area and University of Pavia scene.

Cancer survival analysis using semi-supervised learning method based on Cox and AFT models with L1/2 regularization.

PubMed

Liang, Yong; Chai, Hua; Liu, Xiao-Ying; Xu, Zong-Ben; Zhang, Hai; Leung, Kwong-Sak

2016-03-01

One of the most important objectives of the clinical cancer research is to diagnose cancer more accurately based on the patients' gene expression profiles. Both Cox proportional hazards model (Cox) and accelerated failure time model (AFT) have been widely adopted to the high risk and low risk classification or survival time prediction for the patients' clinical treatment. Nevertheless, two main dilemmas limit the accuracy of these prediction methods. One is that the small sample size and censored data remain a bottleneck for training robust and accurate Cox classification model. In addition to that, similar phenotype tumours and prognoses are actually completely different diseases at the genotype and molecular level. Thus, the utility of the AFT model for the survival time prediction is limited when such biological differences of the diseases have not been previously identified. To try to overcome these two main dilemmas, we proposed a novel semi-supervised learning method based on the Cox and AFT models to accurately predict the treatment risk and the survival time of the patients. Moreover, we adopted the efficient L1/2 regularization approach in the semi-supervised learning method to select the relevant genes, which are significantly associated with the disease. The results of the simulation experiments show that the semi-supervised learning model can significant improve the predictive performance of Cox and AFT models in survival analysis. The proposed procedures have been successfully applied to four real microarray gene expression and artificial evaluation datasets. The advantages of our proposed semi-supervised learning method include: 1) significantly increase the available training samples from censored data; 2) high capability for identifying the survival risk classes of patient in Cox model; 3) high predictive accuracy for patients' survival time in AFT model; 4) strong capability of the relevant biomarker selection. Consequently, our proposed semi-supervised learning model is one more appropriate tool for survival analysis in clinical cancer research.

A semi-supervised Support Vector Machine model for predicting the language outcomes following cochlear implantation based on pre-implant brain fMRI imaging.

PubMed

Tan, Lirong; Holland, Scott K; Deshpande, Aniruddha K; Chen, Ye; Choo, Daniel I; Lu, Long J

2015-12-01

We developed a machine learning model to predict whether or not a cochlear implant (CI) candidate will develop effective language skills within 2 years after the CI surgery by using the pre-implant brain fMRI data from the candidate. The language performance was measured 2 years after the CI surgery by the Clinical Evaluation of Language Fundamentals-Preschool, Second Edition (CELF-P2). Based on the CELF-P2 scores, the CI recipients were designated as either effective or ineffective CI users. For feature extraction from the fMRI data, we constructed contrast maps using the general linear model, and then utilized the Bag-of-Words (BoW) approach that we previously published to convert the contrast maps into feature vectors. We trained both supervised models and semi-supervised models to classify CI users as effective or ineffective. Compared with the conventional feature extraction approach, which used each single voxel as a feature, our BoW approach gave rise to much better performance for the classification of effective versus ineffective CI users. The semi-supervised model with the feature set extracted by the BoW approach from the contrast of speech versus silence achieved a leave-one-out cross-validation AUC as high as 0.97. Recursive feature elimination unexpectedly revealed that two features were sufficient to provide highly accurate classification of effective versus ineffective CI users based on our current dataset. We have validated the hypothesis that pre-implant cortical activation patterns revealed by fMRI during infancy correlate with language performance 2 years after cochlear implantation. The two brain regions highlighted by our classifier are potential biomarkers for the prediction of CI outcomes. Our study also demonstrated the superiority of the semi-supervised model over the supervised model. It is always worthwhile to try a semi-supervised model when unlabeled data are available.

Semi-supervised SVM for individual tree crown species classification

NASA Astrophysics Data System (ADS)

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

2015-12-01

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

Restricted Boltzmann machines based oversampling and semi-supervised learning for false positive reduction in breast CAD.

PubMed

Cao, Peng; Liu, Xiaoli; Bao, Hang; Yang, Jinzhu; Zhao, Dazhe

2015-01-01

The false-positive reduction (FPR) is a crucial step in the computer aided detection system for the breast. The issues of imbalanced data distribution and the limitation of labeled samples complicate the classification procedure. To overcome these challenges, we propose oversampling and semi-supervised learning methods based on the restricted Boltzmann machines (RBMs) to solve the classification of imbalanced data with a few labeled samples. To evaluate the proposed method, we conducted a comprehensive performance study and compared its results with the commonly used techniques. Experiments on benchmark dataset of DDSM demonstrate the effectiveness of the RBMs based oversampling and semi-supervised learning method in terms of geometric mean (G-mean) for false positive reduction in Breast CAD.

Experiences of burnout among drug counselors in a large opioid treatment program: A qualitative investigation.

PubMed

Beitel, Mark; Oberleitner, Lindsay; Muthulingam, Dharushana; Oberleitner, David; Madden, Lynn M; Marcus, Ruthanne; Eller, Anthony; Bono, Madeline H; Barry, Declan T

2018-03-09

Little is known about possible experiences of burnout among drug counselors in opioid treatment programs that are scaling up capacity to address the current opioid treatment gap. Participants in this quality improvement study were 31 drug counselors employed by large opioid treatment programs whose treatment capacities were expanding. Experiences of burnout and approaches for managing and/or preventing burnout were examined using individual semi-structured interviews, which were audiotaped, transcribed, and systematically coded by a multidisciplinary team using grounded theory. Rates of reported burnout (in response to an open-ended question) were lower than expected, with approximately 26% of participants reporting burnout. Counselor descriptions of burnout included cognitive, affective, behavioral, and physiological symptoms; and job-related demands were identified as a frequent cause. Participants described both self-initiated (e.g., engaging in pleasurable activities, exercising, taking breaks during workday) and system-supported strategies for managing or preventing burnout (e.g., availing of supervision and paid time off). Counselors provided recommendations for system-level changes to attenuate counselor risk of burnout (e.g., increased staff-wide encounters, improved communication, accessible paid time off, and increased clinical supervision). Findings suggest that drug counselor burnout is not inevitable, even in opioid treatment program settings whose treatment capacities are expanding. Organizations might benefit from routinely assessing counselor feedback about burnout and implementing feasible recommendations to attenuate burnout and promote work engagement.

Optimizing area under the ROC curve using semi-supervised learning

PubMed Central

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

2014-01-01

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

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

PubMed

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

2015-01-01

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

Active Semi-Supervised Community Detection Based on Must-Link and Cannot-Link Constraints

PubMed Central

Cheng, Jianjun; Leng, Mingwei; Li, Longjie; Zhou, Hanhai; Chen, Xiaoyun

2014-01-01

Community structure detection is of great importance because it can help in discovering the relationship between the function and the topology structure of a network. Many community detection algorithms have been proposed, but how to incorporate the prior knowledge in the detection process remains a challenging problem. In this paper, we propose a semi-supervised community detection algorithm, which makes full utilization of the must-link and cannot-link constraints to guide the process of community detection and thereby extracts high-quality community structures from networks. To acquire the high-quality must-link and cannot-link constraints, we also propose a semi-supervised component generation algorithm based on active learning, which actively selects nodes with maximum utility for the proposed semi-supervised community detection algorithm step by step, and then generates the must-link and cannot-link constraints by accessing a noiseless oracle. Extensive experiments were carried out, and the experimental results show that the introduction of active learning into the problem of community detection makes a success. Our proposed method can extract high-quality community structures from networks, and significantly outperforms other comparison methods. PMID:25329660

Active learning for semi-supervised clustering based on locally linear propagation reconstruction.

PubMed

Chang, Chin-Chun; Lin, Po-Yi

2015-03-01

The success of semi-supervised clustering relies on the effectiveness of side information. To get effective side information, a new active learner learning pairwise constraints known as must-link and cannot-link constraints is proposed in this paper. Three novel techniques are developed for learning effective pairwise constraints. The first technique is used to identify samples less important to cluster structures. This technique makes use of a kernel version of locally linear embedding for manifold learning. Samples neither important to locally linear propagation reconstructions of other samples nor on flat patches in the learned manifold are regarded as unimportant samples. The second is a novel criterion for query selection. This criterion considers not only the importance of a sample to expanding the space coverage of the learned samples but also the expected number of queries needed to learn the sample. To facilitate semi-supervised clustering, the third technique yields inferred must-links for passing information about flat patches in the learned manifold to semi-supervised clustering algorithms. Experimental results have shown that the learned pairwise constraints can capture the underlying cluster structures and proven the feasibility of the proposed approach. Copyright © 2014 Elsevier Ltd. All rights reserved.

Deep Learning @15 Petaflops/second: Semi-supervised pattern detection for 15 Terabytes of climate data

NASA Astrophysics Data System (ADS)

Collins, W. D.; Wehner, M. F.; Prabhat, M.; Kurth, T.; Satish, N.; Mitliagkas, I.; Zhang, J.; Racah, E.; Patwary, M.; Sundaram, N.; Dubey, P.

2017-12-01

Anthropogenically-forced climate changes in the number and character of extreme storms have the potential to significantly impact human and natural systems. Current high-performance computing enables multidecadal simulations with global climate models at resolutions of 25km or finer. Such high-resolution simulations are demonstrably superior in simulating extreme storms such as tropical cyclones than the coarser simulations available in the Coupled Model Intercomparison Project (CMIP5) and provide the capability to more credibly project future changes in extreme storm statistics and properties. The identification and tracking of storms in the voluminous model output is very challenging as it is impractical to manually identify storms due to the enormous size of the datasets, and therefore automated procedures are used. Traditionally, these procedures are based on a multi-variate set of physical conditions based on known properties of the class of storms in question. In recent years, we have successfully demonstrated that Deep Learning produces state of the art results for pattern detection in climate data. We have developed supervised and semi-supervised convolutional architectures for detecting and localizing tropical cyclones, extra-tropical cyclones and atmospheric rivers in simulation data. One of the primary challenges in the applicability of Deep Learning to climate data is in the expensive training phase. Typical networks may take days to converge on 10GB-sized datasets, while the climate science community has ready access to O(10 TB)-O(PB) sized datasets. In this work, we present the most scalable implementation of Deep Learning to date. We successfully scale a unified, semi-supervised convolutional architecture on all of the Cori Phase II supercomputer at NERSC. We use IntelCaffe, MKL and MLSL libraries. We have optimized single node MKL libraries to obtain 1-4 TF on single KNL nodes. We have developed a novel hybrid parameter update strategy to improve scaling to 9600 KNL nodes (600,000 cores). We obtain 15PF performance over the course of the training run; setting a new watermark for the HPC and Deep Learning communities. This talk will share insights on how to obtain this extreme level of performance, current gaps/challenges and implications for the climate science community.

Semantic classification of urban buildings combining VHR image and GIS data: An improved random forest approach

NASA Astrophysics Data System (ADS)

Du, Shihong; Zhang, Fangli; Zhang, Xiuyuan

2015-07-01

While most existing studies have focused on extracting geometric information on buildings, only a few have concentrated on semantic information. The lack of semantic information cannot satisfy many demands on resolving environmental and social issues. This study presents an approach to semantically classify buildings into much finer categories than those of existing studies by learning random forest (RF) classifier from a large number of imbalanced samples with high-dimensional features. First, a two-level segmentation mechanism combining GIS and VHR image produces single image objects at a large scale and intra-object components at a small scale. Second, a semi-supervised method chooses a large number of unbiased samples by considering the spatial proximity and intra-cluster similarity of buildings. Third, two important improvements in RF classifier are made: a voting-distribution ranked rule for reducing the influences of imbalanced samples on classification accuracy and a feature importance measurement for evaluating each feature's contribution to the recognition of each category. Fourth, the semantic classification of urban buildings is practically conducted in Beijing city, and the results demonstrate that the proposed approach is effective and accurate. The seven categories used in the study are finer than those in existing work and more helpful to studying many environmental and social problems.

TopicLens: Efficient Multi-Level Visual Topic Exploration of Large-Scale Document Collections.

PubMed

Kim, Minjeong; Kang, Kyeongpil; Park, Deokgun; Choo, Jaegul; Elmqvist, Niklas

2017-01-01

Topic modeling, which reveals underlying topics of a document corpus, has been actively adopted in visual analytics for large-scale document collections. However, due to its significant processing time and non-interactive nature, topic modeling has so far not been tightly integrated into a visual analytics workflow. Instead, most such systems are limited to utilizing a fixed, initial set of topics. Motivated by this gap in the literature, we propose a novel interaction technique called TopicLens that allows a user to dynamically explore data through a lens interface where topic modeling and the corresponding 2D embedding are efficiently computed on the fly. To support this interaction in real time while maintaining view consistency, we propose a novel efficient topic modeling method and a semi-supervised 2D embedding algorithm. Our work is based on improving state-of-the-art methods such as nonnegative matrix factorization and t-distributed stochastic neighbor embedding. Furthermore, we have built a web-based visual analytics system integrated with TopicLens. We use this system to measure the performance and the visualization quality of our proposed methods. We provide several scenarios showcasing the capability of TopicLens using real-world datasets.

Instructional Supervision in Public Secondary Schools in Kenya

ERIC Educational Resources Information Center

Wanzare, Zachariah

2012-01-01

This article reports some findings of study regarding practices and procedures of internal instructional supervision in public secondary schools in Kenya. The findings are part of a large-scale project undertaken in Kenya to determine the perceptions of headteachers, teachers and senior government education officers regarding the practices of…

Wire connector classification with machine vision and a novel hybrid SVM

NASA Astrophysics Data System (ADS)

Chauhan, Vedang; Joshi, Keyur D.; Surgenor, Brian W.

2018-04-01

A machine vision-based system has been developed and tested that uses a novel hybrid Support Vector Machine (SVM) in a part inspection application with clear plastic wire connectors. The application required the system to differentiate between 4 different known styles of connectors plus one unknown style, for a total of 5 classes. The requirement to handle an unknown class is what necessitated the hybrid approach. The system was trained with the 4 known classes and tested with 5 classes (the 4 known plus the 1 unknown). The hybrid classification approach used two layers of SVMs: one layer was semi-supervised and the other layer was supervised. The semi-supervised SVM was a special case of unsupervised machine learning that classified test images as one of the 4 known classes (to accept) or as the unknown class (to reject). The supervised SVM classified test images as one of the 4 known classes and consequently would give false positives (FPs). Two methods were tested. The difference between the methods was that the order of the layers was switched. The method with the semi-supervised layer first gave an accuracy of 80% with 20% FPs. The method with the supervised layer first gave an accuracy of 98% with 0% FPs. Further work is being conducted to see if the hybrid approach works with other applications that have an unknown class requirement.

Machine learning applications in genetics and genomics.

PubMed

Libbrecht, Maxwell W; Noble, William Stafford

2015-06-01

The field of machine learning, which aims to develop computer algorithms that improve with experience, holds promise to enable computers to assist humans in the analysis of large, complex data sets. Here, we provide an overview of machine learning applications for the analysis of genome sequencing data sets, including the annotation of sequence elements and epigenetic, proteomic or metabolomic data. We present considerations and recurrent challenges in the application of supervised, semi-supervised and unsupervised machine learning methods, as well as of generative and discriminative modelling approaches. We provide general guidelines to assist in the selection of these machine learning methods and their practical application for the analysis of genetic and genomic data sets.

FRaC: a feature-modeling approach for semi-supervised and unsupervised anomaly detection.

PubMed

Noto, Keith; Brodley, Carla; Slonim, Donna

2012-01-01

Anomaly detection involves identifying rare data instances (anomalies) that come from a different class or distribution than the majority (which are simply called "normal" instances). Given a training set of only normal data, the semi-supervised anomaly detection task is to identify anomalies in the future. Good solutions to this task have applications in fraud and intrusion detection. The unsupervised anomaly detection task is different: Given unlabeled, mostly-normal data, identify the anomalies among them. Many real-world machine learning tasks, including many fraud and intrusion detection tasks, are unsupervised because it is impractical (or impossible) to verify all of the training data. We recently presented FRaC, a new approach for semi-supervised anomaly detection. FRaC is based on using normal instances to build an ensemble of feature models, and then identifying instances that disagree with those models as anomalous. In this paper, we investigate the behavior of FRaC experimentally and explain why FRaC is so successful. We also show that FRaC is a superior approach for the unsupervised as well as the semi-supervised anomaly detection task, compared to well-known state-of-the-art anomaly detection methods, LOF and one-class support vector machines, and to an existing feature-modeling approach.

FRaC: a feature-modeling approach for semi-supervised and unsupervised anomaly detection

PubMed Central

Brodley, Carla; Slonim, Donna

2011-01-01

Anomaly detection involves identifying rare data instances (anomalies) that come from a different class or distribution than the majority (which are simply called “normal” instances). Given a training set of only normal data, the semi-supervised anomaly detection task is to identify anomalies in the future. Good solutions to this task have applications in fraud and intrusion detection. The unsupervised anomaly detection task is different: Given unlabeled, mostly-normal data, identify the anomalies among them. Many real-world machine learning tasks, including many fraud and intrusion detection tasks, are unsupervised because it is impractical (or impossible) to verify all of the training data. We recently presented FRaC, a new approach for semi-supervised anomaly detection. FRaC is based on using normal instances to build an ensemble of feature models, and then identifying instances that disagree with those models as anomalous. In this paper, we investigate the behavior of FRaC experimentally and explain why FRaC is so successful. We also show that FRaC is a superior approach for the unsupervised as well as the semi-supervised anomaly detection task, compared to well-known state-of-the-art anomaly detection methods, LOF and one-class support vector machines, and to an existing feature-modeling approach. PMID:22639542

Semi-supervised learning for ordinal Kernel Discriminant Analysis.

PubMed

Pérez-Ortiz, M; Gutiérrez, P A; Carbonero-Ruz, M; Hervás-Martínez, C

2016-12-01

Ordinal classification considers those classification problems where the labels of the variable to predict follow a given order. Naturally, labelled data is scarce or difficult to obtain in this type of problems because, in many cases, ordinal labels are given by a user or expert (e.g. in recommendation systems). Firstly, this paper develops a new strategy for ordinal classification where both labelled and unlabelled data are used in the model construction step (a scheme which is referred to as semi-supervised learning). More specifically, the ordinal version of kernel discriminant learning is extended for this setting considering the neighbourhood information of unlabelled data, which is proposed to be computed in the feature space induced by the kernel function. Secondly, a new method for semi-supervised kernel learning is devised in the context of ordinal classification, which is combined with our developed classification strategy to optimise the kernel parameters. The experiments conducted compare 6 different approaches for semi-supervised learning in the context of ordinal classification in a battery of 30 datasets, showing (1) the good synergy of the ordinal version of discriminant analysis and the use of unlabelled data and (2) the advantage of computing distances in the feature space induced by the kernel function. Copyright © 2016 Elsevier Ltd. All rights reserved.

A Large Scale Computer Terminal Output Controller.

ERIC Educational Resources Information Center

Tucker, Paul Thomas

This paper describes the design and implementation of a large scale computer terminal output controller which supervises the transfer of information from a Control Data 6400 Computer to a PLATO IV data network. It discusses the cost considerations leading to the selection of educational television channels rather than telephone lines for…

Efficient use of unlabeled data for protein sequence classification: a comparative study.

PubMed

Kuksa, Pavel; Huang, Pai-Hsi; Pavlovic, Vladimir

2009-04-29

Recent studies in computational primary protein sequence analysis have leveraged the power of unlabeled data. For example, predictive models based on string kernels trained on sequences known to belong to particular folds or superfamilies, the so-called labeled data set, can attain significantly improved accuracy if this data is supplemented with protein sequences that lack any class tags-the unlabeled data. In this study, we present a principled and biologically motivated computational framework that more effectively exploits the unlabeled data by only using the sequence regions that are more likely to be biologically relevant for better prediction accuracy. As overly-represented sequences in large uncurated databases may bias the estimation of computational models that rely on unlabeled data, we also propose a method to remove this bias and improve performance of the resulting classifiers. Combined with state-of-the-art string kernels, our proposed computational framework achieves very accurate semi-supervised protein remote fold and homology detection on three large unlabeled databases. It outperforms current state-of-the-art methods and exhibits significant reduction in running time. The unlabeled sequences used under the semi-supervised setting resemble the unpolished gemstones; when used as-is, they may carry unnecessary features and hence compromise the classification accuracy but once cut and polished, they improve the accuracy of the classifiers considerably.

A Phenomenological Study: The Experience of Live Supervision during a Pre-Practicum Counseling Techniques Course

ERIC Educational Resources Information Center

Koltz, Rebecca L.; Feit, Stephen S.

2012-01-01

The experiences of live supervision for three, master's level, pre-practicum counseling students were explored using a phenomenological methodology. Using semi-structured interviews, this study resulted in a thick description of the experience of live supervision capturing participants' thoughts, emotions, and behaviors. Data revealed that live…

Factors influencing the perceived quality of clinical supervision of occupational therapists in a large Australian state.

PubMed

Martin, Priya; Kumar, Saravana; Lizarondo, Lucylynn; Tyack, Zephanie

2016-10-01

Clinical supervision is important for effective health service delivery, professional development and practice. Despite its importance there is a lack of evidence regarding the factors that improve its quality. This study aimed to investigate the factors that influence the quality of clinical supervision of occupational therapists employed in a large public sector health service covering mental health, paediatrics, adult physical and other practice areas. A mixed method, sequential explanatory study design was used consisting of two phases. This article reports the quantitative phase (Phase One) which involved administration of the Manchester Clinical Supervision Scale (MCSS-26) to 207 occupational therapists. Frequency of supervision sessions, choice of supervisor and the type of supervision were found to be the predictor variables with a positive and significant influence on the quality of clinical supervision. Factors such as age, length of supervision and the area of practice were found to be the predictor variables with a negative and significant influence on the quality of clinical supervision. Factors that influence the perceived quality of clinical supervision among occupational therapists have been identified. High quality clinical supervision is an important component of clinical governance and has been shown to be beneficial to practitioners, patients and the organisation. Information on factors that make clinical supervision effective identified in this study can be added to existing supervision training and practices to improve the quality of clinical supervision. © 2016 Occupational Therapy Australia.

Effects of automation and task load on task switching during human supervision of multiple semi-autonomous robots in a dynamic environment.

PubMed

Squire, P N; Parasuraman, R

2010-08-01

The present study assessed the impact of task load and level of automation (LOA) on task switching in participants supervising a team of four or eight semi-autonomous robots in a simulated 'capture the flag' game. Participants were faster to perform the same task than when they chose to switch between different task actions. They also took longer to switch between different tasks when supervising the robots at a high compared to a low LOA. Task load, as manipulated by the number of robots to be supervised, did not influence switch costs. The results suggest that the design of future unmanned vehicle (UV) systems should take into account not simply how many UVs an operator can supervise, but also the impact of LOA and task operations on task switching during supervision of multiple UVs. The findings of this study are relevant for the ergonomics practice of UV systems. This research extends the cognitive theory of task switching to inform the design of UV systems and results show that switching between UVs is an important factor to consider.

Constrained Deep Weak Supervision for Histopathology Image Segmentation.

PubMed

Jia, Zhipeng; Huang, Xingyi; Chang, Eric I-Chao; Xu, Yan

2017-11-01

In this paper, we develop a new weakly supervised learning algorithm to learn to segment cancerous regions in histopathology images. This paper is under a multiple instance learning (MIL) framework with a new formulation, deep weak supervision (DWS); we also propose an effective way to introduce constraints to our neural networks to assist the learning process. The contributions of our algorithm are threefold: 1) we build an end-to-end learning system that segments cancerous regions with fully convolutional networks (FCNs) in which image-to-image weakly-supervised learning is performed; 2) we develop a DWS formulation to exploit multi-scale learning under weak supervision within FCNs; and 3) constraints about positive instances are introduced in our approach to effectively explore additional weakly supervised information that is easy to obtain and enjoy a significant boost to the learning process. The proposed algorithm, abbreviated as DWS-MIL, is easy to implement and can be trained efficiently. Our system demonstrates the state-of-the-art results on large-scale histopathology image data sets and can be applied to various applications in medical imaging beyond histopathology images, such as MRI, CT, and ultrasound images.

Newton Methods for Large Scale Problems in Machine Learning

ERIC Educational Resources Information Center

Hansen, Samantha Leigh

2014-01-01

The focus of this thesis is on practical ways of designing optimization algorithms for minimizing large-scale nonlinear functions with applications in machine learning. Chapter 1 introduces the overarching ideas in the thesis. Chapters 2 and 3 are geared towards supervised machine learning applications that involve minimizing a sum of loss…

An Effective Big Data Supervised Imbalanced Classification Approach for Ortholog Detection in Related Yeast Species

PubMed Central

Galpert, Deborah; del Río, Sara; Herrera, Francisco; Ancede-Gallardo, Evys; Antunes, Agostinho; Agüero-Chapin, Guillermin

2015-01-01

Orthology detection requires more effective scaling algorithms. In this paper, a set of gene pair features based on similarity measures (alignment scores, sequence length, gene membership to conserved regions, and physicochemical profiles) are combined in a supervised pairwise ortholog detection approach to improve effectiveness considering low ortholog ratios in relation to the possible pairwise comparison between two genomes. In this scenario, big data supervised classifiers managing imbalance between ortholog and nonortholog pair classes allow for an effective scaling solution built from two genomes and extended to other genome pairs. The supervised approach was compared with RBH, RSD, and OMA algorithms by using the following yeast genome pairs: Saccharomyces cerevisiae-Kluyveromyces lactis, Saccharomyces cerevisiae-Candida glabrata, and Saccharomyces cerevisiae-Schizosaccharomyces pombe as benchmark datasets. Because of the large amount of imbalanced data, the building and testing of the supervised model were only possible by using big data supervised classifiers managing imbalance. Evaluation metrics taking low ortholog ratios into account were applied. From the effectiveness perspective, MapReduce Random Oversampling combined with Spark SVM outperformed RBH, RSD, and OMA, probably because of the consideration of gene pair features beyond alignment similarities combined with the advances in big data supervised classification. PMID:26605337

An Effective Big Data Supervised Imbalanced Classification Approach for Ortholog Detection in Related Yeast Species.

PubMed

Galpert, Deborah; Del Río, Sara; Herrera, Francisco; Ancede-Gallardo, Evys; Antunes, Agostinho; Agüero-Chapin, Guillermin

2015-01-01

Orthology detection requires more effective scaling algorithms. In this paper, a set of gene pair features based on similarity measures (alignment scores, sequence length, gene membership to conserved regions, and physicochemical profiles) are combined in a supervised pairwise ortholog detection approach to improve effectiveness considering low ortholog ratios in relation to the possible pairwise comparison between two genomes. In this scenario, big data supervised classifiers managing imbalance between ortholog and nonortholog pair classes allow for an effective scaling solution built from two genomes and extended to other genome pairs. The supervised approach was compared with RBH, RSD, and OMA algorithms by using the following yeast genome pairs: Saccharomyces cerevisiae-Kluyveromyces lactis, Saccharomyces cerevisiae-Candida glabrata, and Saccharomyces cerevisiae-Schizosaccharomyces pombe as benchmark datasets. Because of the large amount of imbalanced data, the building and testing of the supervised model were only possible by using big data supervised classifiers managing imbalance. Evaluation metrics taking low ortholog ratios into account were applied. From the effectiveness perspective, MapReduce Random Oversampling combined with Spark SVM outperformed RBH, RSD, and OMA, probably because of the consideration of gene pair features beyond alignment similarities combined with the advances in big data supervised classification.

Semi-supervised spectral algorithms for community detection in complex networks based on equivalence of clustering methods

NASA Astrophysics Data System (ADS)

Ma, Xiaoke; Wang, Bingbo; Yu, Liang

2018-01-01

Community detection is fundamental for revealing the structure-functionality relationship in complex networks, which involves two issues-the quantitative function for community as well as algorithms to discover communities. Despite significant research on either of them, few attempt has been made to establish the connection between the two issues. To attack this problem, a generalized quantification function is proposed for community in weighted networks, which provides a framework that unifies several well-known measures. Then, we prove that the trace optimization of the proposed measure is equivalent with the objective functions of algorithms such as nonnegative matrix factorization, kernel K-means as well as spectral clustering. It serves as the theoretical foundation for designing algorithms for community detection. On the second issue, a semi-supervised spectral clustering algorithm is developed by exploring the equivalence relation via combining the nonnegative matrix factorization and spectral clustering. Different from the traditional semi-supervised algorithms, the partial supervision is integrated into the objective of the spectral algorithm. Finally, through extensive experiments on both artificial and real world networks, we demonstrate that the proposed method improves the accuracy of the traditional spectral algorithms in community detection.

Graph-Based Semi-Supervised Hyperspectral Image Classification Using Spatial Information

NASA Astrophysics Data System (ADS)

Jamshidpour, N.; Homayouni, S.; Safari, A.

2017-09-01

Hyperspectral image classification has been one of the most popular research areas in the remote sensing community in the past decades. However, there are still some problems that need specific attentions. For example, the lack of enough labeled samples and the high dimensionality problem are two most important issues which degrade the performance of supervised classification dramatically. The main idea of semi-supervised learning is to overcome these issues by the contribution of unlabeled samples, which are available in an enormous amount. In this paper, we propose a graph-based semi-supervised classification method, which uses both spectral and spatial information for hyperspectral image classification. More specifically, two graphs were designed and constructed in order to exploit the relationship among pixels in spectral and spatial spaces respectively. Then, the Laplacians of both graphs were merged to form a weighted joint graph. The experiments were carried out on two different benchmark hyperspectral data sets. The proposed method performed significantly better than the well-known supervised classification methods, such as SVM. The assessments consisted of both accuracy and homogeneity analyses of the produced classification maps. The proposed spectral-spatial SSL method considerably increased the classification accuracy when the labeled training data set is too scarce.When there were only five labeled samples for each class, the performance improved 5.92% and 10.76% compared to spatial graph-based SSL, for AVIRIS Indian Pine and Pavia University data sets respectively.

Adaptive Sensing and Fusion of Multi-Sensor Data and Historical Information

DTIC Science & Technology

2009-11-06

integrate MTL and semi-supervised learning into a single framework , thereby exploiting two forms of contextual information. A key new objective of the...this report we integrate MTL and semi-supervised learning into a single framework , thereby exploiting two forms of contextual information. A key new...process [8], denoted as X ∼ BeP (B), where B is a measure on Ω. If B is continuous, X is a Poisson process with intensity B and can be constructed as X = N

Failure Analysis of a Complex Learning Framework Incorporating Multi-Modal and Semi-Supervised Learning

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

Pullum, Laura L; Symons, Christopher T

2011-01-01

Machine learning is used in many applications, from machine vision to speech recognition to decision support systems, and is used to test applications. However, though much has been done to evaluate the performance of machine learning algorithms, little has been done to verify the algorithms or examine their failure modes. Moreover, complex learning frameworks often require stepping beyond black box evaluation to distinguish between errors based on natural limits on learning and errors that arise from mistakes in implementation. We present a conceptual architecture, failure model and taxonomy, and failure modes and effects analysis (FMEA) of a semi-supervised, multi-modal learningmore » system, and provide specific examples from its use in a radiological analysis assistant system. The goal of the research described in this paper is to provide a foundation from which dependability analysis of systems using semi-supervised, multi-modal learning can be conducted. The methods presented provide a first step towards that overall goal.« less

Semi-supervised clustering methods

PubMed Central

Bair, Eric

2013-01-01

Cluster analysis methods seek to partition a data set into homogeneous subgroups. It is useful in a wide variety of applications, including document processing and modern genetics. Conventional clustering methods are unsupervised, meaning that there is no outcome variable nor is anything known about the relationship between the observations in the data set. In many situations, however, information about the clusters is available in addition to the values of the features. For example, the cluster labels of some observations may be known, or certain observations may be known to belong to the same cluster. In other cases, one may wish to identify clusters that are associated with a particular outcome variable. This review describes several clustering algorithms (known as “semi-supervised clustering” methods) that can be applied in these situations. The majority of these methods are modifications of the popular k-means clustering method, and several of them will be described in detail. A brief description of some other semi-supervised clustering algorithms is also provided. PMID:24729830

Semi-supervised clustering methods.

PubMed

Bair, Eric

2013-01-01

Cluster analysis methods seek to partition a data set into homogeneous subgroups. It is useful in a wide variety of applications, including document processing and modern genetics. Conventional clustering methods are unsupervised, meaning that there is no outcome variable nor is anything known about the relationship between the observations in the data set. In many situations, however, information about the clusters is available in addition to the values of the features. For example, the cluster labels of some observations may be known, or certain observations may be known to belong to the same cluster. In other cases, one may wish to identify clusters that are associated with a particular outcome variable. This review describes several clustering algorithms (known as "semi-supervised clustering" methods) that can be applied in these situations. The majority of these methods are modifications of the popular k-means clustering method, and several of them will be described in detail. A brief description of some other semi-supervised clustering algorithms is also provided.

Efficient use of unlabeled data for protein sequence classification: a comparative study

PubMed Central

Kuksa, Pavel; Huang, Pai-Hsi; Pavlovic, Vladimir

2009-01-01

Background Recent studies in computational primary protein sequence analysis have leveraged the power of unlabeled data. For example, predictive models based on string kernels trained on sequences known to belong to particular folds or superfamilies, the so-called labeled data set, can attain significantly improved accuracy if this data is supplemented with protein sequences that lack any class tags–the unlabeled data. In this study, we present a principled and biologically motivated computational framework that more effectively exploits the unlabeled data by only using the sequence regions that are more likely to be biologically relevant for better prediction accuracy. As overly-represented sequences in large uncurated databases may bias the estimation of computational models that rely on unlabeled data, we also propose a method to remove this bias and improve performance of the resulting classifiers. Results Combined with state-of-the-art string kernels, our proposed computational framework achieves very accurate semi-supervised protein remote fold and homology detection on three large unlabeled databases. It outperforms current state-of-the-art methods and exhibits significant reduction in running time. Conclusion The unlabeled sequences used under the semi-supervised setting resemble the unpolished gemstones; when used as-is, they may carry unnecessary features and hence compromise the classification accuracy but once cut and polished, they improve the accuracy of the classifiers considerably. PMID:19426450

Semi-supervised Machine Learning for Analysis of Hydrogeochemical Data and Models

NASA Astrophysics Data System (ADS)

Vesselinov, Velimir; O'Malley, Daniel; Alexandrov, Boian; Moore, Bryan

2017-04-01

Data- and model-based analyses such as uncertainty quantification, sensitivity analysis, and decision support using complex physics models with numerous model parameters and typically require a huge number of model evaluations (on order of 10^6). Furthermore, model simulations of complex physics may require substantial computational time. For example, accounting for simultaneously occurring physical processes such as fluid flow and biogeochemical reactions in heterogeneous porous medium may require several hours of wall-clock computational time. To address these issues, we have developed a novel methodology for semi-supervised machine learning based on Non-negative Matrix Factorization (NMF) coupled with customized k-means clustering. The algorithm allows for automated, robust Blind Source Separation (BSS) of groundwater types (contamination sources) based on model-free analyses of observed hydrogeochemical data. We have also developed reduced order modeling tools, which coupling support vector regression (SVR), genetic algorithms (GA) and artificial and convolutional neural network (ANN/CNN). SVR is applied to predict the model behavior within prior uncertainty ranges associated with the model parameters. ANN and CNN procedures are applied to upscale heterogeneity of the porous medium. In the upscaling process, fine-scale high-resolution models of heterogeneity are applied to inform coarse-resolution models which have improved computational efficiency while capturing the impact of fine-scale effects at the course scale of interest. These techniques are tested independently on a series of synthetic problems. We also present a decision analysis related to contaminant remediation where the developed reduced order models are applied to reproduce groundwater flow and contaminant transport in a synthetic heterogeneous aquifer. The tools are coded in Julia and are a part of the MADS high-performance computational framework (https://github.com/madsjulia/Mads.jl).

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.

Can Semi-Supervised Learning Explain Incorrect Beliefs about Categories?

ERIC Educational Resources Information Center

Kalish, Charles W.; Rogers, Timothy T.; Lang, Jonathan; Zhu, Xiaojin

2011-01-01

Three experiments with 88 college-aged participants explored how unlabeled experiences--learning episodes in which people encounter objects without information about their category membership--influence beliefs about category structure. Participants performed a simple one-dimensional categorization task in a brief supervised learning phase, then…

Scaling Up Graph-Based Semisupervised Learning via Prototype Vector Machines

PubMed Central

Zhang, Kai; Lan, Liang; Kwok, James T.; Vucetic, Slobodan; Parvin, Bahram

2014-01-01

When the amount of labeled data are limited, semi-supervised learning can improve the learner's performance by also using the often easily available unlabeled data. In particular, a popular approach requires the learned function to be smooth on the underlying data manifold. By approximating this manifold as a weighted graph, such graph-based techniques can often achieve state-of-the-art performance. However, their high time and space complexities make them less attractive on large data sets. In this paper, we propose to scale up graph-based semisupervised learning using a set of sparse prototypes derived from the data. These prototypes serve as a small set of data representatives, which can be used to approximate the graph-based regularizer and to control model complexity. Consequently, both training and testing become much more efficient. Moreover, when the Gaussian kernel is used to define the graph affinity, a simple and principled method to select the prototypes can be obtained. Experiments on a number of real-world data sets demonstrate encouraging performance and scaling properties of the proposed approach. It also compares favorably with models learned via ℓ1-regularization at the same level of model sparsity. These results demonstrate the efficacy of the proposed approach in producing highly parsimonious and accurate models for semisupervised learning. PMID:25720002

SSEL-ADE: A semi-supervised ensemble learning framework for extracting adverse drug events from social media.

PubMed

Liu, Jing; Zhao, Songzheng; Wang, Gang

2018-01-01

With the development of Web 2.0 technology, social media websites have become lucrative but under-explored data sources for extracting adverse drug events (ADEs), which is a serious health problem. Besides ADE, other semantic relation types (e.g., drug indication and beneficial effect) could hold between the drug and adverse event mentions, making ADE relation extraction - distinguishing ADE relationship from other relation types - necessary. However, conducting ADE relation extraction in social media environment is not a trivial task because of the expertise-dependent, time-consuming and costly annotation process, and the feature space's high-dimensionality attributed to intrinsic characteristics of social media data. This study aims to develop a framework for ADE relation extraction using patient-generated content in social media with better performance than that delivered by previous efforts. To achieve the objective, a general semi-supervised ensemble learning framework, SSEL-ADE, was developed. The framework exploited various lexical, semantic, and syntactic features, and integrated ensemble learning and semi-supervised learning. A series of experiments were conducted to verify the effectiveness of the proposed framework. Empirical results demonstrate the effectiveness of each component of SSEL-ADE and reveal that our proposed framework outperforms most of existing ADE relation extraction methods The SSEL-ADE can facilitate enhanced ADE relation extraction performance, thereby providing more reliable support for pharmacovigilance. Moreover, the proposed semi-supervised ensemble methods have the potential of being applied to effectively deal with other social media-based problems. Copyright © 2017 Elsevier B.V. All rights reserved.

Measurement of faculty anesthesiologists' quality of clinical supervision has greater reliability when controlling for the leniency of the rating anesthesia resident: a retrospective cohort study.

PubMed

Dexter, Franklin; Ledolter, Johannes; Hindman, Bradley J

2017-06-01

Our department monitors the quality of anesthesiologists' clinical supervision and provides each anesthesiologist with periodic feedback. We hypothesized that greater differentiation among anesthesiologists' supervision scores could be obtained by adjusting for leniency of the rating resident. From July 1, 2013 to December 31, 2015, our department has utilized the de Oliveira Filho unidimensional nine-item supervision scale to assess the quality of clinical supervision provided by faculty as rated by residents. We examined all 13,664 ratings of the 97 anesthesiologists (ratees) by the 65 residents (raters). Testing for internal consistency among answers to questions (large Cronbach's alpha > 0.90) was performed to rule out that one or two questions accounted for leniency. Mixed-effects logistic regression was used to compare ratees while controlling for rater leniency vs using Student t tests without rater leniency. The mean supervision scale score was calculated for each combination of the 65 raters and nine questions. The Cronbach's alpha was very large (0.977). The mean score was calculated for each of the 3,421 observed combinations of resident and anesthesiologist. The logits of the percentage of scores equal to the maximum value of 4.00 were normally distributed (residents, P = 0.24; anesthesiologists, P = 0.50). There were 20/97 anesthesiologists identified as significant outliers (13 with below average supervision scores and seven with better than average) using the mixed-effects logistic regression with rater leniency entered as a fixed effect but not by Student's t test. In contrast, there were three of 97 anesthesiologists identified as outliers (all three above average) using Student's t tests but not by logistic regression with leniency. The 20 vs 3 was significant (P < 0.001). Use of logistic regression with leniency results in greater detection of anesthesiologists with significantly better (or worse) clinical supervision scores than use of Student's t tests (i.e., without adjustment for rater leniency).

Adaptive distance metric learning for diffusion tensor image segmentation.

PubMed

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

2014-01-01

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

Adaptive Distance Metric Learning for Diffusion Tensor Image Segmentation

PubMed Central

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

2014-01-01

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

Combining active learning and semi-supervised learning techniques to extract protein interaction sentences.

PubMed

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

2011-11-24

Protein-protein interaction (PPI) extraction has been a focal point of many biomedical research and database curation tools. Both Active Learning and Semi-supervised SVMs have recently been applied to extract PPI automatically. In this paper, we explore combining the AL with the SSL to improve the performance of the PPI task. We propose a novel PPI extraction technique called PPISpotter by combining Deterministic Annealing-based SSL and an AL technique to extract protein-protein interaction. In addition, we extract a comprehensive set of features from MEDLINE records by Natural Language Processing (NLP) techniques, which further improve the SVM classifiers. In our feature selection technique, syntactic, semantic, and lexical properties of text are incorporated into feature selection that boosts the system performance significantly. By conducting experiments with three different PPI corpuses, we show that PPISpotter is superior to the other techniques incorporated into semi-supervised SVMs such as Random Sampling, Clustering, and Transductive SVMs by precision, recall, and F-measure. Our system is a novel, state-of-the-art technique for efficiently extracting protein-protein interaction pairs.

Seizure Classification From EEG Signals Using Transfer Learning, Semi-Supervised Learning and TSK Fuzzy System.

PubMed

Jiang, Yizhang; Wu, Dongrui; Deng, Zhaohong; Qian, Pengjiang; Wang, Jun; Wang, Guanjin; Chung, Fu-Lai; Choi, Kup-Sze; Wang, Shitong

2017-12-01

Recognition of epileptic seizures from offline EEG signals is very important in clinical diagnosis of epilepsy. Compared with manual labeling of EEG signals by doctors, machine learning approaches can be faster and more consistent. However, the classification accuracy is usually not satisfactory for two main reasons: the distributions of the data used for training and testing may be different, and the amount of training data may not be enough. In addition, most machine learning approaches generate black-box models that are difficult to interpret. In this paper, we integrate transductive transfer learning, semi-supervised learning and TSK fuzzy system to tackle these three problems. More specifically, we use transfer learning to reduce the discrepancy in data distribution between the training and testing data, employ semi-supervised learning to use the unlabeled testing data to remedy the shortage of training data, and adopt TSK fuzzy system to increase model interpretability. Two learning algorithms are proposed to train the system. Our experimental results show that the proposed approaches can achieve better performance than many state-of-the-art seizure classification algorithms.

A Semi-Supervised Learning Algorithm for Predicting Four Types MiRNA-Disease Associations by Mutual Information in a Heterogeneous Network.

PubMed

Zhang, Xiaotian; Yin, Jian; Zhang, Xu

2018-03-02

Increasing evidence suggests that dysregulation of microRNAs (miRNAs) may lead to a variety of diseases. Therefore, identifying disease-related miRNAs is a crucial problem. Currently, many computational approaches have been proposed to predict binary miRNA-disease associations. In this study, in order to predict underlying miRNA-disease association types, a semi-supervised model called the network-based label propagation algorithm is proposed to infer multiple types of miRNA-disease associations (NLPMMDA) by mutual information derived from the heterogeneous network. The NLPMMDA method integrates disease semantic similarity, miRNA functional similarity, and Gaussian interaction profile kernel similarity information of miRNAs and diseases to construct a heterogeneous network. NLPMMDA is a semi-supervised model which does not require verified negative samples. Leave-one-out cross validation (LOOCV) was implemented for four known types of miRNA-disease associations and demonstrated the reliable performance of our method. Moreover, case studies of lung cancer and breast cancer confirmed effective performance of NLPMMDA to predict novel miRNA-disease associations and their association types.

Semi-Supervised Novelty Detection with Adaptive Eigenbases, and Application to Radio Transients

NASA Technical Reports Server (NTRS)

Thompson, David R.; Majid, Walid A.; Reed, Colorado J.; Wagstaff, Kiri L.

2011-01-01

We present a semi-supervised online method for novelty detection and evaluate its performance for radio astronomy time series data. Our approach uses adaptive eigenbases to combine 1) prior knowledge about uninteresting signals with 2) online estimation of the current data properties to enable highly sensitive and precise detection of novel signals. We apply the method to the problem of detecting fast transient radio anomalies and compare it to current alternative algorithms. Tests based on observations from the Parkes Multibeam Survey show both effective detection of interesting rare events and robustness to known false alarm anomalies.

The UK Postgraduate Masters Dissertation: An "Elusive Chameleon"?

ERIC Educational Resources Information Center

Pilcher, Nick

2011-01-01

Many studies into the process of producing and supervising dissertations exist, yet little research into the "product" of the Masters dissertation, or into how Masters supervision changes over time exist. Drawing on 62 semi-structured interviews with 31 Maths and Computer Science supervisors over a two-year period, this paper explores…

What's Not Being Said? Recollections of Nondisclosure in Clinical Supervision While in Training

ERIC Educational Resources Information Center

Sweeney, Jennifer; Creaner, Mary

2014-01-01

The aim of this qualitative study was to retrospectively examine nondisclosure in individual supervision while in training. Interviews were conducted with supervisees two years post-qualification. Specific nondisclosures were examined and reasons for these nondisclosures were explored. Six in-depth semi-structured interviews were conducted and…

Computerized breast cancer analysis system using three stage semi-supervised learning method.

PubMed

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

2016-10-01

A large number of labeled medical image data is usually a requirement to train a well-performed computer-aided detection (CAD) system. But the process of data labeling is time consuming, and potential ethical and logistical problems may also present complications. As a result, incorporating unlabeled data into CAD system can be a feasible way to combat these obstacles. In this study we developed a three stage semi-supervised learning (SSL) scheme that combines a small amount of labeled data and larger amount of unlabeled data. The scheme was modified on our existing CAD system using the following three stages: data weighing, feature selection, and newly proposed dividing co-training data labeling algorithm. Global density asymmetry features were incorporated to the feature pool to reduce the false positive rate. Area under the curve (AUC) and accuracy were computed using 10 fold cross validation method to evaluate the performance of our CAD system. The image dataset includes mammograms from 400 women who underwent routine screening examinations, and each pair contains either two cranio-caudal (CC) or two mediolateral-oblique (MLO) view mammograms from the right and the left breasts. From these mammograms 512 regions were extracted and used in this study, and among them 90 regions were treated as labeled while the rest were treated as unlabeled. Using our proposed scheme, the highest AUC observed in our research was 0.841, which included the 90 labeled data and all the unlabeled data. It was 7.4% higher than using labeled data only. With the increasing amount of labeled data, AUC difference between using mixed data and using labeled data only reached its peak when the amount of labeled data was around 60. This study demonstrated that our proposed three stage semi-supervised learning can improve the CAD performance by incorporating unlabeled data. Using unlabeled data is promising in computerized cancer research and may have a significant impact for future CAD system applications. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Maximum margin semi-supervised learning with irrelevant data.

PubMed

Yang, Haiqin; Huang, Kaizhu; King, Irwin; Lyu, Michael R

2015-10-01

Semi-supervised learning (SSL) is a typical learning paradigms training a model from both labeled and unlabeled data. The traditional SSL models usually assume unlabeled data are relevant to the labeled data, i.e., following the same distributions of the targeted labeled data. In this paper, we address a different, yet formidable scenario in semi-supervised classification, where the unlabeled data may contain irrelevant data to the labeled data. To tackle this problem, we develop a maximum margin model, named tri-class support vector machine (3C-SVM), to utilize the available training data, while seeking a hyperplane for separating the targeted data well. Our 3C-SVM exhibits several characteristics and advantages. First, it does not need any prior knowledge and explicit assumption on the data relatedness. On the contrary, it can relieve the effect of irrelevant unlabeled data based on the logistic principle and maximum entropy principle. That is, 3C-SVM approaches an ideal classifier. This classifier relies heavily on labeled data and is confident on the relevant data lying far away from the decision hyperplane, while maximally ignoring the irrelevant data, which are hardly distinguished. Second, theoretical analysis is provided to prove that in what condition, the irrelevant data can help to seek the hyperplane. Third, 3C-SVM is a generalized model that unifies several popular maximum margin models, including standard SVMs, Semi-supervised SVMs (S(3)VMs), and SVMs learned from the universum (U-SVMs) as its special cases. More importantly, we deploy a concave-convex produce to solve the proposed 3C-SVM, transforming the original mixed integer programming, to a semi-definite programming relaxation, and finally to a sequence of quadratic programming subproblems, which yields the same worst case time complexity as that of S(3)VMs. Finally, we demonstrate the effectiveness and efficiency of our proposed 3C-SVM through systematical experimental comparisons. Copyright © 2015 Elsevier Ltd. All rights reserved.

Classifying galaxy spectra at 0.5 < z < 1 with self-organizing maps

NASA Astrophysics Data System (ADS)

Rahmani, S.; Teimoorinia, H.; Barmby, P.

2018-05-01

The spectrum of a galaxy contains information about its physical properties. Classifying spectra using templates helps elucidate the nature of a galaxy's energy sources. In this paper, we investigate the use of self-organizing maps in classifying galaxy spectra against templates. We trained semi-supervised self-organizing map networks using a set of templates covering the wavelength range from far ultraviolet to near infrared. The trained networks were used to classify the spectra of a sample of 142 galaxies with 0.5 < z < 1 and the results compared to classifications performed using K-means clustering, a supervised neural network, and chi-squared minimization. Spectra corresponding to quiescent galaxies were more likely to be classified similarly by all methods while starburst spectra showed more variability. Compared to classification using chi-squared minimization or the supervised neural network, the galaxies classed together by the self-organizing map had more similar spectra. The class ordering provided by the one-dimensional self-organizing maps corresponds to an ordering in physical properties, a potentially important feature for the exploration of large datasets.

The role of imaging based prostate biopsy morphology in a data fusion paradigm for transducing prognostic predictions

NASA Astrophysics Data System (ADS)

Khan, Faisal M.; Kulikowski, Casimir A.

2016-03-01

A major focus area for precision medicine is in managing the treatment of newly diagnosed prostate cancer patients. For patients with a positive biopsy, clinicians aim to develop an individualized treatment plan based on a mechanistic understanding of the disease factors unique to each patient. Recently, there has been a movement towards a multi-modal view of the cancer through the fusion of quantitative information from multiple sources, imaging and otherwise. Simultaneously, there have been significant advances in machine learning methods for medical prognostics which integrate a multitude of predictive factors to develop an individualized risk assessment and prognosis for patients. An emerging area of research is in semi-supervised approaches which transduce the appropriate survival time for censored patients. In this work, we apply a novel semi-supervised approach for support vector regression to predict the prognosis for newly diagnosed prostate cancer patients. We integrate clinical characteristics of a patient's disease with imaging derived metrics for biomarker expression as well as glandular and nuclear morphology. In particular, our goal was to explore the performance of nuclear and glandular architecture within the transduction algorithm and assess their predictive power when compared with the Gleason score manually assigned by a pathologist. Our analysis in a multi-institutional cohort of 1027 patients indicates that not only do glandular and morphometric characteristics improve the predictive power of the semi-supervised transduction algorithm; they perform better when the pathological Gleason is absent. This work represents one of the first assessments of quantitative prostate biopsy architecture versus the Gleason grade in the context of a data fusion paradigm which leverages a semi-supervised approach for risk prognosis.

The Feminist Supervision Scale: A Rational/Theoretical Approach

ERIC Educational Resources Information Center

Szymanski, Dawn M.

2003-01-01

This article reports the development and psychometric properties of the Feminist Supervision Scale (FSS), a new scale designed to assess feminist supervision practices in clinical supervision. This 32-item measure was developed using a rational/theoretical approach of test construction and includes four subscales: (a) collaborative relationships,…

A multimedia retrieval framework based on semi-supervised ranking and relevance feedback.

PubMed

Yang, Yi; Nie, Feiping; Xu, Dong; Luo, Jiebo; Zhuang, Yueting; Pan, Yunhe

2012-04-01

We present a new framework for multimedia content analysis and retrieval which consists of two independent algorithms. First, we propose a new semi-supervised algorithm called ranking with Local Regression and Global Alignment (LRGA) to learn a robust Laplacian matrix for data ranking. In LRGA, for each data point, a local linear regression model is used to predict the ranking scores of its neighboring points. A unified objective function is then proposed to globally align the local models from all the data points so that an optimal ranking score can be assigned to each data point. Second, we propose a semi-supervised long-term Relevance Feedback (RF) algorithm to refine the multimedia data representation. The proposed long-term RF algorithm utilizes both the multimedia data distribution in multimedia feature space and the history RF information provided by users. A trace ratio optimization problem is then formulated and solved by an efficient algorithm. The algorithms have been applied to several content-based multimedia retrieval applications, including cross-media retrieval, image retrieval, and 3D motion/pose data retrieval. Comprehensive experiments on four data sets have demonstrated its advantages in precision, robustness, scalability, and computational efficiency.

Semi-Supervised Tensor-Based Graph Embedding Learning and Its Application to Visual Discriminant Tracking.

PubMed

Hu, Weiming; Gao, Jin; Xing, Junliang; Zhang, Chao; Maybank, Stephen

2017-01-01

An appearance model adaptable to changes in object appearance is critical in visual object tracking. In this paper, we treat an image patch as a two-order tensor which preserves the original image structure. We design two graphs for characterizing the intrinsic local geometrical structure of the tensor samples of the object and the background. Graph embedding is used to reduce the dimensions of the tensors while preserving the structure of the graphs. Then, a discriminant embedding space is constructed. We prove two propositions for finding the transformation matrices which are used to map the original tensor samples to the tensor-based graph embedding space. In order to encode more discriminant information in the embedding space, we propose a transfer-learning- based semi-supervised strategy to iteratively adjust the embedding space into which discriminative information obtained from earlier times is transferred. We apply the proposed semi-supervised tensor-based graph embedding learning algorithm to visual tracking. The new tracking algorithm captures an object's appearance characteristics during tracking and uses a particle filter to estimate the optimal object state. Experimental results on the CVPR 2013 benchmark dataset demonstrate the effectiveness of the proposed tracking algorithm.

A semi-supervised learning approach for RNA secondary structure prediction.

PubMed

Yonemoto, Haruka; Asai, Kiyoshi; Hamada, Michiaki

2015-08-01

RNA secondary structure prediction is a key technology in RNA bioinformatics. Most algorithms for RNA secondary structure prediction use probabilistic models, in which the model parameters are trained with reliable RNA secondary structures. Because of the difficulty of determining RNA secondary structures by experimental procedures, such as NMR or X-ray crystal structural analyses, there are still many RNA sequences that could be useful for training whose secondary structures have not been experimentally determined. In this paper, we introduce a novel semi-supervised learning approach for training parameters in a probabilistic model of RNA secondary structures in which we employ not only RNA sequences with annotated secondary structures but also ones with unknown secondary structures. Our model is based on a hybrid of generative (stochastic context-free grammars) and discriminative models (conditional random fields) that has been successfully applied to natural language processing. Computational experiments indicate that the accuracy of secondary structure prediction is improved by incorporating RNA sequences with unknown secondary structures into training. To our knowledge, this is the first study of a semi-supervised learning approach for RNA secondary structure prediction. This technique will be useful when the number of reliable structures is limited. Copyright © 2015 Elsevier Ltd. All rights reserved.

Accuracy of latent-variable estimation in Bayesian semi-supervised learning.

PubMed

Yamazaki, Keisuke

2015-09-01

Hierarchical probabilistic models, such as Gaussian mixture models, are widely used for unsupervised learning tasks. These models consist of observable and latent variables, which represent the observable data and the underlying data-generation process, respectively. Unsupervised learning tasks, such as cluster analysis, are regarded as estimations of latent variables based on the observable ones. The estimation of latent variables in semi-supervised learning, where some labels are observed, will be more precise than that in unsupervised, and one of the concerns is to clarify the effect of the labeled data. However, there has not been sufficient theoretical analysis of the accuracy of the estimation of latent variables. In a previous study, a distribution-based error function was formulated, and its asymptotic form was calculated for unsupervised learning with generative models. It has been shown that, for the estimation of latent variables, the Bayes method is more accurate than the maximum-likelihood method. The present paper reveals the asymptotic forms of the error function in Bayesian semi-supervised learning for both discriminative and generative models. The results show that the generative model, which uses all of the given data, performs better when the model is well specified. Copyright © 2015 Elsevier Ltd. All rights reserved.

Supervision of tunnelling constructions and software used for their evaluation

NASA Astrophysics Data System (ADS)

Caravanas, Aristotelis; Hilar, Matous

2017-09-01

Supervision is a common instrument for controlling constructions of tunnels. In order to suit relevant project’s purposes a supervision procedure is modified by local conditions, habits, codes and ways of allocating of a particular tunnelling project. The duties of tunnel supervision are specified in an agreement with the client and they can include a wide range of activities. On large scale tunnelling projects the supervision tasks are performed by a high number of people of different professions. Teamwork, smooth communication and coordination are required in order to successfully fulfil supervision tasks. The efficiency and quality of tunnel supervision work are enhanced when specialized software applications are used. Such applications should allow on-line data management and the prompt evaluation, reporting and sharing of relevant construction information and other aspects. The client is provided with an as-built database that contains all the relevant information related to a construction process, which is a valuable tool for the claim management as well as for the evaluation of structure defects that can occur in the future. As a result, the level of risks related to tunnel constructions is decreased.

Towards Automatic Classification of Exoplanet-Transit-Like Signals: A Case Study on Kepler Mission Data

NASA Astrophysics Data System (ADS)

Valizadegan, Hamed; Martin, Rodney; McCauliff, Sean D.; Jenkins, Jon Michael; Catanzarite, Joseph; Oza, Nikunj C.

2015-08-01

Building new catalogues of planetary candidates, astrophysical false alarms, and non-transiting phenomena is a challenging task that currently requires a reviewing team of astrophysicists and astronomers. These scientists need to examine more than 100 diagnostic metrics and associated graphics for each candidate exoplanet-transit-like signal to classify it into one of the three classes. Considering that the NASA Explorer Program's TESS mission and ESA's PLATO mission survey even a larger area of space, the classification of their transit-like signals is more time-consuming for human agents and a bottleneck to successfully construct the new catalogues in a timely manner. This encourages building automatic classification tools that can quickly and reliably classify the new signal data from these missions. The standard tool for building automatic classification systems is the supervised machine learning that requires a large set of highly accurate labeled examples in order to build an effective classifier. This requirement cannot be easily met for classifying transit-like signals because not only are existing labeled signals very limited, but also the current labels may not be reliable (because the labeling process is a subjective task). Our experiments with using different supervised classifiers to categorize transit-like signals verifies that the labeled signals are not rich enough to provide the classifier with enough power to generalize well beyond the observed cases (e.g. to unseen or test signals). That motivated us to utilize a new category of learning techniques, so-called semi-supervised learning, that combines the label information from the costly labeled signals, and distribution information from the cheaply available unlabeled signals in order to construct more effective classifiers. Our study on the Kepler Mission data shows that semi-supervised learning can significantly improve the result of multiple base classifiers (e.g. Support Vector Machines, AdaBoost, and Decision Tree) and is a good technique for automatic classification of exoplanet-transit-like signal.

An online semi-supervised brain-computer interface.

PubMed

Gu, Zhenghui; Yu, Zhuliang; Shen, Zhifang; Li, Yuanqing

2013-09-01

Practical brain-computer interface (BCI) systems should require only low training effort for the user, and the algorithms used to classify the intent of the user should be computationally efficient. However, due to inter- and intra-subject variations in EEG signal, intermittent training/calibration is often unavoidable. In this paper, we present an online semi-supervised P300 BCI speller system. After a short initial training (around or less than 1 min in our experiments), the system is switched to a mode where the user can input characters through selective attention. In this mode, a self-training least squares support vector machine (LS-SVM) classifier is gradually enhanced in back end with the unlabeled EEG data collected online after every character input. In this way, the classifier is gradually enhanced. Even though the user may experience some errors in input at the beginning due to the small initial training dataset, the accuracy approaches that of fully supervised method in a few minutes. The algorithm based on LS-SVM and its sequential update has low computational complexity; thus, it is suitable for online applications. The effectiveness of the algorithm has been validated through data analysis on BCI Competition III dataset II (P300 speller BCI data). The performance of the online system was evaluated through experimental results on eight healthy subjects, where all of them achieved the spelling accuracy of 85 % or above within an average online semi-supervised learning time of around 3 min.

Machinery running state identification based on discriminant semi-supervised local tangent space alignment for feature fusion and extraction

NASA Astrophysics Data System (ADS)

Su, Zuqiang; Xiao, Hong; Zhang, Yi; Tang, Baoping; Jiang, Yonghua

2017-04-01

Extraction of sensitive features is a challenging but key task in data-driven machinery running state identification. Aimed at solving this problem, a method for machinery running state identification that applies discriminant semi-supervised local tangent space alignment (DSS-LTSA) for feature fusion and extraction is proposed. Firstly, in order to extract more distinct features, the vibration signals are decomposed by wavelet packet decomposition WPD, and a mixed-domain feature set consisted of statistical features, autoregressive (AR) model coefficients, instantaneous amplitude Shannon entropy and WPD energy spectrum is extracted to comprehensively characterize the properties of machinery running state(s). Then, the mixed-dimension feature set is inputted into DSS-LTSA for feature fusion and extraction to eliminate redundant information and interference noise. The proposed DSS-LTSA can extract intrinsic structure information of both labeled and unlabeled state samples, and as a result the over-fitting problem of supervised manifold learning and blindness problem of unsupervised manifold learning are overcome. Simultaneously, class discrimination information is integrated within the dimension reduction process in a semi-supervised manner to improve sensitivity of the extracted fusion features. Lastly, the extracted fusion features are inputted into a pattern recognition algorithm to achieve the running state identification. The effectiveness of the proposed method is verified by a running state identification case in a gearbox, and the results confirm the improved accuracy of the running state identification.

Extending bicluster analysis to annotate unclassified ORFs and predict novel functional modules using expression data

PubMed Central

Bryan, Kenneth; Cunningham, Pádraig

2008-01-01

Background Microarrays have the capacity to measure the expressions of thousands of genes in parallel over many experimental samples. The unsupervised classification technique of bicluster analysis has been employed previously to uncover gene expression correlations over subsets of samples with the aim of providing a more accurate model of the natural gene functional classes. This approach also has the potential to aid functional annotation of unclassified open reading frames (ORFs). Until now this aspect of biclustering has been under-explored. In this work we illustrate how bicluster analysis may be extended into a 'semi-supervised' ORF annotation approach referred to as BALBOA. Results The efficacy of the BALBOA ORF classification technique is first assessed via cross validation and compared to a multi-class k-Nearest Neighbour (kNN) benchmark across three independent gene expression datasets. BALBOA is then used to assign putative functional annotations to unclassified yeast ORFs. These predictions are evaluated using existing experimental and protein sequence information. Lastly, we employ a related semi-supervised method to predict the presence of novel functional modules within yeast. Conclusion In this paper we demonstrate how unsupervised classification methods, such as bicluster analysis, may be extended using of available annotations to form semi-supervised approaches within the gene expression analysis domain. We show that such methods have the potential to improve upon supervised approaches and shed new light on the functions of unclassified ORFs and their co-regulation. PMID:18831786

Informality, Power and Relationships in Postgraduate Supervision: Supervising PhD Candidates over Coffee

ERIC Educational Resources Information Center

Hemer, Susan R.

2012-01-01

A great deal of literature in recent years has focused on the supervisory relationship, yet very little has been written about the nature or content of supervisory meetings, beyond commenting on the frequency and length of meetings. Through semi-structured interviews, informal discussions with colleagues and students, a critical review of…

Learning Supervised Topic Models for Classification and Regression from Crowds.

PubMed

Rodrigues, Filipe; Lourenco, Mariana; Ribeiro, Bernardete; Pereira, Francisco C

2017-12-01

The growing need to analyze large collections of documents has led to great developments in topic modeling. Since documents are frequently associated with other related variables, such as labels or ratings, much interest has been placed on supervised topic models. However, the nature of most annotation tasks, prone to ambiguity and noise, often with high volumes of documents, deem learning under a single-annotator assumption unrealistic or unpractical for most real-world applications. In this article, we propose two supervised topic models, one for classification and another for regression problems, which account for the heterogeneity and biases among different annotators that are encountered in practice when learning from crowds. We develop an efficient stochastic variational inference algorithm that is able to scale to very large datasets, and we empirically demonstrate the advantages of the proposed model over state-of-the-art approaches.

Automated gastric cancer diagnosis on H&E-stained sections; ltraining a classifier on a large scale with multiple instance machine learning

NASA Astrophysics Data System (ADS)

Cosatto, Eric; Laquerre, Pierre-Francois; Malon, Christopher; Graf, Hans-Peter; Saito, Akira; Kiyuna, Tomoharu; Marugame, Atsushi; Kamijo, Ken'ichi

2013-03-01

We present a system that detects cancer on slides of gastric tissue sections stained with hematoxylin and eosin (H&E). At its heart is a classi er trained using the semi-supervised multi-instance learning framework (MIL) where each tissue is represented by a set of regions-of-interest (ROI) and a single label. Such labels are readily obtained because pathologists diagnose each tissue independently as part of the normal clinical work ow. From a large dataset of over 26K gastric tissue sections from over 12K patients obtained from a clinical load spanning several months, we train a MIL classi er on a patient-level partition of the dataset (2/3 of the patients) and obtain a very high performance of 96% (AUC), tested on the remaining 1/3 never-seen before patients (over 8K tissues). We show this level of performance to match the more costly supervised approach where individual ROIs need to be labeled manually. The large amount of data used to train this system gives us con dence in its robustness and that it can be safely used in a clinical setting. We demonstrate how it can improve the clinical work ow when used for pre-screening or quality control. For pre-screening, the system can diagnose 47% of the tissues with a very low likelihood (< 1%) of missing cancers, thus halving the clinicians' caseload. For quality control, compared to random rechecking of 33% of the cases, the system achieves a three-fold increase in the likelihood of catching cancers missed by pathologists. The system is currently in regular use at independent pathology labs in Japan where it is used to double-check clinician's diagnoses. At the end of 2012 it will have analyzed over 80,000 slides of gastric and colorectal samples (200,000 tissues).

Semi-supervised anomaly detection - towards model-independent searches of new physics

NASA Astrophysics Data System (ADS)

Kuusela, Mikael; Vatanen, Tommi; Malmi, Eric; Raiko, Tapani; Aaltonen, Timo; Nagai, Yoshikazu

2012-06-01

Most classification algorithms used in high energy physics fall under the category of supervised machine learning. Such methods require a training set containing both signal and background events and are prone to classification errors should this training data be systematically inaccurate for example due to the assumed MC model. To complement such model-dependent searches, we propose an algorithm based on semi-supervised anomaly detection techniques, which does not require a MC training sample for the signal data. We first model the background using a multivariate Gaussian mixture model. We then search for deviations from this model by fitting to the observations a mixture of the background model and a number of additional Gaussians. This allows us to perform pattern recognition of any anomalous excess over the background. We show by a comparison to neural network classifiers that such an approach is a lot more robust against misspecification of the signal MC than supervised classification. In cases where there is an unexpected signal, a neural network might fail to correctly identify it, while anomaly detection does not suffer from such a limitation. On the other hand, when there are no systematic errors in the training data, both methods perform comparably.

[How did health personnel perceive supervision of obstetric institutions?].

PubMed

Arianson, Helga; Elvbakken, Kari Tove; Malterud, Kirsti

2008-05-15

Through audits, the Norwegian Board of Health supervises and ensures that health institutions adhere to rules and regulations that apply to them. Conduct of such supervision should be predictable and the basis for decisions should be documented and challengeable. Those in charge of the supervision must have the necessary professional competence and be able to integrate and understand the collected information so they can draw the right conclusions. The audit team should demonstrate consideration and respect to those they meet during audits. We therefore wanted to study the experience of being audited among health care providers and leaders of institutions and subsequent adjustments after the audit. We used a questionnaire to evaluate the national audit of 26 (of 60 totally) Norwegian obstetric institutions in 2004. A questionnaire was sent to leaders and health care providers in all institutions that had been inspected (208 persons). Data from semi-structured interviews were used to validate and explore the quantitative findings. 89% responded to the questionnaire. The supervision was well received by leaders and health care providers at the obstetric institutions. The respondents confirmed that the audit team's approach and conduct in principle adhered to the rules within the examined domains. The conclusions presented by the audit teams were accepted as correct by most of the respondents. A large number of adjustments were reported after the audits. We conclude that auditing can lead to improvements and that the described programme probably contributed to improving obstetric services in Norway. The audit team's conduct seems to have an effect on acceptance of the supervision. The performance of the teams may have an impact of the acceptance of auditing, but not on reporting of the adjustments carried out.

Information Forests

DTIC Science & Technology

2014-01-01

Classification confidence, or informative content of the subsets, is quantified by the Information Divergence. Our approach relates to active learning , semi-supervised learning, mixed generative/discriminative learning.

A comparative evaluation of supervised and unsupervised representation learning approaches for anaplastic medulloblastoma differentiation

NASA Astrophysics Data System (ADS)

Cruz-Roa, Angel; Arevalo, John; Basavanhally, Ajay; Madabhushi, Anant; González, Fabio

2015-01-01

Learning data representations directly from the data itself is an approach that has shown great success in different pattern recognition problems, outperforming state-of-the-art feature extraction schemes for different tasks in computer vision, speech recognition and natural language processing. Representation learning applies unsupervised and supervised machine learning methods to large amounts of data to find building-blocks that better represent the information in it. Digitized histopathology images represents a very good testbed for representation learning since it involves large amounts of high complex, visual data. This paper presents a comparative evaluation of different supervised and unsupervised representation learning architectures to specifically address open questions on what type of learning architectures (deep or shallow), type of learning (unsupervised or supervised) is optimal. In this paper we limit ourselves to addressing these questions in the context of distinguishing between anaplastic and non-anaplastic medulloblastomas from routine haematoxylin and eosin stained images. The unsupervised approaches evaluated were sparse autoencoders and topographic reconstruct independent component analysis, and the supervised approach was convolutional neural networks. Experimental results show that shallow architectures with more neurons are better than deeper architectures without taking into account local space invariances and that topographic constraints provide useful invariant features in scale and rotations for efficient tumor differentiation.

Semi-supervised classification tool for DubaiSat-2 multispectral imagery

NASA Astrophysics Data System (ADS)

Al-Mansoori, Saeed

2015-10-01

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

Implementing Projects in Calculus on a Large Scale at the University of South Florida

ERIC Educational Resources Information Center

Fox, Gordon A.; Campbell, Scott; Grinshpan, Arcadii; Xu, Xiaoying; Holcomb, John; Bénéteau, Catherine; Lewis, Jennifer E.; Ramachandran, Kandethody

2017-01-01

This paper describes the development of a program of project-based learning in Calculus courses at a large urban research university. In this program, students developed research projects in consultation with a faculty advisor in their major, and supervised by their calculus instructors. Students wrote up their projects in a prescribed format…

Porosity estimation by semi-supervised learning with sparsely available labeled samples

NASA Astrophysics Data System (ADS)

Lima, Luiz Alberto; Görnitz, Nico; Varella, Luiz Eduardo; Vellasco, Marley; Müller, Klaus-Robert; Nakajima, Shinichi

2017-09-01

This paper addresses the porosity estimation problem from seismic impedance volumes and porosity samples located in a small group of exploratory wells. Regression methods, trained on the impedance as inputs and the porosity as output labels, generally suffer from extremely expensive (and hence sparsely available) porosity samples. To optimally make use of the valuable porosity data, a semi-supervised machine learning method was proposed, Transductive Conditional Random Field Regression (TCRFR), showing good performance (Görnitz et al., 2017). TCRFR, however, still requires more labeled data than those usually available, which creates a gap when applying the method to the porosity estimation problem in realistic situations. In this paper, we aim to fill this gap by introducing two graph-based preprocessing techniques, which adapt the original TCRFR for extremely weakly supervised scenarios. Our new method outperforms the previous automatic estimation methods on synthetic data and provides a comparable result to the manual labored, time-consuming geostatistics approach on real data, proving its potential as a practical industrial tool.

IslandFAST: A Semi-numerical Tool for Simulating the Late Epoch of Reionization

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

Xu, Yidong; Chen, Xuelei; Yue, Bin

2017-08-01

We present the algorithm and main results of our semi-numerical simulation, islandFAST, which was developed from 21cmFAST and designed for the late stage of reionization. The islandFAST simulation predicts the evolution and size distribution of the large-scale underdense neutral regions (neutral islands), and we find that the late Epoch of Reionization proceeds very fast, showing a characteristic scale of the neutral islands at each redshift. Using islandFAST, we compare the impact of two types of absorption systems, i.e., the large-scale underdense neutral islands versus small-scale overdense absorbers, in regulating the reionization process. The neutral islands dominate the morphology of themore » ionization field, while the small-scale absorbers dominate the mean-free path of ionizing photons, and also delay and prolong the reionization process. With our semi-numerical simulation, the evolution of the ionizing background can be derived self-consistently given a model for the small absorbers. The hydrogen ionization rate of the ionizing background is reduced by an order of magnitude in the presence of dense absorbers.« less

New Possibilities for High-Resolution, Large-Scale Ecosystem Assessment of the World's Semi-Arid Regions

NASA Astrophysics Data System (ADS)

Burney, J. A.; Goldblatt, R.

2016-12-01

Understanding drivers of land use change - and in particular, levels of ecosystem degradation - in semi-arid regions is of critical importance because these agroecosystems (1) are home to the world's poorest populations, almost all of whom depend on agriculture for their livelihoods, (2) play a critical role in the global carbon and climate cycles, and (3) have in many cases seen dramatic changes in temperature and precipitation, relative to global averages, over the past several decades. However, assessing ecosystem health (or, conversely, degradation) presents a difficult measurement problem. Established methods are very labor intensive and rest on detailed questionnaires and field assessments. High-resolution satellite imagery has a unique role semi-arid ecosystem assessment in that it can be used for rapid (or repeated) and very simple measurements of tree and shrub density, an excellent overall indicator for dryland ecosystem health. Because trees and large shrubs are more sparse in semi-arid regions, sub-meter resolution imagery in conjunction with automated image analysis can be used to assess density differences at high spatial resolution without expensive and time-consuming ground-truthing. This could be used down to the farm level, for example, to better assess the larger-scale ecosystem impacts of different management practices, to assess compliance with REDD+ carbon offset protocols, or to evaluate implementation of conservation goals. Here we present results comparing spatial and spectral remote sensing methods for semi-arid ecosystem assessment across new data sources, using the Brazilian Sertão as an example, and the implications for large-scale use in semi-arid ecosystem science.

The oligonucleotide frequency derived error gradient and its application to the binning of metagenome fragments

PubMed Central

2009-01-01

Background The characterisation, or binning, of metagenome fragments is an important first step to further downstream analysis of microbial consortia. Here, we propose a one-dimensional signature, OFDEG, derived from the oligonucleotide frequency profile of a DNA sequence, and show that it is possible to obtain a meaningful phylogenetic signal for relatively short DNA sequences. The one-dimensional signal is essentially a compact representation of higher dimensional feature spaces of greater complexity and is intended to improve on the tetranucleotide frequency feature space preferred by current compositional binning methods. Results We compare the fidelity of OFDEG against tetranucleotide frequency in both an unsupervised and semi-supervised setting on simulated metagenome benchmark data. Four tests were conducted using assembler output of Arachne and phrap, and for each, performance was evaluated on contigs which are greater than or equal to 8 kbp in length and contigs which are composed of at least 10 reads. Using both G-C content in conjunction with OFDEG gave an average accuracy of 96.75% (semi-supervised) and 95.19% (unsupervised), versus 94.25% (semi-supervised) and 82.35% (unsupervised) for tetranucleotide frequency. Conclusion We have presented an observation of an alternative characteristic of DNA sequences. The proposed feature representation has proven to be more beneficial than the existing tetranucleotide frequency space to the metagenome binning problem. We do note, however, that our observation of OFDEG deserves further anlaysis and investigation. Unsupervised clustering revealed OFDEG related features performed better than standard tetranucleotide frequency in representing a relevant organism specific signal. Further improvement in binning accuracy is given by semi-supervised classification using OFDEG. The emphasis on a feature-driven, bottom-up approach to the problem of binning reveals promising avenues for future development of techniques to characterise short environmental sequences without bias toward cultivable organisms. PMID:19958473

Modelling hydrologic and hydrodynamic processes in basins with large semi-arid wetlands

NASA Astrophysics Data System (ADS)

Fleischmann, Ayan; Siqueira, Vinícius; Paris, Adrien; Collischonn, Walter; Paiva, Rodrigo; Pontes, Paulo; Crétaux, Jean-François; Bergé-Nguyen, Muriel; Biancamaria, Sylvain; Gosset, Marielle; Calmant, Stephane; Tanimoun, Bachir

2018-06-01

Hydrological and hydrodynamic models are core tools for simulation of large basins and complex river systems associated to wetlands. Recent studies have pointed towards the importance of online coupling strategies, representing feedbacks between floodplain inundation and vertical hydrology. Especially across semi-arid regions, soil-floodplain interactions can be strong. In this study, we included a two-way coupling scheme in a large scale hydrological-hydrodynamic model (MGB) and tested different model structures, in order to assess which processes are important to be simulated in large semi-arid wetlands and how these processes interact with water budget components. To demonstrate benefits from this coupling over a validation case, the model was applied to the Upper Niger River basin encompassing the Niger Inner Delta, a vast semi-arid wetland in the Sahel Desert. Simulation was carried out from 1999 to 2014 with daily TMPA 3B42 precipitation as forcing, using both in-situ and remotely sensed data for calibration and validation. Model outputs were in good agreement with discharge and water levels at stations both upstream and downstream of the Inner Delta (Nash-Sutcliffe Efficiency (NSE) >0.6 for most gauges), as well as for flooded areas within the Delta region (NSE = 0.6; r = 0.85). Model estimates of annual water losses across the Delta varied between 20.1 and 30.6 km3/yr, while annual evapotranspiration ranged between 760 mm/yr and 1130 mm/yr. Evaluation of model structure indicated that representation of both floodplain channels hydrodynamics (storage, bifurcations, lateral connections) and vertical hydrological processes (floodplain water infiltration into soil column; evapotranspiration from soil and vegetation and evaporation of open water) are necessary to correctly simulate flood wave attenuation and evapotranspiration along the basin. Two-way coupled models are necessary to better understand processes in large semi-arid wetlands. Finally, such coupled hydrologic and hydrodynamic modelling proves to be an important tool for integrated evaluation of hydrological processes in such poorly gauged, large scale basins. We hope that this model application provides new ways forward for large scale model development in such systems, involving semi-arid regions and complex floodplains.

Interprofessional supervision in an intercultural context: a qualitative study.

PubMed

Chipchase, Lucy; Allen, Shelley; Eley, Diann; McAllister, Lindy; Strong, Jenny

2012-11-01

Our understanding of the qualities and value of clinical supervision is based on uniprofessional clinical education models. There is little research regarding the role and qualities needed in the supervisor role for supporting interprofessional placements. This paper reports the views and perceptions of medical and allied heath students and supervisors on the characteristics of clinical supervision in an interprofessional, international context. A qualitative case study was used involving semi-structured interviews of eight health professional students and four clinical supervisors before and after an interprofessional, international clinical placement. Our findings suggest that supervision from educators whose profession differs from that of the students can be a beneficial and rewarding experience leading to the use of alternative learning strategies. Although all participants valued interprofessional supervision, there was agreement that profession-specific supervision was required throughout the placement. Further research is required to understand this view as interprofessional education aims to prepare graduates for collaborative practice where they may work in teams supervised by staff whose profession may differ from their own.

Content analysis of resident evaluations of faculty anesthesiologists: supervision encompasses some attributes of the professionalism core competency.

PubMed

Dexter, Franklin; Szeluga, Debra; Hindman, Bradley J

2017-05-01

Anesthesiology departments need an instrument with which to assess practicing anesthesiologists' professionalism. The purpose of this retrospective analysis of the content of a cohort of resident evaluations of faculty anesthesiologists was to investigate the relationship between a clinical supervision scale and the multiple attributes of professionalism. From July 1, 2013 to the present, our department has utilized the de Oliveira Filho unidimensional nine-item supervision scale to assess the quality of clinical supervision of residents provided by our anesthesiologists. The "cohort" we examined included all 13,664 resident evaluations of all faculty anesthesiologists from July 1, 2013 through December 31, 2015, including 1,387 accompanying comments. Words and phrases associated with the core competency of professionalism were obtained from previous studies, and the supervision scale was analyzed for the presence of these words and phrases. The supervision scale assesses some attributes of anesthesiologists' professionalism as well as patient care and procedural skills and interpersonal and communication skills. The comments that residents provided with the below-average supervision scores included attributes of professionalism, although numerous words and phrases related to professionalism were not present in any of the residents' comments. The de Oliveira Filho clinical supervision scale includes some attributes of anesthesiologists' professionalism. The core competency of professionalism, however, is multidimensional, and the supervision scale and/or residents' comments did not address many of the other established attributes of professionalism.

Agent-based human-robot interaction of a combat bulldozer

NASA Astrophysics Data System (ADS)

Granot, Reuven; Feldman, Maxim

2004-09-01

A small-scale supervised autonomous bulldozer in a remote site was developed to experience agent based human intervention. The model is based on Lego Mindstorms kit and represents combat equipment, whose job performance does not require high accuracy. The model enables evaluation of system response for different operator interventions, as well as for a small colony of semiautonomous dozers. The supervising human may better react than a fully autonomous system to unexpected contingent events, which are a major barrier to implement full autonomy. The automation is introduced as improved Man Machine Interface (MMI) by developing control agents as intelligent tools to negotiate between human requests and task level controllers as well as negotiate with other elements of the software environment. Current UGVs demand significant communication resources and constant human operation. Therefore they will be replaced by semi-autonomous human supervisory controlled systems (telerobotic). For human intervention at the low layers of the control hierarchy we suggest a task oriented control agent to take care of the fluent transition between the state in which the robot operates and the one imposed by the human. This transition should take care about the imperfections, which are responsible for the improper operation of the robot, by disconnecting or adapting them to the new situation. Preliminary conclusions from the small-scale experiments are presented.

[Development of the role scale for municipal supervising public health nurses].

PubMed

Hatono, Yoko; Suzuki, Hiroko; Masaki, Naoko

2013-05-01

As public health nurses are becoming increasingly decentralized in municipalities, recommendations for allocating supervising public health nurses are being made. This study aimed to develop a scale for measuring the implementation of role of municipal supervising public health nurses and to test its reliability and validity. Scale items were developed using results of a qualitative inductive analysis of interview data, and the items were then revised following an examination of content validity by experts, resulting in a provisional scale of 17 items. A self-administered, written questionnaire was then completed by supervising public health nurses or public health nurses holding the most senior positions in all municipalities nationwide, with the exception of three prefectures in the Tohoku region (total 1,621 locations). In total, 1,036 responses were received, and 931 were used for analysis (valid response rate = 57.4%). Of these, 406 were completed by supervising public health nurses. After deleting one item as a result of item analysis and conducting principal component analysis, factor analysis was conducted using the major factor method and Promax rotation. One item with high loading on multiple factors was deleted, resulting in a scale comprising 15 items and 3 factors. The cumulative contribution ratio was 56.10%. The three factors were labeled "Promotion of health activities across the whole locality," "Coordination as a PHN role leader," and "Development of the skills of public health nurses". The reliability coefficient of the RMSP (Role Scale for Municipal Supervising Public Health Nurses) as a whole was 0.84 using the split-half method (Spearman-Brown formula) and 0.91 using Cronbach's alpha, confirming internal consistency. In terms of validity, an examination was conducted of the correlation of two RMSP scale scores (strength of awareness of role as a supervising public health nurse and confidence as a supervising public health nurse) and scores on existing scales assessing management abilities, and a significant correlation (P < 0.01) was obtained. Additionally, a comparison of the RMSP scores of decentralized local public health nurses according to rank and years of service in areas where there were no supervising public health nurses with the RMSP scores of supervising public health nurses showed that the scores of supervising public health nurses were higher. The developed scale was found to be reliable and valid for measuring the implementation of supervising public health nurses' role.

78 FR 73547 - Medicare Program; Semi-Annual Meeting of the Advisory Panel on Hospital Outpatient Payment (HOP...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-12-06

...) March 10-11, 2014 AGENCY: Centers for Medicare & Medicaid Services (CMS), Department of Health and Human... Advisory Panel on Hospital Outpatient Payment (the Panel) for 2014. The purpose of the Panel is to advise... therapeutic services supervision issues. DATES: Meeting Dates: The first semi-annual meeting in 2014 is...

Scuba: scalable kernel-based gene prioritization.

PubMed

Zampieri, Guido; Tran, Dinh Van; Donini, Michele; Navarin, Nicolò; Aiolli, Fabio; Sperduti, Alessandro; Valle, Giorgio

2018-01-25

The uncovering of genes linked to human diseases is a pressing challenge in molecular biology and precision medicine. This task is often hindered by the large number of candidate genes and by the heterogeneity of the available information. Computational methods for the prioritization of candidate genes can help to cope with these problems. In particular, kernel-based methods are a powerful resource for the integration of heterogeneous biological knowledge, however, their practical implementation is often precluded by their limited scalability. We propose Scuba, a scalable kernel-based method for gene prioritization. It implements a novel multiple kernel learning approach, based on a semi-supervised perspective and on the optimization of the margin distribution. Scuba is optimized to cope with strongly unbalanced settings where known disease genes are few and large scale predictions are required. Importantly, it is able to efficiently deal both with a large amount of candidate genes and with an arbitrary number of data sources. As a direct consequence of scalability, Scuba integrates also a new efficient strategy to select optimal kernel parameters for each data source. We performed cross-validation experiments and simulated a realistic usage setting, showing that Scuba outperforms a wide range of state-of-the-art methods. Scuba achieves state-of-the-art performance and has enhanced scalability compared to existing kernel-based approaches for genomic data. This method can be useful to prioritize candidate genes, particularly when their number is large or when input data is highly heterogeneous. The code is freely available at https://github.com/gzampieri/Scuba .

Semi-supervised protein subcellular localization.

PubMed

Xu, Qian; Hu, Derek Hao; Xue, Hong; Yu, Weichuan; Yang, Qiang

2009-01-30

Protein subcellular localization is concerned with predicting the location of a protein within a cell using computational method. The location information can indicate key functionalities of proteins. Accurate predictions of subcellular localizations of protein can aid the prediction of protein function and genome annotation, as well as the identification of drug targets. Computational methods based on machine learning, such as support vector machine approaches, have already been widely used in the prediction of protein subcellular localization. However, a major drawback of these machine learning-based approaches is that a large amount of data should be labeled in order to let the prediction system learn a classifier of good generalization ability. However, in real world cases, it is laborious, expensive and time-consuming to experimentally determine the subcellular localization of a protein and prepare instances of labeled data. In this paper, we present an approach based on a new learning framework, semi-supervised learning, which can use much fewer labeled instances to construct a high quality prediction model. We construct an initial classifier using a small set of labeled examples first, and then use unlabeled instances to refine the classifier for future predictions. Experimental results show that our methods can effectively reduce the workload for labeling data using the unlabeled data. Our method is shown to enhance the state-of-the-art prediction results of SVM classifiers by more than 10%.

Multisource Data Classification Using A Hybrid Semi-supervised Learning Scheme

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

Vatsavai, Raju; Bhaduri, Budhendra L; Shekhar, Shashi

2009-01-01

In many practical situations thematic classes can not be discriminated by spectral measurements alone. Often one needs additional features such as population density, road density, wetlands, elevation, soil types, etc. which are discrete attributes. On the other hand remote sensing image features are continuous attributes. Finding a suitable statistical model and estimation of parameters is a challenging task in multisource (e.g., discrete and continuous attributes) data classification. In this paper we present a semi-supervised learning method by assuming that the samples were generated by a mixture model, where each component could be either a continuous or discrete distribution. Overall classificationmore » accuracy of the proposed method is improved by 12% in our initial experiments.« less

The helpfulness of category labels in semi-supervised learning depends on category structure.

PubMed

Vong, Wai Keen; Navarro, Daniel J; Perfors, Amy

2016-02-01

The study of semi-supervised category learning has generally focused on how additional unlabeled information with given labeled information might benefit category learning. The literature is also somewhat contradictory, sometimes appearing to show a benefit to unlabeled information and sometimes not. In this paper, we frame the problem differently, focusing on when labels might be helpful to a learner who has access to lots of unlabeled information. Using an unconstrained free-sorting categorization experiment, we show that labels are useful to participants only when the category structure is ambiguous and that people's responses are driven by the specific set of labels they see. We present an extension of Anderson's Rational Model of Categorization that captures this effect.

Developing a "Semi-Systematic" Approach to Using Large-Scale Data-Sets for Small-Scale Interventions: The "Baby Matterz" Initiative as a Case Study

ERIC Educational Resources Information Center

O'Brien, Mark

2011-01-01

The appropriateness of using statistical data to inform the design of any given service development or initiative often depends upon judgements regarding scale. Large-scale data sets, perhaps national in scope, whilst potentially important in informing the design, implementation and roll-out of experimental initiatives, will often remain unused…

Development of the Artistic Supervision Model Scale (ASMS)

ERIC Educational Resources Information Center

Kapusuzoglu, Saduman; Dilekci, Umit

2017-01-01

The purpose of the study is to develop the Artistic Supervision Model Scale in accordance with the perception of inspectors and the elementary and secondary school teachers on artistic supervision. The lack of a measuring instrument related to the model of artistic supervision in the field of literature reveals the necessity of such study. 290…

Rapid Training of Information Extraction with Local and Global Data Views

DTIC Science & Technology

2012-05-01

56 xiii 4.1 An example of words and their bit string representations. Bold ones are transliterated Arabic words...Natural Language Processing ( NLP ) community faces new tasks and new domains all the time. Without enough labeled data of a new task or a new domain to...conduct supervised learning, semi-supervised learning is particularly attractive to NLP researchers since it only requires a handful of labeled examples

Subsampled Hessian Newton Methods for Supervised Learning.

PubMed

Wang, Chien-Chih; Huang, Chun-Heng; Lin, Chih-Jen

2015-08-01

Newton methods can be applied in many supervised learning approaches. However, for large-scale data, the use of the whole Hessian matrix can be time-consuming. Recently, subsampled Newton methods have been proposed to reduce the computational time by using only a subset of data for calculating an approximation of the Hessian matrix. Unfortunately, we find that in some situations, the running speed is worse than the standard Newton method because cheaper but less accurate search directions are used. In this work, we propose some novel techniques to improve the existing subsampled Hessian Newton method. The main idea is to solve a two-dimensional subproblem per iteration to adjust the search direction to better minimize the second-order approximation of the function value. We prove the theoretical convergence of the proposed method. Experiments on logistic regression, linear SVM, maximum entropy, and deep networks indicate that our techniques significantly reduce the running time of the subsampled Hessian Newton method. The resulting algorithm becomes a compelling alternative to the standard Newton method for large-scale data classification.

Genetic Classification of Populations Using Supervised Learning

PubMed Central

Bridges, Michael; Heron, Elizabeth A.; O'Dushlaine, Colm; Segurado, Ricardo; Morris, Derek; Corvin, Aiden; Gill, Michael; Pinto, Carlos

2011-01-01

There are many instances in genetics in which we wish to determine whether two candidate populations are distinguishable on the basis of their genetic structure. Examples include populations which are geographically separated, case–control studies and quality control (when participants in a study have been genotyped at different laboratories). This latter application is of particular importance in the era of large scale genome wide association studies, when collections of individuals genotyped at different locations are being merged to provide increased power. The traditional method for detecting structure within a population is some form of exploratory technique such as principal components analysis. Such methods, which do not utilise our prior knowledge of the membership of the candidate populations. are termed unsupervised. Supervised methods, on the other hand are able to utilise this prior knowledge when it is available. In this paper we demonstrate that in such cases modern supervised approaches are a more appropriate tool for detecting genetic differences between populations. We apply two such methods, (neural networks and support vector machines) to the classification of three populations (two from Scotland and one from Bulgaria). The sensitivity exhibited by both these methods is considerably higher than that attained by principal components analysis and in fact comfortably exceeds a recently conjectured theoretical limit on the sensitivity of unsupervised methods. In particular, our methods can distinguish between the two Scottish populations, where principal components analysis cannot. We suggest, on the basis of our results that a supervised learning approach should be the method of choice when classifying individuals into pre-defined populations, particularly in quality control for large scale genome wide association studies. PMID:21589856

Data integration modeling applied to drill hole planning through semi-supervised learning: A case study from the Dalli Cu-Au porphyry deposit in the central Iran

NASA Astrophysics Data System (ADS)

Fatehi, Moslem; Asadi, Hooshang H.

2017-04-01

In this study, the application of a transductive support vector machine (TSVM), an innovative semi-supervised learning algorithm, has been proposed for mapping the potential drill targets at a detailed exploration stage. The semi-supervised learning method is a hybrid of supervised and unsupervised learning approach that simultaneously uses both training and non-training data to design a classifier. By using the TSVM algorithm, exploration layers at the Dalli porphyry Cu-Au deposit in the central Iran were integrated to locate the boundary of the Cu-Au mineralization for further drilling. By applying this algorithm on the non-training (unlabeled) and limited training (labeled) Dalli exploration data, the study area was classified in two domains of Cu-Au ore and waste. Then, the results were validated by the earlier block models created, using the available borehole and trench data. In addition to TSVM, the support vector machine (SVM) algorithm was also implemented on the study area for comparison. Thirty percent of the labeled exploration data was used to evaluate the performance of these two algorithms. The results revealed 87 percent correct recognition accuracy for the TSVM algorithm and 82 percent for the SVM algorithm. The deepest inclined borehole, recently drilled in the western part of the Dalli deposit, indicated that the boundary of Cu-Au mineralization, as identified by the TSVM algorithm, was only 15 m off from the actual boundary intersected by this borehole. According to the results of the TSVM algorithm, six new boreholes were suggested for further drilling at the Dalli deposit. This study showed that the TSVM algorithm could be a useful tool for enhancing the mineralization zones and consequently, ensuring a more accurate drill hole planning.

Semi-supervised clustering for parcellating brain regions based on resting state fMRI data

NASA Astrophysics Data System (ADS)

Cheng, Hewei; Fan, Yong

2014-03-01

Many unsupervised clustering techniques have been adopted for parcellating brain regions of interest into functionally homogeneous subregions based on resting state fMRI data. However, the unsupervised clustering techniques are not able to take advantage of exiting knowledge of the functional neuroanatomy readily available from studies of cytoarchitectonic parcellation or meta-analysis of the literature. In this study, we propose a semi-supervised clustering method for parcellating amygdala into functionally homogeneous subregions based on resting state fMRI data. Particularly, the semi-supervised clustering is implemented under the framework of graph partitioning, and adopts prior information and spatial consistent constraints to obtain a spatially contiguous parcellation result. The graph partitioning problem is solved using an efficient algorithm similar to the well-known weighted kernel k-means algorithm. Our method has been validated for parcellating amygdala into 3 subregions based on resting state fMRI data of 28 subjects. The experiment results have demonstrated that the proposed method is more robust than unsupervised clustering and able to parcellate amygdala into centromedial, laterobasal, and superficial parts with improved functionally homogeneity compared with the cytoarchitectonic parcellation result. The validity of the parcellation results is also supported by distinctive functional and structural connectivity patterns of the subregions and high consistency between coactivation patterns derived from a meta-analysis and functional connectivity patterns of corresponding subregions.

Cell segmentation in phase contrast microscopy images via semi-supervised classification over optics-related features.

PubMed

Su, Hang; Yin, Zhaozheng; Huh, Seungil; Kanade, Takeo

2013-10-01

Phase-contrast microscopy is one of the most common and convenient imaging modalities to observe long-term multi-cellular processes, which generates images by the interference of lights passing through transparent specimens and background medium with different retarded phases. Despite many years of study, computer-aided phase contrast microscopy analysis on cell behavior is challenged by image qualities and artifacts caused by phase contrast optics. Addressing the unsolved challenges, the authors propose (1) a phase contrast microscopy image restoration method that produces phase retardation features, which are intrinsic features of phase contrast microscopy, and (2) a semi-supervised learning based algorithm for cell segmentation, which is a fundamental task for various cell behavior analysis. Specifically, the image formation process of phase contrast microscopy images is first computationally modeled with a dictionary of diffraction patterns; as a result, each pixel of a phase contrast microscopy image is represented by a linear combination of the bases, which we call phase retardation features. Images are then partitioned into phase-homogeneous atoms by clustering neighboring pixels with similar phase retardation features. Consequently, cell segmentation is performed via a semi-supervised classification technique over the phase-homogeneous atoms. Experiments demonstrate that the proposed approach produces quality segmentation of individual cells and outperforms previous approaches. Copyright © 2013 Elsevier B.V. All rights reserved.

The development and implementation of a theory-informed, integrated mother-child intervention in rural Uganda.

PubMed

Singla, Daisy R; Kumbakumba, Elias

2015-12-01

A randomised cluster effectiveness trial of a parenting intervention in rural Uganda found benefits to child development among children 12-36 months, relevant parenting practices related to stimulation, hygiene and diet, and prevented the worsening of mothers' depressive symptoms. An examination of underlying implementation processes allows researchers and program developers to determine whether the program was implemented as intended and highlight barriers and facilitators that may influence replication and scale-up. The objectives of this study were to describe and critically examine (a) perceived barriers and facilitators related to implementation processes of intervention content, training and supervision and delivery from the perspectives of delivery agents and supervisors; (b) perceived barriers and facilitators related to enactment of practices from the perspective of intervention mothers participating in the parenting program; and c) whether the program was implemented as intended. Semi-structured interviews were conducted at midline with peer delivery agents (n = 12) and intervention mothers (n = 31) and at endline with supervisors (n = 4). Content analysis was used to analyze qualitative data in terms of barriers and facilitators of intervention content, training and supervision, delivery and enactment. Additionally, mothers' recall and enactment of practices were coded and analyzed statistically. Monitoring of group sessions and home visits were examined to reveal whether the program was implemented as intended. Among the program's five key messages, 'love and respect' targeting maternal psychological well-being was the most practiced by mothers, easiest to implement by delivery agents, and mothers reported the most internal facilitators for this message. A detailed manual and structured monitoring forms were perceived to facilitate training, intervention delivery, and supervision. Interactive and active strategies based on social-cognitive learning theory were reported as facilitators to intervention delivery. Only program attendance, but not barriers, facilitators or message recall, was significantly positively related to message enactment. Monitoring of group sessions and home visits showed that the program was largely implemented as intended. This implementation assessment revealed a number of important barriers and facilitators from the perspectives of delivery agents, supervisors and program participants. The methods and results are useful to examining and informing the content, delivery, and scaling up of the current program as well as future mother-child interventions in LMIC settings. Copyright © 2015 Elsevier Ltd. All rights reserved.

Weakly supervised visual dictionary learning by harnessing image attributes.

PubMed

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

2014-12-01

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

Satellite monitoring of vegetation and geology in semi-arid environments. [Tanzania

NASA Technical Reports Server (NTRS)

Kihlblom, U.; Johansson, D. (Principal Investigator)

1980-01-01

The possibility of mapping various characteristics of the natural environment of Tanzania by various LANDSAT techniques was assessed. Interpretation and mapping were carried out using black and white as well as color infrared images on the scale of 1:250,000. The advantages of several computer techniques were also assessed, including contrast-stretched rationing, differential edge enhancement; supervised classification; multitemporal classification; and change detection. Results Show the most useful image for interpretation comes from band 5, with additional information being obtained from either band 6 or band 7. The advantages of using color infrared images for interpreting vegetation and geology are so great that black and white should be used only to supplement the colored images.

The Dynamics of a Semi-Arid Region in Response to Climate and Water - Use Policy

NASA Technical Reports Server (NTRS)

Mustard, John F.; Hamburg, Steve; Grant, John A.; Manning, Sara J.; Steinwand, Aaron; Howard, Chris

2000-01-01

The objectives of this project were to determine the response of semi-arid ecosystems to the combined forcings of climate variability and anthropogenic stress. Arid and semi-arid systems encompass close to 40% of the worlds land surface. The ecology of these regions are principally limited by water, and as the water resources wax and wane, so should the health and vigor of the ecosystems. Water, however, is a necessary and critical resource for humans living in these same regions. Thus for many and and semi-arid regions the natural systems and human systems are in direct competition for a limited resource. Increasing competition through development of and and semi-arid regions, export of water resources, as well as potential persistent changes in weather patterns are likely to lead to fundamental changes in carrying capacity, resilience, and ecology of these regions. A detailed understanding of these systems respond to forcing on a regional and local scale is required in order to better prepare for and manage future changes in the availability of water. In the Owens Valley CA, decadal changes in rainfall and increased use of groundwater resources by Los Angles (which derives 60-70% of its water from this region) have resulted in a large-scale experiment on the impacts of these changes in semi-arid ecosystems. This project works directly with the Inyo County Water Department (local water authority) and the Los Angles Department of Water and Power (regional demand on water resources) to understand changes, their causes, and impacts. Very detailed records have been kept for a number of selected sites in the valley which provide essential ground truth. These results are then scaled up through remote sensed data to regions scale to assess large scale patterns and link them to the fundamental decisions regarding the water resources of this region. A fundamental goal is to understand how resilient the native ecosystems are to large changes in water resources. Are they are on a spring (remove and return resources, do the systems return to the original state) or a vector (when water returns have the systems fundamentally changed).

Arrangement and Applying of Movement Patterns in the Cerebellum Based on Semi-supervised Learning.

PubMed

Solouki, Saeed; Pooyan, Mohammad

2016-06-01

Biological control systems have long been studied as a possible inspiration for the construction of robotic controllers. The cerebellum is known to be involved in the production and learning of smooth, coordinated movements. Therefore, highly regular structure of the cerebellum has been in the core of attention in theoretical and computational modeling. However, most of these models reflect some special features of the cerebellum without regarding the whole motor command computational process. In this paper, we try to make a logical relation between the most significant models of the cerebellum and introduce a new learning strategy to arrange the movement patterns: cerebellar modular arrangement and applying of movement patterns based on semi-supervised learning (CMAPS). We assume here the cerebellum like a big archive of patterns that has an efficient organization to classify and recall them. The main idea is to achieve an optimal use of memory locations by more than just a supervised learning and classification algorithm. Surely, more experimental and physiological researches are needed to confirm our hypothesis.

Presenting an Approach for Conducting Knowledge Architecture within Large-Scale Organizations

PubMed Central

Varaee, Touraj; Habibi, Jafar; Mohaghar, Ali

2015-01-01

Knowledge architecture (KA) establishes the basic groundwork for the successful implementation of a short-term or long-term knowledge management (KM) program. An example of KA is the design of a prototype before a new vehicle is manufactured. Due to a transformation to large-scale organizations, the traditional architecture of organizations is undergoing fundamental changes. This paper explores the main strengths and weaknesses in the field of KA within large-scale organizations and provides a suitable methodology and supervising framework to overcome specific limitations. This objective was achieved by applying and updating the concepts from the Zachman information architectural framework and the information architectural methodology of enterprise architecture planning (EAP). The proposed solution may be beneficial for architects in knowledge-related areas to successfully accomplish KM within large-scale organizations. The research method is descriptive; its validity is confirmed by performing a case study and polling the opinions of KA experts. PMID:25993414

Presenting an Approach for Conducting Knowledge Architecture within Large-Scale Organizations.

PubMed

Varaee, Touraj; Habibi, Jafar; Mohaghar, Ali

2015-01-01

Knowledge architecture (KA) establishes the basic groundwork for the successful implementation of a short-term or long-term knowledge management (KM) program. An example of KA is the design of a prototype before a new vehicle is manufactured. Due to a transformation to large-scale organizations, the traditional architecture of organizations is undergoing fundamental changes. This paper explores the main strengths and weaknesses in the field of KA within large-scale organizations and provides a suitable methodology and supervising framework to overcome specific limitations. This objective was achieved by applying and updating the concepts from the Zachman information architectural framework and the information architectural methodology of enterprise architecture planning (EAP). The proposed solution may be beneficial for architects in knowledge-related areas to successfully accomplish KM within large-scale organizations. The research method is descriptive; its validity is confirmed by performing a case study and polling the opinions of KA experts.

Combining semi-automated image analysis techniques with machine learning algorithms to accelerate large-scale genetic studies.

PubMed

Atkinson, Jonathan A; Lobet, Guillaume; Noll, Manuel; Meyer, Patrick E; Griffiths, Marcus; Wells, Darren M

2017-10-01

Genetic analyses of plant root systems require large datasets of extracted architectural traits. To quantify such traits from images of root systems, researchers often have to choose between automated tools (that are prone to error and extract only a limited number of architectural traits) or semi-automated ones (that are highly time consuming). We trained a Random Forest algorithm to infer architectural traits from automatically extracted image descriptors. The training was performed on a subset of the dataset, then applied to its entirety. This strategy allowed us to (i) decrease the image analysis time by 73% and (ii) extract meaningful architectural traits based on image descriptors. We also show that these traits are sufficient to identify the quantitative trait loci that had previously been discovered using a semi-automated method. We have shown that combining semi-automated image analysis with machine learning algorithms has the power to increase the throughput of large-scale root studies. We expect that such an approach will enable the quantification of more complex root systems for genetic studies. We also believe that our approach could be extended to other areas of plant phenotyping. © The Authors 2017. Published by Oxford University Press.

Combining semi-automated image analysis techniques with machine learning algorithms to accelerate large-scale genetic studies

PubMed Central

Atkinson, Jonathan A.; Lobet, Guillaume; Noll, Manuel; Meyer, Patrick E.; Griffiths, Marcus

2017-01-01

Abstract Genetic analyses of plant root systems require large datasets of extracted architectural traits. To quantify such traits from images of root systems, researchers often have to choose between automated tools (that are prone to error and extract only a limited number of architectural traits) or semi-automated ones (that are highly time consuming). We trained a Random Forest algorithm to infer architectural traits from automatically extracted image descriptors. The training was performed on a subset of the dataset, then applied to its entirety. This strategy allowed us to (i) decrease the image analysis time by 73% and (ii) extract meaningful architectural traits based on image descriptors. We also show that these traits are sufficient to identify the quantitative trait loci that had previously been discovered using a semi-automated method. We have shown that combining semi-automated image analysis with machine learning algorithms has the power to increase the throughput of large-scale root studies. We expect that such an approach will enable the quantification of more complex root systems for genetic studies. We also believe that our approach could be extended to other areas of plant phenotyping. PMID:29020748

Bridging the Gap Between Large-scale Data Sets and Analyses: Semi-automated Methods to Facilitate Length Polymorphism Scoring and Data Analyses.

EPA Science Inventory

Amplified fragment length polymorphism (AFLP) markers can be developed more quickly and at a lower cost than microsatellite and single nucleotide polymorphism markers, which makes them ideal markers for large-scale studies of understudied taxa — such as species at risk. However,...

Semi-Supervised Data Summarization: Using Spectral Libraries to Improve Hyperspectral Clustering

NASA Technical Reports Server (NTRS)

Wagstaff, K. L.; Shu, H. P.; Mazzoni, D.; Castano, R.

2005-01-01

Hyperspectral imagers produce very large images, with each pixel recorded at hundreds or thousands of different wavelengths. The ability to automatically generate summaries of these data sets enables several important applications, such as quickly browsing through a large image repository or determining the best use of a limited bandwidth link (e.g., determining which images are most critical for full transmission). Clustering algorithms can be used to generate these summaries, but traditional clustering methods make decisions based only on the information contained in the data set. In contrast, we present a new method that additionally leverages existing spectral libraries to identify materials that are likely to be present in the image target area. We find that this approach simultaneously reduces runtime and produces summaries that are more relevant to science goals.

Elementary Administrators' Mathematics Supervision and Self-Efficacy Development

ERIC Educational Resources Information Center

Johnson, Kelly M. Gomez

2017-01-01

Mathematics curriculum reform is changing the content and resources in today's elementary classrooms as well as the culture of mathematics teaching and learning. Administrators face the challenge of leading large-scale curricular change efforts with limited prior knowledge or experiences with reform curricula structures. Administrators, as the…

Effects of Supervised Counseling on Dominance

ERIC Educational Resources Information Center

Ostrand, Janet L.

1976-01-01

To ascertain effects of supervised counselor experience on measures of dominance, graduate students (N=32) were given the dominance and self-acceptance scales of the California Psychological Inventory and the California Fascism Scale. Results implied a supervised counseling practicum effected more of an increase in participants' feelings of…

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

PubMed Central

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

2017-01-01

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

Nonlinear Semi-Supervised Metric Learning Via Multiple Kernels and Local Topology.

PubMed

Li, Xin; Bai, Yanqin; Peng, Yaxin; Du, Shaoyi; Ying, Shihui

2018-03-01

Changing the metric on the data may change the data distribution, hence a good distance metric can promote the performance of learning algorithm. In this paper, we address the semi-supervised distance metric learning (ML) problem to obtain the best nonlinear metric for the data. First, we describe the nonlinear metric by the multiple kernel representation. By this approach, we project the data into a high dimensional space, where the data can be well represented by linear ML. Then, we reformulate the linear ML by a minimization problem on the positive definite matrix group. Finally, we develop a two-step algorithm for solving this model and design an intrinsic steepest descent algorithm to learn the positive definite metric matrix. Experimental results validate that our proposed method is effective and outperforms several state-of-the-art ML methods.

A Modular Hierarchical Approach to 3D Electron Microscopy Image Segmentation

PubMed Central

Liu, Ting; Jones, Cory; Seyedhosseini, Mojtaba; Tasdizen, Tolga

2014-01-01

The study of neural circuit reconstruction, i.e., connectomics, is a challenging problem in neuroscience. Automated and semi-automated electron microscopy (EM) image analysis can be tremendously helpful for connectomics research. In this paper, we propose a fully automatic approach for intra-section segmentation and inter-section reconstruction of neurons using EM images. A hierarchical merge tree structure is built to represent multiple region hypotheses and supervised classification techniques are used to evaluate their potentials, based on which we resolve the merge tree with consistency constraints to acquire final intra-section segmentation. Then, we use a supervised learning based linking procedure for the inter-section neuron reconstruction. Also, we develop a semi-automatic method that utilizes the intermediate outputs of our automatic algorithm and achieves intra-segmentation with minimal user intervention. The experimental results show that our automatic method can achieve close-to-human intra-segmentation accuracy and state-of-the-art inter-section reconstruction accuracy. We also show that our semi-automatic method can further improve the intra-segmentation accuracy. PMID:24491638

Community cooperatives and insecticide-treated materials for malaria control: a new experience in Latin America.

PubMed

Kroeger, Axel; Aviñna, Ana; Ordoñnez-Gonzalez, José; Escandon, Celia

2002-11-15

Insecticide-treated materials (ITMs) are effective in substantially reducing the burden of malaria and other vector-borne diseases; but how can high coverage rates of ITMs be achieved and maintained? In south Mexico and on the Pacific and Atlantic coasts of Colombia 14 community-based cooperatives offering three different kinds of ITM services (sale of impregnation services; sale of impregnated nets; production of nets and sale of impregnated nets) were formed and supervised by a national health service (IMSS-SOLIDARIDAD, Mexico) and by an academic institution (the Colombian Institute of Tropical Medicine) along with local district health services. The objectives of this research were to analyse the processes and results of this approach and to identify the favourable and limiting factors. The methods used for data collection and analysis were group discussions, individual and semi-structured interviews with users and non-users of ITMs, individual in-depth interviews with cooperative members and supervisors, checks of sales book and observation of impregnation services. Coverage with unimpregnated nets was above 50% in all study areas. The fastest increase of ITM coverage was achieved through the exclusive sale of impregnation services. Low-cost social marketing techniques were used to increase demand. The large-scale production of nets in two cooperatives was only possible with the aid of an international NGO which ordered impregnated bednets for their target group. A number of favourable and limiting factors relating to the success of ITM cooperatives were identified. Of particular importance for the more successful Mexican cooperatives were: a) support by health services, b) smaller size, c) lesser desire for quick returns and d) lower ITM unit costs. ITM community cooperatives supported and supervised by the health services have good potential in the Latin American context for achieving and maintaining high impregnation rates.

Semi-supervised prediction of SH2-peptide interactions from imbalanced high-throughput data.

PubMed

Kundu, Kousik; Costa, Fabrizio; Huber, Michael; Reth, Michael; Backofen, Rolf

2013-01-01

Src homology 2 (SH2) domains are the largest family of the peptide-recognition modules (PRMs) that bind to phosphotyrosine containing peptides. Knowledge about binding partners of SH2-domains is key for a deeper understanding of different cellular processes. Given the high binding specificity of SH2, in-silico ligand peptide prediction is of great interest. Currently however, only a few approaches have been published for the prediction of SH2-peptide interactions. Their main shortcomings range from limited coverage, to restrictive modeling assumptions (they are mainly based on position specific scoring matrices and do not take into consideration complex amino acids inter-dependencies) and high computational complexity. We propose a simple yet effective machine learning approach for a large set of known human SH2 domains. We used comprehensive data from micro-array and peptide-array experiments on 51 human SH2 domains. In order to deal with the high data imbalance problem and the high signal-to-noise ration, we casted the problem in a semi-supervised setting. We report competitive predictive performance w.r.t. state-of-the-art. Specifically we obtain 0.83 AUC ROC and 0.93 AUC PR in comparison to 0.71 AUC ROC and 0.87 AUC PR previously achieved by the position specific scoring matrices (PSSMs) based SMALI approach. Our work provides three main contributions. First, we showed that better models can be obtained when the information on the non-interacting peptides (negative examples) is also used. Second, we improve performance when considering high order correlations between the ligand positions employing regularization techniques to effectively avoid overfitting issues. Third, we developed an approach to tackle the data imbalance problem using a semi-supervised strategy. Finally, we performed a genome-wide prediction of human SH2-peptide binding, uncovering several findings of biological relevance. We make our models and genome-wide predictions, for all the 51 SH2-domains, freely available to the scientific community under the following URLs: http://www.bioinf.uni-freiburg.de/Software/SH2PepInt/SH2PepInt.tar.gz and http://www.bioinf.uni-freiburg.de/Software/SH2PepInt/Genome-wide-predictions.tar.gz, respectively.

Semi-Supervised Learning of Lift Optimization of Multi-Element Three-Segment Variable Camber Airfoil

NASA Technical Reports Server (NTRS)

Kaul, Upender K.; Nguyen, Nhan T.

2017-01-01

This chapter describes a new intelligent platform for learning optimal designs of morphing wings based on Variable Camber Continuous Trailing Edge Flaps (VCCTEF) in conjunction with a leading edge flap called the Variable Camber Krueger (VCK). The new platform consists of a Computational Fluid Dynamics (CFD) methodology coupled with a semi-supervised learning methodology. The CFD component of the intelligent platform comprises of a full Navier-Stokes solution capability (NASA OVERFLOW solver with Spalart-Allmaras turbulence model) that computes flow over a tri-element inboard NASA Generic Transport Model (GTM) wing section. Various VCCTEF/VCK settings and configurations were considered to explore optimal design for high-lift flight during take-off and landing. To determine globally optimal design of such a system, an extremely large set of CFD simulations is needed. This is not feasible to achieve in practice. To alleviate this problem, a recourse was taken to a semi-supervised learning (SSL) methodology, which is based on manifold regularization techniques. A reasonable space of CFD solutions was populated and then the SSL methodology was used to fit this manifold in its entirety, including the gaps in the manifold where there were no CFD solutions available. The SSL methodology in conjunction with an elastodynamic solver (FiDDLE) was demonstrated in an earlier study involving structural health monitoring. These CFD-SSL methodologies define the new intelligent platform that forms the basis for our search for optimal design of wings. Although the present platform can be used in various other design and operational problems in engineering, this chapter focuses on the high-lift study of the VCK-VCCTEF system. Top few candidate design configurations were identified by solving the CFD problem in a small subset of the design space. The SSL component was trained on the design space, and was then used in a predictive mode to populate a selected set of test points outside of the given design space. The new design test space thus populated was evaluated by using the CFD component by determining the error between the SSL predictions and the true (CFD) solutions, which was found to be small. This demonstrates the proposed CFD-SSL methodologies for isolating the best design of the VCK-VCCTEF system, and it holds promise for quantitatively identifying best designs of flight systems, in general.

NetVLAD: CNN Architecture for Weakly Supervised Place Recognition.

PubMed

Arandjelovic, Relja; Gronat, Petr; Torii, Akihiko; Pajdla, Tomas; Sivic, Josef

2018-06-01

We tackle the problem of large scale visual place recognition, where the task is to quickly and accurately recognize the location of a given query photograph. We present the following four principal contributions. First, we develop a convolutional neural network (CNN) architecture that is trainable in an end-to-end manner directly for the place recognition task. The main component of this architecture, NetVLAD, is a new generalized VLAD layer, inspired by the "Vector of Locally Aggregated Descriptors" image representation commonly used in image retrieval. The layer is readily pluggable into any CNN architecture and amenable to training via backpropagation. Second, we create a new weakly supervised ranking loss, which enables end-to-end learning of the architecture's parameters from images depicting the same places over time downloaded from Google Street View Time Machine. Third, we develop an efficient training procedure which can be applied on very large-scale weakly labelled tasks. Finally, we show that the proposed architecture and training procedure significantly outperform non-learnt image representations and off-the-shelf CNN descriptors on challenging place recognition and image retrieval benchmarks.

Simulating Daily and Sub-daily Water Flow in Large, Semi-arid Watershed Using SWAT: A Case Study of Nueces River Basin, Texas

NASA Astrophysics Data System (ADS)

Bassam, S.; Ren, J.

2015-12-01

Runoff generated during heavy rainfall imposes quick, but often intense, changes in the flow of streams, which increase the chance of flash floods in the vicinity of the streams. Understanding the temporal response of streams to heavy rainfall requires a hydrological model that considers meteorological, hydrological, and geological components of the streams and their watersheds. SWAT is a physically-based, semi-distributed model that is capable of simulating water flow within watersheds with both long-term, i.e. annually and monthly, and short-term (daily and sub-daily) time scales. However, the capability of SWAT in sub-daily water flow modeling within large watersheds has not been studied much, compare to long-term and daily time scales. In this study we are investigating the water flow in a large, semi-arid watershed, Nueces River Basin (NRB) with the drainage area of 16950 mi2 located in South Texas, with daily and sub-daily time scales. The objectives of this study are: (1) simulating the response of streams to heavy, and often quick, rainfall, (2) evaluating SWAT performance in sub-daily modeling of water flow within a large watershed, and (3) examining means for model performance improvement during model calibration and verification based on results of sensitivity and uncertainty analysis. The results of this study can provide important information for water resources planning during flood seasons.

A qualitative investigation of the nature of "informal supervision" among therapists in training.

PubMed

Coren, Sidney; Farber, Barry A

2017-11-29

This study investigated how, when, why, and with whom therapists in training utilize "informal supervision"-that is, engage individuals who are not their formally assigned supervisors in significant conversations about their clinical work. Participants were 16 doctoral trainees in clinical and counseling psychology programs. Semi-structured interviews were conducted and analyzed using the Consensual Qualitative Research (CQR) method. Seven domains emerged from the analysis, indicating that, in general, participants believe that informal and formal supervision offer many of the same benefits, including validation, support, and reassurance; freedom and safety to discuss doubts, anxieties, strong personal reactions to patients, clinical mistakes and challenges; and alternative approaches to clinical interventions. However, several differences also emerged between these modes of learning-for example, formal supervision is seen as more focused on didactics per se ("what to do"), whereas informal supervision is seen as providing more of a "holding environment." Overall, the findings of this study suggest that informal supervision is an important and valuable adjunctive practice by which clinical trainees augment their professional competencies. Recommendations are proposed for clinical practice and training, including the need to further specify the ethical boundaries of this unique and essentially unregulated type of supervision.

Development of a semi-autonomous service robot with telerobotic capabilities

NASA Technical Reports Server (NTRS)

Jones, J. E.; White, D. R.

1987-01-01

The importance to the United States of semi-autonomous systems for application to a large number of manufacturing and service processes is very clear. Two principal reasons emerge as the primary driving forces for development of such systems: enhanced national productivity and operation in environments whch are hazardous to humans. Completely autonomous systems may not currently be economically feasible. However, autonomous systems that operate in a limited operation domain or that are supervised by humans are within the technology capability of this decade and will likely provide reasonable return on investment. The two research and development efforts of autonomy and telerobotics are distinctly different, yet interconnected. The first addresses the communication of an intelligent electronic system with a robot while the second requires human communication and ergonomic consideration. Discussed here are work in robotic control, human/robot team implementation, expert system robot operation, and sensor development by the American Welding Institute, MTS Systems Corporation, and the Colorado School of Mines--Center for Welding Research.

Modeling High Temperature Deformation Behavior of Large-Scaled Mg-Al-Zn Magnesium Alloy Fabricated by Semi-continuous Casting

NASA Astrophysics Data System (ADS)

Li, Jianping; Xia, Xiangsheng

2015-09-01

In order to improve the understanding of the hot deformation and dynamic recrystallization (DRX) behaviors of large-scaled AZ80 magnesium alloy fabricated by semi-continuous casting, compression tests were carried out in the temperature range from 250 to 400 °C and strain rate range from 0.001 to 0.1 s-1 on a Gleeble 1500 thermo-mechanical machine. The effects of the temperature and strain rate on the hot deformation behavior have been expressed by means of the conventional hyperbolic sine equation, and the influence of the strain has been incorporated in the equation by considering its effect on different material constants for large-scaled AZ80 magnesium alloy. In addition, the DRX behavior has been discussed. The result shows that the deformation temperature and strain rate exerted remarkable influences on the flow stress. The constitutive equation of large-scaled AZ80 magnesium alloy for hot deformation at steady-state stage (ɛ = 0.5) was The true stress-true strain curves predicted by the extracted model were in good agreement with the experimental results, thereby confirming the validity of the developed constitutive relation. The DRX kinetic model of large-scaled AZ80 magnesium alloy was established as X d = 1 - exp[-0.95((ɛ - ɛc)/ɛ*)2.4904]. The rate of DRX increases with increasing deformation temperature, and high temperature is beneficial for achieving complete DRX in the large-scaled AZ80 magnesium alloy.

A deep learning and novelty detection framework for rapid phenotyping in high-content screening

PubMed Central

Sommer, Christoph; Hoefler, Rudolf; Samwer, Matthias; Gerlich, Daniel W.

2017-01-01

Supervised machine learning is a powerful and widely used method for analyzing high-content screening data. Despite its accuracy, efficiency, and versatility, supervised machine learning has drawbacks, most notably its dependence on a priori knowledge of expected phenotypes and time-consuming classifier training. We provide a solution to these limitations with CellCognition Explorer, a generic novelty detection and deep learning framework. Application to several large-scale screening data sets on nuclear and mitotic cell morphologies demonstrates that CellCognition Explorer enables discovery of rare phenotypes without user training, which has broad implications for improved assay development in high-content screening. PMID:28954863

Satisfaction and Experience With a Supervised Home-Based Real-Time Videoconferencing Telerehabilitation Exercise Program in People with Chronic Obstructive Pulmonary Disease (COPD)

PubMed Central

TSAI, LING LING Y.; MCNAMARA, RENAE J.; DENNIS, SARAH M.; MODDEL, CHLOE; ALISON, JENNIFER A.; MCKENZIE, DAVID K.; MCKEOUGH, ZOE J.

2016-01-01

Telerehabilitation, consisting of supervised home-based exercise training via real-time videoconferencing, is an alternative method to deliver pulmonary rehabilitation with potential to improve access. The aims were to determine the level of satisfaction and experience of an eight-week supervised home-based telerehabilitation exercise program using real-time videoconferencing in people with COPD. Quantitative measures were the Client Satisfaction Questionnaire-8 (CSQ-8) and a purpose-designed satisfaction survey. A qualitative component was conducted using semi-structured interviews. Nineteen participants (mean (SD) age 73 (8) years, forced expiratory volume in 1 second (FEV1) 60 (23) % predicted) showed a high level of satisfaction in the CSQ-8 score and 100% of participants reported a high level of satisfaction with the quality of exercise sessions delivered using real-time videoconferencing in participant satisfaction survey. Eleven participants undertook semi-structured interviews. Key themes in four areas relating to the telerehabilitation service emerged: positive virtual interaction through technology; health benefits; and satisfaction with the convenience and use of equipment. Participants were highly satisfied with the telerehabilitation exercise program delivered via videoconferencing. PMID:28775799

An immune-inspired semi-supervised algorithm for breast cancer diagnosis.

PubMed

Peng, Lingxi; Chen, Wenbin; Zhou, Wubai; Li, Fufang; Yang, Jin; Zhang, Jiandong

2016-10-01

Breast cancer is the most frequently and world widely diagnosed life-threatening cancer, which is the leading cause of cancer death among women. Early accurate diagnosis can be a big plus in treating breast cancer. Researchers have approached this problem using various data mining and machine learning techniques such as support vector machine, artificial neural network, etc. The computer immunology is also an intelligent method inspired by biological immune system, which has been successfully applied in pattern recognition, combination optimization, machine learning, etc. However, most of these diagnosis methods belong to a supervised diagnosis method. It is very expensive to obtain labeled data in biology and medicine. In this paper, we seamlessly integrate the state-of-the-art research on life science with artificial intelligence, and propose a semi-supervised learning algorithm to reduce the need for labeled data. We use two well-known benchmark breast cancer datasets in our study, which are acquired from the UCI machine learning repository. Extensive experiments are conducted and evaluated on those two datasets. Our experimental results demonstrate the effectiveness and efficiency of our proposed algorithm, which proves that our algorithm is a promising automatic diagnosis method for breast cancer. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Filtering large-scale event collections using a combination of supervised and unsupervised learning for event trigger classification.

PubMed

Mehryary, Farrokh; Kaewphan, Suwisa; Hakala, Kai; Ginter, Filip

2016-01-01

Biomedical event extraction is one of the key tasks in biomedical text mining, supporting various applications such as database curation and hypothesis generation. Several systems, some of which have been applied at a large scale, have been introduced to solve this task. Past studies have shown that the identification of the phrases describing biological processes, also known as trigger detection, is a crucial part of event extraction, and notable overall performance gains can be obtained by solely focusing on this sub-task. In this paper we propose a novel approach for filtering falsely identified triggers from large-scale event databases, thus improving the quality of knowledge extraction. Our method relies on state-of-the-art word embeddings, event statistics gathered from the whole biomedical literature, and both supervised and unsupervised machine learning techniques. We focus on EVEX, an event database covering the whole PubMed and PubMed Central Open Access literature containing more than 40 million extracted events. The top most frequent EVEX trigger words are hierarchically clustered, and the resulting cluster tree is pruned to identify words that can never act as triggers regardless of their context. For rarely occurring trigger words we introduce a supervised approach trained on the combination of trigger word classification produced by the unsupervised clustering method and manual annotation. The method is evaluated on the official test set of BioNLP Shared Task on Event Extraction. The evaluation shows that the method can be used to improve the performance of the state-of-the-art event extraction systems. This successful effort also translates into removing 1,338,075 of potentially incorrect events from EVEX, thus greatly improving the quality of the data. The method is not solely bound to the EVEX resource and can be thus used to improve the quality of any event extraction system or database. The data and source code for this work are available at: http://bionlp-www.utu.fi/trigger-clustering/.

Mental health nurses' experiences of managing work-related emotions through supervision.

PubMed

MacLaren, Jessica; Stenhouse, Rosie; Ritchie, Deborah

2016-10-01

The aim of this study was to explore emotion cultures constructed in supervision and consider how supervision functions as an emotionally safe space promoting critical reflection. Research published between 1995-2015 suggests supervision has a positive impact on nurses' emotional well-being, but there is little understanding of the processes involved in this and how styles of emotion interaction are established in supervision. A narrative approach was used to investigate mental health nurses' understandings and experiences of supervision. Eight semi-structured interviews were conducted with community mental health nurses in the UK during 2011. Analysis of audio data used features of speech to identify narrative discourse and illuminate meanings. A topic-centred analysis of interview narratives explored discourses shared between the participants. This supported the identification of feeling rules in participants' narratives and the exploration of the emotion context of supervision. Effective supervision was associated with three feeling rules: safety and reflexivity; staying professional; managing feelings. These feeling rules allowed the expression and exploration of emotions, promoting critical reflection. A contrast was identified between the emotion culture of supervision and the nurses' experience of their workplace cultures as requiring the suppression of difficult emotions. Despite this, contrast supervision functioned as an emotion micro-culture with its own distinctive feeling rules. The analytical construct of feeling rules allows us to connect individual emotional experiences to shared normative discourses, highlighting how these shape emotional processes taking place in supervision. This understanding supports an explanation of how supervision may positively influence nurses' emotion management and perhaps reduce burnout. © 2016 John Wiley & Sons Ltd.

Semi-Supervised Learning to Identify UMLS Semantic Relations.

PubMed

Luo, Yuan; Uzuner, Ozlem

2014-01-01

The UMLS Semantic Network is constructed by experts and requires periodic expert review to update. We propose and implement a semi-supervised approach for automatically identifying UMLS semantic relations from narrative text in PubMed. Our method analyzes biomedical narrative text to collect semantic entity pairs, and extracts multiple semantic, syntactic and orthographic features for the collected pairs. We experiment with seeded k-means clustering with various distance metrics. We create and annotate a ground truth corpus according to the top two levels of the UMLS semantic relation hierarchy. We evaluate our system on this corpus and characterize the learning curves of different clustering configuration. Using KL divergence consistently performs the best on the held-out test data. With full seeding, we obtain macro-averaged F-measures above 70% for clustering the top level UMLS relations (2-way), and above 50% for clustering the second level relations (7-way).

Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media

PubMed Central

Yazdavar, Amir Hossein; Al-Olimat, Hussein S.; Ebrahimi, Monireh; Bajaj, Goonmeet; Banerjee, Tanvi; Thirunarayan, Krishnaprasad; Pathak, Jyotishman; Sheth, Amit

2017-01-01

With the rise of social media, millions of people are routinely expressing their moods, feelings, and daily struggles with mental health issues on social media platforms like Twitter. Unlike traditional observational cohort studies conducted through questionnaires and self-reported surveys, we explore the reliable detection of clinical depression from tweets obtained unobtrusively. Based on the analysis of tweets crawled from users with self-reported depressive symptoms in their Twitter profiles, we demonstrate the potential for detecting clinical depression symptoms which emulate the PHQ-9 questionnaire clinicians use today. Our study uses a semi-supervised statistical model to evaluate how the duration of these symptoms and their expression on Twitter (in terms of word usage patterns and topical preferences) align with the medical findings reported via the PHQ-9. Our proactive and automatic screening tool is able to identify clinical depressive symptoms with an accuracy of 68% and precision of 72%. PMID:29707701

Automatic Selection of Order Parameters in the Analysis of Large Scale Molecular Dynamics Simulations.

PubMed

Sultan, Mohammad M; Kiss, Gert; Shukla, Diwakar; Pande, Vijay S

2014-12-09

Given the large number of crystal structures and NMR ensembles that have been solved to date, classical molecular dynamics (MD) simulations have become powerful tools in the atomistic study of the kinetics and thermodynamics of biomolecular systems on ever increasing time scales. By virtue of the high-dimensional conformational state space that is explored, the interpretation of large-scale simulations faces difficulties not unlike those in the big data community. We address this challenge by introducing a method called clustering based feature selection (CB-FS) that employs a posterior analysis approach. It combines supervised machine learning (SML) and feature selection with Markov state models to automatically identify the relevant degrees of freedom that separate conformational states. We highlight the utility of the method in the evaluation of large-scale simulations and show that it can be used for the rapid and automated identification of relevant order parameters involved in the functional transitions of two exemplary cell-signaling proteins central to human disease states.

Semi-Supervised Sparse Representation Based Classification for Face Recognition With Insufficient Labeled Samples

NASA Astrophysics Data System (ADS)

Gao, Yuan; Ma, Jiayi; Yuille, Alan L.

2017-05-01

This paper addresses the problem of face recognition when there is only few, or even only a single, labeled examples of the face that we wish to recognize. Moreover, these examples are typically corrupted by nuisance variables, both linear (i.e., additive nuisance variables such as bad lighting, wearing of glasses) and non-linear (i.e., non-additive pixel-wise nuisance variables such as expression changes). The small number of labeled examples means that it is hard to remove these nuisance variables between the training and testing faces to obtain good recognition performance. To address the problem we propose a method called Semi-Supervised Sparse Representation based Classification (S$^3$RC). This is based on recent work on sparsity where faces are represented in terms of two dictionaries: a gallery dictionary consisting of one or more examples of each person, and a variation dictionary representing linear nuisance variables (e.g., different lighting conditions, different glasses). The main idea is that (i) we use the variation dictionary to characterize the linear nuisance variables via the sparsity framework, then (ii) prototype face images are estimated as a gallery dictionary via a Gaussian Mixture Model (GMM), with mixed labeled and unlabeled samples in a semi-supervised manner, to deal with the non-linear nuisance variations between labeled and unlabeled samples. We have done experiments with insufficient labeled samples, even when there is only a single labeled sample per person. Our results on the AR, Multi-PIE, CAS-PEAL, and LFW databases demonstrate that the proposed method is able to deliver significantly improved performance over existing methods.

A combined Fisher and Laplacian score for feature selection in QSAR based drug design using compounds with known and unknown activities.

PubMed

Valizade Hasanloei, Mohammad Amin; Sheikhpour, Razieh; Sarram, Mehdi Agha; Sheikhpour, Elnaz; Sharifi, Hamdollah

2018-02-01

Quantitative structure-activity relationship (QSAR) is an effective computational technique for drug design that relates the chemical structures of compounds to their biological activities. Feature selection is an important step in QSAR based drug design to select the most relevant descriptors. One of the most popular feature selection methods for classification problems is Fisher score which aim is to minimize the within-class distance and maximize the between-class distance. In this study, the properties of Fisher criterion were extended for QSAR models to define the new distance metrics based on the continuous activity values of compounds with known activities. Then, a semi-supervised feature selection method was proposed based on the combination of Fisher and Laplacian criteria which exploits both compounds with known and unknown activities to select the relevant descriptors. To demonstrate the efficiency of the proposed semi-supervised feature selection method in selecting the relevant descriptors, we applied the method and other feature selection methods on three QSAR data sets such as serine/threonine-protein kinase PLK3 inhibitors, ROCK inhibitors and phenol compounds. The results demonstrated that the QSAR models built on the selected descriptors by the proposed semi-supervised method have better performance than other models. This indicates the efficiency of the proposed method in selecting the relevant descriptors using the compounds with known and unknown activities. The results of this study showed that the compounds with known and unknown activities can be helpful to improve the performance of the combined Fisher and Laplacian based feature selection methods.

Individualized Functional Parcellation of the Human Amygdala Using a Semi-supervised Clustering Method: A 7T Resting State fMRI Study.

PubMed

Zhang, Xianchang; Cheng, Hewei; Zuo, Zhentao; Zhou, Ke; Cong, Fei; Wang, Bo; Zhuo, Yan; Chen, Lin; Xue, Rong; Fan, Yong

2018-01-01

The amygdala plays an important role in emotional functions and its dysfunction is considered to be associated with multiple psychiatric disorders in humans. Cytoarchitectonic mapping has demonstrated that the human amygdala complex comprises several subregions. However, it's difficult to delineate boundaries of these subregions in vivo even if using state of the art high resolution structural MRI. Previous attempts to parcellate this small structure using unsupervised clustering methods based on resting state fMRI data suffered from the low spatial resolution of typical fMRI data, and it remains challenging for the unsupervised methods to define subregions of the amygdala in vivo . In this study, we developed a novel brain parcellation method to segment the human amygdala into spatially contiguous subregions based on 7T high resolution fMRI data. The parcellation was implemented using a semi-supervised spectral clustering (SSC) algorithm at an individual subject level. Under guidance of prior information derived from the Julich cytoarchitectonic atlas, our method clustered voxels of the amygdala into subregions according to similarity measures of their functional signals. As a result, three distinct amygdala subregions can be obtained in each hemisphere for every individual subject. Compared with the cytoarchitectonic atlas, our method achieved better performance in terms of subregional functional homogeneity. Validation experiments have also demonstrated that the amygdala subregions obtained by our method have distinctive, lateralized functional connectivity (FC) patterns. Our study has demonstrated that the semi-supervised brain parcellation method is a powerful tool for exploring amygdala subregional functions.

A combined Fisher and Laplacian score for feature selection in QSAR based drug design using compounds with known and unknown activities

NASA Astrophysics Data System (ADS)

Valizade Hasanloei, Mohammad Amin; Sheikhpour, Razieh; Sarram, Mehdi Agha; Sheikhpour, Elnaz; Sharifi, Hamdollah

2018-02-01

Quantitative structure-activity relationship (QSAR) is an effective computational technique for drug design that relates the chemical structures of compounds to their biological activities. Feature selection is an important step in QSAR based drug design to select the most relevant descriptors. One of the most popular feature selection methods for classification problems is Fisher score which aim is to minimize the within-class distance and maximize the between-class distance. In this study, the properties of Fisher criterion were extended for QSAR models to define the new distance metrics based on the continuous activity values of compounds with known activities. Then, a semi-supervised feature selection method was proposed based on the combination of Fisher and Laplacian criteria which exploits both compounds with known and unknown activities to select the relevant descriptors. To demonstrate the efficiency of the proposed semi-supervised feature selection method in selecting the relevant descriptors, we applied the method and other feature selection methods on three QSAR data sets such as serine/threonine-protein kinase PLK3 inhibitors, ROCK inhibitors and phenol compounds. The results demonstrated that the QSAR models built on the selected descriptors by the proposed semi-supervised method have better performance than other models. This indicates the efficiency of the proposed method in selecting the relevant descriptors using the compounds with known and unknown activities. The results of this study showed that the compounds with known and unknown activities can be helpful to improve the performance of the combined Fisher and Laplacian based feature selection methods.

Enhanced low-rank representation via sparse manifold adaption for semi-supervised learning.

PubMed

Peng, Yong; Lu, Bao-Liang; Wang, Suhang

2015-05-01

Constructing an informative and discriminative graph plays an important role in various pattern recognition tasks such as clustering and classification. Among the existing graph-based learning models, low-rank representation (LRR) is a very competitive one, which has been extensively employed in spectral clustering and semi-supervised learning (SSL). In SSL, the graph is composed of both labeled and unlabeled samples, where the edge weights are calculated based on the LRR coefficients. However, most of existing LRR related approaches fail to consider the geometrical structure of data, which has been shown beneficial for discriminative tasks. In this paper, we propose an enhanced LRR via sparse manifold adaption, termed manifold low-rank representation (MLRR), to learn low-rank data representation. MLRR can explicitly take the data local manifold structure into consideration, which can be identified by the geometric sparsity idea; specifically, the local tangent space of each data point was sought by solving a sparse representation objective. Therefore, the graph to depict the relationship of data points can be built once the manifold information is obtained. We incorporate a regularizer into LRR to make the learned coefficients preserve the geometric constraints revealed in the data space. As a result, MLRR combines both the global information emphasized by low-rank property and the local information emphasized by the identified manifold structure. Extensive experimental results on semi-supervised classification tasks demonstrate that MLRR is an excellent method in comparison with several state-of-the-art graph construction approaches. Copyright © 2015 Elsevier Ltd. All rights reserved.

Regional estimates of reef carbonate dynamics and productivity Using Landsat 7 ETM+, and potential impacts from ocean acidification

USGS Publications Warehouse

Moses, C.S.; Andrefouet, S.; Kranenburg, C.; Muller-Karger, F. E.

2009-01-01

Using imagery at 30 m spatial resolution from the most recent Landsat satellite, the Landsat 7 Enhanced Thematic Mapper Plus (ETM+), we scale up reef metabolic productivity and calcification from local habitat-scale (10 -1 to 100 km2) measurements to regional scales (103 to 104 km2). Distribution and spatial extent of the North Florida Reef Tract (NFRT) habitats come from supervised classification of the Landsat imagery within independent Landsat-derived Millennium Coral Reef Map geomorphologic classes. This system minimizes the depth range and variability of benthic habitat characteristics found in the area of supervised classification and limits misclassification. Classification of Landsat imagery into 5 biotopes (sand, dense live cover, sparse live cover, seagrass, and sparse seagrass) by geomorphologic class is >73% accurate at regional scales. Based on recently published habitat-scale in situ metabolic measurements, gross production (P = 3.01 ?? 109 kg C yr -1), excess production (E = -5.70 ?? 108 kg C yr -1), and calcification (G = -1.68 ?? 106 kg CaCO 3 yr-1) are estimated over 2711 km2 of the NFRT. Simple models suggest sensitivity of these values to ocean acidification, which will increase local dissolution of carbonate sediments. Similar approaches could be applied over large areas with poorly constrained bathymetry or water column properties and minimal metabolic sampling. This tool has potential applications for modeling and monitoring large-scale environmental impacts on reef productivity, such as the influence of ocean acidification on coral reef environments. ?? Inter-Research 2009.

Detecting Visually Observable Disease Symptoms from Faces.

PubMed

Wang, Kuan; Luo, Jiebo

2016-12-01

Recent years have witnessed an increasing interest in the application of machine learning to clinical informatics and healthcare systems. A significant amount of research has been done on healthcare systems based on supervised learning. In this study, we present a generalized solution to detect visually observable symptoms on faces using semi-supervised anomaly detection combined with machine vision algorithms. We rely on the disease-related statistical facts to detect abnormalities and classify them into multiple categories to narrow down the possible medical reasons of detecting. Our method is in contrast with most existing approaches, which are limited by the availability of labeled training data required for supervised learning, and therefore offers the major advantage of flagging any unusual and visually observable symptoms.

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.

Home exercise improves the quality of sleep and daytime sleepiness of elderlies: a randomized controlled trial.

PubMed

Brandão, Glauber Sá; Gomes, Glaucia Sá Brandão Freitas; Brandão, Glaudson Sá; Callou Sampaio, Antônia A; Donner, Claudio F; Oliveira, Luis V F; Camelier, Aquiles Assunção

2018-01-01

Aging causes physiological changes which affect the quality of sleep. Supervised physical exercise is an important therapeutic resource to improve the sleep of the elderlies, however there is a low adherence to those type of programs, so it is necessary to implement an exercise program which is feasible and effective. The study aimed to test the hypothesis that a semi-supervised home exercise program, improves sleep quality and daytime sleepiness of elderlies of the community who present poor sleep quality. This was a randomized controlled trial study, conducted from May to September 2017, in Northeastern Brazil, with elderlies of the community aging 60 years old or older, sedentary, with lower scores or equal to 5 at the Pittsburgh Sleep Quality Index (PSQI) and without cognitive decline. From one hundred ninety-one potential participants twenty-eight refused to participate, therefore, one hundred thirty-one (mean age 68 ± 7 years), and 88% female, were randomly assigned to an intervention group - IG (home exercise and sleep hygiene, n  = 65) and a control group - CG (sleep hygiene only, n  = 66). Sleep assessment tools were used: PSQI, Epworth sleepiness scale (ESS) and clinical questionnaire of Berlin. The level of physical activity has been assessed by means of International Physical Activity Questionnaire adapted for the elderly (IPAQ) and Mini-Mental State Examination for cognitive decline. All participants were assessed before and after the 12-week intervention period and, also, the assessors were blind. The IG showed significant improvement in quality of sleep with a mean reduction of 4.9 ± 2.7 points in the overall PSQI ( p  < 0.01) and in all its 7 components of evaluation ( p  < 0.05), and improvement of secondary endpoint, daytime sleepiness, a decline of 2.8 ± 2.2 points in the ESS (p < 0.01). Our results suggest that semi-supervised home exercise is effective in improving the quality of sleep and self-referred daytime sleepiness of sedentary elderlies of the community who presented sleep disorders. Ensaiosclinicos.gov.br process number: RBR-3cqzfy.

The Dimensionality of Supervisor Roles: Counselor Trainees' Perceptions of Supervision.

ERIC Educational Resources Information Center

Ellis, Michael V.; And Others

A study was conducted which continued the investigation of the underlying structure of supervision by empirically testing Bernard's (1979) model of supervision using a confirmatory multidimensional scaling paradigm. To accomplish this, counselor trainees' perceptions of the underlying structure (dimensionality or cognitive map) of supervision were…

Towards harmonized seismic analysis across Europe using supervised machine learning approaches

NASA Astrophysics Data System (ADS)

Zaccarelli, Riccardo; Bindi, Dino; Cotton, Fabrice; Strollo, Angelo

2017-04-01

In the framework of the Thematic Core Services for Seismology of EPOS-IP (European Plate Observing System-Implementation Phase), a service for disseminating a regionalized logic-tree of ground motions models for Europe is under development. While for the Mediterranean area the large availability of strong motion data qualified and disseminated through the Engineering Strong Motion database (ESM-EPOS), supports the development of both selection criteria and ground motion models, for the low-to-moderate seismic regions of continental Europe the development of ad-hoc models using weak motion recordings of moderate earthquakes is unavoidable. Aim of this work is to present a platform for creating application-oriented earthquake databases by retrieving information from EIDA (European Integrated Data Archive) and applying supervised learning models for earthquake records selection and processing suitable for any specific application of interest. Supervised learning models, i.e. the task of inferring a function from labelled training data, have been extensively used in several fields such as spam detection, speech and image recognition and in general pattern recognition. Their suitability to detect anomalies and perform a semi- to fully- automated filtering on large waveform data set easing the effort of (or replacing) human expertise is therefore straightforward. Being supervised learning algorithms capable of learning from a relatively small training set to predict and categorize unseen data, its advantage when processing large amount of data is crucial. Moreover, their intrinsic ability to make data driven predictions makes them suitable (and preferable) in those cases where explicit algorithms for detection might be unfeasible or too heuristic. In this study, we consider relatively simple statistical classifiers (e.g., Naive Bayes, Logistic Regression, Random Forest, SVMs) where label are assigned to waveform data based on "recognized classes" needed for our use case. These classes might be a simply binary case (e.g., "good for analysis" vs "bad") or more complex one (e.g., "good for analysis" vs "low SNR", "multi-event", "bad coda envelope"). It is important to stress the fact that our approach can be generalized to any use case providing, as in any supervised approach, an adequate training set of labelled data, a feature-set, a statistical classifier, and finally model validation and evaluation. Examples of use cases considered to develop the system prototype are the characterization of the ground motion in low seismic areas; harmonized spectral analysis across Europe for source and attenuation studies; magnitude calibration; coda analysis for attenuation studies.

A qualitative inquiry into the challenges and complexities of research supervision: viewpoints of postgraduate students and faculty members.

PubMed

Yousefi, Alireza; Bazrafkan, Leila; Yamani, Nikoo

2015-07-01

The supervision of academic theses at the Universities of Medical Sciences is one of the most important issues with several challenges. The aim of the present study is to discover the nature of problems and challenges of thesis supervision in Iranian universities of medical sciences. The study was conducted with a qualitative method using conventional content analysis approach. Nineteen faculty members, using purposive sampling, and 11 postgraduate medical sciences students (Ph.D students and residents) were selected on the basis of theoretical sampling. The data were gathered through semi-structured interviews and field observations in Shiraz and Isfahan universities of medical sciences from September 2012 to December 2014. The qualitative content analysis was used with a conventional approach to analyze the data. While experiencing the nature of research supervision process, faculties and the students faced some complexities and challenges in the research supervision process. The obtained codes were categorized under 4 themes Based on the characteristics; included "contextual problem", "role ambiguity in thesis supervision", "poor reflection in supervision" and "ethical problems". The result of this study revealed that there is a need for more attention to planning and defining the supervisory, and research supervision. Also, improvement of the quality of supervisor and students relationship must be considered behind the research context improvement in research supervisory area.

Webly-Supervised Fine-Grained Visual Categorization via Deep Domain Adaptation.

PubMed

Xu, Zhe; Huang, Shaoli; Zhang, Ya; Tao, Dacheng

2018-05-01

Learning visual representations from web data has recently attracted attention for object recognition. Previous studies have mainly focused on overcoming label noise and data bias and have shown promising results by learning directly from web data. However, we argue that it might be better to transfer knowledge from existing human labeling resources to improve performance at nearly no additional cost. In this paper, we propose a new semi-supervised method for learning via web data. Our method has the unique design of exploiting strong supervision, i.e., in addition to standard image-level labels, our method also utilizes detailed annotations including object bounding boxes and part landmarks. By transferring as much knowledge as possible from existing strongly supervised datasets to weakly supervised web images, our method can benefit from sophisticated object recognition algorithms and overcome several typical problems found in webly-supervised learning. We consider the problem of fine-grained visual categorization, in which existing training resources are scarce, as our main research objective. Comprehensive experimentation and extensive analysis demonstrate encouraging performance of the proposed approach, which, at the same time, delivers a new pipeline for fine-grained visual categorization that is likely to be highly effective for real-world applications.

Quantitative Missense Variant Effect Prediction Using Large-Scale Mutagenesis Data.

PubMed

Gray, Vanessa E; Hause, Ronald J; Luebeck, Jens; Shendure, Jay; Fowler, Douglas M

2018-01-24

Large datasets describing the quantitative effects of mutations on protein function are becoming increasingly available. Here, we leverage these datasets to develop Envision, which predicts the magnitude of a missense variant's molecular effect. Envision combines 21,026 variant effect measurements from nine large-scale experimental mutagenesis datasets, a hitherto untapped training resource, with a supervised, stochastic gradient boosting learning algorithm. Envision outperforms other missense variant effect predictors both on large-scale mutagenesis data and on an independent test dataset comprising 2,312 TP53 variants whose effects were measured using a low-throughput approach. This dataset was never used for hyperparameter tuning or model training and thus serves as an independent validation set. Envision prediction accuracy is also more consistent across amino acids than other predictors. Finally, we demonstrate that Envision's performance improves as more large-scale mutagenesis data are incorporated. We precompute Envision predictions for every possible single amino acid variant in human, mouse, frog, zebrafish, fruit fly, worm, and yeast proteomes (https://envision.gs.washington.edu/). Copyright © 2017 Elsevier Inc. All rights reserved.

A device for automatically measuring and supervising the critical care patient's urine output.

PubMed

Otero, Abraham; Palacios, Francisco; Akinfiev, Teodor; Fernández, Roemi

2010-01-01

Critical care units are equipped with commercial monitoring devices capable of sensing patients' physiological parameters and supervising the achievement of the established therapeutic goals. This avoids human errors in this task and considerably decreases the workload of the healthcare staff. However, at present there still is a very relevant physiological parameter that is measured and supervised manually by the critical care units' healthcare staff: urine output. This paper presents a patent-pending device capable of automatically recording and supervising the urine output of a critical care patient. A high precision scale is used to measure the weight of a commercial urine meter. On the scale's pan there is a support frame made up of Bosch profiles that isolates the scale from force transmission from the patient's bed, and guarantees that the urine flows properly through the urine meter input tube. The scale's readings are sent to a PC via Bluetooth where an application supervises the achievement of the therapeutic goals. The device is currently undergoing tests at a research unit associated with the University Hospital of Getafe in Spain.

Stream Discharge and Evapotranspiration Responses to Climate Change and Their Associated Uncertainties in a Large Semi-Arid Basin

NASA Astrophysics Data System (ADS)

Bassam, S.; Ren, J.

2017-12-01

Predicting future water availability in watersheds is very important for proper water resources management, especially in semi-arid regions with scarce water resources. Hydrological models have been considered as powerful tools in predicting future hydrological conditions in watershed systems in the past two decades. Streamflow and evapotranspiration are the two important components in watershed water balance estimation as the former is the most commonly-used indicator of the overall water budget estimation, and the latter is the second biggest component of water budget (biggest outflow from the system). One of the main concerns in watershed scale hydrological modeling is the uncertainties associated with model prediction, which could arise from errors in model parameters and input meteorological data, or errors in model representation of the physics of hydrological processes. Understanding and quantifying these uncertainties are vital to water resources managers for proper decision making based on model predictions. In this study, we evaluated the impacts of different climate change scenarios on the future stream discharge and evapotranspiration, and their associated uncertainties, throughout a large semi-arid basin using a stochastically-calibrated, physically-based, semi-distributed hydrological model. The results of this study could provide valuable insights in applying hydrological models in large scale watersheds, understanding the associated sensitivity and uncertainties in model parameters, and estimating the corresponding impacts on interested hydrological process variables under different climate change scenarios.

Oceans apart, yet connected: Findings from a qualitative study on professional supervision in rural and remote allied health services.

PubMed

Ducat, Wendy; Martin, Priya; Kumar, Saravana; Burge, Vanessa; Abernathy, LuJuana

2016-02-01

Improving the quality and safety of health care in Australia is imperative to ensure the right treatment is delivered to the right person at the right time. Achieving this requires appropriate clinical governance and support for health professionals, including professional supervision. This study investigates the usefulness and effectiveness of and barriers to supervision in rural and remote Queensland. As part of the evaluation of the Allied Health Rural and Remote Training and Support program, a qualitative descriptive study was conducted involving semi-structured interviews with 42 rural or remote allied health professionals, nine operational managers and four supervisors. The interviews explored perspectives on their supervision arrangements, including the perceived usefulness, effect on practice and barriers. Themes of reduced isolation; enhanced professional enthusiasm, growth and commitment to the organisation; enhanced clinical skills, knowledge and confidence; and enhanced patient safety were identified as perceived outcomes of professional supervision. Time, technology and organisational factors were identified as potential facilitators as well as potential barriers to effective supervision. This research provides current evidence on the impact of professional supervision in rural and remote Queensland. A multidimensional model of organisational factors associated with effective supervision in rural and remote settings is proposed identifying positive supervision culture and a good supervisor-supervisee fit as key factors associated with effective arrangements. © 2015 Commonwealth of Australia. Australian Journal of Rural Health published by Wiley Publishing Asia Pty Ltd. on behalf of National Rural Health Alliance Inc.

Enablers of and barriers to high quality clinical supervision among occupational therapists across Queensland in Australia: findings from a qualitative study.

PubMed

Martin, Priya; Kumar, Saravana; Lizarondo, Lucylynn; VanErp, Ans

2015-09-24

Health professionals practising in countries with dispersed populations such as Australia rely on clinical supervision for professional support. While there are directives and guidelines in place to govern clinical supervision, little is known about how it is actually conducted and what makes it effective. The purpose of this study was to explore the enablers of and barriers to high quality clinical supervision among occupational therapists across Queensland in Australia. This qualitative study took place as part of a broader project. Individual, in-depth, semi-structured interviews were conducted with occupational therapy supervisees in Queensland. The interviews explored the enablers of and barriers to high quality clinical supervision in this group. They further explored some findings from the initial quantitative study. Content analysis of the interview data resulted in eight themes. These themes were broadly around the importance of the supervisory relationship, the impact of clinical supervision and the enablers of and barriers to high quality clinical supervision. This study identified a number of factors that were perceived to be associated with high quality clinical supervision. Supervisor-supervisee matching and fit, supervisory relationship and availability of supervisor for support in between clinical supervision sessions appeared to be associated with perceptions of higher quality of clinical supervision received. Some face-to-face contact augmented with telesupervision was found to improve perceptions of the quality of clinical supervision received via telephone. Lastly, dual roles where clinical supervision and line management were provided by the same person were not considered desirable by supervisees. A number of enablers of and barriers to high quality clinical supervision were also identified. With clinical supervision gaining increasing prominence as part of organisational and professional governance, this study provides important lessons for successful and sustainable clinical supervision in practice contexts.

Health Sciences Graduate Students' Perceptions of the Quality of their Supervision: A Measurement Scale

ERIC Educational Resources Information Center

Bravo, Gina; Saint-Mleux, Julie; Dubois, Marie-France

2007-01-01

We developed and evaluated the G3S-SP, a scale measuring health sciences graduate students' perceptions of the quality of their supervision. The scale was developed from a literature review and existing questionnaires. Feedback from health sciences graduate students and supervisors led to a revised version of the scale that was mailed to 215…

A procedural method for the efficient implementation of full-custom VLSI designs

NASA Technical Reports Server (NTRS)

Belk, P.; Hickey, N.

1987-01-01

An imbedded language system for the layout of very large scale integration (VLSI) circuits is examined. It is shown that through the judicious use of this system, a large variety of circuits can be designed with circuit density and performance comparable to traditional full-custom design methods, but with design costs more comparable to semi-custom design methods. The high performance of this methodology is attributable to the flexibility of procedural descriptions of VLSI layouts and to a number of automatic and semi-automatic tools within the system.

Use of Large-Scale Multi-Configuration EMI Measurements to Characterize Subsurface Structures of the Vadose Zone.

NASA Astrophysics Data System (ADS)

Huisman, J. A.; Brogi, C.; Pätzold, S.; Weihermueller, L.; von Hebel, C.; Van Der Kruk, J.; Vereecken, H.

2017-12-01

Subsurface structures of the vadose zone can play a key role in crop yield potential, especially during water stress periods. Geophysical techniques like electromagnetic induction EMI can provide information about dominant shallow subsurface features. However, previous studies with EMI have typically not reached beyond the field scale. We used high-resolution large-scale multi-configuration EMI measurements to characterize patterns of soil structural organization (layering and texture) and their impact on crop productivity at the km2 scale. We collected EMI data on an agricultural area of 1 km2 (102 ha) near Selhausen (NRW, Germany). The area consists of 51 agricultural fields cropped in rotation. Therefore, measurements were collected between April and December 2016, preferably within few days after the harvest. EMI data were automatically filtered, temperature corrected, and interpolated onto a common grid of 1 m resolution. Inspecting the ECa maps, we identified three main sub-areas with different subsurface heterogeneity. We also identified small-scale geomorphological structures as well as anthropogenic activities such as soil management and buried drainage networks. To identify areas with similar subsurface structures, we applied image classification techniques. We fused ECa maps obtained with different coil distances in a multiband image and applied supervised and unsupervised classification methodologies. Both showed good results in reconstructing observed patterns in plant productivity and the subsurface structures associated with them. However, the supervised methodology proved more efficient in classifying the whole study area. In a second step, we selected hundred locations within the study area and obtained a soil profile description with type, depth, and thickness of the soil horizons. Using this ground truth data it was possible to assign a typical soil profile to each of the main classes obtained from the classification. The proposed methodology was effective in producing a high resolution subsurface model in a large and complex study area that extends well beyond the field scale.

Semi-Supervised Multi-View Learning for Gene Network Reconstruction

PubMed Central

Ceci, Michelangelo; Pio, Gianvito; Kuzmanovski, Vladimir; Džeroski, Sašo

2015-01-01

The task of gene regulatory network reconstruction from high-throughput data is receiving increasing attention in recent years. As a consequence, many inference methods for solving this task have been proposed in the literature. It has been recently observed, however, that no single inference method performs optimally across all datasets. It has also been shown that the integration of predictions from multiple inference methods is more robust and shows high performance across diverse datasets. Inspired by this research, in this paper, we propose a machine learning solution which learns to combine predictions from multiple inference methods. While this approach adds additional complexity to the inference process, we expect it would also carry substantial benefits. These would come from the automatic adaptation to patterns on the outputs of individual inference methods, so that it is possible to identify regulatory interactions more reliably when these patterns occur. This article demonstrates the benefits (in terms of accuracy of the reconstructed networks) of the proposed method, which exploits an iterative, semi-supervised ensemble-based algorithm. The algorithm learns to combine the interactions predicted by many different inference methods in the multi-view learning setting. The empirical evaluation of the proposed algorithm on a prokaryotic model organism (E. coli) and on a eukaryotic model organism (S. cerevisiae) clearly shows improved performance over the state of the art methods. The results indicate that gene regulatory network reconstruction for the real datasets is more difficult for S. cerevisiae than for E. coli. The software, all the datasets used in the experiments and all the results are available for download at the following link: http://figshare.com/articles/Semi_supervised_Multi_View_Learning_for_Gene_Network_Reconstruction/1604827. PMID:26641091

A Semi-Supervised Learning Approach to Enhance Health Care Community–Based Question Answering: A Case Study in Alcoholism

PubMed Central

Klabjan, Diego; Jonnalagadda, Siddhartha Reddy

2016-01-01

Background Community-based question answering (CQA) sites play an important role in addressing health information needs. However, a significant number of posted questions remain unanswered. Automatically answering the posted questions can provide a useful source of information for Web-based health communities. Objective In this study, we developed an algorithm to automatically answer health-related questions based on past questions and answers (QA). We also aimed to understand information embedded within Web-based health content that are good features in identifying valid answers. Methods Our proposed algorithm uses information retrieval techniques to identify candidate answers from resolved QA. To rank these candidates, we implemented a semi-supervised leaning algorithm that extracts the best answer to a question. We assessed this approach on a curated corpus from Yahoo! Answers and compared against a rule-based string similarity baseline. Results On our dataset, the semi-supervised learning algorithm has an accuracy of 86.2%. Unified medical language system–based (health related) features used in the model enhance the algorithm’s performance by proximately 8%. A reasonably high rate of accuracy is obtained given that the data are considerably noisy. Important features distinguishing a valid answer from an invalid answer include text length, number of stop words contained in a test question, a distance between the test question and other questions in the corpus, and a number of overlapping health-related terms between questions. Conclusions Overall, our automated QA system based on historical QA pairs is shown to be effective according to the dataset in this case study. It is developed for general use in the health care domain, which can also be applied to other CQA sites. PMID:27485666

Effectiveness of Group Supervision versus Combined Group and Individual Supervision.

ERIC Educational Resources Information Center

Ray, Dee; Altekruse, Michael

2000-01-01

Investigates the effectiveness of different types of supervision (large group, small group, combined group, individual supervision) with counseling students (N=64). Analyses revealed that all supervision formats resulted in similar progress in counselor effectiveness and counselor development. Participants voiced a preference for individual…

Comparison Between a Self-Administered and Supervised Version of a Web-Based Cognitive Test Battery: Results From the NutriNet-Santé Cohort Study.

PubMed

Assmann, Karen E; Bailet, Marion; Lecoffre, Amandine C; Galan, Pilar; Hercberg, Serge; Amieva, Hélène; Kesse-Guyot, Emmanuelle

2016-04-05

Dementia is a major public health problem, and repeated cognitive data from large epidemiological studies could help to develop efficient measures of early prevention. Data collection by self-administered online tools could drastically reduce the logistical and financial burden of such large-scale investigations. In this context, it is important to obtain data concerning the comparability of such new online tools with traditional, supervised modes of cognitive assessment. Our objective was to compare self-administration of the Web-based NutriNet-Santé cognitive test battery (NutriCog) with administration by a neuropsychologist. The test battery included four tests, measuring, among others aspects, psychomotor speed, attention, executive function, episodic memory, working memory, and associative memory. Both versions of the cognitive battery were completed by 189 volunteers (either self-administered version first, n=99, or supervised version first, n=90). Subjects also completed a satisfaction questionnaire. Concordance was assessed by Spearman correlation. Agreement between both versions varied according to the investigated cognitive task and outcome variable. Spearman correlations ranged between .42 and .73. Moreover, a majority of participants responded that they "absolutely" or "rather" agreed that the duration of the self-administered battery was acceptable (184/185, 99.5%), that the tasks were amusing (162/185, 87.6%), that the instructions were sufficiently detailed (168/185; 90.8%) and understandable (164/185, 88.7%), and that they had overall enjoyed the test battery (182/185, 98.4%). The self-administered version of the Web-based NutriCog cognitive test battery provided similar information as the supervised version. Thus, integrating repeated cognitive evaluations into large cohorts via the implementation of self-administered online versions of traditional test batteries appears to be feasible.

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

PubMed

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

2015-10-01

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

A Scale for Evaluating Practicum Students in Counseling and Supervision

ERIC Educational Resources Information Center

Myrick, Robert D.; Kelly, F. Donald, Jr.

1971-01-01

This article presents an instrument, the Counselor Evaluation Rating Scale, which can be used as an aid in the systematic evaluation of a student counselor in a supervised counseling experience. Development of the CERS and its reliability are discussed. (Author)

Feature extraction based on semi-supervised kernel Marginal Fisher analysis and its application in bearing fault diagnosis

NASA Astrophysics Data System (ADS)

Jiang, Li; Xuan, Jianping; Shi, Tielin

2013-12-01

Generally, the vibration signals of faulty machinery are non-stationary and nonlinear under complicated operating conditions. Therefore, it is a big challenge for machinery fault diagnosis to extract optimal features for improving classification accuracy. This paper proposes semi-supervised kernel Marginal Fisher analysis (SSKMFA) for feature extraction, which can discover the intrinsic manifold structure of dataset, and simultaneously consider the intra-class compactness and the inter-class separability. Based on SSKMFA, a novel approach to fault diagnosis is put forward and applied to fault recognition of rolling bearings. SSKMFA directly extracts the low-dimensional characteristics from the raw high-dimensional vibration signals, by exploiting the inherent manifold structure of both labeled and unlabeled samples. Subsequently, the optimal low-dimensional features are fed into the simplest K-nearest neighbor (KNN) classifier to recognize different fault categories and severities of bearings. The experimental results demonstrate that the proposed approach improves the fault recognition performance and outperforms the other four feature extraction methods.

Semi-supervised vibration-based classification and condition monitoring of compressors

NASA Astrophysics Data System (ADS)

Potočnik, Primož; Govekar, Edvard

2017-09-01

Semi-supervised vibration-based classification and condition monitoring of the reciprocating compressors installed in refrigeration appliances is proposed in this paper. The method addresses the problem of industrial condition monitoring where prior class definitions are often not available or difficult to obtain from local experts. The proposed method combines feature extraction, principal component analysis, and statistical analysis for the extraction of initial class representatives, and compares the capability of various classification methods, including discriminant analysis (DA), neural networks (NN), support vector machines (SVM), and extreme learning machines (ELM). The use of the method is demonstrated on a case study which was based on industrially acquired vibration measurements of reciprocating compressors during the production of refrigeration appliances. The paper presents a comparative qualitative analysis of the applied classifiers, confirming the good performance of several nonlinear classifiers. If the model parameters are properly selected, then very good classification performance can be obtained from NN trained by Bayesian regularization, SVM and ELM classifiers. The method can be effectively applied for the industrial condition monitoring of compressors.

An efficient semi-supervised community detection framework in social networks.

PubMed

Li, Zhen; Gong, Yong; Pan, Zhisong; Hu, Guyu

2017-01-01

Community detection is an important tasks across a number of research fields including social science, biology, and physics. In the real world, topology information alone is often inadequate to accurately find out community structure due to its sparsity and noise. The potential useful prior information such as pairwise constraints which contain must-link and cannot-link constraints can be obtained from domain knowledge in many applications. Thus, combining network topology with prior information to improve the community detection accuracy is promising. Previous methods mainly utilize the must-link constraints while cannot make full use of cannot-link constraints. In this paper, we propose a semi-supervised community detection framework which can effectively incorporate two types of pairwise constraints into the detection process. Particularly, must-link and cannot-link constraints are represented as positive and negative links, and we encode them by adding different graph regularization terms to penalize closeness of the nodes. Experiments on multiple real-world datasets show that the proposed framework significantly improves the accuracy of community detection.

Supervised Learning Using Spike-Timing-Dependent Plasticity of Memristive Synapses.

PubMed

Nishitani, Yu; Kaneko, Yukihiro; Ueda, Michihito

2015-12-01

We propose a supervised learning model that enables error backpropagation for spiking neural network hardware. The method is modeled by modifying an existing model to suit the hardware implementation. An example of a network circuit for the model is also presented. In this circuit, a three-terminal ferroelectric memristor (3T-FeMEM), which is a field-effect transistor with a gate insulator composed of ferroelectric materials, is used as an electric synapse device to store the analog synaptic weight. Our model can be implemented by reflecting the network error to the write voltage of the 3T-FeMEMs and introducing a spike-timing-dependent learning function to the device. An XOR problem was successfully demonstrated as a benchmark learning by numerical simulations using the circuit properties to estimate the learning performance. In principle, the learning time per step of this supervised learning model and the circuit is independent of the number of neurons in each layer, promising a high-speed and low-power calculation in large-scale neural networks.

On the Fidelity of Semi-distributed Hydrologic Model Simulations for Large Scale Catchment Applications

NASA Astrophysics Data System (ADS)

Ajami, H.; Sharma, A.; Lakshmi, V.

2017-12-01

Application of semi-distributed hydrologic modeling frameworks is a viable alternative to fully distributed hyper-resolution hydrologic models due to computational efficiency and resolving fine-scale spatial structure of hydrologic fluxes and states. However, fidelity of semi-distributed model simulations is impacted by (1) formulation of hydrologic response units (HRUs), and (2) aggregation of catchment properties for formulating simulation elements. Here, we evaluate the performance of a recently developed Soil Moisture and Runoff simulation Toolkit (SMART) for large catchment scale simulations. In SMART, topologically connected HRUs are delineated using thresholds obtained from topographic and geomorphic analysis of a catchment, and simulation elements are equivalent cross sections (ECS) representative of a hillslope in first order sub-basins. Earlier investigations have shown that formulation of ECSs at the scale of a first order sub-basin reduces computational time significantly without compromising simulation accuracy. However, the implementation of this approach has not been fully explored for catchment scale simulations. To assess SMART performance, we set-up the model over the Little Washita watershed in Oklahoma. Model evaluations using in-situ soil moisture observations show satisfactory model performance. In addition, we evaluated the performance of a number of soil moisture disaggregation schemes recently developed to provide spatially explicit soil moisture outputs at fine scale resolution. Our results illustrate that the statistical disaggregation scheme performs significantly better than the methods based on topographic data. Future work is focused on assessing the performance of SMART using remotely sensed soil moisture observations using spatially based model evaluation metrics.

Weakly Supervised Segmentation-Aided Classification of Urban Scenes from 3d LIDAR Point Clouds

NASA Astrophysics Data System (ADS)

Guinard, S.; Landrieu, L.

2017-05-01

We consider the problem of the semantic classification of 3D LiDAR point clouds obtained from urban scenes when the training set is limited. We propose a non-parametric segmentation model for urban scenes composed of anthropic objects of simple shapes, partionning the scene into geometrically-homogeneous segments which size is determined by the local complexity. This segmentation can be integrated into a conditional random field classifier (CRF) in order to capture the high-level structure of the scene. For each cluster, this allows us to aggregate the noisy predictions of a weakly-supervised classifier to produce a higher confidence data term. We demonstrate the improvement provided by our method over two publicly-available large-scale data sets.

Deep Neural Network Based Supervised Speech Segregation Generalizes to Novel Noises through Large-scale Training

DTIC Science & Technology

2015-01-01

Table 2: Segregation results in terms of STOI on a variety of novel noises (SNR=-2 dB) Babble-20 Cafeteria Factory Babble-100 Living Room Cafe Park...NOISEX-92 corpus [13], and a living room, a cafe and a park noise from the DEMAND corpus [12]. To put the performance of the noise-independent model in

Value, Cost, and Sharing: Open Issues in Constrained Clustering

NASA Technical Reports Server (NTRS)

Wagstaff, Kiri L.

2006-01-01

Clustering is an important tool for data mining, since it can identify major patterns or trends without any supervision (labeled data). Over the past five years, semi-supervised (constrained) clustering methods have become very popular. These methods began with incorporating pairwise constraints and have developed into more general methods that can learn appropriate distance metrics. However, several important open questions have arisen about which constraints are most useful, how they can be actively acquired, and when and how they should be propagated to neighboring points. This position paper describes these open questions and suggests future directions for constrained clustering research.

10 CFR 712.3 - Definitions.

Code of Federal Regulations, 2010 CFR

2010-01-01

... derogatory under the criteria listed in 10 CFR part 710, subpart A. Semi-structured interview means an interview by a Designated Psychologist, or a psychologist under his or her supervision, who has the latitude to vary the focus and content of the questions depending on the interviewee's responses. Site...

10 CFR 712.3 - Definitions.

Code of Federal Regulations, 2011 CFR

2011-01-01

... derogatory under the criteria listed in 10 CFR part 710, subpart A. Semi-structured interview means an interview by a Designated Psychologist, or a psychologist under his or her supervision, who has the latitude to vary the focus and content of the questions depending on the interviewee's responses. Site...

Rapid Training of Information Extraction with Local and Global Data Views

DTIC Science & Technology

2012-05-01

relation type extension system based on active learning a relation type extension system based on semi-supervised learning, and a crossdomain...bootstrapping system for domain adaptive named entity extraction. The active learning procedure adopts features extracted at the sentence level as the local

Spectral Learning for Supervised Topic Models.

PubMed

Ren, Yong; Wang, Yining; Zhu, Jun

2018-03-01

Supervised topic models simultaneously model the latent topic structure of large collections of documents and a response variable associated with each document. Existing inference methods are based on variational approximation or Monte Carlo sampling, which often suffers from the local minimum defect. Spectral methods have been applied to learn unsupervised topic models, such as latent Dirichlet allocation (LDA), with provable guarantees. This paper investigates the possibility of applying spectral methods to recover the parameters of supervised LDA (sLDA). We first present a two-stage spectral method, which recovers the parameters of LDA followed by a power update method to recover the regression model parameters. Then, we further present a single-phase spectral algorithm to jointly recover the topic distribution matrix as well as the regression weights. Our spectral algorithms are provably correct and computationally efficient. We prove a sample complexity bound for each algorithm and subsequently derive a sufficient condition for the identifiability of sLDA. Thorough experiments on synthetic and real-world datasets verify the theory and demonstrate the practical effectiveness of the spectral algorithms. In fact, our results on a large-scale review rating dataset demonstrate that our single-phase spectral algorithm alone gets comparable or even better performance than state-of-the-art methods, while previous work on spectral methods has rarely reported such promising performance.

A spectral radius scaling semi-implicit iterative time stepping method for reactive flow simulations with detailed chemistry

NASA Astrophysics Data System (ADS)

Xie, Qing; Xiao, Zhixiang; Ren, Zhuyin

2018-09-01

A spectral radius scaling semi-implicit time stepping scheme has been developed for simulating unsteady compressible reactive flows with detailed chemistry, in which the spectral radius in the LUSGS scheme has been augmented to account for viscous/diffusive and reactive terms and a scalar matrix is proposed to approximate the chemical Jacobian using the minimum species destruction timescale. The performance of the semi-implicit scheme, together with a third-order explicit Runge-Kutta scheme and a Strang splitting scheme, have been investigated in auto-ignition and laminar premixed and nonpremixed flames of three representative fuels, e.g., hydrogen, methane, and n-heptane. Results show that the minimum species destruction time scale can well represent the smallest chemical time scale in reactive flows and the proposed scheme can significantly increase the allowable time steps in simulations. The scheme is stable when the time step is as large as 10 μs, which is about three to five orders of magnitude larger than the smallest time scales in various tests considered. For the test flames considered, the semi-implicit scheme achieves second order of accuracy in time. Moreover, the errors in quantities of interest are smaller than those from the Strang splitting scheme indicating the accuracy gain when the reaction and transport terms are solved coupled. Results also show that the relative efficiency of different schemes depends on fuel mechanisms and test flames. When the minimum time scale in reactive flows is governed by transport processes instead of chemical reactions, the proposed semi-implicit scheme is more efficient than the splitting scheme. Otherwise, the relative efficiency depends on the cost in sub-iterations for convergence within each time step and in the integration for chemistry substep. Then, the capability of the compressible reacting flow solver and the proposed semi-implicit scheme is demonstrated for capturing the hydrogen detonation waves. Finally, the performance of the proposed method is demonstrated in a two-dimensional hydrogen/air diffusion flame.

Predicting protein complexes using a supervised learning method combined with local structural information.

PubMed

Dong, Yadong; Sun, Yongqi; Qin, Chao

2018-01-01

The existing protein complex detection methods can be broadly divided into two categories: unsupervised and supervised learning methods. Most of the unsupervised learning methods assume that protein complexes are in dense regions of protein-protein interaction (PPI) networks even though many true complexes are not dense subgraphs. Supervised learning methods utilize the informative properties of known complexes; they often extract features from existing complexes and then use the features to train a classification model. The trained model is used to guide the search process for new complexes. However, insufficient extracted features, noise in the PPI data and the incompleteness of complex data make the classification model imprecise. Consequently, the classification model is not sufficient for guiding the detection of complexes. Therefore, we propose a new robust score function that combines the classification model with local structural information. Based on the score function, we provide a search method that works both forwards and backwards. The results from experiments on six benchmark PPI datasets and three protein complex datasets show that our approach can achieve better performance compared with the state-of-the-art supervised, semi-supervised and unsupervised methods for protein complex detection, occasionally significantly outperforming such methods.

A new semi-supervised learning model combined with Cox and SP-AFT models in cancer survival analysis.

PubMed

Chai, Hua; Li, Zi-Na; Meng, De-Yu; Xia, Liang-Yong; Liang, Yong

2017-10-12

Gene selection is an attractive and important task in cancer survival analysis. Most existing supervised learning methods can only use the labeled biological data, while the censored data (weakly labeled data) far more than the labeled data are ignored in model building. Trying to utilize such information in the censored data, a semi-supervised learning framework (Cox-AFT model) combined with Cox proportional hazard (Cox) and accelerated failure time (AFT) model was used in cancer research, which has better performance than the single Cox or AFT model. This method, however, is easily affected by noise. To alleviate this problem, in this paper we combine the Cox-AFT model with self-paced learning (SPL) method to more effectively employ the information in the censored data in a self-learning way. SPL is a kind of reliable and stable learning mechanism, which is recently proposed for simulating the human learning process to help the AFT model automatically identify and include samples of high confidence into training, minimizing interference from high noise. Utilizing the SPL method produces two direct advantages: (1) The utilization of censored data is further promoted; (2) the noise delivered to the model is greatly decreased. The experimental results demonstrate the effectiveness of the proposed model compared to the traditional Cox-AFT model.

Statistical wave climate projections for coastal impact assessments

NASA Astrophysics Data System (ADS)

Camus, P.; Losada, I. J.; Izaguirre, C.; Espejo, A.; Menéndez, M.; Pérez, J.

2017-09-01

Global multimodel wave climate projections are obtained at 1.0° × 1.0° scale from 30 Coupled Model Intercomparison Project Phase 5 (CMIP5) global circulation model (GCM) realizations. A semi-supervised weather-typing approach based on a characterization of the ocean wave generation areas and the historical wave information from the recent GOW2 database are used to train the statistical model. This framework is also applied to obtain high resolution projections of coastal wave climate and coastal impacts as port operability and coastal flooding. Regional projections are estimated using the collection of weather types at spacing of 1.0°. This assumption is feasible because the predictor is defined based on the wave generation area and the classification is guided by the local wave climate. The assessment of future changes in coastal impacts is based on direct downscaling of indicators defined by empirical formulations (total water level for coastal flooding and number of hours per year with overtopping for port operability). Global multimodel projections of the significant wave height and peak period are consistent with changes obtained in previous studies. Statistical confidence of expected changes is obtained due to the large number of GCMs to construct the ensemble. The proposed methodology is proved to be flexible to project wave climate at different spatial scales. Regional changes of additional variables as wave direction or other statistics can be estimated from the future empirical distribution with extreme values restricted to high percentiles (i.e., 95th, 99th percentiles). The statistical framework can also be applied to evaluate regional coastal impacts integrating changes in storminess and sea level rise.

Semi-Supervised Clustering for High-Dimensional and Sparse Features

ERIC Educational Resources Information Center

Yan, Su

2010-01-01

Clustering is one of the most common data mining tasks, used frequently for data organization and analysis in various application domains. Traditional machine learning approaches to clustering are fully automated and unsupervised where class labels are unknown a priori. In real application domains, however, some "weak" form of side…

Generating region proposals for histopathological whole slide image retrieval.

PubMed

Ma, Yibing; Jiang, Zhiguo; Zhang, Haopeng; Xie, Fengying; Zheng, Yushan; Shi, Huaqiang; Zhao, Yu; Shi, Jun

2018-06-01

Content-based image retrieval is an effective method for histopathological image analysis. However, given a database of huge whole slide images (WSIs), acquiring appropriate region-of-interests (ROIs) for training is significant and difficult. Moreover, histopathological images can only be annotated by pathologists, resulting in the lack of labeling information. Therefore, it is an important and challenging task to generate ROIs from WSI and retrieve image with few labels. This paper presents a novel unsupervised region proposing method for histopathological WSI based on Selective Search. Specifically, the WSI is over-segmented into regions which are hierarchically merged until the WSI becomes a single region. Nucleus-oriented similarity measures for region mergence and Nucleus-Cytoplasm color space for histopathological image are specially defined to generate accurate region proposals. Additionally, we propose a new semi-supervised hashing method for image retrieval. The semantic features of images are extracted with Latent Dirichlet Allocation and transformed into binary hashing codes with Supervised Hashing. The methods are tested on a large-scale multi-class database of breast histopathological WSIs. The results demonstrate that for one WSI, our region proposing method can generate 7.3 thousand contoured regions which fit well with 95.8% of the ROIs annotated by pathologists. The proposed hashing method can retrieve a query image among 136 thousand images in 0.29 s and reach precision of 91% with only 10% of images labeled. The unsupervised region proposing method can generate regions as predictions of lesions in histopathological WSI. The region proposals can also serve as the training samples to train machine-learning models for image retrieval. The proposed hashing method can achieve fast and precise image retrieval with small amount of labels. Furthermore, the proposed methods can be potentially applied in online computer-aided-diagnosis systems. Copyright © 2018 Elsevier B.V. All rights reserved.

Community cooperatives and insecticide-treated materials for malaria control: a new experience in Latin America

PubMed Central

Kroeger, Axel; Aviñna, Ana; Ordoñnez-Gonzalez, José; Escandon, Celia

2002-01-01

Background and objectives Insecticide-treated materials (ITMs) are effective in substantially reducing the burden of malaria and other vector-borne diseases; but how can high coverage rates of ITMs be achieved and maintained? In south Mexico and on the Pacific and Atlantic coasts of Colombia 14 community-based cooperatives offering three different kinds of ITM services (sale of impregnation services; sale of impregnated nets; production of nets and sale of impregnated nets) were formed and supervised by a national health service (IMSS-SOLIDARIDAD, Mexico) and by an academic institution (the Colombian Institute of Tropical Medicine) along with local district health services. The objectives of this research were to analyse the processes and results of this approach and to identify the favourable and limiting factors. Methods The methods used for data collection and analysis were group discussions, individual and semi-structured interviews with users and non-users of ITMs, individual in-depth interviews with cooperative members and supervisors, checks of sales book and observation of impregnation services. Results Coverage with unimpregnated nets was above 50% in all study areas. The fastest increase of ITM coverage was achieved through the exclusive sale of impregnation services. Low-cost social marketing techniques were used to increase demand. The large-scale production of nets in two cooperatives was only possible with the aid of an international NGO which ordered impregnated bednets for their target group. A number of favourable and limiting factors relating to the success of ITM cooperatives were identified. Of particular importance for the more successful Mexican cooperatives were: a) support by health services, b) smaller size, c) lesser desire for quick returns and d) lower ITM unit costs. Conclusions ITM community cooperatives supported and supervised by the health services have good potential in the Latin American context for achieving and maintaining high impregnation rates. PMID:12473181

SU-E-J-107: Supervised Learning Model of Aligned Collagen for Human Breast Carcinoma Prognosis

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

Bredfeldt, J; Liu, Y; Conklin, M

Purpose: Our goal is to develop and apply a set of optical and computational tools to enable large-scale investigations of the interaction between collagen and tumor cells. Methods: We have built a novel imaging system for automating the capture of whole-slide second harmonic generation (SHG) images of collagen in registry with bright field (BF) images of hematoxylin and eosin stained tissue. To analyze our images, we have integrated a suite of supervised learning tools that semi-automatically model and score collagen interactions with tumor cells via a variety of metrics, a method we call Electronic Tumor Associated Collagen Signatures (eTACS). Thismore » group of tools first segments regions of epithelial cells and collagen fibers from BF and SHG images respectively. We then associate fibers with groups of epithelial cells and finally compute features based on the angle of interaction and density of the collagen surrounding the epithelial cell clusters. These features are then processed with a support vector machine to separate cancer patients into high and low risk groups. Results: We validated our model by showing that eTACS produces classifications that have statistically significant correlation with manual classifications. In addition, our system generated classification scores that accurately predicted breast cancer patient survival in a cohort of 196 patients. Feature rank analysis revealed that TACS positive fibers are more well aligned with each other, generally lower density, and terminate within or near groups of epithelial cells. Conclusion: We are working to apply our model to predict survival in larger cohorts of breast cancer patients with a diversity of breast cancer types, predict response to treatments such as COX2 inhibitors, and to study collagen architecture changes in other cancer types. In the future, our system may be used to provide metastatic potential information to cancer patients to augment existing clinical assays.« less

Achieving online consent to participation in large-scale gene-environment studies: a tangible destination.

PubMed

Wood, Fiona; Kowalczuk, Jenny; Elwyn, Glyn; Mitchell, Clive; Gallacher, John

2011-08-01

Population based genetics studies are dependent on large numbers of individuals in the pursuit of small effect sizes. Recruiting and consenting a large number of participants is both costly and time consuming. We explored whether an online consent process for large-scale genetics studies is acceptable for prospective participants using an example online genetics study. We conducted semi-structured interviews with 42 members of the public stratified by age group, gender and newspaper readership (a measure of social status). Respondents were asked to use a website designed to recruit for a large-scale genetic study. After using the website a semi-structured interview was conducted to explore opinions and any issues they would have. Responses were analysed using thematic content analysis. The majority of respondents said they would take part in the research (32/42). Those who said they would decline to participate saw fewer benefits from the research, wanted more information and expressed a greater number of concerns about the study. Younger respondents had concerns over time commitment. Middle aged respondents were concerned about privacy and security. Older respondents were more altruistic in their motivation to participate. Common themes included trust in the authenticity of the website, security of personal data, curiosity about their own genetic profile, operational concerns and a desire for more information about the research. Online consent to large-scale genetic studies is likely to be acceptable to the public. The online consent process must establish trust quickly and effectively by asserting authenticity and credentials, and provide access to a range of information to suit different information preferences.

Large Scale Processes and Extreme Floods in Brazil

NASA Astrophysics Data System (ADS)

Ribeiro Lima, C. H.; AghaKouchak, A.; Lall, U.

2016-12-01

Persistent large scale anomalies in the atmospheric circulation and ocean state have been associated with heavy rainfall and extreme floods in water basins of different sizes across the world. Such studies have emerged in the last years as a new tool to improve the traditional, stationary based approach in flood frequency analysis and flood prediction. Here we seek to advance previous studies by evaluating the dominance of large scale processes (e.g. atmospheric rivers/moisture transport) over local processes (e.g. local convection) in producing floods. We consider flood-prone regions in Brazil as case studies and the role of large scale climate processes in generating extreme floods in such regions is explored by means of observed streamflow, reanalysis data and machine learning methods. The dynamics of the large scale atmospheric circulation in the days prior to the flood events are evaluated based on the vertically integrated moisture flux and its divergence field, which are interpreted in a low-dimensional space as obtained by machine learning techniques, particularly supervised kernel principal component analysis. In such reduced dimensional space, clusters are obtained in order to better understand the role of regional moisture recycling or teleconnected moisture in producing floods of a given magnitude. The convective available potential energy (CAPE) is also used as a measure of local convection activities. We investigate for individual sites the exceedance probability in which large scale atmospheric fluxes dominate the flood process. Finally, we analyze regional patterns of floods and how the scaling law of floods with drainage area responds to changes in the climate forcing mechanisms (e.g. local vs large scale).

Self-reported needs for improving the supervision competence of PhD supervisors from the medical sciences in Denmark.

PubMed

Raffing, Rie; Jensen, Thor Bern; Tønnesen, Hanne

2017-10-23

Quality of supervision is a major predictor for successful PhD projects. A survey showed that almost all PhD students in the Health Sciences in Denmark indicated that good supervision was important for the completion of their PhD study. Interestingly, approximately half of the students who withdrew from their program had experienced insufficient supervision. This led the Research Education Committee at the University of Copenhagen to recommend that supervisors further develop their supervision competence. The aim of this study was to explore PhD supervisors' self-reported needs and wishes regarding the content of a new program in supervision, with a special focus on the supervision of PhD students in medical fields. A semi-structured interview guide was developed, and 20 PhD supervisors from the Graduate School of Health and Medical Sciences at the Faculty of Health and Medical Sciences at the University of Copenhagen were interviewed. Empirical data were analysed using qualitative methods of analysis. Overall, the results indicated a general interest in improved competence and development of a new supervision programme. Those who were not interested argued that, due to their extensive experience with supervision, they had no need to participate in such a programme. The analysis revealed seven overall themes to be included in the course. The clinical context offers PhD supervisors additional challenges that include the following sub-themes: patient recruitment, writing the first article, agreements and scheduled appointments and two main groups of students, in addition to the main themes. The PhD supervisors reported the clear need and desire for a competence enhancement programme targeting the supervision of PhD students at the Faculty of Health and Medical Sciences. Supervision in the clinical context appeared to require additional competence. The Scientific Ethical Committee for the Capital Region of Denmark. Number: H-3-2010-101, date: 2010.09.29.

Spatial distribution of GRBs and large scale structure of the Universe

NASA Astrophysics Data System (ADS)

Bagoly, Zsolt; Rácz, István I.; Balázs, Lajos G.; Tóth, L. Viktor; Horváth, István

We studied the space distribution of the starburst galaxies from Millennium XXL database at z = 0.82. We examined the starburst distribution in the classical Millennium I (De Lucia et al. (2006)) using a semi-analytical model for the genesis of the galaxies. We simulated a starburst galaxies sample with Markov Chain Monte Carlo method. The connection between the large scale structures homogenous and starburst groups distribution (Kofman and Shandarin 1998), Suhhonenko et al. (2011), Liivamägi et al. (2012), Park et al. (2012), Horvath et al. (2014), Horvath et al. (2015)) on a defined scale were checked too.

Revealing "The Space Between": Creating an Observation Scale to Understand Infant Mental Health Reflective Supervision

ERIC Educational Resources Information Center

Watson, Christopher L.; Harrison, Mary E.; Hennes, Jill E.; Harris, Maren M.

2016-01-01

The Reflective Interaction Observation Scale (RIOS) describes and operationalizes the nature of the interactions between a supervisor and supervisee(s) during reflective supervision. Developed in collaboration among researchers and clinicians from the University of Minnesota, the Minnesota Association for Infant and Early Childhood Mental Health,…

Effective Yard Supervision: From Needs Assessment to Customized Training

ERIC Educational Resources Information Center

Sharkey, Jill D.; Hunnicutt, Kayleigh L.; Mayworm, Ashley M.; Schiedel, K. Chris; Calcagnotto, Leandro

2014-01-01

Most educational scholars agree that appropriate supervision of children is critical for positive youth development. Supervision is especially important during situations where children have a large degree of freedom and unstructured interaction, such as during recess. Despite the apparent importance of supervision of children at recess, there is…

Assessing support for supervised injection services among community stakeholders in London, Canada.

PubMed

Bardwell, Geoff; Scheim, Ayden; Mitra, Sanjana; Kerr, Thomas

2017-10-01

Few qualitative studies have examined support for supervised injection services (SIS), and these have been restricted to large cities. This study aimed to assess support for SIS among a diverse representation of community stakeholders in London, a mid-sized city in southwestern Ontario, Canada. This qualitative study was undertaken as part of the Ontario Integrated Supervised Injection Services Feasibility Study. We used purposive sampling methods to recruit a diversity of key informants (n=20) from five sectors: healthcare; social services; government and municipal services; police and emergency services; and the business and community sector. Interview data, collected via one-to-one semi structured interviews, were coded and analyzed using thematic analyses through NVivo 10 software. Interview participants unanimously supported the implementation of SIS in London. However, participant support for SIS was met with some implementation-related preferences and/or conditions. These included centralization or decentralization of SIS; accessibility of SIS for people who inject drugs; proximity of SIS to interview participants; and other services and strategies offered alongside SIS. The results of this study challenge the assumptions that smaller cities like London may be unlikely to support SIS. Community stakeholders were supportive of the implementation of SIS with some preferences or conditions. Interview participants had differing perspectives, but ultimately supported similar end goals of accessibility and reducing community harms associated with injection drug use. Future research and SIS programming should consider these factors when determining optimal service delivery in ways that increase support from a diversity of community stakeholders. Copyright © 2017 Elsevier B.V. All rights reserved.

Initial experiences and innovations in supervising community health workers for maternal, newborn, and child health in Morogoro region, Tanzania.

PubMed

Roberton, Timothy; Applegate, Jennifer; Lefevre, Amnesty E; Mosha, Idda; Cooper, Chelsea M; Silverman, Marissa; Feldhaus, Isabelle; Chebet, Joy J; Mpembeni, Rose; Semu, Helen; Killewo, Japhet; Winch, Peter; Baqui, Abdullah H; George, Asha S

2015-04-09

Supervision is meant to improve the performance and motivation of community health workers (CHWs). However, most evidence on supervision relates to facility health workers. The Integrated Maternal, Newborn, and Child Health (MNCH) Program in Morogoro region, Tanzania, implemented a CHW pilot with a cascade supervision model where facility health workers were trained in supportive supervision for volunteer CHWs, supported by regional and district staff, and with village leaders to further support CHWs. We examine the initial experiences of CHWs, their supervisors, and village leaders to understand the strengths and challenges of such a supervision model for CHWs. Quantitative and qualitative data were collected concurrently from CHWs, supervisors, and village leaders. A survey was administered to 228 (96%) of the CHWs in the Integrated MNCH Program and semi-structured interviews were conducted with 15 CHWs, 8 supervisors, and 15 village leaders purposefully sampled to represent different actor perspectives from health centre catchment villages in Morogoro region. Descriptive statistics analysed the frequency and content of CHW supervision, while thematic content analysis explored CHW, supervisor, and village leader experiences with CHW supervision. CHWs meet with their facility-based supervisors an average of 1.2 times per month. CHWs value supervision and appreciate the sense of legitimacy that arises when supervisors visit them in their village. Village leaders and district staff are engaged and committed to supporting CHWs. Despite these successes, facility-based supervisors visit CHWs in their village an average of only once every 2.8 months, CHWs and supervisors still see supervision primarily as an opportunity to check reports, and meetings with district staff are infrequent and not well scheduled. Supervision of CHWs could be strengthened by streamlining supervision protocols to focus less on report checking and more on problem solving and skills development. Facility health workers, while important for technical oversight, may not be the best mentors for certain tasks such as community relationship-building. We suggest further exploring CHW supervision innovations, such as an enhanced role for community actors, who may be more suitable to support CHWs engaged primarily in health promotion than scarce and over-worked facility health workers.

Hypotheses generation as supervised link discovery with automated class labeling on large-scale biomedical concept networks

PubMed Central

2012-01-01

Computational approaches to generate hypotheses from biomedical literature have been studied intensively in recent years. Nevertheless, it still remains a challenge to automatically discover novel, cross-silo biomedical hypotheses from large-scale literature repositories. In order to address this challenge, we first model a biomedical literature repository as a comprehensive network of biomedical concepts and formulate hypotheses generation as a process of link discovery on the concept network. We extract the relevant information from the biomedical literature corpus and generate a concept network and concept-author map on a cluster using Map-Reduce frame-work. We extract a set of heterogeneous features such as random walk based features, neighborhood features and common author features. The potential number of links to consider for the possibility of link discovery is large in our concept network and to address the scalability problem, the features from a concept network are extracted using a cluster with Map-Reduce framework. We further model link discovery as a classification problem carried out on a training data set automatically extracted from two network snapshots taken in two consecutive time duration. A set of heterogeneous features, which cover both topological and semantic features derived from the concept network, have been studied with respect to their impacts on the accuracy of the proposed supervised link discovery process. A case study of hypotheses generation based on the proposed method has been presented in the paper. PMID:22759614

Drift in Children's Categories: When Experienced Distributions Conflict with Prior Learning

ERIC Educational Resources Information Center

Kalish, Charles W.; Zhu, XiaoJin; Rogers, Timothy T.

2015-01-01

Psychological intuitions about natural category structure do not always correspond to the true structure of the world. The current study explores young children's responses to conflict between intuitive structure and authoritative feedback using a semi-supervised learning (Zhu et al., 2007) paradigm. In three experiments, 160 children between the…

Application of graph-based semi-supervised learning for development of cyber COP and network intrusion detection

NASA Astrophysics Data System (ADS)

Levchuk, Georgiy; Colonna-Romano, John; Eslami, Mohammed

2017-05-01

The United States increasingly relies on cyber-physical systems to conduct military and commercial operations. Attacks on these systems have increased dramatically around the globe. The attackers constantly change their methods, making state-of-the-art commercial and military intrusion detection systems ineffective. In this paper, we present a model to identify functional behavior of network devices from netflow traces. Our model includes two innovations. First, we define novel features for a host IP using detection of application graph patterns in IP's host graph constructed from 5-min aggregated packet flows. Second, we present the first application, to the best of our knowledge, of Graph Semi-Supervised Learning (GSSL) to the space of IP behavior classification. Using a cyber-attack dataset collected from NetFlow packet traces, we show that GSSL trained with only 20% of the data achieves higher attack detection rates than Support Vector Machines (SVM) and Naïve Bayes (NB) classifiers trained with 80% of data points. We also show how to improve detection quality by filtering out web browsing data, and conclude with discussion of future research directions.

Modeling EEG Waveforms with Semi-Supervised Deep Belief Nets: Fast Classification and Anomaly Measurement

PubMed Central

Wulsin, D. F.; Gupta, J. R.; Mani, R.; Blanco, J. A.; Litt, B.

2011-01-01

Clinical electroencephalography (EEG) records vast amounts of human complex data yet is still reviewed primarily by human readers. Deep Belief Nets (DBNs) are a relatively new type of multi-layer neural network commonly tested on two-dimensional image data, but are rarely applied to times-series data such as EEG. We apply DBNs in a semi-supervised paradigm to model EEG waveforms for classification and anomaly detection. DBN performance was comparable to standard classifiers on our EEG dataset, and classification time was found to be 1.7 to 103.7 times faster than the other high-performing classifiers. We demonstrate how the unsupervised step of DBN learning produces an autoencoder that can naturally be used in anomaly measurement. We compare the use of raw, unprocessed data—a rarity in automated physiological waveform analysis—to hand-chosen features and find that raw data produces comparable classification and better anomaly measurement performance. These results indicate that DBNs and raw data inputs may be more effective for online automated EEG waveform recognition than other common techniques. PMID:21525569

Nonlinear Deep Kernel Learning for Image Annotation.

PubMed

Jiu, Mingyuan; Sahbi, Hichem

2017-02-08

Multiple kernel learning (MKL) is a widely used technique for kernel design. Its principle consists in learning, for a given support vector classifier, the most suitable convex (or sparse) linear combination of standard elementary kernels. However, these combinations are shallow and often powerless to capture the actual similarity between highly semantic data, especially for challenging classification tasks such as image annotation. In this paper, we redefine multiple kernels using deep multi-layer networks. In this new contribution, a deep multiple kernel is recursively defined as a multi-layered combination of nonlinear activation functions, each one involves a combination of several elementary or intermediate kernels, and results into a positive semi-definite deep kernel. We propose four different frameworks in order to learn the weights of these networks: supervised, unsupervised, kernel-based semisupervised and Laplacian-based semi-supervised. When plugged into support vector machines (SVMs), the resulting deep kernel networks show clear gain, compared to several shallow kernels for the task of image annotation. Extensive experiments and analysis on the challenging ImageCLEF photo annotation benchmark, the COREL5k database and the Banana dataset validate the effectiveness of the proposed method.

Multi-Scale Analysis for Characterizing Near-Field Constituent Concentrations in the Context of a Macro-Scale Semi-Lagrangian Numerical Model

NASA Astrophysics Data System (ADS)

Yearsley, J. R.

2017-12-01

The semi-Lagrangian numerical scheme employed by RBM, a model for simulating time-dependent, one-dimensional water quality constituents in advection-dominated rivers, is highly scalable both in time and space. Although the model has been used at length scales of 150 meters and time scales of three hours, the majority of applications have been at length scales of 1/16th degree latitude/longitude (about 5 km) or greater and time scales of one day. Applications of the method at these scales has proven successful for characterizing the impacts of climate change on water temperatures in global rivers and on the vulnerability of thermoelectric power plants to changes in cooling water temperatures in large river systems. However, local effects can be very important in terms of ecosystem impacts, particularly in the case of developing mixing zones for wastewater discharges with pollutant loadings limited by regulations imposed by the Federal Water Pollution Control Act (FWPCA). Mixing zone analyses have usually been decoupled from large-scale watershed influences by developing scenarios that represent critical scenarios for external processes associated with streamflow and weather conditions . By taking advantage of the particle-tracking characteristics of the numerical scheme, RBM can provide results at any point in time within the model domain. We develop a proof of concept for locations in the river network where local impacts such as mixing zones may be important. Simulated results from the semi-Lagrangian numerical scheme are treated as input to a finite difference model of the two-dimensional diffusion equation for water quality constituents such as water temperature or toxic substances. Simulations will provide time-dependent, two-dimensional constituent concentration in the near-field in response to long-term basin-wide processes. These results could provide decision support to water quality managers for evaluating mixing zone characteristics.

Perceptions of Supervision Processes and Practices in Initial Contract, Tenured, and Distinguished-Rated Teachers as They Relate to Self-Learning and Growth in One Large Suburban School District

ERIC Educational Resources Information Center

Watters, Chad M.

2017-01-01

The purpose of this mixed methods study is to examine the perceptions of supervision practices in initial contract, tenured, and distinguished-rated teachers at the elementary level in one large, suburban school district. This study described teacher perceptions of clinical and alternative supervision practices. Six research questions guided this…

A Novel Semi-Supervised Methodology for Extracting Tumor Type-Specific MRS Sources in Human Brain Data

PubMed Central

Ortega-Martorell, Sandra; Ruiz, Héctor; Vellido, Alfredo; Olier, Iván; Romero, Enrique; Julià-Sapé, Margarida; Martín, José D.; Jarman, Ian H.; Arús, Carles; Lisboa, Paulo J. G.

2013-01-01

Background The clinical investigation of human brain tumors often starts with a non-invasive imaging study, providing information about the tumor extent and location, but little insight into the biochemistry of the analyzed tissue. Magnetic Resonance Spectroscopy can complement imaging by supplying a metabolic fingerprint of the tissue. This study analyzes single-voxel magnetic resonance spectra, which represent signal information in the frequency domain. Given that a single voxel may contain a heterogeneous mix of tissues, signal source identification is a relevant challenge for the problem of tumor type classification from the spectroscopic signal. Methodology/Principal Findings Non-negative matrix factorization techniques have recently shown their potential for the identification of meaningful sources from brain tissue spectroscopy data. In this study, we use a convex variant of these methods that is capable of handling negatively-valued data and generating sources that can be interpreted as tumor class prototypes. A novel approach to convex non-negative matrix factorization is proposed, in which prior knowledge about class information is utilized in model optimization. Class-specific information is integrated into this semi-supervised process by setting the metric of a latent variable space where the matrix factorization is carried out. The reported experimental study comprises 196 cases from different tumor types drawn from two international, multi-center databases. The results indicate that the proposed approach outperforms a purely unsupervised process by achieving near perfect correlation of the extracted sources with the mean spectra of the tumor types. It also improves tissue type classification. Conclusions/Significance We show that source extraction by unsupervised matrix factorization benefits from the integration of the available class information, so operating in a semi-supervised learning manner, for discriminative source identification and brain tumor labeling from single-voxel spectroscopy data. We are confident that the proposed methodology has wider applicability for biomedical signal processing. PMID:24376744

Classifying GABAergic interneurons with semi-supervised projected model-based clustering.

PubMed

Mihaljević, Bojan; Benavides-Piccione, Ruth; Guerra, Luis; DeFelipe, Javier; Larrañaga, Pedro; Bielza, Concha

2015-09-01

A recently introduced pragmatic scheme promises to be a useful catalog of interneuron names. We sought to automatically classify digitally reconstructed interneuronal morphologies according to this scheme. Simultaneously, we sought to discover possible subtypes of these types that might emerge during automatic classification (clustering). We also investigated which morphometric properties were most relevant for this classification. A set of 118 digitally reconstructed interneuronal morphologies classified into the common basket (CB), horse-tail (HT), large basket (LB), and Martinotti (MA) interneuron types by 42 of the world's leading neuroscientists, quantified by five simple morphometric properties of the axon and four of the dendrites. We labeled each neuron with the type most commonly assigned to it by the experts. We then removed this class information for each type separately, and applied semi-supervised clustering to those cells (keeping the others' cluster membership fixed), to assess separation from other types and look for the formation of new groups (subtypes). We performed this same experiment unlabeling the cells of two types at a time, and of half the cells of a single type at a time. The clustering model is a finite mixture of Gaussians which we adapted for the estimation of local (per-cluster) feature relevance. We performed the described experiments on three different subsets of the data, formed according to how many experts agreed on type membership: at least 18 experts (the full data set), at least 21 (73 neurons), and at least 26 (47 neurons). Interneurons with more reliable type labels were classified more accurately. We classified HT cells with 100% accuracy, MA cells with 73% accuracy, and CB and LB cells with 56% and 58% accuracy, respectively. We identified three subtypes of the MA type, one subtype of CB and LB types each, and no subtypes of HT (it was a single, homogeneous type). We got maximum (adapted) Silhouette width and ARI values of 1, 0.83, 0.79, and 0.42, when unlabeling the HT, CB, LB, and MA types, respectively, confirming the quality of the formed cluster solutions. The subtypes identified when unlabeling a single type also emerged when unlabeling two types at a time, confirming their validity. Axonal morphometric properties were more relevant that dendritic ones, with the axonal polar histogram length in the [π, 2π) angle interval being particularly useful. The applied semi-supervised clustering method can accurately discriminate among CB, HT, LB, and MA interneuron types while discovering potential subtypes, and is therefore useful for neuronal classification. The discovery of potential subtypes suggests that some of these types are more heterogeneous that previously thought. Finally, axonal variables seem to be more relevant than dendritic ones for distinguishing among the CB, HT, LB, and MA interneuron types. Copyright © 2015 Elsevier B.V. All rights reserved.

Detection of facilities in satellite imagery using semi-supervised image classification and auxiliary contextual observables

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

Harvey, Neal R; Ruggiero, Christy E; Pawley, Norma H

2009-01-01

Detecting complex targets, such as facilities, in commercially available satellite imagery is a difficult problem that human analysts try to solve by applying world knowledge. Often there are known observables that can be extracted by pixel-level feature detectors that can assist in the facility detection process. Individually, each of these observables is not sufficient for an accurate and reliable detection, but in combination, these auxiliary observables may provide sufficient context for detection by a machine learning algorithm. We describe an approach for automatic detection of facilities that uses an automated feature extraction algorithm to extract auxiliary observables, and a semi-supervisedmore » assisted target recognition algorithm to then identify facilities of interest. We illustrate the approach using an example of finding schools in Quickbird image data of Albuquerque, New Mexico. We use Los Alamos National Laboratory's Genie Pro automated feature extraction algorithm to find a set of auxiliary features that should be useful in the search for schools, such as parking lots, large buildings, sports fields and residential areas and then combine these features using Genie Pro's assisted target recognition algorithm to learn a classifier that finds schools in the image data.« less

Smart Annotation of Cyclic Data Using Hierarchical Hidden Markov Models.

PubMed

Martindale, Christine F; Hoenig, Florian; Strohrmann, Christina; Eskofier, Bjoern M

2017-10-13

Cyclic signals are an intrinsic part of daily life, such as human motion and heart activity. The detailed analysis of them is important for clinical applications such as pathological gait analysis and for sports applications such as performance analysis. Labeled training data for algorithms that analyze these cyclic data come at a high annotation cost due to only limited annotations available under laboratory conditions or requiring manual segmentation of the data under less restricted conditions. This paper presents a smart annotation method that reduces this cost of labeling for sensor-based data, which is applicable to data collected outside of strict laboratory conditions. The method uses semi-supervised learning of sections of cyclic data with a known cycle number. A hierarchical hidden Markov model (hHMM) is used, achieving a mean absolute error of 0.041 ± 0.020 s relative to a manually-annotated reference. The resulting model was also used to simultaneously segment and classify continuous, 'in the wild' data, demonstrating the applicability of using hHMM, trained on limited data sections, to label a complete dataset. This technique achieved comparable results to its fully-supervised equivalent. Our semi-supervised method has the significant advantage of reduced annotation cost. Furthermore, it reduces the opportunity for human error in the labeling process normally required for training of segmentation algorithms. It also lowers the annotation cost of training a model capable of continuous monitoring of cycle characteristics such as those employed to analyze the progress of movement disorders or analysis of running technique.

Comparison Between a Self-Administered and Supervised Version of a Web-Based Cognitive Test Battery: Results From the NutriNet-Santé Cohort Study

PubMed Central

Bailet, Marion; Lecoffre, Amandine C; Galan, Pilar; Hercberg, Serge; Amieva, Hélène; Kesse-Guyot, Emmanuelle

2016-01-01

Background Dementia is a major public health problem, and repeated cognitive data from large epidemiological studies could help to develop efficient measures of early prevention. Data collection by self-administered online tools could drastically reduce the logistical and financial burden of such large-scale investigations. In this context, it is important to obtain data concerning the comparability of such new online tools with traditional, supervised modes of cognitive assessment. Objective Our objective was to compare self-administration of the Web-based NutriNet-Santé cognitive test battery (NutriCog) with administration by a neuropsychologist. Methods The test battery included four tests, measuring, among others aspects, psychomotor speed, attention, executive function, episodic memory, working memory, and associative memory. Both versions of the cognitive battery were completed by 189 volunteers (either self-administered version first, n=99, or supervised version first, n=90). Subjects also completed a satisfaction questionnaire. Concordance was assessed by Spearman correlation. Results Agreement between both versions varied according to the investigated cognitive task and outcome variable. Spearman correlations ranged between .42 and .73. Moreover, a majority of participants responded that they “absolutely” or “rather” agreed that the duration of the self-administered battery was acceptable (184/185, 99.5%), that the tasks were amusing (162/185, 87.6%), that the instructions were sufficiently detailed (168/185; 90.8%) and understandable (164/185, 88.7%), and that they had overall enjoyed the test battery (182/185, 98.4%). Conclusions The self-administered version of the Web-based NutriCog cognitive test battery provided similar information as the supervised version. Thus, integrating repeated cognitive evaluations into large cohorts via the implementation of self-administered online versions of traditional test batteries appears to be feasible. PMID:27049114

Modeling the Webgraph: How Far We Are

NASA Astrophysics Data System (ADS)

Donato, Debora; Laura, Luigi; Leonardi, Stefano; Millozzi, Stefano

The following sections are included: * Introduction * Preliminaries * WebBase * In-degree and out-degree * PageRank * Bipartite cliques * Strongly connected components * Stochastic models of the webgraph * Models of the webgraph * A multi-layer model * Large scale simulation * Algorithmic techniques for generating and measuring webgraphs * Data representation and multifiles * Generating webgraphs * Traversal with two bits for each node * Semi-external breadth first search * Semi-external depth first search * Computation of the SCCs * Computation of the bow-tie regions * Disjoint bipartite cliques * PageRank * Summary and outlook

International Conference on Numerical Ship Hydrodynamics (6th), Held in Iowa City, Iowa on 2-5 August 1993,

DTIC Science & Technology

1994-01-01

length scales mensional hydrofoil and tip vortex flow around a F circulation three dimensional hydrofoil. The simulated mean v molecular viscosity flow...Unstructured Grid for Free Surface Flow Simulations , by T. Hino, L. Martinelli, and A. Jameson 173 "A Semi-Implicit Semi-Lagrangian Finite Element Model...Haussling Solid-Fluid Juncture Boundary Layer and Wake with Waves, by J.E. Choi and F. Stern 215 Direct Numerical and Large-Eddy Simulations of Turbulent

Supervision Anxiety as a Predictor for Organizational Cynicism in Teachers

ERIC Educational Resources Information Center

Gündüz, Hasan Basri; Ömür, Yunus Emre

2016-01-01

The purpose of this is study is to reveal how the anxiety that the teachers who work in the Beyoglu district of Istanbul experience, due to the supervision process, predict their organizational cynicism levels. With this respect, the study was conducted on 274 teachers with the relational screening model. The "Supervision Anxiety Scale"…

How Do I Know That My Supervision Is Reflective? Identifying Factors and Validity of the Reflective Supervision Rating Scale

ERIC Educational Resources Information Center

Gallen, Robert T.; Ash, Jordana; Smith, Conner; Franco, Allison; Willford, Jennifer A.

2016-01-01

Reflective supervision and consultation (RS/C) is often defined as a "relationship for learning"(Fenichel, 1992, p.9). As such, measurement tools should include the perspective of each participant in the supervisory relationship when assessing RS/C fidelity, delivery quality, and the supervisee's experience. The Reflective Supervision…

Supervised exercise reduces cancer-related fatigue: a systematic review.

PubMed

Meneses-Echávez, José F; González-Jiménez, Emilio; Ramírez-Vélez, Robinson

2015-01-01

Does supervised physical activity reduce cancer-related fatigue? Systematic review with meta-analysis of randomised trials. People diagnosed with any type of cancer, without restriction to a particular stage of diagnosis or treatment. Supervised physical activity interventions (eg, aerobic, resistance and stretching exercise), defined as any planned or structured body movement causing an increase in energy expenditure, designed to maintain or enhance health-related outcomes, and performed with systematic frequency, intensity and duration. The primary outcome measure was fatigue. Secondary outcomes were physical and functional wellbeing assessed using the Functional Assessment of Cancer Therapy Fatigue Scale, European Organisation for Research and Treatment of Cancer Quality of Life QUESTIONnaire, Piper Fatigue Scale, Schwartz Cancer Fatigue Scale and the Multidimensional Fatigue Inventory. Methodological quality, including risk of bias of the studies, was evaluated using the PEDro Scale. Eleven studies involving 1530 participants were included in the review. The assessment of quality showed a mean score of 6.5 (SD 1.1), indicating a low overall risk of bias. The pooled effect on fatigue, calculated as a standardised mean difference (SMD) using a random-effects model, was -1.69 (95% CI -2.99 to -0.39). Beneficial reductions in fatigue were also found with combined aerobic and resistance training with supervision (SMD=-0.41, 95% CI -0.70 to -0.13) and with combined aerobic, resistance and stretching training with supervision (SMD=-0.67, 95% CI -1.17 to -0.17). Supervised physical activity interventions reduce cancer-related fatigue. These findings suggest that combined aerobic and resistance exercise regimens with or without stretching should be included as part of rehabilitation programs for people who have been diagnosed with cancer. PROSPERO CRD42013005803. Copyright © 2014 Australian Physiotherapy Association. Published by Elsevier B.V. All rights reserved.

Jovian meterology: Large-scale moist convection without a lower boundary

NASA Technical Reports Server (NTRS)

Gierasch, P. J.

1975-01-01

It is proposed that Jupiter's cloud bands represent large scale convection whose character is determined by the phase change of water at a level where the temperature is about 275K. It is argued that there are three important layers in the atmosphere: a tropopause layer where emission to space occurs; an intermediate layer between the tropopause and the water cloud base; and the deep layer below the water cloud. All arguments are only semi-quantitative. It is pointed out that these ingredients are essential to Jovian meteorology.

An exploration of facilitators and challenges in the scale-up of a national, public sector community health worker cadre in Zambia: a qualitative study.

PubMed

Phiri, Sydney Chauwa; Prust, Margaret Lippitt; Chibawe, Caroline Phiri; Misapa, Ronald; van den Broek, Jan Willem; Wilmink, Nikhil

2017-06-24

In 2010 a public sector cadre of community health workers called Community Health Assistants (CHAs) was created in Zambia through the National Community Health Worker Strategy to expand access to health services. This cadre continues to be scaled up to meet the growing demands of Zambia's rural population. We summarize factors that have facilitated the scale-up of the CHA program into a nationwide CHW cadre and the challenges of introducing and institutionalizing the cadre within the Zambian health system. Semi-structured, individual interviews were held across 5 districts with 16 CHAs and 6 CHA supervisors, and 10 focus group discussions were held with 93 community members. Audio recordings of interviews and focus group discussions were transcribed and thematically coded using Dedoose web-based software. The study showed that the CHAs play a critical role in providing a wide range of services at the community level, as described by supervisors and community members. Some challenges still remain, that may inhibit the CHAs ability to provide health services effectively. In particular, the respondents highlighted infrequent supervision, lack of medical and non-medical supplies for outreach services, and challenges with the mobile data reporting system. The study shows that in order to optimize the impact of CHAs or other community health workers, key health-system support structures need to be functioning effectively, such as supervision, community surveillance systems, supplies, and reporting. The Ministry of Health with support from partners are currently addressing these challenges through nationwide supervisor and community data trainings, as well as advocating for adding primary health care as a specific focus area in the new National Health Strategy Plan 2017-2021. This study contributes to the evidence base on the introduction of formalized community health worker cadres in developing countries.

12 CFR 502.26 - How does OTS calculate the semi-annual assessment for savings and loan holding companies?

Code of Federal Regulations, 2010 CFR

2010-01-01

... holding company's business, a component based on its organizational form, and a component based on its...-site supervision of a noncomplex, low risk savings and loan holding company structure. OTS will... company is the registered holding company at the highest level of ownership in a holding company structure...

Fusion And Inference From Multiple And Massive Disparate Distributed Dynamic Data Sets

DTIC Science & Technology

2017-07-01

principled methodology for two-sample graph testing; designed a provably almost-surely perfect vertex clustering algorithm for block model graphs; proved...3.7 Semi-Supervised Clustering Methodology ...................................................................... 9 3.8 Robust Hypothesis Testing...dimensional Euclidean space – allows the full arsenal of statistical and machine learning methodology for multivariate Euclidean data to be deployed for

Validity of Level of Supervision Scales for Assessing Pediatric Fellows on the Common Pediatric Subspecialty Entrustable Professional Activities.

PubMed

Mink, Richard B; Schwartz, Alan; Herman, Bruce E; Turner, David A; Curran, Megan L; Myers, Angela; Hsu, Deborah C; Kesselheim, Jennifer C; Carraccio, Carol L

2018-02-01

Entrustable professional activities (EPAs) represent the routine and essential activities that physicians perform in practice. Although some level of supervision scales have been proposed, they have not been validated. In this study, the investigators created level of supervision scales for EPAs common to the pediatric subspecialties and then examined their validity in a study conducted by the Subspecialty Pediatrics Investigator Network (SPIN). SPIN Steering Committee members used a modified Delphi process to develop unique scales for six of the seven common EPAs. The investigators sought validity evidence in a multisubspecialty study in which pediatric fellowship program directors and Clinical Competency Committees used the scales to evaluate fellows in fall 2014 and spring 2015. Separate scales for the six EPAs, each with five levels of progressive entrustment, were created. In both fall and spring, more than 300 fellows in each year of training from over 200 programs were assessed. In both periods and for each EPA, there was a progressive increase in entrustment levels, with second-year fellows rated higher than first-year fellows (P < .001) and third-year fellows rated higher than second-year fellows (P < .001). For each EPA, spring ratings were higher (P < .001) than those in the fall. Interrater reliability was high (Janson and Olsson's iota = 0.73). The supervision scales developed for these six common pediatric subspecialty EPAs demonstrated strong validity evidence for use in EPA-based assessment of pediatric fellows. They may also inform the development of scales in other specialties.

Paediatric specialist registrars' views of educational supervision and how it can be improved: a questionnaire study.

PubMed

Lloyd, B W; Becker, D

2007-08-01

To determine what paediatric specialist registrars think of the educational supervision they have received and what advice they would give to a consultant who wanted to be a more effective educational supervisor. A questionnaire study. The North Thames Deanery, UK. 129 year 3, 4 or 5 paediatric specialist registrars in the North Thames Deanery. Reported value of educational supervision on a Likert scale; what elements of educational supervision were reported to be most useful; what elements of educational supervision were reported to be done poorly; what advice would specialist registrars give to a consultant who wanted to be a more effective educational supervisor. 86/129 specialist registrars responded (67%). The mean score on the Likert scale (0-a complete waste of time; 100-excellent) was 57 with 37% of respondents giving a score of less than 50. The most valued aspects of educational supervision were: feedback on performance--cited by 50 respondents (56% of respondents); career advice--cited by 43 (48%); objective setting--cited by 36 (40%); pastoral support--cited by 25 (28%). Aspects of educational supervision that were reported to be often not done well were: commitment to educational supervision--cited by 44 respondents (49% of respondents); ensuring sessions are bleep-free-cited by 43 (48%); listening rather than talking--cited by 23 (26%); being encouraging--cited by 18 (20%). Advice to consultants about how to improve educational supervision included: listen rather than talk; be encouraging; treat the trainee as an individual with individual needs. We can find no other study of trainees' views about how educational supervision can be improved. Although some trainees found educational supervision very valuable, many did not. Educational supervision should only be carried out by consultants who are committed to the task. An educational supervisor should listen carefully in order to understand the trainee's individual ambitions and needs, should provide specific feedback on performance and should be encouraging.

Comparison of Effectiveness of Supervised Exercise Program and Cyriax Physiotherapy in Patients with Tennis Elbow (Lateral Epicondylitis): A Randomized Clinical Trial

PubMed Central

Viswas, Rajadurai; Ramachandran, Rejeeshkumar; Korde Anantkumar, Payal

2012-01-01

Objective. To compare the effectiveness of supervised exercise program and Cyriax physiotherapy in the treatment of tennis elbow (lateral epicondylitis). Design. Randomized clinical trial. Setting. Physiotherapy and rehabilitation centre. Subjects. This study was carried out with 20 patients, who had tennis elbow (lateral epicondylitis). Intervention. Group A (n = 10) had received supervised exercise program. Group B (n = 10) was treated with Cyriax physiotherapy. All patients received three treatment sessions per week for four weeks (12 treatment sessions). Outcome measures. Pain was evaluated using a visual analogue scale (VAS), and functional status was evaluated by completion of the Tennis Elbow Function Scale (TEFS) which were recorded at base line and at the end of fourth week. Results. Both the supervised exercise program and Cyriax physiotherapy were found to be significantly effective in reduction of pain and in the improvement of functional status. The supervised exercise programme resulted in greater improvement in comparison to those who received Cyriax physiotherapy. Conclusion. The results of this clinical trial demonstrate that the supervised exercise program may be the first treatment choice for therapist in managing tennis elbow. PMID:22629225

Investigation of Seal-to-Floor Effects on Semi-Span Transonic Models

NASA Technical Reports Server (NTRS)

Sleppy, Mark A.; Engel, Eric A.; Watson, Kevin T.; Atler, Douglas M.

2009-01-01

In an effort to achieve the maximum possible Reynolds number (Re) when conducting production testing for flight loads aerodynamic databases, it has been the preferred practice of The Boeing Company / Commercial Airplanes (BCA) -- Loads and Dynamics Group since the early 1990's to test large scale semi-span models in the 11- By 11-Foot Transonic Wind Tunnel (TWT) leg of the Unitary Plan Wind Tunnel (UPWT) at the NASA Ames Research Center (ARC). There are many problems related to testing large scale semi-span models of high aspect ratio flexible transport wings, such as; floor boundary layer effects, wing spanwise wall effects, solid blockage buoyancy effects, floor mechanical interference effects, airflow under the model effects, or tunnel flow gradient effects. For most of these issues, BCA has developed and implemented either standard testing methods or numerical correction schemes and these will not be discussed in this document. Other researchers have reported on semi-span transonic testing correction issues, however most of the reported research has been for low Mach testing. Some of the reports for low Mach testing address the difficult problem of preventing undesirable airflow under a semi-span model while ensuring unrestricted main balance functionality, however, for transonic models this issue has gone unresolved. BCA has been cognizant for sometime that there are marked differences in wing pressure distributions from semi-span transonic model testing than from full model or flight testing. It has been suspected that these differences are at least in part due to airflow under the model. Previous efforts by BCA to address this issue have proven to be ineffective or inconclusive and in one situation resulted in broken hardware. This paper reports on a Boeing-NASA collaborative investigation based on a series of small tests conducted between June 2006 and November 2007 in the 11 by 11 foot Transonic Wind Tunnel at NASA Ames on three large commercial jet transport configurations to assess the effects of sealing a semi-span model to the floor and to investigate efficient sealing and testing techniques. This document will show how sealing the model to the floor has a small but remarkably far reaching spanwise effect on wing pressures, wing local section forces and wing force summations.

An integrated network of Arabidopsis growth regulators and its use for gene prioritization.

PubMed

Sabaghian, Ehsan; Drebert, Zuzanna; Inzé, Dirk; Saeys, Yvan

2015-12-01

Elucidating the molecular mechanisms that govern plant growth has been an important topic in plant research, and current advances in large-scale data generation call for computational tools that efficiently combine these different data sources to generate novel hypotheses. In this work, we present a novel, integrated network that combines multiple large-scale data sources to characterize growth regulatory genes in Arabidopsis, one of the main plant model organisms. The contributions of this work are twofold: first, we characterized a set of carefully selected growth regulators with respect to their connectivity patterns in the integrated network, and, subsequently, we explored to which extent these connectivity patterns can be used to suggest new growth regulators. Using a large-scale comparative study, we designed new supervised machine learning methods to prioritize growth regulators. Our results show that these methods significantly improve current state-of-the-art prioritization techniques, and are able to suggest meaningful new growth regulators. In addition, the integrated network is made available to the scientific community, providing a rich data source that will be useful for many biological processes, not necessarily restricted to plant growth.

L1-norm locally linear representation regularization multi-source adaptation learning.

PubMed

Tao, Jianwen; Wen, Shiting; Hu, Wenjun

2015-09-01

In most supervised domain adaptation learning (DAL) tasks, one has access only to a small number of labeled examples from target domain. Therefore the success of supervised DAL in this "small sample" regime needs the effective utilization of the large amounts of unlabeled data to extract information that is useful for generalization. Toward this end, we here use the geometric intuition of manifold assumption to extend the established frameworks in existing model-based DAL methods for function learning by incorporating additional information about the target geometric structure of the marginal distribution. We would like to ensure that the solution is smooth with respect to both the ambient space and the target marginal distribution. In doing this, we propose a novel L1-norm locally linear representation regularization multi-source adaptation learning framework which exploits the geometry of the probability distribution, which has two techniques. Firstly, an L1-norm locally linear representation method is presented for robust graph construction by replacing the L2-norm reconstruction measure in LLE with L1-norm one, which is termed as L1-LLR for short. Secondly, considering the robust graph regularization, we replace traditional graph Laplacian regularization with our new L1-LLR graph Laplacian regularization and therefore construct new graph-based semi-supervised learning framework with multi-source adaptation constraint, which is coined as L1-MSAL method. Moreover, to deal with the nonlinear learning problem, we also generalize the L1-MSAL method by mapping the input data points from the input space to a high-dimensional reproducing kernel Hilbert space (RKHS) via a nonlinear mapping. Promising experimental results have been obtained on several real-world datasets such as face, visual video and object. Copyright © 2015 Elsevier Ltd. All rights reserved.

Hydrometeor classification through statistical clustering of polarimetric radar measurements: a semi-supervised approach

NASA Astrophysics Data System (ADS)

Besic, Nikola; Ventura, Jordi Figueras i.; Grazioli, Jacopo; Gabella, Marco; Germann, Urs; Berne, Alexis

2016-09-01

Polarimetric radar-based hydrometeor classification is the procedure of identifying different types of hydrometeors by exploiting polarimetric radar observations. The main drawback of the existing supervised classification methods, mostly based on fuzzy logic, is a significant dependency on a presumed electromagnetic behaviour of different hydrometeor types. Namely, the results of the classification largely rely upon the quality of scattering simulations. When it comes to the unsupervised approach, it lacks the constraints related to the hydrometeor microphysics. The idea of the proposed method is to compensate for these drawbacks by combining the two approaches in a way that microphysical hypotheses can, to a degree, adjust the content of the classes obtained statistically from the observations. This is done by means of an iterative approach, performed offline, which, in a statistical framework, examines clustered representative polarimetric observations by comparing them to the presumed polarimetric properties of each hydrometeor class. Aside from comparing, a routine alters the content of clusters by encouraging further statistical clustering in case of non-identification. By merging all identified clusters, the multi-dimensional polarimetric signatures of various hydrometeor types are obtained for each of the studied representative datasets, i.e. for each radar system of interest. These are depicted by sets of centroids which are then employed in operational labelling of different hydrometeors. The method has been applied on three C-band datasets, each acquired by different operational radar from the MeteoSwiss Rad4Alp network, as well as on two X-band datasets acquired by two research mobile radars. The results are discussed through a comparative analysis which includes a corresponding supervised and unsupervised approach, emphasising the operational potential of the proposed method.

Social work in oncology-managing vicarious trauma-the positive impact of professional supervision.

PubMed

Joubert, Lynette; Hocking, Alison; Hampson, Ralph

2013-01-01

This exploratory study focused on the experience and management of vicarious trauma in a team of social workers (N = 16) at a specialist cancer hospital in Melbourne. Respondents completed the Traumatic Stress Institute Belief Scale (TSIBS), the Professional Quality of Life Scale (ProQOL), and participated in four focus groups. The results from the TSIBS and the ProQol scales confirm that there is a stress associated with the social work role within a cancer service, as demonstrated by the high scores related to stress. However at the same time the results indicated a high level of satisfaction which acted as a mitigating factor. The study also highlighted the importance of supervision and management support. A model for clinical social work supervision is proposed to reduce the risks associated with vicarious trauma.

Methods of Sparse Modeling and Dimensionality Reduction to Deal with Big Data

DTIC Science & Technology

2015-04-01

supervised learning (c). Our framework consists of two separate phases: (a) first find an initial space in an unsupervised manner; then (b) utilize label...model that can learn thousands of topics from a large set of documents and infer the topic mixture of each document, 2) a supervised dimension reduction...model that can learn thousands of topics from a large set of documents and infer the topic mixture of each document, (i) a method of supervised

Culture modes and financial evaluation of two oleaginous microalgae for biodiesel production in desert area with open raceway pond.

PubMed

He, Qiaoning; Yang, Haijian; Hu, Chunxiang

2016-10-01

Cultivation modes of autotrophic microalgae for biodiesel production utilizing open raceway pond were analyzed in this study. Five before screened good microalgae were tested their lipid productivity and biodiesel quality again in outdoor 1000L ORP. Then, Chlorella sp. L1 and Monoraphidium dybowskii Y2 were selected due to their stronger environmental adaptability, higher lipid productivity and better biodiesel properties. Further scale up cultivation for two species with batch and semi-continuous culture was conducted. In 40,000L ORP, higher lipid productivity (5.15 versus 4.06gm(-2)d(-1) for Chlorella sp. L1, 5.35 versus 3.00gm(-2)d(-1) for M. dybowskii Y2) was achieved in semi-continuous mode. Moreover, the financial costs of 14.18$gal(-1) and 13.31$gal(-1) for crude biodiesel in two microalgae with semi-continuous mode were more economically feasible for commercial production on large scale outdoors. Copyright © 2016 Elsevier Ltd. All rights reserved.

Towards a deeper understanding of parenting on farms: A qualitative study.

PubMed

Elliot, Valerie; Cammer, Allison; Pickett, William; Marlenga, Barbara; Lawson, Joshua; Dosman, James; Hagel, Louise; Koehncke, Niels; Trask, Catherine

2018-01-01

Children living on farms experience exceptionally high risks for traumatic injury. There is a large body of epidemiological research documenting this phenomenon, yet few complementary studies that have explored the deep underlying reasons for such trends. Fundamental to this is understanding the decision-making processes of parents surrounding their choice to bring children, or not, into the farm worksite. To (1) document farm parent views of the risks and benefits of raising children on a family farm, and, (2) understand more deeply why children are brought into the farm worksite. Interviews were conducted as part of a larger cohort study, The Saskatchewan Farm Injury Cohort. Subsequent to an initial mail-out question focused on parental decision-making, 11 semi-structured telephone interviews were conducted with rural Saskatchewan farm parents. Interviews were digitally recorded and transcribed verbatim, then thematically analyzed using interpretive description methodology. This parental decision-making process on farms fundamentally involves weighing the risks vs. benefits of bringing children into the worksite, as if on a balance scale. One side of this scale holds potential risks such as exposure to physical and chemical farm hazards, in the absence of full supervision. The other side holds potential benefits such as meeting family needs for childcare, labour, and family time; building work ethic and pride; and the positive impacts of involvement and responsibility. Decision-making 'tips the scales', in part dependent upon parental perceptions of the risk-benefit trade-off. This 'perceptual lens' is influenced by factors such as: the agricultural way of life, parents' prior knowledge and past experience, characteristics of children, and safety norms. This novel qualitative study provides deep insight into how Saskatchewan farm parents approach a fundamental decision-making process associated with their parenting. The proposed model provides insight into the etiology of pediatric farm injuries as well as their prevention.

Towards a deeper understanding of parenting on farms: A qualitative study

PubMed Central

Elliot, Valerie; Cammer, Allison; Pickett, William; Marlenga, Barbara; Lawson, Joshua; Dosman, James; Hagel, Louise; Koehncke, Niels

2018-01-01

Background Children living on farms experience exceptionally high risks for traumatic injury. There is a large body of epidemiological research documenting this phenomenon, yet few complementary studies that have explored the deep underlying reasons for such trends. Fundamental to this is understanding the decision-making processes of parents surrounding their choice to bring children, or not, into the farm worksite. Objectives To (1) document farm parent views of the risks and benefits of raising children on a family farm, and, (2) understand more deeply why children are brought into the farm worksite. Methods Interviews were conducted as part of a larger cohort study, The Saskatchewan Farm Injury Cohort. Subsequent to an initial mail-out question focused on parental decision-making, 11 semi-structured telephone interviews were conducted with rural Saskatchewan farm parents. Interviews were digitally recorded and transcribed verbatim, then thematically analyzed using interpretive description methodology. Findings This parental decision-making process on farms fundamentally involves weighing the risks vs. benefits of bringing children into the worksite, as if on a balance scale. One side of this scale holds potential risks such as exposure to physical and chemical farm hazards, in the absence of full supervision. The other side holds potential benefits such as meeting family needs for childcare, labour, and family time; building work ethic and pride; and the positive impacts of involvement and responsibility. Decision-making 'tips the scales', in part dependent upon parental perceptions of the risk-benefit trade-off. This 'perceptual lens' is influenced by factors such as: the agricultural way of life, parents' prior knowledge and past experience, characteristics of children, and safety norms. Conclusions This novel qualitative study provides deep insight into how Saskatchewan farm parents approach a fundamental decision-making process associated with their parenting. The proposed model provides insight into the etiology of pediatric farm injuries as well as their prevention. PMID:29897960

Interactive Effects of Large- and Small-Scale Sources of Feral Honey-Bees for Sunflower in the Argentine Pampas

PubMed Central

Sáez, Agustín; Sabatino, Malena; Aizen, Marcelo A.

2012-01-01

Pollinators for animal pollinated crops can be provided by natural and semi-natural habitats, ranging from large vegetation remnants to small areas of non-crop land in an otherwise highly modified landscape. It is unknown, however, how different small- and large-scale habitat patches interact as pollinator sources. In the intensively managed Argentine Pampas, we studied the additive and interactive effects of large expanses (up to 2200 ha) of natural habitat, represented by untilled isolated “sierras”, and narrow (3–7 m wide) strips of semi-natural habitat, represented by field margins, as pollinator sources for sunflower (Helianthus annus). We estimated visitation rates by feral honey-bees, Apis mellifera, and native flower visitors (as a group) at 1, 5, 25, 50 and 100 m from a field margin in 17 sunflower fields 0–10 km distant from the nearest sierra. Honey-bees dominated the pollinator assemblage accounting for >90% of all visits to sunflower inflorescences. Honey-bee visitation was strongly affected by proximity to the sierras decreasing by about 70% in the most isolated fields. There was also a decline in honey-bee visitation with distance from the field margin, which was apparent with increasing field isolation, but undetected in fields nearby large expanses of natural habitat. The probability of observing a native visitor decreased with isolation from the sierras, but in other respects visitation by flower visitors other than honey-bees was mostly unaffected by the habitat factors assessed in this study. Overall, we found strong hierarchical and interactive effects between the study large and small-scale pollinator sources. These results emphasize the importance of preserving natural habitats and managing actively field verges in the absence of large remnants of natural habitat for improving pollinator services. PMID:22303477

Interactive effects of large- and small-scale sources of feral honey-bees for sunflower in the Argentine Pampas.

PubMed

Sáez, Agustín; Sabatino, Malena; Aizen, Marcelo A

2012-01-01

Pollinators for animal pollinated crops can be provided by natural and semi-natural habitats, ranging from large vegetation remnants to small areas of non-crop land in an otherwise highly modified landscape. It is unknown, however, how different small- and large-scale habitat patches interact as pollinator sources. In the intensively managed Argentine Pampas, we studied the additive and interactive effects of large expanses (up to 2200 ha) of natural habitat, represented by untilled isolated "sierras", and narrow (3-7 m wide) strips of semi-natural habitat, represented by field margins, as pollinator sources for sunflower (Helianthus annus). We estimated visitation rates by feral honey-bees, Apis mellifera, and native flower visitors (as a group) at 1, 5, 25, 50 and 100 m from a field margin in 17 sunflower fields 0-10 km distant from the nearest sierra. Honey-bees dominated the pollinator assemblage accounting for >90% of all visits to sunflower inflorescences. Honey-bee visitation was strongly affected by proximity to the sierras decreasing by about 70% in the most isolated fields. There was also a decline in honey-bee visitation with distance from the field margin, which was apparent with increasing field isolation, but undetected in fields nearby large expanses of natural habitat. The probability of observing a native visitor decreased with isolation from the sierras, but in other respects visitation by flower visitors other than honey-bees was mostly unaffected by the habitat factors assessed in this study. Overall, we found strong hierarchical and interactive effects between the study large and small-scale pollinator sources. These results emphasize the importance of preserving natural habitats and managing actively field verges in the absence of large remnants of natural habitat for improving pollinator services.

A Hybrid Semi-Supervised Anomaly Detection Model for High-Dimensional Data.

PubMed

Song, Hongchao; Jiang, Zhuqing; Men, Aidong; Yang, Bo

2017-01-01

Anomaly detection, which aims to identify observations that deviate from a nominal sample, is a challenging task for high-dimensional data. Traditional distance-based anomaly detection methods compute the neighborhood distance between each observation and suffer from the curse of dimensionality in high-dimensional space; for example, the distances between any pair of samples are similar and each sample may perform like an outlier. In this paper, we propose a hybrid semi-supervised anomaly detection model for high-dimensional data that consists of two parts: a deep autoencoder (DAE) and an ensemble k -nearest neighbor graphs- ( K -NNG-) based anomaly detector. Benefiting from the ability of nonlinear mapping, the DAE is first trained to learn the intrinsic features of a high-dimensional dataset to represent the high-dimensional data in a more compact subspace. Several nonparametric KNN-based anomaly detectors are then built from different subsets that are randomly sampled from the whole dataset. The final prediction is made by all the anomaly detectors. The performance of the proposed method is evaluated on several real-life datasets, and the results confirm that the proposed hybrid model improves the detection accuracy and reduces the computational complexity.

A Hybrid Semi-Supervised Anomaly Detection Model for High-Dimensional Data

PubMed Central

Jiang, Zhuqing; Men, Aidong; Yang, Bo

2017-01-01

Anomaly detection, which aims to identify observations that deviate from a nominal sample, is a challenging task for high-dimensional data. Traditional distance-based anomaly detection methods compute the neighborhood distance between each observation and suffer from the curse of dimensionality in high-dimensional space; for example, the distances between any pair of samples are similar and each sample may perform like an outlier. In this paper, we propose a hybrid semi-supervised anomaly detection model for high-dimensional data that consists of two parts: a deep autoencoder (DAE) and an ensemble k-nearest neighbor graphs- (K-NNG-) based anomaly detector. Benefiting from the ability of nonlinear mapping, the DAE is first trained to learn the intrinsic features of a high-dimensional dataset to represent the high-dimensional data in a more compact subspace. Several nonparametric KNN-based anomaly detectors are then built from different subsets that are randomly sampled from the whole dataset. The final prediction is made by all the anomaly detectors. The performance of the proposed method is evaluated on several real-life datasets, and the results confirm that the proposed hybrid model improves the detection accuracy and reduces the computational complexity. PMID:29270197

An Efficient Semi-supervised Learning Approach to Predict SH2 Domain Mediated Interactions.

PubMed

Kundu, Kousik; Backofen, Rolf

2017-01-01

Src homology 2 (SH2) domain is an important subclass of modular protein domains that plays an indispensable role in several biological processes in eukaryotes. SH2 domains specifically bind to the phosphotyrosine residue of their binding peptides to facilitate various molecular functions. For determining the subtle binding specificities of SH2 domains, it is very important to understand the intriguing mechanisms by which these domains recognize their target peptides in a complex cellular environment. There are several attempts have been made to predict SH2-peptide interactions using high-throughput data. However, these high-throughput data are often affected by a low signal to noise ratio. Furthermore, the prediction methods have several additional shortcomings, such as linearity problem, high computational complexity, etc. Thus, computational identification of SH2-peptide interactions using high-throughput data remains challenging. Here, we propose a machine learning approach based on an efficient semi-supervised learning technique for the prediction of 51 SH2 domain mediated interactions in the human proteome. In our study, we have successfully employed several strategies to tackle the major problems in computational identification of SH2-peptide interactions.

The large-scale effect of environment on galactic conformity

NASA Astrophysics Data System (ADS)

Sun, Shuangpeng; Guo, Qi; Wang, Lan; Lacey, Cedric G.; Wang, Jie; Gao, Liang; Pan, Jun

2018-07-01

We use a volume-limited galaxy sample from the Sloan Digital Sky Survey Data Release 7 to explore the dependence of galactic conformity on the large-scale environment, measured on ˜4 Mpc scales. We find that the star formation activity of neighbour galaxies depends more strongly on the environment than on the activity of their primary galaxies. In underdense regions most neighbour galaxies tend to be active, while in overdense regions neighbour galaxies are mostly passive, regardless of the activity of their primary galaxies. At a given stellar mass, passive primary galaxies reside in higher density regions than active primary galaxies, leading to the apparently strong conformity signal. The dependence of the activity of neighbour galaxies on environment can be explained by the corresponding dependence of the fraction of satellite galaxies. Similar results are found for galaxies in a semi-analytical model, suggesting that no new physics is required to explain the observed large-scale conformity.

Cosmic Rays and Gamma-Rays in Large-Scale Structure

NASA Astrophysics Data System (ADS)

Inoue, Susumu; Nagashima, Masahiro; Suzuki, Takeru K.; Aoki, Wako

2004-12-01

During the hierarchical formation of large scale structure in the universe, the progressive collapse and merging of dark matter should inevitably drive shocks into the gas, with nonthermal particle acceleration as a natural consequence. Two topics in this regard are discussed, emphasizing what important things nonthermal phenomena may tell us about the structure formation (SF) process itself. 1. Inverse Compton gamma-rays from large scale SF shocks and non-gravitational effects, and the implications for probing the warm-hot intergalactic medium. We utilize a semi-analytic approach based on Monte Carlo merger trees that treats both merger and accretion shocks self-consistently. 2. Production of 6Li by cosmic rays from SF shocks in the early Galaxy, and the implications for probing Galaxy formation and uncertain physics on sub-Galactic scales. Our new observations of metal-poor halo stars with the Subaru High Dispersion Spectrograph are highlighted.

ENSO elicits opposing responses of semi-arid vegetation between Hemispheres

NASA Astrophysics Data System (ADS)

Zhang, Anzhi; Jia, Gensuo; Epstein, Howard E.; Xia, Jiangjiang

2017-02-01

Semi-arid ecosystems are key contributors to the global carbon cycle and may even dominate the inter-annual variability (IAV) and trends of the land carbon sink, driven largely by the El Niño-Southern Oscillation (ENSO). The linkages between dynamics of semi-arid ecosystems and climate at the hemispheric scale however are not well known. Here, we use satellite data and climate observations from 2000 to 2014 to explore the impacts of ENSO on variability of semi-arid ecosystems, using the Ensemble Empirical Mode Decomposition method. We show that the responses of semi-arid vegetation to ENSO occur in opposite directions, resulting from opposing controls of ENSO on precipitation between the Northern Hemisphere (positively correlated to ENSO) and the Southern Hemisphere (negatively correlated to ENSO). Also, the Southern Hemisphere, with a robust negative coupling of temperature and precipitation anomalies, exhibits stronger and faster responses of semi-arid ecosystems to ENSO than the Northern Hemisphere. Our findings suggest that natural coherent variability in semi-arid ecosystem productivity responded to ENSO in opposite ways between two hemispheres, which may imply potential prediction of global semi-arid ecosystem variability, particularly based on variability in tropical Pacific Sea Surface Temperatures.

[Validity and Reliability of the Korean Version Scale of the Clinical Learning Environment, Supervision and Nurse Teacher Evaluation Scale (CLES+T)].

PubMed

Kim, Sun Hee; Yoo, So Yeon; Kim, Yae Young

2018-02-01

This study was conducted to evaluate the validity and reliability of the Korean version of the clinical learning environment, supervision and nurse teacher evaluation scale (CLES+T) that measures the clinical learning environment and the conditions associated with supervision and nurse teachers. The English CLES+T was translated into Korean with forward and back translation. Survey data were collected from 434 nursing students who had more than four days of clinical practice in Korean hospitals. Internal consistency reliability and construct validity using confirmatory and exploratory factor analysis were conducted. SPSS 20.0 and AMOS 22.0 programs were used for data analysis. The exploratory factor analysis revealed seven factors for the thirty three-item scale. Confirmatory factor analysis supported good convergent and discriminant validities. The Cronbach's alpha for the overall scale was .94 and for the seven subscales ranged from .78 to .94. The findings suggest that the 33-items Korean CLES+T is an appropriate instrument to measure Korean nursing students'clinical learning environment with good validity and reliability. © 2018 Korean Society of Nursing Science.

Semi-Automated Air-Coupled Impact-Echo Method for Large-Scale Parkade Structure.

PubMed

Epp, Tyler; Svecova, Dagmar; Cha, Young-Jin

2018-03-29

Structural Health Monitoring (SHM) has moved to data-dense systems, utilizing numerous sensor types to monitor infrastructure, such as bridges and dams, more regularly. One of the issues faced in this endeavour is the scale of the inspected structures and the time it takes to carry out testing. Installing automated systems that can provide measurements in a timely manner is one way of overcoming these obstacles. This study proposes an Artificial Neural Network (ANN) application that determines intact and damaged locations from a small training sample of impact-echo data, using air-coupled microphones from a reinforced concrete beam in lab conditions and data collected from a field experiment in a parking garage. The impact-echo testing in the field is carried out in a semi-autonomous manner to expedite the front end of the in situ damage detection testing. The use of an ANN removes the need for a user-defined cutoff value for the classification of intact and damaged locations when a least-square distance approach is used. It is postulated that this may contribute significantly to testing time reduction when monitoring large-scale civil Reinforced Concrete (RC) structures.

Semi-Automated Air-Coupled Impact-Echo Method for Large-Scale Parkade Structure

PubMed Central

Epp, Tyler; Svecova, Dagmar; Cha, Young-Jin

2018-01-01

Structural Health Monitoring (SHM) has moved to data-dense systems, utilizing numerous sensor types to monitor infrastructure, such as bridges and dams, more regularly. One of the issues faced in this endeavour is the scale of the inspected structures and the time it takes to carry out testing. Installing automated systems that can provide measurements in a timely manner is one way of overcoming these obstacles. This study proposes an Artificial Neural Network (ANN) application that determines intact and damaged locations from a small training sample of impact-echo data, using air-coupled microphones from a reinforced concrete beam in lab conditions and data collected from a field experiment in a parking garage. The impact-echo testing in the field is carried out in a semi-autonomous manner to expedite the front end of the in situ damage detection testing. The use of an ANN removes the need for a user-defined cutoff value for the classification of intact and damaged locations when a least-square distance approach is used. It is postulated that this may contribute significantly to testing time reduction when monitoring large-scale civil Reinforced Concrete (RC) structures. PMID:29596332

Classification of simulated and actual NOAA-6 AVHRR data for hydrologic land-surface feature definition. [Advanced Very High Resolution Radiometer

NASA Technical Reports Server (NTRS)

Ormsby, J. P.

1982-01-01

An examination of the possibilities of using Landsat data to simulate NOAA-6 Advanced Very High Resolution Radiometer (AVHRR) data on two channels, as well as using actual NOAA-6 imagery, for large-scale hydrological studies is presented. A running average was obtained of 18 consecutive pixels of 1 km resolution taken by the Landsat scanners were scaled up to 8-bit data and investigated for different gray levels. AVHRR data comprising five channels of 10-bit, band-interleaved information covering 10 deg latitude were analyzed and a suitable pixel grid was chosen for comparison with the Landsat data in a supervised classification format, an unsupervised mode, and with ground truth. Landcover delineation was explored by removing snow, water, and cloud features from the cluster analysis, and resulted in less than 10% difference. Low resolution large-scale data was determined useful for characterizing some landcover features if weekly and/or monthly updates are maintained.

Administrative clinical supervision as evaluated by the first-line managers in one health care organization district.

PubMed

Sirola-Karvinen, Pirjo; Hyrkäs, Kristiina

2008-07-01

The aim of this article is to increase knowledge and understanding of administrative clinical supervision. Administrative clinical supervision is a learning process for leaders that is based on experiences. Only a few studies have focused on administrative clinical supervision. The materials for this study were evaluations collected in 2002-2005 using a clinical supervision evaluation scale (MCSS). The respondents (n = 126) in the study were nursing leaders representing different specialties. The data were analysed statistically. The findings showed that the supervision succeeded very well. The contents of the sessions differed depending on the nurse leader's position. Significant differences were found in the evaluations between specialties and within years of work experience. Clinical supervision was utilized best in the psychiatric and mental health sector. The supervisees' who had long work experience scored the importance and value of clinical supervision as high. Clinical supervision is beneficial for nursing leaders. The experiences were positive and the nursing leaders appreciated the importance and value of clinical supervision. It is important to plan and coordinate a longitudinal evaluation so that clinical supervision for nursing leaders is systematically implemented and continuously developed.

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.

Topic Identification and Categorization of Public Information in Community-Based Social Media

NASA Astrophysics Data System (ADS)

Kusumawardani, RP; Basri, MH

2017-01-01

This paper presents a work on a semi-supervised method for topic identification and classification of short texts in the social media, and its application on tweets containing dialogues in a large community of dwellers in a city, written mostly in Indonesian. These dialogues comprise a wealth of information about the city, shared in real-time. We found that despite the high irregularity of the language used, and the scarcity of suitable linguistic resources, a meaningful identification of topics could be performed by clustering the tweets using the K-Means algorithm. The resulting clusters are found to be robust enough to be the basis of a classification. On three grouping schemes derived from the clusters, we get accuracy of 95.52%, 95.51%, and 96.7 using linear SVMs, reflecting the applicability of applying this method for generating topic identification and classification on such data.

AN EVALUATION OF HYDROLOGIC RESPONSE TO 25 YEARS OF LANDSCAPE CHANGE IN A SEMI-ARID WATERSHED

EPA Science Inventory

The assessment of land use and land cover is an extremely important activity for contemporary land management. A large body of current literature suggests that human land-use practices are the most important factor influencing natural resource management at multiple scales. D...

Small white matter lesion detection in cerebral small vessel disease

NASA Astrophysics Data System (ADS)

Ghafoorian, Mohsen; Karssemeijer, Nico; van Uden, Inge; de Leeuw, Frank E.; Heskes, Tom; Marchiori, Elena; Platel, Bram

2015-03-01

Cerebral small vessel disease (SVD) is a common finding on magnetic resonance images of elderly people. White matter lesions (WML) are important markers for not only the small vessel disease, but also neuro-degenerative diseases including multiple sclerosis, Alzheimer's disease and vascular dementia. Volumetric measurements such as the "total lesion load", have been studied and related to these diseases. With respect to SVD we conjecture that small lesions are important, as they have been observed to grow over time and they form the majority of lesions in number. To study these small lesions they need to be annotated, which is a complex and time-consuming task. Existing (semi) automatic methods have been aimed at volumetric measurements and large lesions, and are not suitable for the detection of small lesions. In this research we established a supervised voxel classification CAD system, optimized and trained to exclusively detect small WMLs. To achieve this, several preprocessing steps were taken, which included a robust standardization of subject intensities to reduce inter-subject intensity variability as much as possible. A number of features that were found to be well identifying small lesions were calculated including multimodal intensities, tissue probabilities, several features for accurate location description, a number of second order derivative features as well as multi-scale annular filter for blobness detection. Only small lesions were used to learn the target concept via Adaboost using random forests as its basic classifiers. Finally the results were evaluated using Free-response receiver operating characteristic.

Effect of Home Exercise Program in Patients With Knee Osteoarthritis: A Systematic Review and Meta-analysis.

PubMed

Anwer, Shahnawaz; Alghadir, Ahmad; Brismée, Jean-Michel

2016-01-01

The Osteoarthritis Research Society International recommended that nonpharmacological methods include patient education programs, weight reduction, coping strategies, and exercise programs for the management of knee osteoarthritis (OA). However, neither a systematic review nor a meta-analysis has been published regarding the effectiveness of home exercise programs for the management of knee OA. The purpose of this systematic review was to examine the evidence regarding the effect of home exercise programs with and without supervised clinic-based exercises in the management of knee OA. We searched PubMed, CINAHL, Embase, Scopus, and PEDro for research articles published prior to September 2014 using key words such as pain, exercise, home exercise program, rehabilitation, supervised exercise program, and physiotherapy in combination with Medical Subject Headings "Osteoarthritis knee." We selected randomized and case-controlled trials published in English language. To verify the quality of the selected studies, we applied the PEDro Scale. Two evaluators individually selected the studies based on titles, excluding those articles that were not related to the objectives of this review. One evaluator extracted data from the included studies. A second evaluator independently verified extracted data for accuracy. A total of 31 studies were found in the search. Of these, 19 studies met the inclusion criteria and were further analyzed. Seventeen of these 19 studies reached high methodological quality on the PEDro scale. Although the methods and home exercise program interventions varied widely in these studies, most found significant improvements in pain and function in individuals with knee OA. The analysis indicated that both home exercise programs with and without supervised clinic-based exercises were beneficial in the management of knee OA. The large evidence of high-quality trials supports the effectiveness of home exercise programs with and without supervised clinic-based exercises in the rehabilitation of knee OA. In addition, small but growing evidence supports the effectiveness of other types of exercise such as tai chi, balance, and proprioceptive training for individuals with knee OA.

Effect of Clinical Supervision on Job Satisfaction and Burnout among School Psychologists

ERIC Educational Resources Information Center

Kucer, Priscilla Naomi

2018-01-01

This study examined the effect of clinical supervision on job satisfaction and burnout among school psychologists in large urban school districts in Florida. The theory of work adjustment, Maslach and Jackson's three-dimensional model of burnout, and Atkinson and Woods's triadic model of supervision were the theoretical foundations and/or…

Semi-supervised learning for photometric supernova classification

NASA Astrophysics Data System (ADS)

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

2012-01-01

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

Toward global crop type mapping using a hybrid machine learning approach and multi-sensor imagery

NASA Astrophysics Data System (ADS)

Wang, S.; Le Bras, S.; Azzari, G.; Lobell, D. B.

2017-12-01

Current global scale datasets on agricultural land use do not have sufficient spatial or temporal resolution to meet the needs of many applications. The recent rapid increase in public availability of fine- to moderate-resolution satellite imagery from Landsat OLI and Copernicus Sentinel-2 provides a unique opportunity to improve agricultural land use datasets. This project leverages these new satellite data streams, existing census data, and a novel training approach to develop global, annual maps that indicate the presence of (i) cropland and (ii) specific crops at a 20m resolution. Our machine learning methodology consists of two steps. The first is a supervised classifier trained with explicitly labelled data to distinguish between crop and non-crop pixels, creating a binary mask. For ground truth, we use labels collected by previous mapping efforts (e.g. IIASA's crowdsourced data (Fritz et al. 2015) and AFSIS's geosurvey data) in combination with new data collected manually. The crop pixels output by the binary mask are input to the second step: a semi-supervised clustering algorithm to resolve different crop types and generate a crop type map. We do not use field-level information on crop type to train the algorithm, making this approach scalable spatially and temporally. We instead incorporate size constraints on clusters based on aggregated agricultural land use statistics and other, more generalizable domain knowledge. We employ field-level data from the U.S., Southern Europe, and Eastern Africa to validate crop-to-cluster assignments.

The Neutral Islands during the Late Epoch of Reionization

NASA Astrophysics Data System (ADS)

Xu, Yidong; Yue, Bin; Chen, Xuelei

2018-05-01

The large-scale structure of the ionization field during the epoch of reionization (EoR) can be modeled by the excursion set theory. While the growth of ionized regions during the early stage are described by the ``bubble model'', the shrinking process of neutral regions after the percolation of the ionized region calls for an ``island model''. An excursion set based analytical model and a semi-numerical code (islandFAST) have been developed. The ionizing background and the bubbles inside the islands are also included in the treatment. With two kinds of absorbers of ionizing photons, i.e. the large-scale under-dense neutral islands and the small-scale over-dense clumps, the ionizing background are self-consistently evolved in the model.

'It gave me a new lease of life … ': GPs' views and experiences of supervising foundation doctors in general practice.

PubMed

Sabey, Abigail; Harris, Michael; van Hamel, Clare

2016-03-01

General practice is a popular placement in the second year of Foundation training. Evaluations suggest this is a positive experience for most trainee doctors and benefits their perceptions of primary care, but the impact on primary care supervisors has not been considered. At a time when placements may need to increase, understanding the experience of the GP supervisors responsible for these placements is important. To explore the views, experiences and needs of GPs who supervise F2 doctors in their practices including their perceptions of the benefits to individuals and practices. A qualitative approach with GPs from across Severn Postgraduate Medical Education who supervise F2 doctors. Semi-structured interviews with 15 GPs between December 2012 and April 2013. GP supervisors are enthusiastic about helping F2 doctors to appreciate the uniqueness of primary care. Workload and responsibility around supervision is considerable making a supportive team important. Working with young, enthusiastic doctors boosts morale in the team. The presence of freshly trained minds prompts GPs to consider their own learning needs. Being a supervisor can increase job satisfaction; the teaching role gives respite from the demanding nature of GP work. Supervisors are positive about working with F2s, who lift morale in the team and challenge GPs in their own practice and learning. This boosts job and personal satisfaction. Nonetheless, consideration should be given to managing teaching workload and team support for supervision.

An evaluation of semi-automated methods for collecting ecosystem-level data in temperate marine systems.

PubMed

Griffin, Kingsley J; Hedge, Luke H; González-Rivero, Manuel; Hoegh-Guldberg, Ove I; Johnston, Emma L

2017-07-01

Historically, marine ecologists have lacked efficient tools that are capable of capturing detailed species distribution data over large areas. Emerging technologies such as high-resolution imaging and associated machine-learning image-scoring software are providing new tools to map species over large areas in the ocean. Here, we combine a novel diver propulsion vehicle (DPV) imaging system with free-to-use machine-learning software to semi-automatically generate dense and widespread abundance records of a habitat-forming algae over ~5,000 m 2 of temperate reef. We employ replicable spatial techniques to test the effectiveness of traditional diver-based sampling, and better understand the distribution and spatial arrangement of one key algal species. We found that the effectiveness of a traditional survey depended on the level of spatial structuring, and generally 10-20 transects (50 × 1 m) were required to obtain reliable results. This represents 2-20 times greater replication than have been collected in previous studies. Furthermore, we demonstrate the usefulness of fine-resolution distribution modeling for understanding patterns in canopy algae cover at multiple spatial scales, and discuss applications to other marine habitats. Our analyses demonstrate that semi-automated methods of data gathering and processing provide more accurate results than traditional methods for describing habitat structure at seascape scales, and therefore represent vastly improved techniques for understanding and managing marine seascapes.

Major challenges to scale up of visual inspection-based cervical cancer prevention programs: the experience of Guatemalan NGOs.

PubMed

Chary, Anita Nandkumar; Rohloff, Peter J

2014-08-01

Like many other low- and middle-income countries, Guatemala has adopted visual inspection with acetic acid (VIA) as a low-resource alternative to the Pap smear for cervical cancer screening. Nongovernmental organizations (NGOs) introduced VIA to Guatemala in 2004, and a growing number of NGOs, working both independently and in collaboration with the Guatemalan Ministry of Health, employ VIA in cervical cancer prevention programs today. While much research describes VIA efficacy and feasibility in Latin America, little is known about NGO involvement with VIA programming or experiences with VIA outside the context of clinical trials and pilot projects in the region. To explore challenges faced by NGOs implementing VIA programs in Guatemala, we conducted semi-structured interviews with 36 NGO staff members involved with 20 VIA programs as direct service providers, program administrators, and training course instructors. Additionally, we collected data through observation at 30 NGO-sponsored cervical cancer screening campaigns, 8 cervical cancer prevention conferences, and 1 week-long NGO-sponsored VIA training course. Frequently highlighted challenges included staff turnover, concerns over training quality, a need for opportunities for continued supervision, and problems with cryotherapy referrals when immediate treatment for VIA-positive women was unavailable. Reducing staff turnover, budgeting to train replacement providers, standardizing training curricula, and offering continued supervision are key strategies to improve VIA service quality and program sustainability. Alternative training methods, such as on-the-job mentoring and course prerequisites of online learning, could help increase training time available for clinical supervision. Efforts should be made to ensure that VIA testing is coupled with immediate cryotherapy, that providers trained in VIA are also trained in cryotherapy, and that cryotherapy supplies and equipment are maintained. Where this is not possible and only VIA screening is available, referral systems must be strengthened.

Major challenges to scale up of visual inspection-based cervical cancer prevention programs: the experience of Guatemalan NGOs

PubMed Central

Chary, Anita Nandkumar; Rohloff, Peter J

2014-01-01

ABSTRACT Background: Like many other low- and middle-income countries, Guatemala has adopted visual inspection with acetic acid (VIA) as a low-resource alternative to the Pap smear for cervical cancer screening. Nongovernmental organizations (NGOs) introduced VIA to Guatemala in 2004, and a growing number of NGOs, working both independently and in collaboration with the Guatemalan Ministry of Health, employ VIA in cervical cancer prevention programs today. While much research describes VIA efficacy and feasibility in Latin America, little is known about NGO involvement with VIA programming or experiences with VIA outside the context of clinical trials and pilot projects in the region. Methods: To explore challenges faced by NGOs implementing VIA programs in Guatemala, we conducted semi-structured interviews with 36 NGO staff members involved with 20 VIA programs as direct service providers, program administrators, and training course instructors. Additionally, we collected data through observation at 30 NGO-sponsored cervical cancer screening campaigns, 8 cervical cancer prevention conferences, and 1 week-long NGO-sponsored VIA training course. Results: Frequently highlighted challenges included staff turnover, concerns over training quality, a need for opportunities for continued supervision, and problems with cryotherapy referrals when immediate treatment for VIA-positive women was unavailable. Conclusions: Reducing staff turnover, budgeting to train replacement providers, standardizing training curricula, and offering continued supervision are key strategies to improve VIA service quality and program sustainability. Alternative training methods, such as on-the-job mentoring and course prerequisites of online learning, could help increase training time available for clinical supervision. Efforts should be made to ensure that VIA testing is coupled with immediate cryotherapy, that providers trained in VIA are also trained in cryotherapy, and that cryotherapy supplies and equipment are maintained. Where this is not possible and only VIA screening is available, referral systems must be strengthened. PMID:25276590

The Intern's Experience as Supervisor: Managing Resistance, Identification, and Countertransference While Feeling Insecure

ERIC Educational Resources Information Center

DiMino, John L.; Risler, Robin

2014-01-01

This article focuses on the experiences of predoctoral interns supervising the clinical work of less experienced externs in psychology and social work as part of a training program in a large university counseling center. After 4 years of running a relationally based supervision of supervision group, the authors believe that providing supervision…

AQBE — QBE Style Queries for Archetyped Data

NASA Astrophysics Data System (ADS)

Sachdeva, Shelly; Yaginuma, Daigo; Chu, Wanming; Bhalla, Subhash

Large-scale adoption of electronic healthcare applications requires semantic interoperability. The new proposals propose an advanced (multi-level) DBMS architecture for repository services for health records of patients. These also require query interfaces at multiple levels and at the level of semi-skilled users. In this regard, a high-level user interface for querying the new form of standardized Electronic Health Records system has been examined in this study. It proposes a step-by-step graphical query interface to allow semi-skilled users to write queries. Its aim is to decrease user effort and communication ambiguities, and increase user friendliness.

Lidar Cloud Detection with Fully Convolutional Networks

NASA Astrophysics Data System (ADS)

Cromwell, E.; Flynn, D.

2017-12-01

The vertical distribution of clouds from active remote sensing instrumentation is a widely used data product from global atmospheric measuring sites. The presence of clouds can be expressed as a binary cloud mask and is a primary input for climate modeling efforts and cloud formation studies. Current cloud detection algorithms producing these masks do not accurately identify the cloud boundaries and tend to oversample or over-represent the cloud. This translates as uncertainty for assessing the radiative impact of clouds and tracking changes in cloud climatologies. The Atmospheric Radiation Measurement (ARM) program has over 20 years of micro-pulse lidar (MPL) and High Spectral Resolution Lidar (HSRL) instrument data and companion automated cloud mask product at the mid-latitude Southern Great Plains (SGP) and the polar North Slope of Alaska (NSA) atmospheric observatory. Using this data, we train a fully convolutional network (FCN) with semi-supervised learning to segment lidar imagery into geometric time-height cloud locations for the SGP site and MPL instrument. We then use transfer learning to train a FCN for (1) the MPL instrument at the NSA site and (2) for the HSRL. In our semi-supervised approach, we pre-train the classification layers of the FCN with weakly labeled lidar data. Then, we facilitate end-to-end unsupervised pre-training and transition to fully supervised learning with ground truth labeled data. Our goal is to improve the cloud mask accuracy and precision for the MPL instrument to 95% and 80%, respectively, compared to the current cloud mask algorithms of 89% and 50%. For the transfer learning based FCN for the HSRL instrument, our goal is to achieve a cloud mask accuracy of 90% and a precision of 80%.

Unsupervised Transfer Learning via Multi-Scale Convolutional Sparse Coding for Biomedical Applications

PubMed Central

Chang, Hang; Han, Ju; Zhong, Cheng; Snijders, Antoine M.; Mao, Jian-Hua

2017-01-01

The capabilities of (I) learning transferable knowledge across domains; and (II) fine-tuning the pre-learned base knowledge towards tasks with considerably smaller data scale are extremely important. Many of the existing transfer learning techniques are supervised approaches, among which deep learning has the demonstrated power of learning domain transferrable knowledge with large scale network trained on massive amounts of labeled data. However, in many biomedical tasks, both the data and the corresponding label can be very limited, where the unsupervised transfer learning capability is urgently needed. In this paper, we proposed a novel multi-scale convolutional sparse coding (MSCSC) method, that (I) automatically learns filter banks at different scales in a joint fashion with enforced scale-specificity of learned patterns; and (II) provides an unsupervised solution for learning transferable base knowledge and fine-tuning it towards target tasks. Extensive experimental evaluation of MSCSC demonstrates the effectiveness of the proposed MSCSC in both regular and transfer learning tasks in various biomedical domains. PMID:28129148

Nanoliter-Scale Protein Crystallization and Screening with a Microfluidic Droplet Robot

PubMed Central

Zhu, Ying; Zhu, Li-Na; Guo, Rui; Cui, Heng-Jun; Ye, Sheng; Fang, Qun

2014-01-01

Large-scale screening of hundreds or even thousands of crystallization conditions while with low sample consumption is in urgent need, in current structural biology research. Here we describe a fully-automated droplet robot for nanoliter-scale crystallization screening that combines the advantages of both automated robotics technique for protein crystallization screening and the droplet-based microfluidic technique. A semi-contact dispensing method was developed to achieve flexible, programmable and reliable liquid-handling operations for nanoliter-scale protein crystallization experiments. We applied the droplet robot in large-scale screening of crystallization conditions of five soluble proteins and one membrane protein with 35–96 different crystallization conditions, study of volume effects on protein crystallization, and determination of phase diagrams of two proteins. The volume for each droplet reactor is only ca. 4–8 nL. The protein consumption significantly reduces 50–500 fold compared with current crystallization stations. PMID:24854085

Nanoliter-scale protein crystallization and screening with a microfluidic droplet robot.

PubMed

Zhu, Ying; Zhu, Li-Na; Guo, Rui; Cui, Heng-Jun; Ye, Sheng; Fang, Qun

2014-05-23

Large-scale screening of hundreds or even thousands of crystallization conditions while with low sample consumption is in urgent need, in current structural biology research. Here we describe a fully-automated droplet robot for nanoliter-scale crystallization screening that combines the advantages of both automated robotics technique for protein crystallization screening and the droplet-based microfluidic technique. A semi-contact dispensing method was developed to achieve flexible, programmable and reliable liquid-handling operations for nanoliter-scale protein crystallization experiments. We applied the droplet robot in large-scale screening of crystallization conditions of five soluble proteins and one membrane protein with 35-96 different crystallization conditions, study of volume effects on protein crystallization, and determination of phase diagrams of two proteins. The volume for each droplet reactor is only ca. 4-8 nL. The protein consumption significantly reduces 50-500 fold compared with current crystallization stations.

Improved understanding of hydrology and erosion processes and enhanced application of the Rangeland Hydrology and Erosion Model (RHEM) for disturbed rangelands

USDA-ARS?s Scientific Manuscript database

Large-scale disturbances such as fire and woodland encroachment continue to plague the sustainability of semi-arid regions around the world. Land managers are challenged with predicting and mitigating such disturbances to stabilize soil and ecological degradation of vast landscapes. Scientists fro...

Costs of landscape silviculture for fire and habitat management.

Treesearch

S. Hummel; D.E. Calkin

2005-01-01

In forest reserves of the U.S. Pacific Northwest, management objectives include protecting late-semi habitat structure by reducing the threat of large-scale disturbances like wildfire. We simulated how altering within- and among-stand structure with silvicultural treatments of differing intensity affected late-seral forest (LSF) structure and fire threat (FT) reduction...

Intraseasonal to interannual variations in the tropical wave activity revealed in reanalyses and their potential impact on the QBO

NASA Astrophysics Data System (ADS)

Kim, Young-Ha; Yoo, Changhyun

2017-04-01

We investigate activities of tropical waves represented in reanalysis products. The wave activities are quantified by the Eliassen-Palm (EP) flux at 100 hPa, after decomposed into the following four components: equatorially trapped Kelvin waves and mixed Rossby-gravity waves, gravity waves, and Rossby waves. Monthly EP fluxes of the four waves exhibit considerable temporal variations at intraseasonal and interannual, along with seasonal, time scales. These variations are discussed with the tropical large-scale variabilities, including the Madden-Julian Oscillation (MJO), the El Ninõ-Southern Oscillation, and the stratospheric quasi-biennial oscillation (QBO). We find that during boreal winter, the interannual variation of Kelvin wave activity is in phase with that of the MJO amplitude, while such a simultaneous variation cannot be seen in other seasons. The gravity wave is dominated by a semi-annual cycle, while the departure from its semi-annual cycle is largely correlated with the QBO phase in the stratosphere. Potential impacts of the variations in the wave activity upon the QBO properties will be assessed using a simple one-dimensional QBO model.

Pharmaceutical Raw Material Identification Using Miniature Near-Infrared (MicroNIR) Spectroscopy and Supervised Pattern Recognition Using Support Vector Machine

PubMed Central

Hsiung, Chang; Pederson, Christopher G.; Zou, Peng; Smith, Valton; von Gunten, Marc; O’Brien, Nada A.

2016-01-01

Near-infrared spectroscopy as a rapid and non-destructive analytical technique offers great advantages for pharmaceutical raw material identification (RMID) to fulfill the quality and safety requirements in pharmaceutical industry. In this study, we demonstrated the use of portable miniature near-infrared (MicroNIR) spectrometers for NIR-based pharmaceutical RMID and solved two challenges in this area, model transferability and large-scale classification, with the aid of support vector machine (SVM) modeling. We used a set of 19 pharmaceutical compounds including various active pharmaceutical ingredients (APIs) and excipients and six MicroNIR spectrometers to test model transferability. For the test of large-scale classification, we used another set of 253 pharmaceutical compounds comprised of both chemically and physically different APIs and excipients. We compared SVM with conventional chemometric modeling techniques, including soft independent modeling of class analogy, partial least squares discriminant analysis, linear discriminant analysis, and quadratic discriminant analysis. Support vector machine modeling using a linear kernel, especially when combined with a hierarchical scheme, exhibited excellent performance in both model transferability and large-scale classification. Hence, ultra-compact, portable and robust MicroNIR spectrometers coupled with SVM modeling can make on-site and in situ pharmaceutical RMID for large-volume applications highly achievable. PMID:27029624

A semi-automatic traffic sign detection, classification, and positioning system

NASA Astrophysics Data System (ADS)

Creusen, I. M.; Hazelhoff, L.; de With, P. H. N.

2012-01-01

The availability of large-scale databases containing street-level panoramic images offers the possibility to perform semi-automatic surveying of real-world objects such as traffic signs. These inventories can be performed significantly more efficiently than using conventional methods. Governmental agencies are interested in these inventories for maintenance and safety reasons. This paper introduces a complete semi-automatic traffic sign inventory system. The system consists of several components. First, a detection algorithm locates the 2D position of the traffic signs in the panoramic images. Second, a classification algorithm is used to identify the traffic sign. Third, the 3D position of the traffic sign is calculated using the GPS position of the photographs. Finally, the results are listed in a table for quick inspection and are also visualized in a web browser.

Self-consistent implementation of meta-GGA functionals for the ONETEP linear-scaling electronic structure package.

PubMed

Womack, James C; Mardirossian, Narbe; Head-Gordon, Martin; Skylaris, Chris-Kriton

2016-11-28

Accurate and computationally efficient exchange-correlation functionals are critical to the successful application of linear-scaling density functional theory (DFT). Local and semi-local functionals of the density are naturally compatible with linear-scaling approaches, having a general form which assumes the locality of electronic interactions and which can be efficiently evaluated by numerical quadrature. Presently, the most sophisticated and flexible semi-local functionals are members of the meta-generalized-gradient approximation (meta-GGA) family, and depend upon the kinetic energy density, τ, in addition to the charge density and its gradient. In order to extend the theoretical and computational advantages of τ-dependent meta-GGA functionals to large-scale DFT calculations on thousands of atoms, we have implemented support for τ-dependent meta-GGA functionals in the ONETEP program. In this paper we lay out the theoretical innovations necessary to implement τ-dependent meta-GGA functionals within ONETEP's linear-scaling formalism. We present expressions for the gradient of the τ-dependent exchange-correlation energy, necessary for direct energy minimization. We also derive the forms of the τ-dependent exchange-correlation potential and kinetic energy density in terms of the strictly localized, self-consistently optimized orbitals used by ONETEP. To validate the numerical accuracy of our self-consistent meta-GGA implementation, we performed calculations using the B97M-V and PKZB meta-GGAs on a variety of small molecules. Using only a minimal basis set of self-consistently optimized local orbitals, we obtain energies in excellent agreement with large basis set calculations performed using other codes. Finally, to establish the linear-scaling computational cost and applicability of our approach to large-scale calculations, we present the outcome of self-consistent meta-GGA calculations on amyloid fibrils of increasing size, up to tens of thousands of atoms.

Self-consistent implementation of meta-GGA functionals for the ONETEP linear-scaling electronic structure package

NASA Astrophysics Data System (ADS)

Womack, James C.; Mardirossian, Narbe; Head-Gordon, Martin; Skylaris, Chris-Kriton

2016-11-01

Accurate and computationally efficient exchange-correlation functionals are critical to the successful application of linear-scaling density functional theory (DFT). Local and semi-local functionals of the density are naturally compatible with linear-scaling approaches, having a general form which assumes the locality of electronic interactions and which can be efficiently evaluated by numerical quadrature. Presently, the most sophisticated and flexible semi-local functionals are members of the meta-generalized-gradient approximation (meta-GGA) family, and depend upon the kinetic energy density, τ, in addition to the charge density and its gradient. In order to extend the theoretical and computational advantages of τ-dependent meta-GGA functionals to large-scale DFT calculations on thousands of atoms, we have implemented support for τ-dependent meta-GGA functionals in the ONETEP program. In this paper we lay out the theoretical innovations necessary to implement τ-dependent meta-GGA functionals within ONETEP's linear-scaling formalism. We present expressions for the gradient of the τ-dependent exchange-correlation energy, necessary for direct energy minimization. We also derive the forms of the τ-dependent exchange-correlation potential and kinetic energy density in terms of the strictly localized, self-consistently optimized orbitals used by ONETEP. To validate the numerical accuracy of our self-consistent meta-GGA implementation, we performed calculations using the B97M-V and PKZB meta-GGAs on a variety of small molecules. Using only a minimal basis set of self-consistently optimized local orbitals, we obtain energies in excellent agreement with large basis set calculations performed using other codes. Finally, to establish the linear-scaling computational cost and applicability of our approach to large-scale calculations, we present the outcome of self-consistent meta-GGA calculations on amyloid fibrils of increasing size, up to tens of thousands of atoms.

The impact of supervision training on genetic counselor supervisory identity development.

PubMed

Atzinger, Carrie L; Lewis, Kimberly; Martin, Lisa J; Yager, Geoffrey; Ramstetter, Catherine; Wusik, Katie

2014-12-01

Supervision is critical to the training of genetic counselors. Limited research exists on the influence of supervision training and experience on the development of genetic counseling supervisors. The purpose of this study was to investigate the impact of supervision training in addition to supervisory and clinical experience on supervisory identity development, and the perceived confidence and competence supervisors have in their own supervisory skills. In addition, we explored genetic counselors' (N = 291) interest in and barriers to training as well as perspectives on requirements for supervisors. Results indicated clinical experience, supervision experience, and formal supervision training are positively associated with genetic counselors' supervisory identity development as measured by the Psychotherapy Supervisory Development Scale (PSDS) (p < 0.05). Despite a moderate correlation between supervision experience and formal training (ρ = 0.42, p < 0.001), both had independent effects on PSDS scores (p < 0.04). A majority of genetic counselors were interested in receiving supervision training but noted lack of available training as a barrier. The majority of participants indicated that supervisors should be certified as genetic counselors, but there was no consensus on training requirements. Development of additional supervision training opportunities for genetic counselors should be considered.

Learning Biological Networks via Bootstrapping with Optimized GO-based Gene Similarity

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

Taylor, Ronald C.; Sanfilippo, Antonio P.; McDermott, Jason E.

2010-08-02

Microarray gene expression data provide a unique information resource for learning biological networks using "reverse engineering" methods. However, there are a variety of cases in which we know which genes are involved in a given pathology of interest, but we do not have enough experimental evidence to support the use of fully-supervised/reverse-engineering learning methods. In this paper, we explore a novel semi-supervised approach in which biological networks are learned from a reference list of genes and a partial set of links for these genes extracted automatically from PubMed abstracts, using a knowledge-driven bootstrapping algorithm. We show how new relevant linksmore » across genes can be iteratively derived using a gene similarity measure based on the Gene Ontology that is optimized on the input network at each iteration. We describe an application of this approach to the TGFB pathway as a case study and show how the ensuing results prove the feasibility of the approach as an alternate or complementary technique to fully supervised methods.« less

Optimal reinforcement of training datasets in semi-supervised landmark-based segmentation

NASA Astrophysics Data System (ADS)

Ibragimov, Bulat; Likar, Boštjan; Pernuš, Franjo; Vrtovec, Tomaž

2015-03-01

During the last couple of decades, the development of computerized image segmentation shifted from unsupervised to supervised methods, which made segmentation results more accurate and robust. However, the main disadvantage of supervised segmentation is a need for manual image annotation that is time-consuming and subjected to human error. To reduce the need for manual annotation, we propose a novel learning approach for training dataset reinforcement in the area of landmark-based segmentation, where newly detected landmarks are optimally combined with reference landmarks from the training dataset and therefore enriches the training process. The approach is formulated as a nonlinear optimization problem, where the solution is a vector of weighting factors that measures how reliable are the detected landmarks. The detected landmarks that are found to be more reliable are included into the training procedure with higher weighting factors, whereas the detected landmarks that are found to be less reliable are included with lower weighting factors. The approach is integrated into the landmark-based game-theoretic segmentation framework and validated against the problem of lung field segmentation from chest radiographs.

Constraining calving front processes on W Greenland outlet glaciers using inertial-corrected laser scanning & swath-bathymetry

NASA Astrophysics Data System (ADS)

Bates, R.; Hubbard, A.; Neale, M.; Woodward, J.; Box, J. E.; Nick, F.

2010-12-01

Calving and submarine melt account for the majority of loss from the Antarctic and over 50% of that from the Greenland Ice Sheet. These ice-ocean processes are highly efficient mass-loss mechanisms, providing a rapid link between terrestrial ice (storage) and the oceanic sink (sea level/freshwater flux) which renders the ocean-outlet-ice sheet system potentially highly non-linear. Despite this, the controls on tidewater processes are poorly understood and a process based description of them is lacking from the present generation of coupled ice sheet models. We present details from an innovative study where two survey techniques are integrated to enable the construction of accurate, ~m resolution 3d digital terrain models (DTMs) of the aerial and submarine ice front of calving outlet glaciers. A 2km range terrestrial laser scanner was combined with a 416KHz swath-interferometric system and corrected via an inertial motion unit stabilized by RTK GPS and gyro-compass data. The system was mounted aboard a heavy displacement (20,000kg) yacht in addition to a light displacement (100kg) semi-autonomous boat and used to image the aerial and submarine calving fronts of two large outlet glaciers in W Greenland. Six daily surveys, each 2.5km long were repeated across Lille Glacier during which significant ice flow, melt and calving events were observed and captured from on-ice GPS stations and time-lapse sequences. A curtain of CTD and velocity casts were also conducted to constrain the fresh and oceanic mass and energy fluxes within the fjord. The residual of successive DTMs yield the spatial pattern of frontal change enabling the processes of aerial and submarine calving and melt to be quantified and constrained in unprecedented detail. These observed frontal changes are tentatively related to local dynamic, atmospheric and oceanographic processes that drive them. A partial survey of Store Glacier (~7km calving front & W Greenland 2nd largest outlet after Jakobshavn Isbrae) was conducted, indicating that the technique is successful up to ~500m from the ice front and to a similar water depth. These data sets show that it is possible to integrate and build 3d DTMs at the metre-scale both above and below the water surface. The successful acquisition from our semi-autonomous vessel supervised up to 2km away greatly eases repeat surveys and reduces the exposure of equipment and personnel to the risks posed by large, active calving glaciers. Lille Glacier and s/v Gambo surveyed & photographed from the semi-autonomous vessel. Mock-up of Lille Glacier calving front and fore-bay submarine topography imaged by interferometric swath-bathymetry.

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

Improving the Efficiency and Effectiveness of Community Detection via Prior-Induced Equivalent Super-Network.

PubMed

Yang, Liang; Jin, Di; He, Dongxiao; Fu, Huazhu; Cao, Xiaochun; Fogelman-Soulie, Francoise

2017-03-29

Due to the importance of community structure in understanding network and a surge of interest aroused on community detectability, how to improve the community identification performance with pairwise prior information becomes a hot topic. However, most existing semi-supervised community detection algorithms only focus on improving the accuracy but ignore the impacts of priors on speeding detection. Besides, they always require to tune additional parameters and cannot guarantee pairwise constraints. To address these drawbacks, we propose a general, high-speed, effective and parameter-free semi-supervised community detection framework. By constructing the indivisible super-nodes according to the connected subgraph of the must-link constraints and by forming the weighted super-edge based on network topology and cannot-link constraints, our new framework transforms the original network into an equivalent but much smaller Super-Network. Super-Network perfectly ensures the must-link constraints and effectively encodes cannot-link constraints. Furthermore, the time complexity of super-network construction process is linear in the original network size, which makes it efficient. Meanwhile, since the constructed super-network is much smaller than the original one, any existing community detection algorithm is much faster when using our framework. Besides, the overall process will not introduce any additional parameters, making it more practical.

Guided Text Search Using Adaptive Visual Analytics

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

Steed, Chad A; Symons, Christopher T; Senter, James K

This research demonstrates the promise of augmenting interactive visualizations with semi- supervised machine learning techniques to improve the discovery of significant associations and insights in the search and analysis of textual information. More specifically, we have developed a system called Gryffin that hosts a unique collection of techniques that facilitate individualized investigative search pertaining to an ever-changing set of analytical questions over an indexed collection of open-source documents related to critical national infrastructure. The Gryffin client hosts dynamic displays of the search results via focus+context record listings, temporal timelines, term-frequency views, and multiple coordinate views. Furthermore, as the analyst interactsmore » with the display, the interactions are recorded and used to label the search records. These labeled records are then used to drive semi-supervised machine learning algorithms that re-rank the unlabeled search records such that potentially relevant records are moved to the top of the record listing. Gryffin is described in the context of the daily tasks encountered at the US Department of Homeland Security s Fusion Center, with whom we are collaborating in its development. The resulting system is capable of addressing the analysts information overload that can be directly attributed to the deluge of information that must be addressed in the search and investigative analysis of textual information.« less

Positive diversity–invasibility relationship in species-rich semi-natural grassland at the neighbourhood scale

PubMed Central

Zeiter, Michaela; Stampfli, Andreas

2012-01-01

Background and Aims Attempts to answer the old question of whether high diversity causes high invasion resistance have resulted in an invasion paradox: while large-scale studies often find a positive relationship between diversity and invasibility, small-scale experimental studies often find a negative relationship. Many of the small-scale studies are conducted in artificial communities of even-aged plants. Species in natural communities, however, do not represent one simultaneous cohort and occur at various levels of spatial aggregation at different scales. This study used natural patterns of diversity to assess the relationship between diversity and invasibility within a uniformly managed, semi-natural community. Methods In species-rich grassland, one seed of each of ten species was added to each of 50 contiguous 16 cm2 quadrats within seven plots (8 × 100 cm). The emergence of these species was recorded in seven control plots, and establishment success was measured in relation to the species diversity of the resident vegetation at two spatial scales, quadrat (64 cm2) within plots (800 cm2) and between plots within the site (approx. 400 m2) over 46 months. Key Results Invader success was positively related to resident species diversity and richness over a range of 28–37 species per plot. This relationship emerged 7 months after seed addition and remained over time despite continuous mortality of invaders. Conclusions Biotic resistance to plant invasion may play only a sub-ordinate role in species-rich, semi-natural grassland. As possible alternative explanations for the positive diversity–invasibility relationship are not clear, it is recommended that future studies elaborate fine-scale environmental heterogeneity in resource supplies or potential resource flows from resident species to seedlings by means of soil biological networks established by arbuscular mycorrhizal fungi. PMID:22956533

A unifying framework for systems modeling, control systems design, and system operation

NASA Technical Reports Server (NTRS)

Dvorak, Daniel L.; Indictor, Mark B.; Ingham, Michel D.; Rasmussen, Robert D.; Stringfellow, Margaret V.

2005-01-01

Current engineering practice in the analysis and design of large-scale multi-disciplinary control systems is typified by some form of decomposition- whether functional or physical or discipline-based-that enables multiple teams to work in parallel and in relative isolation. Too often, the resulting system after integration is an awkward marriage of different control and data mechanisms with poor end-to-end accountability. System of systems engineering, which faces this problem on a large scale, cries out for a unifying framework to guide analysis, design, and operation. This paper describes such a framework based on a state-, model-, and goal-based architecture for semi-autonomous control systems that guides analysis and modeling, shapes control system software design, and directly specifies operational intent. This paper illustrates the key concepts in the context of a large-scale, concurrent, globally distributed system of systems: NASA's proposed Array-based Deep Space Network.

Remotely-Sensed Regional-Scale Evapotranspiration of a Semi-Arid Great Basin Desert and its Relationship to Geomorphology, Soils, and Vegetation

NASA Technical Reports Server (NTRS)

Laymon, C.; Quattrochi, D.; Malek, E.; Hipps, L.; Boettinger, J.; McCurdy, G.

1998-01-01

Landsat thematic mapper data are used to estimate instantaneous regional-scale surface water and energy fluxes in a semi-arid Great Basin desert of the western United States. Results suggest that it is possible to scale from point measurements of environmental state variables to regional estimates of water and energy exchange. This research characterizes the unifying thread in the classical climate-topography-soil-vegetation relation -the surface water and energy balance-through maps of the partitioning of energy throughout the landscape. The study was conducted in Goshute Valley of northeastern Nevada, which is characteristic of most faulted graben valleys of the Basin and Range Province of the western United States. The valley comprises a central playa and lake plain bordered by alluvial fans emanating from the surrounding mountains. The distribution of evapotranspiration (ET) is lowest in the middle reaches of the fans where the water table is deep and plants are small, resulting in low evaporation and transpiration. Highest ET occurs in the center of the valley, particularly in the playa, where limited to no vegetation occurs, but evaporation is relatively high because of a shallow water table and silty clay soil capable of large capillary movement. Intermediate values of ET are associated with large shrubs and is dominated by transpiration.

Remotely-Sensed Regional-Scale Evapotranspiration of a Semi-Arid Great Basin Desert and its Relationship to Geomorphology, Soils, and Vegetation

NASA Technical Reports Server (NTRS)

Laymon, C.; Quattrochi, D.; Malek, E.; Hipps, L.; Boettinger, J.; McCurdy, G.

1997-01-01

Landsat Thematic Mapper data is used to estimate instantaneous regional-scale surface water and energy fluxes in a semi-arid Great Basin desert of the western United States. Results suggest that it is possible to scale from point measurements of environmental state variables to regional estimates of water and energy exchange. This research characterizes the unifying thread in the classical climate-topography-soil-vegetation relation-the surface water and energy balance-through maps of the partitioning of energy throughout the landscape. The study was conducted in Goshute Valley of northeastern Nevada, which is characteristic of most faulted graben valleys of the Basin and Range Province of the western United States. The valley comprises a central playa and lake plain bordered by alluvial fans emanating from the surrounding mountains. The distribution of evapotranspiration (ET) is lowest in the middle reaches of the fans where the water table is deep and plants are small, resulting in low evaporation and transpiration. Highest ET occurs in the center of the valley, particularly in the playa, where limited to no vegetation occurs, but evaporation is relatively high because of a shallow water table and silty clay soil capable of large capillary movement. Intermediate values of ET are associated with large shrubs and is dominated by transpiration.

Exploring the Use of Tablets for Student Teaching Supervision

ERIC Educational Resources Information Center

Johnson, Joseph A.; Wesley, Whitney M.; Yerrick, Randy

2016-01-01

While research on the use of tablets in the field of education is emerging, existing research on the use of this tool for the purposes of student teaching supervision is very limited. This study aimed to explore the application of iPad tablets for student teacher supervision in a teacher preparation program at a large state university in the…

Automatic scoring of dicentric chromosomes as a tool in large scale radiation accidents.

PubMed

Romm, H; Ainsbury, E; Barnard, S; Barrios, L; Barquinero, J F; Beinke, C; Deperas, M; Gregoire, E; Koivistoinen, A; Lindholm, C; Moquet, J; Oestreicher, U; Puig, R; Rothkamm, K; Sommer, S; Thierens, H; Vandersickel, V; Vral, A; Wojcik, A

2013-08-30

Mass casualty scenarios of radiation exposure require high throughput biological dosimetry techniques for population triage in order to rapidly identify individuals who require clinical treatment. The manual dicentric assay is a highly suitable technique, but it is also very time consuming and requires well trained scorers. In the framework of the MULTIBIODOSE EU FP7 project, semi-automated dicentric scoring has been established in six European biodosimetry laboratories. Whole blood was irradiated with a Co-60 gamma source resulting in 8 different doses between 0 and 4.5Gy and then shipped to the six participating laboratories. To investigate two different scoring strategies, cell cultures were set up with short term (2-3h) or long term (24h) colcemid treatment. Three classifiers for automatic dicentric detection were applied, two of which were developed specifically for these two different culture techniques. The automation procedure included metaphase finding, capture of cells at high resolution and detection of dicentric candidates. The automatically detected dicentric candidates were then evaluated by a trained human scorer, which led to the term 'semi-automated' being applied to the analysis. The six participating laboratories established at least one semi-automated calibration curve each, using the appropriate classifier for their colcemid treatment time. There was no significant difference between the calibration curves established, regardless of the classifier used. The ratio of false positive to true positive dicentric candidates was dose dependent. The total staff effort required for analysing 150 metaphases using the semi-automated approach was 2 min as opposed to 60 min for manual scoring of 50 metaphases. Semi-automated dicentric scoring is a useful tool in a large scale radiation accident as it enables high throughput screening of samples for fast triage of potentially exposed individuals. Furthermore, the results from the participating laboratories were comparable which supports networking between laboratories for this assay. Copyright © 2013 Elsevier B.V. All rights reserved.

Clinical Supervision of Mental Health Professionals Serving Youth: Format and Microskills.

PubMed

Bailin, Abby; Bearman, Sarah Kate; Sale, Rafaella

2018-03-21

Clinical supervision is an element of quality assurance in routine mental health care settings serving children; however, there is limited scientific evaluation of its components. This study examines the format and microskills of routine supervision. Supervisors (n = 13) and supervisees (n = 20) reported on 100 supervision sessions, and trained coders completed observational coding on a subset of recorded sessions (n = 57). Results indicate that microskills shown to enhance supervisee competency in effectiveness trials and experiments were largely absent from routine supervision, highlighting potential missed opportunities to impart knowledge to therapists. Findings suggest areas for quality improvement within routine care settings.

The nature and structure of supervision in health visiting with victims of child sexual abuse.

PubMed

Scott, L

1999-03-01

Part of a higher research degree to explore professional practice. To explore how health visitors work with victims of child sexual abuse and the supervision systems to support them. To seek the views and experiences of practising health visitors relating to complex care in order to consider the nature and structure of supervision. The research reported in this paper used a qualitative method of research and semi-structured interviews with practising health visitors of varying levels of experience in venues around England. Qualitative research enabled the exploration of experiences. Identification of the need for regular, structured, accountable opportunities in a 'private setting' to discuss whole caseload work and current practice issues. Supervision requires a structured, formalized process, in both regularity and content, as a means to explore and acknowledge work with increasingly complex care, to enable full discussion of whole caseloads. Supervision is demonstrated as a vehicle to enable the sharing of good practices and for weak practices to be identified and managed appropriately. Supervision seeks to fulfil the above whilst promoting a stimulating, learning experience, accommodating the notion that individuals learn at their own pace and bring a wealth of human experience to the service. The size of the study was dictated by the amount of time available within which to complete a research master's degree course primarily in the author's own time, over a 2-year period. The majority of participants volunteered their accounts in their own time. For others I obtained permission from their employers for them to participate once they approached me with an interest in being interviewed. This research provides a model of supervision based on practitioner views and experiences. The article highlights the value of research and evidence-based information to enhance practice accountability and the quality of care. Proactive risk management can safeguard the health and safety of the public, the practitioner and the organization.

Identification and optimization of parameters for the semi-continuous production of garbage enzyme from pre-consumer organic waste by green RP-HPLC method.

PubMed

Arun, C; Sivashanmugam, P

2015-10-01

Reuse and management of organic solid waste, reduce the environmental impact on human health and increase the economic status by generating valuable products for current and novel applications. Garbage enzyme is one such product produced from fermentation of organic solid waste and it can be used as liquid fertilizer, antimicrobial agents, treatment of domestic wastewater, municipal and industrial sludge treatment, etc. The semi-continuous production of garbage enzyme in large quantity at minimal time period and at lesser cost is needed to cater for treatment of increasing quantities of industrial waste activated sludge. This necessitates a parameter for monitoring and control for the scaling up of current process on semi-continuous basis. In the present study a RP-HPLC (Reversed Phase-High Performance Liquid Chromatography) method is used for quantification of standard organic acid at optimized condition 30°C column oven temperature, pH 2.7, and 0.7 ml/min flow rate of the mobile phase (potassium dihydrogen phosphate in water) at 50mM concentration. The garbage enzyme solution collected in 15, 30, 45, 60, 75 and 90 days were used as sample to determine the concentration of organic acid. Among these, 90th day sample showed the maximum concentration of 78.14 g/l of acetic acid in garbage enzyme, whereas other organic acids concentration got decreased when compare to the 15th day sample. This result confirms that the matured garbage enzyme contains a higher concentration of acetic acid and thus it can be used as a monitoring parameter for semi-continuous production of garbage enzyme in large scale. Copyright © 2015 Elsevier Ltd. All rights reserved.

Comparison of start-up strategies and process performance during semi-continuous anaerobic digestion of sugarcane filter cake co-digested with bagasse.

PubMed

Janke, Leandro; Leite, Athaydes F; Nikolausz, Marcell; Radetski, Claudemir M; Nelles, Michael; Stinner, Walter

2016-02-01

The anaerobic digestion of sugarcane filter cake and the option of co-digestion with bagasse were investigated in a semi-continuous feeding regime to assess the main parameters used for large-scale process designing. Moreover, fresh cattle manure was considered as alternative inoculum for the start-up of biogas reactors in cases where digestate from a biogas plant would not be available in remote rural areas. Experiments were carried out in 6 lab-scale semi-continuous stirred-tank reactors at mesophilic conditions (38±1°C) while the main anaerobic digestion process parameters monitored. Fresh cattle manure demonstrated to be appropriate for the start-up process. However, an acclimation period was required due to the high initial volatile fatty acids concentration (8.5gL(-1)). Regardless the mono-digestion of filter cake presented 50% higher biogas yield (480mLgVS(-1)) than co-digestion with bagasse (320mLgVS(-1)) during steady state conditions. A large-scale co-digestion system would produce 58% more biogas (1008m(3)h(-1)) than mono-digestion of filter cake (634m(3)h(-1)) due to its higher biomass availability for biogas conversion. Considering that the biogas production rate was the technical parameter that displayed the most relevant differences between the analyzed substrate options (0.99-1.45m(3)biogasm(3)d(-1)). The decision of which substrate option should be implemented in practice would be mainly driven by the available construction techniques, since economically efficient tanks could compensate the lower biogas production rate of co-digestion option. Copyright © 2015 Elsevier Ltd. All rights reserved.

Event recognition in personal photo collections via multiple instance learning-based classification of multiple images

NASA Astrophysics Data System (ADS)

Ahmad, Kashif; Conci, Nicola; Boato, Giulia; De Natale, Francesco G. B.

2017-11-01

Over the last few years, a rapid growth has been witnessed in the number of digital photos produced per year. This rapid process poses challenges in the organization and management of multimedia collections, and one viable solution consists of arranging the media on the basis of the underlying events. However, album-level annotation and the presence of irrelevant pictures in photo collections make event-based organization of personal photo albums a more challenging task. To tackle these challenges, in contrast to conventional approaches relying on supervised learning, we propose a pipeline for event recognition in personal photo collections relying on a multiple instance-learning (MIL) strategy. MIL is a modified form of supervised learning and fits well for such applications with weakly labeled data. The experimental evaluation of the proposed approach is carried out on two large-scale datasets including a self-collected and a benchmark dataset. On both, our approach significantly outperforms the existing state-of-the-art.

Effects of Small-scale Vegetation-related Roughness on Overland Flow and Infiltration in Semi-arid Grassland and Shrublands

NASA Astrophysics Data System (ADS)

Bedford, D.

2012-12-01

We studied the effects of small-scale roughness on overland flow/runoff and the spatial pattern of infiltration. Our semi-arid sites include a grassland and shrubland in Central New Mexico and a shrubland in the Eastern Mojave Desert. Vegetation exerts strong controls on small-scale surface roughness in the form of plant mounds and other microtopography such as depressions and rills. We quantified the effects of densely measured soil surface heterogeneity using model simulations of runoff and infiltration. Microtopographic roughness associated with vegetation patterns, on the scale of mm-cm's in height, has a larger effect on runoff and infiltration than spatially correlated saturated conductivity. The magnitude and pattern of the effect of roughness largely depends on the vegetation and landform type, and rainfall depth and intensity. In all cases, runoff and infiltration amount and patterns were most strongly affected by depression storage. In the grassland we studied in central New Mexico, soil surface roughness had a large effect on runoff and infiltration where vegetation mounds coalesced, forming large storage volumes that require filling and overtopping in order for overland flow to concentrate into runoff. Total discharge over rough surfaces was reduced 100-200% compared to simulations in which no surface roughness was accounted for. For shrublands, total discharge was reduced 30-40% by microtopography on gently sloping alluvial fans and only 10-20% on steep hillslopes. This difference is largely due to the lack of storage elements on steep slopes. For our sites, we found that overland flow can increase infiltration by up to 2.5 times the total rainfall by filling depressions. The redistribution of water via overland flow can affect up to 20% of an area but varies with vegetation type and landform. This infiltration augmentation by overland flow tends to occur near the edges of vegetation canopies where overland flow depths are deep and infiltration rates are moderate. Infiltration augmentation is greatest in microtopographic depressions and flow threads. These results show that some vegetation-landform settings are efficient at trapping and concentrating the primary limiting resource, and demonstrate the importance of micro-scale soil characteristics for the ecohydrologic function of semi-arid environments. Since other essential attributes for plant ecosystems, such as nutrients, likely co-vary with water availability, further research is needed to elucidate ecosystem dynamics that may lead to self-organized behavior and determine thresholds for ecosystem stability.

Biofeedback in Partial Weight Bearing: Usability of Two Different Devices from a Patient's and Physical Therapist's Perspective.

PubMed

van Lieshout, Remko; Pisters, Martijn F; Vanwanseele, Benedicte; de Bie, Rob A; Wouters, Eveline J; Stukstette, Mirelle J

2016-01-01

Partial weight bearing is frequently instructed by physical therapists in patients after lower-limb trauma or surgery. The use of biofeedback devices seems promising to improve the patient's compliance with weight-bearing instructions. SmartStep and OpenGo-Science are biofeedback devices that provide real-time feedback. For a successful implementation, usability of the devices is a critical aspect and should be tested from a user's perspective. To describe the usability from the physical therapists' and a patients' perspective of Smartstep and OpenGo-Science to provide feedback on partial weight bearing during supervised rehabilitation of patients after lower-limb trauma or surgery. In a convergent mixed-methods design, qualitative and quantitative data were collected. Usability was subdivided into user performance, satisfaction and acceptability. Patients prescribed with partial weight bearing and their physical therapists were asked to use SmartStep and OpenGo-Science during supervised rehabilitation. Usability was qualitatively tested by a think-aloud method and a semi-structured interview and quantitatively tested by the System-Usability-Scale (SUS) and closed questions. For the qualitative data thematic content analyses were used. Nine pairs of physical therapists and their patients participated. The mean SUS scores for patients and physical therapists were for SmartStep 70 and 53, and for OpenGo-Science 79 and 81, respectively. Scores were interpreted with the Curved Grading Scale. The qualitative data showed that there were mixed views and perceptions from patients and physical therapists on satisfaction and acceptability. This study gives insight in the usability of two biofeedback devices from the patient's and physical therapist's perspective. The overall usability from both perspectives seemed to be acceptable for OpenGo-Science. For SmartStep, overall usability seemed only acceptable from the patient's perspective. The study findings could help clinicians to decide which biofeedback device is appropriate for their given situation and provide information for future development of biofeedback devices.

Modeling the spreading of large-scale wildland fires

Treesearch

Mohamed Drissi

2015-01-01

The objective of the present study is twofold. First, the last developments and validation results of a hybrid model designed to simulate fire patterns in heterogeneous landscapes are presented. The model combines the features of a stochastic small-world network model with those of a deterministic semi-physical model of the interaction between burning and non-burning...

Scalability of Semi-Implicit Time Integrators for Nonhydrostatic Galerkin-based Atmospheric Models on Large Scale Cluster

DTIC Science & Technology

2011-01-01

present performance statistics to explain the scalability behavior. Keywords-atmospheric models, time intergrators , MPI, scal- ability, performance; I...across inter-element bound- aries. Basis functions are constructed as tensor products of Lagrange polynomials ψi (x) = hα(ξ) ⊗ hβ(η) ⊗ hγ(ζ)., where hα

[Exploring Flow and Supervision of Medical Instruments by Standing on Frontier of the Reform of Free Trade Zone].

PubMed

Shen, Jianhua; Han, Meixian; Lu, Fei

2017-11-30

Shanghai Waigaoqiao Free Trade Zone as one of the special customs supervision areas of China (Shanghai) free trade pilot area, gathered a large number of general agent enterprises related to medical apparatus and instruments. This article analyzes the characteristics of special environment and medical equipment business in Shanghai Waigaoqiao Free Trade Zone in order to further implement the national administrative examination and approval reform. According to the latest requirement in laws and regulations of medical instruments, and trend of development in the industry of medical instruments, as well as research on the basis of practices of market supervision in countries around the world, this article also proposes measures about precision supervision, coordination of supervision, classification supervision and dynamic supervision to establish a new order of fair and standardized competition in market, and create conditions for establishment of allocation and transport hub of international medicine.

District health managers' perceptions of supervision in Malawi and Tanzania.

PubMed

Bradley, Susan; Kamwendo, Francis; Masanja, Honorati; de Pinho, Helen; Waxman, Rachel; Boostrom, Camille; McAuliffe, Eilish

2013-09-05

Mid-level cadres are being used to address human resource shortages in many African contexts, but insufficient and ineffective human resource management is compromising their performance. Supervision plays a key role in performance and motivation, but is frequently characterised by periodic inspection and control, rather than support and feedback to improve performance. This paper explores the perceptions of district health management teams in Tanzania and Malawi on their role as supervisors and on the challenges to effective supervision at the district level. This qualitative study took place as part of a broader project, "Health Systems Strengthening for Equity: The Power and Potential of Mid-Level Providers". Semi-structured interviews were conducted with 20 district health management team personnel in Malawi and 37 council health team members in Tanzania. The interviews covered a range of human resource management issues, including supervision and performance assessment, staff job descriptions and roles, motivation and working conditions. Participants displayed varying attitudes to the nature and purpose of the supervision process. Much of the discourse in Malawi centred on inspection and control, while interviewees in Tanzania were more likely to articulate a paradigm characterised by support and improvement. In both countries, facility level performance metrics dominated. The lack of competency-based indicators or clear standards to assess individual health worker performance were considered problematic. Shortages of staff, at both district and facility level, were described as a major impediment to carrying out regular supervisory visits. Other challenges included conflicting and multiple responsibilities of district health team staff and financial constraints. Supervision is a central component of effective human resource management. Policy level attention is crucial to ensure a systematic, structured process that is based on common understandings of the role and purpose of supervision. This is particularly important in a context where the majority of staff are mid-level cadres for whom regulation and guidelines may not be as formalised or well-developed as for traditional cadres, such as registered nurses and medical doctors. Supervision needs to be adequately resourced and supported in order to improve performance and retention at the district level.

District health managers’ perceptions of supervision in Malawi and Tanzania

PubMed Central

2013-01-01

Background Mid-level cadres are being used to address human resource shortages in many African contexts, but insufficient and ineffective human resource management is compromising their performance. Supervision plays a key role in performance and motivation, but is frequently characterised by periodic inspection and control, rather than support and feedback to improve performance. This paper explores the perceptions of district health management teams in Tanzania and Malawi on their role as supervisors and on the challenges to effective supervision at the district level. Methods This qualitative study took place as part of a broader project, “Health Systems Strengthening for Equity: The Power and Potential of Mid-Level Providers”. Semi-structured interviews were conducted with 20 district health management team personnel in Malawi and 37 council health team members in Tanzania. The interviews covered a range of human resource management issues, including supervision and performance assessment, staff job descriptions and roles, motivation and working conditions. Results Participants displayed varying attitudes to the nature and purpose of the supervision process. Much of the discourse in Malawi centred on inspection and control, while interviewees in Tanzania were more likely to articulate a paradigm characterised by support and improvement. In both countries, facility level performance metrics dominated. The lack of competency-based indicators or clear standards to assess individual health worker performance were considered problematic. Shortages of staff, at both district and facility level, were described as a major impediment to carrying out regular supervisory visits. Other challenges included conflicting and multiple responsibilities of district health team staff and financial constraints. Conclusion Supervision is a central component of effective human resource management. Policy level attention is crucial to ensure a systematic, structured process that is based on common understandings of the role and purpose of supervision. This is particularly important in a context where the majority of staff are mid-level cadres for whom regulation and guidelines may not be as formalised or well-developed as for traditional cadres, such as registered nurses and medical doctors. Supervision needs to be adequately resourced and supported in order to improve performance and retention at the district level. PMID:24007354

How supportive supervision influences immunization session site practices: a quasi-experimental study in Odisha, India.

PubMed

Panda, Bhuputra; Pati, Sanghamitra; Nallala, Srinivas; Chauhan, Abhimanyu S; Anasuya, Anita; Som, Meena; Zodpey, Sanjay

2015-01-01

Routine immunization (RI) is a key child survival intervention. Ensuring acceptable standards of RI service delivery is critical for optimal outcomes. Accumulated evidences suggest that 'supportive supervision' improves the quality of health care services in general. During 2009-2010, the Government of Odisha and UNICEF jointly piloted this strategy in four districts to improve RI program outcomes. The present study aims to assess the effect of this strategy on improvement of skills and practices at immunization session sites. A quasi-experimental 'post-test only' study design was adopted to compare the opinion and practices of frontline health workers and their supervisors in four intervention districts (IDs) with two control districts (CDs). Altogether, we interviewed 111 supervisor-supervisee (health worker) pairs using semi-structured interview schedules and case vignettes. We also directly observed health workers' practices during immunization sessions at 111 sites. Data were analyzed with SPSS version 16.0. The mean knowledge score of supervisors in CDs was significantly higher than in intervention groups. Variegated responses were obtained on case vignettes. The control group performed better in solving certain hypothetically asked problems, whereas the intervention group scored better in others. Health workers in IDs gave a lower rating to their respective supervisors' knowledge, skill, and frequency of supervision. Logistics and vaccine availability were better in CDs. Notwithstanding other limitations, supportive supervision may not have independent effects on improving the quality of immunization services. Addressing systemic issues, such as the availability of essential logistics, supply chain management, timely indenting, and financial resources, could complement the supportive supervision strategy in improving immunization service delivery.

The Relationship between the Supervision Role and Compassion Fatigue and Burnout in Genetic Counseling.

PubMed

Allsbrook, Katlin; Atzinger, Carrie; He, Hua; Engelhard, Chalee; Yager, Geoffrey; Wusik, Katie

2016-12-01

Many genetic counselors provide supervision to students during their career. Previous studies have shown genetic counselors, in general, are at increased risk for developing compassion fatigue. The purpose of this study was to determine if there was a difference in compassion fatigue and burnout levels in genetic counselors who currently supervise compared to genetic counselors who do not. Genetic counselors who currently practice in a clinical setting (N = 391) completed an online survey containing demographic questions, the Professional Quality of Life Scale, the State-Trait Anxiety Inventory, and questions specific to the genetic counselor's experiences with supervision. Overall, when controlling for trait-anxiety, the supervision role by itself was not independently associated with the risk for compassion fatigue and burnout among genetic counselors. Within supervisors, however, there were several factors which were associated with this risk. Those with less supervision experience reported more secondary traumatic stress. Those supervisors reporting less confidence had decreased compassion satisfaction. Those with less experience or less confidence in their supervision role were most likely to be at increased risk for developing compassion fatigue. Training in supervision and support for dealing with compassion fatigue and burnout may be beneficial to supervisors with less experience.

Work stress among newly graduated nurses in relation to workplace and clinical group supervision.

PubMed

Blomberg, Karin; Isaksson, Ann-Kristin; Allvin, Renée; Bisholt, Birgitta; Ewertsson, Mona; Kullén Engström, Agneta; Ohlsson, Ulla; Sundler Johansson, Annelie; Gustafsson, Margareta

2016-01-01

The aim was to investigate occupational stress among newly graduated nurses in relation to the workplace and clinical group supervision. Being a newly graduated nurse is particularly stressful. What remains unclear is whether the workplace and clinical group supervision affect the stress. A cross-sectional comparative study was performed in 2012. Data were collected by means of a numerical scale measuring occupational stress, questions about workplace and clinical group supervision. One hundred and thirteen nurses who had recently graduated from three Swedish universities were included in the study. The stress was high among the newly graduated nurses but it differed significantly between workplaces, surgical departments generating the most stress. Nurses who had received clinical group supervision reported significantly less stress. The stress between workplaces remained significant also when participation in clinical group supervision was taken into account. Newly graduated nurses experience great stress and need support, especially those in surgical departments. Nurses participating in clinical group supervision reported significantly less stress. It is important to develop strategies that help to adapt the work situation so as to give nurses the necessary support. Clinical group supervision should be considered as an option for reducing stress. © 2014 John Wiley & Sons Ltd.

A Semi-supervised Heat Kernel Pagerank MBO Algorithm for Data Classification

DTIC Science & Technology

2016-07-01

financial predictions, etc. and is finding growing use in text mining studies. In this paper, we present an efficient algorithm for classification of high...video data, set of images, hyperspectral data, medical data, text data, etc. Moreover, the framework provides a way to analyze data whose different...also be incorporated. For text classification, one can use tfidf (term frequency inverse document frequency) to form feature vectors for each document

Semi-Supervised Multiple Feature Analysis for Action Recognition

DTIC Science & Technology

2013-11-26

Technology and Electrical Engineering, University of Queensland, Brisbane, Australia ( e -mail: sen.wang@uq.edu.au; yi.yang@uq.edu.au). Z. Ma is with...the Language Technologies Institute, Carnegie Mellon Univer- sity, Pittsburgh, PA 15213 USA ( e -mail: kevinma@cs.cmu.edu). X. Li is with the School of...Service Computing in Cyber Physical Society, Chongqing University, Chongqing, China ( e -mail: xueli@itee.uq.edu.au). C. Pang is with the Australian e

Highly multiplexed targeted proteomics using precise control of peptide retention time.

PubMed

Gallien, Sebastien; Peterman, Scott; Kiyonami, Reiko; Souady, Jamal; Duriez, Elodie; Schoen, Alan; Domon, Bruno

2012-04-01

Large-scale proteomics applications using SRM analysis on triple quadrupole mass spectrometers present new challenges to LC-MS/MS experimental design. Despite the automation of building large-scale LC-SRM methods, the increased numbers of targeted peptides can compromise the balance between sensitivity and selectivity. To facilitate large target numbers, time-scheduled SRM transition acquisition is performed. Previously published results have demonstrated incorporation of a well-characterized set of synthetic peptides enabled chromatographic characterization of the elution profile for most endogenous peptides. We have extended this application of peptide trainer kits to not only build SRM methods but to facilitate real-time elution profile characterization that enables automated adjustment of the scheduled detection windows. Incorporation of dynamic retention time adjustments better facilitate targeted assays lasting several days without the need for constant supervision. This paper provides an overview of how the dynamic retention correction approach identifies and corrects for commonly observed LC variations. This adjustment dramatically improves robustness in targeted discovery experiments as well as routine quantification experiments. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Vision in semi-aquatic snakes: Intraocular morphology, accommodation, and eye: Body allometry

NASA Astrophysics Data System (ADS)

Plylar, Helen Bond

Vision in vertebrates generally relies on the refractive power of the cornea and crystalline lens to facilitate vision. Light from the environment enters the eye and is refracted by the cornea and lens onto the retina for production of an image. When an animal with a system designed for air submerges underwater, the refractive power of the cornea is lost. Semi-aquatic animals (e.g., water snakes, turtles, aquatic mammals) must overcome this loss of corneal refractive power through visual accommodation. Accommodation relies on change of the position or shape of the lens to change the focal length of the optical system. Intraocular muscles and fibers facilitate lenticular displacement and deformation. Snakes, in general, are largely unstudied in terms of visual acuity and intraocular morphology. I used light microscopy and scanning electron microscopy to examine differences in eye anatomy between five sympatric colubrid snake species (Nerodia cyclopion, N. fasciata, N. rhombifer, Pantherophis obsoletus, and Thamnophis proximus) from Southeast Louisiana. I discovered previously undescribed structures associated with the lens in semi-aquatic species. Photorefractive methods were used to assess refractive error. While all species overcame the expected hyperopia imposed by submergence, there was interspecific variation in refractive error. To assess scaling of eye size with body size, I measure of eye size, head size, and body size in Nerodia cyclopion and N. fasciata from the SLU Vertebrate Museum. In both species, body size increases at a significantly faster rate than head size and eye size (negative allometry). Small snakes have large eyes relative to body size, and large snakes have relatively small eyes. There were interspecific differences in scaling of eye size with body size, where N. fasciata had larger eye diameter, but N. cyclopion had longer eyes (axial length).

Producing Information for Corine Database by Using Classification Method: a Case Study of Sazlidere Basin, Istanbul

NASA Astrophysics Data System (ADS)

Sarıyılmaz, F. B.; Musaoğlu, N.; Uluğtekin, N.

2017-11-01

The Sazlidere Basin is located on the European side of Istanbul within the borders of Arnavutkoy and Basaksehir districts. The total area of the basin, which is largely located within the province of Arnavutkoy, is approximately 177 km2. The Sazlidere Basin is faced with intense urbanization pressures and land use / cover change due to the Northern Marmara Motorway, 3rd airport and Channel Istanbul Projects, which are planned to be realized in the Arnavutkoy region. Due to the mentioned projects, intense land use /cover changes occur in the basin. In this study, 2000 and 2012 dated LANDSAT images were supervised classified based on CORINE Land Cover first level to determine the land use/cover classes. As a result, four information classes were identified. These classes are water bodies, forest and semi-natural areas, agricultural areas and artificial surfaces. Accuracy analysis of the images were performed following the classification process. The supervised classified images that have the smallest mapping units 0.09 ha and 0.64 ha were generalized to be compatible with the CORINE Land Cover data. The image pixels have been rearranged by using the thematic pixel aggregation method as the smallest mapping unit is 25 ha. These results were compared with CORINE Land Cover 2000 and CORINE Land Cover 2012, which were obtained by digitizing land cover and land use classes on satellite images. It has been determined that the compared results are compatible with each other in terms of quality and quantity.

Factors influencing antibiotic-prescribing decisions among inpatient physicians: a qualitative investigation

PubMed Central

Livorsi, D; Comer, AR; Matthias, MS; Perencevich, EN; Bair, MJ

2016-01-01

Objective To understand the professional and psychosocial factors that influence physicians' antibiotic-prescribing habits in the inpatient setting. Design We conducted semi-structured interviews with 30 inpatient physicians. Interviews consisted of open-ended questions and flexible probes based on participants' responses. Interviews were audio recorded, transcribed, de-identified, and reviewed for accuracy and completeness. Data were analyzed using emergent thematic analysis. Setting Two teaching hospitals in Indianapolis, IN Participants Thirty inpatient physicians (10 physicians-in-training, 20 supervising staff) Results Participants recognized that antibiotics are over-used, and many admitted to prescribing antibiotics even when the clinical evidence of infection was uncertain. Over-prescription was largely driven by anxiety about missing an infection while potential adverse effects of antibiotics did not strongly influence decision-making. Participants did not routinely disclose potential adverse effects of antibiotics to inpatients. Physicians-in-training were strongly influenced by the antibiotic prescribing behavior of their supervising staff physicians. Participants sometimes questioned their colleagues' antibiotic-prescribing decisions but frequently avoided providing direct feedback or critique, citing obstacles of hierarchy, infrequent face-to-face encounters, and the awkwardness of these conversations. Conclusion There is a physician-based culture of prescribing antibiotics, which involves over-using antibiotics and not challenging colleagues' decisions. The potential adverse effects of antibiotics do not strongly influence decision-making in this sample. A better understanding of these factors could be leveraged in future efforts to improve antibiotic-prescribing in the inpatient setting. PMID:26078017

A Parallel, Multi-Scale Watershed-Hydrologic-Inundation Model with Adaptively Switching Mesh for Capturing Flooding and Lake Dynamics

NASA Astrophysics Data System (ADS)

Ji, X.; Shen, C.

2017-12-01

Flood inundation presents substantial societal hazards and also changes biogeochemistry for systems like the Amazon. It is often expensive to simulate high-resolution flood inundation and propagation in a long-term watershed-scale model. Due to the Courant-Friedrichs-Lewy (CFL) restriction, high resolution and large local flow velocity both demand prohibitively small time steps even for parallel codes. Here we develop a parallel surface-subsurface process-based model enhanced by multi-resolution meshes that are adaptively switched on or off. The high-resolution overland flow meshes are enabled only when the flood wave invades to floodplains. This model applies semi-implicit, semi-Lagrangian (SISL) scheme in solving dynamic wave equations, and with the assistant of the multi-mesh method, it also adaptively chooses the dynamic wave equation only in the area of deep inundation. Therefore, the model achieves a balance between accuracy and computational cost.

Large Eddy Simulation ... Where Do We Stand? International Workshop Held in St. Petersburg Beach, Florida on 19-21 December 1990.

DTIC Science & Technology

1990-01-01

S. Orszag, Chairman 1. P. Moin Some Issues in Computation of Turbulent Flows. 2. M. Lesieur, P. Comte, X. Normand, 0. Metais and A. Silveira Spectral...Richtmeyer’s computational experience with one-dimensional shock waves (1950) indicated the value of a non-linear artificial viscosity. Charney and... computer architecture and the advantages of semi-Lagrangian advective schemes may lure large-scale atmospheric modelers back to finite-difference

Single polymer dynamics in semi-dilute unentangled and entangled solutions: from molecular conformation to normal stress

NASA Astrophysics Data System (ADS)

Schroeder, Charles

Semi-dilute polymer solutions are encountered in a wide array of applications such as advanced 3D printing technologies. Semi-dilute solutions are characterized by large fluctuations in concentration, such that hydrodynamic interactions, excluded volume interactions, and transient chain entanglements may be important, which greatly complicates analytical modeling and theoretical treatment. Despite recent progress, we still lack a complete molecular-level understanding of polymer dynamics in these systems. In this talk, I will discuss three recent projects in my group to study semi-dilute solutions that focus on single molecule studies of linear and ring polymers and a new method to measure normal stresses in microfluidic devices based on the Stokes trap. In the first effort, we use single polymer techniques to investigate the dynamics of semi-dilute unentangled and semi-dilute entangled DNA solutions in extensional flow, including polymer relaxation from high stretch, transient stretching dynamics in step-strain experiments, and steady-state stretching in flow. In the semi-dilute unentangled regime, our results show a power-law scaling of the longest polymer relaxation time that is consistent with scaling arguments based on the double cross-over regime. Upon increasing concentration, we observe a transition region in dynamics to the entangled regime. We also studied the transient and steady-state stretching dynamics in extensional flow using the Stokes trap, and our results show a decrease in transient polymer stretch and a milder coil-to-stretch transition for semi-dilute polymer solutions compared to dilute solutions, which is interpreted in the context of a critical Weissenberg number Wi at the coil-to-stretch transition. Interestingly, we observe a unique set of polymer conformations in semi-dilute unentangled solutions that are highly suggestive of transient topological entanglements in solutions that are nominally unentangled at equilibrium. Taken together, these results suggest that the transient stretching pathways in semi-dilute solution extensional flows are qualitatively different than for both dilute solutions and for semi-dilute solutions in shear flow. In a second effort, we studied the dynamics of ring polymers in background solutions of semi-dilute linear polymers. Interestingly, we observe strikingly large fluctuations in steady-state polymer extension for ring polymers in flow, which occurs due to the interplay between polymer topology and concentration leading to chain `threading' in flow. In a third effort, we developed a new microfluidic method to measure normal stress and extensional viscosity that can be loosely described as passive yet non-linear microrheology. In particular, we incorporated 3-D particle imaging velocimetry (PIV) with the Stokes trap to study extensional flow-induced particle migration in semi-dilute polymer solutions. Experimental results are analyzed using the framework of a second-order-fluid model, which allows for measurement of normal stress and extensional viscosity in semi-dilute polymer solutions, all of which is a first-of-its-kind demonstration. Microfluidic measurements of extensional viscosity are directly compared to the dripping-onto-substrate or DOS method, and good agreement is generally observed. Overall, our work aims to provide a molecular-level understanding of the role of polymer topology and concentration on bulk rheological properties by using single polymer techniques.

Effects of land use pattern on soil water in revegetation watersheds in semi-arid Chinese Loess Plateau

NASA Astrophysics Data System (ADS)

Yang, Lei; Chen, Liding; Wei, Wei

2017-04-01

Soil water stored below rainfall infiltration depth is a reliable water resource for plant growth in arid and semi-arid regions. For decreasing serious soil erosion, large-scale human-introduced vegetation restoration was initiated in Chinese Loess Plateau in late 1990s. However, these activities may result in excessive water consumption and soil water deficit if no appropriate scientific guidance were offered. This in turn impacts the regional ecological restoration and sustainable management of water resources. In this study, soil water content data in depth of 0-5 m was obtained by long-term field observation and geostatistical method in 6 small watersheds covered with different land use pattern. Profile characteristics and spatial-temporal patterns of soil water were compared between different land use types, hillslopes, and watersheds. The results showed that: (1) Introduced vegetation consumed excessive amount of water when compared with native grassland and farmland, and induced temporally stable soil desiccation in depth of 0-5 m. The introduced vegetation decreased soil water content to levels lower than the reference value representing no human impact in all soil layers. (2) The analysis of differences in soil water at hillslope and watershed scales indicated that land use determined the spatial and temporal variability of soil water. Soil water at watershed scale increased with the increasing area of farmland, and decreased with increasing percentage of introduced vegetation. Land use structure determined the soil water condition and land use pattern determined the spatial-temporal variability of soil water at watershed scale. (3) Large-scale revegetation with introduced vegetation diminished the spatial heterogeneity of soil water at different scales. Land use pattern adjustment could be used to improve the water resources management and maintain the sustainability of vegetation restoration.

Termites Are Resistant to the Effects of Fire at Multiple Spatial Scales.

PubMed

Avitabile, Sarah C; Nimmo, Dale G; Bennett, Andrew F; Clarke, Michael F

2015-01-01

Termites play an important ecological role in many ecosystems, particularly in nutrient-poor arid and semi-arid environments. We examined the distribution and occurrence of termites in the fire-prone, semi-arid mallee region of south-eastern Australia. In addition to periodic large wildfires, land managers use fire as a tool to achieve both asset protection and ecological outcomes in this region. Twelve taxa of termites were detected by using systematic searches and grids of cellulose baits at 560 sites, clustered in 28 landscapes selected to represent different fire mosaic patterns. There was no evidence of a significant relationship between the occurrence of termite species and time-since-fire at the site scale. Rather, the occurrence of species was related to habitat features such as the density of mallee trees and large logs (>10 cm diameter). Species richness was greater in chenopod mallee vegetation on heavier soils in swales, rather than Triodia mallee vegetation of the sandy dune slopes. At the landscape scale, there was little evidence that the frequency of occurrence of termite species was related to fire, and no evidence that habitat heterogeneity generated by fire influenced termite species richness. The most influential factor at the landscape scale was the environmental gradient represented by average annual rainfall. Although termites may be associated with flammable habitat components (e.g. dead wood), they appear to be buffered from the effects of fire by behavioural traits, including nesting underground, and the continued availability of dead wood after fire. There is no evidence to support the hypothesis that a fine-scale, diverse mosaic of post-fire age-classes will enhance the diversity of termites. Rather, termites appear to be resistant to the effects of fire at multiple spatial scales.

Termites Are Resistant to the Effects of Fire at Multiple Spatial Scales

PubMed Central

Avitabile, Sarah C.; Nimmo, Dale G.; Bennett, Andrew F.; Clarke, Michael F.

2015-01-01

Termites play an important ecological role in many ecosystems, particularly in nutrient-poor arid and semi-arid environments. We examined the distribution and occurrence of termites in the fire-prone, semi-arid mallee region of south-eastern Australia. In addition to periodic large wildfires, land managers use fire as a tool to achieve both asset protection and ecological outcomes in this region. Twelve taxa of termites were detected by using systematic searches and grids of cellulose baits at 560 sites, clustered in 28 landscapes selected to represent different fire mosaic patterns. There was no evidence of a significant relationship between the occurrence of termite species and time-since-fire at the site scale. Rather, the occurrence of species was related to habitat features such as the density of mallee trees and large logs (>10 cm diameter). Species richness was greater in chenopod mallee vegetation on heavier soils in swales, rather than Triodia mallee vegetation of the sandy dune slopes. At the landscape scale, there was little evidence that the frequency of occurrence of termite species was related to fire, and no evidence that habitat heterogeneity generated by fire influenced termite species richness. The most influential factor at the landscape scale was the environmental gradient represented by average annual rainfall. Although termites may be associated with flammable habitat components (e.g. dead wood), they appear to be buffered from the effects of fire by behavioural traits, including nesting underground, and the continued availability of dead wood after fire. There is no evidence to support the hypothesis that a fine-scale, diverse mosaic of post-fire age-classes will enhance the diversity of termites. Rather, termites appear to be resistant to the effects of fire at multiple spatial scales. PMID:26571383

The Need for Data-Informed Clinical Supervision in Substance Use Disorder Treatment

PubMed Central

Ramsey, Alex T.; Baumann, Ana; Silver Wolf, David Patterson; Yan, Yan; Cooper, Ben; Proctor, Enola

2017-01-01

Background Effective clinical supervision is necessary for high-quality care in community-based substance use disorder treatment settings, yet little is known about current supervision practices. Some evidence suggests that supervisors and counselors differ in their experiences of clinical supervision; however, the impact of this misalignment on supervision quality is unclear. Clinical information monitoring systems may support supervision in substance use disorder treatment, but the potential use of these tools must first be explored. Aims First, this study examines the extent to which misaligned supervisor-counselor perceptions impact supervision satisfaction and emphasis on evidence-based treatments. This study also reports on formative work to develop a supervision-based clinical dashboard, an electronic information monitoring system and data visualization tool providing real-time clinical information to engage supervisors and counselors in a coordinated and data-informed manner, help align supervisor-counselor perceptions about supervision, and improve supervision effectiveness. Methods Clinical supervisors and frontline counselors (N=165) from five Midwestern agencies providing substance abuse services completed an online survey using Research Electronic Data Capture (REDCap) software, yielding a 75% response rate. Valid quantitative measures of supervision effectiveness were assessed, along with qualitative perceptions of a supervision-based clinical dashboard. Results Through within-dyad analyses, misalignment between supervisor and counselor perceptions of supervision practices was negatively associated with satisfaction of supervision and reported frequency of discussing several important clinical supervision topics, including evidence-based treatments and client rapport. Participants indicated the most useful clinical dashboard functions and reported important benefits and challenges to using the proposed tool. Discussion Clinical supervision tends to be largely an informal and unstructured process in substance abuse treatment, which may compromise the quality of care. Clinical dashboards may be a well-targeted approach to facilitate data-informed clinical supervision in community-based treatment agencies. PMID:28166480

The need for data-informed clinical supervision in substance use disorder treatment.

PubMed

Ramsey, Alex T; Baumann, Ana; Patterson Silver Wolf, David; Yan, Yan; Cooper, Ben; Proctor, Enola

2017-01-01

Effective clinical supervision is necessary for high-quality care in community-based substance use disorder treatment settings, yet little is known about current supervision practices. Some evidence suggests that supervisors and counselors differ in their experiences of clinical supervision; however, the impact of this misalignment on supervision quality is unclear. Clinical information monitoring systems may support supervision in substance use disorder treatment, but the potential use of these tools must first be explored. First, the current study examines the extent to which misaligned supervisor-counselor perceptions impact supervision satisfaction and emphasis on evidence-based treatments. This study also reports on formative work to develop a supervision-based clinical dashboard, an electronic information monitoring system and data visualization tool providing real-time clinical information to engage supervisors and counselors in a coordinated and data-informed manner, help align supervisor-counselor perceptions about supervision, and improve supervision effectiveness. Clinical supervisors and frontline counselors (N = 165) from five Midwestern agencies providing substance abuse services completed an online survey using Research Electronic Data Capture software, yielding a 75% response rate. Valid quantitative measures of supervision effectiveness were administered, along with qualitative perceptions of a supervision-based clinical dashboard. Through within-dyad analyses, misalignment between supervisor and counselor perceptions of supervision practices was negatively associated with satisfaction of supervision and reported frequency of discussing several important clinical supervision topics, including evidence-based treatments and client rapport. Participants indicated the most useful clinical dashboard functions and reported important benefits and challenges to using the proposed tool. Clinical supervision tends to be largely an informal and unstructured process in substance abuse treatment, which may compromise the quality of care. Clinical dashboards may be a well-targeted approach to facilitate data-informed clinical supervision in community-based treatment agencies.

Iterative quantization: a Procrustean approach to learning binary codes for large-scale image retrieval.

PubMed

Gong, Yunchao; Lazebnik, Svetlana; Gordo, Albert; Perronnin, Florent

2013-12-01

This paper addresses the problem of learning similarity-preserving binary codes for efficient similarity search in large-scale image collections. We formulate this problem in terms of finding a rotation of zero-centered data so as to minimize the quantization error of mapping this data to the vertices of a zero-centered binary hypercube, and propose a simple and efficient alternating minimization algorithm to accomplish this task. This algorithm, dubbed iterative quantization (ITQ), has connections to multiclass spectral clustering and to the orthogonal Procrustes problem, and it can be used both with unsupervised data embeddings such as PCA and supervised embeddings such as canonical correlation analysis (CCA). The resulting binary codes significantly outperform several other state-of-the-art methods. We also show that further performance improvements can result from transforming the data with a nonlinear kernel mapping prior to PCA or CCA. Finally, we demonstrate an application of ITQ to learning binary attributes or "classemes" on the ImageNet data set.

Supervision, monitoring and evaluation of nationwide scale-up of antiretroviral therapy in Malawi.

PubMed Central

Libamba, Edwin; Makombe, Simon; Mhango, Eustice; de Ascurra Teck, Olga; Limbambala, Eddie; Schouten, Erik J.; Harries, Anthony D.

2006-01-01

OBJECTIVE: To describe the supervision, monitoring and evaluation strategies used to assess the delivery of antiretroviral therapy during nationwide scale-up of treatment in Malawi. METHODS: In the first quarter of 2005, the HIV Unit of the Ministry of Health and its partners (the Lighthouse Clinic; Médecins Sans Frontières-Belgium, Thyolo district; and WHO's Country Office) undertook structured supervision and monitoring of all public sector health facilities in Malawi delivering antiretroviral therapy. FINDINGS: Data monitoring showed that by the end of 2004, there were 13,183 patients (5274 (40%) male, 12 527 (95%) adults) who had ever started antiretroviral therapy. Of patients who had ever started, 82% (10 761/13,183) were alive and taking antiretrovirals; 8% (1026/13,183) were dead; 8% (1039/13,183) had been lost to follow up; <1% (106/13,183) had stopped treatment; and 2% (251/13,183) had transferred to another facility. Of those alive and on antiretrovirals, 98% (7098/7258) were ambulatory; 85% (6174/7258) were fit to work; 10% (456/4687) had significant side effects; and, based on pill counts, 96% (6824/7114) had taken their treatment correctly. Mistakes in the registration and monitoring of patients were identified and corrected. Drug stocks were checked, and one potential drug stock-out was averted. As a result of the supervisory visits, by the end of March 2005 recruitment of patients to facilities scheduled to start delivering antiretroviral therapy had increased. CONCLUSION: This report demonstrates the importance of early supervision for sites that are starting to deliver antiretroviral therapy, and it shows the value of combining data collection with supervision. Making regular supervisory and monitoring visits to delivery sites are essential for tracking the national scale-up of delivery of antiretrovirals. PMID:16628306

Defining the transcriptome assembly and its use for genome dynamics and transcriptome profiling studies in pigeonpea (Cajanus cajan L.)

USDA-ARS?s Scientific Manuscript database

This study reports generation of large-scale genomic resources for pigeonpea, a so-called ‘orphan crop species’ of the semi-arid tropic regions. Roche FLX/454 sequencing was carried out on a normalized cDNA pool prepared from 31 tissues produced 494,353 short transcript reads (STRs). Cluster analysi...

No positive feedback between fire and a nonnative perennial grass

Treesearch

Erika L. Geiger; Guy R. McPherson

2005-01-01

Semi-desert grasslands flank the “Sky Island” mountains in southern Arizona and Northern Mexico. Many of these grasslands are dominated by nonnative grasses, which potentially alter native biotic communities. One specific concern is the potential for a predicted feedback between nonnative grasses and fire. In a large-scale experiment in southern Arizona we investigated...

A method to identify the variable ecosystem services relationship across time: a case study on Yanhe Basin, China

Treesearch

Zhenmin Zheng; Bojie Fu; Haitang Hu; Ge Sun

2014-01-01

Ecosystem services are increasingly recognized as the foundations of a well-functioning society. Large-scale ecological restoration projects have been implemented around China with the goal of restoring and sustaining ecosystem services, especially in vulnerable semi-arid regions where soil and water resources are most stressed due to historic human activities. The...

Effectiveness of clinical supervision of physiotherapists: a survey.

PubMed

Snowdon, David A; Millard, Geraldine; Taylor, Nicholas F

2015-04-01

Limited literature exists on the practice of clinical supervision (CS) of professional physiotherapists despite current Australian safety and quality health standards stating that CS is to be provided to all physiotherapists. The aim of the present study was to evaluate the effectiveness of CS of physiotherapists working in an Australian public health service. CS was measured using the allied health-specific 26-item modified Manchester Clinical Supervision Scale (MCSS-26). Subscales of the MCSS-26 were summed for three domain scores (normative, restorative and formative) and a total score was calculated, which was compared with the reported threshold score of 73 for effective supervision. Sixty registered physiotherapists (response rate 92%), working for a large metropolitan public health service, with six different site locations, completed the survey. The mean (± s.d.) total MCSS-26 score was 71.0 ± 14.3 (95% confidence interval (CI) 67.4-74.6). Hospital site was the only variable that had a significant effect on total MCSS-26 score (P=0.005); there was no effect for supervisor or supervisee experience, or hospital setting (acute vs subacute). Physiotherapists scored a significantly lower mean percentage MCSS-26 score on the normative domain compared with the restorative domain (mean difference 7.8%; 95% CI 2.9-12.7; P=0.002) and the formative domain (mean difference 9.6%; 95% CI 6.3-13.0; P<0.001). Of the two subscales that form the normative domain, 'finding time' had a significantly lower mean percentage MCSS-26 score than 'importance/value of CS' (mean difference 35.4%; 95% CI 31.3-39.4; P<0.001). Within this publicly funded physiotherapy department there was uncertainty about the effectiveness of CS, with more than half the physiotherapists rating their supervision as less than effective, suggesting there is opportunity for improvement in the practice of physiotherapy CS. Physiotherapists scored lowest in the normative domain, indicating that they found it difficult to find time for CS.

The Critical Role of Supervision in Retaining Staff in Obstetric Services: A Three Country Study

PubMed Central

McAuliffe, Eilish; Daly, Michael; Kamwendo, Francis; Masanja, Honorati; Sidat, Mohsin; de Pinho, Helen

2013-01-01

Millennium Development Goal (MDG) 5 commits us to reducing maternal mortality rates by three quarters and MDG 4 commits us to reducing child mortality by two-thirds between 1990 and 2015. In order to reach these goals, greater access to basic emergency obstetric care (EmOC) as well as comprehensive EmOC which includes safe Caesarean section, is needed.. The limited capacity of health systems to meet demand for obstetric services has led several countries to utilize mid-level cadres as a substitute to more extensively trained and more internationally mobile healthcare workers. Although this does provide greater capacity for service delivery, concern about the performance and motivation of these workers is emerging. We propose that poor leadership characterized by inadequate and unstructured supervision underlies much of the dissatisfaction and turnover that has been shown to exist amongst these mid-level healthcare workers and indeed health workers more generally. To investigate this, we conducted a large-scale survey of 1,561 mid-level cadre healthcare workers (health workers trained for shorter periods to perform specific tasks e.g. clinical officers) delivering obstetric care in Malawi, Tanzania, and Mozambique. Participants indicated the primary supervision method used in their facility and we assessed their job satisfaction and intentions to leave their current workplace. In all three countries we found robust evidence indicating that a formal supervision process predicted high levels of job satisfaction and low intentions to leave. We find no evidence that facility level factors modify the link between supervisory methods and key outcomes. We interpret this evidence as strongly supporting the need to strengthen leadership and implement a framework and mechanism for systematic supportive supervision. This will promote better job satisfaction and improve the retention and performance of obstetric care workers, something which has the potential to improve maternal and neonatal outcomes in the countdown to 2015. PMID:23555581

Effect of a School-Based Supervised Tooth Brushing Program In Mexico City: A Cluster Randomized Intervention.

PubMed

Borges-Yáñez, S Aída; Castrejón-Pérez, Roberto Carlos; Camacho, María Esther Irigoyen

Large-scale school-based programs effectively provide health education and preventive strategies. SaludARTE is a school-based program, including supervised tooth brushing, implemented in 51 elementary schools in Mexico City. To assess the three-month efficacy of supervised tooth brushing in reducing dental plaque, gingival inflammation, and bleeding on probing in schoolchildren participating in SaludARTE. This was a pragmatic cluster randomized intervention, with two parallel branches. Four randomly selected schools participating in SaludARTE (n=200) and one control school, which did not participate in the program (CG) (n=50), were assessed. Clusters were not randomly allocated to intervention. The main outcomes were as follows: mean percentage gingival units with no inflammation, dental surfaces with no dental plaque, and gingival margins with no bleeding. The independent variable was supervised tooth brushing at school once a day after a meal. Guardians and children responded to a questionnaire on sociodemographic and oral hygiene practices, and children were examined dentally. Mean percentage differences were compared (baseline and follow-up). A total of 75% of guardians from the intervention group (IG) and 77% from the CG answered the questionnaire. Of these, 89.3% were women, with a mean age of 36.9±8.5 years. No differences in sociodemographic variables were observed between groups, and 151 children from the IG and 35 from the CG were examined at baseline and follow-up. Mean percentage differences for plaque-free surfaces (8.8±28.5%) and healthy gingival units (23.3%±23.2%) were significantly higher in the IG. The school-supervised tooth brushing program is effective in improving oral hygiene and had a greater impact on plaque and gingivitis than on gingival bleeding. It is necessary to reinforce the oral health education component of the program.

Can clinical supervision sustain our workforce in the current healthcare landscape? Findings from a Queensland study of allied health professionals.

PubMed

Saxby, Christine; Wilson, Jill; Newcombe, Peter

2015-09-01

Clinical supervision is widely recognised as a mechanism for providing professional support, professional development and clinical governance for healthcare workers. There have been limited studies about the effectiveness of clinical supervision for allied health and minimal studies conducted within the Australian health context. The aim of the present study was to identify whether clinical supervision was perceived to be effective by allied health professionals and to identify components that contributed to effectiveness. Participants completed an anonymous online questionnaire, administered through the health service's intranet. A cross-sectional study was conducted with community allied health workers (n = 82) 8 months after implementation of structured clinical supervision. Demographic data (age, gender), work-related history (profession employment level, years of experience), and supervision practice (number and length of supervision sessions) were collected through an online survey. The outcome measure, clinical supervision effectiveness, was operationalised using the Manchester Clinical Supervision Scale-26 (MCSS-26). Data were analysed with Pearson correlation (r) and independent sample t-tests (t) with significance set at 0.05 (ie the probability of significant difference set at P < 0.05). The length of the supervision sessions (r(s) ≥ 0.44), the number of sessions (r(s) ≥ 0.35) and the total period supervision had been received (r(s) ≥ 0.42) were all significantly positively correlated with the MCSS-26 domains of clinical supervision effectiveness. Three individual variables, namely 'receiving clinical supervision', 'having some choice in the allocation of clinical supervisor' and 'having a completed clinical supervision agreement', were also significantly associated with higher total MCSS-26 scores (P(s) < 0.014). The results of the study demonstrate that when clinical supervision uses best practice principles, it can provide professional support for allied health workers, even during times of rapid organisational change.

Optimized Graph Learning Using Partial Tags and Multiple Features for Image and Video Annotation.

PubMed

Song, Jingkuan; Gao, Lianli; Nie, Feiping; Shen, Heng Tao; Yan, Yan; Sebe, Nicu

2016-11-01

In multimedia annotation, due to the time constraints and the tediousness of manual tagging, it is quite common to utilize both tagged and untagged data to improve the performance of supervised learning when only limited tagged training data are available. This is often done by adding a geometry-based regularization term in the objective function of a supervised learning model. In this case, a similarity graph is indispensable to exploit the geometrical relationships among the training data points, and the graph construction scheme essentially determines the performance of these graph-based learning algorithms. However, most of the existing works construct the graph empirically and are usually based on a single feature without using the label information. In this paper, we propose a semi-supervised annotation approach by learning an optimized graph (OGL) from multi-cues (i.e., partial tags and multiple features), which can more accurately embed the relationships among the data points. Since OGL is a transductive method and cannot deal with novel data points, we further extend our model to address the out-of-sample issue. Extensive experiments on image and video annotation show the consistent superiority of OGL over the state-of-the-art methods.

Multilabel user classification using the community structure of online networks

PubMed Central

Papadopoulos, Symeon; Kompatsiaris, Yiannis

2017-01-01

We study the problem of semi-supervised, multi-label user classification of networked data in the online social platform setting. We propose a framework that combines unsupervised community extraction and supervised, community-based feature weighting before training a classifier. We introduce Approximate Regularized Commute-Time Embedding (ARCTE), an algorithm that projects the users of a social graph onto a latent space, but instead of packing the global structure into a matrix of predefined rank, as many spectral and neural representation learning methods do, it extracts local communities for all users in the graph in order to learn a sparse embedding. To this end, we employ an improvement of personalized PageRank algorithms for searching locally in each user’s graph structure. Then, we perform supervised community feature weighting in order to boost the importance of highly predictive communities. We assess our method performance on the problem of user classification by performing an extensive comparative study among various recent methods based on graph embeddings. The comparison shows that ARCTE significantly outperforms the competition in almost all cases, achieving up to 35% relative improvement compared to the second best competing method in terms of F1-score. PMID:28278242

Multilabel user classification using the community structure of online networks.

PubMed

Rizos, Georgios; Papadopoulos, Symeon; Kompatsiaris, Yiannis

2017-01-01

We study the problem of semi-supervised, multi-label user classification of networked data in the online social platform setting. We propose a framework that combines unsupervised community extraction and supervised, community-based feature weighting before training a classifier. We introduce Approximate Regularized Commute-Time Embedding (ARCTE), an algorithm that projects the users of a social graph onto a latent space, but instead of packing the global structure into a matrix of predefined rank, as many spectral and neural representation learning methods do, it extracts local communities for all users in the graph in order to learn a sparse embedding. To this end, we employ an improvement of personalized PageRank algorithms for searching locally in each user's graph structure. Then, we perform supervised community feature weighting in order to boost the importance of highly predictive communities. We assess our method performance on the problem of user classification by performing an extensive comparative study among various recent methods based on graph embeddings. The comparison shows that ARCTE significantly outperforms the competition in almost all cases, achieving up to 35% relative improvement compared to the second best competing method in terms of F1-score.

Classifying epileptic EEG signals with delay permutation entropy and Multi-Scale K-means.

PubMed

Zhu, Guohun; Li, Yan; Wen, Peng Paul; Wang, Shuaifang

2015-01-01

Most epileptic EEG classification algorithms are supervised and require large training datasets, that hinder their use in real time applications. This chapter proposes an unsupervised Multi-Scale K-means (MSK-means) MSK-means algorithm to distinguish epileptic EEG signals and identify epileptic zones. The random initialization of the K-means algorithm can lead to wrong clusters. Based on the characteristics of EEGs, the MSK-means MSK-means algorithm initializes the coarse-scale centroid of a cluster with a suitable scale factor. In this chapter, the MSK-means algorithm is proved theoretically superior to the K-means algorithm on efficiency. In addition, three classifiers: the K-means, MSK-means MSK-means and support vector machine (SVM), are used to identify seizure and localize epileptogenic zone using delay permutation entropy features. The experimental results demonstrate that identifying seizure with the MSK-means algorithm and delay permutation entropy achieves 4. 7 % higher accuracy than that of K-means, and 0. 7 % higher accuracy than that of the SVM.

Contaminant source identification using semi-supervised machine learning

NASA Astrophysics Data System (ADS)

Vesselinov, Velimir V.; Alexandrov, Boian S.; O'Malley, Daniel

2018-05-01

Identification of the original groundwater types present in geochemical mixtures observed in an aquifer is a challenging but very important task. Frequently, some of the groundwater types are related to different infiltration and/or contamination sources associated with various geochemical signatures and origins. The characterization of groundwater mixing processes typically requires solving complex inverse models representing groundwater flow and geochemical transport in the aquifer, where the inverse analysis accounts for available site data. Usually, the model is calibrated against the available data characterizing the spatial and temporal distribution of the observed geochemical types. Numerous different geochemical constituents and processes may need to be simulated in these models which further complicates the analyses. In this paper, we propose a new contaminant source identification approach that performs decomposition of the observation mixtures based on Non-negative Matrix Factorization (NMF) method for Blind Source Separation (BSS), coupled with a custom semi-supervised clustering algorithm. Our methodology, called NMFk, is capable of identifying (a) the unknown number of groundwater types and (b) the original geochemical concentration of the contaminant sources from measured geochemical mixtures with unknown mixing ratios without any additional site information. NMFk is tested on synthetic and real-world site data. The NMFk algorithm works with geochemical data represented in the form of concentrations, ratios (of two constituents; for example, isotope ratios), and delta notations (standard normalized stable isotope ratios).

Exploring the roles of cannot-link constraint in community detection via Multi-variance Mixed Gaussian Generative Model.

PubMed

Yang, Liang; Ge, Meng; Jin, Di; He, Dongxiao; Fu, Huazhu; Wang, Jing; Cao, Xiaochun

2017-01-01

Due to the demand for performance improvement and the existence of prior information, semi-supervised community detection with pairwise constraints becomes a hot topic. Most existing methods have been successfully encoding the must-link constraints, but neglect the opposite ones, i.e., the cannot-link constraints, which can force the exclusion between nodes. In this paper, we are interested in understanding the role of cannot-link constraints and effectively encoding pairwise constraints. Towards these goals, we define an integral generative process jointly considering the network topology, must-link and cannot-link constraints. We propose to characterize this process as a Multi-variance Mixed Gaussian Generative (MMGG) Model to address diverse degrees of confidences that exist in network topology and pairwise constraints and formulate it as a weighted nonnegative matrix factorization problem. The experiments on artificial and real-world networks not only illustrate the superiority of our proposed MMGG, but also, most importantly, reveal the roles of pairwise constraints. That is, though the must-link is more important than cannot-link when either of them is available, both must-link and cannot-link are equally important when both of them are available. To the best of our knowledge, this is the first work on discovering and exploring the importance of cannot-link constraints in semi-supervised community detection.

Contaminant source identification using semi-supervised machine learning

DOE PAGES

Vesselinov, Velimir Valentinov; Alexandrov, Boian S.; O’Malley, Dan

2017-11-08

Identification of the original groundwater types present in geochemical mixtures observed in an aquifer is a challenging but very important task. Frequently, some of the groundwater types are related to different infiltration and/or contamination sources associated with various geochemical signatures and origins. The characterization of groundwater mixing processes typically requires solving complex inverse models representing groundwater flow and geochemical transport in the aquifer, where the inverse analysis accounts for available site data. Usually, the model is calibrated against the available data characterizing the spatial and temporal distribution of the observed geochemical types. Numerous different geochemical constituents and processes may needmore » to be simulated in these models which further complicates the analyses. In this paper, we propose a new contaminant source identification approach that performs decomposition of the observation mixtures based on Non-negative Matrix Factorization (NMF) method for Blind Source Separation (BSS), coupled with a custom semi-supervised clustering algorithm. Our methodology, called NMFk, is capable of identifying (a) the unknown number of groundwater types and (b) the original geochemical concentration of the contaminant sources from measured geochemical mixtures with unknown mixing ratios without any additional site information. NMFk is tested on synthetic and real-world site data. Finally, the NMFk algorithm works with geochemical data represented in the form of concentrations, ratios (of two constituents; for example, isotope ratios), and delta notations (standard normalized stable isotope ratios).« less

Contaminant source identification using semi-supervised machine learning

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

Vesselinov, Velimir Valentinov; Alexandrov, Boian S.; O’Malley, Dan

Identification of the original groundwater types present in geochemical mixtures observed in an aquifer is a challenging but very important task. Frequently, some of the groundwater types are related to different infiltration and/or contamination sources associated with various geochemical signatures and origins. The characterization of groundwater mixing processes typically requires solving complex inverse models representing groundwater flow and geochemical transport in the aquifer, where the inverse analysis accounts for available site data. Usually, the model is calibrated against the available data characterizing the spatial and temporal distribution of the observed geochemical types. Numerous different geochemical constituents and processes may needmore » to be simulated in these models which further complicates the analyses. In this paper, we propose a new contaminant source identification approach that performs decomposition of the observation mixtures based on Non-negative Matrix Factorization (NMF) method for Blind Source Separation (BSS), coupled with a custom semi-supervised clustering algorithm. Our methodology, called NMFk, is capable of identifying (a) the unknown number of groundwater types and (b) the original geochemical concentration of the contaminant sources from measured geochemical mixtures with unknown mixing ratios without any additional site information. NMFk is tested on synthetic and real-world site data. Finally, the NMFk algorithm works with geochemical data represented in the form of concentrations, ratios (of two constituents; for example, isotope ratios), and delta notations (standard normalized stable isotope ratios).« less

Exploring the roles of cannot-link constraint in community detection via Multi-variance Mixed Gaussian Generative Model

PubMed Central

Ge, Meng; Jin, Di; He, Dongxiao; Fu, Huazhu; Wang, Jing; Cao, Xiaochun

2017-01-01

Due to the demand for performance improvement and the existence of prior information, semi-supervised community detection with pairwise constraints becomes a hot topic. Most existing methods have been successfully encoding the must-link constraints, but neglect the opposite ones, i.e., the cannot-link constraints, which can force the exclusion between nodes. In this paper, we are interested in understanding the role of cannot-link constraints and effectively encoding pairwise constraints. Towards these goals, we define an integral generative process jointly considering the network topology, must-link and cannot-link constraints. We propose to characterize this process as a Multi-variance Mixed Gaussian Generative (MMGG) Model to address diverse degrees of confidences that exist in network topology and pairwise constraints and formulate it as a weighted nonnegative matrix factorization problem. The experiments on artificial and real-world networks not only illustrate the superiority of our proposed MMGG, but also, most importantly, reveal the roles of pairwise constraints. That is, though the must-link is more important than cannot-link when either of them is available, both must-link and cannot-link are equally important when both of them are available. To the best of our knowledge, this is the first work on discovering and exploring the importance of cannot-link constraints in semi-supervised community detection. PMID:28678864

Out-of-Sample Extrapolation utilizing Semi-Supervised Manifold Learning (OSE-SSL): Content Based Image Retrieval for Histopathology Images

PubMed Central

Sparks, Rachel; Madabhushi, Anant

2016-01-01

Content-based image retrieval (CBIR) retrieves database images most similar to the query image by (1) extracting quantitative image descriptors and (2) calculating similarity between database and query image descriptors. Recently, manifold learning (ML) has been used to perform CBIR in a low dimensional representation of the high dimensional image descriptor space to avoid the curse of dimensionality. ML schemes are computationally expensive, requiring an eigenvalue decomposition (EVD) for every new query image to learn its low dimensional representation. We present out-of-sample extrapolation utilizing semi-supervised ML (OSE-SSL) to learn the low dimensional representation without recomputing the EVD for each query image. OSE-SSL incorporates semantic information, partial class label, into a ML scheme such that the low dimensional representation co-localizes semantically similar images. In the context of prostate histopathology, gland morphology is an integral component of the Gleason score which enables discrimination between prostate cancer aggressiveness. Images are represented by shape features extracted from the prostate gland. CBIR with OSE-SSL for prostate histology obtained from 58 patient studies, yielded an area under the precision recall curve (AUPRC) of 0.53 ± 0.03 comparatively a CBIR with Principal Component Analysis (PCA) to learn a low dimensional space yielded an AUPRC of 0.44 ± 0.01. PMID:27264985

The beginnings of psychoanalytic supervision: the crucial role of Max Eitingon.

PubMed

Watkins, C Edward

2013-09-01

Psychoanalytic supervision is moving well into its 2nd century of theory, practice, and (to a limited extent) research. In this paper, I take a look at the pioneering first efforts to define psychoanalytic supervision and its importance to the psychoanalytic education process. Max Eitingon, the "almost forgotten man" of psychoanalysis, looms large in any such consideration. His writings or organizational reports were seemingly the first psychoanalytic published material to address the following supervision issues: rationale, screening, notes, responsibility, supervisee learning/personality issues, and the extent and length of supervision itself. Although Eitingon never wrote formally on supervision, his pioneering work in the area has continued to echo across the decades and can still be seen reflected in contemporary supervision practice. I also recognize the role of Karen Horney-one of the founders of the Berlin Institute and Poliklinik, friend of Eitingon, and active, vital participant in Eitingon's efforts-in contributing to and shaping the beginnings of psychoanalytic education.

Healthcare students' evaluation of the clinical learning environment and supervision - a cross-sectional study.

PubMed

Pitkänen, Salla; Kääriäinen, Maria; Oikarainen, Ashlee; Tuomikoski, Anna-Maria; Elo, Satu; Ruotsalainen, Heidi; Saarikoski, Mikko; Kärsämänoja, Taina; Mikkonen, Kristina

2018-03-01

The purpose of clinical placements and supervision is to promote the development of healthcare students´ professional skills. High-quality clinical learning environments and supervision were shown to have significant influence on healthcare students´ professional development. This study aimed to describe healthcare students` evaluation of the clinical learning environment and supervision, and to identify the factors that affect these. The study was performed as a cross-sectional study. The data (n = 1973) were gathered through an online survey using the Clinical Learning Environment, Supervision and Nurse Teacher scale during the academic year 2015-2016 from all healthcare students (N = 2500) who completed their clinical placement at a certain university hospital in Finland. The data were analysed using descriptive statistics and binary logistic regression analysis. More than half of the healthcare students had a named supervisor and supervision was completed as planned. The students evaluated the clinical learning environment and supervision as 'good'. The students´ readiness to recommend the unit to other students and the frequency of separate private unscheduled sessions with the supervisor were the main factors that affect healthcare students` evaluation of the clinical learning environment and supervision. Individualized and goal-oriented supervision in which the student had a named supervisor and where supervision was completed as planned in a positive environment that supported learning had a significant impact on student's learning. The clinical learning environment and supervision support the development of future healthcare professionals' clinical competence. The supervisory relationship was shown to have a significant effect on the outcomes of students' experiences. We recommend the planning of educational programmes for supervisors of healthcare students for the enhancement of supervisors' pedagogical competencies in supervising students in the clinical practice. Copyright © 2018 Elsevier Ltd. All rights reserved.

Semi-industrial scale (30 m3) fed-batch fermentation for the production of D-lactate by Escherichia coli strain HBUT-D15.

PubMed

Fu, Xiangmin; Wang, Yongze; Wang, Jinhua; Garza, Erin; Manow, Ryan; Zhou, Shengde

2017-02-01

D(-)-lactic acid is needed for manufacturing of stereo-complex poly-lactic acid polymer. Large scale D-lactic acid fermentation, however, has yet to be demonstrated. A genetically engineered Escherichia coli strain, HBUT-D, was adaptively evolved in a 15% calcium lactate medium for improved lactate tolerance. The resulting strain, HBUT-D15, was tested at a lab scale (7 L) by fed-batch fermentation with up to 200 g L -1 of glucose, producing 184-191 g L -1 of D-lactic acid, with a volumetric productivity of 4.38 g L -1  h -1 , a yield of 92%, and an optical purity of 99.9%. The HBUT-D15 was then evaluated at a semi-industrial scale (30 m 3 ) via fed-batch fermentation with up to 160 g L -1 of glucose, producing 146-150 g L -1 of D-lactic acid, with a volumetric productivity of 3.95-4.29 g L -1  h -1 , a yield of 91-94%, and an optical purity of 99.8%. These results are comparable to that of current industrial scale L(+)-lactic acid fermentation.

Test-retest reliability of the Clinical Learning Environment, Supervision and Nurse Teacher (CLES + T) scale.

PubMed

Gustafsson, Margareta; Blomberg, Karin; Holmefur, Marie

2015-07-01

The Clinical Learning Environment, Supervision and Nurse Teacher (CLES + T) scale evaluates the student nurses' perception of the learning environment and supervision within the clinical placement. It has never been tested in a replication study. The aim of the present study was to evaluate the test-retest reliability of the CLES + T scale. The CLES + T scale was administered twice to a group of 42 student nurses, with a one-week interval. Test-retest reliability was determined by calculations of Intraclass Correlation Coefficients (ICCs) and weighted Kappa coefficients. Standard Error of Measurements (SEM) and Smallest Detectable Difference (SDD) determined the precision of individual scores. Bland-Altman plots were created for analyses of systematic differences between the test occasions. The results of the study showed that the stability over time was good to excellent (ICC 0.88-0.96) in the sub-dimensions "Supervisory relationship", "Pedagogical atmosphere on the ward" and "Role of the nurse teacher". Measurements of "Premises of nursing on the ward" and "Leadership style of the manager" had lower but still acceptable stability (ICC 0.70-0.75). No systematic differences occurred between the test occasions. This study supports the usefulness of the CLES + T scale as a reliable measure of the student nurses' perception of the learning environment within the clinical placement at a hospital. Copyright © 2015 Elsevier Ltd. All rights reserved.

Residents' Ratings of Their Clinical Supervision and Their Self-Reported Medical Errors: Analysis of Data From 2009.

PubMed

Baldwin, DeWitt C; Daugherty, Steven R; Ryan, Patrick M; Yaghmour, Nicholas A; Philibert, Ingrid

2018-04-01

Medical errors and patient safety are major concerns for the medical and medical education communities. Improving clinical supervision for residents is important in avoiding errors, yet little is known about how residents perceive the adequacy of their supervision and how this relates to medical errors and other education outcomes, such as learning and satisfaction. We analyzed data from a 2009 survey of residents in 4 large specialties regarding the adequacy and quality of supervision they receive as well as associations with self-reported data on medical errors and residents' perceptions of their learning environment. Residents' reports of working without adequate supervision were lower than data from a 1999 survey for all 4 specialties, and residents were least likely to rate "lack of supervision" as a problem. While few residents reported that they received inadequate supervision, problems with supervision were negatively correlated with sufficient time for clinical activities, overall ratings of the residency experience, and attending physicians as a source of learning. Problems with supervision were positively correlated with resident reports that they had made a significant medical error, had been belittled or humiliated, or had observed others falsifying medical records. Although working without supervision was not a pervasive problem in 2009, when it happened, it appeared to have negative consequences. The association between inadequate supervision and medical errors is of particular concern.

Effect of supportive supervision on routine immunization service delivery-a randomized post-test study in Odisha.

PubMed

Som, Meena; Panda, Bhuputra; Pati, Sanghamitra; Nallala, Srinivas; Anasuya, Anita; Chauhan, Abhimanyu Singh; Sen, Ashish Kumar; Zodpey, Sanjay

2014-06-30

Routine immunization is a key child survival intervention. Issues related to quality of service delivery pose operational challenges in delivering effective immunization services. Accumulated evidences suggest that "supportive supervision" improves the quality of health care services. During 2009-10, Govt. of Odisha (GoO) and UNICEF jointly piloted this strategy in four districts to improve routine immunization. The present study aims to assess the effect of supportive supervision strategy on improvement of knowledge and practices on routine immunization among service providers. We adopted a 'post-test only' study design to compare the knowledge and practices of frontline health workers and their supervisors in four intervention districts with that of two control districts. Altogether we interviewed 170 supervisors and supervisees (health workers), each, using semi-structured interview schedules. We also directly observed 25 ice lined refrigerator (ILR) points in both groups of districts. The findings were compared with the baseline information, available only for the intervention districts. The health workers in the intervention districts displayed a higher knowledge score in selected items than in the control group. No significant difference in knowledge was observed between control and intervention supervisors. The management practices at ILR points on key routine immunization components were found to have improved significantly in intervention districts. The observed improvements in the ILR management practices indicate positive influence of supportive supervision. Higher level of domain knowledge among intervention health workers on specific items related to routine immunization could be due to successful transfer of knowledge from supervisors. A 'pre-post' study design should be undertaken to gain insights into the effectiveness of supportive supervision in improving routine immunization services.

"Kicked out into the real world": prostate cancer patients' experiences with transitioning from hospital-based supervised exercise to unsupervised exercise in the community.

PubMed

Schmidt, Mette L K; Østergren, Peter; Cormie, Prue; Ragle, Anne-Mette; Sønksen, Jens; Midtgaard, Julie

2018-06-21

Regular exercise is recommended to mitigate the adverse effects of androgen deprivation therapy in men with prostate cancer. The purpose of this study was to explore the experience of transition to unsupervised, community-based exercise among men who had participated in a hospital-based supervised exercise programme in order to propose components that supported transition to unsupervised exercise. Participants were selected by means of purposive, criteria-based sampling. Men undergoing androgen deprivation therapy who had completed a 12-week hospital-based, supervised, group exercise intervention were invited to participate. The programme involved aerobic and resistance training using machines and included a structured transition to a community-based fitness centre. Data were collected by means of semi-structured focus group interviews and analysed using thematic analysis. Five focus group interviews were conducted with a total of 29 men, of whom 25 reported to have continued to exercise at community-based facilities. Three thematic categories emerged: Development and practice of new skills; Establishing social relationships; and Familiarising with bodily well-being. These were combined into an overarching theme: From learning to doing. Components suggested to support transition were as follows: a structured transition involving supervised exercise sessions at a community-based facility; strategies to facilitate peer support; transferable tools including an individual exercise chart; and access to 'check-ups' by qualified exercise specialists. Hospital-based, supervised exercise provides a safe learning environment. Transferring to community-based exercise can be experienced as a confrontation with the real world and can be eased through securing a structured transition, having transferable tools, sustained peer support and monitoring.

Last interglacial semi-desert expansions in southern Africa

NASA Astrophysics Data System (ADS)

Urrego, D. H.; Sanchez Goni, M.; Lechevrel, S.; Daniau, A.

2013-05-01

While our understanding of the effects of orbital-scale variability on the vegetation has grown during the past decades, empirical data from some climatically important periods and regions are still lacking. Scarce data exist for instance for deep-time glacial-interglacial cycles that could provide suitable analogs for current climate change. Recent global-scale syntheses of vegetation responses to rapid events during the last glacial have been useful, however, these global compilations clearly show that some regions, namely the southern tropics and subtropics, remain understudied. We use pollen analysis of marine sediments from core MD96-2098 to produce a paleoenvironmental record from southern Africa spanning MIS 6 to 3. Our interpretations are supported by an analysis of present-day pollen-vegetation-climate relationships for the region. We applied canonical correspondence analysis (CCA) and detrended correspondence analysis (DCA) on pollen spectra from terrestrial surface samples to investigate these relationships and to identify pollen taxa that are suitable bioclimatic indicators for the different South African biomes. Semi-desert vegetation dominated southern Africa during the MIS 5 interglacial. Expansion of the semi-desert biome into the Namib desert likely resulted from the reduction of the Benguela upwelling and a relative decrease in aridity. In its eastern boundary, the semi-desert likely expanded at the expense of grasslands as a result of increased subtropical high pressure and reduced summer precipitation. Semi-desert expansion in its southern boundary probably resulted from reduced influence of the southern westerlies and decreased winter precipitation. This atmospheric configuration was probably exacerbated during the three warm substages of MIS 5. During glacial isotopic stages MIS 6, 4 and 3 grasslands gained area over the semi-desert as summer precipitation increased. The area occupied by Fynbos vegetation was particularly large at the transition MIS 5e to 5d, and the end of MIS 4 with an increased influence of the southern westerlies and austral-winter precipitation. Small and short-lived increases of Podocarpus during transitions between isotopic stages and substages indicated increases of humidity. Our record also suggested an increase in millennial-scale variability after ca. 100 ka that could be associated with enhanced variability of the trade-wind intensity.

Deep Visual Attention Prediction

NASA Astrophysics Data System (ADS)

Wang, Wenguan; Shen, Jianbing

2018-05-01

In this work, we aim to predict human eye fixation with view-free scenes based on an end-to-end deep learning architecture. Although Convolutional Neural Networks (CNNs) have made substantial improvement on human attention prediction, it is still needed to improve CNN based attention models by efficiently leveraging multi-scale features. Our visual attention network is proposed to capture hierarchical saliency information from deep, coarse layers with global saliency information to shallow, fine layers with local saliency response. Our model is based on a skip-layer network structure, which predicts human attention from multiple convolutional layers with various reception fields. Final saliency prediction is achieved via the cooperation of those global and local predictions. Our model is learned in a deep supervision manner, where supervision is directly fed into multi-level layers, instead of previous approaches of providing supervision only at the output layer and propagating this supervision back to earlier layers. Our model thus incorporates multi-level saliency predictions within a single network, which significantly decreases the redundancy of previous approaches of learning multiple network streams with different input scales. Extensive experimental analysis on various challenging benchmark datasets demonstrate our method yields state-of-the-art performance with competitive inference time.

Extracting PICO Sentences from Clinical Trial Reports using Supervised Distant Supervision

PubMed Central

Wallace, Byron C.; Kuiper, Joël; Sharma, Aakash; Zhu, Mingxi (Brian); Marshall, Iain J.

2016-01-01

Systematic reviews underpin Evidence Based Medicine (EBM) by addressing precise clinical questions via comprehensive synthesis of all relevant published evidence. Authors of systematic reviews typically define a Population/Problem, Intervention, Comparator, and Outcome (a PICO criteria) of interest, and then retrieve, appraise and synthesize results from all reports of clinical trials that meet these criteria. Identifying PICO elements in the full-texts of trial reports is thus a critical yet time-consuming step in the systematic review process. We seek to expedite evidence synthesis by developing machine learning models to automatically extract sentences from articles relevant to PICO elements. Collecting a large corpus of training data for this task would be prohibitively expensive. Therefore, we derive distant supervision (DS) with which to train models using previously conducted reviews. DS entails heuristically deriving ‘soft’ labels from an available structured resource. However, we have access only to unstructured, free-text summaries of PICO elements for corresponding articles; we must derive from these the desired sentence-level annotations. To this end, we propose a novel method – supervised distant supervision (SDS) – that uses a small amount of direct supervision to better exploit a large corpus of distantly labeled instances by learning to pseudo-annotate articles using the available DS. We show that this approach tends to outperform existing methods with respect to automated PICO extraction. PMID:27746703

Reliability and validity of assessing subspecialty level of faculty anesthesiologists' supervision of anesthesiology residents.

PubMed

De Oliveira, Gildasio S; Dexter, Franklin; Bialek, Jane M; McCarthy, Robert J

2015-01-01

Supervision of anesthesiology residents is a major responsibility of faculty (academic) anesthesiologists. Supervision can be evaluated daily for individual anesthesiologists using a 9-question instrument. Faculty anesthesiologists with lesser individual scores contribute to lesser departmental (global) scores. Low (<3, "frequent") department-wide evaluations of supervision are associated with more mistakes with negative consequences to patients. With the long-term aim for residency programs to be evaluated partly based on the quality of their resident supervision, we assessed the 9-item instrument's reliability and validity when used to compare anesthesia programs' rotations nationwide. One thousand five hundred residents in the American Society of Anesthesiologists' directory of anesthesia trainees were randomly selected to be participants. Residents were contacted via e-mail and requested to complete a Web-based survey. Nonrespondents were mailed a paper version of the survey. Internal consistency of the supervision scale was excellent, with Cronbach's α = 0.909 (95% CI, 0.896-0.922, n = 641 respondents). Discriminant validity was found based on absence of rank correlation of supervision score with characteristics of the respondents and programs (all P > 0.10): age, hours worked per week, female, year of anesthesia training, weeks in the current rotation, sequence of survey response, size of residency class, and number of survey respondents from the current rotation and program. Convergent validity was found based on significant positive correlation between supervision score and variables related to safety culture (all P < 0.0001): "Overall perceptions of patient safety," "Teamwork within units," "Nonpunitive response to errors," "Handoffs and transitions," "Feedback and communication about error," "Communication openness," and rotation's "overall grade on patient safety." Convergent validity was found also based on significant negative correlation with variables related to the individual resident's burnout (all P < 0.0001): "I feel burnout from my work," "I have become more callous toward people since I took this job," and numbers of "errors with potential negative consequences to patients [that you have] made and/or witnessed." Usefulness was shown by supervision being predicted by the same 1 variable for each of 3 regression tree criteria: "Teamwork within [the rotation]" (e.g., "When one area in this rotation gets busy, others help out"). Evaluation of the overall quality of supervision of residents by faculty anesthesiologists depends on the reliability and validity of the instrument. Our results show that the 9-item de Oliveira Filho et al. supervision scale can be applied for overall (department, rotation) assessment of anesthesia training programs.

Mapping Expert Supervisors' Cognitions

ERIC Educational Resources Information Center

Kemer, Gulsah

2012-01-01

Since the essential role of counseling supervision for counselor growth and effectiveness was emphasized in several seminal articles in the 1980s (Blocher, 1983; Loganbill, Hardy, & Delworth, 1982), many researchers have investigated the complex factors involved in effective counseling supervision. However, within this large body of work, very…

Model for investigating the benefits of clinical supervision in psychiatric nursing: a survey study.

PubMed

Gonge, Henrik; Buus, Niels

2011-04-01

The objective of this study was to test a model for analysing the possible benefits of clinical supervision. The model suggested a pathway from participation to effectiveness to benefits of clinical supervision, and included possible influences of individual and workplace factors. The study sample was 136 nursing staff members in permanent employment on nine general psychiatric wards and at four community mental health centres at a Danish psychiatric university hospital. Data were collected by means of a set of questionnaires. Participation in clinical supervision was associated with the effectiveness of clinical supervision, as measured by the Manchester Clinical Supervision Scale (MCSS). Furthermore, MCSS scores were associated with benefits, such as increased job satisfaction, vitality, rational coping and less stress, emotional exhaustion, and depersonalization. Multivariate analyses indicated that certain individual and workplace factors were related to subscales of the MCSS, as well as some of the benefits. The study supported the suggested model, but methodological limitations apply. © 2011 The Authors. International Journal of Mental Health Nursing © 2011 Australian College of Mental Health Nurses Inc.

A review of supervised object-based land-cover image classification

NASA Astrophysics Data System (ADS)

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

2017-08-01

Object-based image classification for land-cover mapping purposes using remote-sensing imagery has attracted significant attention in recent years. Numerous studies conducted over the past decade have investigated a broad array of sensors, feature selection, classifiers, and other factors of interest. However, these research results have not yet been synthesized to provide coherent guidance on the effect of different supervised object-based land-cover classification processes. In this study, we first construct a database with 28 fields using qualitative and quantitative information extracted from 254 experimental cases described in 173 scientific papers. Second, the results of the meta-analysis are reported, including general characteristics of the studies (e.g., the geographic range of relevant institutes, preferred journals) and the relationships between factors of interest (e.g., spatial resolution and study area or optimal segmentation scale, accuracy and number of targeted classes), especially with respect to the classification accuracy of different sensors, segmentation scale, training set size, supervised classifiers, and land-cover types. Third, useful data on supervised object-based image classification are determined from the meta-analysis. For example, we find that supervised object-based classification is currently experiencing rapid advances, while development of the fuzzy technique is limited in the object-based framework. Furthermore, spatial resolution correlates with the optimal segmentation scale and study area, and Random Forest (RF) shows the best performance in object-based classification. The area-based accuracy assessment method can obtain stable classification performance, and indicates a strong correlation between accuracy and training set size, while the accuracy of the point-based method is likely to be unstable due to mixed objects. In addition, the overall accuracy benefits from higher spatial resolution images (e.g., unmanned aerial vehicle) or agricultural sites where it also correlates with the number of targeted classes. More than 95.6% of studies involve an area less than 300 ha, and the spatial resolution of images is predominantly between 0 and 2 m. Furthermore, we identify some methods that may advance supervised object-based image classification. For example, deep learning and type-2 fuzzy techniques may further improve classification accuracy. Lastly, scientists are strongly encouraged to report results of uncertainty studies to further explore the effects of varied factors on supervised object-based image classification.

From 5 Million to 20 Million a Year: The Challenge of Scale, Quality and Relevance in India's TVET

ERIC Educational Resources Information Center

Mehrotra, Santosh

2014-01-01

In the first decade of this century, India became one of the world's fastest growing large economies, and began to face serious skill-related shortages of workers. Its TVET system has not responded adequately to the growth in demand for semi-skilled and skilled workers. This article describes six sets of reforms that India's educational planners…

Can biochar be used as a seed coating to improve native plant germination and growth in arid conditions?

Treesearch

Mary I. Williams; R. Kasten Dumroese; Deborah S. Page-Dumroese; Stuart P. Hardegree

2016-01-01

Direct seeding is a common large-scale restoration practice for revegetating arid and semi-arid lands, but success can be limited by moisture and temperature. Seed coating technologies that use biochar may have the potential to overcome moisture and temperature limitations on native plant germination and growth. Biochar is a popular agronomic tool for improving soil...

The Galics Project: Virtual Galaxy: from Cosmological N-body Simulations

NASA Astrophysics Data System (ADS)

Guiderdoni, B.

The GalICS project develops extensive semi-analytic post-processing of large cosmological simulations to describe hierarchical galaxy formation. The multiwavelength statistical properties of high-redshift and local galaxies are predicted within the large-scale structures. The fake catalogs and mock images that are generated from the outputs are used for the analysis and preparation of deep surveys. The whole set of results is now available in an on-line database that can be easily queried. The GalICS project represents a first step towards a 'Virtual Observatory of virtual galaxies'.

Identification of Alfalfa Leaf Diseases Using Image Recognition Technology

PubMed Central

Qin, Feng; Liu, Dongxia; Sun, Bingda; Ruan, Liu; Ma, Zhanhong; Wang, Haiguang

2016-01-01

Common leaf spot (caused by Pseudopeziza medicaginis), rust (caused by Uromyces striatus), Leptosphaerulina leaf spot (caused by Leptosphaerulina briosiana) and Cercospora leaf spot (caused by Cercospora medicaginis) are the four common types of alfalfa leaf diseases. Timely and accurate diagnoses of these diseases are critical for disease management, alfalfa quality control and the healthy development of the alfalfa industry. In this study, the identification and diagnosis of the four types of alfalfa leaf diseases were investigated using pattern recognition algorithms based on image-processing technology. A sub-image with one or multiple typical lesions was obtained by artificial cutting from each acquired digital disease image. Then the sub-images were segmented using twelve lesion segmentation methods integrated with clustering algorithms (including K_means clustering, fuzzy C-means clustering and K_median clustering) and supervised classification algorithms (including logistic regression analysis, Naive Bayes algorithm, classification and regression tree, and linear discriminant analysis). After a comprehensive comparison, the segmentation method integrating the K_median clustering algorithm and linear discriminant analysis was chosen to obtain lesion images. After the lesion segmentation using this method, a total of 129 texture, color and shape features were extracted from the lesion images. Based on the features selected using three methods (ReliefF, 1R and correlation-based feature selection), disease recognition models were built using three supervised learning methods, including the random forest, support vector machine (SVM) and K-nearest neighbor methods. A comparison of the recognition results of the models was conducted. The results showed that when the ReliefF method was used for feature selection, the SVM model built with the most important 45 features (selected from a total of 129 features) was the optimal model. For this SVM model, the recognition accuracies of the training set and the testing set were 97.64% and 94.74%, respectively. Semi-supervised models for disease recognition were built based on the 45 effective features that were used for building the optimal SVM model. For the optimal semi-supervised models built with three ratios of labeled to unlabeled samples in the training set, the recognition accuracies of the training set and the testing set were both approximately 80%. The results indicated that image recognition of the four alfalfa leaf diseases can be implemented with high accuracy. This study provides a feasible solution for lesion image segmentation and image recognition of alfalfa leaf disease. PMID:27977767

Identification of Alfalfa Leaf Diseases Using Image Recognition Technology.

PubMed

Qin, Feng; Liu, Dongxia; Sun, Bingda; Ruan, Liu; Ma, Zhanhong; Wang, Haiguang

2016-01-01

Common leaf spot (caused by Pseudopeziza medicaginis), rust (caused by Uromyces striatus), Leptosphaerulina leaf spot (caused by Leptosphaerulina briosiana) and Cercospora leaf spot (caused by Cercospora medicaginis) are the four common types of alfalfa leaf diseases. Timely and accurate diagnoses of these diseases are critical for disease management, alfalfa quality control and the healthy development of the alfalfa industry. In this study, the identification and diagnosis of the four types of alfalfa leaf diseases were investigated using pattern recognition algorithms based on image-processing technology. A sub-image with one or multiple typical lesions was obtained by artificial cutting from each acquired digital disease image. Then the sub-images were segmented using twelve lesion segmentation methods integrated with clustering algorithms (including K_means clustering, fuzzy C-means clustering and K_median clustering) and supervised classification algorithms (including logistic regression analysis, Naive Bayes algorithm, classification and regression tree, and linear discriminant analysis). After a comprehensive comparison, the segmentation method integrating the K_median clustering algorithm and linear discriminant analysis was chosen to obtain lesion images. After the lesion segmentation using this method, a total of 129 texture, color and shape features were extracted from the lesion images. Based on the features selected using three methods (ReliefF, 1R and correlation-based feature selection), disease recognition models were built using three supervised learning methods, including the random forest, support vector machine (SVM) and K-nearest neighbor methods. A comparison of the recognition results of the models was conducted. The results showed that when the ReliefF method was used for feature selection, the SVM model built with the most important 45 features (selected from a total of 129 features) was the optimal model. For this SVM model, the recognition accuracies of the training set and the testing set were 97.64% and 94.74%, respectively. Semi-supervised models for disease recognition were built based on the 45 effective features that were used for building the optimal SVM model. For the optimal semi-supervised models built with three ratios of labeled to unlabeled samples in the training set, the recognition accuracies of the training set and the testing set were both approximately 80%. The results indicated that image recognition of the four alfalfa leaf diseases can be implemented with high accuracy. This study provides a feasible solution for lesion image segmentation and image recognition of alfalfa leaf disease.

The Supervisory Process of EFL Teachers: A Case Study

ERIC Educational Resources Information Center

Chen, Cheryl Wei-Yu; Cheng, Yuh-show

2013-01-01

Supervision is an essential part of language teachers' professional experiences. The literature on language teacher supervision from the past few decades consists largely of descriptions of supervisory approaches (Bailey, 2009) and analysis of the supervisory discourse (Hooton, 2008; Wajnryb, 1994; 1995; 1998; Wallace & Woolger, 1991). This…

Semi supervised Learning of Feature Hierarchies for Object Detection in a Video (Open Access)

DTIC Science & Technology

2013-10-03

dataset: PETS2009 Dataset, Oxford Town Center dataset [3], PNNL Parking Lot datasets [25] and CAVIAR cols1 dataset [1] for human detection. Be- sides, we...level features from TownCen- ter, ParkingLot, PETS09 and CAVIAR . As we can see that, the four set of features are visually very different from each other...information is more distinguished for detecting a person in the TownCen- ter than CAVIAR . Comparing figure 5(a) with 6(a), interest- ingly, the color

Supervised Versus Home Exercise Training Programs on Functional Balance in Older Subjects.

PubMed

Youssef, Enas Fawzy; Shanb, Alsayed Abd Elhameed

2016-11-01

Aging is associated with a progressive decline in physical capabilities and a disturbance of both postural control and daily living activities. The aim of this study was to evaluate the effects of supervised versus home exercise programs on muscle strength, balance and functional activities in older participants. Forty older participants were equally assigned to a supervised exercise program (group-I) or a home exercise program (group-II). Each participant performed the exercise program for 35-45 minutes, two times per week for four months. Balance indices and isometric muscle strength were measured with the Biodex Balance System and Hand-Held Dynamometer. Functional activities were evaluated by the Berg Balance Scale (BBS) and the timed get-up-and-go test (TUG). The mean values of the Biodex balance indices and the BBS improved significantly after both the supervised and home exercise programs ( P < 0.05). However, the mean values of the TUG and muscle strength at the ankle, knee and hip improved significantly only after the supervised program. A comparison between the supervised and home exercise programs revealed there were only significant differences in the BBS, TUG and muscle strength. Both the supervised and home exercise training programs significantly increased balance performance. The supervised program was superior to the home program in restoring functional activities and isometric muscle strength in older participants.

Measuring the Effectiveness of a Genetic Counseling Supervision Training Conference.

PubMed

Atzinger, Carrie L; He, Hua; Wusik, Katie

2016-08-01

Genetic counselors who receive formal training report increased confidence and competence in their supervisory roles. The effectiveness of specific formal supervision training has not been assessed previously. A day-long GC supervision conference was designed based on published supervision competencies and was attended by 37 genetic counselors. Linear Mixed Model and post-hoc paired t-test was used to compare Psychotherapy Supervisor Development Scale (PSDS) scores among/between individuals pre and post conference. Generalized Estimating Equation (GEE) model and post-hoc McNemar's test was used to determine if the conference had an effect on GC supervision competencies. PSDS scores were significantly increased 1 week (p < 0.001) and 6 months (p < 0.001) following the conference. For three supervision competencies, attendees were more likely to agree they were able to perform them after the conference than before. These effects remained significant 6 months later. For the three remaining competencies, the majority of supervisors agreed they could perform these before the conference; therefore, no change was found. This exploratory study showed this conference increased the perceived confidence and competence of the supervisors who attended and increased their self-reported ability to perform certain supervision competencies. While still preliminary, this supports the idea that a one day conference on supervision has the potential to impact supervisor development.

Amount of balance necessary for the independence of transfer and stair-climbing in stroke inpatients.

PubMed

Fujita, Takaaki; Sato, Atsushi; Ohashi, Yuji; Nishiyama, Kazutaka; Ohashi, Takuro; Yamane, Kazuhiro; Yamamoto, Yuichi; Tsuchiya, Kenji; Otsuki, Koji; Tozato, Fusae

2018-05-01

The purpose of this study was to clarify the amount of balance necessary for the independence of transfer and stair-climbing in stroke patients. This study included 111 stroke inpatients. Simple and multiple regression analyses were conducted to establish the association between the FIM ® instrument scores for transfer or stair-climbing and Berg Balance Scale. Furthermore, receiver operating characteristic curves were used to elucidate the amount of balance necessary for the independence of transfer and stair-climbing. Simple and multiple regression analyses showed that the FIM ® instrument scores for transfer and stair-climbing were strongly associated with Berg Balance Scale. On comparison of the independent and supervision-dependent groups, Berg Balance Scale cut-off values for transfer and stair-climbing were 41/40 and 54/53 points, respectively. On comparison of the independent-supervision and dependent groups, the cut-off values for transfer and stair-climbing were 30/29 and 41/40 points, respectively. The calculated cut-off values indicated the amount of balance necessary for the independence of transfer and stair-climbing, with and without supervision, in stroke patients. Berg Balance Scale has a good discriminatory ability and cut-off values are clinically useful to determine the appropriate independence levels of transfer and stair-climbing in hospital wards. Implications for rehabilitation The Berg Balance Scale's (BBS) strong association with transfer and stair-climbing independence and performance indicates that establishing cut-off values is vitally important for the established use of the BBS clinically. The cut-off values calculated herein accurately demonstrate the level of balance necessary for transfer and stair-climbing independence, with and without supervision, in stroke patients. These criteria should be employed clinically for determining the level of independence for transfer and stair-climbing as well as for setting balance training goals aimed at improving transfer and stair-climbing.

Estimating and interpreting migration of Amazonian forests using spatially implicit and semi-explicit neutral models.

PubMed

Pos, Edwin; Guevara Andino, Juan Ernesto; Sabatier, Daniel; Molino, Jean-François; Pitman, Nigel; Mogollón, Hugo; Neill, David; Cerón, Carlos; Rivas-Torres, Gonzalo; Di Fiore, Anthony; Thomas, Raquel; Tirado, Milton; Young, Kenneth R; Wang, Ophelia; Sierra, Rodrigo; García-Villacorta, Roosevelt; Zagt, Roderick; Palacios Cuenca, Walter; Aulestia, Milton; Ter Steege, Hans

2017-06-01

With many sophisticated methods available for estimating migration, ecologists face the difficult decision of choosing for their specific line of work. Here we test and compare several methods, performing sanity and robustness tests, applying to large-scale data and discussing the results and interpretation. Five methods were selected to compare for their ability to estimate migration from spatially implicit and semi-explicit simulations based on three large-scale field datasets from South America (Guyana, Suriname, French Guiana and Ecuador). Space was incorporated semi-explicitly by a discrete probability mass function for local recruitment, migration from adjacent plots or from a metacommunity. Most methods were able to accurately estimate migration from spatially implicit simulations. For spatially semi-explicit simulations, estimation was shown to be the additive effect of migration from adjacent plots and the metacommunity. It was only accurate when migration from the metacommunity outweighed that of adjacent plots, discrimination, however, proved to be impossible. We show that migration should be considered more an approximation of the resemblance between communities and the summed regional species pool. Application of migration estimates to simulate field datasets did show reasonably good fits and indicated consistent differences between sets in comparison with earlier studies. We conclude that estimates of migration using these methods are more an approximation of the homogenization among local communities over time rather than a direct measurement of migration and hence have a direct relationship with beta diversity. As betadiversity is the result of many (non)-neutral processes, we have to admit that migration as estimated in a spatial explicit world encompasses not only direct migration but is an ecological aggregate of these processes. The parameter m of neutral models then appears more as an emerging property revealed by neutral theory instead of being an effective mechanistic parameter and spatially implicit models should be rejected as an approximation of forest dynamics.

Barriers Inhibiting Inquiry-Based Science Teaching and Potential Solutions: Perceptions of Positively Inclined Early Adopters

NASA Astrophysics Data System (ADS)

Fitzgerald, Michael; Danaia, Lena; McKinnon, David H.

2017-07-01

In recent years, calls for the adoption of inquiry-based pedagogies in the science classroom have formed a part of the recommendations for large-scale high school science reforms. However, these pedagogies have been problematic to implement at scale. This research explores the perceptions of 34 positively inclined early-adopter teachers in relation to their implementation of inquiry-based pedagogies. The teachers were part of a large-scale Australian high school intervention project based around astronomy. In a series of semi-structured interviews, the teachers identified a number of common barriers that prevented them from implementing inquiry-based approaches. The most important barriers identified include the extreme time restrictions on all scales, the poverty of their common professional development experiences, their lack of good models and definitions for what inquiry-based teaching actually is, and the lack of good resources enabling the capacity for change. Implications for expectations of teachers and their professional learning during educational reform and curriculum change are discussed.

Use of a mobile social networking intervention for weight management: a mixed-methods study protocol.

PubMed

Laranjo, Liliana; Lau, Annie Y S; Martin, Paige; Tong, Huong Ly; Coiera, Enrico

2017-07-12

Obesity and physical inactivity are major societal challenges and significant contributors to the global burden of disease and healthcare costs. Information and communication technologies are increasingly being used in interventions to promote behaviour change in diet and physical activity. In particular, social networking platforms seem promising for the delivery of weight control interventions.We intend to pilot test an intervention involving the use of a social networking mobile application and tracking devices ( Fitbit Flex 2 and Fitbit Aria scale) to promote the social comparison of weight and physical activity, in order to evaluate whether mechanisms of social influence lead to changes in those outcomes over the course of the study. Mixed-methods study involving semi-structured interviews and a pre-post quasi-experimental pilot with one arm, where healthy participants in different body mass index (BMI) categories, aged between 19 and 35 years old, will be subjected to a social networking intervention over a 6-month period. The primary outcome is the average difference in weight before and after the intervention. Secondary outcomes include BMI, number of steps per day, engagement with the intervention, social support and system usability. Semi-structured interviews will assess participants' expectations and perceptions regarding the intervention. Ethics approval was granted by Macquarie University's Human Research Ethics Committee for Medical Sciences on 3 November 2016 (ethics reference number 5201600716).The social network will be moderated by a researcher with clinical expertise, who will monitor and respond to concerns raised by participants. Monitoring will involve daily observation of measures collected by the fitness tracker and the wireless scale, as well as continuous supervision of forum interactions and posts. Additionally, a protocol is in place to monitor for participant misbehaviour and direct participants-in-need to appropriate sources of help. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Use of a mobile social networking intervention for weight management: a mixed-methods study protocol

PubMed Central

Lau, Annie Y S; Martin, Paige; Tong, Huong Ly; Coiera, Enrico

2017-01-01

Introduction Obesity and physical inactivity are major societal challenges and significant contributors to the global burden of disease and healthcare costs. Information and communication technologies are increasingly being used in interventions to promote behaviour change in diet and physical activity. In particular, social networking platforms seem promising for the delivery of weight control interventions. We intend to pilot test an intervention involving the use of a social networking mobile application and tracking devices (Fitbit Flex 2 and Fitbit Aria scale) to promote the social comparison of weight and physical activity, in order to evaluate whether mechanisms of social influence lead to changes in those outcomes over the course of the study. Methods and analysis Mixed-methods study involving semi-structured interviews and a pre–post quasi-experimental pilot with one arm, where healthy participants in different body mass index (BMI) categories, aged between 19 and 35 years old, will be subjected to a social networking intervention over a 6-month period. The primary outcome is the average difference in weight before and after the intervention. Secondary outcomes include BMI, number of steps per day, engagement with the intervention, social support and system usability. Semi-structured interviews will assess participants’ expectations and perceptions regarding the intervention. Ethics and dissemination Ethics approval was granted by Macquarie University’s Human Research Ethics Committee for Medical Sciences on 3 November 2016 (ethics reference number 5201600716). The social network will be moderated by a researcher with clinical expertise, who will monitor and respond to concerns raised by participants. Monitoring will involve daily observation of measures collected by the fitness tracker and the wireless scale, as well as continuous supervision of forum interactions and posts. Additionally, a protocol is in place to monitor for participant misbehaviour and direct participants-in-need to appropriate sources of help. PMID:28706104

Cost and resource implications of clinical supervision in nursing: an Australian perspective.

PubMed

White, Edward; Winstanley, Julie

2006-11-01

The aim of this article was to explore the resource and management issues in introducing and maintaining a clinical supervision programme for nurses. A number of federal, state and non-governmental agency reports have recently indicted the quality of present-day mental health service provision in Australia. Clinical supervision in nursing has been widely embraced in many parts of the developed world, as a positive contribution to the clinical governance agenda, but remains largely underdeveloped in Australia. Using data derived from several empirical clinical supervision research studies conducted in mental health nursing settings, preliminary financial modelling has provided new information for Nurse Managers, about the material implications of implementing clinical supervision. It is suggested that, on average, the cost of giving peer group one-to-one supervision to any nurse represented about 1% of an annual salary. When interpreted as a vanishingly small cap on clinical nursing practice necessary to reap demonstrable benefits, it behoves Nurse Managers to comprehend clinical supervision as bona fide nursing work, not an activity which is separate from nursing work.

Entanglement in Self-Supervised Dynamics

NASA Technical Reports Server (NTRS)

Zak, Michail

2011-01-01

A new type of correlation has been developed similar to quantum entanglement in self-supervised dynamics (SSD). SSDs have been introduced as a quantum-classical hybrid based upon the Madelung equation in which the quantum potential is replaced by an information potential. As a result, SSD preserves the quantum topology along with superposition, entanglement, and wave-particle duality. At the same time, it can be implemented in any scale including the Newtonian scale. The main properties of SSD associated with simulating intelligence have been formulated. The attention with this innovation is focused on intelligent agents interaction based upon the new fundamental non-New tonian effect; namely, entanglement.

Learning relevant features of data with multi-scale tensor networks

NASA Astrophysics Data System (ADS)

Miles Stoudenmire, E.

2018-07-01

Inspired by coarse-graining approaches used in physics, we show how similar algorithms can be adapted for data. The resulting algorithms are based on layered tree tensor networks and scale linearly with both the dimension of the input and the training set size. Computing most of the layers with an unsupervised algorithm, then optimizing just the top layer for supervised classification of the MNIST and fashion MNIST data sets gives very good results. We also discuss mixing a prior guess for supervised weights together with an unsupervised representation of the data, yielding a smaller number of features nevertheless able to give good performance.

[Object-oriented aquatic vegetation extracting approach based on visible vegetation indices.

PubMed

Jing, Ran; Deng, Lei; Zhao, Wen Ji; Gong, Zhao Ning

2016-05-01

Using the estimation of scale parameters (ESP) image segmentation tool to determine the ideal image segmentation scale, the optimal segmented image was created by the multi-scale segmentation method. Based on the visible vegetation indices derived from mini-UAV imaging data, we chose a set of optimal vegetation indices from a series of visible vegetation indices, and built up a decision tree rule. A membership function was used to automatically classify the study area and an aquatic vegetation map was generated. The results showed the overall accuracy of image classification using the supervised classification was 53.7%, and the overall accuracy of object-oriented image analysis (OBIA) was 91.7%. Compared with pixel-based supervised classification method, the OBIA method improved significantly the image classification result and further increased the accuracy of extracting the aquatic vegetation. The Kappa value of supervised classification was 0.4, and the Kappa value based OBIA was 0.9. The experimental results demonstrated that using visible vegetation indices derived from the mini-UAV data and OBIA method extracting the aquatic vegetation developed in this study was feasible and could be applied in other physically similar areas.

Statistical Learning Theory for High Dimensional Prediction: Application to Criterion-Keyed Scale Development

PubMed Central

Chapman, Benjamin P.; Weiss, Alexander; Duberstein, Paul

2016-01-01

Statistical learning theory (SLT) is the statistical formulation of machine learning theory, a body of analytic methods common in “big data” problems. Regression-based SLT algorithms seek to maximize predictive accuracy for some outcome, given a large pool of potential predictors, without overfitting the sample. Research goals in psychology may sometimes call for high dimensional regression. One example is criterion-keyed scale construction, where a scale with maximal predictive validity must be built from a large item pool. Using this as a working example, we first introduce a core principle of SLT methods: minimization of expected prediction error (EPE). Minimizing EPE is fundamentally different than maximizing the within-sample likelihood, and hinges on building a predictive model of sufficient complexity to predict the outcome well, without undue complexity leading to overfitting. We describe how such models are built and refined via cross-validation. We then illustrate how three common SLT algorithms–Supervised Principal Components, Regularization, and Boosting—can be used to construct a criterion-keyed scale predicting all-cause mortality, using a large personality item pool within a population cohort. Each algorithm illustrates a different approach to minimizing EPE. Finally, we consider broader applications of SLT predictive algorithms, both as supportive analytic tools for conventional methods, and as primary analytic tools in discovery phase research. We conclude that despite their differences from the classic null-hypothesis testing approach—or perhaps because of them–SLT methods may hold value as a statistically rigorous approach to exploratory regression. PMID:27454257

ChemNet: A Transferable and Generalizable Deep Neural Network for Small-Molecule Property Prediction

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

Goh, Garrett B.; Siegel, Charles M.; Vishnu, Abhinav

With access to large datasets, deep neural networks through representation learning have been able to identify patterns from raw data, achieving human-level accuracy in image and speech recognition tasks. However, in chemistry, availability of large standardized and labelled datasets is scarce, and with a multitude of chemical properties of interest, chemical data is inherently small and fragmented. In this work, we explore transfer learning techniques in conjunction with the existing Chemception CNN model, to create a transferable and generalizable deep neural network for small-molecule property prediction. Our latest model, ChemNet learns in a semi-supervised manner from inexpensive labels computed frommore » the ChEMBL database. When fine-tuned to the Tox21, HIV and FreeSolv dataset, which are 3 separate chemical tasks that ChemNet was not originally trained on, we demonstrate that ChemNet exceeds the performance of existing Chemception models, contemporary MLP models that trains on molecular fingerprints, and it matches the performance of the ConvGraph algorithm, the current state-of-the-art. Furthermore, as ChemNet has been pre-trained on a large diverse chemical database, it can be used as a universal “plug-and-play” deep neural network, which accelerates the deployment of deep neural networks for the prediction of novel small-molecule chemical properties.« less

Psychological mechanism linking abusive supervision and compulsory citizenship behavior: a moderated mediation study.

PubMed

Zhao, Hongdan; Peng, Zhenglong; Han, Yong; Sheard, Geoff; Hudson, Alan

2013-01-01

This study seeks to examine the effect of abusive supervision on the "dark side" of organizational citizenship behavior (OCB) and, specifically, compulsory citizenship behavior (CCB). The study focuses on the mediating role of psychological safety underpinning the relationship between abusive supervision and CCB, and the moderating role of Chinese traditionality in influencing the mediation. The authors tested the model with data of 434 dyads (employee-coworker pairs) in a large Chinese service company. Results indicated that psychological safety fully mediated the relationship between abusive supervision and CCB. The authors also found that Chinese traditionality moderated the strength of the mediated relationship between abusive supervision and CCB via psychological safety, such that the mediated relationship is weaker under high Chinese traditionality than under low Chinese traditionality. The article also discusses the implications, limitations, and future research directions.

Does Fire Influence the Landscape-Scale Distribution of an Invasive Mesopredator?

PubMed Central

Payne, Catherine J.; Ritchie, Euan G.; Kelly, Luke T.; Nimmo, Dale G.

2014-01-01

Predation and fire shape the structure and function of ecosystems globally. However, studies exploring interactions between these two processes are rare, especially at large spatial scales. This knowledge gap is significant not only for ecological theory, but also in an applied context, because it limits the ability of landscape managers to predict the outcomes of manipulating fire and predators. We examined the influence of fire on the occurrence of an introduced and widespread mesopredator, the red fox (Vulpes vulpes), in semi-arid Australia. We used two extensive and complimentary datasets collected at two spatial scales. At the landscape-scale, we surveyed red foxes using sand-plots within 28 study landscapes – which incorporated variation in the diversity and proportional extent of fire-age classes – located across a 104 000 km2 study area. At the site-scale, we surveyed red foxes using camera traps at 108 sites stratified along a century-long post-fire chronosequence (0–105 years) within a 6630 km2 study area. Red foxes were widespread both at the landscape and site-scale. Fire did not influence fox distribution at either spatial scale, nor did other environmental variables that we measured. Our results show that red foxes exploit a broad range of environmental conditions within semi-arid Australia. The presence of red foxes throughout much of the landscape is likely to have significant implications for native fauna, particularly in recently burnt habitats where reduced cover may increase prey species’ predation risk. PMID:25291186

Maternal factors and the probability of a planned home birth.

PubMed

Anthony, S; Buitendijk, S E; Offerhaus, P M; Dommelen, P; Pal-de Bruin, K M

2005-06-01

In the Netherlands, approximately one-third of births are planned home births, mostly supervised by a midwife. The relationship between maternal demographic factors and home births supervised by midwives was examined. Cross-sectional study. Setting Dutch national perinatal registries of the year 2000. All women starting their pregnancy care under the supervision of a midwife, because these women have the possibility of having a planned home birth. The possible groups of birth were as follows: planned home birth or short stay hospital birth, both under the supervision of a midwife, or hospital birth under the supervision of an obstetrician after referral from the midwife during pregnancy or birth. The studied demographic factors were maternal age, parity, ethnicity and degree of urbanisation. Probabilities of having a planned home birth were calculated for women with different demographic profiles. Place of birth. In all age groups, the planned home birth percentage in primiparous women was lower than in multiparous women (23.5%vs 42.8%). A low home birth percentage was observed in women younger than 25 years. Dutch and non-Dutch women showed almost similar percentages of obstetrician-supervised hospital births but large differences in percentage of planned home births (36.5%vs 17.3%). Fewer home births were observed in large cities (30.5%) compared with small cities (35.7%) and rural areas (35.8%). This study demonstrates a clear relationship between maternal demographic factors and the place of birth and type of caregiver and therefore the probability of a planned home birth.

Quality assurance of the clinical learning environment in Austria: Construct validity of the Clinical Learning Environment, Supervision and Nurse Teacher Scale (CLES+T scale).

PubMed

Mueller, Gerhard; Mylonas, Demetrius; Schumacher, Petra

2018-07-01

Within nursing education, the clinical learning environment is of a high importance in regards to the development of competencies and abilities. The organization, atmosphere, and supervision in the clinical learning environment are only a few factors that influence this development. In Austria there is currently no valid instrument available for the evaluation of influencing factors. The aim of the study was to test the construct validity with principal component analysis as well as the internal consistency of the German Clinical Learning Environment, Supervision and Teacher Scale (CLES+T scale) in Austria. The present validation study has a descriptive-quantitative cross-sectional design. The sample consisted of 385 nursing students from thirteen training institutions in Austria. The data collection was carried out online between March and April 2016. Starting with a polychoric correlation matrix, a parallel analysis with principal component extraction and promax rotation was carried out due to the ordinal data. The exploratory ordinal factor analysis supported a four-component solution and explained 73% of the total variance. The internal consistency of all 25 items reached a Cronbach's α of 0.95 and the four components ranged between 0.83 and 0.95. The German version of the CLES+T scale seems to be a useful instrument for identifying potential areas of improvement in clinical practice in order to derive specific quality measures for the practical learning environment. Copyright © 2018 Elsevier Ltd. All rights reserved.

Study of alumina-trichite reinforcement of a nickel-based matric by means of powder metallurgy

NASA Technical Reports Server (NTRS)

Walder, A.; Hivert, A.

1982-01-01

Research was conducted on reinforcing nickel based matrices with alumina trichites by using powder metallurgy. Alumina trichites previously coated with nickel are magnetically aligned. The felt obtained is then sintered under a light pressure at a temperature just below the melting point of nickel. The halogenated atmosphere technique makes it possible to incorporate a large number of additive elements such as chromium, titanium, zirconium, tantalum, niobium, aluminum, etc. It does not appear that going from laboratory scale to a semi-industrial scale in production would create any major problems.

Organisational innovation and control practices: the case of public-private mix in tuberculosis control in India.

PubMed

Engel, Nora; van Lente, Harro

2014-07-01

Partnerships between public and private healthcare providers are often seen as an important way to improve health care in resource-constrained settings. Despite the reconfirmed policy support for including private providers into public tuberculosis control in India, the public-private mix (PPM) activities continue to face apprehension at local implementation sites. This article investigates the causes for those difficulties by examining PPM initiatives as cases of organisational innovation. It examines findings from semi-structured interviews, observations and document analyses in India around three different PPM models and the attempts of innovating and scaling up. The results reveal that in PPM initiatives underlying problem definitions and different control practices, including supervision, standardisation and culture, continue to clash and ultimately hinder the scaling up of PPM. Successful PPM initiatives require organisational control practices which are rooted in different professions to be bridged. This entails difficult balancing acts between innovation and control. The innovators handle those differently, based on their own ideas of the problem that PPM should address and their own control practices. We offer new perspectives on why collaboration is so difficult and show a possible way to mitigate the established apprehensions between professions in order to make organisational innovations, such as PPM, sustainable and scalable. © 2013 The Authors Sociology of Health & Illness © 2013 Foundation for the Sociology of Health & Illness/John Wiley & Sons Ltd.

Distant Supervision with Transductive Learning for Adverse Drug Reaction Identification from Electronic Medical Records

PubMed Central

Ikeda, Mitsuru

2017-01-01

Information extraction and knowledge discovery regarding adverse drug reaction (ADR) from large-scale clinical texts are very useful and needy processes. Two major difficulties of this task are the lack of domain experts for labeling examples and intractable processing of unstructured clinical texts. Even though most previous works have been conducted on these issues by applying semisupervised learning for the former and a word-based approach for the latter, they face with complexity in an acquisition of initial labeled data and ignorance of structured sequence of natural language. In this study, we propose automatic data labeling by distant supervision where knowledge bases are exploited to assign an entity-level relation label for each drug-event pair in texts, and then, we use patterns for characterizing ADR relation. The multiple-instance learning with expectation-maximization method is employed to estimate model parameters. The method applies transductive learning to iteratively reassign a probability of unknown drug-event pair at the training time. By investigating experiments with 50,998 discharge summaries, we evaluate our method by varying large number of parameters, that is, pattern types, pattern-weighting models, and initial and iterative weightings of relations for unlabeled data. Based on evaluations, our proposed method outperforms the word-based feature for NB-EM (iEM), MILR, and TSVM with F1 score of 11.3%, 9.3%, and 6.5% improvement, respectively. PMID:29090077

Multi-View Budgeted Learning under Label and Feature Constraints Using Label-Guided Graph-Based Regularization

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

Symons, Christopher T; Arel, Itamar

2011-01-01

Budgeted learning under constraints on both the amount of labeled information and the availability of features at test time pertains to a large number of real world problems. Ideas from multi-view learning, semi-supervised learning, and even active learning have applicability, but a common framework whose assumptions fit these problem spaces is non-trivial to construct. We leverage ideas from these fields based on graph regularizers to construct a robust framework for learning from labeled and unlabeled samples in multiple views that are non-independent and include features that are inaccessible at the time the model would need to be applied. We describemore » examples of applications that fit this scenario, and we provide experimental results to demonstrate the effectiveness of knowledge carryover from training-only views. As learning algorithms are applied to more complex applications, relevant information can be found in a wider variety of forms, and the relationships between these information sources are often quite complex. The assumptions that underlie most learning algorithms do not readily or realistically permit the incorporation of many of the data sources that are available, despite an implicit understanding that useful information exists in these sources. When multiple information sources are available, they are often partially redundant, highly interdependent, and contain noise as well as other information that is irrelevant to the problem under study. In this paper, we are focused on a framework whose assumptions match this reality, as well as the reality that labeled information is usually sparse. Most significantly, we are interested in a framework that can also leverage information in scenarios where many features that would be useful for learning a model are not available when the resulting model will be applied. As with constraints on labels, there are many practical limitations on the acquisition of potentially useful features. A key difference in the case of feature acquisition is that the same constraints often don't pertain to the training samples. This difference provides an opportunity to allow features that are impractical in an applied setting to nevertheless add value during the model-building process. Unfortunately, there are few machine learning frameworks built on assumptions that allow effective utilization of features that are only available at training time. In this paper we formulate a knowledge carryover framework for the budgeted learning scenario with constraints on features and labels. The approach is based on multi-view and semi-supervised learning methods that use graph-encoded regularization. Our main contributions are the following: (1) we propose and provide justification for a methodology for ensuring that changes in the graph regularizer using alternate views are performed in a manner that is target-concept specific, allowing value to be obtained from noisy views; and (2) we demonstrate how this general set-up can be used to effectively improve models by leveraging features unavailable at test time. The rest of the paper is structured as follows. In Section 2, we outline real-world problems to motivate the approach and describe relevant prior work. Section 3 describes the graph construction process and the learning methodologies that are employed. Section 4 provides preliminary discussion regarding theoretical motivation for the method. In Section 5, effectiveness of the approach is demonstrated in a series of experiments employing modified versions of two well-known semi-supervised learning algorithms. Section 6 concludes the paper.« less

Illness Management & Recovery (IMR) in the Netherlands; a naturalistic pilot study to explore the feasibility of a randomized controlled trial.

PubMed

Roosenschoon, Bert-Jan; van Weeghel, Jaap; Bogaards, Moniek; Deen, Mathijs L; Mulder, Cornelis L

2016-11-09

Illness Management & Recovery (IMR) is a curriculum-based program for people with severe and persistent mental illness. To date, four randomized controlled trials (RCTs) have been published on it. As these produced mixed results, we conducted a pilot study to test the feasibility of conducting a new RCT in a Dutch psychiatric institute. Because our primary objective was to evaluate support for implementing IMR on a broader scale, we examined participant recruitment, client outcomes, and clients' and clinicians' satisfaction. Secondary objectives were to evaluate fidelity, trainers' training and supervision, and to explore program duration, dropout, and client characteristics related to dropout. For reporting, we used the checklist for pilot studies adopted from the CONSORT Statement. This program evaluation included a process-evaluation and an outcome evaluation with a One Group Pre-Posttest Design (N = 81). Interviews and internal reports were used to monitor participant numbers, program duration, dropout, and completers' characteristics. Clients' and clinicians' satisfaction and provision of trainers' training and supervision were assessed through interviews. Fidelity was assessed on the IMR Fidelity Scale; client outcomes were assessed on the IMR scale (client and clinician versions) and the Recovery Markers Questionnaire (RMQ). Eighty-one participants were recruited of 167 people who were assessed for eligibility. Completers and clinicians were satisfied, and scores for completers improved significantly on the IMR scale (clinician version) (d = 0.84) and RMQ (d = 0.52), and not significantly on the IMR scale client version (d = 0.41). Mean fidelity was good, but three groups had only moderate fidelity. Our feasibility criterion for trainers' education and supervision was partly attained. Dropout from treatment was 51 %; female participants and people who scored higher on both IMR-scales at baseline had a significantly lower chance of dropping out. The duration of IMR varied (M = 12.7 months, SD = 2.87). Results suggested that feasibility of conducting an RCT on IMR was good. Special attention is required to fidelity, IMR duration, trainers' education and supervision, and dropout, especially of men. One study limitation was our inability to conduct follow-up measurements of non-completers.

Supervised and non-supervised Nordic walking in the treatment of chronic low back pain: a single blind randomized clinical trial

PubMed Central

2010-01-01

Background Active approaches including both specific and unspecific exercise are probably the most widely recommended treatment for patients with chronic low back pain but it is not known exactly which types of exercise provide the most benefit. Nordic Walking - power walking using ski poles - is a popular and fast growing type of exercise in Northern Europe that has been shown to improve cardiovascular metabolism. Until now, no studies have been performed to investigate whether Nordic Walking has beneficial effects in relation to back pain. Methods A total of 151 patients with low back and/or leg pain of greater than eight weeks duration were recruited from a hospital based outpatient back pain clinic. Patients continuing to have pain greater than three on the 11-point numeric rating scale after a multidisciplinary intervention were included. Fifteen patients were unable to complete the baseline evaluation and 136 patients were randomized to receive A) Nordic walking supervised by a specially trained instructor twice a week for eight weeks B) One-hour instruction in Nordic walking by a specially trained instructor followed by advice to perform Nordic walking at home as much as they liked for eight weeks or C) Individual oral information consisting of advice to remain active and about maintaining the daily function level that they had achieved during their stay at the backcenter. Primary outcome measures were pain and disability using the Low Back Pain Rating Scale, and functional limitation further assessed using the Patient Specific Function Scale. Furthermore, information on time off work, use of medication, and concurrent treatment for their low back pain was collected. Objective measurements of physical activity levels for the supervised and unsupervised Nordic walking groups were performed using accelerometers. Data were analyzed on an intention-to-treat basis. Results No mean differences were found between the three groups in relation to any of the outcomes at baseline. For pain, disability, and patient specific function the supervised Nordic walking group generally faired best however no statistically significant differences were found. Regarding the secondary outcome measures, patients in the supervised group tended to use less pain medication, to seek less concurrent care for their back pain, at the eight-week follow-up. There was no difference between physical activity levels for the supervised and unsupervised Nordic walking groups. No negative side effects were reported. Conclusion We did not find statistically significant differences between eight weeks of supervised or unsupervised Nordic walking and advice to remain active in a group of chronic low back pain patients. Nevertheless, the greatest average improvement tended to favor the supervised Nordic walking group and - taking into account other health related benefits of Nordic walking - this form of exercise may potentially be of benefit to selected groups of chronic back pain patients. Trial registration http://www.ClinicalTrials.gov # NCT00209820 PMID:20146793

Effect of an isolated semi-arid pine forest on the boundary layer height

NASA Astrophysics Data System (ADS)

Brugger, Peter; Banerjee, Tirtha; Kröniger, Konstantin; Preisler, Yakir; Rotenberg, Eyal; Tatarinov, Fedor; Yakir, Dan; Mauder, Matthias

2017-04-01

Forests play an important role for earth's climate by influencing the surface energy balance and CO2 concentrations in the atmosphere. Semi-arid forests and their effects on the local and regional climate are studied within the CliFF project (Climate Feedbacks and benefits of semi-arid Forests). This requires understanding of the atmospheric boundary layer over semi-arid forests, because it links the surface and the free atmosphere and determines the exchange of momentum, heat and trace gases. Our study site, Yatir, is a semi-arid isolated pine forest in the Negev desert in Israel. Higher roughness and lower albedo compared to the surrounding shrubland make it interesting to study the influences of the semi-arid Yatir forest on the boundary layer. Previous studies of the forest focused on the energy balance and secondary circulations. This study focuses on the boundary layer structure above the forest, in particular the boundary layer height. The boundary layer height is an essential parameter for many applications (e.g. construction of convective scaling parameters or air pollution modeling). We measured the boundary layer height upwind, over and downwind of the forest. In addition we measured at two sites wind profiles within the boundary layer and turbulent fluxes at the surface. This allows us to quantify the effects of the forest on boundary layer compared to the surrounding shrubland. Results show that the forest increases the boundary layer height in absence of a strong boundary layer top inversion. A model of the boundary layer height based on eddy-covariance data shows some agreement to the measurements, but fails during anticyclonic conditions and the transition to the nocturnal boundary layer. More complex models accounting for large scale influences are investigated. Further influences of the forest and surrounding shrubland on the turbulent transport of energy are discussed in a companion presentation (EGU2017-2219).

[A novel approach to NIR spectral quantitative analysis: semi-supervised least-squares support vector regression machine].

PubMed

Li, Lin; Xu, Shuo; An, Xin; Zhang, Lu-Da

2011-10-01

In near infrared spectral quantitative analysis, the precision of measured samples' chemical values is the theoretical limit of those of quantitative analysis with mathematical models. However, the number of samples that can obtain accurately their chemical values is few. Many models exclude the amount of samples without chemical values, and consider only these samples with chemical values when modeling sample compositions' contents. To address this problem, a semi-supervised LS-SVR (S2 LS-SVR) model is proposed on the basis of LS-SVR, which can utilize samples without chemical values as well as those with chemical values. Similar to the LS-SVR, to train this model is equivalent to solving a linear system. Finally, the samples of flue-cured tobacco were taken as experimental material, and corresponding quantitative analysis models were constructed for four sample compositions' content(total sugar, reducing sugar, total nitrogen and nicotine) with PLS regression, LS-SVR and S2 LS-SVR. For the S2 LS-SVR model, the average relative errors between actual values and predicted ones for the four sample compositions' contents are 6.62%, 7.56%, 6.11% and 8.20%, respectively, and the correlation coefficients are 0.974 1, 0.973 3, 0.923 0 and 0.948 6, respectively. Experimental results show the S2 LS-SVR model outperforms the other two, which verifies the feasibility and efficiency of the S2 LS-SVR model.

Graph-based semi-supervised learning with genomic data integration using condition-responsive genes applied to phenotype classification.

PubMed

Doostparast Torshizi, Abolfazl; Petzold, Linda R

2018-01-01

Data integration methods that combine data from different molecular levels such as genome, epigenome, transcriptome, etc., have received a great deal of interest in the past few years. It has been demonstrated that the synergistic effects of different biological data types can boost learning capabilities and lead to a better understanding of the underlying interactions among molecular levels. In this paper we present a graph-based semi-supervised classification algorithm that incorporates latent biological knowledge in the form of biological pathways with gene expression and DNA methylation data. The process of graph construction from biological pathways is based on detecting condition-responsive genes, where 3 sets of genes are finally extracted: all condition responsive genes, high-frequency condition-responsive genes, and P-value-filtered genes. The proposed approach is applied to ovarian cancer data downloaded from the Human Genome Atlas. Extensive numerical experiments demonstrate superior performance of the proposed approach compared to other state-of-the-art algorithms, including the latest graph-based classification techniques. Simulation results demonstrate that integrating various data types enhances classification performance and leads to a better understanding of interrelations between diverse omics data types. The proposed approach outperforms many of the state-of-the-art data integration algorithms. © The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Knowledge based word-concept model estimation and refinement for biomedical text mining.

PubMed

Jimeno Yepes, Antonio; Berlanga, Rafael

2015-02-01

Text mining of scientific literature has been essential for setting up large public biomedical databases, which are being widely used by the research community. In the biomedical domain, the existence of a large number of terminological resources and knowledge bases (KB) has enabled a myriad of machine learning methods for different text mining related tasks. Unfortunately, KBs have not been devised for text mining tasks but for human interpretation, thus performance of KB-based methods is usually lower when compared to supervised machine learning methods. The disadvantage of supervised methods though is they require labeled training data and therefore not useful for large scale biomedical text mining systems. KB-based methods do not have this limitation. In this paper, we describe a novel method to generate word-concept probabilities from a KB, which can serve as a basis for several text mining tasks. This method not only takes into account the underlying patterns within the descriptions contained in the KB but also those in texts available from large unlabeled corpora such as MEDLINE. The parameters of the model have been estimated without training data. Patterns from MEDLINE have been built using MetaMap for entity recognition and related using co-occurrences. The word-concept probabilities were evaluated on the task of word sense disambiguation (WSD). The results showed that our method obtained a higher degree of accuracy than other state-of-the-art approaches when evaluated on the MSH WSD data set. We also evaluated our method on the task of document ranking using MEDLINE citations. These results also showed an increase in performance over existing baseline retrieval approaches. Copyright © 2014 Elsevier Inc. All rights reserved.

SPECTRAL LINE DE-CONFUSION IN AN INTENSITY MAPPING SURVEY

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

Cheng, Yun-Ting; Bock, James; Bradford, C. Matt

2016-12-01

Spectral line intensity mapping (LIM) has been proposed as a promising tool to efficiently probe the cosmic reionization and the large-scale structure. Without detecting individual sources, LIM makes use of all available photons and measures the integrated light in the source confusion limit to efficiently map the three-dimensional matter distribution on large scales as traced by a given emission line. One particular challenge is the separation of desired signals from astrophysical continuum foregrounds and line interlopers. Here we present a technique to extract large-scale structure information traced by emission lines from different redshifts, embedded in a three-dimensional intensity mapping data cube.more » The line redshifts are distinguished by the anisotropic shape of the power spectra when projected onto a common coordinate frame. We consider the case where high-redshift [C ii] lines are confused with multiple low-redshift CO rotational lines. We present a semi-analytic model for [C ii] and CO line estimates based on the cosmic infrared background measurements, and show that with a modest instrumental noise level and survey geometry, the large-scale [C ii] and CO power spectrum amplitudes can be successfully extracted from a confusion-limited data set, without external information. We discuss the implications and limits of this technique for possible LIM experiments.« less

HClass: Automatic classification tool for health pathologies using artificial intelligence techniques.

PubMed

Garcia-Chimeno, Yolanda; Garcia-Zapirain, Begonya

2015-01-01

The classification of subjects' pathologies enables a rigorousness to be applied to the treatment of certain pathologies, as doctors on occasions play with so many variables that they can end up confusing some illnesses with others. Thanks to Machine Learning techniques applied to a health-record database, it is possible to make using our algorithm. hClass contains a non-linear classification of either a supervised, non-supervised or semi-supervised type. The machine is configured using other techniques such as validation of the set to be classified (cross-validation), reduction in features (PCA) and committees for assessing the various classifiers. The tool is easy to use, and the sample matrix and features that one wishes to classify, the number of iterations and the subjects who are going to be used to train the machine all need to be introduced as inputs. As a result, the success rate is shown either via a classifier or via a committee if one has been formed. A 90% success rate is obtained in the ADABoost classifier and 89.7% in the case of a committee (comprising three classifiers) when PCA is applied. This tool can be expanded to allow the user to totally characterise the classifiers by adjusting them to each classification use.

Development of an Independent Global Land Cover Validation Dataset

NASA Astrophysics Data System (ADS)

Sulla-Menashe, D. J.; Olofsson, P.; Woodcock, C. E.; Holden, C.; Metcalfe, M.; Friedl, M. A.; Stehman, S. V.; Herold, M.; Giri, C.

2012-12-01

Accurate information related to the global distribution and dynamics in global land cover is critical for a large number of global change science questions. A growing number of land cover products have been produced at regional to global scales, but the uncertainty in these products and the relative strengths and weaknesses among available products are poorly characterized. To address this limitation we are compiling a database of high spatial resolution imagery to support international land cover validation studies. Validation sites were selected based on a probability sample, and may therefore be used to estimate statistically defensible accuracy statistics and associated standard errors. Validation site locations were identified using a stratified random design based on 21 strata derived from an intersection of Koppen climate classes and a population density layer. In this way, the two major sources of global variation in land cover (climate and human activity) are explicitly included in the stratification scheme. At each site we are acquiring high spatial resolution (< 1-m) satellite imagery for 5-km x 5-km blocks. The response design uses an object-oriented hierarchical legend that is compatible with the UN FAO Land Cover Classification System. Using this response design, we are classifying each site using a semi-automated algorithm that blends image segmentation with a supervised RandomForest classification algorithm. In the long run, the validation site database is designed to support international efforts to validate land cover products. To illustrate, we use the site database to validate the MODIS Collection 4 Land Cover product, providing a prototype for validating the VIIRS Surface Type Intermediate Product scheduled to start operational production early in 2013. As part of our analysis we evaluate sources of error in coarse resolution products including semantic issues related to the class definitions, mixed pixels, and poor spectral separation between classes.

HAPEX-Sahel: A large-scale study of land-atmosphere interactions in the semi-arid tropics

NASA Technical Reports Server (NTRS)

Gutorbe, J-P.; Lebel, T.; Tinga, A.; Bessemoulin, P.; Brouwer, J.; Dolman, A.J.; Engman, E. T.; Gash, J. H. C.; Hoepffner, M.; Kabat, P.

1994-01-01

The Hydrologic Atmospheric Pilot EXperiment in the Sahel (HAPEX-Sahel) was carried out in Niger, West Africa, during 1991-1992, with an intensive observation period (IOP) in August-October 1992. It aims at improving the parameteriztion of land surface atmospheric interactions at the Global Circulation Model (GCM) gridbox scale. The experiment combines remote sensing and ground based measurements with hydrological and meteorological modeling to develop aggregation techniques for use in large scale estimates of the hydrological and meteorological behavior of large areas in the Sahel. The experimental strategy consisted of a period of intensive measurements during the transition period of the rainy to the dry season, backed up by a series of long term measurements in a 1 by 1 deg square in Niger. Three 'supersites' were instrumented with a variety of hydrological and (micro) meteorological equipment to provide detailed information on the surface energy exchange at the local scale. Boundary layer measurements and aircraft measurements were used to provide information at scales of 100-500 sq km. All relevant remote sensing images were obtained for this period. This program of measurements is now being analyzed and an extensive modelling program is under way to aggregate the information at all scales up to the GCM grid box scale. The experimental strategy and some preliminary results of the IOP are described.

Controlling the Schools: An Essay on the Bureaucratization of School Supervision in the Late Nineteenth Century.

ERIC Educational Resources Information Center

Glanz, Jeffrey

Focusing on factors which shaped and influenced public school supervision, the paper investigates educational developments in the late 19th century. During this period the movement toward centralization in urban public schools gained considerable momentum. Educational historians have largely ignored the role school superintendents played in the…

A systematic review of evidence relating to clinical supervision for nurses, midwives and allied health professionals.

PubMed

Pollock, Alex; Campbell, Pauline; Deery, Ruth; Fleming, Mick; Rankin, Jean; Sloan, Graham; Cheyne, Helen

2017-08-01

The aim of this study was to systematically review evidence relating to clinical supervision for nurses, midwives and allied health professionals. Since 1902 statutory supervision has been a requirement for UK midwives, but this is due to change. Evidence relating to clinical supervision for nurses and allied health professions could inform a new model of clinical supervision for midwives. A systematic review with a contingent design, comprising a broad map of research relating to clinical supervision and two focussed syntheses answering specific review questions. Electronic databases were searched from 2005 - September 2015, limited to English-language peer-reviewed publications. Systematic reviews evaluating the effectiveness of clinical supervision were included in Synthesis 1. Primary research studies including a description of a clinical supervision intervention were included in Synthesis 2. Quality of reviews were judged using a risk of bias tool and review results summarized in tables. Data describing the key components of clinical supervision interventions were extracted from studies included in Synthesis 2, categorized using a reporting framework and a narrative account provided. Ten reviews were included in Synthesis 1; these demonstrated an absence of convincing empirical evidence and lack of agreement over the nature of clinical supervision. Nineteen primary studies were included in Synthesis 2; these highlighted a lack of consistency and large variations between delivered interventions. Despite insufficient evidence to directly inform the selection and implementation of a framework, the limited available evidence can inform the design of a new model of clinical supervision for UK-based midwives. © 2017 John Wiley & Sons Ltd.

The effects of clinical supervision on supervisees and patients in cognitive-behavioral therapy: a study protocol for a systematic review.

PubMed

Alfonsson, Sven; Spännargård, Åsa; Parling, Thomas; Andersson, Gerhard; Lundgren, Tobias

2017-05-11

Clinical supervision by a senior therapist is a very common practice in psychotherapist training and psychiatric care settings. Though clinical supervision is advocated by most educational and governing institutions, the effects of clinical supervision on the supervisees' competence, e.g., attitudes, behaviors, and skills, as well as on treatment outcomes and other patient variables are debated and largely unknown. Evidence-based practice is advocated in clinical settings but has not yet been fully implemented in educational or clinical training settings. The aim of this systematic review is to synthesize and present the empirical literature regarding effects of clinical supervision in cognitive-behavioral therapy. This study will include a systematic review of the literature to identify studies that have empirically investigated the effects of supervision on supervised psychotherapists and/or the supervisees' patients. A comprehensive search strategy will be conducted to identify published controlled studies indexed in the MEDLINE, EMBASE, PsycINFO, and Cochrane Library databases. Data on supervision outcomes in both psychotherapists and their patients will be extracted, synthesized, and reported. Risk of bias and quality of the included studies will be assessed systematically. This systematic review will rigorously follow established guidelines for systematic reviews in order to summarize and present the evidence base for clinical supervision in cognitive-behavioral therapy and may aid further research and discussion in this area. PROSPERO CRD42016046834.

Semi-natural areas of Tarim Basin in northwest China: Linkage to desertification.

PubMed

Liu, Fang; Zhang, Hongqi; Qin, Yuanwei; Dong, Jinwei; Xu, Erqi; Yang, Yang; Zhang, Geli; Xiao, Xiangming

2016-12-15

Semi-natural lands are not intensively managed lands, which have ecological significance in protecting artificial oasis and preventing desertification in arid regions. The significant shrinkage and degradation of semi-natural lands in the land-use intensification process have caused severe desertification. However, there is a knowledge gap regarding the spatio-temporal pattern and detailed classification of semi-natural lands and its quantitative relationship with desertification. Taking the Tarim Basin as an example, we proposed a comprehensive classification system to identify semi-natural lands for 1990, 2000, and 2010, respectively, using multi-source datasets at large scales. Spatio-temporal changes of semi-natural lands were then characterized by map comparisons at decade intervals. Finally, statistical relationships between semi-natural lands and desertification were explored based on 241 watersheds. The area of semi-natural lands in Tarim Basin was 10.77×10 4 km 2 in 2010, and desert-vegetation type, native-oasis type, artificial-oasis type, saline type and wetland type accounted for 59.59%, 14.65%, 11.25%, 9.63% and 4.88% of the total area, respectively. A rapid loss of semi-natural lands (9769.05km 2 ) was demonstrated from 1990 to 2010. In the fragile watersheds, the semi-natural lands were mainly converted to desert; while in the watersheds with advanced oasis agriculture, artificial-oasis type reclaimed to arable land was the major change. The occurrence of desertification was closely related to the type, area proportion and combination patterns of semi-natural lands. Desertification was prone to occur in regions abundant in desert-vegetation type and saline type, while less serious desertification was observed in regions with high proportion of artificial-oasis type and wetland type. Policy intervention and reasonable water resource allocation were encouraged to prevent the substantial loss of semi-natural lands, especially for the water-limiting watersheds and periods. Copyright © 2016 Elsevier B.V. All rights reserved.

Identification of the key ecological factors influencing vegetation degradation in semi-arid agro-pastoral ecotone considering spatial scales

NASA Astrophysics Data System (ADS)

Peng, Yu; Wang, Qinghui; Fan, Min

2017-11-01

When assessing re-vegetation project performance and optimizing land management, identification of the key ecological factors inducing vegetation degradation has crucial implications. Rainfall, temperature, elevation, slope, aspect, land use type, and human disturbance are ecological factors affecting the status of vegetation index. However, at different spatial scales, the key factors may vary. Using Helin County, Inner-Mongolia, China as the study site and combining remote sensing image interpretation, field surveying, and mathematical methods, this study assesses key ecological factors affecting vegetation degradation under different spatial scales in a semi-arid agro-pastoral ecotone. It indicates that the key factors are different at various spatial scales. Elevation, rainfall, and temperature are identified as crucial for all spatial extents. Elevation, rainfall and human disturbance are key factors for small-scale quadrats of 300 m × 300 m and 600 m × 600 m, temperature and land use type are key factors for a medium-scale quadrat of 1 km × 1 km, and rainfall, temperature, and land use are key factors for large-scale quadrats of 2 km × 2 km and 5 km × 5 km. For this region, human disturbance is not the key factor for vegetation degradation across spatial scales. It is necessary to consider spatial scale for the identification of key factors determining vegetation characteristics. The eco-restoration programs at various spatial scales should identify key influencing factors according their scales so as to take effective measurements. The new understanding obtained in this study may help to explore the forces which driving vegetation degradation in the degraded regions in the world.

CLAss-Specific Subspace Kernel Representations and Adaptive Margin Slack Minimization for Large Scale Classification.

PubMed

Yu, Yinan; Diamantaras, Konstantinos I; McKelvey, Tomas; Kung, Sun-Yuan

2018-02-01

In kernel-based classification models, given limited computational power and storage capacity, operations over the full kernel matrix becomes prohibitive. In this paper, we propose a new supervised learning framework using kernel models for sequential data processing. The framework is based on two components that both aim at enhancing the classification capability with a subset selection scheme. The first part is a subspace projection technique in the reproducing kernel Hilbert space using a CLAss-specific Subspace Kernel representation for kernel approximation. In the second part, we propose a novel structural risk minimization algorithm called the adaptive margin slack minimization to iteratively improve the classification accuracy by an adaptive data selection. We motivate each part separately, and then integrate them into learning frameworks for large scale data. We propose two such frameworks: the memory efficient sequential processing for sequential data processing and the parallelized sequential processing for distributed computing with sequential data acquisition. We test our methods on several benchmark data sets and compared with the state-of-the-art techniques to verify the validity of the proposed techniques.

Supervised graph hashing for histopathology image retrieval and classification.

PubMed

Shi, Xiaoshuang; Xing, Fuyong; Xu, KaiDi; Xie, Yuanpu; Su, Hai; Yang, Lin

2017-12-01

In pathology image analysis, morphological characteristics of cells are critical to grade many diseases. With the development of cell detection and segmentation techniques, it is possible to extract cell-level information for further analysis in pathology images. However, it is challenging to conduct efficient analysis of cell-level information on a large-scale image dataset because each image usually contains hundreds or thousands of cells. In this paper, we propose a novel image retrieval based framework for large-scale pathology image analysis. For each image, we encode each cell into binary codes to generate image representation using a novel graph based hashing model and then conduct image retrieval by applying a group-to-group matching method to similarity measurement. In order to improve both computational efficiency and memory requirement, we further introduce matrix factorization into the hashing model for scalable image retrieval. The proposed framework is extensively validated with thousands of lung cancer images, and it achieves 97.98% classification accuracy and 97.50% retrieval precision with all cells of each query image used. Copyright © 2017 Elsevier B.V. All rights reserved.

Limits on transverse momentum dependent evolution from semi-inclusive deep inelastic scattering at moderate Q

NASA Astrophysics Data System (ADS)

Aidala, C. A.; Field, B.; Gamberg, L. P.; Rogers, T. C.

2014-05-01

In the QCD evolution of transverse momentum dependent parton distribution and fragmentation functions, the Collins-Soper evolution kernel includes both a perturbative short-distance contribution and a large-distance nonperturbative, but strongly universal, contribution. In the past, global fits, based mainly on larger Q Drell-Yan-like processes, have found substantial contributions from nonperturbative regions in the Collins-Soper evolution kernel. In this article, we investigate semi-inclusive deep inelastic scattering measurements in the region of relatively small Q, of the order of a few GeV, where sensitivity to nonperturbative transverse momentum dependence may become more important or even dominate the evolution. Using recently available deep inelastic scattering data from the COMPASS experiment, we provide estimates of the regions of coordinate space that dominate in transverse momentum dependent (TMD) processes when the hard scale is of the order of only a few GeV. We find that distance scales that are much larger than those commonly probed in large Q measurements become important, suggesting that the details of nonperturbative effects in TMD evolution are especially significant in the region of intermediate Q. We highlight the strongly universal nature of the nonperturbative component of evolution and its potential to be tightly constrained by fits from a wide variety of observables that include both large and moderate Q. On this basis, we recommend detailed treatments of the nonperturbative component of the Collins-Soper evolution kernel for future TMD studies.

Comparison Of Semi-Automatic And Automatic Slick Detection Algorithms For Jiyeh Power Station Oil Spill, Lebanon

NASA Astrophysics Data System (ADS)

Osmanoglu, B.; Ozkan, C.; Sunar, F.

2013-10-01

After air strikes on July 14 and 15, 2006 the Jiyeh Power Station started leaking oil into the eastern Mediterranean Sea. The power station is located about 30 km south of Beirut and the slick covered about 170 km of coastline threatening the neighboring countries Turkey and Cyprus. Due to the ongoing conflict between Israel and Lebanon, cleaning efforts could not start immediately resulting in 12 000 to 15 000 tons of fuel oil leaking into the sea. In this paper we compare results from automatic and semi-automatic slick detection algorithms. The automatic detection method combines the probabilities calculated for each pixel from each image to obtain a joint probability, minimizing the adverse effects of atmosphere on oil spill detection. The method can readily utilize X-, C- and L-band data where available. Furthermore wind and wave speed observations can be used for a more accurate analysis. For this study, we utilize Envisat ASAR ScanSAR data. A probability map is generated based on the radar backscatter, effect of wind and dampening value. The semi-automatic algorithm is based on supervised classification. As a classifier, Artificial Neural Network Multilayer Perceptron (ANN MLP) classifier is used since it is more flexible and efficient than conventional maximum likelihood classifier for multisource and multi-temporal data. The learning algorithm for ANN MLP is chosen as the Levenberg-Marquardt (LM). Training and test data for supervised classification are composed from the textural information created from SAR images. This approach is semiautomatic because tuning the parameters of classifier and composing training data need a human interaction. We point out the similarities and differences between the two methods and their results as well as underlining their advantages and disadvantages. Due to the lack of ground truth data, we compare obtained results to each other, as well as other published oil slick area assessments.

A Customized Attention-Based Long Short-Term Memory Network for Distant Supervised Relation Extraction.

PubMed

He, Dengchao; Zhang, Hongjun; Hao, Wenning; Zhang, Rui; Cheng, Kai

2017-07-01

Distant supervision, a widely applied approach in the field of relation extraction can automatically generate large amounts of labeled training corpus with minimal manual effort. However, the labeled training corpus may have many false-positive data, which would hurt the performance of relation extraction. Moreover, in traditional feature-based distant supervised approaches, extraction models adopt human design features with natural language processing. It may also cause poor performance. To address these two shortcomings, we propose a customized attention-based long short-term memory network. Our approach adopts word-level attention to achieve better data representation for relation extraction without manually designed features to perform distant supervision instead of fully supervised relation extraction, and it utilizes instance-level attention to tackle the problem of false-positive data. Experimental results demonstrate that our proposed approach is effective and achieves better performance than traditional methods.

Maximum Margin Clustering of Hyperspectral Data

NASA Astrophysics Data System (ADS)

Niazmardi, S.; Safari, A.; Homayouni, S.

2013-09-01

In recent decades, large margin methods such as Support Vector Machines (SVMs) are supposed to be the state-of-the-art of supervised learning methods for classification of hyperspectral data. However, the results of these algorithms mainly depend on the quality and quantity of available training data. To tackle down the problems associated with the training data, the researcher put effort into extending the capability of large margin algorithms for unsupervised learning. One of the recent proposed algorithms is Maximum Margin Clustering (MMC). The MMC is an unsupervised SVMs algorithm that simultaneously estimates both the labels and the hyperplane parameters. Nevertheless, the optimization of the MMC algorithm is a non-convex problem. Most of the existing MMC methods rely on the reformulating and the relaxing of the non-convex optimization problem as semi-definite programs (SDP), which are computationally very expensive and only can handle small data sets. Moreover, most of these algorithms are two-class classification, which cannot be used for classification of remotely sensed data. In this paper, a new MMC algorithm is used that solve the original non-convex problem using Alternative Optimization method. This algorithm is also extended for multi-class classification and its performance is evaluated. The results of the proposed algorithm show that the algorithm has acceptable results for hyperspectral data clustering.

Comparison of physical and semi-empirical hydraulic models for flood inundation mapping

NASA Astrophysics Data System (ADS)

Tavakoly, A. A.; Afshari, S.; Omranian, E.; Feng, D.; Rajib, A.; Snow, A.; Cohen, S.; Merwade, V.; Fekete, B. M.; Sharif, H. O.; Beighley, E.

2016-12-01

Various hydraulic/GIS-based tools can be used for illustrating spatial extent of flooding for first-responders, policy makers and the general public. The objective of this study is to compare four flood inundation modeling tools: HEC-RAS-2D, Gridded Surface Subsurface Hydrologic Analysis (GSSHA), AutoRoute and Height Above the Nearest Drainage (HAND). There is a trade-off among accuracy, workability and computational demand in detailed, physics-based flood inundation models (e.g. HEC-RAS-2D and GSSHA) in contrast with semi-empirical, topography-based, computationally less expensive approaches (e.g. AutoRoute and HAND). The motivation for this study is to evaluate this trade-off and offer guidance to potential large-scale application in an operational prediction system. The models were assessed and contrasted via comparability analysis (e.g. overlapping statistics) by using three case studies in the states of Alabama, Texas, and West Virginia. The sensitivity and accuracy of physical and semi-eimpirical models in producing inundation extent were evaluated for the following attributes: geophysical characteristics (e.g. high topographic variability vs. flat natural terrain, urbanized vs. rural zones, effect of surface roughness paratermer value), influence of hydraulic structures such as dams and levees compared to unobstructed flow condition, accuracy in large vs. small study domain, effect of spatial resolution in topographic data (e.g. 10m National Elevation Dataset vs. 0.3m LiDAR). Preliminary results suggest that semi-empericial models tend to underestimate in a flat, urbanized area with controlled/managed river channel around 40% of the inundation extent compared to the physical models, regardless of topographic resolution. However, in places where there are topographic undulations, semi-empericial models attain relatively higher level of accuracy than they do in flat non-urbanized terrain.

A semi-automated Raman micro-spectroscopy method for morphological and chemical characterizations of microplastic litter.

PubMed

L, Frère; I, Paul-Pont; J, Moreau; P, Soudant; C, Lambert; A, Huvet; E, Rinnert

2016-12-15

Every step of microplastic analysis (collection, extraction and characterization) is time-consuming, representing an obstacle to the implementation of large scale monitoring. This study proposes a semi-automated Raman micro-spectroscopy method coupled to static image analysis that allows the screening of a large quantity of microplastic in a time-effective way with minimal machine operator intervention. The method was validated using 103 particles collected at the sea surface spiked with 7 standard plastics: morphological and chemical characterization of particles was performed in <3h. The method was then applied to a larger environmental sample (n=962 particles). The identification rate was 75% and significantly decreased as a function of particle size. Microplastics represented 71% of the identified particles and significant size differences were observed: polystyrene was mainly found in the 2-5mm range (59%), polyethylene in the 1-2mm range (40%) and polypropylene in the 0.335-1mm range (42%). Copyright © 2016 Elsevier Ltd. All rights reserved.

The Relation Between Supervisors' Big Five Personality Traits and Employees' Experiences of Abusive Supervision.

PubMed

Camps, Jeroen; Stouten, Jeroen; Euwema, Martin

2016-01-01

The present study investigates the relation between supervisors' personality traits and employees' experiences of supervisory abuse, an area that - to date - remained largely unexplored in previous research. Field data collected from 103 supervisor-subordinate dyads showed that contrary to our expectations supervisors' agreeableness and neuroticism were not significantly related to abusive supervision, nor were supervisors' extraversion or openness to experience. Interestingly, however, our findings revealed a positive relation between supervisors' conscientiousness and abusive supervision. That is, supervisors high in conscientiousness were more likely to be perceived as an abusive supervisor by their employees. Overall, our findings do suggest that supervisors' Big Five personality traits explain only a limited amount of the variability in employees' experiences of abusive supervision.

The effect of a supervised community-based exercise program on balance, balance confidence, and gait in individuals with lower limb amputation.

PubMed

Miller, Carol A; Williams, Jennifer E; Durham, Katey L; Hom, Selena C; Smith, Julie L

2017-10-01

Many individuals with lower limb loss report concern with walking ability after completing structured traditional rehabilitation. The purpose of this study was to explore the impact of a supervised community-based exercise program on balance, balance confidence, and gait in individuals with lower limb amputation. Repeated measures. The supervised exercise program was offered biweekly for 6 weeks. The GAITRite System by CIR Systems, Inc., the Figure-of-8 Walk Test, and Activity-specific Balance Confidence Scale were used to measure clinical outcomes pre- and post-intervention. In total, 16 participants with lower limb amputation (mean age: 50.8 years) completed the study. A multivariate, repeated measures analysis of variance indicated a statistically significant effect of training across six clinical outcome measures ( F(6, 10) = 4.514, p = .018). Moderate effect sizes were found for the Figure-of-8 Walk Test ( η 2 = .586), Activity-specific Balance Confidence Scale ( η 2 = .504), and gait velocity at comfortable walking speed ( η 2 = .574). The average increase in gait speed was clinically meaningful at .14 m/s. The supervised community-based exercise program implemented in this study was designed to address specific functional needs for individuals with lower limb loss. Each participant experienced clinically meaningful improvements in balance, balance confidence, and walking ability. Clinical relevance The provision of a supervised community-based exercise program, after traditional rehabilitation, provides opportunity to offer a continuum of care that may enhance prosthetic functional ability and active participation in the community for individuals with lower limb amputation.

Biofeedback in Partial Weight Bearing: Usability of Two Different Devices from a Patient’s and Physical Therapist’s Perspective

PubMed Central

van Lieshout, Remko; Pisters, Martijn F.; Vanwanseele, Benedicte; de Bie, Rob A.; Wouters, Eveline J.; Stukstette, Mirelle J.

2016-01-01

Background Partial weight bearing is frequently instructed by physical therapists in patients after lower-limb trauma or surgery. The use of biofeedback devices seems promising to improve the patient’s compliance with weight-bearing instructions. SmartStep and OpenGo-Science are biofeedback devices that provide real-time feedback. For a successful implementation, usability of the devices is a critical aspect and should be tested from a user’s perspective. Aim To describe the usability from the physical therapists’ and a patients’ perspective of Smartstep and OpenGo-Science to provide feedback on partial weight bearing during supervised rehabilitation of patients after lower-limb trauma or surgery. Methods In a convergent mixed-methods design, qualitative and quantitative data were collected. Usability was subdivided into user performance, satisfaction and acceptability. Patients prescribed with partial weight bearing and their physical therapists were asked to use SmartStep and OpenGo-Science during supervised rehabilitation. Usability was qualitatively tested by a think-aloud method and a semi-structured interview and quantitatively tested by the System-Usability-Scale (SUS) and closed questions. For the qualitative data thematic content analyses were used. Results Nine pairs of physical therapists and their patients participated. The mean SUS scores for patients and physical therapists were for SmartStep 70 and 53, and for OpenGo-Science 79 and 81, respectively. Scores were interpreted with the Curved Grading Scale. The qualitative data showed that there were mixed views and perceptions from patients and physical therapists on satisfaction and acceptability. Conclusion This study gives insight in the usability of two biofeedback devices from the patient’s and physical therapist’s perspective. The overall usability from both perspectives seemed to be acceptable for OpenGo-Science. For SmartStep, overall usability seemed only acceptable from the patient’s perspective. Implication The study findings could help clinicians to decide which biofeedback device is appropriate for their given situation and provide information for future development of biofeedback devices. PMID:27798674

An experimental method to verify soil conservation by check dams on the Loess Plateau, China.

PubMed

Xu, X Z; Zhang, H W; Wang, G Q; Chen, S C; Dang, W Q

2009-12-01

A successful experiment with a physical model requires necessary conditions of similarity. This study presents an experimental method with a semi-scale physical model. The model is used to monitor and verify soil conservation by check dams in a small watershed on the Loess Plateau of China. During experiments, the model-prototype ratio of geomorphic variables was kept constant under each rainfall event. Consequently, experimental data are available for verification of soil erosion processes in the field and for predicting soil loss in a model watershed with check dams. Thus, it can predict the amount of soil loss in a catchment. This study also mentions four criteria: similarities of watershed geometry, grain size and bare land, Froude number (Fr) for rainfall event, and soil erosion in downscaled models. The efficacy of the proposed method was confirmed using these criteria in two different downscaled model experiments. The B-Model, a large scale model, simulates watershed prototype. The two small scale models, D(a) and D(b), have different erosion rates, but are the same size. These two models simulate hydraulic processes in the B-Model. Experiment results show that while soil loss in the small scale models was converted by multiplying the soil loss scale number, it was very close to that of the B-Model. Obviously, with a semi-scale physical model, experiments are available to verify and predict soil loss in a small watershed area with check dam system on the Loess Plateau, China.

Clinical supervision, emotional exhaustion, and turnover intention: a study of substance abuse treatment counselors in the Clinical Trials Network of the National Institute on Drug Abuse.

PubMed

Knudsen, Hannah K; Ducharme, Lori J; Roman, Paul M

2008-12-01

An intriguing hypothesis is that clinical supervision may protect against counselor turnover. This idea has been mentioned in recent discussions of the substance abuse treatment workforce. To test this hypothesis, we extend our previous research on emotional exhaustion and turnover intention among counselors by estimating the associations between clinical supervision and these variables in a large sample (N = 823). An exploratory analysis reveals that clinical supervision was negatively associated with emotional exhaustion and turnover intention. Given our previous findings that emotional exhaustion and turnover intention were associated with job autonomy, procedural justice, and distributive justice, we estimate a structural equation model to examine whether these variables mediated clinical supervision's associations with emotional exhaustion and turnover intention. These data support the fully mediated model. We found that the perceived quality of clinical supervision is strongly associated with counselors' perceptions of job autonomy, procedural justice, and distributive justice, which are, in turn, associated with emotional exhaustion and turnover intention. These data offer support for the protective role of clinical supervision in substance abuse treatment counselors' turnover and occupational well-being.

Kinetic modelling of anaerobic hydrolysis of solid wastes, including disintegration processes.

PubMed

García-Gen, Santiago; Sousbie, Philippe; Rangaraj, Ganesh; Lema, Juan M; Rodríguez, Jorge; Steyer, Jean-Philippe; Torrijos, Michel

2015-01-01

A methodology to estimate disintegration and hydrolysis kinetic parameters of solid wastes and validate an ADM1-based anaerobic co-digestion model is presented. Kinetic parameters of the model were calibrated from batch reactor experiments treating individually fruit and vegetable wastes (among other residues) following a new protocol for batch tests. In addition, decoupled disintegration kinetics for readily and slowly biodegradable fractions of solid wastes was considered. Calibrated parameters from batch assays of individual substrates were used to validate the model for a semi-continuous co-digestion operation treating simultaneously 5 fruit and vegetable wastes. The semi-continuous experiment was carried out in a lab-scale CSTR reactor for 15 weeks at organic loading rate ranging between 2.0 and 4.7 gVS/Ld. The model (built in Matlab/Simulink) fit to a large extent the experimental results in both batch and semi-continuous mode and served as a powerful tool to simulate the digestion or co-digestion of solid wastes. Copyright © 2014 Elsevier Ltd. All rights reserved.

Evaluating the implementation of a national disclosure policy for large-scale adverse events in an integrated health care system: identification of gaps and successes.

PubMed

Maguire, Elizabeth M; Bokhour, Barbara G; Wagner, Todd H; Asch, Steven M; Gifford, Allen L; Gallagher, Thomas H; Durfee, Janet M; Martinello, Richard A; Elwy, A Rani

2016-11-11

Many healthcare organizations have developed disclosure policies for large-scale adverse events, including the Veterans Health Administration (VA). This study evaluated VA's national large-scale disclosure policy and identifies gaps and successes in its implementation. Semi-structured qualitative interviews were conducted with leaders, hospital employees, and patients at nine sites to elicit their perceptions of recent large-scale adverse events notifications and the national disclosure policy. Data were coded using the constructs of the Consolidated Framework for Implementation Research (CFIR). We conducted 97 interviews. Insights included how to handle the communication of large-scale disclosures through multiple levels of a large healthcare organization and manage ongoing communications about the event with employees. Of the 5 CFIR constructs and 26 sub-constructs assessed, seven were prominent in interviews. Leaders and employees specifically mentioned key problem areas involving 1) networks and communications during disclosure, 2) organizational culture, 3) engagement of external change agents during disclosure, and 4) a need for reflecting on and evaluating the policy implementation and disclosure itself. Patients shared 5) preferences for personal outreach by phone in place of the current use of certified letters. All interviewees discussed 6) issues with execution and 7) costs of the disclosure. CFIR analysis reveals key problem areas that need to be addresses during disclosure, including: timely communication patterns throughout the organization, establishing a supportive culture prior to implementation, using patient-approved, effective communications strategies during disclosures; providing follow-up support for employees and patients, and sharing lessons learned.

Slip Continuity in Explicit Crystal Plasticity Simulations Using Nonlocal Continuum and Semi-discrete Approaches

DTIC Science & Technology

2013-01-01

Based Micropolar Single Crystal Plasticity: Comparison of Multi - and Single Criterion Theories. J. Mech. Phys. Solids 2011, 59, 398–422. ALE3D ...element boundaries in a multi -step constitutive evaluation (Becker, 2011). The results showed the desired effects of smoothing the deformation field...Implementation The model was implemented in the large-scale parallel, explicit finite element code ALE3D (2012). The crystal plasticity

Active learning based segmentation of Crohns disease from abdominal MRI.

PubMed

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

2016-05-01

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

Effects of Supervised vs. Unsupervised Training Programs on Balance and Muscle Strength in Older Adults: A Systematic Review and Meta-Analysis.

PubMed

Lacroix, André; Hortobágyi, Tibor; Beurskens, Rainer; Granacher, Urs

2017-11-01

Balance and resistance training can improve healthy older adults' balance and muscle strength. Delivering such exercise programs at home without supervision may facilitate participation for older adults because they do not have to leave their homes. To date, no systematic literature analysis has been conducted to determine if supervision affects the effectiveness of these programs to improve healthy older adults' balance and muscle strength/power. The objective of this systematic review and meta-analysis was to quantify the effectiveness of supervised vs. unsupervised balance and/or resistance training programs on measures of balance and muscle strength/power in healthy older adults. In addition, the impact of supervision on training-induced adaptive processes was evaluated in the form of dose-response relationships by analyzing randomized controlled trials that compared supervised with unsupervised trials. A computerized systematic literature search was performed in the electronic databases PubMed, Web of Science, and SportDiscus to detect articles examining the role of supervision in balance and/or resistance training in older adults. The initially identified 6041 articles were systematically screened. Studies were included if they examined balance and/or resistance training in adults aged ≥65 years with no relevant diseases and registered at least one behavioral balance (e.g., time during single leg stance) and/or muscle strength/power outcome (e.g., time for 5-Times-Chair-Rise-Test). Finally, 11 studies were eligible for inclusion in this meta-analysis. Weighted mean standardized mean differences between subjects (SMD bs ) of supervised vs. unsupervised balance/resistance training studies were calculated. The included studies were coded for the following variables: number of participants, sex, age, number and type of interventions, type of balance/strength tests, and change (%) from pre- to post-intervention values. Additionally, we coded training according to the following modalities: period, frequency, volume, modalities of supervision (i.e., number of supervised/unsupervised sessions within the supervised or unsupervised training groups, respectively). Heterogeneity was computed using I 2 and χ 2 statistics. The methodological quality of the included studies was evaluated using the Physiotherapy Evidence Database scale. Our analyses revealed that in older adults, supervised balance/resistance training was superior compared with unsupervised balance/resistance training in improving measures of static steady-state balance (mean SMD bs  = 0.28, p = 0.39), dynamic steady-state balance (mean SMD bs  = 0.35, p = 0.02), proactive balance (mean SMD bs  = 0.24, p = 0.05), balance test batteries (mean SMD bs  = 0.53, p = 0.02), and measures of muscle strength/power (mean SMD bs  = 0.51, p = 0.04). Regarding the examined dose-response relationships, our analyses showed that a number of 10-29 additional supervised sessions in the supervised training groups compared with the unsupervised training groups resulted in the largest effects for static steady-state balance (mean SMD bs  = 0.35), dynamic steady-state balance (mean SMD bs  = 0.37), and muscle strength/power (mean SMD bs  = 1.12). Further, ≥30 additional supervised sessions in the supervised training groups were needed to produce the largest effects on proactive balance (mean SMD bs  = 0.30) and balance test batteries (mean SMD bs  = 0.77). Effects in favor of supervised programs were larger for studies that did not include any supervised sessions in their unsupervised programs (mean SMD bs : 0.28-1.24) compared with studies that implemented a few supervised sessions in their unsupervised programs (e.g., three supervised sessions throughout the entire intervention program; SMD bs : -0.06 to 0.41). The present findings have to be interpreted with caution because of the low number of eligible studies and the moderate methodological quality of the included studies, which is indicated by a median Physiotherapy Evidence Database scale score of 5. Furthermore, we indirectly compared dose-response relationships across studies and not from single controlled studies. Our analyses suggest that supervised balance and/or resistance training improved measures of balance and muscle strength/power to a greater extent than unsupervised programs in older adults. Owing to the small number of available studies, we were unable to establish a clear dose-response relationship with regard to the impact of supervision. However, the positive effects of supervised training are particularly prominent when compared with completely unsupervised training programs. It is therefore recommended to include supervised sessions (i.e., two out of three sessions/week) in balance/resistance training programs to effectively improve balance and muscle strength/power in older adults.

SU-F-I-10: Spatially Local Statistics for Adaptive Image Filtering

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

Iliopoulos, AS; Sun, X; Floros, D

Purpose: To facilitate adaptive image filtering operations, addressing spatial variations in both noise and signal. Such issues are prevalent in cone-beam projections, where physical effects such as X-ray scattering result in spatially variant noise, violating common assumptions of homogeneous noise and challenging conventional filtering approaches to signal extraction and noise suppression. Methods: We present a computational mechanism for probing into and quantifying the spatial variance of noise throughout an image. The mechanism builds a pyramid of local statistics at multiple spatial scales; local statistical information at each scale includes (weighted) mean, median, standard deviation, median absolute deviation, as well asmore » histogram or dynamic range after local mean/median shifting. Based on inter-scale differences of local statistics, the spatial scope of distinguishable noise variation is detected in a semi- or un-supervised manner. Additionally, we propose and demonstrate the incorporation of such information in globally parametrized (i.e., non-adaptive) filters, effectively transforming the latter into spatially adaptive filters. The multi-scale mechanism is materialized by efficient algorithms and implemented in parallel CPU/GPU architectures. Results: We demonstrate the impact of local statistics for adaptive image processing and analysis using cone-beam projections of a Catphan phantom, fitted within an annulus to increase X-ray scattering. The effective spatial scope of local statistics calculations is shown to vary throughout the image domain, necessitating multi-scale noise and signal structure analysis. Filtering results with and without spatial filter adaptation are compared visually, illustrating improvements in imaging signal extraction and noise suppression, and in preserving information in low-contrast regions. Conclusion: Local image statistics can be incorporated in filtering operations to equip them with spatial adaptivity to spatial signal/noise variations. An efficient multi-scale computational mechanism is developed to curtail processing latency. Spatially adaptive filtering may impact subsequent processing tasks such as reconstruction and numerical gradient computations for deformable registration. NIH Grant No. R01-184173.« less

Machine Learning Toolkit for Extreme Scale

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

2014-03-31

Support Vector Machines (SVM) is a popular machine learning technique, which has been applied to a wide range of domains such as science, finance, and social networks for supervised learning. MaTEx undertakes the challenge of designing a scalable parallel SVM training algorithm for large scale systems, which includes commodity multi-core machines, tightly connected supercomputers and cloud computing systems. Several techniques are proposed for improved speed and memory space usage including adaptive and aggressive elimination of samples for faster convergence , and sparse format representation of data samples. Several heuristics for earliest possible to lazy elimination of non-contributing samples are consideredmore » in MaTEx. In many cases, where an early sample elimination might result in a false positive, low overhead mechanisms for reconstruction of key data structures are proposed. The proposed algorithm and heuristics are implemented and evaluated on various publicly available datasets« less

Morphodynamics of semi-alluvial streams in northern Fennoscandia: a flume experiment to determine bedform self-organization

NASA Astrophysics Data System (ADS)

Polvi, Lina

2017-04-01

Streams in northern Fennoscandia have two characteristics that complicate a process-based understanding of sediment transport affecting channel form: (1) they are typically semi-alluvial, in that they contain coarse glacial legacy sediment, and (2) numerous mainstem lakes buffer sediment and water fluxes. Systematic studies of these streams are complicated because natural reference sites are lacking due to over a century of widespread channel simplification to aid timber-floating. This research is part of a larger project to determine controls on channel geometry and sediment transport at: (1) the catchment scale, examining downstream hydraulic geometry, (2) the reach scale, examining sediment transport, and (3) the bedform scale, examining the potential for predictable bedform formation. The objective of the current study, targeting the bedform scale, was to use a flume experiment to determine whether sediment self-organizes and creates bedforms in semi-alluvial channels. The prototype channels, tributaries to the unregulated Vindel River in northern Sweden that are being restored after timber-floating, contain coarse sediment (D16: 55 mm, D50:250 mm, D84:620 mm) with moderately steep slopes (2-5%) and typically experience snowmelt-flooding and flooding due to ice jams. Using a scaling factor of 8 for Froude number similitude, an 8-m long, 1.1 m wide fixed-bed flume was set up at the Colorado State University Engineering Research Center with a scaled-down sediment distribution analogous to the prototype channels. For two flume setups, with bed slopes of 2% and 5%, four runs were conducted with flows analogous to QBF, Q2, Q10 and Q50 flows in the prototype channels until equilibrium conditions were reached. Digital elevation models (DEMs) of bed topography were constructed before and after each run using structure-from-motion photogrammetry. To examine self-organization of sediment, DEMs of difference between pre-flow conditions and after each flow were created; scour and deposition in relation to large immobile clasts were examined. Preliminary results show that at high flows at the lower slope (2%), fine sediment was deposited above immobile clasts and scour was common below. High flows at the higher slope (5%) caused scour above and occasionally directly below immobile clasts, with fine sediment deposited nearby scour zones above immobile clasts. These results indicate that these channels experience a shielding effect by large immobile clasts, inhibiting bedload transport and creating pockets of fine sediment upstream of large boulders. Additionally, pools downstream of immobile boulders may experience velocity reversals, causing scour instead of deposition in low-velocity zones. In addition, the combined aggradation and degradation between the Q50 and Q10 flows was less than between the Q10 and Q2 flows. This is most likely because the snowmelt-dominated flow regime of northern Sweden with buffering capacity of lakes precludes extremely high flows, causing a small difference in intermediate- and high-recurrence interval flow magnitudes. Therefore, flows with an intermediate recurrence interval likely do the most geomorphic work, but major sediment self-organization as seen in alluvial mountain streams is unlikely barring an extreme event. In conclusion, classical slope-dependent bedform relationships found in alluvial gravel-bed streams may not be applicable in semi-alluvial channels in northern Fennoscandia.

Clinical supervision, emotional exhaustion, and turnover intention: A study of substance abuse treatment counselors in NIDA’s Clinical Trials Network

PubMed Central

Knudsen, Hannah K.; Ducharme, Lori J.; Roman, Paul M

2008-01-01

An intriguing hypothesis is that clinical supervision may protect against counselor turnover. This idea has been mentioned in recent discussions of the substance abuse treatment workforce. To test this hypothesis, we extend our previous research on emotional exhaustion and turnover intention among counselors by estimating the associations between clinical supervision and these variables in a large sample (n = 823). An exploratory analysis reveals that clinical supervision was negatively associated with emotional exhaustion and turnover intention. Given our previous findings that emotional exhaustion and turnover intention were associated with job autonomy, procedural justice, and distributive justice, we estimate a structural equation model to examine whether these variables mediated clinical supervision’s associations with emotional exhaustion and turnover intention. These data support the fully mediated model. We found the perceived quality of clinical supervision is strongly associated with counselors’ perceptions of job autonomy, procedural justice, and distributive justice, which are, in turn, associated with emotional exhaustion and turnover intention. These data offer support for the protective role of clinical supervision in substance abuse treatment counselors’ turnover and occupational wellbeing. PMID:18424048

Mitigation benefits of forestation greatly varies on short spatial scale

NASA Astrophysics Data System (ADS)

Yakir, Dan; Rotenberg, Eyal; Rohatin, Shani; Ramati, Efrat; Asaf, David; Dicken, Uri

2016-04-01

Mitigation of global warming by forestation is controversial because of its linkage to increasing surface energy load and associated surface warming. Such tradeoffs between cooling associated with carbon sequestration and warming associated with radiative effects have been considered predominantly on large spatial scales, indicating benefits of forestation mainly in the tropics but not in the boreal regions. Using mobile laboratory for measuring CO2, water and energy flux in forest and non-forest ecosystem along the climatic gradient in Israel over three years, we show that the balance between cooling and warming effects of forestation can be transformed across small spatial scale. While converting shrubland to pine forest in a semi-arid site (280 mm annual precipitations) requires several decades of carbon sequestration to balance the radiative warming effects, similar land use change under moist Mediterranean conditions (780 mm annual precipitation) just ~200 km away showed reversal of this balance. Specifically, the results indicated that in the study region (semi-arid to humid Mediterranean), net absorb radiation in pine forests is always larger than in open space ecosystems, resulting in surface warming effects (the so-called albedo effect). Similarly, depression of thermal radiation emission, mainly due canopy skin surface cooling associated with the 'convector effect' in forests compared with shrubland ecosystems also appears in all sites. But both effects decrease by about 1/2 in going from the semi-arid to the humid Mediterranean sites, while enhanced productivity of forest compared to grassland increase about fourfold. The results indicate a greater potential for forestation as climate change mitigation strategy than previously assumed.

DisPATCh as a tool to evaluate coarse-scale remotely sensed soil moisture using localized in situ measurements: Application to SMOS and AMSR-E data in Southeastern Australia

NASA Astrophysics Data System (ADS)

Malbéteau, Yoann; Merlin, Olivier; Molero, Beatriz; Rüdiger, Christoph; Bacon, Stephan

2016-03-01

Validating coarse-scale satellite soil moisture data still represents a big challenge, notably due to the large mismatch existing between the spatial resolution (> 10 km) of microwave radiometers and the representativeness scale (several m) of localized in situ measurements. This study aims to examine the potential of DisPATCh (Disaggregation based on Physical and Theoretical scale Change) for validating SMOS (Soil Moisture and Ocean Salinity) and AMSR-E (Advanced Microwave Scanning Radiometer-Earth observation system) level-3 soil moisture products. The ∽40-50 km resolution SMOS and AMSR-E data are disaggregated at 1 km resolution over the Murrumbidgee catchment in Southeastern Australia during a one year period in 2010-2011, and the satellite products are compared with the in situ measurements of 38 stations distributed within the study area. It is found that disaggregation improves the mean difference, correlation coefficient and slope of the linear regression between satellite and in situ data in 77%, 92% and 94% of cases, respectively. Nevertheless, the downscaling efficiency is lower in winter than during the hotter months when DisPATCh performance is optimal. Consistently, better results are obtained in the semi-arid than in a temperate zone of the catchment. In the semi-arid Yanco region, disaggregation in summer increases the correlation coefficient from 0.63 to 0.78 and from 0.42 to 0.71 for SMOS and AMSR-E in morning overpasses and from 0.37 to 0.63 and from 0.47 to 0.73 for SMOS and AMSR-E in afternoon overpasses, respectively. DisPATCh has strong potential in low vegetated semi-arid areas where it can be used as a tool to evaluate coarse-scale remotely sensed soil moisture by explicitly representing the sub-pixel variability.

Statistical learning theory for high dimensional prediction: Application to criterion-keyed scale development.

PubMed

Chapman, Benjamin P; Weiss, Alexander; Duberstein, Paul R

2016-12-01

Statistical learning theory (SLT) is the statistical formulation of machine learning theory, a body of analytic methods common in "big data" problems. Regression-based SLT algorithms seek to maximize predictive accuracy for some outcome, given a large pool of potential predictors, without overfitting the sample. Research goals in psychology may sometimes call for high dimensional regression. One example is criterion-keyed scale construction, where a scale with maximal predictive validity must be built from a large item pool. Using this as a working example, we first introduce a core principle of SLT methods: minimization of expected prediction error (EPE). Minimizing EPE is fundamentally different than maximizing the within-sample likelihood, and hinges on building a predictive model of sufficient complexity to predict the outcome well, without undue complexity leading to overfitting. We describe how such models are built and refined via cross-validation. We then illustrate how 3 common SLT algorithms-supervised principal components, regularization, and boosting-can be used to construct a criterion-keyed scale predicting all-cause mortality, using a large personality item pool within a population cohort. Each algorithm illustrates a different approach to minimizing EPE. Finally, we consider broader applications of SLT predictive algorithms, both as supportive analytic tools for conventional methods, and as primary analytic tools in discovery phase research. We conclude that despite their differences from the classic null-hypothesis testing approach-or perhaps because of them-SLT methods may hold value as a statistically rigorous approach to exploratory regression. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Large Scale Helium Liquefaction and Considerations for Site Services for a Plant Located in Algeria

NASA Astrophysics Data System (ADS)

Froehlich, P.; Clausen, J. J.

2008-03-01

The large-scale liquefaction of helium extracted from natural gas is depicted. Based on a block diagram the process chain, starting with the pipeline downstream of the natural-gas plant to the final storage of liquid helium, is explained. Information will be provided about the recent experiences during installation and start-up of a bulk helium liquefaction plant located in Skikda, Algeria, including part-load operation based on a reduced feed gas supply. The local working and ambient conditions are described, including challenging logistic problems like shipping and receiving of parts, qualified and semi-qualified subcontractors, basic provisions and tools on site, and precautions to sea water and ambient conditions. Finally, the differences in commissioning (technically and evaluation of time and work packages) to European locations and standards will be discussed.

Large-scale computations in fluid mechanics; Proceedings of the Fifteenth Summer Seminar on Applied Mathematics, University of California, La Jolla, CA, June 27-July 8, 1983. Parts 1 & 2

NASA Technical Reports Server (NTRS)

Engquist, B. E. (Editor); Osher, S. (Editor); Somerville, R. C. J. (Editor)

1985-01-01

Papers are presented on such topics as the use of semi-Lagrangian advective schemes in meteorological modeling; computation with high-resolution upwind schemes for hyperbolic equations; dynamics of flame propagation in a turbulent field; a modified finite element method for solving the incompressible Navier-Stokes equations; computational fusion magnetohydrodynamics; and a nonoscillatory shock capturing scheme using flux-limited dissipation. Consideration is also given to the use of spectral techniques in numerical weather prediction; numerical methods for the incorporation of mountains in atmospheric models; techniques for the numerical simulation of large-scale eddies in geophysical fluid dynamics; high-resolution TVD schemes using flux limiters; upwind-difference methods for aerodynamic problems governed by the Euler equations; and an MHD model of the earth's magnetosphere.

Clinical supervision of allied health professionals in country South Australia: A mixed methods pilot study.

PubMed

Kumar, Saravana; Osborne, Kate; Lehmann, Tanya

2015-10-01

Recent times have witnessed dramatic changes in health care with overt recognition for quality and safety to underpin health care service delivery. In addition to systems-wide focus, the importance of supporting and mentoring people delivering the care has also been recognised. This can be achieved through quality clinical supervision. In 2010, Country Health South Australia Local Health Network developed a holistic allied health clinical governance structure, which was implemented in 2011. This research reports on emergent findings from the evaluation of the clinical governance structure, which included mandating clinical supervision for all allied health staff. A mixed method approach was chosen with evaluation of the impact of clinical supervision undertaken by a psychometrically sound instrument (Manchester Clinical Supervision Scale 26-item version), collected through an anonymous online survey and qualitative data collected through semistructured interviews and focus groups. Overall, 189 allied health professionals responded to the survey. Survey responses indicated allied health professionals recognised the importance of and valued receiving clinical supervision (normative domain), had levels of trust and rapport with, and were supported by supervisors (restorative domain) and positively affected their delivery of care and improvement in skills (formative domain). Qualitative data identified enablers such as profession specific gains, improved opportunities and consistency for clinical supervision and barriers such as persistent organisational issues, lack of clarity (delineation of roles) and communication issues. The findings from this research highlight that while clinical supervision has an important role to play, it is not a panacea for all the ills of the health care system. © 2015 National Rural Health Alliance Inc.

The Relation Between Supervisors’ Big Five Personality Traits and Employees’ Experiences of Abusive Supervision

PubMed Central

Camps, Jeroen; Stouten, Jeroen; Euwema, Martin

2016-01-01

The present study investigates the relation between supervisors’ personality traits and employees’ experiences of supervisory abuse, an area that – to date – remained largely unexplored in previous research. Field data collected from 103 supervisor-subordinate dyads showed that contrary to our expectations supervisors’ agreeableness and neuroticism were not significantly related to abusive supervision, nor were supervisors’ extraversion or openness to experience. Interestingly, however, our findings revealed a positive relation between supervisors’ conscientiousness and abusive supervision. That is, supervisors high in conscientiousness were more likely to be perceived as an abusive supervisor by their employees. Overall, our findings do suggest that supervisors’ Big Five personality traits explain only a limited amount of the variability in employees’ experiences of abusive supervision. PMID:26903919

The effectiveness of Technology-assisted Cascade Training and Supervision of community health workers in delivering the Thinking Healthy Program for perinatal depression in a post-conflict area of Pakistan - study protocol for a randomized controlled trial.

PubMed

Zafar, Shamsa; Sikander, Siham; Hamdani, Syed Usman; Atif, Najia; Akhtar, Parveen; Nazir, Huma; Maselko, Joanna; Rahman, Atif

2016-04-06

Rates of perinatal depression in low and middle income countries are reported to be very high. Perinatal depression not only has profound impact on women's health, disability and functioning, it is associated with poor child health outcomes such as pre-term birth, under-nutrition and stunting, which ultimately have an adverse trans-generational impact. There is strong evidence in the medical literature that perinatal depression can be effectively managed with psychological treatments delivered by non-specialists. Our previous research in Pakistan led to the development of a successful perinatal depression intervention, the Thinking Healthy Program (THP). The THP is a psychological treatment delivered by community health workers. The burden of perinatal depression can be reduced through scale-up of this proven intervention; however, training of health workers at scale is a major barrier. To enhance access to such interventions there is a need to look at technological solutions to training and supervision. This is a non-inferiority, single-blinded randomized controlled trial. Eighty community health workers called Lady Health Workers (LHWs) working in a post-conflict rural area in Pakistan (Swat) will be recruited through the LHW program. LHWs will be randomly allocated to Technology-assisted Cascade Training and Supervision (TACTS) or to specialist-delivered training (40 in each group). The TACTS group will receive training in THP through LHW supervisors using a tablet-based training package, whereas the comparison group will receive training directly from mental health specialists. Our hypothesis is that both groups will achieve equal competence. Primary outcome measure will be competence of health workers at delivering THP using a modified ENhancing Assessment of Common Therapeutic factors (ENACT) rating scale immediately post training and after 3 months of supervision. Independent assessors will be blinded to the LHW allocation status. Women living in post-conflict areas are at higher risk of depression compared to the general population. Implementation of evidence-based interventions for depression in such situations is a challenge because health systems are weak and human resources are scarce. The key innovation to be tested in this trial is a Technology-assisted Cascade Training and Supervision system to assist scale-up of the THP. Registered with ClinicalTrials.gov as GCC-THP-TACTS-2015, Identifier: NCT02644902 .

Gauge invariance and kaon production in deep inelastic scattering at low scales

NASA Astrophysics Data System (ADS)

Guerrero, Juan V.; Accardi, Alberto

2018-06-01

This paper focuses on hadron mass effects in calculations of semi-inclusive kaon production in lepton-Deuteron deeply inelastic scattering at HERMES and COMPASS kinematics. In the collinear factorization framework, the corresponding cross section is shown to factorize, at leading order and leading twist, into products of parton distributions and fragmentation functions evaluated in terms of kaon- and nucleon-mass-dependent scaling variables, and to respect gauge invariance. It is found that hadron mass corrections for integrated kaon multiplicities sizeably reduce the apparent large discrepancy between measurements of K++K- multiplicities performed by the two collaborations, and fully reconcile their K+/K- ratios.

Splitting of the weak hypercharge quantum

NASA Astrophysics Data System (ADS)

Nielsen, H. B.; Brene, N.

1991-08-01

The ratio between the weak hypercharge quantum for particles having no coupling to the gauge bosons corresponding to the semi-simple component of the gauge group and the smallest hypercharge quantum for particles that do have such couplings is exceptionally large for the standard model, considering its rank. To compare groups with respect to this property we propose a quantity χ which depends on the rank of the group and the splitting ratio of the hypercharge(s) to be found in the group. The quantity χ has maximal value for the gauge group of the standard model. This suggests that the hypercharge splitting may play an important rôle either in the origin of the gauge symmetry at a fundamental scale or in some kind of selection mechanism at a scale perhaps nearer to the experimental scale. Such a selection mechanism might be what we have called confusion which removes groups with many (so-called generalized) automorphisms. The quantity χ tends to be large for groups with few generalized automorphisms.

Determination of semi-arid landscape endmembers and seasonal trends using convex geometry spectral unmixing techniques

NASA Technical Reports Server (NTRS)

Yuhas, Roberta H.; Boardman, Joseph W.; Goetz, Alexander F. H.

1993-01-01

Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data were acquired during three consecutive growing seasons (26 September 1989, 22 March 1990, and 7 August 1990) over an area of the High Plains east of Greeley, Colorado (40 deg 20 min N and 104 deg 16 min W). A repeat visit to assess vegetation at its peak growth was flown on 6 June 1993. This region contains extensive eolian deposits in the form of stabilized dune complexes (small scale parabolic dunes superimposed on large scale longitudinal and parabolic dunes). Due to the dunes' large scale (2-10 km) and low relief (1-5 m), the scaling and morphological relationships that contribute to the evolution of this landscape are nearly impossible to understand without the use of remote sensing. Additionally, this area and regions similarly situated could be the first to experience the effects caused by global climate change. During the past 10,000 years there were at least four periods of extensive sand activity due to climate change, followed by periods of landscape stability, as shown in the stratigraphic record of this area.

Equipment-based Pilates reduces work-related chronic low back pain and disability: A pilot study.

PubMed

Stieglitz, Dana Duval; Vinson, David R; Hampton, Michelle De Coux

2016-01-01

This study investigated effectiveness of an equipment-based Pilates protocol for reducing pain and disability in individuals with work-related chronic low back pain (CLBP). Twelve workers with non-specific CLBP participated in a quasi-experimental, one-group, pretest-posttest pilot study of supervised 6-week equipment-based Pilates exercise. Pain severity was assessed using a 100-mm visual analog scale (VAS). Physical function was assessed using the Oswestry disability index (ODI). The Pilates intervention significantly reduced pain (mean decrease in VAS 30.75 ± 20.27, p < 0.0001) and disability (mean decrease in ODI 11.25 ± 13.20, p < 0.02) with large and borderline large effect sizes, respectively. Rehabilitative Pilates exercise reduced pain and disability in workers with CLBP. Further research is needed to investigate Pilates exercise for rehabilitation of work-related injuries in large populations. Copyright © 2015 Elsevier Ltd. All rights reserved.

On-line Machine Learning and Event Detection in Petascale Data Streams

NASA Astrophysics Data System (ADS)

Thompson, David R.; Wagstaff, K. L.

2012-01-01

Traditional statistical data mining involves off-line analysis in which all data are available and equally accessible. However, petascale datasets have challenged this premise since it is often impossible to store, let alone analyze, the relevant observations. This has led the machine learning community to investigate adaptive processing chains where data mining is a continuous process. Here pattern recognition permits triage and followup decisions at multiple stages of a processing pipeline. Such techniques can also benefit new astronomical instruments such as the Large Synoptic Survey Telescope (LSST) and Square Kilometre Array (SKA) that will generate petascale data volumes. We summarize some machine learning perspectives on real time data mining, with representative cases of astronomical applications and event detection in high volume datastreams. The first is a "supervised classification" approach currently used for transient event detection at the Very Long Baseline Array (VLBA). It injects known signals of interest - faint single-pulse anomalies - and tunes system parameters to recover these events. This permits meaningful event detection for diverse instrument configurations and observing conditions whose noise cannot be well-characterized in advance. Second, "semi-supervised novelty detection" finds novel events based on statistical deviations from previous patterns. It detects outlier signals of interest while considering known examples of false alarm interference. Applied to data from the Parkes pulsar survey, the approach identifies anomalous "peryton" phenomena that do not match previous event models. Finally, we consider online light curve classification that can trigger adaptive followup measurements of candidate events. Classifier performance analyses suggest optimal survey strategies, and permit principled followup decisions from incomplete data. These examples trace a broad range of algorithm possibilities available for online astronomical data mining. This talk describes research performed at the Jet Propulsion Laboratory, California Institute of Technology. Copyright 2012, All Rights Reserved. U.S. Government support acknowledged.

Psoriasis image representation using patch-based dictionary learning for erythema severity scoring.

PubMed

George, Yasmeen; Aldeen, Mohammad; Garnavi, Rahil

2018-06-01

Psoriasis is a chronic skin disease which can be life-threatening. Accurate severity scoring helps dermatologists to decide on the treatment. In this paper, we present a semi-supervised computer-aided system for automatic erythema severity scoring in psoriasis images. Firstly, the unsupervised stage includes a novel image representation method. We construct a dictionary, which is then used in the sparse representation for local feature extraction. To acquire the final image representation vector, an aggregation method is exploited over the local features. Secondly, the supervised phase is where various multi-class machine learning (ML) classifiers are trained for erythema severity scoring. Finally, we compare the proposed system with two popular unsupervised feature extractor methods, namely: bag of visual words model (BoVWs) and AlexNet pretrained model. Root mean square error (RMSE) and F1 score are used as performance measures for the learned dictionaries and the trained ML models, respectively. A psoriasis image set consisting of 676 images, is used in this study. Experimental results demonstrate that the use of the proposed procedure can provide a setup where erythema scoring is accurate and consistent. Also, it is revealed that dictionaries with large number of atoms and small patch sizes yield the best representative erythema severity features. Further, random forest (RF) outperforms other classifiers with F1 score 0.71, followed by support vector machine (SVM) and boosting with 0.66 and 0.64 scores, respectively. Furthermore, the conducted comparative studies confirm the effectiveness of the proposed approach with improvement of 9% and 12% over BoVWs and AlexNet based features, respectively. Crown Copyright © 2018. Published by Elsevier Ltd. All rights reserved.

Medical electives in sub-Saharan Africa: a host perspective.

PubMed

Kumwenda, Ben; Dowell, Jon; Daniels, Katy; Merrylees, Neil

2015-06-01

Electives are part of most Western medical school curricula. It is estimated that each year 3000-4000 undergraduate medical students from the UK alone undertake an elective in a developing country. The impact of these electives has given some cause for concern, but the views of elective hosts are largely missing from the debate. The purpose of this study was to evaluate the organisation, outcomes and impacts of medical electives in sub-Saharan Africa from a host perspective. A qualitative analysis of 14 semi-structured interviews with elective hosts at seven elective sites in Malawi, Zambia and Tanzania was carried out. A framework analysis approach was used to analyse 483 minutes of audio-recorded data. Hosts were committed to providing elective experiences but their reasons for doing so varied considerably, in particular between urban or teaching hospitals and rural or mission hospitals. Nurturing a group of professionals who will understand the provision of health care from a global perspective was the main reason reported for hosting an elective, along with generating potential future staff. Hosts argued that the quality of supervision should be judged according to local context. Typical concerns cited in the literature with reference to clinical activities, safety and ethics did not emerge as issues for these hosts. However, in under-resourced clinical contexts, the training of local students sometimes had to take priority. Electives could be improved with greater student preparation and some contribution from sending institutions to support teaching, supervision or patient care. The challenge to both students and their sending institutions is to progress towards giving something proportionate back in return for the learning experiences received. There is clearly room to improve electives from the hosts' perspective, but individually host institutions lack the opportunity or ability to achieve change. © 2015 John Wiley & Sons Ltd.

Supervised classification of continental shelf sediment off western Donegal, Ireland

NASA Astrophysics Data System (ADS)

Monteys, X.; Craven, K.; McCarron, S. G.

2017-12-01

Managing human impacts on marine ecosystems requires natural regions to be identified and mapped over a range of hierarchically nested scales. In recent years (2000-present) the Irish National Seabed Survey (INSS) and Integrated Mapping for the Sustainable Development of Ireland's Marine Resources programme (INFOMAR) (Geological Survey Ireland and Marine Institute collaborations) has provided unprecedented quantities of high quality data on Ireland's offshore territories. The increasing availability of large, detailed digital representations of these environments requires the application of objective and quantitative analyses. This study presents results of a new approach for sea floor sediment mapping based on an integrated analysis of INFOMAR multibeam bathymetric data (including the derivatives of slope and relative position), backscatter data (including derivatives of angular response analysis) and sediment groundtruthing over the continental shelf, west of Donegal. It applies a Geographic-Object-Based Image Analysis software package to provide a supervised classification of the surface sediment. This approach can provide a statistically robust, high resolution classification of the seafloor. Initial results display a differentiation of sediment classes and a reduction in artefacts from previously applied methodologies. These results indicate a methodology that could be used during physical habitat mapping and classification of marine environments.

Object-Location-Aware Hashing for Multi-Label Image Retrieval via Automatic Mask Learning.

PubMed

Huang, Chang-Qin; Yang, Shang-Ming; Pan, Yan; Lai, Han-Jiang

2018-09-01

Learning-based hashing is a leading approach of approximate nearest neighbor search for large-scale image retrieval. In this paper, we develop a deep supervised hashing method for multi-label image retrieval, in which we propose to learn a binary "mask" map that can identify the approximate locations of objects in an image, so that we use this binary "mask" map to obtain length-limited hash codes which mainly focus on an image's objects but ignore the background. The proposed deep architecture consists of four parts: 1) a convolutional sub-network to generate effective image features; 2) a binary "mask" sub-network to identify image objects' approximate locations; 3) a weighted average pooling operation based on the binary "mask" to obtain feature representations and hash codes that pay most attention to foreground objects but ignore the background; and 4) the combination of a triplet ranking loss designed to preserve relative similarities among images and a cross entropy loss defined on image labels. We conduct comprehensive evaluations on four multi-label image data sets. The results indicate that the proposed hashing method achieves superior performance gains over the state-of-the-art supervised or unsupervised hashing baselines.

Closing the Barn Door: The Effect of Parental Supervision on Canadian Children's Online Privacy

ERIC Educational Resources Information Center

Steeves, Valerie; Webster, Cheryl

2008-01-01

Empirical data from a large sample of Canadian youth aged 13 to 17 years suggest that, although the current privacy policy framework is having a positive effect on the extent to which young people are complying with the types of behavior promoted by adults as privacy protective, its primary focus on parental supervision is inadequate to fully…

Should First-Year Doctoral Students Be Supervised Collectively or Individually? Effects on Thesis Completion and Time to Completion

ERIC Educational Resources Information Center

Agné, Hans; Mörkenstam, Ulf

2018-01-01

Whether supervision of doctoral students is best pursued individually or collectively is a recurring but unresolved question in debates on higher education. The rarity of longitudinal data and the common usage of qualitative methods to analyse a limited number of cases have left the effectiveness of either model largely untested. To assist with…

Compact Representation of High-Dimensional Feature Vectors for Large-Scale Image Recognition and Retrieval.

PubMed

Zhang, Yu; Wu, Jianxin; Cai, Jianfei

2016-05-01

In large-scale visual recognition and image retrieval tasks, feature vectors, such as Fisher vector (FV) or the vector of locally aggregated descriptors (VLAD), have achieved state-of-the-art results. However, the combination of the large numbers of examples and high-dimensional vectors necessitates dimensionality reduction, in order to reduce its storage and CPU costs to a reasonable range. In spite of the popularity of various feature compression methods, this paper shows that the feature (dimension) selection is a better choice for high-dimensional FV/VLAD than the feature (dimension) compression methods, e.g., product quantization. We show that strong correlation among the feature dimensions in the FV and the VLAD may not exist, which renders feature selection a natural choice. We also show that, many dimensions in FV/VLAD are noise. Throwing them away using feature selection is better than compressing them and useful dimensions altogether using feature compression methods. To choose features, we propose an efficient importance sorting algorithm considering both the supervised and unsupervised cases, for visual recognition and image retrieval, respectively. Combining with the 1-bit quantization, feature selection has achieved both higher accuracy and less computational cost than feature compression methods, such as product quantization, on the FV and the VLAD image representations.

Fully kinetic 3D simulations of the Hermean magnetosphere under realistic conditions: a new approach

NASA Astrophysics Data System (ADS)

Amaya, Jorge; Gonzalez-Herrero, Diego; Lembège, Bertrand; Lapenta, Giovanni

2017-04-01

Simulations of the magnetosphere of planets are usually performed using the MHD and the hybrid approaches. However, these two methods still rely on approximations for the computation of the pressure tensor, and require the neutrality of the plasma at every point of the domain by construction. These approximations undermine the role of electrons on the emergence of plasma features in the magnetosphere of planets. The high mobility of electrons, their characteristic time and space scales, and the lack of perfect neutrality, are the source of many observed phenomena in the magnetospheres, including the turbulence energy cascade, the magnetic reconnection, the particle acceleration in the shock front and the formation of current systems around the magnetosphere. Fully kinetic codes are extremely demanding of computing time, and have been unable to perform simulations of the full magnetosphere at the real scales of a planet with realistic plasma conditions. This is caused by two main reasons: 1) explicit codes must resolve the electron scales limiting the time and space discretisation, and 2) current versions of semi-implicit codes are unstable for cell sizes larger than a few Debye lengths. In this work we present new simulations performed with ECsim, an Energy Conserving semi-implicit method [1], that can overcome these two barriers. We compare the solutions obtained with ECsim with the solutions obtained by the classic semi-implicit code iPic3D [2]. The new simulations with ECsim demand a larger computational effort, but the time and space discretisations are larger than those in iPic3D allowing for a faster simulation time of the full planetary environment. The new code, ECsim, can reach a resolution allowing the capture of significant large scale physics without loosing kinetic electron information, such as wave-electron interaction and non-Maxwellian electron velocity distributions [3]. The code is able to better capture the thickness of the different boundary layers of the magnetosphere of Mercury. Electron kinetics are consistent with the spatial and temporal scale resolutions. Simulations are compared with measurements from the MESSENGER spacecraft showing a better fit when compared against the classic fully kinetic code iPic3D. These results show that the new generation of Energy Conserving semi-implicit codes can be used for an accurate analysis and interpretation of particle data from magnetospheric missions like BepiColombo and MMS, including electron velocity distributions and electron temperature anisotropies. [1] Lapenta, G. (2016). Exactly Energy Conserving Implicit Moment Particle in Cell Formulation. arXiv preprint arXiv:1602.06326. [2] Markidis, S., & Lapenta, G. (2010). Multi-scale simulations of plasma with iPIC3D. Mathematics and Computers in Simulation, 80(7), 1509-1519. [3] Lapenta, G., Gonzalez-Herrero, D., & Boella, E. (2016). Multiple scale kinetic simulations with the energy conserving semi implicit particle in cell (PIC) method. arXiv preprint arXiv:1612.08289.

Effect of Training Supervision on Effectiveness of Strength Training for Reducing Neck/Shoulder Pain and Headache in Office Workers: Cluster Randomized Controlled Trial

PubMed Central

Gram, Bibi; Andersen, Christoffer; Zebis, Mette K.; Bredahl, Thomas; Pedersen, Mogens T.; Mortensen, Ole S.; Jensen, Rigmor H.; Andersen, Lars L.; Sjøgaard, Gisela

2014-01-01

Objective. To investigate the effect of workplace neck/shoulder strength training with and without regular supervision on neck/shoulder pain and headache among office workers. Method. A 20-week cluster randomized controlled trial among 351 office workers was randomized into three groups: two training groups with the same total amount of planned exercises three times per week (1) with supervision (3WS) throughout the intervention period, (2) with minimal supervision (3MS) only initially, and (3) a reference group (REF). Main outcome is self-reported pain intensity in neck and shoulder (scale 0–9) and headache (scale 0–10). Results. Intention-to-treat analyses showed a significant decrease in neck pain intensity the last 7 days in 3MS compared with REF: −0.5 ± 0.2 (P < 0.02) and a tendency for 3WS versus REF: −0.4 ± 0.2 (P < 0.07). Intensity of headache the last month decreased in both training groups: 3WS versus REF: −1.1 ± 0.2 (P < 0.001) and 3MS versus REF: −1.1 ± 0.2 (P < 0.001). Additionally, days of headache decreased 1.0 ± 0.5 in 3WS and 1.3 ± 0.5 in 3MS versus REF. There were no differences between the two training groups for any of the variables. Conclusion. Neck/shoulder training at the workplace reduced neck pain and headache among office workers independently of the extent of supervision. This finding has important practical implications for future workplace interventions. PMID:24701581

On The Evidence For Large-Scale Galactic Conformity In The Local Universe

NASA Astrophysics Data System (ADS)

Sin, Larry P. T.; Lilly, Simon J.; Henriques, Bruno M. B.

2017-10-01

We re-examine the observational evidence for large-scale (4 Mpc) galactic conformity in the local Universe, as presented in Kauffmann et al. We show that a number of methodological features of their analysis act to produce a misleadingly high amplitude of the conformity signal. These include a weighting in favour of central galaxies in very high density regions, the likely misclassification of satellite galaxies as centrals in the same high-density regions and the use of medians to characterize bimodal distributions. We show that the large-scale conformity signal in Kauffmann et al. clearly originates from a very small number of central galaxies in the vicinity of just a few very massive clusters, whose effect is strongly amplified by the methodological issues that we have identified. Some of these 'centrals' are likely misclassified satellites, but some may be genuine centrals showing a real conformity effect. Regardless, this analysis suggests that conformity on 4 Mpc scales is best viewed as a relatively short-range effect (at the virial radius) associated with these very large neighbouring haloes, rather than a very long-range effect (at tens of virial radii) associated with the relatively low-mass haloes that host the nominal central galaxies in the analysis. A mock catalogue constructed from a recent semi-analytic model shows very similar conformity effects to the data when analysed in the same way, suggesting that there is no need to introduce new physical processes to explain galactic conformity on 4 Mpc scales.

Using patient experiences on Dutch social media to supervise health care services: exploratory study.

PubMed

van de Belt, Tom H; Engelen, Lucien J L P G; Verhoef, Lise M; van der Weide, Marian J A; Schoonhoven, Lisette; Kool, Rudolf B

2015-01-15

Social media has become mainstream and a growing number of people use it to share health care-related experiences, for example on health care rating sites. These users' experiences and ratings on social media seem to be associated with quality of care. Therefore, information shared by citizens on social media could be of additional value for supervising the quality and safety of health care services by regulatory bodies, thereby stimulating participation by consumers. The objective of the study was to identify the added value of social media for two types of supervision by the Dutch Healthcare Inspectorate (DHI), which is the regulatory body charged with supervising the quality and safety of health care services in the Netherlands. These were (1) supervision in response to incidents reported by individuals, and (2) risk-based supervision. We performed an exploratory study in cooperation with the DHI and searched different social media sources such as Twitter, Facebook, and healthcare rating sites to find additional information for these incidents and topics, from five different sectors. Supervision experts determined the added value for each individual result found, making use of pre-developed scales. Searches in social media resulted in relevant information for six of 40 incidents studied and provided relevant additional information in 72 of 116 cases in risk-based supervision of long-term elderly care. The results showed that social media could be used to include the patient's perspective in supervision. However, it appeared that the rating site ZorgkaartNederland was the only source that provided information that was of additional value for the DHI, while other sources such as forums and social networks like Twitter and Facebook did not result in additional information. This information could be of importance for health care inspectorates, particularly for its enforcement by risk-based supervision in care of the elderly. Further research is needed to determine the added value for other health care sectors.

Using Patient Experiences on Dutch Social Media to Supervise Health Care Services: Exploratory Study

PubMed Central

Engelen, Lucien JLPG; Verhoef, Lise M; van der Weide, Marian JA; Schoonhoven, Lisette; Kool, Rudolf B

2015-01-01

Background Social media has become mainstream and a growing number of people use it to share health care-related experiences, for example on health care rating sites. These users’ experiences and ratings on social media seem to be associated with quality of care. Therefore, information shared by citizens on social media could be of additional value for supervising the quality and safety of health care services by regulatory bodies, thereby stimulating participation by consumers. Objective The objective of the study was to identify the added value of social media for two types of supervision by the Dutch Healthcare Inspectorate (DHI), which is the regulatory body charged with supervising the quality and safety of health care services in the Netherlands. These were (1) supervision in response to incidents reported by individuals, and (2) risk-based supervision. Methods We performed an exploratory study in cooperation with the DHI and searched different social media sources such as Twitter, Facebook, and healthcare rating sites to find additional information for these incidents and topics, from five different sectors. Supervision experts determined the added value for each individual result found, making use of pre-developed scales. Results Searches in social media resulted in relevant information for six of 40 incidents studied and provided relevant additional information in 72 of 116 cases in risk-based supervision of long-term elderly care. Conclusions The results showed that social media could be used to include the patient’s perspective in supervision. However, it appeared that the rating site ZorgkaartNederland was the only source that provided information that was of additional value for the DHI, while other sources such as forums and social networks like Twitter and Facebook did not result in additional information. This information could be of importance for health care inspectorates, particularly for its enforcement by risk-based supervision in care of the elderly. Further research is needed to determine the added value for other health care sectors. PMID:25592481

Vaccinium meridionale Swartz Supercritical CO₂ Extraction: Effect of Process Conditions and Scaling Up.

PubMed

López-Padilla, Alexis; Ruiz-Rodriguez, Alejandro; Restrepo Flórez, Claudia Estela; Rivero Barrios, Diana Marsela; Reglero, Guillermo; Fornari, Tiziana

2016-06-25

Vaccinium meridionale Swartz (Mortiño or Colombian blueberry) is one of the Vaccinium species abundantly found across the Colombian mountains, which are characterized by high contents of polyphenolic compounds (anthocyanins and flavonoids). The supercritical fluid extraction (SFE) of Vaccinium species has mainly focused on the study of V. myrtillus L. (blueberry). In this work, the SFE of Mortiño fruit from Colombia was studied in a small-scale extraction cell (273 cm³) and different extraction pressures (20 and 30 MPa) and temperatures (313 and 343 K) were investigated. Then, process scaling-up to a larger extraction cell (1350 cm³) was analyzed using well-known semi-empirical engineering approaches. The Broken and Intact Cell (BIC) model was adjusted to represent the kinetic behavior of the low-scale extraction and to simulate the large-scale conditions. Extraction yields obtained were in the range 0.1%-3.2%. Most of the Mortiño solutes are readily accessible and, thus, 92% of the extractable material was recovered in around 30 min. The constant CO₂ residence time criterion produced excellent results regarding the small-scale kinetic curve according to the BIC model, and this conclusion was experimentally validated in large-scale kinetic experiments.

Vaccinium meridionale Swartz Supercritical CO2 Extraction: Effect of Process Conditions and Scaling Up

PubMed Central

López-Padilla, Alexis; Ruiz-Rodriguez, Alejandro; Restrepo Flórez, Claudia Estela; Rivero Barrios, Diana Marsela; Reglero, Guillermo; Fornari, Tiziana

2016-01-01

Vaccinium meridionale Swartz (Mortiño or Colombian blueberry) is one of the Vaccinium species abundantly found across the Colombian mountains, which are characterized by high contents of polyphenolic compounds (anthocyanins and flavonoids). The supercritical fluid extraction (SFE) of Vaccinium species has mainly focused on the study of V. myrtillus L. (blueberry). In this work, the SFE of Mortiño fruit from Colombia was studied in a small-scale extraction cell (273 cm3) and different extraction pressures (20 and 30 MPa) and temperatures (313 and 343 K) were investigated. Then, process scaling-up to a larger extraction cell (1350 cm3) was analyzed using well-known semi-empirical engineering approaches. The Broken and Intact Cell (BIC) model was adjusted to represent the kinetic behavior of the low-scale extraction and to simulate the large-scale conditions. Extraction yields obtained were in the range 0.1%–3.2%. Most of the Mortiño solutes are readily accessible and, thus, 92% of the extractable material was recovered in around 30 min. The constant CO2 residence time criterion produced excellent results regarding the small-scale kinetic curve according to the BIC model, and this conclusion was experimentally validated in large-scale kinetic experiments. PMID:28773640

Biological classification with RNA-Seq data: Can alternatively spliced transcript expression enhance machine learning classifier?

PubMed

Johnson, Nathan T; Dhroso, Andi; Hughes, Katelyn J; Korkin, Dmitry

2018-06-25

The extent to which the genes are expressed in the cell can be simplistically defined as a function of one or more factors of the environment, lifestyle, and genetics. RNA sequencing (RNA-Seq) is becoming a prevalent approach to quantify gene expression, and is expected to gain better insights to a number of biological and biomedical questions, compared to the DNA microarrays. Most importantly, RNA-Seq allows to quantify expression at the gene and alternative splicing isoform levels. However, leveraging the RNA-Seq data requires development of new data mining and analytics methods. Supervised machine learning methods are commonly used approaches for biological data analysis, and have recently gained attention for their applications to the RNA-Seq data. In this work, we assess the utility of supervised learning methods trained on RNA-Seq data for a diverse range of biological classification tasks. We hypothesize that the isoform-level expression data is more informative for biological classification tasks than the gene-level expression data. Our large-scale assessment is done through utilizing multiple datasets, organisms, lab groups, and RNA-Seq analysis pipelines. Overall, we performed and assessed 61 biological classification problems that leverage three independent RNA-Seq datasets and include over 2,000 samples that come from multiple organisms, lab groups, and RNA-Seq analyses. These 61 problems include predictions of the tissue type, sex, or age of the sample, healthy or cancerous phenotypes and, the pathological tumor stage for the samples from the cancerous tissue. For each classification problem, the performance of three normalization techniques and six machine learning classifiers was explored. We find that for every single classification problem, the isoform-based classifiers outperform or are comparable with gene expression based methods. The top-performing supervised learning techniques reached a near perfect classification accuracy, demonstrating the utility of supervised learning for RNA-Seq based data analysis. Published by Cold Spring Harbor Laboratory Press for the RNA Society.

Effects of clinical supervision on resident learning and patient care during simulated ICU scenarios.

PubMed

Piquette, Dominique; Tarshis, Jordan; Regehr, Glenn; Fowler, Robert A; Pinto, Ruxandra; LeBlanc, Vicki R

2013-12-01

Closer supervision of residents' clinical activities has been promoted to improve patient safety, but may additionally affect resident participation in patient care and learning. The objective of this study was to determine the effects of closer supervision on patient care, resident participation, and the development of resident ability to care independently for critically ill patients during simulated scenarios. This quantitative study represents a component of a larger mixed-methods study. Residents were randomized to one of three levels of supervision, defined by the physical proximity of the supervisor (distant, immediately available, and direct). Each resident completed a simulation scenario under the supervision of a critical care fellow, immediately followed by a modified scenario of similar content without supervision. The simulation center of a tertiary, university-affiliated academic center in a large urban city. Fifty-three residents completing a critical care rotation and 24 critical care fellows were recruited between April 2009 and June 2010. None. During the supervised scenarios, lower team performance checklist scores were obtained for distant supervision compared with immediately available and direct supervision (mean [SD], direct: 72% [12%] vs immediately available: 77% [10%] vs distant: 61% [11%]; p = 0.0013). The percentage of checklist items completed by the residents themselves was significantly lower during direct supervision (median [interquartile range], direct: 40% [21%] vs immediately available: 58% [16%] vs distant: 55% [11%]; p = 0.005). During unsupervised scenarios, no significant differences were found on the outcome measures. Care delivered in the presence of senior supervising physicians was more comprehensive than care delivered without access to a bedside supervisor, but was associated with lower resident participation. However, subsequent resident performance during unsupervised scenarios was not adversely affected. Direct supervision of residents leads to improved care process and does not diminish the subsequent ability of residents to function independently.

Effects of Large-Scale Releases on the Genetic Structure of Red Sea Bream (Pagrus major, Temminck et Schlegel) Populations in Japan.

PubMed

Blanco Gonzalez, Enrique; Aritaki, Masato; Knutsen, Halvor; Taniguchi, Nobuhiko

2015-01-01

Large-scale hatchery releases are carried out for many marine fish species worldwide; nevertheless, the long-term effects of this practice on the genetic structure of natural populations remains unclear. The lack of knowledge is especially evident when independent stock enhancement programs are conducted simultaneously on the same species at different geographical locations, as occurs with red sea bream (Pagrus major, Temminck et Schlegel) in Japan. In this study, we examined the putative effects of intensive offspring releases on the genetic structure of red sea bream populations along the Japanese archipelago by genotyping 848 fish at fifteen microsatellite loci. Our results suggests weak but consistent patterns of genetic divergence (F(ST) = 0.002, p < 0.001). Red sea bream in Japan appeared spatially structured with several patches of distinct allelic composition, which corresponded to areas receiving an important influx of fish of hatchery origin, either released intentionally or from unintentional escapees from aquaculture operations. In addition to impacts upon local populations inhabiting semi-enclosed embayments, large-scale releases (either intentionally or from unintentional escapes) appeared also to have perturbed genetic structure in open areas. Hence, results of the present study suggest that independent large-scale marine stock enhancement programs conducted simultaneously on one species at different geographical locations may compromise native genetic structure and lead to patchy patterns in population genetic structure.

Study on the removal efficiency of UF membranes using bacteriophages in bench-scale and semi-technical scale.

PubMed

Kreissel, K; Bösl, M; Lipp, P; Franzreb, M; Hambsch, B

2012-01-01

To determine the removal efficiency of ultrafiltration (UF) membranes for nano-particles in the size range of viruses the state of the art uses challenge tests with virus-spiked water. This work focuses on bench-scale and semi-technical scale experiments. Different experimental parameters influencing the removal efficiency of the tested UF membrane modules were analyzed and evaluated for bench- and semi-technical scale experiments. Organic matter in the water matrix highly influenced the removal of the tested bacteriophages MS2 and phiX174. Less membrane fouling (low ΔTMP) led to a reduced phage reduction. Increased flux positively affected phage removal in natural waters. The tested bacteriophages MS2 and phiX174 revealed different removal properties. MS2, which is widely used as a model organism to determine virus removal efficiencies of membranes, mostly showed a better removal than phiX174 for the natural water qualities tested. It seems that MS2 is possibly a less conservative surrogate for human enteric virus removal than phiX174. In bench-scale experiments log removal values (LRV) for MS2 of 2.5-6.0 and of 2.5-4.5 for phiX174 were obtained for the examined range of parameters. Phage removal obtained with differently fabricated semi-technical modules was quite variable for comparable parameter settings, indicating that module fabrication can lead to differing results. Potting temperature and module size were identified as influencing factors. In conclusion, careful attention has to be paid to the choice of experimental settings and module potting when using bench-scale or semi-technical scale experiments for UF membrane challenge tests.

Use of rice-based oral rehydration solution in a large diarrhoea treatment centre in Bangladesh: in-house production, use and relative cost.

PubMed

Islam, M A; Mahalanabis, D; Majid, N

1994-12-01

Glucose-based oral rehydration salt (ORS) is an appropriate and cost-effective tool to treat diarrhoeal dehydration. In patients with a high purging rate, particularly due to cholera, rice-based ORS has been shown to substantially reduce stool output compared to glucose ORS. However, it is not used in the hospitals or diarrhoea treatment centres largely because of the non-availability of a ready-to-use inexpensive packaged product and because of the problem of cooking. In a large diarrhoea treatment centre in Bangladesh (with an annual ORS consumption of approximately 140,000 litres), we have maintained in-house production of rice ORS and used it routinely for more than 600,000 patients over the last nine years. Semi-literate health workers cook rice ORS and supervise mothers in its use. Rice ORS is less costly (US $0.15 per patient treated compared with US $0.37 for glucose ORS) and is well accepted. It is an attractive alternative to glucose ORS in many fixed facility treatment centres in countries where rice is a staple and cholera is endemic. The process of its in-house preparation and use is described in this report which may assist hospitals wishing to use rice ORS in treating diarrhoea patients. Availability of a low cost ready-to-use rice ORS packet (which needs no cooking) with adequate shelf-life will increase its use at fixed facilities.

Gram-scale purification of aconitine and identification of lappaconitine in Aconitum karacolicum.

PubMed

Tarbe, M; de Pomyers, H; Mugnier, L; Bertin, D; Ibragimov, T; Gigmes, D; Mabrouk, K

2017-07-01

Aconitum karacolicum from northern Kyrgyzstan (Alatau area) contains about 0.8-1% aconitine as well as other aconite derivatives that have already been identified. In this paper, we compare several methods for the further purification of an Aconitum karacolicum extract initially containing 80% of aconitine. Reverse-phase flash chromatography, reverse-phase semi-preparative HPLC, centrifugal partition chromatography (CPC) and recrystallization techniques were evaluated regarding first their efficiency to get the highest purity of aconitine (over 96%) and secondly their applicability in a semi-industrial scale purification process (in our case, 150g of plant extract). Even if the CPC technique shows the highest purification yield (63%), the recrystallization remains the method of choice to purify a large amount of aconitine as i) it can be easily carried out in safe conditions; ii) an aprotic solvent is used, avoiding aconitine degradation. Moreover, this study led us to the identification of lappaconitine in Aconitum karacolicum, a well-known alkaloid never found in this Aconitum species. Copyright © 2017 Elsevier B.V. All rights reserved.

Materials for suspension (semi-solid) electrodes for energy and water technologies

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

Hatzell, Kelsey B.; Boota, Muhammad; Gogotsi, Yury

2015-01-01

Suspension or semi-solid electrodes have recently gained increased attention for large-scale applications such as grid energy storage, capacitive water deionization, and wastewater treatment. A suspension electrode is a multiphase material system comprised of an active (charge storing) material suspended in ionic solution (electrolyte). Gravimetrically, the electrolyte is the majority component and aids in physical transport of the active material. For the first time, this principle enables, scalability of electrochemical energy storage devices (supercapacitors and batteries) previously limited to small and medium scale applications. This critical review describes the ongoing material challenges encompassing suspension-based systems. The research described here combines classicalmore » aspects of electrochemistry, colloidal science, material science, fluid mechanics, and rheology to describe ion and charge percolation, adsorption of ions, and redox charge storage processes in suspension electrodes. Our review summarizes the growing inventory of material systems, methods and practices used to characterize suspension electrodes, and describes universal material system properties (rheological, electrical, and electrochemical) that are pivotal in the design of high performing systems. We include a discussion of the primary challenges and future research directions.« less

Trait-specific responses of wild bee communities to landscape composition, configuration and local factors.

PubMed

Hopfenmüller, Sebastian; Steffan-Dewenter, Ingolf; Holzschuh, Andrea

2014-01-01

Land-use intensification and loss of semi-natural habitats have induced a severe decline of bee diversity in agricultural landscapes. Semi-natural habitats like calcareous grasslands are among the most important bee habitats in central Europe, but they are threatened by decreasing habitat area and quality, and by homogenization of the surrounding landscape affecting both landscape composition and configuration. In this study we tested the importance of habitat area, quality and connectivity as well as landscape composition and configuration on wild bees in calcareous grasslands. We made detailed trait-specific analyses as bees with different traits might differ in their response to the tested factors. Species richness and abundance of wild bees were surveyed on 23 calcareous grassland patches in Southern Germany with independent gradients in local and landscape factors. Total wild bee richness was positively affected by complex landscape configuration, large habitat area and high habitat quality (i.e. steep slopes). Cuckoo bee richness was positively affected by complex landscape configuration and large habitat area whereas habitat specialists were only affected by the local factors habitat area and habitat quality. Small social generalists were positively influenced by habitat area whereas large social generalists (bumblebees) were positively affected by landscape composition (high percentage of semi-natural habitats). Our results emphasize a strong dependence of habitat specialists on local habitat characteristics, whereas cuckoo bees and bumblebees are more likely affected by the surrounding landscape. We conclude that a combination of large high-quality patches and heterogeneous landscapes maintains high bee species richness and communities with diverse trait composition. Such diverse communities might stabilize pollination services provided to crops and wild plants on local and landscape scales.

Trait-Specific Responses of Wild Bee Communities to Landscape Composition, Configuration and Local Factors

PubMed Central

Hopfenmüller, Sebastian; Steffan-Dewenter, Ingolf; Holzschuh, Andrea

2014-01-01

Land-use intensification and loss of semi-natural habitats have induced a severe decline of bee diversity in agricultural landscapes. Semi-natural habitats like calcareous grasslands are among the most important bee habitats in central Europe, but they are threatened by decreasing habitat area and quality, and by homogenization of the surrounding landscape affecting both landscape composition and configuration. In this study we tested the importance of habitat area, quality and connectivity as well as landscape composition and configuration on wild bees in calcareous grasslands. We made detailed trait-specific analyses as bees with different traits might differ in their response to the tested factors. Species richness and abundance of wild bees were surveyed on 23 calcareous grassland patches in Southern Germany with independent gradients in local and landscape factors. Total wild bee richness was positively affected by complex landscape configuration, large habitat area and high habitat quality (i.e. steep slopes). Cuckoo bee richness was positively affected by complex landscape configuration and large habitat area whereas habitat specialists were only affected by the local factors habitat area and habitat quality. Small social generalists were positively influenced by habitat area whereas large social generalists (bumblebees) were positively affected by landscape composition (high percentage of semi-natural habitats). Our results emphasize a strong dependence of habitat specialists on local habitat characteristics, whereas cuckoo bees and bumblebees are more likely affected by the surrounding landscape. We conclude that a combination of large high-quality patches and heterogeneous landscapes maintains high bee species richness and communities with diverse trait composition. Such diverse communities might stabilize pollination services provided to crops and wild plants on local and landscape scales. PMID:25137311

Preface paper to the Semi-Arid Land-Surface-Atmosphere (SALSA) Program special issue

USGS Publications Warehouse

Goodrich, D.C.; Chehbouni, A.; Goff, B.; MacNish, B.; Maddock, T.; Moran, S.; Shuttleworth, W.J.; Williams, D.G.; Watts, C.; Hipps, L.H.; Cooper, D.I.; Schieldge, J.; Kerr, Y.H.; Arias, H.; Kirkland, M.; Carlos, R.; Cayrol, P.; Kepner, W.; Jones, B.; Avissar, R.; Begue, A.; Bonnefond, J.-M.; Boulet, G.; Branan, B.; Brunel, J.P.; Chen, L.C.; Clarke, T.; Davis, M.R.; DeBruin, H.; Dedieu, G.; Elguero, E.; Eichinger, W.E.; Everitt, J.; Garatuza-Payan, J.; Gempko, V.L.; Gupta, H.; Harlow, C.; Hartogensis, O.; Helfert, M.; Holifield, C.; Hymer, D.; Kahle, A.; Keefer, T.; Krishnamoorthy, S.; Lhomme, J.-P.; Lagouarde, J.-P.; Lo, Seen D.; Luquet, D.; Marsett, R.; Monteny, B.; Ni, W.; Nouvellon, Y.; Pinker, R.; Peters, C.; Pool, D.; Qi, J.; Rambal, S.; Rodriguez, J.; Santiago, F.; Sano, E.; Schaeffer, S.M.; Schulte, M.; Scott, R.; Shao, X.; Snyder, K.A.; Sorooshian, S.; Unkrich, C.L.; Whitaker, M.; Yucel, I.

2000-01-01

The Semi-Arid Land-Surface-Atmosphere Program (SALSA) is a multi-agency, multi-national research effort that seeks to evaluate the consequences of natural and human-induced environmental change in semi-arid regions. The ultimate goal of SALSA is to advance scientific understanding of the semi-arid portion of the hydrosphere-biosphere interface in order to provide reliable information for environmental decision making. SALSA approaches this goal through a program of long-term, integrated observations, process research, modeling, assessment, and information management that is sustained by cooperation among scientists and information users. In this preface to the SALSA special issue, general program background information and the critical nature of semi-arid regions is presented. A brief description of the Upper San Pedro River Basin, the initial location for focused SALSA research follows. Several overarching research objectives under which much of the interdisciplinary research contained in the special issue was undertaken are discussed. Principal methods, primary research sites and data collection used by numerous investigators during 1997-1999 are then presented. Scientists from about 20 US, five European (four French and one Dutch), and three Mexican agencies and institutions have collaborated closely to make the research leading to this special issue a reality. The SALSA Program has served as a model of interagency cooperation by breaking new ground in the approach to large scale interdisciplinary science with relatively limited resources.

A Fast Optimization Method for General Binary Code Learning.

PubMed

Shen, Fumin; Zhou, Xiang; Yang, Yang; Song, Jingkuan; Shen, Heng; Tao, Dacheng

2016-09-22

Hashing or binary code learning has been recognized to accomplish efficient near neighbor search, and has thus attracted broad interests in recent retrieval, vision and learning studies. One main challenge of learning to hash arises from the involvement of discrete variables in binary code optimization. While the widely-used continuous relaxation may achieve high learning efficiency, the pursued codes are typically less effective due to accumulated quantization error. In this work, we propose a novel binary code optimization method, dubbed Discrete Proximal Linearized Minimization (DPLM), which directly handles the discrete constraints during the learning process. Specifically, the discrete (thus nonsmooth nonconvex) problem is reformulated as minimizing the sum of a smooth loss term with a nonsmooth indicator function. The obtained problem is then efficiently solved by an iterative procedure with each iteration admitting an analytical discrete solution, which is thus shown to converge very fast. In addition, the proposed method supports a large family of empirical loss functions, which is particularly instantiated in this work by both a supervised and an unsupervised hashing losses, together with the bits uncorrelation and balance constraints. In particular, the proposed DPLM with a supervised `2 loss encodes the whole NUS-WIDE database into 64-bit binary codes within 10 seconds on a standard desktop computer. The proposed approach is extensively evaluated on several large-scale datasets and the generated binary codes are shown to achieve very promising results on both retrieval and classification tasks.

A methodology for small scale rural land use mapping in semi-arid developing countries using orbital imagery. 1: Introduction

NASA Technical Reports Server (NTRS)

Vangenderen, J. L. (Principal Investigator); Lock, B. F.

1976-01-01

The author has identified the following significant results. This research program has developed a viable methodology for producing small scale rural land use maps in semi-arid developing countries using imagery obtained from orbital multispectral scanners.

Fingerprint Liveness Detection in the Presence of Capable Intruders.

PubMed

Sequeira, Ana F; Cardoso, Jaime S

2015-06-19

Fingerprint liveness detection methods have been developed as an attempt to overcome the vulnerability of fingerprint biometric systems to spoofing attacks. Traditional approaches have been quite optimistic about the behavior of the intruder assuming the use of a previously known material. This assumption has led to the use of supervised techniques to estimate the performance of the methods, using both live and spoof samples to train the predictive models and evaluate each type of fake samples individually. Additionally, the background was often included in the sample representation, completely distorting the decision process. Therefore, we propose that an automatic segmentation step should be performed to isolate the fingerprint from the background and truly decide on the liveness of the fingerprint and not on the characteristics of the background. Also, we argue that one cannot aim to model the fake samples completely since the material used by the intruder is unknown beforehand. We approach the design by modeling the distribution of the live samples and predicting as fake the samples very unlikely according to that model. Our experiments compare the performance of the supervised approaches with the semi-supervised ones that rely solely on the live samples. The results obtained differ from the ones obtained by the more standard approaches which reinforces our conviction that the results in the literature are misleadingly estimating the true vulnerability of the biometric system.

Fingerprint Liveness Detection in the Presence of Capable Intruders

PubMed Central

Sequeira, Ana F.; Cardoso, Jaime S.

2015-01-01

Fingerprint liveness detection methods have been developed as an attempt to overcome the vulnerability of fingerprint biometric systems to spoofing attacks. Traditional approaches have been quite optimistic about the behavior of the intruder assuming the use of a previously known material. This assumption has led to the use of supervised techniques to estimate the performance of the methods, using both live and spoof samples to train the predictive models and evaluate each type of fake samples individually. Additionally, the background was often included in the sample representation, completely distorting the decision process. Therefore, we propose that an automatic segmentation step should be performed to isolate the fingerprint from the background and truly decide on the liveness of the fingerprint and not on the characteristics of the background. Also, we argue that one cannot aim to model the fake samples completely since the material used by the intruder is unknown beforehand. We approach the design by modeling the distribution of the live samples and predicting as fake the samples very unlikely according to that model. Our experiments compare the performance of the supervised approaches with the semi-supervised ones that rely solely on the live samples. The results obtained differ from the ones obtained by the more standard approaches which reinforces our conviction that the results in the literature are misleadingly estimating the true vulnerability of the biometric system. PMID:26102491

Barriers to providing quality emergency obstetric care in Addis Ababa, Ethiopia: Healthcare providers' perspectives on training, referrals and supervision, a mixed methods study.

PubMed

Austin, Anne; Gulema, Hanna; Belizan, Maria; Colaci, Daniela S; Kendall, Tamil; Tebeka, Mahlet; Hailemariam, Mengistu; Bekele, Delayehu; Tadesse, Lia; Berhane, Yemane; Langer, Ana

2015-03-29

Increasing women's access to and use of facilities for childbirth is a critical national strategy to improve maternal health outcomes in Ethiopia; however coverage alone is not enough as the quality of emergency obstetric services affects maternal mortality and morbidity. Addis Ababa has a much higher proportion of facility-based births (82%) than the national average (11%), but timely provision of quality emergency obstetric care remains a significant challenge for reducing maternal mortality and improving maternal health. The purpose of this study was to assess barriers to the provision of emergency obstetric care in Addis Ababa from the perspective of healthcare providers by analyzing three factors: implementation of national referral guidelines, staff training, and staff supervision. A mixed methods approach was used to assess barriers to quality emergency obstetric care. Qualitative analyses included twenty-nine, semi-structured, key informant interviews with providers from an urban referral network consisting of a hospital and seven health centers. Quantitative survey data were collected from 111 providers, 80% (111/138) of those providing maternal health services in the same referral network. Respondents identified a lack of transportation and communication infrastructure, overcrowding at the referral hospital, insufficient pre-service and in-service training, and absence of supportive supervision as key barriers to provision of quality emergency obstetric care. Dedicated transportation and communication infrastructure, improvements in pre-service and in-service training, and supportive supervision are needed to maximize the effective use of existing human resources and infrastructure, thus increasing access to and the provision of timely, high quality emergency obstetric care in Addis Ababa, Ethiopia.

Influence of supervision ratios by anesthesiologists on first-case starts and critical portions of anesthetics.

PubMed

Epstein, Richard H; Dexter, Franklin

2012-03-01

Anesthesia groups may wish to decrease the supervision ratio for nontrainee providers. Because hospitals offer many first-case starts and focus on starting these cases on time, the number of anesthesiologists needed is sensitive to this ratio. The number of operating rooms that an anesthesiologist can supervise concurrently is determined by the probability of multiple simultaneous critical portions of cases (i.e., requiring presence) and the availability of cross-coverage. A simulation study showed peak occurrence of critical portions during first cases, and frequent supervision lapses. These predictions were tested using real data from an anesthesia information management system. The timing and duration of critical portions of cases were determined from 1 yr of data at a tertiary care hospital. The percentages of days with at least one supervision lapse occurring at supervision ratios between 1:1 and 1:3 were determined. Even at a supervision ratio of 1:2, lapses occurred on 35% of days (lower 95% confidence limit = 30%). The peak incidence occurred before 8:00 AM, P < 0.0001 for the hypothesis that most (i.e., >50%) lapses occurred before this time. The average time from operating room entry until ready for prepping and draping (i.e., anesthesia release time) during first case starts was 22.2 min (95% confidence interval 21.8-22.8 min). Decreasing the supervision ratio from 1:2 to 1:3 has a large effect on supervision lapses during first-case starts. To mitigate such lapses, either staggered starts or additional anesthesiologists working at the start of the day would be required.

Consultancy on Large-Scale Submerged Aerobic Cultivation Process Design - Final Technical Report: February 1, 2016 -- June 30, 2016

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

Crater, Jason; Galleher, Connor; Lievense, Jeff

NREL is developing an advanced aerobic bubble column model using Aspen Custom Modeler (ACM). The objective of this work is to integrate the new fermentor model with existing techno-economic models in Aspen Plus and Excel to establish a new methodology for guiding process design. To assist this effort, NREL has contracted Genomatica to critique and make recommendations for improving NREL's bioreactor model and large scale aerobic bioreactor design for biologically producing lipids at commercial scale. Genomatica has highlighted a few areas for improving the functionality and effectiveness of the model. Genomatica recommends using a compartment model approach with an integratedmore » black-box kinetic model of the production microbe. We also suggest including calculations for stirred tank reactors to extend the models functionality and adaptability for future process designs. Genomatica also suggests making several modifications to NREL's large-scale lipid production process design. The recommended process modifications are based on Genomatica's internal techno-economic assessment experience and are focused primarily on minimizing capital and operating costs. These recommendations include selecting/engineering a thermotolerant yeast strain with lipid excretion; using bubble column fermentors; increasing the size of production fermentors; reducing the number of vessels; employing semi-continuous operation; and recycling cell mass.« less

Geomorphic analysis of large alluvial rivers

NASA Astrophysics Data System (ADS)

Thorne, Colin R.

2002-05-01

Geomorphic analysis of a large river presents particular challenges and requires a systematic and organised approach because of the spatial scale and system complexity involved. This paper presents a framework and blueprint for geomorphic studies of large rivers developed in the course of basic, strategic and project-related investigations of a number of large rivers. The framework demonstrates the need to begin geomorphic studies early in the pre-feasibility stage of a river project and carry them through to implementation and post-project appraisal. The blueprint breaks down the multi-layered and multi-scaled complexity of a comprehensive geomorphic study into a number of well-defined and semi-independent topics, each of which can be performed separately to produce a clearly defined, deliverable product. Geomorphology increasingly plays a central role in multi-disciplinary river research and the importance of effective quality assurance makes it essential that audit trails and quality checks are hard-wired into study design. The structured approach presented here provides output products and production trails that can be rigorously audited, ensuring that the results of a geomorphic study can stand up to the closest scrutiny.

A novel class sensitive hashing technique for large-scale content-based remote sensing image retrieval

NASA Astrophysics Data System (ADS)

Reato, Thomas; Demir, Begüm; Bruzzone, Lorenzo

2017-10-01

This paper presents a novel class sensitive hashing technique in the framework of large-scale content-based remote sensing (RS) image retrieval. The proposed technique aims at representing each image with multi-hash codes, each of which corresponds to a primitive (i.e., land cover class) present in the image. To this end, the proposed method consists of a three-steps algorithm. The first step is devoted to characterize each image by primitive class descriptors. These descriptors are obtained through a supervised approach, which initially extracts the image regions and their descriptors that are then associated with primitives present in the images. This step requires a set of annotated training regions to define primitive classes. A correspondence between the regions of an image and the primitive classes is built based on the probability of each primitive class to be present at each region. All the regions belonging to the specific primitive class with a probability higher than a given threshold are highly representative of that class. Thus, the average value of the descriptors of these regions is used to characterize that primitive. In the second step, the descriptors of primitive classes are transformed into multi-hash codes to represent each image. This is achieved by adapting the kernel-based supervised locality sensitive hashing method to multi-code hashing problems. The first two steps of the proposed technique, unlike the standard hashing methods, allow one to represent each image by a set of primitive class sensitive descriptors and their hash codes. Then, in the last step, the images in the archive that are very similar to a query image are retrieved based on a multi-hash-code-matching scheme. Experimental results obtained on an archive of aerial images confirm the effectiveness of the proposed technique in terms of retrieval accuracy when compared to the standard hashing methods.