The Spiritual Genogram in Training and Supervision.

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Frame, Marsha Wiggins

2001-01-01

Describes the spiritual genogram, a blueprint of family members' multigenerational religious and spiritual affiliations, events, and conflicts. Used as a tool in both training and supervision, the spiritual genogram enables students and supervisees to make sense of their own religious and spiritual heritage and to explore the ways in which their…

Learning outcomes using video in supervision and peer feedback during clinical skills training

DEFF Research Database (Denmark)

Lauridsen, Henrik Hein; Toftgård, Rie Castella; Nørgaard, Cita

supervision of clinical skills (formative assessment). Demonstrations of these principles will be presented as video podcasts during the session. The learning outcomes of video supervision and peer-feedback were assessed in an online questionnaire survey. Results Results of the supervision showed large self......Objective New technology and learning principles were introduced in a clinical skills training laboratory (iLab). The intension was to move from apprenticeship to active learning principles including peer feedback and supervision using video. The objective of this study was to evaluate student...... learning outcomes in a manual skills training subject using video during feedback and supervision. Methods The iLab classroom was designed to fit four principles of teaching using video. Two of these principles were (a) group work using peer-feedback on videos produced by the students and, (b) video...

Informal sources of supervision in clinical training.

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Farber, Barry A; Hazanov, Valery

2014-11-01

Although formal, assigned supervision is a potent source of learning and guidance for psychotherapy trainees, many beginning psychotherapists use other, informal sources of supervision or consultation for advice and support. Results of an online survey of beginning trainees (N = 146) indicate that other than their formally assigned supervisor, trainees most often consult with colleagues in their program, their own psychotherapist, and their significant other; that they're most likely to seek these other sources of help when they're feeling stuck or feel they've made a clinical mistake; that they do so because they need extra reassurance and suggestions; that they feel the advice given from these sources is helpful; and that they don't especially regret sharing this information. Several case examples are used to illustrate these points. Discussing clinical material with informal sources is, apparently, a great deal more common than typically acknowledged, and as such, has implications for training programs (including discussions of ethics) and formal supervision. © 2014 Wiley Periodicals, Inc.

Automatic Classification Using Supervised Learning in a Medical Document Filtering Application.

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Mostafa, J.; Lam, W.

2000-01-01

Presents a multilevel model of the information filtering process that permits document classification. Evaluates a document classification approach based on a supervised learning algorithm, measures the accuracy of the algorithm in a neural network that was trained to classify medical documents on cell biology, and discusses filtering…

Automatic segmentation of MR brain images of preterm infants using supervised classification.

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Moeskops, Pim; Benders, Manon J N L; Chiţ, Sabina M; Kersbergen, Karina J; Groenendaal, Floris; de Vries, Linda S; Viergever, Max A; Išgum, Ivana

2015-09-01

Preterm birth is often associated with impaired brain development. The state and expected progression of preterm brain development can be evaluated using quantitative assessment of MR images. Such measurements require accurate segmentation of different tissue types in those images. This paper presents an algorithm for the automatic segmentation of unmyelinated white matter (WM), cortical grey matter (GM), and cerebrospinal fluid in the extracerebral space (CSF). The algorithm uses supervised voxel classification in three subsequent stages. In the first stage, voxels that can easily be assigned to one of the three tissue types are labelled. In the second stage, dedicated analysis of the remaining voxels is performed. The first and the second stages both use two-class classification for each tissue type separately. Possible inconsistencies that could result from these tissue-specific segmentation stages are resolved in the third stage, which performs multi-class classification. A set of T1- and T2-weighted images was analysed, but the optimised system performs automatic segmentation using a T2-weighted image only. We have investigated the performance of the algorithm when using training data randomly selected from completely annotated images as well as when using training data from only partially annotated images. The method was evaluated on images of preterm infants acquired at 30 and 40weeks postmenstrual age (PMA). When the method was trained using random selection from the completely annotated images, the average Dice coefficients were 0.95 for WM, 0.81 for GM, and 0.89 for CSF on an independent set of images acquired at 30weeks PMA. When the method was trained using only the partially annotated images, the average Dice coefficients were 0.95 for WM, 0.78 for GM and 0.87 for CSF for the images acquired at 30weeks PMA, and 0.92 for WM, 0.80 for GM and 0.85 for CSF for the images acquired at 40weeks PMA. Even though the segmentations obtained using training data

Transfer of communication skills training from workshop to workplace: the impact of clinical supervision.

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Heaven, Cathy; Clegg, Jenny; Maguire, Peter

2006-03-01

Recent studies have recognised that the communication skills learned in the training environment are not always transferred back into the clinical setting. This paper reports a study which investigated the potential of clinical supervision in enhancing the transfer process. A randomised controlled trial was conducted involving 61 clinical nurse specialists. All attended a 3-day communication skills training workshop. Twenty-nine were then randomised to 4 weeks of clinical supervision, aimed at facilitating transfer of newly acquired skills into practice. Assessments, using real and simulated patients, were carried out before the course, immediately after the supervision period and 3 months later. Interviews were rated objectively using the Medical Interview Aural Rating Scale (MIARS) to assess nurses' ability to use key skills, respond to patient cues and identify patient concerns. Assessments with simulated patients showed that the training programme was extremely effective in changing competence in all three key areas. However, only those who experienced supervision showed any evidence of transfer. Improvements were found in the supervised groups' use of open questions, negotiation and psychological exploration. Whilst neither group facilitated more disclosure of cues or concerns, those in the experimental group responded more effectively to the cues disclosed, reduced their distancing behaviour and increasing their exploration of cues. The study has shown that whilst training enhances skills, without intervention, it may have little effect on clinical practice. The potential role of clinical supervision as one way of enhancing the clinical effectiveness of communication skills training programmes has been demonstrated. PRACTISE IMPLICATIONS: This study raises questions about the effectiveness of training programmes which do not incorporate a transfer element, and provides evidence to support the need for clinical supervision for clinical nurse specialist.

A Novel Semi-Supervised Electronic Nose Learning Technique: M-Training

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Pengfei Jia

2016-03-01

Full Text Available When an electronic nose (E-nose is used to distinguish different kinds of gases, the label information of the target gas could be lost due to some fault of the operators or some other reason, although this is not expected. Another fact is that the cost of getting the labeled samples is usually higher than for unlabeled ones. In most cases, the classification accuracy of an E-nose trained using labeled samples is higher than that of the E-nose trained by unlabeled ones, so gases without label information should not be used to train an E-nose, however, this wastes resources and can even delay the progress of research. In this work a novel multi-class semi-supervised learning technique called M-training is proposed to train E-noses with both labeled and unlabeled samples. We employ M-training to train the E-nose which is used to distinguish three indoor pollutant gases (benzene, toluene and formaldehyde. Data processing results prove that the classification accuracy of E-nose trained by semi-supervised techniques (tri-training and M-training is higher than that of an E-nose trained only with labeled samples, and the performance of M-training is better than that of tri-training because more base classifiers can be employed by M-training.

A Device for Automatically Measuring and Supervising the Critical Care Patient’S Urine Output

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Roemi Fernández

2010-01-01

Full Text Available 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.

Utilizing technological innovations to enhance psychotherapy supervision, training, and outcomes.

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Barnett, Jeffrey E

2011-06-01

Recent technological advances in the use of the Internet and video technologies has greatly impacted the provision of psychotherapy and other clinical services as well as how the training of psychotherapists may be conducted. When utilized appropriately these technologies may provide greater access to needed services to include treatment, consultation, supervision, and training. Specific ethical challenges and pitfalls are discussed and recommendations are made for the ethical use of these technologies. Additionally, innovative practices from the seven articles in the special section that follows are highlighted and reviewed. These articles present a number of innovations that can take psychotherapy training, research, supervision, and treatment forward toward increased effectiveness. Recommendations for integrating these innovations into ongoing practices are provided and for additional research to build on the important work of the authors in this special section are provided.

Transfer learning improves supervised image segmentation across imaging protocols

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van Opbroek, Annegreet; Ikram, M. Arfan; Vernooij, Meike W.

2015-01-01

with slightly different characteristics. The performance of the four transfer classifiers was compared to that of standard supervised classification on two MRI brain-segmentation tasks with multi-site data: white matter, gray matter, and CSF segmentation; and white-matter- /MS-lesion segmentation......The variation between images obtained with different scanners or different imaging protocols presents a major challenge in automatic segmentation of biomedical images. This variation especially hampers the application of otherwise successful supervised-learning techniques which, in order to perform...... well, often require a large amount of labeled training data that is exactly representative of the target data. We therefore propose to use transfer learning for image segmentation. Transfer-learning techniques can cope with differences in distributions between training and target data, and therefore...

Does training frequency and supervision affect compliance, performance and muscular health? A cluster randomized controlled trial.

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Dalager, Tina; Bredahl, Thomas G V; Pedersen, Mogens T; Boyle, Eleanor; Andersen, Lars L; Sjøgaard, Gisela

2015-10-01

The aim was to determine the effect of one weekly hour of specific strength training within working hours, performed with the same total training volume but with different training frequencies and durations, or with different levels of supervision, on compliance, muscle health and performance, behavior and work performance. In total, 573 office workers were cluster-randomized to: 1 WS: one 60-min supervised session/week, 3 WS: three 20-min supervised sessions/week, 9 WS: nine 7-min supervised sessions/week, 3 MS: three 20-min sessions/week with minimal supervision, or REF: a reference group without training. Outcomes were diary-based compliance, total training volume, muscle performance and questionnaire-based health, behavior and work performance. Comparisons were made among the WS training groups and between 3 WS and 3 MS. If no difference, training groups were collapsed (TG) and compared with REF. Results demonstrated similar degrees of compliance, mean(range) of 39(33-44)%, and total training volume, 13.266(11.977-15.096)kg. Musculoskeletal pain in neck and shoulders were reduced with approx. 50% in TG, which was significant compared with REF. Only the training groups improved significantly their muscle strength 8(4-13)% and endurance 27(12-37)%, both being significant compared with REF. No change in workability, productivity or self-rated health was demonstrated. Secondary analysis showed exercise self-efficacy to be a significant predictor of compliance. Regardless of training schedule and supervision, similar degrees of compliance were shown together with reduced musculoskeletal pain and improved muscle performance. These findings provide evidence that a great degree of flexibility is legitimate for companies in planning future implementation of physical exercise programs at the workplace. ClinicalTrials.gov, number NCT01027390. Copyright © 2015 Elsevier Ltd. All rights reserved.

Reflections on the supervised relationship and interaction in nursing clinical training

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Inês Alves da Rocha e Silva Rocha

2013-05-01

Full Text Available This reflection fits into the area of Clinical Supervision in Nursing focusing on relationships that the student develops during clinical training. It seems that in clinical training the student not only learns but also consolidates verbal, procedural and attitudinal contents. The relationship established with peers, teachers and tutors will contribute to their professional identity. It is underlined the importance of the contribution of clinical supervision for the development of a reflective thinking in students in Portugal, as highlighted here, which influences the changes in nursing practices, as well as ensuring the quality and safety of the care provided. The characteristics that the tutor must have to improve the development of the students are also explained as well as the possible solutions for the existing limitations of clinical training contexts.

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

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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.

Literature mining of protein-residue associations with graph rules learned through distant supervision

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Ravikumar KE

2012-10-01

Full Text Available Abstract Background We propose a method for automatic extraction of protein-specific residue mentions from the biomedical literature. The method searches text for mentions of amino acids at specific sequence positions and attempts to correctly associate each mention with a protein also named in the text. The methods presented in this work will enable improved protein functional site extraction from articles, ultimately supporting protein function prediction. Our method made use of linguistic patterns for identifying the amino acid residue mentions in text. Further, we applied an automated graph-based method to learn syntactic patterns corresponding to protein-residue pairs mentioned in the text. We finally present an approach to automated construction of relevant training and test data using the distant supervision model. Results The performance of the method was assessed by extracting protein-residue relations from a new automatically generated test set of sentences containing high confidence examples found using distant supervision. It achieved a F-measure of 0.84 on automatically created silver corpus and 0.79 on a manually annotated gold data set for this task, outperforming previous methods. Conclusions The primary contributions of this work are to (1 demonstrate the effectiveness of distant supervision for automatic creation of training data for protein-residue relation extraction, substantially reducing the effort and time involved in manual annotation of a data set and (2 show that the graph-based relation extraction approach we used generalizes well to the problem of protein-residue association extraction. This work paves the way towards effective extraction of protein functional residues from the literature.

Literature mining of protein-residue associations with graph rules learned through distant supervision.

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Ravikumar, Ke; Liu, Haibin; Cohn, Judith D; Wall, Michael E; Verspoor, Karin

2012-10-05

We propose a method for automatic extraction of protein-specific residue mentions from the biomedical literature. The method searches text for mentions of amino acids at specific sequence positions and attempts to correctly associate each mention with a protein also named in the text. The methods presented in this work will enable improved protein functional site extraction from articles, ultimately supporting protein function prediction. Our method made use of linguistic patterns for identifying the amino acid residue mentions in text. Further, we applied an automated graph-based method to learn syntactic patterns corresponding to protein-residue pairs mentioned in the text. We finally present an approach to automated construction of relevant training and test data using the distant supervision model. The performance of the method was assessed by extracting protein-residue relations from a new automatically generated test set of sentences containing high confidence examples found using distant supervision. It achieved a F-measure of 0.84 on automatically created silver corpus and 0.79 on a manually annotated gold data set for this task, outperforming previous methods. The primary contributions of this work are to (1) demonstrate the effectiveness of distant supervision for automatic creation of training data for protein-residue relation extraction, substantially reducing the effort and time involved in manual annotation of a data set and (2) show that the graph-based relation extraction approach we used generalizes well to the problem of protein-residue association extraction. This work paves the way towards effective extraction of protein functional residues from the literature.

Semi-supervised Learning for Phenotyping Tasks.

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Dligach, Dmitriy; Miller, Timothy; Savova, Guergana K

2015-01-01

Supervised learning is the dominant approach to automatic electronic health records-based phenotyping, but it is expensive due to the cost of manual chart review. Semi-supervised learning takes advantage of both scarce labeled and plentiful unlabeled data. In this work, we study a family of semi-supervised learning algorithms based on Expectation Maximization (EM) in the context of several phenotyping tasks. We first experiment with the basic EM algorithm. When the modeling assumptions are violated, basic EM leads to inaccurate parameter estimation. Augmented EM attenuates this shortcoming by introducing a weighting factor that downweights the unlabeled data. Cross-validation does not always lead to the best setting of the weighting factor and other heuristic methods may be preferred. We show that accurate phenotyping models can be trained with only a few hundred labeled (and a large number of unlabeled) examples, potentially providing substantial savings in the amount of the required manual chart review.

Self-supervised Chinese ontology learning from online encyclopedias.

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Hu, Fanghuai; Shao, Zhiqing; Ruan, Tong

2014-01-01

Constructing ontology manually is a time-consuming, error-prone, and tedious task. We present SSCO, a self-supervised learning based chinese ontology, which contains about 255 thousand concepts, 5 million entities, and 40 million facts. We explore the three largest online Chinese encyclopedias for ontology learning and describe how to transfer the structured knowledge in encyclopedias, including article titles, category labels, redirection pages, taxonomy systems, and InfoBox modules, into ontological form. In order to avoid the errors in encyclopedias and enrich the learnt ontology, we also apply some machine learning based methods. First, we proof that the self-supervised machine learning method is practicable in Chinese relation extraction (at least for synonymy and hyponymy) statistically and experimentally and train some self-supervised models (SVMs and CRFs) for synonymy extraction, concept-subconcept relation extraction, and concept-instance relation extraction; the advantages of our methods are that all training examples are automatically generated from the structural information of encyclopedias and a few general heuristic rules. Finally, we evaluate SSCO in two aspects, scale and precision; manual evaluation results show that the ontology has excellent precision, and high coverage is concluded by comparing SSCO with other famous ontologies and knowledge bases; the experiment results also indicate that the self-supervised models obviously enrich SSCO.

The role of supervision in the training of behavior therapists: Case study

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Raquel Martins Sartori

2014-08-01

Full Text Available The therapeutic process therapist requires skills that go beyond the theoretical and technical knowledge, the therapeutic relationship is a prerequisite for the success of behavioral psychotherapy variable. Supervision of clinical care is a fundamental skill development of the future therapist educational resource as well as to increase the supply conditions of a more appropriate psychotherapeutic customer service. The article reports on supervisory experience in the first client of a therapist in training showed behavioral patterns of aggression. The default client produced in therapist behaviors and feelings that hindered progress and therapeutic success. Supervision thus occupied a role in analyzing and modeling the behavior therapist as a strategy to increase the chances of success of the case. As a result of the strategies adopted in supervision, there were changes in the pattern of interaction between therapist and client training with his progress in the case.

Automatic Training of Rat Cyborgs for Navigation.

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Yu, Yipeng; Wu, Zhaohui; Xu, Kedi; Gong, Yongyue; Zheng, Nenggan; Zheng, Xiaoxiang; Pan, Gang

2016-01-01

A rat cyborg system refers to a biological rat implanted with microelectrodes in its brain, via which the outer electrical stimuli can be delivered into the brain in vivo to control its behaviors. Rat cyborgs have various applications in emergency, such as search and rescue in disasters. Prior to a rat cyborg becoming controllable, a lot of effort is required to train it to adapt to the electrical stimuli. In this paper, we build a vision-based automatic training system for rat cyborgs to replace the time-consuming manual training procedure. A hierarchical framework is proposed to facilitate the colearning between rats and machines. In the framework, the behavioral states of a rat cyborg are visually sensed by a camera, a parameterized state machine is employed to model the training action transitions triggered by rat's behavioral states, and an adaptive adjustment policy is developed to adaptively adjust the stimulation intensity. The experimental results of three rat cyborgs prove the effectiveness of our system. To the best of our knowledge, this study is the first to tackle automatic training of animal cyborgs.

Effects of Supervised vs. Unsupervised Training Programs on Balance and Muscle Strength in Older Adults: A Systematic Review and Meta-Analysis.

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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

Training Level, Acculturation, Role Ambiguity, and Multicultural Discussions in Training and Supervising International Counseling Students in the United States

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Ng, Kok-Mun; Smith, Shannon D.

2012-01-01

This research partially replicated Nilsson and Anderson's "Professional Psychology: Research and Practice" (2004) study on training and supervising international students. It investigated the relationships among international counseling students' training level, acculturation, supervisory working alliance (SWA), counseling self-efficacy (COSE),…

Cultivating Self-Awareness in Counselors-in-Training through Group Supervision

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Del Moro, Ronald R.

2012-01-01

This study investigated processes, strategies, and frameworks that took place during group supervision classes, which best cultivate the self-awareness of Mental Health and Marriage and Family Counselors-in-Training (CITs). It was designed to explore factors across multiple theoretical models, which contributed to the cultivation of self-awareness…

Internet and video technology in psychotherapy supervision and training.

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Wolf, Abraham W

2011-06-01

The seven articles in this special section on the use of Internet and video technology represent the latest growth on one branch of the increasingly prolific and differentiated work in the technology of psychotherapy. In addition to the work presented here on video and the Internet applications to supervision and training, information technology is changing the field of psychotherapy through computer assisted therapies and virtual reality interventions.

Comparison Of Semi-Automatic And Automatic Slick Detection Algorithms For Jiyeh Power Station Oil Spill, Lebanon

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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.

SUPERVISED PHYSICAL TRAINING IMPROVES FINE MOTOR SKILLS OF 5-YEAR-OLD CHILDREN

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Yugang Qi

Full Text Available ABSTRACT Introduction: Fine motor skills are important for children not only in the activities of daily living, but also for learning activities. In the present study, the effects of supervised physical training were investigated in normal children. Objective: To evaluate the effects of supervised training by combining full-body exercise and the eye-hand coordination activities to improve fine motor skills in a group of five-year-old normal children. Methods: Fifty-two children were selected and randomized in exercise and control groups. The exercise group participated in three 30-minute training sessions per week for 24 weeks. Results: The fine motor skills and hand grip strength of the exercise group were significantly increased, while there was no significant change in the control group during the experimental period. Conclusion: The results indicate that the current exercise training program is effective and can be applied to 5-year-old normal children to improve their fine motor skills. In addition, this program has simple physical activities that are appropriate to the physical and mental level of child development. The 30-minute training session would be easily implemented in the kindergarten program. Level of Evidence I; High quality randomized trial with statistically significant difference or no statistically significant difference but narrow confidence intervals.

Automatic classification of time-variable X-ray sources

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Lo, Kitty K.; Farrell, Sean; Murphy, Tara; Gaensler, B. M. [Sydney Institute for Astronomy, School of Physics, The University of Sydney, Sydney, NSW 2006 (Australia)

2014-05-01

To maximize the discovery potential of future synoptic surveys, especially in the field of transient science, it will be necessary to use automatic classification to identify some of the astronomical sources. The data mining technique of supervised classification is suitable for this problem. Here, we present a supervised learning method to automatically classify variable X-ray sources in the Second XMM-Newton Serendipitous Source Catalog (2XMMi-DR2). Random Forest is our classifier of choice since it is one of the most accurate learning algorithms available. Our training set consists of 873 variable sources and their features are derived from time series, spectra, and other multi-wavelength contextual information. The 10 fold cross validation accuracy of the training data is ∼97% on a 7 class data set. We applied the trained classification model to 411 unknown variable 2XMM sources to produce a probabilistically classified catalog. Using the classification margin and the Random Forest derived outlier measure, we identified 12 anomalous sources, of which 2XMM J180658.7–500250 appears to be the most unusual source in the sample. Its X-ray spectra is suggestive of a ultraluminous X-ray source but its variability makes it highly unusual. Machine-learned classification and anomaly detection will facilitate scientific discoveries in the era of all-sky surveys.

Automatic classification of time-variable X-ray sources

International Nuclear Information System (INIS)

Lo, Kitty K.; Farrell, Sean; Murphy, Tara; Gaensler, B. M.

2014-01-01

To maximize the discovery potential of future synoptic surveys, especially in the field of transient science, it will be necessary to use automatic classification to identify some of the astronomical sources. The data mining technique of supervised classification is suitable for this problem. Here, we present a supervised learning method to automatically classify variable X-ray sources in the Second XMM-Newton Serendipitous Source Catalog (2XMMi-DR2). Random Forest is our classifier of choice since it is one of the most accurate learning algorithms available. Our training set consists of 873 variable sources and their features are derived from time series, spectra, and other multi-wavelength contextual information. The 10 fold cross validation accuracy of the training data is ∼97% on a 7 class data set. We applied the trained classification model to 411 unknown variable 2XMM sources to produce a probabilistically classified catalog. Using the classification margin and the Random Forest derived outlier measure, we identified 12 anomalous sources, of which 2XMM J180658.7–500250 appears to be the most unusual source in the sample. Its X-ray spectra is suggestive of a ultraluminous X-ray source but its variability makes it highly unusual. Machine-learned classification and anomaly detection will facilitate scientific discoveries in the era of all-sky surveys.

International Doctoral Students in Counselor Education: Coping Strategies in Supervision Training

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Woo, Hongryun; Jang, Yoo Jin; Henfield, Malik S.

2015-01-01

This study explores 8 international doctoral students' perceptions of coping strategies used in supervision training in counselor education programs. Using human agency as a conceptual framework, the authors found 3 categories: (a) personal and professional self-directed strategies as personal agency, (b) support and care from mentors as proxy…

Supervision of the thermal performance of heat exchanger trains

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Negrao, C.O.R.; Tonin, P.C.; Madi, M. [Federal University of Technology Parana UTFPR, Post-graduate Program in Mechanical and Materials Engineering PPGEM, Thermal Science Laboratory LACIT, Av. Sete de Setembro, 3165, CEP 80230-901, Curitiba, Parana (Brazil)

2007-02-15

In oil refining, heat exchanger networks are employed to recover heat and therefore save energy of the plant. However, many heat exchangers in crude oil pre-heat trains are under high risk of fouling. Under fouling conditions, the thermal performance of heat exchangers is continuously reduced and its supervision becomes an important task. The large number of heat exchangers in pre-heat trains and the change of operation conditions and feedstock charges make the daily supervision a difficult task. This work applies an approach to follow the performance of heat exchangers [M.A.S. Jeronimo, L.F. Melo, A.S. Braga, P.J.B.F. Ferreira, C. Martins, Monitoring the thermal efficiency of fouled heat exchangers - A simplified method, Experimental Thermal and Fluid Science 14 (1997) 455-463] and extends it to monitor the whole train. The approach is based on the comparison of measured and predicted heat exchanger effectiveness. The measured value is computed from the four inlet and outlet temperatures of a heat exchanger unit. The predicted clean and dirty values of effectiveness are calculated from classical literature relations as a function of NTU and of heat capacity ratio (R). NTU and R are continuously adjusted according to mass flow rate changes. An index of fouling is defined for the whole network and the results show the performance degradation of the network with time. The work also suggests that Jeronimo's index of fouling can be used to estimate the fouling thermal resistance of heat exchangers. (author)

Medical-pedagogical supervisions for sportsmen short-trek in a period of еducational and trainings employments

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Zaycev V.P.

2010-04-01

Full Text Available The results of inspection of sportsmen are generalized. 8 highly skilled sportsmen were inspected. A method is presented medical-pedagogical supervisions. Age of sportsmen: 16-18 years - 2 sportsmen, 19-20 years - 3 sportsmen, 22-25 years - 3 sportsmen. The results of the use of clinical inspection and visual supervisions are rotined. Frequency of heart-throbs and arteriotony is certain. Information of pulsator is resulted, functional tests and tests, dynamometer. For renewal of organism after educational and training employment employment it is recommended to accept water procedures, vitaminized food, autogenic training, active and passive rest.

Supervised Versus Home Exercise Training Programs on Functional Balance in Older Subjects.

Science.gov (United States)

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 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.

Wibangbe: the making of a documentary about the training and supervision of traditional birth attendants in Zaire.

Science.gov (United States)

Vansintejan, G A; Glaser, W A

1988-01-01

During the 1980's in Karawa, Northwestern Zaire, a motion picture was produced which showed the interaction of the modern and traditional systems. The maternity center of the Karawa hospital was central to this effort. Traditional birth attendants (TBAs) became leading participants. Locally trained midwives were key trainers. The training and supervision programs had been ongoing for 2 years when Karawa was chosen as the movie site in 1986. The script was written by a midwife who had trained trainers of TBAs and TBAs themselves. All the steps in the selection, training, supervision, and supplying of TBAs in Karawa and its neighboring villages are included in the script. A Zairian team shot the script. The 5-member crew were employees of the Office Zairois de la Radio-Television (OZRT), the country's official television, radio, and film service. "Wibange" has separate sound tracks in French and English. Costs of the movie were met by contributions from both the US Agency for International Development and from Zaire. "Wibange--Traditional Birth Attendants: Their Training and Supervision" was developed in New York City. There are 2 final productions, a French and an English version. Running time is 23 minutes.

Semi-supervised adaptation in ssvep-based brain-computer interface using tri-training

DEFF Research Database (Denmark)

Bender, Thomas; Kjaer, Troels W.; Thomsen, Carsten E.

2013-01-01

This paper presents a novel and computationally simple tri-training based semi-supervised steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI). It is implemented with autocorrelation-based features and a Naïve-Bayes classifier (NBC). The system uses nine characters...

Competency in integrative psychotherapy: perspectives on training and supervision.

Science.gov (United States)

Boswell, James F; Nelson, Dana L; Nordberg, Samuel S; McAleavey, Andrew A; Castonguay, Louis G

2010-03-01

Increasingly, many psychotherapists identify with an integrative approach to psychotherapy. In recent years, more attention has been directed toward the operationalization and evaluation of competence in professional psychology and health care service delivery. Aspects of integrative psychotherapy competency may differ from competency in other psychotherapy orientations, although convergence is more often the case. Despite the potential differences, there exist very few formal training programs or guidelines to systematically guide clinicians in developing a competent integrative practice. This paper attempts to distill the essential elements of competent integrative psychotherapy practice and focuses on how these might be developed in training and supervision. We address most of these complex issues from a specific integrative perspective: principle-based assimilative integration. PsycINFO Database Record (c) 2010 APA, all rights reserved

Task sharing in rural Haiti: Qualitative assessment of a brief, structured training with and without apprenticeship supervision for community health workers

Science.gov (United States)

McLean, Kristen E; Kaiser, Bonnie N; Hagaman, Ashley K; Wagenaar, Bradley H; Therosme, Tatiana P; Kohrt, Brandon A

2015-01-01

Despite growing support for supervision after task sharing trainings in humanitarian settings, there is limited research on the experience of trainees in apprenticeship and other supervision approaches. Studying apprenticeships from trainees’ perspectives is crucial to refine supervision and enhance motivation for service implementation. The authors implemented a multi-stage, transcultural adaptation for a pilot task sharing training in Haiti entailing three phases: 1) literature review and qualitative research to adapt a mental health and psychosocial support training; 2) implementation and qualitative process evaluation of a brief, structured group training; and 3) implementation and qualitative evaluation of an apprenticeship training, including a two year follow-up of trainees. Structured group training revealed limited knowledge acquisition, low motivation, time and resource constraints on mastery, and limited incorporation of skills into practice. Adding an apprenticeship component was associated with subjective clinical competency, increased confidence regarding utilising skills, and career advancement. Qualitative findings support the added value of apprenticeship according to trainees. PMID:26190953

Artificial neural network classification using a minimal training set - Comparison to conventional supervised classification

Science.gov (United States)

Hepner, George F.; Logan, Thomas; Ritter, Niles; Bryant, Nevin

1990-01-01

Recent research has shown an artificial neural network (ANN) to be capable of pattern recognition and the classification of image data. This paper examines the potential for the application of neural network computing to satellite image processing. A second objective is to provide a preliminary comparison and ANN classification. An artificial neural network can be trained to do land-cover classification of satellite imagery using selected sites representative of each class in a manner similar to conventional supervised classification. One of the major problems associated with recognition and classifications of pattern from remotely sensed data is the time and cost of developing a set of training sites. This reseach compares the use of an ANN back propagation classification procedure with a conventional supervised maximum likelihood classification procedure using a minimal training set. When using a minimal training set, the neural network is able to provide a land-cover classification superior to the classification derived from the conventional classification procedure. This research is the foundation for developing application parameters for further prototyping of software and hardware implementations for artificial neural networks in satellite image and geographic information processing.

Effect of training supervision on effectiveness of strength training for reducing neck/shoulder pain and headache in office workers

DEFF Research Database (Denmark)

Gram, Bibi; Andersen, Christoffer; Zebis, Mette Kreutzfeldt

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 trai...

Skærpet bevidsthed om supervision

DEFF Research Database (Denmark)

Pedersen, Inge Nygaard

2002-01-01

This article presents a historical survey of the initiatives which have taken place in european music therapy towards developing a deeper consciousness about supervision. Supervision as a disciplin in music therapy training, as a maintenance of music therapy profession and as a postgraduate...... training for examined music therapists. Definitions are presented and methods developed by working groups in european music therapy supervision are presented....

AUTOMATIC TRAINING SITE SELECTION FOR AGRICULTURAL CROP CLASSIFICATION: A CASE STUDY ON KARACABEY PLAIN, TURKEY

Directory of Open Access Journals (Sweden)

A. Ozdarici Ok

2012-09-01

Full Text Available This study implements a traditional supervised classification method to an optical image composed of agricultural crops by means of a unique way, selecting the training samples automatically. Panchromatic (1m and multispectral (4m Kompsat-2 images (July 2008 of Karacabey Plain (~100km2, located in Marmara region, are used to evaluate the proposed approach. Due to the characteristic of rich, loamy soils combined with reasonable weather conditions, the Karacabey Plain is one of the most valuable agricultural regions of Turkey. Analyses start with applying an image fusion algorithm on the panchromatic and multispectral image. As a result of this process, 1m spatial resolution colour image is produced. In the next step, the four-band fused (1m image and multispectral (4m image are orthorectified. Next, the fused image (1m is segmented using a popular segmentation method, Mean- Shift. The Mean-Shift is originally a method based on kernel density estimation and it shifts each pixel to the mode of clusters. In the segmentation procedure, three parameters must be defined: (i spatial domain (hs, (ii range domain (hr, and (iii minimum region (MR. In this study, in total, 176 parameter combinations (hs, hr, and MR are tested on a small part of the area (~10km2 to find an optimum segmentation result, and a final parameter combination (hs=18, hr=20, and MR=1000 is determined after evaluating multiple goodness measures. The final segmentation output is then utilized to the classification framework. The classification operation is applied on the four-band multispectral image (4m to minimize the mixed pixel effect. Before the image classification, each segment is overlaid with the bands of the image fused, and several descriptive statistics of each segment are computed for each band. To select the potential homogeneous regions that are eligible for the selection of training samples, a user-defined threshold is applied. After finding those potential regions, the

Training And Supervision Did Not Meaningfully Improve Quality Of Care For Pregnant Women Or Sick Children In Sub-Saharan Africa.

Science.gov (United States)

Leslie, Hannah H; Gage, Anna; Nsona, Humphreys; Hirschhorn, Lisa R; Kruk, Margaret E

2016-09-01

In-service training courses and supportive supervision of health workers are among the most common interventions to improve the quality of health care in low- and middle-income countries. Despite extensive investment from donors, evaluations of the long-term effect of these two interventions are scarce. We used nationally representative surveys of health systems in seven countries in sub-Saharan Africa to examine the association of in-service training and supervision with provider quality in antenatal and sick child care. The results of our analysis showed that observed quality of care was poor, with fewer than half of evidence-based actions completed by health workers, on average. In-service training and supervision were associated with quality of sick child care; they were associated with quality of antenatal care only when provided jointly. All associations were modest-at most, improvements related to interventions were equivalent to 2 additional provider actions out of the 18-40 actions expected per visit. In-service training and supportive supervision as delivered were not sufficient to meaningfully improve the quality of care in these countries. Greater attention to the quality of health professional education and national health system performance will be required to provide the standard of health care that patients deserve. Project HOPE—The People-to-People Health Foundation, Inc.

Providing effective supervision in clinical neuropsychology.

Science.gov (United States)

Stucky, Kirk J; Bush, Shane; Donders, Jacobus

2010-01-01

A specialty like clinical neuropsychology is shaped by its selection of trainees, educational standards, expected competencies, and the structure of its training programs. The development of individual competency in this specialty is dependent to a considerable degree on the provision of competent supervision to its trainees. In clinical neuropsychology, as in other areas of professional health-service psychology, supervision is the most frequently used method for teaching a variety of skills, including assessment, report writing, differential diagnosis, and treatment. Although much has been written about the provision of quality supervision in clinical and counseling psychology, very little published guidance is available regarding the teaching and provision of supervision in clinical neuropsychology. The primary focus of this article is to provide a framework and guidance for the development of suggested competency standards for training of neuropsychological supervisors, particularly at the residency level. In this paper we outline important components of supervision for neuropsychology trainees and suggest ways in which clinicians can prepare for supervisory roles. Similar to Falender and Shafranske (2004), we propose a competency-based approach to supervision that advocates for a science-informed, formalized, and objective process that clearly delineates the competencies required for good supervisory practice. As much as possible, supervisory competencies are related to foundational and functional competencies in professional psychology, as well as recent legislative initiatives mandating training in supervision. It is our hope that this article will foster further discussion regarding this complex topic, and eventually enhance training in clinical neuropsychology.

Consistently Trained Artificial Neural Network for Automatic Ship Berthing Control

Directory of Open Access Journals (Sweden)

Y.A. Ahmed

2015-09-01

Full Text Available In this paper, consistently trained Artificial Neural Network controller for automatic ship berthing is discussed. Minimum time course changing manoeuvre is utilised to ensure such consistency and a new concept named ‘virtual window’ is introduced. Such consistent teaching data are then used to train two separate multi-layered feed forward neural networks for command rudder and propeller revolution output. After proper training, several known and unknown conditions are tested to judge the effectiveness of the proposed controller using Monte Carlo simulations. After getting acceptable percentages of success, the trained networks are implemented for the free running experiment system to judge the network’s real time response for Esso Osaka 3-m model ship. The network’s behaviour during such experiments is also investigated for possible effect of initial conditions as well as wind disturbances. Moreover, since the final goal point of the proposed controller is set at some distance from the actual pier to ensure safety, therefore a study on automatic tug assistance is also discussed for the final alignment of the ship with actual pier.

High-Throughput Automatic Training System for Odor-Based Learned Behaviors in Head-Fixed Mice

Directory of Open Access Journals (Sweden)

Zhe Han

2018-02-01

Full Text Available Understanding neuronal mechanisms of learned behaviors requires efficient behavioral assays. We designed a high-throughput automatic training system (HATS for olfactory behaviors in head-fixed mice. The hardware and software were constructed to enable automatic training with minimal human intervention. The integrated system was composed of customized 3D-printing supporting components, an odor-delivery unit with fast response, Arduino based hardware-controlling and data-acquisition unit. Furthermore, the customized software was designed to enable automatic training in all training phases, including lick-teaching, shaping and learning. Using HATS, we trained mice to perform delayed non-match to sample (DNMS, delayed paired association (DPA, Go/No-go (GNG, and GNG reversal tasks. These tasks probed cognitive functions including sensory discrimination, working memory, decision making and cognitive flexibility. Mice reached stable levels of performance within several days in the tasks. HATS enabled an experimenter to train eight mice simultaneously, therefore greatly enhanced the experimental efficiency. Combined with causal perturbation and activity recording techniques, HATS can greatly facilitate our understanding of the neural-circuitry mechanisms underlying learned behaviors.

Clinical Supervision in Denmark

DEFF Research Database (Denmark)

Jacobsen, Claus Haugaard

2011-01-01

Core Questionnaire (DPCCQ) has only few questions on supervision. To rectify this limitation, a recent Danish version of the DPCCQ included two new sections on supervision, one focusing on supervisees and another on supervisors and their supervisory training. This paper presents our initial findings...

Mr. Traore introduces team supervision. Case scenarios for training and group discussion.

Science.gov (United States)

1993-01-01

This supplement to "The Family Planning Manager" presents a case example and five case discussion questions to illustrate the concept of team supervision. In contrast to traditional supervision, where an emphasis is placed on inspection and the uncovering of deficiencies, team supervision uses a facilitative, advocacy-oriented approach. Problem-solving and decision-making responsibilities are assumed by the clinic staff, who identify and analyze problems in group meetings. Thus, the focus shifts from assessing individual performance to evaluating how well they meet clinic objectives as a team. In the team meetings, the visiting supervisor asks the team as a whole to analyze clinic problems and ensures that all staff members are aware of the significance of their contributions. The supervisor also clarifies the division of labor required for implementing solutions and performance standards. Staff are asked if they have concerns they would like communicated to the next organizational level. The supervisory report of the visit can serve as a guide for implementing the recommendations. This approach may require that supervisors and clinic managers receive training in problem solving, motivating staff, team building, and providing constructive feedback.

The efficacy of early initiated, supervised, progressive resistance training compared to unsupervised, home-based exercise after unicompartmental knee arthroplasty

DEFF Research Database (Denmark)

Jørgensen, Peter Bo; Bogh, Søren B; Kierkegaard, Signe

2017-01-01

OBJECTIVE: To examine if supervised progressive resistance training was superior to home-based exercise in rehabilitation after unicompartmental knee arthroplasty. DESIGN: Single blinded, randomized clinical trial. SETTING: Surgery, progressive resistance training and testing was carried out...

Group Supervision in Psychotherapy. Main Findings from a Swedish Research Project on Psychotherapy Supervision in a Group Format

Science.gov (United States)

Ogren, Marie-Louise; Sundin, Eva C.

2009-01-01

Psychotherapy supervision is considered crucial for psychotherapists in training. During the last decades, group supervision has been a frequently used format in many countries. Until recently, very few studies had evaluated the small-group format for training of beginner psychotherapists and psychotherapy supervisors. This article aims to…

Gymnasium-based unsupervised exercise maintains benefits in oxygen uptake kinetics obtained following supervised training in type 2 diabetes.

Science.gov (United States)

Macananey, Oscar; O'Shea, Donal; Warmington, Stuart A; Green, Simon; Egaña, Mikel

2012-08-01

Supervised exercise (SE) in patients with type 2 diabetes improves oxygen uptake kinetics at the onset of exercise. Maintenance of these improvements, however, has not been examined when supervision is removed. We explored if potential improvements in oxygen uptake kinetics following a 12-week SE that combined aerobic and resistance training were maintained after a subsequent 12-week unsupervised exercise (UE). The involvement of cardiac output (CO) in these improvements was also tested. Nineteen volunteers with type 2 diabetes were recruited. Oxygen uptake kinetics and CO (inert gas rebreathing) responses to constant-load cycling at 50% ventilatory threshold (V(T)), 80% V(T), and mid-point between V(T) and peak workload (50% Δ) were examined at baseline (on 2 occasions) and following each 12-week training period. Participants decided to exercise at a local gymnasium during the UE. Thirteen subjects completed all the interventions. The time constant of phase 2 of oxygen uptake was significantly faster (p exercise maintained benefits in oxygen uptake kinetics obtained during a supervised exercise in subjects with diabetes, and these benefits were associated with a faster dynamic response of heart rate after training.

A SURVEY OF SEMI-SUPERVISED LEARNING

OpenAIRE

Amrita Sadarangani *, Dr. Anjali Jivani

2016-01-01

Semi Supervised Learning involves using both labeled and unlabeled data to train a classifier or for clustering. Semi supervised learning finds usage in many applications, since labeled data can be hard to find in many cases. Currently, a lot of research is being conducted in this area. This paper discusses the different algorithms of semi supervised learning and then their advantages and limitations are compared. The differences between supervised classification and semi-supervised classific...

A training approach to improve stepping automaticity while dual-tasking in Parkinson's disease

Science.gov (United States)

Chomiak, Taylor; Watts, Alexander; Meyer, Nicole; Pereira, Fernando V.; Hu, Bin

2017-01-01

Abstract Background: Deficits in motor movement automaticity in Parkinson's disease (PD), especially during multitasking, are early and consistent hallmarks of cognitive function decline, which increases fall risk and reduces quality of life. This study aimed to test the feasibility and potential efficacy of a wearable sensor-enabled technological platform designed for an in-home music-contingent stepping-in-place (SIP) training program to improve step automaticity during dual-tasking (DT). Methods: This was a 4-week prospective intervention pilot study. The intervention uses a sensor system and algorithm that runs off the iPod Touch which calculates step height (SH) in real-time. These measurements were then used to trigger auditory (treatment group, music; control group, radio podcast) playback in real-time through wireless headphones upon maintenance of repeated large amplitude stepping. With small steps or shuffling, auditory playback stops, thus allowing participants to use anticipatory motor control to regain positive feedback. Eleven participants were recruited from an ongoing trial (Trial Number: ISRCTN06023392). Fear of falling (FES-I), general cognitive functioning (MoCA), self-reported freezing of gait (FOG-Q), and DT step automaticity were evaluated. Results: While we found no significant effect of training on FES-I, MoCA, or FOG-Q, we did observe a significant group (music vs podcast) by training interaction in DT step automaticity (Ptraining to increase motor automaticity for people living with PD. The training approach described here can be implemented at home to meet the growing demand for self-management of symptoms by patients. PMID:28151878

Educational Supervision Appropriate for Psychiatry Trainee's Needs

Science.gov (United States)

Rele, Kiran; Tarrant, C. Jane

2010-01-01

Objective: The authors studied the regularity and content of supervision sessions in one of the U.K. postgraduate psychiatric training schemes (Mid-Trent). Methods: A questionnaire sent to psychiatry trainees assessed the timing and duration of supervision, content and protection of supervision time, and overall quality of supervision. The authors…

Optimistic semi-supervised least squares classification

DEFF Research Database (Denmark)

Krijthe, Jesse H.; Loog, Marco

2017-01-01

The goal of semi-supervised learning is to improve supervised classifiers by using additional unlabeled training examples. In this work we study a simple self-learning approach to semi-supervised learning applied to the least squares classifier. We show that a soft-label and a hard-label variant ...

Supervised Exercise Training Counterbalances the Adverse Effects of Insulin Therapy in Overweight/Obese Subjects With Type 2 Diabetes

OpenAIRE

Balducci, Stefano; Zanuso, Silvano; Cardelli, Patrizia; Salerno, Gerardo; Fallucca, Sara; Nicolucci, Antonio; Pugliese, Giuseppe

2011-01-01

OBJECTIVE To examine the effect of supervised exercise on traditional and nontraditional cardiovascular risk factors in sedentary, overweight/obese insulin-treated subjects with type 2 diabetes from the Italian Diabetes Exercise Study (IDES). RESEARCH DESIGN AND METHODS The study randomized 73 insulin-treated patients to twice weekly supervised aerobic and resistance training plus structured exercise counseling (EXE) or to counseling alone (CON) for 12 months. Clinical and laboratory paramete...

Academic Writing in Reflexive Professional Writing: Citations of Scientific Literature in Supervised Pre-Service Training Reports

Directory of Open Access Journals (Sweden)

Lívia Chaves de Melo

2013-06-01

Full Text Available In this paper we investigate citation practices of scientific literature in reflexive writing from the genre of supervised pre-service training report produced by pre-service teachers enrolled in the mandatory pre-service training subject of English Language Teaching, at an undergraduate language teaching course. The aim of this research is to analyze how these pre-services teacher represent themselves based on citation practices of scientific literature, and characterize some of the functions deployed by the citations in the reflexive writing emerging in the academic sphere. We use the dialogic approach to language from Bakhtinian studies as a theoretical base, as well as theoretical and methodological contributions regarding types of sequences and of discourse proposed by Adam and Bronckart. The results of this research show that the practice of citation of scientific literature is an invocation of authority as a form of erudition, amplification and ornamentation of the discourse produced. This practice can also guide pedagogical action developed by pre-service teachers in their supervised training.

Method and device for automatic supervision of plants

International Nuclear Information System (INIS)

Pekrul, P.J.; Thiele, A.W.

1976-01-01

Method and device for the supervision of plants with respect to anomalous events and especially for monitoring dynamic signals from components of plants which are in operation, e.g. nuclear power plants, and not readily accessible for an inspection. (orig./RW) [de

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

Science.gov (United States)

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.

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

Science.gov (United States)

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.

Multicultural Supervision: What Difference Does Difference Make?

Science.gov (United States)

Eklund, Katie; Aros-O'Malley, Megan; Murrieta, Imelda

2014-01-01

Multicultural sensitivity and competency represent critical components to contemporary practice and supervision in school psychology. Internship and supervision experiences are a capstone experience for many new school psychologists; however, few receive formal training and supervision in multicultural competencies. As an increased number of…

Supervised pelvic floor muscle training versus attention-control massage treatment in patients with faecal incontinence

DEFF Research Database (Denmark)

Ussing, Anja; Dahn, Inge; Due, Ulla

2017-01-01

supplements is recommended as first-line treatment for faecal incontinence. Despite this, the effect of pelvic floor muscle training for faecal incontinence is unclear. No previous trials have investigated the efficacy of supervised pelvic floor muscle training in combination with conservative treatment...... treatment and conservative treatment. The primary outcome is participants' rating of symptom changes after 16 weeks of treatment using the Patient Global Impression of Improvement Scale. Secondary outcomes are the Vaizey Incontinence Score, the Fecal Incontinence Severity Index, the Fecal Incontinence...

Extended apprenticeship learning in doctoral training and supervision - moving beyond 'cookbook recipes'

DEFF Research Database (Denmark)

Tanggaard, Lene; Wegener, Charlotte

An apprenticeship perspective on learning in academia sheds light on the potential for mutual learning and production, and also reveals the diverse range of learning resources beyond the formal novice-–expert relationship. Although apprenticeship is a well-known concept in educational research......, in this case apprenticeship offers an innovative perspective on future practice and research in academia allowing more students access to high high-quality research training and giving supervisors a chance to combine their own research with their supervision obligations....

Robust head pose estimation via supervised manifold learning.

Science.gov (United States)

Wang, Chao; Song, Xubo

2014-05-01

Head poses can be automatically estimated using manifold learning algorithms, with the assumption that with the pose being the only variable, the face images should lie in a smooth and low-dimensional manifold. However, this estimation approach is challenging due to other appearance variations related to identity, head location in image, background clutter, facial expression, and illumination. To address the problem, we propose to incorporate supervised information (pose angles of training samples) into the process of manifold learning. The process has three stages: neighborhood construction, graph weight computation and projection learning. For the first two stages, we redefine inter-point distance for neighborhood construction as well as graph weight by constraining them with the pose angle information. For Stage 3, we present a supervised neighborhood-based linear feature transformation algorithm to keep the data points with similar pose angles close together but the data points with dissimilar pose angles far apart. The experimental results show that our method has higher estimation accuracy than the other state-of-art algorithms and is robust to identity and illumination variations. Copyright © 2014 Elsevier Ltd. All rights reserved.

QUEST: Eliminating Online Supervised Learning for Efficient Classification Algorithms.

Science.gov (United States)

Zwartjes, Ardjan; Havinga, Paul J M; Smit, Gerard J M; Hurink, Johann L

2016-10-01

In this work, we introduce QUEST (QUantile Estimation after Supervised Training), an adaptive classification algorithm for Wireless Sensor Networks (WSNs) that eliminates the necessity for online supervised learning. Online processing is important for many sensor network applications. Transmitting raw sensor data puts high demands on the battery, reducing network life time. By merely transmitting partial results or classifications based on the sampled data, the amount of traffic on the network can be significantly reduced. Such classifications can be made by learning based algorithms using sampled data. An important issue, however, is the training phase of these learning based algorithms. Training a deployed sensor network requires a lot of communication and an impractical amount of human involvement. QUEST is a hybrid algorithm that combines supervised learning in a controlled environment with unsupervised learning on the location of deployment. Using the SITEX02 dataset, we demonstrate that the presented solution works with a performance penalty of less than 10% in 90% of the tests. Under some circumstances, it even outperforms a network of classifiers completely trained with supervised learning. As a result, the need for on-site supervised learning and communication for training is completely eliminated by our solution.

Automatic creation of simulation configuration

International Nuclear Information System (INIS)

Oudot, G.; Poizat, F.

1993-01-01

SIPA, which stands for 'Simulator for Post Accident', includes: 1) a sophisticated software oriented workshop SWORD (which stands for 'Software Workshop Oriented towards Research and Development') designed in the ADA language including integrated CAD system and software tools for automatic generation of simulation software and man-machine interface in order to operate run-time simulation; 2) a 'simulator structure' based on hardware equipment and software for supervision and communications; 3) simulation configuration generated by SWORD, operated under the control of the 'simulator structure' and run on a target computer. SWORD has already been used to generate two simulation configurations (French 900 MW and 1300 MW nuclear power plants), which are now fully operational on the SIPA training simulator. (Z.S.) 1 ref

The Cryogenic Supervision System in NSRRC

CERN Document Server

Li, Hsing-Chieh; Chiou, Wen-Song; Hsiao, Feng-Zone; Tsai, Zong-Da

2005-01-01

The helium cryogenic system in NSRRC is a fully automatic PLC system using the Siemens SIMATIC 300 controller. Modularization in both hardware and software makes it easy in the program reading, the system modification and the problem debug. Based on the Laview program we had developed a supervision system taking advantage of the Internet technology to get system's real-time information in any place. The functions of this supervision system include the real-time data accessing with more than 300 digital/analog signals, the data restore, the history trend display, and the human machine interface. The data is accessed via a Profibus line connecting the PLC system and the supervision system with a maximum baud rate 1.5 Mbit/s. Due to this supervision system, it is easy to master the status of the cryogenic system within a short time and diagnose the problem.

Cliché, Gossip, and Anecdote as Supervision Training

Science.gov (United States)

Grealy, Liam

2016-01-01

This article expands on a co-authored project with Timothy Laurie on the practices and ethics of higher degree research (HDR) supervision (or advising): "What does good HDR supervision look like?" in contemporary universities. It connects that project with scholarship on the relevance of "common sense" to questions of…

SemiBoost: boosting for semi-supervised learning.

Science.gov (United States)

Mallapragada, Pavan Kumar; Jin, Rong; Jain, Anil K; Liu, Yi

2009-11-01

Semi-supervised learning has attracted a significant amount of attention in pattern recognition and machine learning. Most previous studies have focused on designing special algorithms to effectively exploit the unlabeled data in conjunction with labeled data. Our goal is to improve the classification accuracy of any given supervised learning algorithm by using the available unlabeled examples. We call this as the Semi-supervised improvement problem, to distinguish the proposed approach from the existing approaches. We design a metasemi-supervised learning algorithm that wraps around the underlying supervised algorithm and improves its performance using unlabeled data. This problem is particularly important when we need to train a supervised learning algorithm with a limited number of labeled examples and a multitude of unlabeled examples. We present a boosting framework for semi-supervised learning, termed as SemiBoost. The key advantages of the proposed semi-supervised learning approach are: 1) performance improvement of any supervised learning algorithm with a multitude of unlabeled data, 2) efficient computation by the iterative boosting algorithm, and 3) exploiting both manifold and cluster assumption in training classification models. An empirical study on 16 different data sets and text categorization demonstrates that the proposed framework improves the performance of several commonly used supervised learning algorithms, given a large number of unlabeled examples. We also show that the performance of the proposed algorithm, SemiBoost, is comparable to the state-of-the-art semi-supervised learning algorithms.

Closing the loop: from paper to protein annotation using supervised Gene Ontology classification.

Science.gov (United States)

Gobeill, Julien; Pasche, Emilie; Vishnyakova, Dina; Ruch, Patrick

2014-01-01

Gene function curation of the literature with Gene Ontology (GO) concepts is one particularly time-consuming task in genomics, and the help from bioinformatics is highly requested to keep up with the flow of publications. In 2004, the first BioCreative challenge already designed a task of automatic GO concepts assignment from a full text. At this time, results were judged far from reaching the performances required by real curation workflows. In particular, supervised approaches produced the most disappointing results because of lack of training data. Ten years later, the available curation data have massively grown. In 2013, the BioCreative IV GO task revisited the automatic GO assignment task. For this issue, we investigated the power of our supervised classifier, GOCat. GOCat computes similarities between an input text and already curated instances contained in a knowledge base to infer GO concepts. The subtask A consisted in selecting GO evidence sentences for a relevant gene in a full text. For this, we designed a state-of-the-art supervised statistical approach, using a naïve Bayes classifier and the official training set, and obtained fair results. The subtask B consisted in predicting GO concepts from the previous output. For this, we applied GOCat and reached leading results, up to 65% for hierarchical recall in the top 20 outputted concepts. Contrary to previous competitions, machine learning has this time outperformed standard dictionary-based approaches. Thanks to BioCreative IV, we were able to design a complete workflow for curation: given a gene name and a full text, this system is able to select evidence sentences for curation and to deliver highly relevant GO concepts. Contrary to previous competitions, machine learning this time outperformed dictionary-based systems. Observed performances are sufficient for being used in a real semiautomatic curation workflow. GOCat is available at http://eagl.unige.ch/GOCat/. http://eagl.unige.ch/GOCat4FT/. �

46 CFR 131.420 - Manning and supervision.

Science.gov (United States)

2010-10-01

... 46 Shipping 4 2010-10-01 2010-10-01 false Manning and supervision. 131.420 Section 131.420 Shipping COAST GUARD, DEPARTMENT OF HOMELAND SECURITY (CONTINUED) OFFSHORE SUPPLY VESSELS OPERATIONS Sufficiency and Supervision of Crew of Survival Craft § 131.420 Manning and supervision. (a) There must be enough trained persons aboard each survival craf...

Short- and long-term effectiveness of a supervised training program in spirometry use for primary care professionals.

Science.gov (United States)

Represas-Represas, Cristina; Botana-Rial, Maribel; Leiro-Fernández, Virginia; González-Silva, Ana Isabel; García-Martínez, Ana; Fernández-Villar, Alberto

2013-09-01

Despite the importance of spirometry, its use and quality are limited in the Primary Care setting. There are few accredited training programs that have demonstrated improvement in the quality of spirometric studies. In this paper, we analyze the short- and long-term effectiveness of a supervised training program for performing and interpreting spirometries. Ours is an intervention study with before and after measurements. The target population included teams of physicians and nursing staff at 26 health-care centers in the area of Vigo (Galicia, Spain). The structured training program involved 2 theoretical and practical training sessions (that were 2months apart), an intermediate period of 30 supervised spirometries performed in the respective centers and weekly e-mail exercises. Effectiveness was evaluated using exercises at the beginning (test 1) and the end (test 2) of the 1st day, 2nd day (test 3) and one year later (test 4), as well as the analysis of spirometries done in month1, month2 and one year later. Participants also completed a survey about their satisfaction. 74 participants initiated the program; 72 completed the program, but only 45 participated in the one-year evaluation. Mean test scores were: 4.1±1.9 on test 1; 7.5±1.6 on test 2; 8.9±1.3 on test 3, and 8.8±1.4 on test 4. During month1, the percentage of correctly done/interpreted tests was 71%, in month two it was 91% and after one year it was 83% (Ptraining program based on theoretical and practical workshops and a supervised follow-up of spirometries significantly improved the ability of Primary Care professionals to carry out and interpret spirometric testing, although the quality of the tests diminished over time. Copyright © 2012 SEPAR. Published by Elsevier Espana. All rights reserved.

QUEST: Eliminating Online Supervised Learning for Efficient Classification Algorithms

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Ardjan Zwartjes

2016-10-01

Full Text Available In this work, we introduce QUEST (QUantile Estimation after Supervised Training, an adaptive classification algorithm for Wireless Sensor Networks (WSNs that eliminates the necessity for online supervised learning. Online processing is important for many sensor network applications. Transmitting raw sensor data puts high demands on the battery, reducing network life time. By merely transmitting partial results or classifications based on the sampled data, the amount of traffic on the network can be significantly reduced. Such classifications can be made by learning based algorithms using sampled data. An important issue, however, is the training phase of these learning based algorithms. Training a deployed sensor network requires a lot of communication and an impractical amount of human involvement. QUEST is a hybrid algorithm that combines supervised learning in a controlled environment with unsupervised learning on the location of deployment. Using the SITEX02 dataset, we demonstrate that the presented solution works with a performance penalty of less than 10% in 90% of the tests. Under some circumstances, it even outperforms a network of classifiers completely trained with supervised learning. As a result, the need for on-site supervised learning and communication for training is completely eliminated by our solution.

Educational Technology and Distance Supervision in Counselor Education

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Carlisle, Robert Milton; Hays, Danica G.; Pribesh, Shana L.; Wood, Chris T.

2017-01-01

The authors used a nonexperimental descriptive design to examine the prevalence of distance supervision in counselor education programs, educational technology used in supervision, training on technology in supervision, and participants' (N = 673) perceptions of legal and ethical compliance. Program policies are recommended to guide the training…

Model-Based Reasoning in Humans Becomes Automatic with Training.

Directory of Open Access Journals (Sweden)

Marcos Economides

2015-09-01

Full Text Available Model-based and model-free reinforcement learning (RL have been suggested as algorithmic realizations of goal-directed and habitual action strategies. Model-based RL is more flexible than model-free but requires sophisticated calculations using a learnt model of the world. This has led model-based RL to be identified with slow, deliberative processing, and model-free RL with fast, automatic processing. In support of this distinction, it has recently been shown that model-based reasoning is impaired by placing subjects under cognitive load--a hallmark of non-automaticity. Here, using the same task, we show that cognitive load does not impair model-based reasoning if subjects receive prior training on the task. This finding is replicated across two studies and a variety of analysis methods. Thus, task familiarity permits use of model-based reasoning in parallel with other cognitive demands. The ability to deploy model-based reasoning in an automatic, parallelizable fashion has widespread theoretical implications, particularly for the learning and execution of complex behaviors. It also suggests a range of important failure modes in psychiatric disorders.

Clinical Supervision of International Supervisees: Suggestions for Multicultural Supervision

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Lee, Ahram

2018-01-01

An increase of international students in various settings has been noted in a range of disciplines including counseling and other mental health professions. The author examined the literature on international counseling students related to their experiences in counseling training, particularly in supervision. From the counseling literature, five…

Supervised progressive cross-continuum strength training compared with usual care in older medical patients

DEFF Research Database (Denmark)

Pedersen, Mette Merete; Petersen, Janne; Beyer, Nina

2016-01-01

Background: Hospitalization in older adults is characterized by physical inactivity and a risk of losing function and independence. Systematic strength training can improve muscle strength and functional performance in older adults. Few studies have examined the effect of a program initiated during...... hospitalization and continued after discharge. We conducted a feasibility study prior to this trial and found a progression model for loaded sit-to-stands feasible in older medical patients. This study aims to determine whether a simple supervised strength training program for the lower extremities (based...... on the model), combined with post-training protein supplementation initiated during hospitalization and continued at home for 4 weeks, is superior to usual care on change in mobility 4 weeks after discharge in older medical patients. Methods: Eighty older medical patients (65 years or older) acutely admitted...

A training approach to improve stepping automaticity while dual-tasking in Parkinson's disease

OpenAIRE

Chomiak, Taylor; Watts, Alexander; Meyer, Nicole; Pereira, Fernando V.; Hu, Bin

2017-01-01

Abstract Background: Deficits in motor movement automaticity in Parkinson's disease (PD), especially during multitasking, are early and consistent hallmarks of cognitive function decline, which increases fall risk and reduces quality of life. This study aimed to test the feasibility and potential efficacy of a wearable sensor-enabled technological platform designed for an in-home music-contingent stepping-in-place (SIP) training program to improve step automaticity during dual-tasking (DT). M...

Effects of a Supervised versus an Unsupervised Combined Balance and Strength Training Program on Balance and Muscle Power in Healthy Older Adults: A Randomized Controlled Trial.

Science.gov (United States)

Lacroix, André; Kressig, Reto W; Muehlbauer, Thomas; Gschwind, Yves J; Pfenninger, Barbara; Bruegger, Othmar; Granacher, Urs

2016-01-01

Losses in lower extremity muscle strength/power, muscle mass and deficits in static and particularly dynamic balance due to aging are associated with impaired functional performance and an increased fall risk. It has been shown that the combination of balance and strength training (BST) mitigates these age-related deficits. However, it is unresolved whether supervised versus unsupervised BST is equally effective in improving muscle power and balance in older adults. This study examined the impact of a 12-week BST program followed by 12 weeks of detraining on measures of balance and muscle power in healthy older adults enrolled in supervised (SUP) or unsupervised (UNSUP) training. Sixty-six older adults (men: 25, women: 41; age 73 ± 4 years) were randomly assigned to a SUP group (2/week supervised training, 1/week unsupervised training; n = 22), an UNSUP group (3/week unsupervised training; n = 22) or a passive control group (CON; n = 22). Static (i.e., Romberg Test) and dynamic (i.e., 10-meter walk test) steady-state, proactive (i.e., Timed Up and Go Test, Functional Reach Test), and reactive balance (e.g., Push and Release Test), as well as lower extremity muscle power (i.e., Chair Stand Test; Stair Ascent and Descent Test) were tested before and after the active training phase as well as after detraining. Adherence rates to training were 92% for SUP and 97% for UNSUP. BST resulted in significant group × time interactions. Post hoc analyses showed, among others, significant training-related improvements for the Romberg Test, stride velocity, Timed Up and Go Test, and Chair Stand Test in favor of the SUP group. Following detraining, significantly enhanced performances (compared to baseline) were still present in 13 variables for the SUP group and in 10 variables for the UNSUP group. Twelve weeks of BST proved to be safe (no training-related injuries) and feasible (high attendance rates of >90%). Deficits of balance and lower extremity muscle power can be

Wellness Model of Supervision: A Comparative Analysis

Science.gov (United States)

Lenz, A. Stephen; Sangganjanavanich, Varunee Faii; Balkin, Richard S.; Oliver, Marvarene; Smith, Robert L.

2012-01-01

This quasi-experimental study compared the effectiveness of the Wellness Model of Supervision (WELMS; Lenz & Smith, 2010) with alternative supervision models for developing wellness constructs, total personal wellness, and helping skills among counselors-in-training. Participants were 32 master's-level counseling students completing their…

Effects of gastric bypass surgery followed by supervised physical training on inflammation and endothelial function

DEFF Research Database (Denmark)

Stolberg, Charlotte Røn; Mundbjerg, Lene Hymøller; Funch-Jensen, Peter

2018-01-01

Background and aims: Obesity and physical inactivity are both associated with low-grade inflammation and endothelial dysfunction. Bariatric surgery improves markers of inflammation and endothelial function, but it is unknown if physical training after bariatric surgery can improve these markers...... even further. Therefore, we aimed to investigate the effects of Roux-en-Y gastric bypass (RYGB) followed by physical training on markers of low-grade inflammation and endothelial function. Methods: Sixty patients approved for RYGB underwent examinations pre-surgery, 6, 12, and 24 months post......-surgery. Six months post-surgery, they were randomized 1:1 to an intervention group or a control group. The interventions consisted of two weekly sessions of supervised moderate intensity physical training for a period of 26 weeks. Fasting blood samples were analyzed for concentrations of interleukin 6 (IL-6...

General collaboration offer of Johnson Controls regarding the performance of air conditioning automatic control systems and other buildings` automatic control systems

Energy Technology Data Exchange (ETDEWEB)

Gniazdowski, J.

1995-12-31

JOHNSON CONTROLS manufactures measuring and control equipment (800 types) and is as well a {open_quotes}turn-key{close_quotes} supplier of complete automatic controls systems for heating, air conditioning, ventilation and refrigerating engineering branches. The Company also supplies Buildings` Computer-Based Supervision and Monitoring Systems that may be applied in both small and large structures. Since 1990 the company has been performing full-range trade and contracting activities on the Polish market. We have our own well-trained technical staff and we collaborate with a series of designing and contracting enterprises that enable us to have our projects carried out all over Poland. The prices of our supplies and services correspond with the level of the Polish market.

Nursing supervision for care comprehensiveness

Directory of Open Access Journals (Sweden)

Lucieli Dias Pedreschi Chaves

Full Text Available ABSTRACT Objective: To reflect on nursing supervision as a management tool for care comprehensiveness by nurses, considering its potential and limits in the current scenario. Method: A reflective study based on discourse about nursing supervision, presenting theoretical and practical concepts and approaches. Results: Limits on the exercise of supervision are related to the organization of healthcare services based on the functional and clinical model of care, in addition to possible gaps in the nurse training process and work overload. Regarding the potential, researchers emphasize that supervision is a tool for coordinating care and management actions, which may favor care comprehensiveness, and stimulate positive attitudes toward cooperation and contribution within teams, co-responsibility, and educational development at work. Final considerations: Nursing supervision may help enhance care comprehensiveness by implying continuous reflection on including the dynamics of the healthcare work process and user needs in care networks.

Projected estimators for robust semi-supervised classification

DEFF Research Database (Denmark)

Krijthe, Jesse H.; Loog, Marco

2017-01-01

For semi-supervised techniques to be applied safely in practice we at least want methods to outperform their supervised counterparts. We study this question for classification using the well-known quadratic surrogate loss function. Unlike other approaches to semi-supervised learning, the procedure...... specifically, we prove that, measured on the labeled and unlabeled training data, this semi-supervised procedure never gives a lower quadratic loss than the supervised alternative. To our knowledge this is the first approach that offers such strong, albeit conservative, guarantees for improvement over...... the supervised solution. The characteristics of our approach are explicated using benchmark datasets to further understand the similarities and differences between the quadratic loss criterion used in the theoretical results and the classification accuracy typically considered in practice....

[Feedback on the training and supervision of student nurses].

Science.gov (United States)

Papas, Anne; Bourgois, Monique

2015-03-01

In order to harmonise the supervision of student nurses in the different departments of the same unit, a Parisian hospital team has created a working group. An IT tool for supervising the students to be used by the whole unit is also under development. Copyright © 2014 Elsevier Masson SAS. All rights reserved.

Cross-Domain Semi-Supervised Learning Using Feature Formulation.

Science.gov (United States)

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.

Discriminative Localization in CNNs for Weakly-Supervised Segmentation of Pulmonary Nodules.

Science.gov (United States)

Feng, Xinyang; Yang, Jie; Laine, Andrew F; Angelini, Elsa D

2017-09-01

Automated detection and segmentation of pulmonary nodules on lung computed tomography (CT) scans can facilitate early lung cancer diagnosis. Existing supervised approaches for automated nodule segmentation on CT scans require voxel-based annotations for training, which are labor- and time-consuming to obtain. In this work, we propose a weakly-supervised method that generates accurate voxel-level nodule segmentation trained with image-level labels only. By adapting a convolutional neural network (CNN) trained for image classification, our proposed method learns discriminative regions from the activation maps of convolution units at different scales, and identifies the true nodule location with a novel candidate-screening framework. Experimental results on the public LIDC-IDRI dataset demonstrate that, our weakly-supervised nodule segmentation framework achieves competitive performance compared to a fully-supervised CNN-based segmentation method.

Current Risk Management Practices in Psychotherapy Supervision.

Science.gov (United States)

Mehrtens, Ilayna K; Crapanzano, Kathleen; Tynes, L Lee

2017-12-01

Psychotherapy competence is a core skill for psychiatry residents, and psychotherapy supervision is a time-honored approach to teaching this skill. To explore the current supervision practices of psychiatry training programs, a 24-item questionnaire was sent to all program directors of Accreditation Council for Graduate Medical Education (ACGME)-approved adult psychiatry programs. The questionnaire included items regarding adherence to recently proposed therapy supervision practices aimed at reducing potential liability risk. The results suggested that current therapy supervision practices do not include sufficient management of the potential liability involved in therapy supervision. Better protections for patients, residents, supervisors and the institutions would be possible with improved credentialing practices and better documentation of informed consent and supervision policies and procedures. © 2017 American Academy of Psychiatry and the Law.

Genetic Counseling Supervisors' Self-Efficacy for Select Clinical Supervision Competencies.

Science.gov (United States)

Finley, Sabra Ledare; Veach, Pat McCarthy; MacFarlane, Ian M; LeRoy, Bonnie S; Callanan, Nancy

2016-04-01

Supervision is a primary instructional vehicle for genetic counseling student clinical training. Approximately two-thirds of genetic counselors report teaching and education roles, which include supervisory roles. Recently, Eubanks Higgins and colleagues published the first comprehensive list of empirically-derived genetic counseling supervisor competencies. Studies have yet to evaluate whether supervisors possess these competencies and whether their competencies differ as a function of experience. This study investigated three research questions: (1) What are genetic counselor supervisors' perceptions of their capabilities (self-efficacy) for a select group of supervisor competencies?, (2) Are there differences in self-efficacy as a function of their supervision experience or their genetic counseling experience, and 3) What training methods do they use and prefer to develop supervision skills? One-hundred thirty-one genetic counselor supervisors completed an anonymous online survey assessing demographics, self-efficacy (self-perceived capability) for 12 goal setting and 16 feedback competencies (Scale: 0-100), competencies that are personally challenging, and supervision training experiences and preferences (open-ended). A MANOVA revealed significant positive effects of supervision experience but not genetic counseling experience on participants' self-efficacy. Although mean self-efficacy ratings were high (>83.7), participant comments revealed several challenging competencies (e.g., incorporating student's report of feedback from previous supervisors into goal setting, and providing feedback about student behavior rather than personal traits). Commonly preferred supervision training methods included consultation with colleagues, peer discussion, and workshops/seminars.

A training approach to improve stepping automaticity while dual-tasking in Parkinson's disease: A prospective pilot study.

Science.gov (United States)

Chomiak, Taylor; Watts, Alexander; Meyer, Nicole; Pereira, Fernando V; Hu, Bin

2017-02-01

Deficits in motor movement automaticity in Parkinson's disease (PD), especially during multitasking, are early and consistent hallmarks of cognitive function decline, which increases fall risk and reduces quality of life. This study aimed to test the feasibility and potential efficacy of a wearable sensor-enabled technological platform designed for an in-home music-contingent stepping-in-place (SIP) training program to improve step automaticity during dual-tasking (DT). This was a 4-week prospective intervention pilot study. The intervention uses a sensor system and algorithm that runs off the iPod Touch which calculates step height (SH) in real-time. These measurements were then used to trigger auditory (treatment group, music; control group, radio podcast) playback in real-time through wireless headphones upon maintenance of repeated large amplitude stepping. With small steps or shuffling, auditory playback stops, thus allowing participants to use anticipatory motor control to regain positive feedback. Eleven participants were recruited from an ongoing trial (Trial Number: ISRCTN06023392). Fear of falling (FES-I), general cognitive functioning (MoCA), self-reported freezing of gait (FOG-Q), and DT step automaticity were evaluated. While we found no significant effect of training on FES-I, MoCA, or FOG-Q, we did observe a significant group (music vs podcast) by training interaction in DT step automaticity (Ptraining to increase motor automaticity for people living with PD. The training approach described here can be implemented at home to meet the growing demand for self-management of symptoms by patients.

Supervised learning with restricted training sets: a generating functional analysis

Energy Technology Data Exchange (ETDEWEB)

Heimel, J.A.F.; Coolen, A.C.C. [Department of Mathematics, King' s College London, Strand, London (United Kingdom)

2001-10-26

We study the dynamics of supervised on-line learning of realizable tasks in feed-forward neural networks. We focus on the regime where the number of examples used for training is proportional to the number of input channels N. Using generating functional techniques from spin glass theory, we are able to average over the composition of the training set and transform the problem for N{yields}{infinity} to an effective single pattern system described completely by the student autocovariance, the student-teacher overlap and the student response function with exact closed equations. Our method applies to arbitrary learning rules, i.e., not necessarily of a gradient-descent type. The resulting exact macroscopic dynamical equations can be integrated without finite-size effects up to any degree of accuracy, but their main value is in providing an exact and simple starting point for analytical approximation schemes. Finally, we show how, in the region of absent anomalous response and using the hypothesis that (as in detailed balance systems) the short-time part of the various operators can be transformed away, one can describe the stationary state of the network successfully by a set of coupled equations involving only four scalar order parameters. (author)

Training, supervision and quality of care in selected integrated community case management (iCCM) programmes: A scoping review of programmatic evidence.

Science.gov (United States)

Bosch-Capblanch, Xavier; Marceau, Claudine

2014-12-01

To describe the training, supervision and quality of care components of integrated Community Case Management (iCCM) programmes and to draw lessons learned from existing evaluations of those programmes. Scoping review of reports from 29 selected iCCM programmes purposively provided by stakeholders containing any information relevant to understand quality of care issues. The number of people reached by iCCM programmes varied from the tens of thousands to more than a million. All programmes aimed at improving access of vulnerable populations to health care, focusing on the main childhood illnesses, managed by Community Health Workers (CHW), often selected bycommunities. Training and supervision were widely implemented, in different ways and intensities, and often complemented with tools (eg, guides, job aids), supplies, equipment and incentives. Quality of care was measured using many outcomes (eg, access or appropriate treatment). Overall, there seemed to be positive effects for those strategies that involved policy change, organisational change, standardisation of clinical practices and alignment with other programmes. Positive effects were mostly achieved in large multi-component programmes. Mild or no effects have been described on mortality reduction amongst the few programmes for which data on this outcome was available to us. Promising strategies included teaming-up of CHW, micro-franchising or social franchising. On-site training and supervision of CHW have been shown to improve clinical practices. Effects on caregivers seemed positive, with increases in knowledge, care seeking behaviour, or caregivers' basic disease management. Evidence on iCCM is often of low quality, cannot relate specific interventions or the ways they are implemented with outcomes and lacks standardisation; this limits the capacity to identify promising strategies to improve quality of care. Large, multi-faceted, iCCM programmes, with strong components of training, supervision, which

Training, supervision and quality of care in selected integrated community case management (iCCM) programmes: A scoping review of programmatic evidence

Science.gov (United States)

Bosch–Capblanch, Xavier; Marceau, Claudine

2014-01-01

Aim To describe the training, supervision and quality of care components of integrated Community Case Management (iCCM) programmes and to draw lessons learned from existing evaluations of those programmes. Methods Scoping review of reports from 29 selected iCCM programmes purposively provided by stakeholders containing any information relevant to understand quality of care issues. Results The number of people reached by iCCM programmes varied from the tens of thousands to more than a million. All programmes aimed at improving access of vulnerable populations to health care, focusing on the main childhood illnesses, managed by Community Health Workers (CHW), often selected bycommunities. Training and supervision were widely implemented, in different ways and intensities, and often complemented with tools (eg, guides, job aids), supplies, equipment and incentives. Quality of care was measured using many outcomes (eg, access or appropriate treatment). Overall, there seemed to be positive effects for those strategies that involved policy change, organisational change, standardisation of clinical practices and alignment with other programmes. Positive effects were mostly achieved in large multi–component programmes. Mild or no effects have been described on mortality reduction amongst the few programmes for which data on this outcome was available to us. Promising strategies included teaming–up of CHW, micro–franchising or social franchising. On–site training and supervision of CHW have been shown to improve clinical practices. Effects on caregivers seemed positive, with increases in knowledge, care seeking behaviour, or caregivers’ basic disease management. Evidence on iCCM is often of low quality, cannot relate specific interventions or the ways they are implemented with outcomes and lacks standardisation; this limits the capacity to identify promising strategies to improve quality of care. Conclusion Large, multi–faceted, iCCM programmes, with strong

Supervised exercise training counterbalances the adverse effects of insulin therapy in overweight/obese subjects with type 2 diabetes.

Science.gov (United States)

Balducci, Stefano; Zanuso, Silvano; Cardelli, Patrizia; Salerno, Gerardo; Fallucca, Sara; Nicolucci, Antonio; Pugliese, Giuseppe

2012-01-01

To examine the effect of supervised exercise on traditional and nontraditional cardiovascular risk factors in sedentary, overweight/obese insulin-treated subjects with type 2 diabetes from the Italian Diabetes Exercise Study (IDES). The study randomized 73 insulin-treated patients to twice weekly supervised aerobic and resistance training plus structured exercise counseling (EXE) or to counseling alone (CON) for 12 months. Clinical and laboratory parameters were assessed at baseline and at the end of the study. The volume of physical activity was significantly higher in the EXE versus the CON group. Values for hemoglobin A(1c), BMI, waist circumference, high-sensitivity C-reactive protein, blood pressure, LDL cholesterol, and the coronary heart disease risk score were significantly reduced only in the EXE group. No major adverse events were observed. In insulin-treated subjects with type 2 diabetes, supervised exercise is safe and effective in improving glycemic control and markers of adiposity and inflammation, thus counterbalancing the adverse effects of insulin on these parameters.

Protocols and Results of Resident Neurosurgeon's Transfemoral Catheter Angiography Training Supervised by Neuroendovascular Specialists

Science.gov (United States)

Shin, Dong-Seong; Yeo, Dong-Kyu; Hwang, Sun-Chul; Park, Sukh-Que

2013-01-01

Objective Transfemoral catheter angiography (TFCA) is a basic procedure in neurovascular surgery with increasing importance in surgical and non-invasive treatments. Unfortunately, resident neurosurgeons have relatively few opportunities to perform TFCA in most institutions. We report a method developed in our hospital for training resident neurosurgeons to perform TFCA and evaluate the efficacy of this training. Methods From May 2011 to September 2011, a total of 112 consecutive patients underwent TFCA by one resident neurosurgeon supervised by two neuroendovascular specialists. Patients who underwent elective diagnostic procedures were included in this study. Patients who underwent endovascular treatment were excluded. Demographic data, indications for TFCA, side of approach, number of selected arteries, and complications were analyzed. Results This study included 64 males and 48 females with a mean age of 51.6 (12-81) years. All procedures were performed in the angiography suite. Common indications for procedures were as follows: stroke-induced symptoms in 61 patients (54.5%), Moyamoya disease and arteriovenous malformation in 13 patients (11.6%), and unruptured intracranial aneurysm in eight patients (7.1%). Right and left femoral puncture was performed in 98.2% and 1.8% of patients, respectively. A total of 465 selective angiographies were performed without complications. Angiographic examination was performed on 4.15 vessels per patient. Conclusion TFCA can be performed safely by resident neurosurgeons based on anatomical study and a meticulous protocol under the careful supervision of neuroendovascular specialists. PMID:24175020

A Delphi Study and Initial Validation of Counselor Supervision Competencies

Science.gov (United States)

Neuer Colburn, Anita A.; Grothaus, Tim; Hays, Danica G.; Milliken, Tammi

2016-01-01

The authors addressed the lack of supervision training standards for doctoral counseling graduates by developing and validating an initial list of supervision competencies. They used content analysis, Delphi polling, and content validity methods to generate a list, vetted by 2 different panels of supervision experts, of 33 competencies grouped…

Fast and robust segmentation of white blood cell images by self-supervised learning.

Science.gov (United States)

Zheng, Xin; Wang, Yong; Wang, Guoyou; Liu, Jianguo

2018-04-01

A fast and accurate white blood cell (WBC) segmentation remains a challenging task, as different WBCs vary significantly in color and shape due to cell type differences, staining technique variations and the adhesion between the WBC and red blood cells. In this paper, a self-supervised learning approach, consisting of unsupervised initial segmentation and supervised segmentation refinement, is presented. The first module extracts the overall foreground region from the cell image by K-means clustering, and then generates a coarse WBC region by touching-cell splitting based on concavity analysis. The second module further uses the coarse segmentation result of the first module as automatic labels to actively train a support vector machine (SVM) classifier. Then, the trained SVM classifier is further used to classify each pixel of the image and achieve a more accurate segmentation result. To improve its segmentation accuracy, median color features representing the topological structure and a new weak edge enhancement operator (WEEO) handling fuzzy boundary are introduced. To further reduce its time cost, an efficient cluster sampling strategy is also proposed. We tested the proposed approach with two blood cell image datasets obtained under various imaging and staining conditions. The experiment results show that our approach has a superior performance of accuracy and time cost on both datasets. Copyright © 2018 Elsevier Ltd. All rights reserved.

Nuclear safety culture and nuclear safety supervision

International Nuclear Information System (INIS)

Chai Jianshe

2013-01-01

In this paper, the author reviews systematically and summarizes up the development process and stage characteristics of nuclear safety culture, analysis the connotation and characteristics of nuclear safety culture, sums up the achievements of our country's nuclear safety supervision, dissects the challenges and problems of nuclear safety supervision. This thesis focused on the relationship between nuclear safety culture and nuclear safety supervision, they are essential differences, but there is a close relationship. Nuclear safety supervision needs to introduce some concepts of nuclear safety culture, lays emphasis on humanistic care and improves its level and efficiency. Nuclear safety supervision authorities must strengthen nuclear safety culture training, conduct the development of nuclear safety culture, make sure that nuclear safety culture can play significant roles. (author)

Semi-Supervised Active Learning for Sound Classification in Hybrid Learning Environments

Science.gov (United States)

Han, Wenjing; Coutinho, Eduardo; Li, Haifeng; Schuller, Björn; Yu, Xiaojie; Zhu, Xuan

2016-01-01

Coping with scarcity of labeled data is a common problem in sound classification tasks. Approaches for classifying sounds are commonly based on supervised learning algorithms, which require labeled data which is often scarce and leads to models that do not generalize well. In this paper, we make an efficient combination of confidence-based Active Learning and Self-Training with the aim of minimizing the need for human annotation for sound classification model training. The proposed method pre-processes the instances that are ready for labeling by calculating their classifier confidence scores, and then delivers the candidates with lower scores to human annotators, and those with high scores are automatically labeled by the machine. We demonstrate the feasibility and efficacy of this method in two practical scenarios: pool-based and stream-based processing. Extensive experimental results indicate that our approach requires significantly less labeled instances to reach the same performance in both scenarios compared to Passive Learning, Active Learning and Self-Training. A reduction of 52.2% in human labeled instances is achieved in both of the pool-based and stream-based scenarios on a sound classification task considering 16,930 sound instances. PMID:27627768

Semi-Supervised Active Learning for Sound Classification in Hybrid Learning Environments.

Science.gov (United States)

Han, Wenjing; Coutinho, Eduardo; Ruan, Huabin; Li, Haifeng; Schuller, Björn; Yu, Xiaojie; Zhu, Xuan

2016-01-01

Coping with scarcity of labeled data is a common problem in sound classification tasks. Approaches for classifying sounds are commonly based on supervised learning algorithms, which require labeled data which is often scarce and leads to models that do not generalize well. In this paper, we make an efficient combination of confidence-based Active Learning and Self-Training with the aim of minimizing the need for human annotation for sound classification model training. The proposed method pre-processes the instances that are ready for labeling by calculating their classifier confidence scores, and then delivers the candidates with lower scores to human annotators, and those with high scores are automatically labeled by the machine. We demonstrate the feasibility and efficacy of this method in two practical scenarios: pool-based and stream-based processing. Extensive experimental results indicate that our approach requires significantly less labeled instances to reach the same performance in both scenarios compared to Passive Learning, Active Learning and Self-Training. A reduction of 52.2% in human labeled instances is achieved in both of the pool-based and stream-based scenarios on a sound classification task considering 16,930 sound instances.

Supervised pelvic floor muscle training versus attention-control massage treatment in patients with faecal incontinence: Statistical analysis plan for a randomised controlled trial.

Science.gov (United States)

Ussing, Anja; Dahn, Inge; Due, Ulla; Sørensen, Michael; Petersen, Janne; Bandholm, Thomas

2017-12-01

Faecal incontinence affects approximately 8-9% of the adult population. The condition is surrounded by taboo; it can have a devastating impact on quality of life and lead to major limitations in daily life. Pelvic floor muscle training in combination with information and fibre supplements is recommended as first-line treatment for faecal incontinence. Despite this, the effect of pelvic floor muscle training for faecal incontinence is unclear. No previous trials have investigated the efficacy of supervised pelvic floor muscle training in combination with conservative treatment and compared this to an attention-control massage treatment including conservative treatment. The aim of this trial is to investigate if 16 weeks of supervised pelvic floor muscle training in combination with conservative treatment is superior to attention-control massage treatment and conservative treatment in patients with faecal incontinence. Randomised, controlled, superiority trial with two parallel arms. 100 participants with faecal incontinence will be randomised to either (1) individually supervised pelvic floor muscle training and conservative treatment or (2) attention-control massage treatment and conservative treatment. The primary outcome is participants' rating of symptom changes after 16 weeks of treatment using the Patient Global Impression of Improvement Scale. Secondary outcomes are the Vaizey Incontinence Score, the Fecal Incontinence Severity Index, the Fecal Incontinence Quality of Life Scale, a 14-day bowel diary, anorectal manometry and rectal capacity measurements. Follow-up assessment at 36 months will be conducted. This paper describes and discusses the rationale, the methods and in particular the statistical analysis plan of this trial.

The supervisions in the field develop nuclear professionals

International Nuclear Information System (INIS)

Fernandez de la Casa, M.; Buedo, J. L.; Gonzalez, F.

2015-01-01

In 2011 Cofrentes Nuclear Power Plants began a training program for improving the supervision of managers in the field: the effort done not only has improved the quality of supervisions but also has defined a way to reinforce behavior expectations of Cofrentes Nuclear Power Plant. (Author)

Guidelines for the training, credentialing, use, and supervision of speech-language pathology assistants. Task Force on Support Personnel.

Science.gov (United States)

1996-01-01

These guidelines are an official statement of the American Speech-Language-Hearing Association. They provide guidance on the training, credentialing, use, and supervision of one category of support personnel in speech-language pathology: speech-language pathology assistants. Guidelines are not official standards of the Association. They were developed by the Task Force on Support Personnel: Dennis J. Arnst, Kenneth D. Barker, Ann Olsen Bird, Sheila Bridges, Linda S. DeYoung, Katherine Formichella, Nena M. Germany, Gilbert C. Hanke, Ann M. Horton, DeAnne M. Owre, Sidney L. Ramsey, Cathy A. Runnels, Brenda Terrell, Gerry W. Werven, Denise West, Patricia A. Mercaitis (consultant), Lisa C. O'Connor (consultant), Frederick T. Spahr (coordinator), Diane Paul-Brown (associate coordinator), Ann L. Carey (Executive Board liaison). The 1994 guidelines supersede the 1981 guidelines entitled, "Guidelines for the Employment and Utilization of Supportive Personnel" (Asha, March 1981, 165-169). Refer to the 1995 position statement on the "Training, Credentialing, Use, and Supervision of Support Personnel in Speech-Language Pathology" (Asha, 37 [Suppl. 14], 21).

Weakly supervised semantic segmentation using fore-background priors

Science.gov (United States)

Han, Zheng; Xiao, Zhitao; Yu, Mingjun

2017-07-01

Weakly-supervised semantic segmentation is a challenge in the field of computer vision. Most previous works utilize the labels of the whole training set and thereby need the construction of a relationship graph about image labels, thus result in expensive computation. In this study, we tackle this problem from a different perspective. We proposed a novel semantic segmentation algorithm based on background priors, which avoids the construction of a huge graph in whole training dataset. Specifically, a random forest classifier is obtained using weakly supervised training data .Then semantic texton forest (STF) feature is extracted from image superpixels. Finally, a CRF based optimization algorithm is proposed. The unary potential of CRF derived from the outputting probability of random forest classifier and the robust saliency map as background prior. Experiments on the MSRC21 dataset show that the new algorithm outperforms some previous influential weakly-supervised segmentation algorithms. Furthermore, the use of efficient decision forests classifier and parallel computing of saliency map significantly accelerates the implementation.

QUEST : Eliminating online supervised learning for efficient classification algorithms

NARCIS (Netherlands)

Zwartjes, Ardjan; Havinga, Paul J.M.; Smit, Gerard J.M.; Hurink, Johann L.

2016-01-01

In this work, we introduce QUEST (QUantile Estimation after Supervised Training), an adaptive classification algorithm for Wireless Sensor Networks (WSNs) that eliminates the necessity for online supervised learning. Online processing is important for many sensor network applications. Transmitting

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

Science.gov (United States)

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.

Training cows to approach the milking unit in response to acoustic signals in an automatic milking system during the grazing season

DEFF Research Database (Denmark)

Wredle, E.; Munksgaard, Lene; Sporndly, E.

2006-01-01

connected to the automatic milking system. The cows were trained indoors using an operant conditioning technique. All cows had 12 training sessions with 7–12 signals given at variable intervals. An evaluation period followed the training period. During evaluation, the trained cows received an individual...... cows housed in a barn with an automatic milking system. A small box emitting an acoustic signal was attached to the collar of the 10 cows. During the training period, the signal was induced manually from a distance and during the evaluation period, signals were activated automatically from a computer...... (with no signal) in early season was 9.7 ± 0.18 h (P 7 ± 0.56 h and 9.0 ± 0.20 h for the five cows trained in late season and a reference group (with no signal), respectively. During the evaluation in a full herd situation, the response ranged between 15 and 75...

Supervised Balance Training and Wii Fit-Based Exercises Lower Falls Risk in Older Adults With Type 2 Diabetes.

Science.gov (United States)

Morrison, Steven; Simmons, Rachel; Colberg, Sheri R; Parson, Henri K; Vinik, Aaron I

2018-02-01

This study examined the benefits of and differences between 12 weeks of thrice-weekly supervised balance training and an unsupervised at-home balance activity (using the Nintendo Wii Fit) for improving balance and reaction time and lowering falls risk in older individuals with type 2 diabetes mellitus (T2DM). Before-after trial. University research laboratory, home environment. Sixty-five older adults with type 2 diabetes were recruited for this study. Participants were randomly allocated to either supervised balance training (mean age 67.8 ± 5.2) or unsupervised training using the Nintendo Wii Fit balance board (mean age 66.1 ± 5.6). The training period for both groups lasted for 12 weeks. Individuals were required to complete three 40-minute sessions per week for a total of 36 sessions. The primary outcome measure was falls risk, which was as derived from the physiological profile assessment. In addition, measures of simple reaction time, lower limb proprioception, postural sway, knee flexion, and knee extension strength were also collected. Persons also self-reported any falls in the previous 6 months. Both training programs resulted in a significant lowering of falls risk (P general balance ability. Interestingly, the reduced falls risk occurred without significant changes in leg strength, suggesting that interventions to reduce falls risk that target intrinsic risk factors related to balance control (over muscle strength) may have positive benefits for the older adult with T2DM at risk for falls. Copyright © 2017 AMDA – The Society for Post-Acute and Long-Term Care Medicine. Published by Elsevier Inc. All rights reserved.

Computerised training improves cognitive performance in chronic pain: a participant-blinded randomised active-controlled trial with remote supervision.

Science.gov (United States)

Baker, Katharine S; Georgiou-Karistianis, Nellie; Lampit, Amit; Valenzuela, Michael; Gibson, Stephen J; Giummarra, Melita J

2018-04-01

Chronic pain is associated with reduced efficiency of cognitive performance, and few studies have investigated methods of remediation. We trialled a computerised cognitive training protocol to determine whether it could attenuate cognitive difficulties in a chronic pain sample. Thirty-nine adults with chronic pain (mean age = 43.3, 61.5% females) were randomised to an 8-week online course (3 sessions/week from home) of game-like cognitive training exercises, or an active control involving watching documentary videos. Participants received weekly supervision by video call. Primary outcomes were a global neurocognitive composite (tests of attention, speed, and executive function) and self-reported cognition. Secondary outcomes were pain (intensity; interference), mood symptoms (depression; anxiety), and coping with pain (catastrophising; self-efficacy). Thirty participants (15 training and 15 control) completed the trial. Mixed model intention-to-treat analyses revealed significant effects of training on the global neurocognitive composite (net effect size [ES] = 0.43, P = 0.017), driven by improved executive function performance (attention switching and working memory). The control group reported improvement in pain intensity (net ES = 0.65, P = 0.022). Both groups reported subjective improvements in cognition (ES = 0.28, P = 0.033) and catastrophising (ES = 0.55, P = 0.006). Depression, anxiety, self-efficacy, and pain interference showed no change in either group. This study provides preliminary evidence that supervised cognitive training may be a viable method for enhancing cognitive skills in persons with chronic pain, but transfer to functional and clinical outcomes remains to be demonstrated. Active control results suggest that activities perceived as relaxing or enjoyable contribute to improved perception of well-being. Weekly contact was pivotal to successful program completion.

Training the Millennial learner through experiential evolutionary scaffolding: implications for clinical supervision in graduate education programs.

Science.gov (United States)

Venne, Vickie L; Coleman, Darrell

2010-12-01

They are the Millennials--Generation Y. Over the next few decades, they will be entering genetic counseling graduate training programs and the workforce. As a group, they are unlike previous youth generations in many ways, including the way they learn. Therefore, genetic counselors who teach and supervise need to understand the Millennials and explore new ways of teaching to ensure that the next cohort of genetic counselors has both skills and knowledge to represent our profession well. This paper will summarize the distinguishing traits of the Millennial generation as well as authentic learning and evolutionary scaffolding theories of learning that can enhance teaching and supervision. We will then use specific aspects of case preparation during clinical rotations to demonstrate how incorporating authentic learning theory into evolutionary scaffolding results in experiential evolutionary scaffolding, a method that potentially offers a more effective approach when teaching Millennials. We conclude with suggestions for future research.

Automated lesion detection on MRI scans using combined unsupervised and supervised methods

International Nuclear Information System (INIS)

Guo, Dazhou; Fridriksson, Julius; Fillmore, Paul; Rorden, Christopher; Yu, Hongkai; Zheng, Kang; Wang, Song

2015-01-01

Accurate and precise detection of brain lesions on MR images (MRI) is paramount for accurately relating lesion location to impaired behavior. In this paper, we present a novel method to automatically detect brain lesions from a T1-weighted 3D MRI. The proposed method combines the advantages of both unsupervised and supervised methods. First, unsupervised methods perform a unified segmentation normalization to warp images from the native space into a standard space and to generate probability maps for different tissue types, e.g., gray matter, white matter and fluid. This allows us to construct an initial lesion probability map by comparing the normalized MRI to healthy control subjects. Then, we perform non-rigid and reversible atlas-based registration to refine the probability maps of gray matter, white matter, external CSF, ventricle, and lesions. These probability maps are combined with the normalized MRI to construct three types of features, with which we use supervised methods to train three support vector machine (SVM) classifiers for a combined classifier. Finally, the combined classifier is used to accomplish lesion detection. We tested this method using T1-weighted MRIs from 60 in-house stroke patients. Using leave-one-out cross validation, the proposed method can achieve an average Dice coefficient of 73.1 % when compared to lesion maps hand-delineated by trained neurologists. Furthermore, we tested the proposed method on the T1-weighted MRIs in the MICCAI BRATS 2012 dataset. The proposed method can achieve an average Dice coefficient of 66.5 % in comparison to the expert annotated tumor maps provided in MICCAI BRATS 2012 dataset. In addition, on these two test datasets, the proposed method shows competitive performance to three state-of-the-art methods, including Stamatakis et al., Seghier et al., and Sanjuan et al. In this paper, we introduced a novel automated procedure for lesion detection from T1-weighted MRIs by combining both an unsupervised and a

TU-C-17A-03: An Integrated Contour Evaluation Software Tool Using Supervised Pattern Recognition for Radiotherapy

Energy Technology Data Exchange (ETDEWEB)

Chen, H; Tan, J; Kavanaugh, J; Dolly, S; Gay, H; Thorstad, W; Anastasio, M; Altman, M; Mutic, S; Li, H [Washington University School of Medicine, Saint Louis, MO (United States)

2014-06-15

Purpose: Radiotherapy (RT) contours delineated either manually or semiautomatically require verification before clinical usage. Manual evaluation is very time consuming. A new integrated software tool using supervised pattern contour recognition was thus developed to facilitate this process. Methods: The contouring tool was developed using an object-oriented programming language C# and application programming interfaces, e.g. visualization toolkit (VTK). The C# language served as the tool design basis. The Accord.Net scientific computing libraries were utilized for the required statistical data processing and pattern recognition, while the VTK was used to build and render 3-D mesh models from critical RT structures in real-time and 360° visualization. Principal component analysis (PCA) was used for system self-updating geometry variations of normal structures based on physician-approved RT contours as a training dataset. The inhouse design of supervised PCA-based contour recognition method was used for automatically evaluating contour normality/abnormality. The function for reporting the contour evaluation results was implemented by using C# and Windows Form Designer. Results: The software input was RT simulation images and RT structures from commercial clinical treatment planning systems. Several abilities were demonstrated: automatic assessment of RT contours, file loading/saving of various modality medical images and RT contours, and generation/visualization of 3-D images and anatomical models. Moreover, it supported the 360° rendering of the RT structures in a multi-slice view, which allows physicians to visually check and edit abnormally contoured structures. Conclusion: This new software integrates the supervised learning framework with image processing and graphical visualization modules for RT contour verification. This tool has great potential for facilitating treatment planning with the assistance of an automatic contour evaluation module in avoiding

Effects of Supervised vs. Unsupervised Training Programs on Balance and Muscle Strength in Older Adults : A Systematic Review and Meta-Analysis

NARCIS (Netherlands)

Lacroix, Andre; Hortobagyi, Tibor; Beurskens, Rainer; Granacher, Urs

Background 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

Effects of Supervised vs. Unsupervised Training Programs on Balance and Muscle Strength in Older Adults : A Systematic Review and Meta-Analysis

NARCIS (Netherlands)

Lacroix, Andre; Hortobagyi, Tibor; Beurskens, Rainer; Granacher, Urs

2017-01-01

Background 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

An Automatic Identification Procedure to Promote the use of FES-Cycling Training for Hemiparetic Patients

Directory of Open Access Journals (Sweden)

Emilia Ambrosini

2014-01-01

Full Text Available Cycling induced by Functional Electrical Stimulation (FES training currently requires a manual setting of different parameters, which is a time-consuming and scarcely repeatable procedure. We proposed an automatic procedure for setting session-specific parameters optimized for hemiparetic patients. This procedure consisted of the identification of the stimulation strategy as the angular ranges during which FES drove the motion, the comparison between the identified strategy and the physiological muscular activation strategy, and the setting of the pulse amplitude and duration of each stimulated muscle. Preliminary trials on 10 healthy volunteers helped define the procedure. Feasibility tests on 8 hemiparetic patients (5 stroke, 3 traumatic brain injury were performed. The procedure maximized the motor output within the tolerance constraint, identified a biomimetic strategy in 6 patients, and always lasted less than 5 minutes. Its reasonable duration and automatic nature make the procedure usable at the beginning of every training session, potentially enhancing the performance of FES-cycling training.

Monitoring the Performance of the Pedestrian Transfer Function of Train Stations Using Automatic Fare Collection Data

NARCIS (Netherlands)

Van den Heuvel, J.P.A.; Hoogenraad, J.H.

2014-01-01

Over the last years all train stations in The Netherlands have been equipped with automatic fare collection gates and/or validators. All public transport passengers use a smart card to pay their fare. In this paper we present a monitor for the performance of the pedestrian function of train stations

Supervised Learning for Visual Pattern Classification

Science.gov (United States)

Zheng, Nanning; Xue, Jianru

This chapter presents an overview of the topics and major ideas of supervised learning for visual pattern classification. Two prevalent algorithms, i.e., the support vector machine (SVM) and the boosting algorithm, are briefly introduced. SVMs and boosting algorithms are two hot topics of recent research in supervised learning. SVMs improve the generalization of the learning machine by implementing the rule of structural risk minimization (SRM). It exhibits good generalization even when little training data are available for machine training. The boosting algorithm can boost a weak classifier to a strong classifier by means of the so-called classifier combination. This algorithm provides a general way for producing a classifier with high generalization capability from a great number of weak classifiers.

Identification of Village Building via Google Earth Images and Supervised Machine Learning Methods

Directory of Open Access Journals (Sweden)

Zhiling Guo

2016-03-01

Full Text Available In this study, a method based on supervised machine learning is proposed to identify village buildings from open high-resolution remote sensing images. We select Google Earth (GE RGB images to perform the classification in order to examine its suitability for village mapping, and investigate the feasibility of using machine learning methods to provide automatic classification in such fields. By analyzing the characteristics of GE images, we design different features on the basis of two kinds of supervised machine learning methods for classification: adaptive boosting (AdaBoost and convolutional neural networks (CNN. To recognize village buildings via their color and texture information, the RGB color features and a large number of Haar-like features in a local window are utilized in the AdaBoost method; with multilayer trained networks based on gradient descent algorithms and back propagation, CNN perform the identification by mining deeper information from buildings and their neighborhood. Experimental results from the testing area at Savannakhet province in Laos show that our proposed AdaBoost method achieves an overall accuracy of 96.22% and the CNN method is also competitive with an overall accuracy of 96.30%.

The efficacy of unsupervised home-based exercise regimens in comparison to supervised lab-based exercise training upon cardio-respiratory health facets

OpenAIRE

Blackwell, J.; Atherton, Philip J.; Smith, Kenneth; Doleman, Brett; Williams, John P.; Lund, Jonathan N.; Phillips, Bethan E.

2017-01-01

Abstract Supervised high?intensity interval training (HIIT) can rapidly improve cardiorespiratory fitness (CRF). However, the effectiveness of time?efficient unsupervised home?based interventions is unknown. Eighteen volunteers completed either: laboratory?HIIT (L?HIIT); home?HIIT (H?HIIT) or home?isometric hand?grip training (H?IHGT). CRF improved significantly in L?HIIT and H?HIIT groups, with blood pressure improvements in the H?IHGT group only. H?HIIT offers a practical, time?efficient ex...

Self-supervised, mobile-application based cognitive training of auditory attention: A behavioral and fMRI evaluation

Directory of Open Access Journals (Sweden)

Josef J. Bless

2014-07-01

Full Text Available Emerging evidence of the validity of collecting data in natural settings using smartphone applications has opened new possibilities for psychological assessment, treatment, and research. In this study we explored the feasibility and effectiveness of using a mobile application for self-supervised training of auditory attention. In addition, we investigated the neural underpinnings of the training procedure with functional magnetic resonance imaging (fMRI, as well as possible transfer effects to untrained cognitive interference tasks. Subjects in the training group performed the training task on an iPod touch two times a day (morning/evening for three weeks; subjects in the control group received no training, but were tested at the same time interval as the training group. Behavioral responses were measured before and after the training period in both groups, together with measures of task-related neural activations by fMRI. The results showed an expected performance increase after training that corresponded to activation decreases in brain regions associated with selective auditory processing (left posterior temporal gyrus and executive functions (right middle frontal gyrus, indicating more efficient processing in task-related neural networks after training. Our study suggests that cognitive training delivered via mobile applications is feasible and improves the ability to focus attention with corresponding effects on neural plasticity. Future research should focus on the clinical benefits of mobile cognitive training. Limitations of the study are discussed including reduced experimental control and lack of transfer effects.

A Hybrid Supervised/Unsupervised Machine Learning Approach to Solar Flare Prediction

Science.gov (United States)

Benvenuto, Federico; Piana, Michele; Campi, Cristina; Massone, Anna Maria

2018-01-01

This paper introduces a novel method for flare forecasting, combining prediction accuracy with the ability to identify the most relevant predictive variables. This result is obtained by means of a two-step approach: first, a supervised regularization method for regression, namely, LASSO is applied, where a sparsity-enhancing penalty term allows the identification of the significance with which each data feature contributes to the prediction; then, an unsupervised fuzzy clustering technique for classification, namely, Fuzzy C-Means, is applied, where the regression outcome is partitioned through the minimization of a cost function and without focusing on the optimization of a specific skill score. This approach is therefore hybrid, since it combines supervised and unsupervised learning; realizes classification in an automatic, skill-score-independent way; and provides effective prediction performances even in the case of imbalanced data sets. Its prediction power is verified against NOAA Space Weather Prediction Center data, using as a test set, data in the range between 1996 August and 2010 December and as training set, data in the range between 1988 December and 1996 June. To validate the method, we computed several skill scores typically utilized in flare prediction and compared the values provided by the hybrid approach with the ones provided by several standard (non-hybrid) machine learning methods. The results showed that the hybrid approach performs classification better than all other supervised methods and with an effectiveness comparable to the one of clustering methods; but, in addition, it provides a reliable ranking of the weights with which the data properties contribute to the forecast.

Feature-space transformation improves supervised segmentation across scanners

DEFF Research Database (Denmark)

van Opbroek, Annegreet; Achterberg, Hakim C.; de Bruijne, Marleen

2015-01-01

Image-segmentation techniques based on supervised classification generally perform well on the condition that training and test samples have the same feature distribution. However, if training and test images are acquired with different scanners or scanning parameters, their feature distributions...

Effects of 12-week supervised treadmill training on spatio-temporal gait parameters in patients with claudication.

Science.gov (United States)

Konik, Anita; Kuklewicz, Stanisław; Rosłoniec, Ewelina; Zając, Marcin; Spannbauer, Anna; Nowobilski, Roman; Mika, Piotr

2016-01-01

The purpose of the study was to evaluate selected temporal and spatial gait parameters in patients with intermittent claudication after completion of 12-week supervised treadmill walking training. The study included 36 patients (26 males and 10 females) aged: mean 64 (SD 7.7) with intermittent claudication. All patients were tested on treadmill (Gait Trainer, Biodex). Before the programme and after its completion, the following gait biomechanical parameters were tested: step length (cm), step cycle (cycle/s), leg support time (%), coefficient of step variation (%) as well as pain-free walking time (PFWT) and maximal walking time (MWT) were measured. Training was conducted in accordance with the current TASC II guidelines. After 12 weeks of training, patients showed significant change in gait biomechanics consisting in decreased frequency of step cycle (p gait was more regular, which was expressed via statistically significant decrease of coefficient of variation (p 0.05). Twelve-week treadmill walking training programme may lead to significant improvement of temporal and spatial gait parameters in patients with intermittent claudication. Twelve-week treadmill walking training programme may lead to significant improvement of pain-free walking time and maximum walking time in patients with intermittent claudication.

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

Science.gov (United States)

Stanescu, Ana; Caragea, Doina

2015-01-01

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

Semi-supervised sparse coding

KAUST Repository

Wang, Jim Jing-Yan; Gao, Xin

2014-01-01

Sparse coding approximates the data sample as a sparse linear combination of some basic codewords and uses the sparse codes as new presentations. In this paper, we investigate learning discriminative sparse codes by sparse coding in a semi-supervised manner, where only a few training samples are labeled. By using the manifold structure spanned by the data set of both labeled and unlabeled samples and the constraints provided by the labels of the labeled samples, we learn the variable class labels for all the samples. Furthermore, to improve the discriminative ability of the learned sparse codes, we assume that the class labels could be predicted from the sparse codes directly using a linear classifier. By solving the codebook, sparse codes, class labels and classifier parameters simultaneously in a unified objective function, we develop a semi-supervised sparse coding algorithm. Experiments on two real-world pattern recognition problems demonstrate the advantage of the proposed methods over supervised sparse coding methods on partially labeled data sets.

Semi-supervised sparse coding

KAUST Repository

Wang, Jim Jing-Yan

2014-07-06

Sparse coding approximates the data sample as a sparse linear combination of some basic codewords and uses the sparse codes as new presentations. In this paper, we investigate learning discriminative sparse codes by sparse coding in a semi-supervised manner, where only a few training samples are labeled. By using the manifold structure spanned by the data set of both labeled and unlabeled samples and the constraints provided by the labels of the labeled samples, we learn the variable class labels for all the samples. Furthermore, to improve the discriminative ability of the learned sparse codes, we assume that the class labels could be predicted from the sparse codes directly using a linear classifier. By solving the codebook, sparse codes, class labels and classifier parameters simultaneously in a unified objective function, we develop a semi-supervised sparse coding algorithm. Experiments on two real-world pattern recognition problems demonstrate the advantage of the proposed methods over supervised sparse coding methods on partially labeled data sets.

Neural-network classifiers for automatic real-world aerial image recognition

Science.gov (United States)

Greenberg, Shlomo; Guterman, Hugo

1996-08-01

We describe the application of the multilayer perceptron (MLP) network and a version of the adaptive resonance theory version 2-A (ART 2-A) network to the problem of automatic aerial image recognition (AAIR). The classification of aerial images, independent of their positions and orientations, is required for automatic tracking and target recognition. Invariance is achieved by the use of different invariant feature spaces in combination with supervised and unsupervised neural networks. The performance of neural-network-based classifiers in conjunction with several types of invariant AAIR global features, such as the Fourier-transform space, Zernike moments, central moments, and polar transforms, are examined. The advantages of this approach are discussed. The performance of the MLP network is compared with that of a classical correlator. The MLP neural-network correlator outperformed the binary phase-only filter (BPOF) correlator. It was found that the ART 2-A distinguished itself with its speed and its low number of required training vectors. However, only the MLP classifier was able to deal with a combination of shift and rotation geometric distortions.

Contrast-based fully automatic segmentation of white matter hyperintensities: method and validation.

Directory of Open Access Journals (Sweden)

Thomas Samaille

Full Text Available White matter hyperintensities (WMH on T2 or FLAIR sequences have been commonly observed on MR images of elderly people. They have been associated with various disorders and have been shown to be a strong risk factor for stroke and dementia. WMH studies usually required visual evaluation of WMH load or time-consuming manual delineation. This paper introduced WHASA (White matter Hyperintensities Automated Segmentation Algorithm, a new method for automatically segmenting WMH from FLAIR and T1 images in multicentre studies. Contrary to previous approaches that were based on intensities, this method relied on contrast: non linear diffusion filtering alternated with watershed segmentation to obtain piecewise constant images with increased contrast between WMH and surroundings tissues. WMH were then selected based on subject dependant automatically computed threshold and anatomical information. WHASA was evaluated on 67 patients from two studies, acquired on six different MRI scanners and displaying a wide range of lesion load. Accuracy of the segmentation was assessed through volume and spatial agreement measures with respect to manual segmentation; an intraclass correlation coefficient (ICC of 0.96 and a mean similarity index (SI of 0.72 were obtained. WHASA was compared to four other approaches: Freesurfer and a thresholding approach as unsupervised methods; k-nearest neighbours (kNN and support vector machines (SVM as supervised ones. For these latter, influence of the training set was also investigated. WHASA clearly outperformed both unsupervised methods, while performing at least as good as supervised approaches (ICC range: 0.87-0.91 for kNN; 0.89-0.94 for SVM. Mean SI: 0.63-0.71 for kNN, 0.67-0.72 for SVM, and did not need any training set.

Automatic Computer Mapping of Terrain

Science.gov (United States)

Smedes, H. W.

1971-01-01

Computer processing of 17 wavelength bands of visible, reflective infrared, and thermal infrared scanner spectrometer data, and of three wavelength bands derived from color aerial film has resulted in successful automatic computer mapping of eight or more terrain classes in a Yellowstone National Park test site. The tests involved: (1) supervised and non-supervised computer programs; (2) special preprocessing of the scanner data to reduce computer processing time and cost, and improve the accuracy; and (3) studies of the effectiveness of the proposed Earth Resources Technology Satellite (ERTS) data channels in the automatic mapping of the same terrain, based on simulations, using the same set of scanner data. The following terrain classes have been mapped with greater than 80 percent accuracy in a 12-square-mile area with 1,800 feet of relief; (1) bedrock exposures, (2) vegetated rock rubble, (3) talus, (4) glacial kame meadow, (5) glacial till meadow, (6) forest, (7) bog, and (8) water. In addition, shadows of clouds and cliffs are depicted, but were greatly reduced by using preprocessing techniques.

Supervised spike-timing-dependent plasticity: a spatiotemporal neuronal learning rule for function approximation and decisions.

Science.gov (United States)

Franosch, Jan-Moritz P; Urban, Sebastian; van Hemmen, J Leo

2013-12-01

How can an animal learn from experience? How can it train sensors, such as the auditory or tactile system, based on other sensory input such as the visual system? Supervised spike-timing-dependent plasticity (supervised STDP) is a possible answer. Supervised STDP trains one modality using input from another one as "supervisor." Quite complex time-dependent relationships between the senses can be learned. Here we prove that under very general conditions, supervised STDP converges to a stable configuration of synaptic weights leading to a reconstruction of primary sensory input.

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

Science.gov (United States)

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.

AUTOMATIC RETINA EXUDATES SEGMENTATION WITHOUT A MANUALLY LABELLED TRAINING SET

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Giancardo, Luca [ORNL; Meriaudeau, Fabrice [ORNL; Karnowski, Thomas Paul [ORNL; Li, Yaquin [University of Tennessee, Knoxville (UTK); Tobin Jr, Kenneth William [ORNL; Chaum, Edward [University of Tennessee, Knoxville (UTK)

2011-01-01

Diabetic macular edema (DME) is a common vision threatening complication of diabetic retinopathy which can be assessed by detecting exudates (a type of bright lesion) in fundus images. In this work, two new methods for the detection of exudates are presented which do not use a supervised learning step and therefore do not require ground-truthed lesion training sets which are time consuming to create, difficult to obtain, and prone to human error. We introduce a new dataset of fundus images from various ethnic groups and levels of DME which we have made publicly available. We evaluate our algorithm with this dataset and compare our results with two recent exudate segmentation algorithms. In all of our tests, our algorithms perform better or comparable with an order of magnitude reduction in computational time.

Late group-based rehabilitation has no advantages compared with supervised home-exercises after total knee arthroplasty

DEFF Research Database (Denmark)

Madsen, Majbritt; Larsen, Kristian; Madsen, Inger Kirkegård

2013-01-01

This study aimed to test whether group-based rehabilitation focusing on strength training, education and self-management is more effective than individual, supervised home-training after fast-track total knee arthroplasty (TKA).......This study aimed to test whether group-based rehabilitation focusing on strength training, education and self-management is more effective than individual, supervised home-training after fast-track total knee arthroplasty (TKA)....

Quasi-supervised scoring of human sleep in polysomnograms using augmented input variables.

Science.gov (United States)

Yaghouby, Farid; Sunderam, Sridhar

2015-04-01

The limitations of manual sleep scoring make computerized methods highly desirable. Scoring errors can arise from human rater uncertainty or inter-rater variability. Sleep scoring algorithms either come as supervised classifiers that need scored samples of each state to be trained, or as unsupervised classifiers that use heuristics or structural clues in unscored data to define states. We propose a quasi-supervised classifier that models observations in an unsupervised manner but mimics a human rater wherever training scores are available. EEG, EMG, and EOG features were extracted in 30s epochs from human-scored polysomnograms recorded from 42 healthy human subjects (18-79 years) and archived in an anonymized, publicly accessible database. Hypnograms were modified so that: 1. Some states are scored but not others; 2. Samples of all states are scored but not for transitional epochs; and 3. Two raters with 67% agreement are simulated. A framework for quasi-supervised classification was devised in which unsupervised statistical models-specifically Gaussian mixtures and hidden Markov models--are estimated from unlabeled training data, but the training samples are augmented with variables whose values depend on available scores. Classifiers were fitted to signal features incorporating partial scores, and used to predict scores for complete recordings. Performance was assessed using Cohen's Κ statistic. The quasi-supervised classifier performed significantly better than an unsupervised model and sometimes as well as a completely supervised model despite receiving only partial scores. The quasi-supervised algorithm addresses the need for classifiers that mimic scoring patterns of human raters while compensating for their limitations. Copyright © 2015 Elsevier Ltd. All rights reserved.

Generative Adversarial Networks-Based Semi-Supervised Learning for Hyperspectral Image Classification

Directory of Open Access Journals (Sweden)

Zhi He

2017-10-01

Full Text Available Classification of hyperspectral image (HSI is an important research topic in the remote sensing community. Significant efforts (e.g., deep learning have been concentrated on this task. However, it is still an open issue to classify the high-dimensional HSI with a limited number of training samples. In this paper, we propose a semi-supervised HSI classification method inspired by the generative adversarial networks (GANs. Unlike the supervised methods, the proposed HSI classification method is semi-supervised, which can make full use of the limited labeled samples as well as the sufficient unlabeled samples. Core ideas of the proposed method are twofold. First, the three-dimensional bilateral filter (3DBF is adopted to extract the spectral-spatial features by naturally treating the HSI as a volumetric dataset. The spatial information is integrated into the extracted features by 3DBF, which is propitious to the subsequent classification step. Second, GANs are trained on the spectral-spatial features for semi-supervised learning. A GAN contains two neural networks (i.e., generator and discriminator trained in opposition to one another. The semi-supervised learning is achieved by adding samples from the generator to the features and increasing the dimension of the classifier output. Experimental results obtained on three benchmark HSI datasets have confirmed the effectiveness of the proposed method , especially with a limited number of labeled samples.

46 CFR 199.100 - Manning of survival craft and supervision.

Science.gov (United States)

2010-10-01

... 46 Shipping 7 2010-10-01 2010-10-01 false Manning of survival craft and supervision. 199.100....100 Manning of survival craft and supervision. (a) There must be a sufficient number of trained... craft and launching arrangements required for abandonment by the total number of persons on board. (c...

Consultative Instructor Supervision and Evaluation

Science.gov (United States)

Lee, William W.

2010-01-01

Organizations vary greatly in how they monitor training instructors. The methods used in monitoring vary greatly. This article presents a systematic process for improving instructor skills that result in better teaching and better learning, which results in better-prepared employees for the workforce. The consultative supervision and evaluation…

On being supervised: getting value from a clinical supervisor and making the relationship work when it is not.

Science.gov (United States)

Parker, Stephen; Suetani, Shuichi; Motamarri, Balaji

2017-12-01

The importance of clinical supervision is emphasised in psychiatric training programs. Despite this, the purpose and processes of supervision are often poorly defined. There is limited guidance available for trainees about their role in making supervision work. This paper considers the nature of supervision in psychiatric training and provides practical advice to help supervisees take active steps to make supervision work. In obtaining value from supervision, the active role of the supervisee in seeking feedback, finding value in criticism and building autonomy is emphasised. Additionally, the importance of exploring what value a supervisor can offer and maintaining realistic expectations is considered. Trainees can benefit from taking an active role in planning and managing their supervision to maximise their learning.

Automatic learning rate adjustment for self-supervising autonomous robot control

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Arras, Michael K.; Protzel, Peter W.; Palumbo, Daniel L.

1992-01-01

Described is an application in which an Artificial Neural Network (ANN) controls the positioning of a robot arm with five degrees of freedom by using visual feedback provided by two cameras. This application and the specific ANN model, local liner maps, are based on the work of Ritter, Martinetz, and Schulten. We extended their approach by generating a filtered, average positioning error from the continuous camera feedback and by coupling the learning rate to this error. When the network learns to position the arm, the positioning error decreases and so does the learning rate until the system stabilizes at a minimum error and learning rate. This abolishes the need for a predetermined cooling schedule. The automatic cooling procedure results in a closed loop control with no distinction between a learning phase and a production phase. If the positioning error suddenly starts to increase due to an internal failure such as a broken joint, or an environmental change such as a camera moving, the learning rate increases accordingly. Thus, learning is automatically activated and the network adapts to the new condition after which the error decreases again and learning is 'shut off'. The automatic cooling is therefore a prerequisite for the autonomy and the fault tolerance of the system.

Supervision and automatic control of robotic systems in nuclear environments

International Nuclear Information System (INIS)

Benner, J.; Leinemann, K.

1992-01-01

This paper describes new developments in controlling remote handling systems for nuclear applications. The main emphasis is to use robotic equipment and methods for reaching a high degree of system autonomy. A remote handling workstation concept is described, supporting various stages of mission planning and supervision by means of suited geometrical, procedural and functional models. The presented control system enables easy switching between semi-autonomous and manual task execution and sensor data integration. Some experimental results of a prototypic implementation are also described

Preparing supervisors to provide safeguarding supervision for healthcare staff.

Science.gov (United States)

Smikle, Marcia

2017-11-28

This paper outlines why experienced supervisors at a London healthcare provider received skills training so they could offer safeguarding supervision to front-line colleagues with case management responsibilities for vulnerable children and young people. It examines how supervisors use the main functions of supervision and a cycle of reflection in clinical practice with supervisees. As well as the professional issues encountered by supervisors in relation to the benefits, the challenges of providing supervision and the action required to make safeguarding supervision a part of the organisational culture are also explored. ©2017 RCN Publishing Company Ltd. All rights reserved. Not to be copied, transmitted or recorded in any way, in whole or part, without prior permission of the publishers.

Supervision to Enhance Educational and Vocational Guidance Practice: A Review

Science.gov (United States)

Reid, Hazel L.

2010-01-01

Supervision to support the work of career practitioners is evident in many countries, but is not universal. This author presents a literature review, intending to emphasise the prime importance of developing supervision for guidance work. The author also considers the issues facing those training to develop the role of supervisors in southeast…

46 CFR 109.323 - Manning of survival craft and supervision.

Science.gov (United States)

2010-10-01

... 46 Shipping 4 2010-10-01 2010-10-01 false Manning of survival craft and supervision. 109.323... DRILLING UNITS OPERATIONS Operation and Stowage of Safety Equipment § 109.323 Manning of survival craft and supervision. (a) There must be a sufficient number of trained persons on board the survival craft for...

Clinical supervision reflected in a Danish DPCCQ sample

DEFF Research Database (Denmark)

Nielsen, Jan; Jacobsen, Claus Haugaard

Core Questionnaire (DPCCQ) has only few questions on supervision. To rectify this limitation, a recent Danish version of the DPCCQ included two new sections on supervision, one focusing on supervisees and another on supervisors and their supervisory training. This paper presents our initial findings...... on giving and receiving clinical supervision as reported by therapists in Denmark. Method: Currently, the Danish sample consists of 350 clinical psychologist doing psychotherapy who completed DPCCQ. Data are currently being prepared for statistical analysis. Results: This paper will focus primarily...... on describing the amount and type of supervision received and given by the sample. Findings from these descriptive statistics will be compared within the sample across demographic parameters such as age and sex, and professional characteristics such as career level, theoretical preferences, type of clients...

Guess Where? Actor-Supervision for Spatiotemporal Action Localization

KAUST Repository

Escorcia, Victor

2018-04-05

This paper addresses the problem of spatiotemporal localization of actions in videos. Compared to leading approaches, which all learn to localize based on carefully annotated boxes on training video frames, we adhere to a weakly-supervised solution that only requires a video class label. We introduce an actor-supervised architecture that exploits the inherent compositionality of actions in terms of actor transformations, to localize actions. We make two contributions. First, we propose actor proposals derived from a detector for human and non-human actors intended for images, which is linked over time by Siamese similarity matching to account for actor deformations. Second, we propose an actor-based attention mechanism that enables the localization of the actions from action class labels and actor proposals and is end-to-end trainable. Experiments on three human and non-human action datasets show actor supervision is state-of-the-art for weakly-supervised action localization and is even competitive to some fully-supervised alternatives.

Guess Where? Actor-Supervision for Spatiotemporal Action Localization

KAUST Repository

Escorcia, Victor; Dao, Cuong D.; Jain, Mihir; Ghanem, Bernard; Snoek, Cees

2018-01-01

This paper addresses the problem of spatiotemporal localization of actions in videos. Compared to leading approaches, which all learn to localize based on carefully annotated boxes on training video frames, we adhere to a weakly-supervised solution that only requires a video class label. We introduce an actor-supervised architecture that exploits the inherent compositionality of actions in terms of actor transformations, to localize actions. We make two contributions. First, we propose actor proposals derived from a detector for human and non-human actors intended for images, which is linked over time by Siamese similarity matching to account for actor deformations. Second, we propose an actor-based attention mechanism that enables the localization of the actions from action class labels and actor proposals and is end-to-end trainable. Experiments on three human and non-human action datasets show actor supervision is state-of-the-art for weakly-supervised action localization and is even competitive to some fully-supervised alternatives.

Development and implementation of full-automatic supervision and control programme for CEFR refueling control system

International Nuclear Information System (INIS)

Zhu Hao; Dong Shengguo; Ma Hongsheng; Zhao Lixia

2011-01-01

In order to make the process of CEFR refueling more convenient and reliable, the computer supervision and control system was designed according to the CEFR refueling technology. Meanwhile, the supervision and control function and database function were developed on the basis of KingView and SQL Server2000. The fuel of reactor core was fully loaded by the system, and full-automation of CEFR refueling process was implemented. (authors)

Challenges for Better thesis supervision.

Science.gov (United States)

Ghadirian, Laleh; Sayarifard, Azadeh; Majdzadeh, Reza; Rajabi, Fatemeh; Yunesian, Masoud

2014-01-01

Conduction of thesis by the students is one of their major academic activities. Thesis quality and acquired experiences are highly dependent on the supervision. Our study is aimed at identifing the challenges in thesis supervision from both students and faculty members point of view. This study was conducted using individual in-depth interviews and Focus Group Discussions (FGD). The participants were 43 students and faculty members selected by purposive sampling. It was carried out in Tehran University of Medical Sciences in 2012. Data analysis was done concurrently with data gathering using content analysis method. Our data analysis resulted in 162 codes, 17 subcategories and 4 major categories, "supervisory knowledge and skills", "atmosphere", "bylaws and regulations relating to supervision" and "monitoring and evaluation". This study showed that more attention and planning in needed for modifying related rules and regulations, qualitative and quantitative improvement in mentorship training, research atmosphere improvement and effective monitoring and evaluation in supervisory area.

Automatic determination of pathological voice transformation coefficients for TDPDOLA using neural network

International Nuclear Information System (INIS)

Belgacem, H.; Cherif, A.

2011-01-01

One of the biggest challenges in vocal transformation with TD-PSOLA technique is the selection of modified parameters that will make a successful speech resynthesis. The best selection methods are by using human ratters. This study focuses on automatic determination of the pathological voice transformation coefficients using an Artificial Neural Network this way by comparing the results to the previous manual work. Four characterizied parameters (RATA-PLP, Jitter, Shimmer and RAP) were chosen. The system is developed with supervised training, consists of recognition (neural network) for synthesis (TD-PSOLA). The experimental results show that the parameter sets selected by the proposed system can be successfully used to resynthesize and demonstrating that our system can assist in vocal of pathological voice's transformation.

ATLAAS: an automatic decision tree-based learning algorithm for advanced image segmentation in positron emission tomography.

Science.gov (United States)

Berthon, Beatrice; Marshall, Christopher; Evans, Mererid; Spezi, Emiliano

2016-07-07

Accurate and reliable tumour delineation on positron emission tomography (PET) is crucial for radiotherapy treatment planning. PET automatic segmentation (PET-AS) eliminates intra- and interobserver variability, but there is currently no consensus on the optimal method to use, as different algorithms appear to perform better for different types of tumours. This work aimed to develop a predictive segmentation model, trained to automatically select and apply the best PET-AS method, according to the tumour characteristics. ATLAAS, the automatic decision tree-based learning algorithm for advanced segmentation is based on supervised machine learning using decision trees. The model includes nine PET-AS methods and was trained on a 100 PET scans with known true contour. A decision tree was built for each PET-AS algorithm to predict its accuracy, quantified using the Dice similarity coefficient (DSC), according to the tumour volume, tumour peak to background SUV ratio and a regional texture metric. The performance of ATLAAS was evaluated for 85 PET scans obtained from fillable and printed subresolution sandwich phantoms. ATLAAS showed excellent accuracy across a wide range of phantom data and predicted the best or near-best segmentation algorithm in 93% of cases. ATLAAS outperformed all single PET-AS methods on fillable phantom data with a DSC of 0.881, while the DSC for H&N phantom data was 0.819. DSCs higher than 0.650 were achieved in all cases. ATLAAS is an advanced automatic image segmentation algorithm based on decision tree predictive modelling, which can be trained on images with known true contour, to predict the best PET-AS method when the true contour is unknown. ATLAAS provides robust and accurate image segmentation with potential applications to radiation oncology.

Self-reflection in cognitive behavioural therapy and supervision.

Science.gov (United States)

Prasko, Jan; Mozny, Petr; Novotny, Miroslav; Slepecky, Milos; Vyskocilova, Jana

2012-12-01

Supervision is a basic part of training and ongoing education in cognitive behavioural therapy. Self-reflection is an important part of supervision. The conscious understanding of one's own emotions, feelings, thoughts, and attitudes at the time of their occurrence, and the ability to continuously follow and recognize them are among the most important abilities of both therapists and supervisors. The objective of this article is to review aspects related to supervision in cognitive behavioural therapy and self-reflection in the literature. This is a narrative review. A literature review was performed using the PubMed, SciVerse Scopus, and Web of Science databases; additional references were found through bibliography reviews of relevant articles published prior to July 2011. The databases were searched for articles containing the following keywords: cognitive behavioural therapy, self-reflection, therapeutic relationship, training, supervision, transference, and countertransference. The review also includes information from monographs referred to by other reviews. We discuss conceptual aspects related to supervision and the role of self-reflection. Self-reflection in therapy is a continuous process which is essential for the establishment of a therapeutic relationship, the professional growth of the therapist, and the ongoing development of therapeutic skills. Recognizing one's own emotions is a basic skill from which other skills necessary for both therapy and emotional self-control stem. Therapists who are skilled in understanding their inner emotions during their encounters with clients are better at making decisions, distinguishing their needs from their clients' needs, understanding transference and countertransference, and considering an optimal response at any time during a session. They know how to handle their feelings so that these correspond with the situation and their response is in the client's best interest. The ability to self-reflect increases the

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

Science.gov (United States)

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.

Semi-Supervised Multiple Feature Analysis for Action Recognition

Science.gov (United States)

2013-11-26

in saving la- beling costs while simultaneously achieving good performance. Most semi-supervised learning methods assume that nearby points are likely...3, 5, 10 and 15) per category in the training set, thus resulting in , , , and randomly la- beled videos, with the remaining training videos unlabeled...with the increase of la- beled training samples, the performance of all algorithms rises. Meanwhile, the performance differences between our method and

Distant Supervision for Relation Extraction with Ranking-Based Methods

Directory of Open Access Journals (Sweden)

Yang Xiang

2016-05-01

Full Text Available Relation extraction has benefited from distant supervision in recent years with the development of natural language processing techniques and data explosion. However, distant supervision is still greatly limited by the quality of training data, due to its natural motivation for greatly reducing the heavy cost of data annotation. In this paper, we construct an architecture called MIML-sort (Multi-instance Multi-label Learning with Sorting Strategies, which is built on the famous MIML framework. Based on MIML-sort, we propose three ranking-based methods for sample selection with which we identify relation extractors from a subset of the training data. Experiments are set up on the KBP (Knowledge Base Propagation corpus, one of the benchmark datasets for distant supervision, which is large and noisy. Compared with previous work, the proposed methods produce considerably better results. Furthermore, the three methods together achieve the best F1 on the official testing set, with an optimal enhancement of F1 from 27.3% to 29.98%.

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

Science.gov (United States)

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.

Clinical Supervision in Alcohol and Drug Abuse Counseling: Principles, Models, Methods.

Science.gov (United States)

Powell, David J.

A case is made for professionalism in clinical training as substance abuse counseling becomes a unique field. Part 1, "Principles," includes: (1) "A Historical Review of Supervision"; (2) "A Working Definition of Supervision"; (3) "Leadership Principles for Supervisors" and; (4) "Traits of an Effective Clinical Supervisor." Part 2, "Models,"…

Teacher training: challenges and possibilities of teaching of reproduction and sexuality in supervised curricular stage

Directory of Open Access Journals (Sweden)

Mayara Lustosa de Oliveira

2012-02-01

Full Text Available This work has its origins in a survey conducted during the supervised curricular stage of Biological Sciences course at Federal University of Goiás. The article describes critically and analytically every step of the curricular stage, especially the classes with the themes: reproduction and sexuality. The classes were taught to elementary students in State College St. Bernadete in Goiânia-GO. To facilitate the process of teaching and learning the trainees divided the themes into subtopics, and several teaching resources were developed for each subtopic. The research was descriptive and exploratory, using interviews with school students, teachers, supervisors and college students to collect data that allowed assessing the success of the teaching methodologies applied by the teachers in training. The field diaries of the trainees were also used to compose the analysis. Through the statements of the participants, it is considered that the methods achieved their goal in clarifying the issues. The resources used not only brought understanding, but encouraged the participation of the students. The article has been organized according to the steps of the curricular stage and it exposes all the impressions of the supervising teachers, school students and undergraduates during the internship, highlighting, comments, concerns, planning and execution process of the activities.

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

Science.gov (United States)

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

The Impact of the School Counselor Supervision Model on the Self-Efficacy of School Counselor Site Supervisors

Science.gov (United States)

Brown, Carleton H.; Olivárez, Artura, Jr.; DeKruyf, Loraine

2018-01-01

Supervision is a critical element in the professional identity development of school counselors; however, available school counseling-specific supervision training is lacking. The authors describe a 4-hour supervision workshop based on the School Counselor Supervision Model (SCSM; Luke & Bernard, 2006) attended by 31 school counselors from…

TU-C-17A-04: BEST IN PHYSICS (THERAPY) - A Supervised Framework for Automatic Contour Assessment for Radiotherapy Planning of Head- Neck Cancer

Energy Technology Data Exchange (ETDEWEB)

Chen, H; Kavanaugh, J; Tan, J; Dolly, S; Gay, H; Thorstad, W; Anastasio, M; Altman, M; Mutic, S; Li, H [Washington University School of Medicine, Saint Louis, MO (United States)

2014-06-15

Purpose: Precise contour delineation of tumor targets and critical structures from CT simulations is essential for accurate radiotherapy (RT) treatment planning. However, manual and automatic delineation processes can be error prone due to limitations in imaging techniques and individual anatomic variability. Tedious and laborious manual verification is hence needed. This study develops a general framework for automatically assessing RT contours for head-neck cancer patients using geometric attribute distribution models (GADMs). Methods: Geometric attributes (centroid and volume) were computed from physician-approved RT contours of 29 head-neck patients. Considering anatomical correlation between neighboring structures, the GADM for each attribute was trained to characterize intra- and interpatient structure variations using principal component analysis. Each trained GADM was scalable and deformable, but constrained by the principal attribute variations of the training contours. A new hierarchical model adaptation algorithm was utilized to assess the RT contour correctness for a given patient. Receiver operating characteristic (ROC) curves were employed to evaluate and tune system parameters for the training models. Results: Experiments utilizing training and non-training data sets with simulated contouring errors were conducted to validate the framework performance. Promising assessment results of contour normality/abnormality for the training contour-based data were achieved with excellent accuracy (0.99), precision (0.99), recall (0.83), and F-score (0.97), while corresponding values of 0.84, 0.96, 0.83, and 0.9 were achieved for the non-training data. Furthermore, the areas under the ROC curves were above 0.9, validating the accuracy of this test. Conclusion: The proposed framework can reliably identify contour normality/abnormality based upon intra- and inter-structure constraints derived from clinically-approved contours. It also allows physicians to

Therapeutic validity and effectiveness of supervised physical exercise training on exercise capacity in patients with chronic obstructive pulmonary disease: A systematic review and meta-analysis

NARCIS (Netherlands)

Vooijs, M.; Siemonsma, P.C.; Heus, I.; Sont, J.K.; Rövekamp, T.A.; Meeteren, N.L. van

2016-01-01

Objective: Our aim was to determine the effectiveness of supervised physical exercise training on exercise capacity in patients with chronic obstructive pulmonary disease taken into consideration indices such as therapeutic validity of interventions, methodological quality of studies, and exercise

REALIZATION OF TRAINING PROGRAMME ON THE BASIS OF LINGUISTIC DATABASE FOR AUTOMATIC TEXTS PROCESSING SYSTEM

Directory of Open Access Journals (Sweden)

M. A. Makarych

2016-01-01

Full Text Available Due to the constant increasing of electronic textual information, modern society needs for the automatic processing of natural language (NL. The main purpose of NL automatic text processing systems is to analyze and create texts and represent their content. The purpose of the paper is the development of linguistic and software bases of an automatic system for processing English publicistic texts. This article discusses the examples of different approaches to the creation of linguistic databases for processing systems. The author gives a detailed description of basic building blocks for a new linguistic processor: lexical-semantic, syntactical and semantic-syntactical. The main advantage of the processor is using special semantic codes in the alphabetical dictionary. The semantic codes have been developed in accordance with a lexical-semantic classification. It helps to precisely define semantic functions of the keywords that are situated in parsing groups and allows the automatic system to avoid typical mistakes. The author also represents the realization of a developed linguistic database in the form of a training computer program.

Artificial intelligence in sports on the example of weight training.

Science.gov (United States)

Novatchkov, Hristo; Baca, Arnold

2013-01-01

The overall goal of the present study was to illustrate the potential of artificial intelligence (AI) techniques in sports on the example of weight training. The research focused in particular on the implementation of pattern recognition methods for the evaluation of performed exercises on training machines. The data acquisition was carried out using way and cable force sensors attached to various weight machines, thereby enabling the measurement of essential displacement and force determinants during training. On the basis of the gathered data, it was consequently possible to deduce other significant characteristics like time periods or movement velocities. These parameters were applied for the development of intelligent methods adapted from conventional machine learning concepts, allowing an automatic assessment of the exercise technique and providing individuals with appropriate feedback. In practice, the implementation of such techniques could be crucial for the investigation of the quality of the execution, the assistance of athletes but also coaches, the training optimization and for prevention purposes. For the current study, the data was based on measurements from 15 rather inexperienced participants, performing 3-5 sets of 10-12 repetitions on a leg press machine. The initially preprocessed data was used for the extraction of significant features, on which supervised modeling methods were applied. Professional trainers were involved in the assessment and classification processes by analyzing the video recorded executions. The so far obtained modeling results showed good performance and prediction outcomes, indicating the feasibility and potency of AI techniques in assessing performances on weight training equipment automatically and providing sportsmen with prompt advice. Key pointsArtificial intelligence is a promising field for sport-related analysis.Implementations integrating pattern recognition techniques enable the automatic evaluation of data

A semi-supervised approach using label propagation to support citation screening.

Science.gov (United States)

Kontonatsios, Georgios; Brockmeier, Austin J; Przybyła, Piotr; McNaught, John; Mu, Tingting; Goulermas, John Y; Ananiadou, Sophia

2017-08-01

Citation screening, an integral process within systematic reviews that identifies citations relevant to the underlying research question, is a time-consuming and resource-intensive task. During the screening task, analysts manually assign a label to each citation, to designate whether a citation is eligible for inclusion in the review. Recently, several studies have explored the use of active learning in text classification to reduce the human workload involved in the screening task. However, existing approaches require a significant amount of manually labelled citations for the text classification to achieve a robust performance. In this paper, we propose a semi-supervised method that identifies relevant citations as early as possible in the screening process by exploiting the pairwise similarities between labelled and unlabelled citations to improve the classification performance without additional manual labelling effort. Our approach is based on the hypothesis that similar citations share the same label (e.g., if one citation should be included, then other similar citations should be included also). To calculate the similarity between labelled and unlabelled citations we investigate two different feature spaces, namely a bag-of-words and a spectral embedding based on the bag-of-words. The semi-supervised method propagates the classification codes of manually labelled citations to neighbouring unlabelled citations in the feature space. The automatically labelled citations are combined with the manually labelled citations to form an augmented training set. For evaluation purposes, we apply our method to reviews from clinical and public health. The results show that our semi-supervised method with label propagation achieves statistically significant improvements over two state-of-the-art active learning approaches across both clinical and public health reviews. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

The Juggling Act of Supervision in Community Mental Health: Implications for Supporting Evidence-Based Treatment.

Science.gov (United States)

Dorsey, Shannon; Pullmann, Michael D; Kerns, Suzanne E U; Jungbluth, Nathaniel; Meza, Rosemary; Thompson, Kelly; Berliner, Lucy

2017-11-01

Supervisors are an underutilized resource for supporting evidence-based treatments (EBTs) in community mental health. Little is known about how EBT-trained supervisors use supervision time. Primary aims were to describe supervision (e.g., modality, frequency), examine functions of individual supervision, and examine factors associated with time allocation to supervision functions. Results from 56 supervisors and 207 clinicians from 25 organizations indicate high prevalence of individual supervision, often alongside group and informal supervision. Individual supervision serves a wide range of functions, with substantial variation at the supervisor-level. Implementation climate was the strongest predictor of time allocation to clinical and EBT-relevant functions.

Supervised Self-Organizing Classification of Superresolution ISAR Images: An Anechoic Chamber Experiment

Directory of Open Access Journals (Sweden)

Radoi Emanuel

2006-01-01

Full Text Available The problem of the automatic classification of superresolution ISAR images is addressed in the paper. We describe an anechoic chamber experiment involving ten-scale-reduced aircraft models. The radar images of these targets are reconstructed using MUSIC-2D (multiple signal classification method coupled with two additional processing steps: phase unwrapping and symmetry enhancement. A feature vector is then proposed including Fourier descriptors and moment invariants, which are calculated from the target shape and the scattering center distribution extracted from each reconstructed image. The classification is finally performed by a new self-organizing neural network called SART (supervised ART, which is compared to two standard classifiers, MLP (multilayer perceptron and fuzzy KNN ( nearest neighbors. While the classification accuracy is similar, SART is shown to outperform the two other classifiers in terms of training speed and classification speed, especially for large databases. It is also easier to use since it does not require any input parameter related to its structure.

A new supervised learning algorithm for spiking neurons.

Science.gov (United States)

Xu, Yan; Zeng, Xiaoqin; Zhong, Shuiming

2013-06-01

The purpose of supervised learning with temporal encoding for spiking neurons is to make the neurons emit a specific spike train encoded by the precise firing times of spikes. If only running time is considered, the supervised learning for a spiking neuron is equivalent to distinguishing the times of desired output spikes and the other time during the running process of the neuron through adjusting synaptic weights, which can be regarded as a classification problem. Based on this idea, this letter proposes a new supervised learning method for spiking neurons with temporal encoding; it first transforms the supervised learning into a classification problem and then solves the problem by using the perceptron learning rule. The experiment results show that the proposed method has higher learning accuracy and efficiency over the existing learning methods, so it is more powerful for solving complex and real-time problems.

Impact of data transformation and preprocessing in supervised ...

African Journals Online (AJOL)

Impact of data transformation and preprocessing in supervised learning ... Nowadays, the ideas of integrating machine learning techniques in power system has ... The proposed algorithm used Python-based split train and k-fold model ...

Introduction to the Special Section on Teaching, Training, and Supervision in Personality and Psychological Assessment.

Science.gov (United States)

Smith, Justin D

2017-01-01

This special section contains empirical and conceptual articles pertaining to the broad topic of teaching, training, and supervision of assessment. Despite some evidence of a decline in recent decades, assessment remains a defining practice of professional psychologists in many subfields, including clinical, counseling, school, and neuropsychology, that consumes a consequential proportion of their time. To restore assessment to its rightful place of prominence, a clear agenda needs to be developed for advancing teaching and training methods, increasing instruction to state-of-the-art methods, and defining aims that could be elucidated through empirical inquiry. The 7 articles in this special section provide a developmental perspective of these issues that collectively provide practical tools for instructors and begin to set the stage for a research agenda in this somewhat neglected area of study that is vital to the identity of professional psychology. Additionally, 2 comments are provided by distinguished figures in the field concerning the implications of the articles in the special section to health services psychology and the competencies-based movement in applied psychology.

Using supervised machine learning to code policy issues: Can classifiers generalize across contexts?

NARCIS (Netherlands)

Burscher, B.; Vliegenthart, R.; de Vreese, C.H.

2015-01-01

Content analysis of political communication usually covers large amounts of material and makes the study of dynamics in issue salience a costly enterprise. In this article, we present a supervised machine learning approach for the automatic coding of policy issues, which we apply to news articles

Supervision functions - Secure operation of sustainable power systems

DEFF Research Database (Denmark)

Morais, Hugo; Zhang, Xinxin; Lind, Morten

2013-01-01

of power systems operation control. The use of PMUs allows more penetration of DG mainly, with technologies based on renewable resources with intermittent and unpredictable operation such a wind power. This paper introduces the Secure Operation of Sustainable Power Systems (SOSPO) project. The SOSPO...... project tries to respond to the question "How to ensure a secure operation of the future power system where the operating point is heavily is fluctuating?" focusing in the Supervision module architecture and in the power system operation states. The main goal of Supervision module is to determine...... the power system operation state based on new stability and security parameters derived from PMUs measurement and coordinate the use of automatic and manual control actions. The coordination of the control action is based not only in the static indicators but also in the performance evaluation of control...

Supervised Transfer Sparse Coding

KAUST Repository

Al-Shedivat, Maruan

2014-07-27

A combination of the sparse coding and transfer learn- ing techniques was shown to be accurate and robust in classification tasks where training and testing objects have a shared feature space but are sampled from differ- ent underlying distributions, i.e., belong to different do- mains. The key assumption in such case is that in spite of the domain disparity, samples from different domains share some common hidden factors. Previous methods often assumed that all the objects in the target domain are unlabeled, and thus the training set solely comprised objects from the source domain. However, in real world applications, the target domain often has some labeled objects, or one can always manually label a small num- ber of them. In this paper, we explore such possibil- ity and show how a small number of labeled data in the target domain can significantly leverage classifica- tion accuracy of the state-of-the-art transfer sparse cod- ing methods. We further propose a unified framework named supervised transfer sparse coding (STSC) which simultaneously optimizes sparse representation, domain transfer and classification. Experimental results on three applications demonstrate that a little manual labeling and then learning the model in a supervised fashion can significantly improve classification accuracy.

Supervised Quality Assessment Of Medical Image Registration: Application to intra-patient CT lung registration

NARCIS (Netherlands)

Muenzing, S.E.; Ginneken, B. van; Murphy, K.; Pluim, J.P.

2012-01-01

A novel method for automatic quality assessment of medical image registration is presented. The method is based on supervised learning of local alignment patterns, which are captured by statistical image features at distinctive landmark points. A two-stage classifier cascade, employing an optimal

Supervised quality assessment of medical image registration : application to intra-patient CT lung registration

NARCIS (Netherlands)

Muenzing, S.E.A.; Ginneken, van B.; Murphy, K.; Pluim, J.P.W.

2012-01-01

A novel method for automatic quality assessment of medical image registration is presented. The method is based on supervised learning of local alignment patterns, which are captured by statistical image features at distinctive landmark points. A two-stage classifier cascade, employing an optimal

Improvement program of state supervision system for radioactive and nuclear installations

International Nuclear Information System (INIS)

Cardenas, J.

1993-01-01

The current program begins as part of a policy to take care of the development of the cuban nuclear program and with the objective of improving the state supervision system of nuclear and radioactive facilities on the basis of the national experience, good skills internationally accepted and taking into account IAEA recommendations. The program develops the following topics: reorientation and restructure of state supervision, review of the current nuclear legislature, update of regulations of facility safety and qualification and training of state supervision personnel

Supervisor training

DEFF Research Database (Denmark)

Pedersen, Inge Nygaard

2015-01-01

on the experience of an integrated supervisor training programme offered in Aalborg, Denmark in 2009/2010. In this programme general issues of professional supervision and the application of artistic media as a core element in the supervisory process were Integrated. It is the hope of the author that this article...... will inspire other music therapists to develop supervisor training programmes for professional music therapists and also to undertake further research into professional supervision....

Automatic Earthquake Detection by Active Learning

Science.gov (United States)

Bergen, K.; Beroza, G. C.

2017-12-01

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

Magazine Picture Collage in Group Supervision

Science.gov (United States)

Shepard, Blythe C.; Guenette, Francis L.

2010-01-01

A magazine picture collage activity was used with three female counsellor education students as a vehicle to support them in processing their experience as counsellors in training. The use of magazine picture collage in group supervision is described, and the benefits and challenges are presented. The collages served as jumping-off points for…

Using Supervised Deep Learning for Human Age Estimation Problem

Science.gov (United States)

Drobnyh, K. A.; Polovinkin, A. N.

2017-05-01

Automatic facial age estimation is a challenging task upcoming in recent years. In this paper, we propose using the supervised deep learning features to improve an accuracy of the existing age estimation algorithms. There are many approaches solving the problem, an active appearance model and the bio-inspired features are two of them which showed the best accuracy. For experiments we chose popular publicly available FG-NET database, which contains 1002 images with a broad variety of light, pose, and expression. LOPO (leave-one-person-out) method was used to estimate the accuracy. Experiments demonstrated that adding supervised deep learning features has improved accuracy for some basic models. For example, adding the features to an active appearance model gave the 4% gain (the error decreased from 4.59 to 4.41).

Fatigue Level Estimation of Bill Based on Acoustic Signal Feature by Supervised SOM

Science.gov (United States)

Teranishi, Masaru; Omatu, Sigeru; Kosaka, Toshihisa

Fatigued bills have harmful influence on daily operation of Automated Teller Machine(ATM). To make the fatigued bills classification more efficient, development of an automatic fatigued bill classification method is desired. We propose a new method to estimate bending rigidity of bill from acoustic signal feature of banking machines. The estimated bending rigidities are used as continuous fatigue level for classification of fatigued bill. By using the supervised Self-Organizing Map(supervised SOM), we estimate the bending rigidity from only the acoustic energy pattern effectively. The experimental result with real bill samples shows the effectiveness of the proposed method.

Automated Detection of Microaneurysms Using Scale-Adapted Blob Analysis and Semi-Supervised Learning

Energy Technology Data Exchange (ETDEWEB)

Adal, Kedir M. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Sidebe, Desire [Univ. of Burgundy, Dijon (France); Ali, Sharib [Univ. of Burgundy, Dijon (France); Chaum, Edward [Univ. of Tennessee, Knoxville, TN (United States); Karnowski, Thomas Paul [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Meriaudeau, Fabrice [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

2014-01-07

Despite several attempts, automated detection of microaneurysm (MA) from digital fundus images still remains to be an open issue. This is due to the subtle nature of MAs against the surrounding tissues. In this paper, the microaneurysm detection problem is modeled as finding interest regions or blobs from an image and an automatic local-scale selection technique is presented. Several scale-adapted region descriptors are then introduced to characterize these blob regions. A semi-supervised based learning approach, which requires few manually annotated learning examples, is also proposed to train a classifier to detect true MAs. The developed system is built using only few manually labeled and a large number of unlabeled retinal color fundus images. The performance of the overall system is evaluated on Retinopathy Online Challenge (ROC) competition database. A competition performance measure (CPM) of 0.364 shows the competitiveness of the proposed system against state-of-the art techniques as well as the applicability of the proposed features to analyze fundus images.

Proficiency in Motivational Interviewing among Nurses in Child Health Services Following Workshop and Supervision with Systematic Feedback.

Directory of Open Access Journals (Sweden)

Johanna Enö Persson

Full Text Available Research on training in motivational interviewing (MI has shown eroding skills after workshops not followed by additional training input (supervision/coaching. There is a need for more research evaluating different types and lengths of post-workshop training with follow-up periods extending six months. This study is an extension of a previous evaluation of the level of proficiency in MI after workshop and four sessions of supervision among nurses in Swedish child health services.To explore the level of MI proficiency among nurses participating in an intervention to prevent childhood obesity (n = 33, after receiving five additional sessions of supervision including feedback on observed practice, as well as level of proficiency at follow-up.Level of proficiency was measured 4 and 12 months after completed supervision using recorded practice samples coded according to the Motivational Interviewing Treatment Integrity (MITI Code. Potential predictors of outcome were investigated.Proficiency remained on the same levels after nine sessions of supervision as after four sessions, and was generally low. The percentage of nurses reaching the proficiency level ranged from 18.2 to 54.5% across indicators. MI-spirit had increased significantly at follow-up, and the rest of the indicators remained on the same levels. No predictors of outcome were found.Comprehensive training programs with prolonged post-workshop supervision and feedback on observed practice may help to sustain but not improve participants' proficiency in MI. Potential explanations to the results and suggestions for future research are discussed.

Constrained Deep Weak Supervision for Histopathology Image Segmentation.

Science.gov (United States)

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.

Extracting microRNA-gene relations from biomedical literature using distant supervision.

Directory of Open Access Journals (Sweden)

Andre Lamurias

Full Text Available Many biomedical relation extraction approaches are based on supervised machine learning, requiring an annotated corpus. Distant supervision aims at training a classifier by combining a knowledge base with a corpus, reducing the amount of manual effort necessary. This is particularly useful for biomedicine because many databases and ontologies have been made available for many biological processes, while the availability of annotated corpora is still limited. We studied the extraction of microRNA-gene relations from text. MicroRNA regulation is an important biological process due to its close association with human diseases. The proposed method, IBRel, is based on distantly supervised multi-instance learning. We evaluated IBRel on three datasets, and the results were compared with a co-occurrence approach as well as a supervised machine learning algorithm. While supervised learning outperformed on two of those datasets, IBRel obtained an F-score 28.3 percentage points higher on the dataset for which there was no training set developed specifically. To demonstrate the applicability of IBRel, we used it to extract 27 miRNA-gene relations from recently published papers about cystic fibrosis. Our results demonstrate that our method can be successfully used to extract relations from literature about a biological process without an annotated corpus. The source code and data used in this study are available at https://github.com/AndreLamurias/IBRel.

Supervising away from home: clinical, cultural and professional challenges.

Science.gov (United States)

Abramovitch, Henry; Wiener, Jan

2017-02-01

This paper explores some challenges of supervising clinical work of trainees, known as 'routers', who live in countries with diverse cultural, social and political traditions, and the analysts who travel to supervise them. It is written as an evolving dialogue between the authors, who explore together the effects of their own culture of origin, and in particular the legacy and values of their own training institutes on the styles and models of analytic supervision. Their dialogue is framed around the meaning of home and experiences of homesickness for analysts working away from home in an interactive field of strangeness in countries where analytical psychology is a relatively new discipline. The authors outline the findings from their own qualitative survey, where other supervisors working abroad, and those they have supervised, describe their experiences and their encounters with difference. The dialogue ends with both authors discussing what they have learned about teaching and supervising abroad, the implications for more flexible use of Jungian concepts, and how such visits have changed their clinical practice in their home countries. © 2017, The Society of Analytical Psychology.

Automatically controlled training systems

International Nuclear Information System (INIS)

Milashenko, A.; Afanasiev, A.

1990-01-01

This paper reports that the computer system for NPP personnel training was developed for training centers in the Soviet Union. The system should be considered as the first step in training, taking into account that further steps are to be devoted to part-task and full scope simulator training. The training room consists of 8-12 IBM PC/AT personal computers combined into a network. A trainee accesses the system in a dialor manner. Software enables the instructor to determine the trainee's progress in different subjects of the program. The quality of any trainee preparedness may be evaluated by Knowledge Control operation. Simplified dynamic models are adopted for separate areas of the program. For example, the system of neutron flux monitoring has a dedicated model. Currently, training, requalification and support of professional qualifications of nuclear power plant operators is being emphasized. A significant number of emergency situations during work are occurring due to operator errors. Based on data from September-October 1989, more than half of all unplanned drops in power and stoppages of power plants were due to operator error. As a comparison, problems due to equipment malfunction accounted for no more than a third of the total. The role of personnel, especially of the operators, is significant during normal operations, since energy production costs as well as losses are influenced by the capability of the staff. These facts all point to the importance of quality training of personnel

Functional measures show improvements after a home exercise program following supervised balance training in older adults with elevated fall risk.

Science.gov (United States)

Tisher, Kristen; Mann, Kimberly; VanDyke, Sarah; Johansson, Charity; Vallabhajosula, Srikant

2018-03-05

Supervised balance training shows immediate benefit for older adults at fall risk. The long-term effectiveness of such training can be enhanced by implementing a safe and simple home exercise program (HEP). We investigated the effects of a12-week unsupervised HEP following supervised clinic-based balance training on functional mobility, balance, fall risk, and gait. Six older adults with an elevated fall risk obtained an HEP and comprised the HEP group (HEPG) and five older adults who were not given an HEP comprised the no HEP group (NoHEPG). The HEP consisted of three static balance exercises: feet-together, single-leg stance, and tandem. Each exercise was to be performed twice for 30-60 s, once per day, 3 days per week for 12 weeks. Participants were educated on proper form, safety, and progression of exercises. Pre- and post-HEP testing included Berg Balance Scale (BBS), Timed Up and Go, Short Physical Performance Battery (SPPB) assessments, Activities-Balance Confidence, Late-Life Functional Disability Instrument and instrumented assessments of balance and gait (Limits of Stability, modified Clinical Test of Sensory Interaction on Balance, Gait). A healthy control group (HCG; n = 11) was also tested. For most of the measures, the HEPG improved to the level of HCG. Though task-specific improvements like BBS and SPPB components were seen, the results did not carry over to more dynamic assessments. Results provide proof of concept that a simple HEP can be independently implemented and effective for sustaining and/or improving balance in older adults at elevated fall-risk after they have undergone a clinic-based balance intervention.

District nurses' experience of supervising nursing students in primary health care: A pre- and post-implementation questionnaire study.

Science.gov (United States)

Bos, Elisabeth; Löfmark, Anna; Törnkvist, Lena

2009-11-01

Nursing students go through clinical supervision in primary health care settings but district nurses' (DNs) circumstances when supervising them are only briefly described in the literature. The aim of this study was to investigate DNs experience of supervising nursing students before and after the implementation of a new supervision model. Ninety-eight (74%) DNs answered a questionnaire before and 84 (65%) after implementation of the new supervision model. The study showed that DNs in most cases felt that conditions for supervision in the workplace were adequate. But about 70% lacked training for the supervisory role and 20% had no specialist district nurse training. They also experienced difficulty in keeping up-to-date with changes in nurse education programmes, in receiving support from the university and from their clinic managers, and in setting aside time for supervision. Improvements after the implementation of a new model chiefly concerned organisation; more DNs stated that one person had primary responsibility for students' clinical practice, that information packages for supervisors and students were available at the health care centres, and that conditions were in place for increasing the number of students they supervised. DNs also stated that supervisors and students benefited from supervision by more than one supervisor. To conclude, implementation of a new supervision model resulted in some improvements.

Automatic training of lemmatization rules that handle morphological changes in pre-, in- and suffixes alike

DEFF Research Database (Denmark)

Jongejan, Bart; Dalianis, Hercules

2009-01-01

We propose a method to automatically train lemmatization rules that handle prefix, infix and suffix changes to generate the lemma from the full form of a word. We explain how the lemmatization rules are created and how the lemmatizer works. We trained this lemmatizer on Danish, Dutch, English......, German, Greek, Icelandic, Norwegian, Polish, Slovene and Swedish full form-lemma pairs respectively. We obtained significant improvements of 24 percent for Polish, 2.3 percent for Dutch, 1.5 percent for English, 1.2 percent for German and 1.0 percent for Swedish compared to plain suffix lemmatization...... using a suffix-only lemmatizer. Icelandic deteriorated with 1.9 percent. We also made an observation regarding the number of produced lemmatization rules as a function of the number of training pairs....

Improving Semi-Supervised Learning with Auxiliary Deep Generative Models

DEFF Research Database (Denmark)

Maaløe, Lars; Sønderby, Casper Kaae; Sønderby, Søren Kaae

Deep generative models based upon continuous variational distributions parameterized by deep networks give state-of-the-art performance. In this paper we propose a framework for extending the latent representation with extra auxiliary variables in order to make the variational distribution more...... expressive for semi-supervised learning. By utilizing the stochasticity of the auxiliary variable we demonstrate how to train discriminative classifiers resulting in state-of-the-art performance within semi-supervised learning exemplified by an 0.96% error on MNIST using 100 labeled data points. Furthermore...

Processes that Inform Multicultural Supervision: A Qualitative Meta-Analysis.

Science.gov (United States)

Tohidian, Nilou B; Quek, Karen Mui-Teng

2017-10-01

As the fields of counseling and psychotherapy have become more cognizant that individuals, couples, and families bring with them a myriad of diversity factors into therapy, multicultural competency has also become a crucial component in the development of clinicians during clinical supervision and training. We employed a qualitative meta-analysis to provide a detailed and comprehensive description of similar themes identified in primary qualitative studies that have investigated supervisory practices with an emphasis on diversity. Findings revealed six meta-categories, namely: (a) Supervisor's Multicultural Stances; (b) Supervisee's Multicultural Encounters; (c) Competency-Based Content in Supervision; (d) Processes Surrounding Multicultural Supervision; (e) Culturally Attuned Interventions; and (f) Multicultural Supervisory Alliance. Implications for practice are discussed. © 2017 American Association for Marriage and Family Therapy.

Using clinical supervision to improve the quality and safety of patient care: a response to Berwick and Francis.

Science.gov (United States)

Tomlinson, Jonathon

2015-06-11

After widely publicised investigations into excess patient deaths at Mid Staffordshire hospital the UK government commissioned reports from Robert Francis QC and Professor Don Berwick. Among their recommendations to improve the quality and safety of patient care were lifelong learning, professional support and 'just culture'. Clinical supervision is in an excellent position to support these activities but opportunities are in danger of being squeezed out by regulatory and managerial demands. Doctors who have completed their training are responsible for complex professional judgements for which narrative supervision is particularly helpful. With reference to the literature and my own practice I propose that all practicing clinicians should have regular clinical supervision. Clinical supervision has patient-safety and the quality of patient care as its primary purposes. After training is completed, doctors may practice for the rest of their career without any clinical supervision, the implication being that the difficulties dealt with in clinical supervision are no longer difficulties, or are better dealt with some other way. Clinical supervision is sufficiently flexible to be adapted to the needs of experienced clinicians as its forms can be varied, though its functions remain focused on patient safety, good quality clinical care and professional wellbeing. The evidence linking clinical supervision to the quality and safety of patient care reveals that supervision is most effective when its educational and supportive functions are separated from its managerial and evaluative functions. Among supervision's different forms, narrative-based-supervision is particularly useful as it has been developed for clinicians who have completed their training. It provides ways to explore the complexity of clinical judgements and encourages doctors to question one another's authority in a supportive culture. To be successful, supervision should also be professionally led and

Automatic recognition of falls in gait-slip training: Harness load cell based criteria.

Science.gov (United States)

Yang, Feng; Pai, Yi-Chung

2011-08-11

Over-head-harness systems, equipped with load cell sensors, are essential to the participants' safety and to the outcome assessment in perturbation training. The purpose of this study was to first develop an automatic outcome recognition criterion among young adults for gait-slip training and then verify such criterion among older adults. Each of 39 young and 71 older subjects, all protected by safety harness, experienced 8 unannounced, repeated slips, while walking on a 7m walkway. Each trial was monitored with a motion capture system, bilateral ground reaction force (GRF), harness force, and video recording. The fall trials were first unambiguously indentified with careful visual inspection of all video records. The recoveries without balance loss (in which subjects' trailing foot landed anteriorly to the slipping foot) were also first fully recognized from motion and GRF analyses. These analyses then set the gold standard for the outcome recognition with load cell measurements. Logistic regression analyses based on young subjects' data revealed that the peak load cell force was the best predictor of falls (with 100% accuracy) at the threshold of 30% body weight. On the other hand, the peak moving average force of load cell across 1s period, was the best predictor (with 100% accuracy) separating recoveries with backward balance loss (in which the recovery step landed posterior to slipping foot) from harness assistance at the threshold of 4.5% body weight. These threshold values were fully verified using the data from older adults (100% accuracy in recognizing falls). Because of the increasing popularity in the perturbation training coupling with the protective over-head-harness system, this new criterion could have far reaching implications in automatic outcome recognition during the movement therapy. Copyright © 2011 Elsevier Ltd. All rights reserved.

Supervision--growing and building a sustainable general practice supervisor system.

Science.gov (United States)

Thomson, Jennifer S; Anderson, Katrina J; Mara, Paul R; Stevenson, Alexander D

2011-06-06

This article explores various models and ideas for future sustainable general practice vocational training supervision in Australia. The general practitioner supervisor in the clinical practice setting is currently central to training the future general practice workforce. Finding ways to recruit, retain and motivate both new and experienced GP teachers is discussed, as is the creation of career paths for such teachers. Some of the newer methods of practice-based teaching are considered for further development, including vertically integrated teaching, e-learning, wave consulting and teaching on the run, teaching teams and remote teaching. Approaches to supporting and resourcing teaching and the required infrastructure are also considered. Further research into sustaining the practice-based general practice supervision model will be required.

Determining effects of non-synonymous SNPs on protein-protein interactions using supervised and semi-supervised learning.

Directory of Open Access Journals (Sweden)

Nan Zhao

2014-05-01

Full Text Available 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

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

Science.gov (United States)

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

Generalization of Supervised Learning for Binary Mask Estimation

DEFF Research Database (Denmark)

May, Tobias; Gerkmann, Timo

2014-01-01

This paper addresses the problem of speech segregation by es- timating the ideal binary mask (IBM) from noisy speech. Two methods will be compared, one supervised learning approach that incorporates a priori knowledge about the feature distri- bution observed during training. The second method...

Cavity contour segmentation in chest radiographs using supervised learning and dynamic programming

International Nuclear Information System (INIS)

Maduskar, Pragnya; Hogeweg, Laurens; Sánchez, Clara I.; Ginneken, Bram van; Jong, Pim A. de; Peters-Bax, Liesbeth; Dawson, Rodney; Ayles, Helen

2014-01-01

Purpose: Efficacy of tuberculosis (TB) treatment is often monitored using chest radiography. Monitoring size of cavities in pulmonary tuberculosis is important as the size predicts severity of the disease and its persistence under therapy predicts relapse. The authors present a method for automatic cavity segmentation in chest radiographs. Methods: A two stage method is proposed to segment the cavity borders, given a user defined seed point close to the center of the cavity. First, a supervised learning approach is employed to train a pixel classifier using texture and radial features to identify the border pixels of the cavity. A likelihood value of belonging to the cavity border is assigned to each pixel by the classifier. The authors experimented with four different classifiers:k-nearest neighbor (kNN), linear discriminant analysis (LDA), GentleBoost (GB), and random forest (RF). Next, the constructed likelihood map was used as an input cost image in the polar transformed image space for dynamic programming to trace the optimal maximum cost path. This constructed path corresponds to the segmented cavity contour in image space. Results: The method was evaluated on 100 chest radiographs (CXRs) containing 126 cavities. The reference segmentation was manually delineated by an experienced chest radiologist. An independent observer (a chest radiologist) also delineated all cavities to estimate interobserver variability. Jaccard overlap measure Ω was computed between the reference segmentation and the automatic segmentation; and between the reference segmentation and the independent observer's segmentation for all cavities. A median overlap Ω of 0.81 (0.76 ± 0.16), and 0.85 (0.82 ± 0.11) was achieved between the reference segmentation and the automatic segmentation, and between the segmentations by the two radiologists, respectively. The best reported mean contour distance and Hausdorff distance between the reference and the automatic segmentation were

Cavity contour segmentation in chest radiographs using supervised learning and dynamic programming

Energy Technology Data Exchange (ETDEWEB)

Maduskar, Pragnya, E-mail: pragnya.maduskar@radboudumc.nl; Hogeweg, Laurens; Sánchez, Clara I.; Ginneken, Bram van [Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, 6525 GA (Netherlands); Jong, Pim A. de [Department of Radiology, University Medical Center Utrecht, 3584 CX (Netherlands); Peters-Bax, Liesbeth [Department of Radiology, Radboud University Medical Center, Nijmegen, 6525 GA (Netherlands); Dawson, Rodney [University of Cape Town Lung Institute, Cape Town 7700 (South Africa); Ayles, Helen [Department of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London WC1E 7HT (United Kingdom)

2014-07-15

Purpose: Efficacy of tuberculosis (TB) treatment is often monitored using chest radiography. Monitoring size of cavities in pulmonary tuberculosis is important as the size predicts severity of the disease and its persistence under therapy predicts relapse. The authors present a method for automatic cavity segmentation in chest radiographs. Methods: A two stage method is proposed to segment the cavity borders, given a user defined seed point close to the center of the cavity. First, a supervised learning approach is employed to train a pixel classifier using texture and radial features to identify the border pixels of the cavity. A likelihood value of belonging to the cavity border is assigned to each pixel by the classifier. The authors experimented with four different classifiers:k-nearest neighbor (kNN), linear discriminant analysis (LDA), GentleBoost (GB), and random forest (RF). Next, the constructed likelihood map was used as an input cost image in the polar transformed image space for dynamic programming to trace the optimal maximum cost path. This constructed path corresponds to the segmented cavity contour in image space. Results: The method was evaluated on 100 chest radiographs (CXRs) containing 126 cavities. The reference segmentation was manually delineated by an experienced chest radiologist. An independent observer (a chest radiologist) also delineated all cavities to estimate interobserver variability. Jaccard overlap measure Ω was computed between the reference segmentation and the automatic segmentation; and between the reference segmentation and the independent observer's segmentation for all cavities. A median overlap Ω of 0.81 (0.76 ± 0.16), and 0.85 (0.82 ± 0.11) was achieved between the reference segmentation and the automatic segmentation, and between the segmentations by the two radiologists, respectively. The best reported mean contour distance and Hausdorff distance between the reference and the automatic segmentation were

Second-order sliding mode controller with model reference adaptation for automatic train operation

Science.gov (United States)

Ganesan, M.; Ezhilarasi, D.; Benni, Jijo

2017-11-01

In this paper, a new approach to model reference based adaptive second-order sliding mode control together with adaptive state feedback is presented to control the longitudinal dynamic motion of a high speed train for automatic train operation with the objective of minimal jerk travel by the passengers. The nonlinear dynamic model for the longitudinal motion of the train comprises of a locomotive and coach subsystems is constructed using multiple point-mass model by considering the forces acting on the vehicle. An adaptation scheme using Lyapunov criterion is derived to tune the controller gains by considering a linear, stable reference model that ensures the stability of the system in closed loop. The effectiveness of the controller tracking performance is tested under uncertain passenger load, coupler-draft gear parameters, propulsion resistance coefficients variations and environmental disturbances due to side wind and wet rail conditions. The results demonstrate improved tracking performance of the proposed control scheme with a least jerk under maximum parameter uncertainties when compared to constant gain second-order sliding mode control.

Supervised exercise reduces cancer-related fatigue: a systematic review

Directory of Open Access Journals (Sweden)

José F Meneses-Echávez

2015-01-01

Full Text Available Question: Does supervised physical activity reduce cancer-related fatigue? Design: Systematic review with meta-analysis of randomised trials. Participants: People diagnosed with any type of cancer, without restriction to a particular stage of diagnosis or treatment. Intervention: 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. Outcome measures: 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. Results: 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. Conclusion: 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

A hybrid approach to automatic de-identification of psychiatric notes.

Science.gov (United States)

Lee, Hee-Jin; Wu, Yonghui; Zhang, Yaoyun; Xu, Jun; Xu, Hua; Roberts, Kirk

2017-11-01

De-identification, or identifying and removing protected health information (PHI) from clinical data, is a critical step in making clinical data available for clinical applications and research. This paper presents a natural language processing system for automatic de-identification of psychiatric notes, which was designed to participate in the 2016 CEGS N-GRID shared task Track 1. The system has a hybrid structure that combines machine leaning techniques and rule-based approaches. The rule-based components exploit the structure of the psychiatric notes as well as characteristic surface patterns of PHI mentions. The machine learning components utilize supervised learning with rich features. In addition, the system performance was boosted with integration of additional data to the training set through domain adaptation. The hybrid system showed overall micro-averaged F-score 90.74 on the test set, second-best among all the participants of the CEGS N-GRID task. Copyright © 2017. Published by Elsevier Inc.

CANDiS: Coupled & Attention-Driven Neural Distant Supervision

OpenAIRE

Nagarajan, Tushar; Sharmistha; Talukdar, Partha

2017-01-01

Distant Supervision for Relation Extraction uses heuristically aligned text data with an existing knowledge base as training data. The unsupervised nature of this technique allows it to scale to web-scale relation extraction tasks, at the expense of noise in the training data. Previous work has explored relationships among instances of the same entity-pair to reduce this noise, but relationships among instances across entity-pairs have not been fully exploited. We explore the use of inter-ins...

GMDH-Based Semi-Supervised Feature Selection for Electricity Load Classification Forecasting

Directory of Open Access Journals (Sweden)

Lintao Yang

2018-01-01

Full Text Available With the development of smart power grids, communication network technology and sensor technology, there has been an exponential growth in complex electricity load data. Irregular electricity load fluctuations caused by the weather and holiday factors disrupt the daily operation of the power companies. To deal with these challenges, this paper investigates a day-ahead electricity peak load interval forecasting problem. It transforms the conventional continuous forecasting problem into a novel interval forecasting problem, and then further converts the interval forecasting problem into the classification forecasting problem. In addition, an indicator system influencing the electricity load is established from three dimensions, namely the load series, calendar data, and weather data. A semi-supervised feature selection algorithm is proposed to address an electricity load classification forecasting issue based on the group method of data handling (GMDH technology. The proposed algorithm consists of three main stages: (1 training the basic classifier; (2 selectively marking the most suitable samples from the unclassified label data, and adding them to an initial training set; and (3 training the classification models on the final training set and classifying the test samples. An empirical analysis of electricity load dataset from four Chinese cities is conducted. Results show that the proposed model can address the electricity load classification forecasting problem more efficiently and effectively than the FW-Semi FS (forward semi-supervised feature selection and GMDH-U (GMDH-based semi-supervised feature selection for customer classification models.

Phenotype classification of zebrafish embryos by supervised learning.

Directory of Open Access Journals (Sweden)

Nathalie Jeanray

Full Text Available Zebrafish is increasingly used to assess biological properties of chemical substances and thus is becoming a specific tool for toxicological and pharmacological studies. The effects of chemical substances on embryo survival and development are generally evaluated manually through microscopic observation by an expert and documented by several typical photographs. Here, we present a methodology to automatically classify brightfield images of wildtype zebrafish embryos according to their defects by using an image analysis approach based on supervised machine learning. We show that, compared to manual classification, automatic classification results in 90 to 100% agreement with consensus voting of biological experts in nine out of eleven considered defects in 3 days old zebrafish larvae. Automation of the analysis and classification of zebrafish embryo pictures reduces the workload and time required for the biological expert and increases the reproducibility and objectivity of this classification.

The efficacy of unsupervised home-based exercise regimens in comparison to supervised laboratory-based exercise training upon cardio-respiratory health facets.

Science.gov (United States)

Blackwell, James; Atherton, Philip J; Smith, Kenneth; Doleman, Brett; Williams, John P; Lund, Jonathan N; Phillips, Bethan E

2017-09-01

Supervised high-intensity interval training (HIIT) can rapidly improve cardiorespiratory fitness (CRF). However, the effectiveness of time-efficient unsupervised home-based interventions is unknown. Eighteen volunteers completed either: laboratory-HIIT (L-HIIT); home-HIIT (H-HIIT) or home-isometric hand-grip training (H-IHGT). CRF improved significantly in L-HIIT and H-HIIT groups, with blood pressure improvements in the H-IHGT group only. H-HIIT offers a practical, time-efficient exercise mode to improve CRF, away from the laboratory environment. H-IHGT potentially provides a viable alternative to modify blood pressure in those unable to participate in whole-body exercise. © 2017 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of The Physiological Society and the American Physiological Society.

The supervisions in the field develop nuclear professionals; Las supervisiones en campo desarrollan profesionales nucleares

Energy Technology Data Exchange (ETDEWEB)

Fernandez de la Casa, M.; Buedo, J. L.; Gonzalez, F.

2015-07-01

In 2011 Cofrentes Nuclear Power Plants began a training program for improving the supervision of managers in the field: the effort done not only has improved the quality of supervisions but also has defined a way to reinforce behavior expectations of Cofrentes Nuclear Power Plant. (Author)

Supervised Gaussian mixture model based remote sensing image ...

African Journals Online (AJOL)

Using the supervised classification technique, both simulated and empirical satellite remote sensing data are used to train and test the Gaussian mixture model algorithm. For the purpose of validating the experiment, the resulting classified satellite image is compared with the ground truth data. For the simulated modelling, ...

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.

Science.gov (United States)

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

Short and long-term effects of supervised versus unsupervised exercise training on health-related quality of life and functional outcomes following lung cancer surgery - a randomized controlled trial.

Science.gov (United States)

Brocki, Barbara Cristina; Andreasen, Jane; Nielsen, Lene Rodkjaer; Nekrasas, Vytautas; Gorst-Rasmussen, Anders; Westerdahl, Elisabeth

2014-01-01

Surgical resection enhances long-term survival after lung cancer, but survivors face functional deficits and report on poor quality of life long time after surgery. This study evaluated short and long-term effects of supervised group exercise training on health-related quality of life and physical performance in patients, who were radically operated for lung cancer. A randomized, assessor-blinded, controlled trial was performed on 78 patients undergoing lung cancer surgery. The intervention group (IG, n=41) participated in supervised out-patient exercise training sessions, one hour once a week for ten weeks. The sessions were based on aerobic exercises with target intensity of 60-80% of work capacity, resistance training and dyspnoea management. The control group (CG, n=37) received one individual instruction in exercise training. Measurements consisted of: health-related quality of life (SF36), six minute walk test (6MWT) and lung function (spirometry), assessed three weeks after surgery and after four and twelve months. Both groups were comparable at baseline on demographic characteristic and outcome values. We found a statistically significant effect after four months in the bodily pain domain of SF36, with an estimated mean difference (EMD) of 15.3 (95% CI:4 to 26.6, p=0.01) and a trend in favour of the intervention for role physical functioning (EMD 12.04, 95% CI: -1 to 25.1, p=0.07) and physical component summary (EMD 3.76, 95% CI:-0.1 to 7.6, p=0.06). At 12 months, the tendency was reversed, with the CG presenting overall slightly better measures. We found no effect of the intervention on 6MWT or lung volumes at any time-point. Supervised compared to unsupervised exercise training resulted in no improvement in health-related quality of life, except for the bodily pain domain, four months after lung cancer surgery. No effects of the intervention were found for any outcome after one year. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Musical Instrument Classification Based on Nonlinear Recurrence Analysis and Supervised Learning

Directory of Open Access Journals (Sweden)

R.Rui

2013-04-01

Full Text Available In this paper, the phase space reconstruction of time series produced by different instruments is discussed based on the nonlinear dynamic theory. The dense ratio, a novel quantitative recurrence parameter, is proposed to describe the difference of wind instruments, stringed instruments and keyboard instruments in the phase space by analyzing the recursive property of every instrument. Furthermore, a novel supervised learning algorithm for automatic classification of individual musical instrument signals is addressed deriving from the idea of supervised non-negative matrix factorization (NMF algorithm. In our approach, the orthogonal basis matrix could be obtained without updating the matrix iteratively, which NMF is unable to do. The experimental results indicate that the accuracy of the proposed method is improved by 3% comparing with the conventional features in the individual instrument classification.

Changes in Perceived Supervision Quality After Introduction of Competency-Based Orthopedic Residency Training: A National 6-Year Follow-Up Study.

Science.gov (United States)

van Vendeloo, Stefan N; Brand, Paul L P; Kollen, Boudewijn J; Verheyen, Cees C P M

2018-04-27

To evaluate the perceived quality of the learning environment, before and after introduction of competency-based postgraduate orthopedic education. From 2009 to 2014, we conducted annual surveys among Dutch orthopedic residents. The validated Dutch Residency Educational Climate Test (D-RECT, 50 items on 11 subscales) was used to assess the quality of the learning environment. Scores range from 1 (poor) to 5 (excellent). Dynamic cohort follow-up study. All Dutch orthopedic residents were surveyed during annual compulsory courses. Over the 6-year period, 641 responses were obtained (response rate 92%). Scores for "supervision" (95% CI for difference 0.06-0.28, p = 0.002) and "coaching and assessment" (95% CI 0.11-0.35, p < 0.001) improved significantly after introduction of competency-based training. There was no significant change in score on the other subscales of the D-RECT. After the introduction of some of the core components of competency-based postgraduate orthopedic education the perceived quality of "supervision" and "coaching and assessment" improved significantly. Copyright © 2018 Association of Program Directors in Surgery. Published by Elsevier Inc. All rights reserved.

Implementing a sustainable clinical supervision model for Isles nurses in Orkney.

Science.gov (United States)

Hall, Ian

2018-03-02

The Isles Network of Care (INOC) community nurses work at the extreme of the remote and rural continuum, working mostly as lone practitioners. Following the development of sustainable clinical supervision model for Isles nurses in Orkney, clinical supervision was found to improve both peer support and governance for this group of isolated staff. A literature overview identified the transition of clinical supervision in general nursing over 24 years from 'carrot' to 'stick'. The study included a questionnaire survey that was sent to the 2017 Queen's Nursing Institute Scotland cohort to elicit information about the nurses' experience of clinical supervision. The survey found that 55% provide supervision and 40% receive it. Health board encouragement of its use was found to be disappointingly low at 40%. The INOC nurses were surveyed about the new peer-support (restorative) model, which relies on video-conference contact to allow face to face interaction between isolated isles nurses. Feedback prompted a review of clinical supervision pairings, and the frequency and methods of meeting. The need for supervisor training led to agreement with the Remote and Rural Health Education Alliance to provide relevant support. The perceived benefits of supervision included increased support and reflection, and improved relationships with isolated colleagues.

Automatic lung segmentation in functional SPECT images using active shape models trained on reference lung shapes from CT.

Science.gov (United States)

Cheimariotis, Grigorios-Aris; Al-Mashat, Mariam; Haris, Kostas; Aletras, Anthony H; Jögi, Jonas; Bajc, Marika; Maglaveras, Nicolaos; Heiberg, Einar

2018-02-01

Image segmentation is an essential step in quantifying the extent of reduced or absent lung function. The aim of this study is to develop and validate a new tool for automatic segmentation of lungs in ventilation and perfusion SPECT images and compare automatic and manual SPECT lung segmentations with reference computed tomography (CT) volumes. A total of 77 subjects (69 patients with obstructive lung disease, and 8 subjects without apparent perfusion of ventilation loss) performed low-dose CT followed by ventilation/perfusion (V/P) SPECT examination in a hybrid gamma camera system. In the training phase, lung shapes from the 57 anatomical low-dose CT images were used to construct two active shape models (right lung and left lung) which were then used for image segmentation. The algorithm was validated in 20 patients, comparing its results to reference delineation of corresponding CT images, and by comparing automatic segmentation to manual delineations in SPECT images. The Dice coefficient between automatic SPECT delineations and manual SPECT delineations were 0.83 ± 0.04% for the right and 0.82 ± 0.05% for the left lung. There was statistically significant difference between reference volumes from CT and automatic delineations for the right (R = 0.53, p = 0.02) and left lung (R = 0.69, p automatic quantification of wide range of measurements.

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

Science.gov (United States)

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.

Supportive supervision for volunteers to deliver reproductive health education: a cluster randomized trial.

Science.gov (United States)

Singh, Debra; Negin, Joel; Orach, Christopher Garimoi; Cumming, Robert

2016-10-03

Community Health Volunteers (CHVs) can be effective in improving pregnancy and newborn outcomes through community education. Inadequate supervision of CHVs, whether due to poor planning, irregular visits, or ineffective supervisory methods, is, however, recognized as a weakness in many programs. There has been little research on best practice supervisory or accompaniment models. From March 2014 to February 2015 a proof of concept study was conducted to compare training alone versus training and supportive supervision by paid CHWs (n = 4) on the effectiveness of CHVs (n = 82) to deliver education about pregnancy, newborn care, family planning and hygiene. The pair-matched cluster randomized trial was conducted in eight villages (four intervention and four control) in Budondo sub-county in Jinja, Uganda. Increases in desired behaviors were seen in both the intervention and control arms over the study period. Both arms showed high retention rates of CHVs (95 %). At 1 year follow-up there was a significantly higher prevalence of installed and functioning tippy taps for hand washing (p services. Supportive supervision involves creating a non-threatening, empowering environment in which both the CHV and the supervising CHW learn together and overcome obstacles that might otherwise demotivate the CHV. While the results seem promising for added value with supportive supervision for CHVs undertaking reproductive health activities, further research on a larger scale will be needed to substantiate the effect.

Exploiting the potential of unlabeled endoscopic video data with self-supervised learning.

Science.gov (United States)

Ross, Tobias; Zimmerer, David; Vemuri, Anant; Isensee, Fabian; Wiesenfarth, Manuel; Bodenstedt, Sebastian; Both, Fabian; Kessler, Philip; Wagner, Martin; Müller, Beat; Kenngott, Hannes; Speidel, Stefanie; Kopp-Schneider, Annette; Maier-Hein, Klaus; Maier-Hein, Lena

2018-04-27

Surgical data science is a new research field that aims to observe all aspects of the patient treatment process in order to provide the right assistance at the right time. Due to the breakthrough successes of deep learning-based solutions for automatic image annotation, the availability of reference annotations for algorithm training is becoming a major bottleneck in the field. The purpose of this paper was to investigate the concept of self-supervised learning to address this issue. Our approach is guided by the hypothesis that unlabeled video data can be used to learn a representation of the target domain that boosts the performance of state-of-the-art machine learning algorithms when used for pre-training. Core of the method is an auxiliary task based on raw endoscopic video data of the target domain that is used to initialize the convolutional neural network (CNN) for the target task. In this paper, we propose the re-colorization of medical images with a conditional generative adversarial network (cGAN)-based architecture as auxiliary task. A variant of the method involves a second pre-training step based on labeled data for the target task from a related domain. We validate both variants using medical instrument segmentation as target task. The proposed approach can be used to radically reduce the manual annotation effort involved in training CNNs. Compared to the baseline approach of generating annotated data from scratch, our method decreases exploratively the number of labeled images by up to 75% without sacrificing performance. Our method also outperforms alternative methods for CNN pre-training, such as pre-training on publicly available non-medical (COCO) or medical data (MICCAI EndoVis2017 challenge) using the target task (in this instance: segmentation). As it makes efficient use of available (non-)public and (un-)labeled data, the approach has the potential to become a valuable tool for CNN (pre-)training.

Quality assurance using outlier detection on an automatic segmentation method for the cerebellar peduncles

Science.gov (United States)

Li, Ke; Ye, Chuyang; Yang, Zhen; Carass, Aaron; Ying, Sarah H.; Prince, Jerry L.

2016-03-01

Cerebellar peduncles (CPs) are white matter tracts connecting the cerebellum to other brain regions. Automatic segmentation methods of the CPs have been proposed for studying their structure and function. Usually the performance of these methods is evaluated by comparing segmentation results with manual delineations (ground truth). However, when a segmentation method is run on new data (for which no ground truth exists) it is highly desirable to efficiently detect and assess algorithm failures so that these cases can be excluded from scientific analysis. In this work, two outlier detection methods aimed to assess the performance of an automatic CP segmentation algorithm are presented. The first one is a univariate non-parametric method using a box-whisker plot. We first categorize automatic segmentation results of a dataset of diffusion tensor imaging (DTI) scans from 48 subjects as either a success or a failure. We then design three groups of features from the image data of nine categorized failures for failure detection. Results show that most of these features can efficiently detect the true failures. The second method—supervised classification—was employed on a larger DTI dataset of 249 manually categorized subjects. Four classifiers—linear discriminant analysis (LDA), logistic regression (LR), support vector machine (SVM), and random forest classification (RFC)—were trained using the designed features and evaluated using a leave-one-out cross validation. Results show that the LR performs worst among the four classifiers and the other three perform comparably, which demonstrates the feasibility of automatically detecting segmentation failures using classification methods.

Automated detection of microaneurysms using scale-adapted blob analysis and semi-supervised learning.

Science.gov (United States)

Adal, Kedir M; Sidibé, Désiré; Ali, Sharib; Chaum, Edward; Karnowski, Thomas P; Mériaudeau, Fabrice

2014-04-01

Despite several attempts, automated detection of microaneurysm (MA) from digital fundus images still remains to be an open issue. This is due to the subtle nature of MAs against the surrounding tissues. In this paper, the microaneurysm detection problem is modeled as finding interest regions or blobs from an image and an automatic local-scale selection technique is presented. Several scale-adapted region descriptors are introduced to characterize these blob regions. A semi-supervised based learning approach, which requires few manually annotated learning examples, is also proposed to train a classifier which can detect true MAs. The developed system is built using only few manually labeled and a large number of unlabeled retinal color fundus images. The performance of the overall system is evaluated on Retinopathy Online Challenge (ROC) competition database. A competition performance measure (CPM) of 0.364 shows the competitiveness of the proposed system against state-of-the art techniques as well as the applicability of the proposed features to analyze fundus images. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Race in Supervision: Let's Talk About It.

Science.gov (United States)

Schen, Cathy R; Greenlee, Alecia

2018-01-01

Addressing race and racial trauma within psychotherapy supervision is increasingly important in psychiatry training. A therapist's ability to discuss race and racial trauma in psychotherapy supervision increases the likelihood that these topics will be explored as they arise in the therapeutic setting. The authors discuss the contextual and sociocultural dynamics that contributed to their own avoidance of race and racial trauma within the supervisory relationship. The authors examine the features that eventually led to a robust discussion of race and culture within the supervisory setting and identify salient themes that occurred during three phases of the conversation about race: pre-dialogue, the conversation, and after the conversation. These themes include building an alliance, supercompetence, avoidance, shared vulnerability, "if I speak on this, I own it," closeness versus distance, and speaking up. This article reviews the key literature in the field of psychiatry and psychology that has shaped how we understand race and racial trauma and concludes with guidelines for supervisors on how to facilitate talking about race in supervision.

Statistical mechanics of semi-supervised clustering in sparse graphs

International Nuclear Information System (INIS)

Ver Steeg, Greg; Galstyan, Aram; Allahverdyan, Armen E

2011-01-01

We theoretically study semi-supervised clustering in sparse graphs in the presence of pair-wise constraints on the cluster assignments of nodes. We focus on bi-cluster graphs and study the impact of semi-supervision for varying constraint density and overlap between the clusters. Recent results for unsupervised clustering in sparse graphs indicate that there is a critical ratio of within-cluster and between-cluster connectivities below which clusters cannot be recovered with better than random accuracy. The goal of this paper is to examine the impact of pair-wise constraints on the clustering accuracy. Our results suggest that the addition of constraints does not provide automatic improvement over the unsupervised case. When the density of the constraints is sufficiently small, their only impact is to shift the detection threshold while preserving the criticality. Conversely, if the density of (hard) constraints is above the percolation threshold, the criticality is suppressed and the detection threshold disappears

Deep Web Search Interface Identification: A Semi-Supervised Ensemble Approach

Directory of Open Access Journals (Sweden)

Hong Wang

2014-12-01

Full Text Available To surface the Deep Web, one crucial task is to predict whether a given web page has a search interface (searchable HyperText Markup Language (HTML form or not. Previous studies have focused on supervised classification with labeled examples. However, labeled data are scarce, hard to get and requires tediousmanual work, while unlabeled HTML forms are abundant and easy to obtain. In this research, we consider the plausibility of using both labeled and unlabeled data to train better models to identify search interfaces more effectively. We present a semi-supervised co-training ensemble learning approach using both neural networks and decision trees to deal with the search interface identification problem. We show that the proposed model outperforms previous methods using only labeled data. We also show that adding unlabeled data improves the effectiveness of the proposed model.

Chemical name extraction based on automatic training data generation and rich feature set.

Science.gov (United States)

Yan, Su; Spangler, W Scott; Chen, Ying

2013-01-01

The automation of extracting chemical names from text has significant value to biomedical and life science research. A major barrier in this task is the difficulty of getting a sizable and good quality data to train a reliable entity extraction model. Another difficulty is the selection of informative features of chemical names, since comprehensive domain knowledge on chemistry nomenclature is required. Leveraging random text generation techniques, we explore the idea of automatically creating training sets for the task of chemical name extraction. Assuming the availability of an incomplete list of chemical names, called a dictionary, we are able to generate well-controlled, random, yet realistic chemical-like training documents. We statistically analyze the construction of chemical names based on the incomplete dictionary, and propose a series of new features, without relying on any domain knowledge. Compared to state-of-the-art models learned from manually labeled data and domain knowledge, our solution shows better or comparable results in annotating real-world data with less human effort. Moreover, we report an interesting observation about the language for chemical names. That is, both the structural and semantic components of chemical names follow a Zipfian distribution, which resembles many natural languages.

Semi-Supervised Generation with Cluster-aware Generative Models

DEFF Research Database (Denmark)

Maaløe, Lars; Fraccaro, Marco; Winther, Ole

2017-01-01

Deep generative models trained with large amounts of unlabelled data have proven to be powerful within the domain of unsupervised learning. Many real life data sets contain a small amount of labelled data points, that are typically disregarded when training generative models. We propose the Clust...... a log-likelihood of −79.38 nats on permutation invariant MNIST, while also achieving competitive semi-supervised classification accuracies. The model can also be trained fully unsupervised, and still improve the log-likelihood performance with respect to related methods.......Deep generative models trained with large amounts of unlabelled data have proven to be powerful within the domain of unsupervised learning. Many real life data sets contain a small amount of labelled data points, that are typically disregarded when training generative models. We propose the Cluster...

Automatic Quantification of Tumour Hypoxia From Multi-Modal Microscopy Images Using Weakly-Supervised Learning Methods.

Science.gov (United States)

Carneiro, Gustavo; Peng, Tingying; Bayer, Christine; Navab, Nassir

2017-07-01

In recently published clinical trial results, hypoxia-modified therapies have shown to provide more positive outcomes to cancer patients, compared with standard cancer treatments. The development and validation of these hypoxia-modified therapies depend on an effective way of measuring tumor hypoxia, but a standardized measurement is currently unavailable in clinical practice. Different types of manual measurements have been proposed in clinical research, but in this paper we focus on a recently published approach that quantifies the number and proportion of hypoxic regions using high resolution (immuno-)fluorescence (IF) and hematoxylin and eosin (HE) stained images of a histological specimen of a tumor. We introduce new machine learning-based methodologies to automate this measurement, where the main challenge is the fact that the clinical annotations available for training the proposed methodologies consist of the total number of normoxic, chronically hypoxic, and acutely hypoxic regions without any indication of their location in the image. Therefore, this represents a weakly-supervised structured output classification problem, where training is based on a high-order loss function formed by the norm of the difference between the manual and estimated annotations mentioned above. We propose four methodologies to solve this problem: 1) a naive method that uses a majority classifier applied on the nodes of a fixed grid placed over the input images; 2) a baseline method based on a structured output learning formulation that relies on a fixed grid placed over the input images; 3) an extension to this baseline based on a latent structured output learning formulation that uses a graph that is flexible in terms of the amount and positions of nodes; and 4) a pixel-wise labeling based on a fully-convolutional neural network. Using a data set of 89 weakly annotated pairs of IF and HE images from eight tumors, we show that the quantitative results of methods (3) and (4

Speech reconstruction using a deep partially supervised neural network.

Science.gov (United States)

McLoughlin, Ian; Li, Jingjie; Song, Yan; Sharifzadeh, Hamid R

2017-08-01

Statistical speech reconstruction for larynx-related dysphonia has achieved good performance using Gaussian mixture models and, more recently, restricted Boltzmann machine arrays; however, deep neural network (DNN)-based systems have been hampered by the limited amount of training data available from individual voice-loss patients. The authors propose a novel DNN structure that allows a partially supervised training approach on spectral features from smaller data sets, yielding very good results compared with the current state-of-the-art.

Trends in Psychotherapy Training: A National Survey of Psychiatry Residency Training

Science.gov (United States)

Sudak, Donna M.; Goldberg, David A.

2012-01-01

Objective: The authors sought to determine current trends in residency training of psychiatrists. Method: The authors surveyed U.S. general-psychiatry training directors about the amount of didactic training, supervised clinical experience, and numbers of patients treated in the RRC-mandated models of psychotherapy (psychodynamic,…

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

Science.gov (United States)

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.

Detection and segmentation of virus plaque using HOG and SVM: toward automatic plaque assay.

Science.gov (United States)

Mao, Yihao; Liu, Hong; Ye, Rong; Shi, Yonghong; Song, Zhijian

2014-01-01

Plaque assaying, measurement of the number, diameter, and area of plaques in a Petri dish image, is a standard procedure gauging the concentration of phage in biology. This paper presented a novel and effective method for implementing automatic plaque assaying. The method was mainly comprised of the following steps: In the training stage, after pre-processing the images for noise suppression, an initial training set was readied by sampling positive (with a plaque at the center) and negative (plaque-free) patches from the training images, and extracting the HOG features from each patch. The linear SVM classifier was trained in a self-learnt supervised learning strategy to avoid possible missing detection. Specifically, the training set which contained positive and negative patches sampled manually from training images was used to train the preliminary classifier which exhaustively searched the training images to predict the label for the unlabeled patches. The mislabeled patches were evaluated by experts and relabeled. And all the newly labeled patches and their corresponding HOG features were added to the initial training set to train the final classifier. In the testing stage, a sliding-window technique was first applied to the unseen image for obtaining HOG features, which were inputted into the classifier to predict whether the patch was positive. Second, a locally adaptive Otsu method was performed on the positive patches to segment the plaques. Finally, after removing the outliers, the parameters of the plaques were measured in the segmented plaques. The experimental results demonstrated that the accuracy of the proposed method was similar to the one measured manually by experts, but it took less than 30 seconds.

Training shortest-path tractography: Automatic learning of spatial priors

DEFF Research Database (Denmark)

Kasenburg, Niklas; Liptrot, Matthew George; Reislev, Nina Linde

2016-01-01

Tractography is the standard tool for automatic delineation of white matter tracts from diffusion weighted images. However, the output of tractography often requires post-processing to remove false positives and ensure a robust delineation of the studied tract, and this demands expert prior...... knowledge. Here we demonstrate how such prior knowledge, or indeed any prior spatial information, can be automatically incorporated into a shortest-path tractography approach to produce more robust results. We describe how such a prior can be automatically generated (learned) from a population, and we...

Effectiveness of Group Supervision versus Combined Group and Individual Supervision.

Science.gov (United States)

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…

Telecommand and monitoring of automatic recloser through cell phone communication; Telecomando e monitoramento de religadoras automaticas via comunicacao celular

Energy Technology Data Exchange (ETDEWEB)

Gardiman, Vitor Luiz G.; Pires Neto, Francisco M.; Rufini, Ricardo; Marques, Rogerio [Bandeirante Energia, Sao Paulo, SP (Brazil)

2004-02-01

This article presents the system of tele command and monitoring automatic recloser of the medium voltage distribution network adopted by the Bandeirante Energia, Brazil, by using the cell phone network. The system, installed in 110 automatic reclosers, presented short term availability of tele supervision and tele control, fast installation, and operation and reliability low costs.

Classification of gene expression data: A hubness-aware semi-supervised approach.

Science.gov (United States)

Buza, Krisztian

2016-04-01

Classification of gene expression data is the common denominator of various biomedical recognition tasks. However, obtaining class labels for large training samples may be difficult or even impossible in many cases. Therefore, semi-supervised classification techniques are required as semi-supervised classifiers take advantage of unlabeled data. Gene expression data is high-dimensional which gives rise to the phenomena known under the umbrella of the curse of dimensionality, one of its recently explored aspects being the presence of hubs or hubness for short. Therefore, hubness-aware classifiers have been developed recently, such as Naive Hubness-Bayesian k-Nearest Neighbor (NHBNN). In this paper, we propose a semi-supervised extension of NHBNN which follows the self-training schema. As one of the core components of self-training is the certainty score, we propose a new hubness-aware certainty score. We performed experiments on publicly available gene expression data. These experiments show that the proposed classifier outperforms its competitors. We investigated the impact of each of the components (classification algorithm, semi-supervised technique, hubness-aware certainty score) separately and showed that each of these components are relevant to the performance of the proposed approach. Our results imply that our approach may increase classification accuracy and reduce computational costs (i.e., runtime). Based on the promising results presented in the paper, we envision that hubness-aware techniques will be used in various other biomedical machine learning tasks. In order to accelerate this process, we made an implementation of hubness-aware machine learning techniques publicly available in the PyHubs software package (http://www.biointelligence.hu/pyhubs) implemented in Python, one of the most popular programming languages of data science. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Self-care assessment as an indicator for clinical supervision in nursing

Directory of Open Access Journals (Sweden)

Sílvia Marlene Monteiro Teixeira

2016-06-01

Full Text Available Objective: to evaluate the needs of clinical supervision for nurses to assess the degree of dependence on self-care and planning of nursing interventions. Methods: analytical study, cross-cutting nature, collecting data from a sample of 110 patients. Results: it was shown the differences in the identification of the degree of dependence between registers and experts, as well as the selection of operations for each self-care and failures to the original assessment of the filling level (no evaluation self-care/no identification of the degree of dependence. Conclusion: there were gaps in the nursing process; they have proposed strategies such as clinical supervision sessions, training, case studies, protocols and guidance documents, to be included in a clinical supervision in nursing model.

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

Science.gov (United States)

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.

Kollegial supervision

DEFF Research Database (Denmark)

Andersen, Ole Dibbern; Petersson, Erling

Publikationen belyser, hvordan kollegial supervision i en kan organiseres i en uddannelsesinstitution......Publikationen belyser, hvordan kollegial supervision i en kan organiseres i en uddannelsesinstitution...

An Automated Self-Learning Quantification System to Identify Visible Areas in Capsule Endoscopy Images.

Science.gov (United States)

Hashimoto, Shinichi; Ogihara, Hiroyuki; Suenaga, Masato; Fujita, Yusuke; Terai, Shuji; Hamamoto, Yoshihiko; Sakaida, Isao

2017-08-01

Visibility in capsule endoscopic images is presently evaluated through intermittent analysis of frames selected by a physician. It is thus subjective and not quantitative. A method to automatically quantify the visibility on capsule endoscopic images has not been reported. Generally, when designing automated image recognition programs, physicians must provide a training image; this process is called supervised learning. We aimed to develop a novel automated self-learning quantification system to identify visible areas on capsule endoscopic images. The technique was developed using 200 capsule endoscopic images retrospectively selected from each of three patients. The rate of detection of visible areas on capsule endoscopic images between a supervised learning program, using training images labeled by a physician, and our novel automated self-learning program, using unlabeled training images without intervention by a physician, was compared. The rate of detection of visible areas was equivalent for the supervised learning program and for our automatic self-learning program. The visible areas automatically identified by self-learning program correlated to the areas identified by an experienced physician. We developed a novel self-learning automated program to identify visible areas in capsule endoscopic images.

Supervised detection of anomalous light curves in massive astronomical catalogs

International Nuclear Information System (INIS)

Nun, Isadora; Pichara, Karim; Protopapas, Pavlos; Kim, Dae-Won

2014-01-01

The development of synoptic sky surveys has led to a massive amount of data for which resources needed for analysis are beyond human capabilities. In order to process this information and to extract all possible knowledge, machine learning techniques become necessary. Here we present a new methodology to automatically discover unknown variable objects in large astronomical catalogs. With the aim of taking full advantage of all information we have about known objects, our method is based on a supervised algorithm. In particular, we train a random forest classifier using known variability classes of objects and obtain votes for each of the objects in the training set. We then model this voting distribution with a Bayesian network and obtain the joint voting distribution among the training objects. Consequently, an unknown object is considered as an outlier insofar it has a low joint probability. By leaving out one of the classes on the training set, we perform a validity test and show that when the random forest classifier attempts to classify unknown light curves (the class left out), it votes with an unusual distribution among the classes. This rare voting is detected by the Bayesian network and expressed as a low joint probability. Our method is suitable for exploring massive data sets given that the training process is performed offline. We tested our algorithm on 20 million light curves from the MACHO catalog and generated a list of anomalous candidates. After analysis, we divided the candidates into two main classes of outliers: artifacts and intrinsic outliers. Artifacts were principally due to air mass variation, seasonal variation, bad calibration, or instrumental errors and were consequently removed from our outlier list and added to the training set. After retraining, we selected about 4000 objects, which we passed to a post-analysis stage by performing a cross-match with all publicly available catalogs. Within these candidates we identified certain known

Supervised Detection of Anomalous Light Curves in Massive Astronomical Catalogs

Science.gov (United States)

Nun, Isadora; Pichara, Karim; Protopapas, Pavlos; Kim, Dae-Won

2014-09-01

The development of synoptic sky surveys has led to a massive amount of data for which resources needed for analysis are beyond human capabilities. In order to process this information and to extract all possible knowledge, machine learning techniques become necessary. Here we present a new methodology to automatically discover unknown variable objects in large astronomical catalogs. With the aim of taking full advantage of all information we have about known objects, our method is based on a supervised algorithm. In particular, we train a random forest classifier using known variability classes of objects and obtain votes for each of the objects in the training set. We then model this voting distribution with a Bayesian network and obtain the joint voting distribution among the training objects. Consequently, an unknown object is considered as an outlier insofar it has a low joint probability. By leaving out one of the classes on the training set, we perform a validity test and show that when the random forest classifier attempts to classify unknown light curves (the class left out), it votes with an unusual distribution among the classes. This rare voting is detected by the Bayesian network and expressed as a low joint probability. Our method is suitable for exploring massive data sets given that the training process is performed offline. We tested our algorithm on 20 million light curves from the MACHO catalog and generated a list of anomalous candidates. After analysis, we divided the candidates into two main classes of outliers: artifacts and intrinsic outliers. Artifacts were principally due to air mass variation, seasonal variation, bad calibration, or instrumental errors and were consequently removed from our outlier list and added to the training set. After retraining, we selected about 4000 objects, which we passed to a post-analysis stage by performing a cross-match with all publicly available catalogs. Within these candidates we identified certain known

Trainees' use of supervision for therapy with sexual minority clients: A qualitative study.

Science.gov (United States)

Chui, Harold; McGann, Kevin J; Ziemer, Kathryn S; Hoffman, Mary Ann; Stahl, Jessica

2018-01-01

In the supervision literature, research on sexual orientation considerations often focuses on sexual minority supervisees and less often on their work with sexual minority clients. Yet both heterosexual and sexual minority supervisees serve sexual minority clients and may have different supervision needs. Twelve predoctoral interns from 12 APA-accredited counseling center internships were interviewed about how they made use of supervision for their work with a sexual minority client. The sample consisted of 6 heterosexual-identified supervisees and 6 supervisees who identified as lesbian, gay, or queer (LGQ). Data were analyzed using consensual qualitative research. All participants reported positive gains from supervision that carried over to their work with heterosexual and sexual minority clients, even when not all supervisors disclosed or discussed their own sexual orientation. Heterosexual supervisees used supervision to ensure that their heterosexuality does not interfere with an affirmative experience for their sexual minority client, whereas LGQ supervisees used supervision to explore differences in sexual identity development between themselves and their client to minimize the negative impact of overidentification. Thus, affirmative supervision may unfold with different foci depending on supervisees' sexual identity. Implications for training and supervision are discussed. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Deconstructing Risk Management in Psychotherapy Supervision.

Science.gov (United States)

Kroll, Jerome; Radden, Jennifer

2017-12-01

In the ongoing controversy over how much regulation and standardization to impose on clinical practice and research, it is not surprising that the activity of psychotherapy supervision should be swept up in the drive for uniformity. The managers amongst us want to regulate and institutionalize all aspects of practice. In opposition, many clinicians resist the relentless march toward the safety of uniformity travel alongside managerial imposition of regulations. Psychotherapy supervision's method of a close apprenticeship relationship between supervisor and trainee and its focus on the process and ethics of professional interaction stand at the humanistic core of what is otherwise becoming an increasingly mechanistic model of providing care to persons with mental illness. Our commentary picks up on these themes as it reviews the work by Mehrtens et al about strengthening awareness of liability in psychiatry residency training programs. We argue that the practice of psychiatry is overburdened by documentation requirements. In imposing further record-keeping on psychotherapy supervision, we lose much more than we gain. We recommend that the supervisory process focus on the characterological virtues essential to functioning as an ethical therapist. We also argue that self-protective rules place restraints on possibilities for imaginative insights and innovations in psychotherapy. © 2017 American Academy of Psychiatry and the Law.

Semi-Supervised Classification for Fault Diagnosis in Nuclear Power Plants

International Nuclear Information System (INIS)

Ma, Jian Ping; Jiang, Jin

2014-01-01

Pattern classification methods have become important tools for fault diagnosis in industrial systems. However, it is normally difficult to obtain reliable labeled data to train a supervised pattern classification model for applications in a nuclear power plant (NPP). However, unlabeled data easily become available through increased deployment of supervisory, control, and data acquisition (SCADA) systems. In this paper, a fault diagnosis scheme based on semi-supervised classification (SSC) method is developed with specific applications for NPP. In this scheme, newly measured plant data are treated as unlabeled data. They are integrated with selected labeled data to train a SSC model which is then used to estimate labels of the new data. Compared to exclusive supervised approaches, the proposed scheme requires significantly less number of labeled data to train a classifier. Furthermore, it is shown that higher degree of uncertainties in the labeled data can be tolerated. The developed scheme has been validated using the data generated from a desktop NPP simulator and also from a physical NPP simulator using a graph-based SSC algorithm. Two case studies have been used in the validation process. In the first case study, three faults have been simulated on the desktop simulator. These faults have all been classified successfully with only four labeled data points per fault case. In the second case, six types of fault are simulated on the physical NPP simulator. All faults have been successfully diagnosed. The results have demonstrated that SSC is a promising tool for fault diagnosis

Supervised and non-supervised Nordic walking in the treatment of chronic low back pain: a single blind randomized clinical trial

Science.gov (United States)

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

Improving the potential of pixel-based supervised classification in ...

African Journals Online (AJOL)

The goal of this paper was to describe the impact of various parameters when applying a supervised Maximum Likelihood Classifier (MLC) to SPOT 5 image analysis in a remote savanna biome. Pair separation indicators and probability thresholds were used to analyse the effect of training area size and heterogeneity as ...

Knowledge-based full-automatic control system for a nuclear ship reactor

International Nuclear Information System (INIS)

Shimazaki, J.; Nakazawa, T.; Yabuuchi, N.

2000-01-01

Plant operations aboard nuclear ships require quick judgements and actions due to changing marine conditions such as wind, waves and currents. Furthermore, additional human support is not available for nuclear ship operation at sea, so advanced automatic operations are necessary to reduce the number of operators required finally. Therefore, an advanced automatic operating system has been developed based on operational knowledge of nuclear ship 'Mutsu' plant. The advanced automatic operating system includes both the automatic operation system and the operator-support system which assists operators in completing actions during plant accidents, anomaly diagnosis and plant supervision. These system are largely being developed using artificial intelligent techniques such as neural network, fuzzy logic and knowledge-based expert. The automatic operation system is fundamentally based upon application of an operator's knowledge of both normal (start-up to rated power level) and abnormal (after scram) operations. Comparing plant behaviors from start-up to power level by the automatic operation with by 'Mutsu' manual operation, stable automatic operation was obtained almost same as manual operation within all operating limits. The abnormal automatic system was for hard work of manual operations after scram or LOCA accidents. An integrating system with the normal and the abnormal automatic systems are being developed for interacting smoothly both systems. (author)

The complexities of power in feminist multicultural psychotherapy supervision.

Science.gov (United States)

Arczynski, Alexis V; Morrow, Susan L

2017-03-01

The goal of the present study was to understand how current feminist multicultural supervisors understand and implement their feminist multicultural principles into clinical supervision. We addressed this aim by answering the following research question: How do self-identified feminist multicultural psychotherapy supervisors conceptualize and practice feminist supervision that is explicitly multicultural? The perspectives of 14 participant supervisors were obtained by using semistructured initial interviews, follow-up interviews, and feedback interviews and were investigated via a feminist constructivist grounded theory design and analysis. Most participants identified as counseling psychologists (n = 12), women (n = 11) and temporarily able-bodied (n = 11); but they identified with diverse racial/ethnic, sexual, spiritual/religious, generational, and nationality statuses. A 7-category empirical framework emerged that explained how the participants anticipated and managed power in supervision. The core category, the complexities of power in supervision, explained how participants conceptualized power in supervisory relationships. The 6 remaining categories were bringing history into the supervision room, creating trust through openness and honesty, using a collaborative process, meeting shifting developmental (a)symmetries, cultivating critical reflexivity, and looking at and counterbalancing the impact of context. Limitations of the study, implications for research, and suggestions to use the theoretical framework to transform supervisory practice and training are discussed. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Supervised Learning for Detection of Duplicates in Genomic Sequence Databases.

Directory of Open Access Journals (Sweden)

Qingyu Chen

Full Text Available First identified as an issue in 1996, duplication in biological databases introduces redundancy and even leads to inconsistency when contradictory information appears. The amount of data makes purely manual de-duplication impractical, and existing automatic systems cannot detect duplicates as precisely as can experts. Supervised learning has the potential to address such problems by building automatic systems that learn from expert curation to detect duplicates precisely and efficiently. While machine learning is a mature approach in other duplicate detection contexts, it has seen only preliminary application in genomic sequence databases.We developed and evaluated a supervised duplicate detection method based on an expert curated dataset of duplicates, containing over one million pairs across five organisms derived from genomic sequence databases. We selected 22 features to represent distinct attributes of the database records, and developed a binary model and a multi-class model. Both models achieve promising performance; under cross-validation, the binary model had over 90% accuracy in each of the five organisms, while the multi-class model maintains high accuracy and is more robust in generalisation. We performed an ablation study to quantify the impact of different sequence record features, finding that features derived from meta-data, sequence identity, and alignment quality impact performance most strongly. The study demonstrates machine learning can be an effective additional tool for de-duplication of genomic sequence databases. All Data are available as described in the supplementary material.

Supervised Learning for Detection of Duplicates in Genomic Sequence Databases.

Science.gov (United States)

Chen, Qingyu; Zobel, Justin; Zhang, Xiuzhen; Verspoor, Karin

2016-01-01

First identified as an issue in 1996, duplication in biological databases introduces redundancy and even leads to inconsistency when contradictory information appears. The amount of data makes purely manual de-duplication impractical, and existing automatic systems cannot detect duplicates as precisely as can experts. Supervised learning has the potential to address such problems by building automatic systems that learn from expert curation to detect duplicates precisely and efficiently. While machine learning is a mature approach in other duplicate detection contexts, it has seen only preliminary application in genomic sequence databases. We developed and evaluated a supervised duplicate detection method based on an expert curated dataset of duplicates, containing over one million pairs across five organisms derived from genomic sequence databases. We selected 22 features to represent distinct attributes of the database records, and developed a binary model and a multi-class model. Both models achieve promising performance; under cross-validation, the binary model had over 90% accuracy in each of the five organisms, while the multi-class model maintains high accuracy and is more robust in generalisation. We performed an ablation study to quantify the impact of different sequence record features, finding that features derived from meta-data, sequence identity, and alignment quality impact performance most strongly. The study demonstrates machine learning can be an effective additional tool for de-duplication of genomic sequence databases. All Data are available as described in the supplementary material.

Supervised Cross-Modal Factor Analysis for Multiple Modal Data Classification

KAUST Repository

Wang, Jingbin; Zhou, Yihua; Duan, Kanghong; Wang, Jim Jing-Yan; Bensmail, Halima

2015-01-01

. In this paper, we improve CFA by incorporating the supervision information to represent and classify both image and text modals of documents. We project both image and text data to a shared data space by factor analysis, and then train a class label predictor

A supervised contextual classifier based on a region-growth algorithm

DEFF Research Database (Denmark)

Lira, Jorge; Maletti, Gabriela Mariel

2002-01-01

A supervised classification scheme to segment optical multi-spectral images has been developed. In this classifier, an automated region-growth algorithm delineates the training sets. This algorithm handles three parameters: an initial pixel seed, a window size and a threshold for each class. A su...

Educating Occupational Therapists in the Use of Theory and Evidence to Enhance Supervision Practice

Directory of Open Access Journals (Sweden)

Melanie J. Roberts

2017-10-01

Full Text Available This paper describes the implementation of a unique learning experience aimed at enhancing the quality of supervision practice in occupational therapy at the Gold Coast Hospital and Health Service. The package was designed by experienced occupational therapy educators based on adult, blended, and flipped learning approaches with content developed following administration of a standardized tool and semi-structured interviews. The learning package focused particularly on the logistics of supervision and the use of occupational therapy theory and evidence with supervision. The training for supervising therapists included a workshop and pre and post workshop learning activities. This collaborative research approach to designing and implementing a learning package as well as the specific content of the ongoing education opportunities could also be transferred to other services.

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

Science.gov (United States)

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.

The effects of clinical supervision on supervisees and patients in cognitive behavioral therapy: a systematic review.

Science.gov (United States)

Alfonsson, Sven; Parling, Thomas; Spännargård, Åsa; Andersson, Gerhard; Lundgren, Tobias

2018-05-01

Clinical supervision is a central part of psychotherapist training but the empirical support for specific supervision theories or features is unclear. The aims of this study were to systematically review the empirical research literature regarding the effects of clinical supervision on therapists' competences and clinical outcomes within Cognitive Behavior Therapy (CBT). A comprehensive database search resulted in 4103 identified publications. Of these, 133 were scrutinized and in the end 5 studies were included in the review for data synthesis. The five studies were heterogeneous in scope and quality and only one provided firm empirical support for the positive effects of clinical supervision on therapists' competence. The remaining four studies suffered from methodological weaknesses, but provided some preliminary support that clinical supervision may be beneficiary for novice therapists. No study could show benefits from supervision for patients. The research literature suggests that clinical supervision may have some potential effects on novice therapists' competence compared to no supervision but the effects on clinical outcomes are still unclear. While bug-in-the-eye live supervision may be more effective than standard delayed supervision, the effects of specific supervision models or features are also unclear. There is a continued need for high-quality empirical studies on the effects of clinical supervision in psychotherapy.

Legislation and supervision

International Nuclear Information System (INIS)

1998-01-01

In this part next aspects are described: (1) Legislative and supervision-related framework (reviews of structure of supervisory bodies; legislation; state supervision in the nuclear safety area, and state supervision in the area of health protection against radiation are given); (2) Operator's responsibility

Good Undergraduate Dissertation Supervision: Perspectives of Supervisors and Dissertation Coordinators

Science.gov (United States)

Roberts, Lynne D.; Seaman, Kristen

2018-01-01

There is a paucity of research, training, and material available to support supervisors of undergraduate dissertation students. This article explores what "good" supervision might look like at this level. Interviews were conducted with eight new supervisors and six dissertation coordinators using a critical incident methodology. Thematic…

A review of supervised object-based land-cover image classification

Science.gov (United States)

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

Collective academic supervision

DEFF Research Database (Denmark)

Nordentoft, Helle Merete; Thomsen, Rie; Wichmann-Hansen, Gitte

2013-01-01

Supervision of students is a core activity in higher education. Previous research on student supervision in higher education focus on individual and relational aspects in the supervisory relationship rather than collective, pedagogical and methodical aspects of the planning of the supervision...... process. This article fills these gaps by discussing potentials and challenges in “Collective Academic Supervision”, a model for supervision at the Master of Education in Guidance at Aarhus University in Denmark. The pedagogical rationale behind the model is that students’ participation and learning...

An ensemble self-training protein interaction article classifier.

Science.gov (United States)

Chen, Yifei; Hou, Ping; Manderick, Bernard

2014-01-01

Protein-protein interaction (PPI) is essential to understand the fundamental processes governing cell biology. The mining and curation of PPI knowledge are critical for analyzing proteomics data. Hence it is desired to classify articles PPI-related or not automatically. In order to build interaction article classification systems, an annotated corpus is needed. However, it is usually the case that only a small number of labeled articles can be obtained manually. Meanwhile, a large number of unlabeled articles are available. By combining ensemble learning and semi-supervised self-training, an ensemble self-training interaction classifier called EST_IACer is designed to classify PPI-related articles based on a small number of labeled articles and a large number of unlabeled articles. A biological background based feature weighting strategy is extended using the category information from both labeled and unlabeled data. Moreover, a heuristic constraint is put forward to select optimal instances from unlabeled data to improve the performance further. Experiment results show that the EST_IACer can classify the PPI related articles effectively and efficiently.

Social constructionism and supervision: experiences of AAMFT supervisors and supervised therapists.

Science.gov (United States)

Hair, Heather J; Fine, Marshall

2012-10-01

A phenomenological research process was used to investigate the supervision experience for supervisors and therapists when supervisors use a social constructionist perspective. Participants of the one-to-one interviews were six AAMFT Approved Supervisors and six therapists providing counseling to individuals, couples and families. The findings suggest supervisors were committed to their self-identified supervision philosophy and intentionally sought out congruence between epistemology and practice. The shared experience of therapists indicates they associated desirable supervision experiences with their supervisors' social constructionist perspective. Our findings also indicated that supervisors' and therapists' understanding of social constructionism included the more controversial concepts of agency and extra-discursiveness. This research has taken an empirical step in the direction of understanding what the social constructionist supervision experience is like for supervisors and therapists. Our findings suggest a linkage between epistemology and supervision practice and a satisfaction with the supervision process. © 2012 American Association for Marriage and Family Therapy.

Computer interfacing of the unified systems for personnel supervising in nuclear units

International Nuclear Information System (INIS)

Staicu, M.

1997-01-01

The dosimetric supervising of the personnel working in nuclear units is based on the information supplied by: 1) the dosimetric data obtained by the method of thermoluminescence; 2) the dosimetric data obtained by the method of photo dosimetry: 3) the records from medical periodic control. To create a unified system of supervising the following elements were combined: a) an Automatic System of TLD Reading and Data Processing (SACDTL). The data from this system are transmitted 'on line' to the computer; b) the measuring line of the optical density of exposed dosimetric films. The interface achieved within the general ensemble SACDTL could be adapted to this line of measurement. The transmission of the data from the measurement line to the computer is made 'on line'; c) the medical surveillance data for each person transmitted 'off line' to the database computer. The unified system resulting from the unification of the three supervising systems will achieve the following general functions: - registering of the personnel working in the nuclear field; - recording the dosimetric data; - processing and presentation of the data; - issuing of measurement bulletins. Thus, by means of unified database, dosimetric intercomparison and correlative studies can be undertaken. (author)

Security system signal supervision

International Nuclear Information System (INIS)

Chritton, M.R.; Matter, J.C.

1991-09-01

This purpose of this NUREG is to present technical information that should be useful to NRC licensees for understanding and applying line supervision techniques to security communication links. A review of security communication links is followed by detailed discussions of link physical protection and DC/AC static supervision and dynamic supervision techniques. Material is also presented on security for atmospheric transmission and video line supervision. A glossary of security communication line supervision terms is appended. 16 figs

Nursing supervision for care comprehensiveness.

Science.gov (United States)

Chaves, Lucieli Dias Pedreschi; Mininel, Vivian Aline; Silva, Jaqueline Alcântara Marcelino da; Alves, Larissa Roberta; Silva, Maria Ferreira da; Camelo, Silvia Helena Henriques

2017-01-01

To reflect on nursing supervision as a management tool for care comprehensiveness by nurses, considering its potential and limits in the current scenario. A reflective study based on discourse about nursing supervision, presenting theoretical and practical concepts and approaches. Limits on the exercise of supervision are related to the organization of healthcare services based on the functional and clinical model of care, in addition to possible gaps in the nurse training process and work overload. Regarding the potential, researchers emphasize that supervision is a tool for coordinating care and management actions, which may favor care comprehensiveness, and stimulate positive attitudes toward cooperation and contribution within teams, co-responsibility, and educational development at work. Nursing supervision may help enhance care comprehensiveness by implying continuous reflection on including the dynamics of the healthcare work process and user needs in care networks. refletir a supervisão de enfermagem como instrumento gerencial do enfermeiro para integralidade do cuidado, considerando suas potencialidades e limitações no cenário atual. estudo reflexivo baseado na formulação discursiva sobre a supervisão de enfermagem, apresentando conceitos e enfoques teóricos e/ou práticos. limitações no exercício da supervisão estão relacionadas à organização dos serviços de saúde embasada no modelo funcional e clínico de atenção, assim como possíveis lacunas no processo de formação do enfermeiro e sobrecarga de trabalho. Quanto às potencialidades, destaca-se a supervisão como instrumento de articulação de ações assistenciais e gerenciais, que pode favorecer integralidade da atenção, estimular atitudes de cooperação e colaboração em equipe, além da corresponsabilização e promoção da educação no trabalho. supervisão de enfermagem pode contribuir para fortalecimento da integralidade do cuidado, pressupondo reflexão cont

32 CFR 634.33 - Training of law enforcement personnel.

Science.gov (United States)

2010-07-01

... 32 National Defense 4 2010-07-01 2010-07-01 true Training of law enforcement personnel. 634.33 Section 634.33 National Defense Department of Defense (Continued) DEPARTMENT OF THE ARMY (CONTINUED) LAW ENFORCEMENT AND CRIMINAL INVESTIGATIONS MOTOR VEHICLE TRAFFIC SUPERVISION Traffic Supervision § 634.33 Training of law enforcement personnel. (a) A...

INCREASING RELIABILITY OF STEPPED AUTOMATIC STARTING AND RHEOSTAT BREAKING SYSTEM OF ELECTRIC TRAINS ER9T AND EPL9T

Directory of Open Access Journals (Sweden)

N. H. Visin

2010-06-01

Full Text Available The article examines transitional processes in the power circuit of tractive motors and their influence on the work of stepped automatic starting of electric trains ER9T and EPL9T. The recommendations for increasing the reliability of operation of multiple-unit rolling stock are proposed.

Automatic humidification system to support the assessment of food drying processes

Science.gov (United States)

Ortiz Hernández, B. D.; Carreño Olejua, A. R.; Castellanos Olarte, J. M.

2016-07-01

This work shows the main features of an automatic humidification system to provide drying air that match environmental conditions of different climate zones. This conditioned air is then used to assess the drying process of different agro-industrial products at the Automation and Control for Agro-industrial Processes Laboratory of the Pontifical Bolivarian University of Bucaramanga, Colombia. The automatic system allows creating and improving control strategies to supply drying air under specified conditions of temperature and humidity. The development of automatic routines to control and acquire real time data was made possible by the use of robust control systems and suitable instrumentation. The signals are read and directed to a controller memory where they are scaled and transferred to a memory unit. Using the IP address is possible to access data to perform supervision tasks. One important characteristic of this automatic system is the Dynamic Data Exchange Server (DDE) to allow direct communication between the control unit and the computer used to build experimental curves.

Paediatric trainee supervision: management changes and perceived education value.

Science.gov (United States)

van den Boom, Mirjam; Pinnock, Ralph; Weller, Jennifer; Reed, Peter; Shulruf, Boaz

2012-07-01

Supervision in postgraduate training is an under-researched area. We measured the amount, type and effect of supervision on patient care and perceived education value in a general paediatric service. We designed a structured observation form and questionnaire to document the type, duration and effect of supervision on patient management and perceived education value. Most supervision occurred without the paediatrician confirming the trainee's findings. Direct observation of the trainee was rare. Management was changed in 30% of patients seen on the inpatient ward round and in 42% of the patients discussed during the chart reviews but not seen by the paediatrician. Management was changed in 48% of the cases when the paediatrician saw the patient with the trainee in outpatients but in only 21% of patients when the patient was but not seen. Changes made to patient management, understanding and perceived education value, differed between inpatient and out patient settings. There was more impact when the paediatrician saw the patient with the trainee in outpatients; while for inpatients, the opposite was true. Trainees rated the value of the supervision more highly than their supervisors did. Trainees' comments on what they learnt from their supervisor related almost exclusively to clinical knowledge rather than professional behaviours. We observed little evidence of supervisors directly observing trainees and trainees learning professional behaviours. A review of supervisory practices to promote more effective learning is needed. Communicating to paediatricians the value their trainees place on their input could have a positive effect on their engagement in supervision. © 2012 The Authors. Journal of Paediatrics and Child Health © 2012 Paediatrics and Child Health Division (Royal Australasian College of Physicians).

Supervised and non-supervised Nordic walking in the treatment of chronic low back pain: a single blind randomized clinical trial

Directory of Open Access Journals (Sweden)

Bendix Tom

2010-02-01

Full Text Available Abstract 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

A Supervised Classification Algorithm for Note Onset Detection

Directory of Open Access Journals (Sweden)

Douglas Eck

2007-01-01

Full Text Available This paper presents a novel approach to detecting onsets in music audio files. We use a supervised learning algorithm to classify spectrogram frames extracted from digital audio as being onsets or nononsets. Frames classified as onsets are then treated with a simple peak-picking algorithm based on a moving average. We present two versions of this approach. The first version uses a single neural network classifier. The second version combines the predictions of several networks trained using different hyperparameters. We describe the details of the algorithm and summarize the performance of both variants on several datasets. We also examine our choice of hyperparameters by describing results of cross-validation experiments done on a custom dataset. We conclude that a supervised learning approach to note onset detection performs well and warrants further investigation.

Man-machine supervision; Supervision homme-machine

Energy Technology Data Exchange (ETDEWEB)

Montmain, J. [CEA Valrho, Dir. de l' Energie Nucleaire (DEN), 30 - Marcoule (France)

2005-05-01

Today's complexity of systems where man is involved has led to the development of more and more sophisticated information processing systems where decision making has become more and more difficult. The operator task has moved from operation to supervision and the production tool has become indissociable from its numerical instrumentation and control system. The integration of more and more numerous and sophisticated control indicators in the control room does not necessary fulfill the expectations of the operation team. It is preferable to develop cooperative information systems which are real situation understanding aids. The stake is not the automation of operators' cognitive tasks but the supply of a reasoning help. One of the challenges of interactive information systems is the selection, organisation and dynamical display of information. The efficiency of the whole man-machine system depends on the communication interface efficiency. This article presents the principles and specificities of man-machine supervision systems: 1 - principle: operator's role in control room, operator and automation, monitoring and diagnosis, characteristics of useful models for supervision; 2 - qualitative reasoning: origin, trends, evolutions; 3 - causal reasoning: causality, causal graph representation, causal and diagnostic graph; 4 - multi-points of view reasoning: multi flow modeling method, Sagace method; 5 - approximate reasoning: the symbolic numerical interface, the multi-criteria decision; 6 - example of application: supervision in a spent-fuel reprocessing facility. (J.S.)

Integrated training support system for PWR operator training simulator

International Nuclear Information System (INIS)

Sakaguchi, Junichi; Komatsu, Yasuki

1999-01-01

The importance of operator training using operator training simulator has been recognized intensively. Since 1986, we have been developing and providing many PWR simulators in Japan. We also have developed some training support systems connected with the simulator and the integrated training support system to improve training effect and to reduce instructor's workload. This paper describes the concept and the effect of the integrated training support system and of the following sub-systems. We have PES (Performance Enhancement System) that evaluates training performance automatically by analyzing many plant parameters and operation data. It can reduce the deviation of training performance evaluation between instructors. PEL (Parameter and Event data Logging system), that is the subset of PES, has some data-logging functions. And we also have TPES (Team Performance Enhancement System) that is used aiming to improve trainees' ability for communication between operators. Trainee can have conversation with virtual trainees that TPES plays automatically. After that, TPES automatically display some advice to be improved. RVD (Reactor coolant system Visual Display) displays the distributed hydraulic-thermal condition of the reactor coolant system in real-time graphically. It can make trainees understand the inside plant condition in more detail. These sub-systems have been used in a training center and have contributed the improvement of operator training and have gained in popularity. (author)

Automatic cloud coverage assessment of Formosat-2 image

Science.gov (United States)

Hsu, Kuo-Hsien

2011-11-01

Formosat-2 satellite equips with the high-spatial-resolution (2m ground sampling distance) remote sensing instrument. It has been being operated on the daily-revisiting mission orbit by National Space organization (NSPO) of Taiwan since May 21 2004. NSPO has also serving as one of the ground receiving stations for daily processing the received Formosat- 2 images. The current cloud coverage assessment of Formosat-2 image for NSPO Image Processing System generally consists of two major steps. Firstly, an un-supervised K-means method is used for automatically estimating the cloud statistic of Formosat-2 image. Secondly, manual estimation of cloud coverage from Formosat-2 image is processed by manual examination. Apparently, a more accurate Automatic Cloud Coverage Assessment (ACCA) method certainly increases the efficiency of processing step 2 with a good prediction of cloud statistic. In this paper, mainly based on the research results from Chang et al, Irish, and Gotoh, we propose a modified Formosat-2 ACCA method which considered pre-processing and post-processing analysis. For pre-processing analysis, cloud statistic is determined by using un-supervised K-means classification, Sobel's method, Otsu's method, non-cloudy pixels reexamination, and cross-band filter method. Box-Counting fractal method is considered as a post-processing tool to double check the results of pre-processing analysis for increasing the efficiency of manual examination.

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

Science.gov (United States)

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.

Good supervision and PBL

DEFF Research Database (Denmark)

Otrel-Cass, Kathrin

This field study was conducted at the Faculty of Social Sciences at Aalborg University with the intention to investigate how students reflect on their experiences with supervision in a PBL environment. The overall aim of this study was to inform about the continued work in strengthening supervision...... at this faculty. This particular study invited Master level students to discuss: • How a typical supervision process proceeds • How they experienced and what they expected of PBL in the supervision process • What makes a good supervision process...

Supervision for School Psychologists in Training: Developing a Framework from Empirical Findings

Science.gov (United States)

Gibbs, Simon; Atkinson, Cathy; Woods, Kevin; Bond, Caroline; Hill, Vivian; Howe, Julia; Morris, Sue

2016-01-01

Similar to other professional disciplines, the importance of supervision within school psychology has attracted considerable attention within recent years. Despite this, systematic review of current literature reveals a dearth of empirical literature proposing underlying theoretical structures. This study extends recent qualitative research by…

Development of a web-based remote load supervision and control system

Energy Technology Data Exchange (ETDEWEB)

Chang, Wei-Fu; Wu, Yu-Chi; Chiu, Chui-Wen [National United University, Miao-Li (Taiwan). Department of Electrical Engineering

2006-07-15

The ability to remotely acquire information and even to control appliances/devices at fingertips over the Internet is becoming desirable to the general public as well as professionals. In this paper, a web-based remote electric load supervision and control (WBRELSAC) system with automatic meter reading and demand control via programmable logic controllers (PLCs) is presented. For both utilities and industrial/commercial customers, the electric load supervision and control (ELSAC) system is a critical function to their load management. However, most high voltage customers do not have enough capital to build a regular-scale supervisory control and data acquisition system as the one for utilities. Therefore, we adopt the industrial-widely-used PLCs in WBRELSAC. In order to make a non-web-based PLC become web-controllable, we develop a graphical-control interface and utilize Internet techniques to implement our system. Based on the performance test conducted under the Laboratory environment, the proposed WBRELSAC architecture is cost-effective and suitable for industrial applications. (author)

Automatic Supervision of Temperature, Humidity, and Luminance with an Assistant Personal Robot

Directory of Open Access Journals (Sweden)

Jordi Palacín

2017-01-01

Full Text Available Smart environments and Ambient Intelligence (AmI technologies are defining the future society where energy optimization and intelligent management are essential for a sustainable advance. Mobile robotics is also making an important contribution to this advance with the integration of sensors and intelligent processing algorithms. This paper presents the application of an Assistant Personal Robot (APR as an autonomous agent for temperature, humidity, and luminance supervision in human-frequented areas. The robot multiagent capabilities allow gathering sensor information while exploring or performing specific tasks and then verifying human comfortability levels. The proposed methodology creates information maps with the distribution of temperature, humidity, and luminance and interprets such information in terms of comfort and warns about corrective actuations if required.

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

Science.gov (United States)

Wang, Yan; Du, Lan; Dai, Hui

2018-01-01

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

Whither Supervision?

Directory of Open Access Journals (Sweden)

Duncan Waite

2006-11-01

Full Text Available This paper inquires if the school supervision is in decadence. Dr. Waite responds that the answer will depend on which perspective you look at it. Dr. Waite suggests taking in consideration three elements that are related: the field itself, the expert in the field (the professor, the theorist, the student and the administrator, and the context. When these three elements are revised, it emphasizes that there is not a consensus about the field of supervision, but there are coincidences related to its importance and that it is related to the improvement of the practice of the students in the school for their benefit. Dr. Waite suggests that the practice on this field is not always in harmony with what the theorists affirm. When referring to the supervisor or the skilled person, the author indicates that his or her perspective depends on his or her epistemological believes or in the way he or she conceives the learning; that is why supervision can be understood in different ways. About the context, Waite suggests that there have to be taken in consideration the social or external forces that influent the people and the society, because through them the education is affected. Dr. Waite concludes that the way to understand the supervision depends on the performer’s perspective. He responds to the initial question saying that the supervision authorities, the knowledge on this field, the performers, and its practice, are maybe spread but not extinct because the supervision will always be part of the great enterprise that we called education.

Reflecting reflection in supervision

DEFF Research Database (Denmark)

Lystbæk, Christian Tang

associated with reflection and an exploration of alternative conceptions that view reflection within the context of settings which have a more group- and team-based orientation. Drawing on an action research project on health care supervision, the paper questions whether we should reject earlier views...... of reflection, rehabilitate them in order to capture broader connotations or move to new ways of regarding reflection that are more in keeping with not only reflective but also emotive, normative and formative views on supervision. The paper presents a critical perspective on supervision that challenge...... the current reflective paradigm I supervision and relate this to emotive, normative and formative views supervision. The paper is relevant for Nordic educational research into the supervision and guidance...

Automatic face morphing for transferring facial animation

NARCIS (Netherlands)

Bui Huu Trung, B.H.T.; Bui, T.D.; Poel, Mannes; Heylen, Dirk K.J.; Nijholt, Antinus; Hamza, H.M.

2003-01-01

In this paper, we introduce a novel method of automatically finding the training set of RBF networks for morphing a prototype face to represent a new face. This is done by automatically specifying and adjusting corresponding feature points on a target face. The RBF networks are then used to transfer

Cardiopulmonary resuscitation and automatic external defibrillator training in schools: "is anyone learning how to save a life?".

Science.gov (United States)

Hart, Devin; Flores-Medrano, Oscar; Brooks, Steve; Buick, Jason E; Morrison, Laurie J

2013-09-01

Bystander resuscitation efforts, such as cardiopulmonary resuscitation (CPR) and use of an automatic external defibrillator (AED), save lives in cardiac arrest cases. School training in CPR and AED use may increase the currently low community rates of bystander resuscitation. The study objective was to determine the rates of CPR and AED training in Toronto secondary schools and to identify barriers to training and training techniques. This prospective study consisted of telephone interviews conducted with key school staff knowledgeable about CPR and AED teaching. An encrypted Web-based tool with prespecified variables and built-in logic was employed to standardize data collection. Of 268 schools contacted, 93% were available for interview and 83% consented to participate. Students and staff were trained in CPR in 51% and 80% of schools, respectively. Private schools had the lowest training rate (39%). Six percent of schools provided AED training to students and 47% provided AED training to staff. Forty-eight percent of schools had at least one AED installed, but 25% were unaware if their AED was registered with emergency services dispatch. Cost (17%), perceived need (11%), and school population size (10%) were common barriers to student training. Frequently employed training techniques were interactive (32%), didactic instruction (30%) and printed material (16%). CPR training rates for staff and students were moderate overall and lowest in private schools, whereas training rates in AED use were poor in all schools. Identified barriers to training include cost and student population size (perceived to be too small to be cost-effective or too large to be implemented). Future studies should assess the application of convenient and cost-effective teaching alternatives not presently in use.

Resistance to group clinical supervision

DEFF Research Database (Denmark)

Buus, Niels; Delgado, Cynthia; Traynor, Michael

2018-01-01

This present study is a report of an interview study exploring personal views on participating in group clinical supervision among mental health nursing staff members who do not participate in supervision. There is a paucity of empirical research on resistance to supervision, which has traditiona......This present study is a report of an interview study exploring personal views on participating in group clinical supervision among mental health nursing staff members who do not participate in supervision. There is a paucity of empirical research on resistance to supervision, which has...... traditionally been theorized as a supervisee's maladaptive coping with anxiety in the supervision process. The aim of the present study was to examine resistance to group clinical supervision by interviewing nurses who did not participate in supervision. In 2015, we conducted semistructured interviews with 24...... Danish mental health nursing staff members who had been observed not to participate in supervision in two periods of 3 months. Interviews were audio-recorded and subjected to discourse analysis. We constructed two discursive positions taken by the informants: (i) 'forced non-participation', where...

Training lay-people to use automatic external defibrillators: are all of their needs being met?

Science.gov (United States)

Harrison-Paul, Russell; Timmons, Stephen; van Schalkwyk, Wilna Dirkse

2006-10-01

We explored the experiences of lay people who have been trained to use automatic external defibrillators. The research questions were: (1) How can training courses help prepare people for dealing with real life situations? (2) Who is ultimately responsible for providing critical incident debriefing and how should this be organised? (3) What is the best process for providing feedback to those who have used an AED? Fifty-three semi-structured, qualitative interviews were conducted, some with those who had been trained and others with trainers. Locations included airports, railway stations, private companies and first responder schemes. Geographically, we covered Nottinghamshire, Lincolnshire, Yorkshire, Staffordshire, Essex and the West Midlands in the UK. Our analysis of the data indicates that most people believe scenarios based within their place of work were most useful in preparing for 'real life'. Many people had not received critical incident debriefing after using an AED. There were a variety of systems in place to provide support after an incident, many of which were informal. Training scenarios should be conducted outside the classroom. There should be more focus on critical incident debriefing during training and a clear identification of who should provide support after an incident. Other issues which were of interest included: (1) people's views on do not attempt resuscitation (DNAR); (2) perceived boundaries of responsibility when using an AED; (3) when is someone no longer 'qualified' to use an AED?

A semi-automatic annotation tool for cooking video

Science.gov (United States)

Bianco, Simone; Ciocca, Gianluigi; Napoletano, Paolo; Schettini, Raimondo; Margherita, Roberto; Marini, Gianluca; Gianforme, Giorgio; Pantaleo, Giuseppe

2013-03-01

In order to create a cooking assistant application to guide the users in the preparation of the dishes relevant to their profile diets and food preferences, it is necessary to accurately annotate the video recipes, identifying and tracking the foods of the cook. These videos present particular annotation challenges such as frequent occlusions, food appearance changes, etc. Manually annotate the videos is a time-consuming, tedious and error-prone task. Fully automatic tools that integrate computer vision algorithms to extract and identify the elements of interest are not error free, and false positive and false negative detections need to be corrected in a post-processing stage. We present an interactive, semi-automatic tool for the annotation of cooking videos that integrates computer vision techniques under the supervision of the user. The annotation accuracy is increased with respect to completely automatic tools and the human effort is reduced with respect to completely manual ones. The performance and usability of the proposed tool are evaluated on the basis of the time and effort required to annotate the same video sequences.

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

Science.gov (United States)

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, Ada

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

Science.gov (United States)

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 supervised learning rule for classification of spatiotemporal spike patterns.

Science.gov (United States)

Lilin Guo; Zhenzhong Wang; Adjouadi, Malek

2016-08-01

This study introduces a novel supervised algorithm for spiking neurons that take into consideration synapse delays and axonal delays associated with weights. It can be utilized for both classification and association and uses several biologically influenced properties, such as axonal and synaptic delays. This algorithm also takes into consideration spike-timing-dependent plasticity as in Remote Supervised Method (ReSuMe). This paper focuses on the classification aspect alone. Spiked neurons trained according to this proposed learning rule are capable of classifying different categories by the associated sequences of precisely timed spikes. Simulation results have shown that the proposed learning method greatly improves classification accuracy when compared to the Spike Pattern Association Neuron (SPAN) and the Tempotron learning rule.

Optimal preventive bank supervision

OpenAIRE

Belhaj, Mohamed; Klimenko, Nataliya

2012-01-01

Early regulator interventions into problem banks is one of the key suggestions of Basel Committee on Banking Supervision. However, no guidance is given on their design. To fill this gap, we outline an incentive-based preventive supervision strategy that eliminates bad asset management in banks. Two supervision techniques are combined: temporary regulatory administration and random audits. Our design ensures good management without excessive supervision costs, through a gradual adjustment of...

Self-Supervised Video Representation Learning With Odd-One-Out Networks : CVPR 2017 : 21-26 July 2016, Honolulu, Hawaii : proceedings

NARCIS (Netherlands)

Fernando, B.; Bilen, H.; Gavves, E.; Gould, S.

2017-01-01

We propose a new self-supervised CNN pre-training technique based on a novel auxiliary task called odd-one-out learning. In this task, the machine is asked to identify the unrelated or odd element from a set of otherwise related elements. We apply this technique to self-supervised video

Supervised Learning

Science.gov (United States)

Rokach, Lior; Maimon, Oded

This chapter summarizes the fundamental aspects of supervised methods. The chapter provides an overview of concepts from various interrelated fields used in subsequent chapters. It presents basic definitions and arguments from the supervised machine learning literature and considers various issues, such as performance evaluation techniques and challenges for data mining tasks.

Supervised machine learning and active learning in classification of radiology reports.

Science.gov (United States)

Nguyen, Dung H M; Patrick, Jon D

2014-01-01

This paper presents an automated system for classifying the results of imaging examinations (CT, MRI, positron emission tomography) into reportable and non-reportable cancer cases. This system is part of an industrial-strength processing pipeline built to extract content from radiology reports for use in the Victorian Cancer Registry. In addition to traditional supervised learning methods such as conditional random fields and support vector machines, active learning (AL) approaches were investigated to optimize training production and further improve classification performance. The project involved two pilot sites in Victoria, Australia (Lake Imaging (Ballarat) and Peter MacCallum Cancer Centre (Melbourne)) and, in collaboration with the NSW Central Registry, one pilot site at Westmead Hospital (Sydney). The reportability classifier performance achieved 98.25% sensitivity and 96.14% specificity on the cancer registry's held-out test set. Up to 92% of training data needed for supervised machine learning can be saved by AL. AL is a promising method for optimizing the supervised training production used in classification of radiology reports. When an AL strategy is applied during the data selection process, the cost of manual classification can be reduced significantly. The most important practical application of the reportability classifier is that it can dramatically reduce human effort in identifying relevant reports from the large imaging pool for further investigation of cancer. The classifier is built on a large real-world dataset and can achieve high performance in filtering relevant reports to support cancer registries. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Psykoterapi og supervision

DEFF Research Database (Denmark)

Jacobsen, Claus Haugaard

2014-01-01

Kapitlet beskriver supervisionen funktioner i forhold til psykoterapi. Supervision af psykoterapi henviser i almindelighed til, at en psykoterapeut konsulterer en ofte mere erfaren kollega (supervisor) med henblik på drøftelse af et konkret igangværende psykoterapeutisk behandlingsforløb. Formålet...... er at fremme denne fagpersons (psykoterapeutens) faglige udvikling samt sikre kvaliteten af behandlingen.kan defineres som i. Der redegøres for, hvorfor supervision er vigtig del af psykoterapeutens profession samt vises, hvorledes supervision foruden den faglige udvikling også er vigtigt redskab i...... psykoterapiens kvalitetssikring. Efter at have drøftet nogle etiske forhold ved supervision, fremlægges endelig nogle få forskningsresultater vedr. psykoterapisupervision af danske psykologer....

Supervised learning for the automated transcription of spacer classification from spoligotype films

Directory of Open Access Journals (Sweden)

Abernethy Neil

2009-08-01

Full Text Available Abstract Background Molecular genotyping of bacteria has revolutionized the study of tuberculosis epidemiology, yet these established laboratory techniques typically require subjective and laborious interpretation by trained professionals. In the context of a Tuberculosis Case Contact study in The Gambia we used a reverse hybridization laboratory assay called spoligotype analysis. To facilitate processing of spoligotype images we have developed tools and algorithms to automate the classification and transcription of these data directly to a database while allowing for manual editing. Results Features extracted from each of the 1849 spots on a spoligo film were classified using two supervised learning algorithms. A graphical user interface allows manual editing of the classification, before export to a database. The application was tested on ten films of differing quality and the results of the best classifier were compared to expert manual classification, giving a median correct classification rate of 98.1% (inter quartile range: 97.1% to 99.2%, with an automated processing time of less than 1 minute per film. Conclusion The software implementation offers considerable time savings over manual processing whilst allowing expert editing of the automated classification. The automatic upload of the classification to a database reduces the chances of transcription errors.

Rethinking Educational Supervision

OpenAIRE

Burhanettin DÖNMEZ; Kadir BEYCİOĞLU

2009-01-01

The history of educational (school) supervision has been influenced by the history of the interaction of intellectual movements in politics, society, philosophy and industrial movements. The purpose of this conceptual and theoretical study is to have a brief look at the concept of educational supervision with related historical developments in the field. The paper also intends to see the terms and issues critically, and to conceptualize some issues associated with educational supervision in...

Automatic recognition of holistic functional brain networks using iteratively optimized convolutional neural networks (IO-CNN) with weak label initialization.

Science.gov (United States)

Zhao, Yu; Ge, Fangfei; Liu, Tianming

2018-07-01

fMRI data decomposition techniques have advanced significantly from shallow models such as Independent Component Analysis (ICA) and Sparse Coding and Dictionary Learning (SCDL) to deep learning models such Deep Belief Networks (DBN) and Convolutional Autoencoder (DCAE). However, interpretations of those decomposed networks are still open questions due to the lack of functional brain atlases, no correspondence across decomposed or reconstructed networks across different subjects, and significant individual variabilities. Recent studies showed that deep learning, especially deep convolutional neural networks (CNN), has extraordinary ability of accommodating spatial object patterns, e.g., our recent works using 3D CNN for fMRI-derived network classifications achieved high accuracy with a remarkable tolerance for mistakenly labelled training brain networks. However, the training data preparation is one of the biggest obstacles in these supervised deep learning models for functional brain network map recognitions, since manual labelling requires tedious and time-consuming labours which will sometimes even introduce label mistakes. Especially for mapping functional networks in large scale datasets such as hundreds of thousands of brain networks used in this paper, the manual labelling method will become almost infeasible. In response, in this work, we tackled both the network recognition and training data labelling tasks by proposing a new iteratively optimized deep learning CNN (IO-CNN) framework with an automatic weak label initialization, which enables the functional brain networks recognition task to a fully automatic large-scale classification procedure. Our extensive experiments based on ABIDE-II 1099 brains' fMRI data showed the great promise of our IO-CNN framework. Copyright © 2018 Elsevier B.V. All rights reserved.

Sparse supervised principal component analysis (SSPCA) for dimension reduction and variable selection

DEFF Research Database (Denmark)

Sharifzadeh, Sara; Ghodsi, Ali; Clemmensen, Line H.

2017-01-01

Principal component analysis (PCA) is one of the main unsupervised pre-processing methods for dimension reduction. When the training labels are available, it is worth using a supervised PCA strategy. In cases that both dimension reduction and variable selection are required, sparse PCA (SPCA...

Semi-supervised detection of intracranial pressure alarms using waveform dynamics

International Nuclear Information System (INIS)

Scalzo, Fabien; Hu, Xiao

2013-01-01

Patient monitoring systems in intensive care units (ICU) are usually set to trigger alarms when abnormal values are detected. Alarms are generated by threshold-crossing rules that lead to high false alarm rates. This is a recognized issue that causes alarm fatigue, waste of human resources, and increased patient risks. Recently developed smart alarm models require alarms to be validated by experts during the training phase. The manual annotation process involved is time-consuming and virtually impossible to achieve for the thousands of alarms recorded in the ICU every week. To tackle this problem, we investigate in this study if the use of semi-supervised learning methods, that can naturally integrate unlabeled data samples in the model, can be used to improve the accuracy of the alarm detection. As a proof of concept, the detection system is evaluated on intracranial pressure (ICP) signal alarms. Specific morphological and trending features are extracted from the ICP signal waveform to capture the dynamic of the signal prior to alarms. This study is based on a comprehensive dataset of 4791 manually labeled alarms recorded from 108 neurosurgical patients. A comparative analysis is provided between kernel spectral regression (SR-KDA) and support vector machine (SVM) both modified for the semi-supervised setting. Results obtained during the experimental evaluations indicate that the two models can significantly reduce false alarms using unlabeled samples; especially in the presence of a restrained number of labeled examples. At a true alarm recognition rate of 99%, the false alarm reduction rates improved from 9% (supervised) to 27% (semi-supervised) for SR-KDA, and from 3% (supervised) to 16% (semi-supervised) for SVM. (paper)

[Analysis of interventions designed to improve clinical supervision of student nurses in Benin].

Science.gov (United States)

Otti, André; Pirson, Magali; Piette, Danielle; Coppieters T Wallant, Yves

2017-12-05

The absence of an explicit and coherent conception of the articulation between theory and practice in the reform of nursing training in Benin has resulted in poor quality clinical supervision of student nurses. The objective of this article is to analyze two interventions designed to improve the quality of supervision. A student welcome booklet developed by means of a consultative and provocative participatory approach was tested with twelve student nurses versus a control group. Content analysis of the data collected by individual semi-directed interviews and during two focus groups demonstrated the value of this tool. Student nurses were also taught to use to training diaries inspired by the ?experiential learning? Training diaries were analysed using a grid based on the descriptive elements of the five types of Scheepers training diaries (2008). According to the student nurses, the welcome booklet provided them with structured information to be used as a reference during their training and a better understanding of their teachers, and allowed them to situate the resources of the training course with a lower level of stress. Fifty-eight per cent of the training diaries were are mosaics, reflecting the reflective practice and self-regulated learning of student nurses. This activity also promoted metacognitive dialogue with their supervisors. The student welcome booklet appeared to facilitate integration of student nurses into the clinical setting and promoted professional and organizational socialization. The training diary improved the quality of clinical learning by repeated reflective observation of student nurses and helped to maintain permanent communication with the supervisors.

Nonparametric Mixture Models for Supervised Image Parcellation.

Science.gov (United States)

Sabuncu, Mert R; Yeo, B T Thomas; Van Leemput, Koen; Fischl, Bruce; Golland, Polina

2009-09-01

We present a nonparametric, probabilistic mixture model for the supervised parcellation of images. The proposed model yields segmentation algorithms conceptually similar to the recently developed label fusion methods, which register a new image with each training image separately. Segmentation is achieved via the fusion of transferred manual labels. We show that in our framework various settings of a model parameter yield algorithms that use image intensity information differently in determining the weight of a training subject during fusion. One particular setting computes a single, global weight per training subject, whereas another setting uses locally varying weights when fusing the training data. The proposed nonparametric parcellation approach capitalizes on recently developed fast and robust pairwise image alignment tools. The use of multiple registrations allows the algorithm to be robust to occasional registration failures. We report experiments on 39 volumetric brain MRI scans with expert manual labels for the white matter, cerebral cortex, ventricles and subcortical structures. The results demonstrate that the proposed nonparametric segmentation framework yields significantly better segmentation than state-of-the-art algorithms.

A qualitative study of intimate partner violence universal screening by family therapy interns: implications for practice, research, training, and supervision.

Science.gov (United States)

Todahl, Jeffrey L; Linville, Deanna; Chou, Liang-Ying; Maher-Cosenza, Patricia

2008-01-01

Although a few family therapy researchers and clinicians have urged universal screening for intimate partner violence (IPV), how screening is implemented-and, in particular, client and therapist response to screening-is vaguely defined and largely untested. This qualitative study examined the dilemmas experienced by couples and family therapy interns when implementing universal screening for IPV in an outpatient clinic setting. Twenty-two graduate students in a COAMFTE-accredited program were interviewed using qualitative research methods grounded in phenomenology. Three domains, 7 main themes, and 26 subthemes were identified. The three domains that emerged in this study include (a) therapist practice of universal screening, (b) client response to universal screening, and (c) therapist response to universal screening. Implications for practice, research, training, and supervision are discussed.

MULTIPERIOD BANKING SUPERVISION

OpenAIRE

KARL-THEODOR EISELE; PHILIPPE ARTZNER

2013-01-01

This paper is based on a general method for multiperiod prudential supervision of companies submitted to hedgeable and non-hedgeable risks. Having treated the case of insurance in an earlier paper, we now consider a quantitative approach to supervision of commercial banks. The various elements under supervision are the bank’s current amount of tradeable assets, the deposit amount, and four flow processes: future trading risk exposures, deposit flows, flows of loan repayments and of deposit re...

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

Science.gov (United States)

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

2012-01-01

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

A Supervision of Solidarity

Science.gov (United States)

Reynolds, Vikki

2010-01-01

This article illustrates an approach to therapeutic supervision informed by a philosophy of solidarity and social justice activism. Called a "Supervision of Solidarity", this approach addresses the particular challenges in the supervision of therapists who work alongside clients who are subjected to social injustice and extreme marginalization. It…

TRADE instructional materials for SARA/OSHA training. Volume 2, Managers and supervisors training

Energy Technology Data Exchange (ETDEWEB)

1989-03-01

This document provides instructional materials for an eight-hour training course for managers and supervisors of hazardous waste sites. It is one of three volumes of course materials TRADE is preparing to help DOE contractor training staff comply with 29 CFR 1910.120, the Occupational Health and Safety Administration (OSHA) rule that implements Title I of the Superfund Amendments and Reauthorization Act (SARA) of 1986. OSHA`s final rule for hazardous waste operators was published in the Federal Register of March 6, 1989 (54 FR 9294). Combined with the materials in Volumes I and III and with appropriate site-specific information, these materials will help DOE contractors to meet the requirements of 1910.120 (e) that ``on-site management and supervisors directly responsible for, or who supervise employees engaged in, hazardous waste operations`` receive the same initial training as that of the employees they supervise and at least eight additional hours of specialized training in managing hazardous waste operations.

Improving head and body pose estimation through semi-supervised manifold alignment

KAUST Repository

Heili, Alexandre

2014-10-27

In this paper, we explore the use of a semi-supervised manifold alignment method for domain adaptation in the context of human body and head pose estimation in videos. We build upon an existing state-of-the-art system that leverages on external labelled datasets for the body and head features, and on the unlabelled test data with weak velocity labels to do a coupled estimation of the body and head pose. While this previous approach showed promising results, the learning of the underlying manifold structure of the features in the train and target data and the need to align them were not explored despite the fact that the pose features between two datasets may vary according to the scene, e.g. due to different camera point of view or perspective. In this paper, we propose to use a semi-supervised manifold alignment method to bring the train and target samples closer within the resulting embedded space. To this end, we consider an adaptation set from the target data and rely on (weak) labels, given for example by the velocity direction whenever they are reliable. These labels, along with the training labels are used to bias the manifold distance within each manifold and to establish correspondences for alignment.

A National Survey of School Counselor Supervision Practices: Administrative, Clinical, Peer, and Technology Mediated Supervision

Science.gov (United States)

Perera-Diltz, Dilani M.; Mason, Kimberly L.

2012-01-01

Supervision is vital for personal and professional development of counselors. Practicing school counselors (n = 1557) across the nation were surveyed to explore current supervision practices. Results indicated that 41.1% of school counselors provide supervision. Although 89% receive some type of supervision, only 10.3% of school counselors receive…

Stochastic microstructure characterization and reconstruction via supervised learning

International Nuclear Information System (INIS)

Bostanabad, Ramin; Bui, Anh Tuan; Xie, Wei; Apley, Daniel W.; Chen, Wei

2016-01-01

Microstructure characterization and reconstruction have become indispensable parts of computational materials science. The main contribution of this paper is to introduce a general methodology for practical and efficient characterization and reconstruction of stochastic microstructures based on supervised learning. The methodology is general in that it can be applied to a broad range of microstructures (clustered, porous, and anisotropic). By treating the digitized microstructure image as a set of training data, we generically learn the stochastic nature of the microstructure via fitting a supervised learning model to it (we focus on classification trees). The fitted supervised learning model provides an implicit characterization of the joint distribution of the collection of pixel phases in the image. Based on this characterization, we propose two different approaches to efficiently reconstruct any number of statistically equivalent microstructure samples. We test the approach on five examples and show that the spatial dependencies within the microstructures are well preserved, as evaluated via correlation and lineal-path functions. The main advantages of our approach stem from having a compact empirically-learned model that characterizes the stochastic nature of the microstructure, which not only makes reconstruction more computationally efficient than existing methods, but also provides insight into morphological complexity.

Ensemble learning with trees and rules: supervised, semi-supervised, unsupervised

Science.gov (United States)

In this article, we propose several new approaches for post processing a large ensemble of conjunctive rules for supervised and semi-supervised learning problems. We show with various examples that for high dimensional regression problems the models constructed by the post processing the rules with ...

Automatic acoustic and vibration monitoring system for nuclear power plants

International Nuclear Information System (INIS)

Tothmatyas, Istvan; Illenyi, Andras; Kiss, Jozsef; Komaromi, Tibor; Nagy, Istvan; Olchvary, Geza

1990-01-01

A diagnostic system for nuclear power plant monitoring is described. Acoustic and vibration diagnostics can be applied to monitor various reactor components and auxiliary equipment including primary circuit machinery, leak detection, integrity of reactor vessel, loose parts monitoring. A noise diagnostic system has been developed for the Paks Nuclear Power Plant, to supervise the vibration state of primary circuit machinery. An automatic data acquisition and processing system is described for digitalizing and analysing diagnostic signals. (R.P.) 3 figs

Effects of supervised slackline training on postural instability, freezing of gait, and falls efficacy in people with Parkinson's disease.

Science.gov (United States)

Santos, Luis; Fernandez-Rio, Javier; Winge, Kristian; Barragán-Pérez, Beatriz; Rodríguez-Pérez, Vicente; González-Díez, Vicente; Blanco-Traba, Miguel; Suman, Oscar E; Philip Gabel, Charles; Rodríguez-Gómez, Javier

2017-08-01

The aim of this study was to assess whether supervised slackline training reduces the risk of falls in people with Parkinson's disease (PD). Twenty-two patients with idiopathic PD were randomized into experimental (EG, N = 11) and control (CG, N = 11) groups. Center of Pressure (CoP), Freezing of Gait (FOG), and Falls Efficacy Scale (FES) were assessed at pre-test, post-test and re-test. Rate perceived exertion (RPE, Borg's 6-20 scale) and local muscle perceived exertion (LRPE) were also assessed at the end of the training sessions. The EG group showed significant improvements in FOG and FES scores from pre-test to post-test. Both decreased at re-test, though they did not return to pre-test levels. No significant differences were detected in CoP parameters. Analysis of RPE and LRPE scores revealed that slackline was associated with minimal fatigue and involved the major lower limb and lumbar muscles. These findings suggest that slacklining is a simple, safe, and challenging training and rehabilitation tool for PD patients. It could be introduced into their physical activity routine to reduce the risk of falls and improve confidence related to fear of falling. Implications for Rehabilitation Individuals with Parkinson's disease (PD) are twice as likely to have falls compared to patients with other neurological conditions. This study support slackline as a simple, safe, and challenging training and rehabilitation tool for people with PD, which reduce their risk of falls and improve confidence related to fear of falling. Slackline in people with PD yields a low tiredness or fatigue impact and involves the major lower limb and lumbar muscles.

Relation of management, supervision, and personnel practices to nuclear power plant safety

International Nuclear Information System (INIS)

Layton, W.L.; Turnage, J.J.

1980-01-01

The knowledge base of industrial/organization psychology suggests three major areas of research with important implications for nuclear power plant safety. These areas are: Management and Supervision: Personnel Selection, Training and Placement; and Organizational Climate. Evidence drawn from several Three Mile Island investigations confirms that organizational structure of plants and supervisory practices, the selection and training of personnel, and organizational climate are important factors. Difficulties in decision making and coordination of personnel are pinpointed. Deficiencies in training are highlighted and the climate of working atmosphere is discussed. These matters are related to nuclear power plant safety. Future research directions are presented

Adequate supervision for children and adolescents.

Science.gov (United States)

Anderst, James; Moffatt, Mary

2014-11-01

Primary care providers (PCPs) have the opportunity to improve child health and well-being by addressing supervision issues before an injury or exposure has occurred and/or after an injury or exposure has occurred. Appropriate anticipatory guidance on supervision at well-child visits can improve supervision of children, and may prevent future harm. Adequate supervision varies based on the child's development and maturity, and the risks in the child's environment. Consideration should be given to issues as wide ranging as swimming pools, falls, dating violence, and social media. By considering the likelihood of harm and the severity of the potential harm, caregivers may provide adequate supervision by minimizing risks to the child while still allowing the child to take "small" risks as needed for healthy development. Caregivers should initially focus on direct (visual, auditory, and proximity) supervision of the young child. Gradually, supervision needs to be adjusted as the child develops, emphasizing a safe environment and safe social interactions, with graduated independence. PCPs may foster adequate supervision by providing concrete guidance to caregivers. In addition to preventing injury, supervision includes fostering a safe, stable, and nurturing relationship with every child. PCPs should be familiar with age/developmentally based supervision risks, adequate supervision based on those risks, characteristics of neglectful supervision based on age/development, and ways to encourage appropriate supervision throughout childhood. Copyright 2014, SLACK Incorporated.

Group supervision for general practitioners

DEFF Research Database (Denmark)

Galina Nielsen, Helena; Sofie Davidsen, Annette; Dalsted, Rikke

2013-01-01

AIM: Group supervision is a sparsely researched method for professional development in general practice. The aim of this study was to explore general practitioners' (GPs') experiences of the benefits of group supervision for improving the treatment of mental disorders. METHODS: One long-establish......AIM: Group supervision is a sparsely researched method for professional development in general practice. The aim of this study was to explore general practitioners' (GPs') experiences of the benefits of group supervision for improving the treatment of mental disorders. METHODS: One long...... considered important prerequisites for disclosing and discussing professional problems. CONCLUSION: The results of this study indicate that participation in a supervision group can be beneficial for maintaining and developing GPs' skills in dealing with patients with mental health problems. Group supervision...... influenced other areas of GPs' professional lives as well. However, more studies are needed to assess the impact of supervision groups....

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

Directory of Open Access Journals (Sweden)

Bin Hou

2016-08-01

Full Text Available 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.

Evaluering af kollegial supervision

DEFF Research Database (Denmark)

Petersen, Anne Line Bjerre Folsgaard; Bager, Lene Tortzen; Jørgensen, Mette Eg

2015-01-01

Videoen er en evaluering af arbejdet med en metodisk tilgang til kollegial supervision på VIA Ergoterapeutuddannelsen gennem et par år. Evalueringen sætter fokus på selve metoden, der er anvendt til kollegial supervision. Derudover er der fokus på erfaringer og udbytte af at arbejde systematisk med...... kollegial supervision blandt undervisere på VIA Ergoterapeutuddannelsen....

Directory of Instructional Programs in Supervision and Management Training.

Science.gov (United States)

Civil Service Commission, Washington, DC. Training Assistance Div.

This directory, which is designed for the use of training officers in the Washington, D.C. area in prescribing learning programs to meet employee training needs, describes available group and self instructional programs used for the training of supervisors and managers. Each of the 21 courses listed contains the pertinent information necessary to…

Rethinking Educational Supervision

Directory of Open Access Journals (Sweden)

Burhanettin DÖNMEZ

2009-08-01

Full Text Available The history of educational (school supervision has been influenced by the history of the interaction of intellectual movements in politics, society, philosophy and industrial movements. The purpose of this conceptual and theoretical study is to have a brief look at the concept of educational supervision with related historical developments in the field. The paper also intends to see the terms and issues critically, and to conceptualize some issues associated with educational supervision in practice. In the paper, the issues are discussed and a number of suggestions are addressed for debate.

Professional Supervision as Storied Experience: Narrative Analysis Findings for Australian-Based Registered Music Therapists.

Science.gov (United States)

Kennelly, Jeanette D; Baker, Felicity A; Daveson, Barbara A

2017-03-01

Limited research exists to inform a music therapist's supervision story from their pre-professional training to their practice as a professional. Evidence is needed to understand the complex nature of supervision experiences and their impact on professional practice. This qualitative study explored the supervisory experiences of Australian-based Registered Music Therapists, according to the: 1) themes that characterize their experiences, 2) influences of the supervisor's professional background, 3) outcomes of supervision, and 4) roles of the employer, the professional music therapy association, and the university in supervision standards and practice. Seven professionals were interviewed for this study. Five stages of narrative analysis were used to create their supervision stories: a life course graph, narrative psychological analysis, component story framework and narrative analysis, analysis of narratives, and final integration of the seven narrative summaries. Findings revealed that supervision practice is influenced by a supervisee's personal and professional needs. A range of supervision models or approaches is recommended, including the access of supervisors from different professional backgrounds to support each stage of learning and development. A quality supervisory experience facilitates shifts in awareness and insight, which results in improved or increased skills, confidence, and accountability of practice. Participants' concern about stakeholders included a limited understanding of the role of the supervisor, a lack of clarity about accountability of supervisory practice, and minimal guidelines, which monitor professional competencies. The benefits of supervision in music therapy depend on the quality of the supervision provided, and clarity about the roles of those involved. Research and guidelines are recommended to target these areas. © the American Music Therapy Association 2017. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com

The efficacy of early initiated, supervised, progressive resistance training compared to unsupervised, home-based exercise after unicompartmental knee arthroplasty: a single-blinded randomized controlled trial.

Science.gov (United States)

Jørgensen, Peter B; Bogh, Søren B; Kierkegaard, Signe; Sørensen, Henrik; Odgaard, Anders; Søballe, Kjeld; Mechlenburg, Inger

2017-01-01

To examine if supervised progressive resistance training was superior to home-based exercise in rehabilitation after unicompartmental knee arthroplasty. Single blinded, randomized clinical trial. Surgery, progressive resistance training and testing was carried out at Aarhus University Hospital and home-based exercise was carried out in the home of the patient. Fifty five patients were randomized to either progressive resistance training or home-based exercise. Patients were randomized to either progressive resistance training (home based exercise five days/week and progressive resistance training two days/week) or control group (home based exercise seven days/week). Preoperative assessment, 10-week (primary endpoint) and one-year follow-up were performed for leg extension power, spatiotemporal gait parameters and knee injury and osteoarthritis outcome score (KOOS). Forty patients (73%) completed 1-year follow-up. Patients in the progressive resistance training group participated in average 11 of 16 training sessions. Leg extension power increased from baseline to 10-week follow-up in progressive resistance training group (progressive resistance training: 0.28 W/kg, P= 0.01, control group: 0.01 W/kg, P=0.93) with no between-group difference. Walking speed and KOOS scores increased from baseline to 10-week follow-up in both groups with no between-group difference (six minutes walk test P=0.63, KOOS P>0.29). Progressive resistance training two days/week combined with home based exercise five days/week was not superior to home based exercise seven days/week in improving leg extension power of the operated leg.

Social construction : discursive perspective towards supervision

OpenAIRE

Naujanienė, Rasa

2010-01-01

The aim of publication is to discuss the development of supervision theory in relation with social and social work theory and practice. Main focus in the analysis is done to social constructionist ideas and its’ relevance to supervision practice. The development of supervision is related with supervision practice. Starting in 19th century supervision from giving practical advices supervision came to 21st century as dialog based on critical and philosophical reflection. Different theory and pr...

"Unscrambling what's in your head": A mixed method evaluation of clinical supervision for midwives.

Science.gov (United States)

Love, Bev; Sidebotham, Mary; Fenwick, Jennifer; Harvey, Susan; Fairbrother, Greg

2017-08-01

As a strategy to promote workforce sustainability a number of midwives working in one health district in New South Wales, Australia were trained to offer a reflective model of clinical supervision. The expectation was that these midwives would then be equipped to facilitate clinical supervision for their colleagues with the organisational aim of supporting professional development and promoting emotional well-being. To identify understanding, uptake, perceptions of impact, and the experiences of midwives accessing clinical supervision. Mixed Methods. In phase one 225 midwives were invited to complete a self-administered survey. Descriptive and inferential statistics were used to analyse the data. In phase two 12 midwives were interviewed. Thematic analysis was used to deepen understanding of midwives' experiences of receiving clinical supervision. Sixty percent of midwives responding in phase one had some experience of clinical supervision. Findings from both phases were complementary with midwives reporting a positive impact on their work, interpersonal skills, situational responses and career goals. Midwives described clinical supervision as a formal, structured and confidential space for 'safe reflection' that was valued as an opportunity for self-care. Barriers included misconceptions, perceived work related pressures and a sense that taking time out was unjustifiable. Education, awareness raising and further research into reflective clinical supervision, to support emotional well-being and professional midwifery practice is needed. In addition, health organisations need to design, implement and evaluate strategies that support the embedding of clinical supervision within midwives' clinical practice. Copyright © 2016 Australian College of Midwives. Published by Elsevier Ltd. All rights reserved.

National survey of psychotherapy training in psychiatry, psychology, and social work.

Science.gov (United States)

Weissman, Myrna M; Verdeli, Helen; Gameroff, Marc J; Bledsoe, Sarah E; Betts, Kathryn; Mufson, Laura; Fitterling, Heidi; Wickramaratne, Priya

2006-08-01

Approximately 3% of the US population receives psychotherapy each year from psychiatrists, psychologists, or social workers. A modest number of psychotherapies are evidence-based therapy (EBT) in that they have been defined in manuals and found efficacious in at least 2 controlled clinical trials with random assignment that include a control condition of psychotherapy, placebo, pill, or other treatment and samples of sufficient power with well-characterized patients. Few practitioners use EBT. To determine the amount of EBT taught in accredited training programs in psychiatry, psychology (PhD and PsyD), and social work and to note whether the training was elective or required and presented as a didactic (coursework) or clinical supervision. A cross-sectional survey of a probability sample of all accredited training programs in psychiatry, psychology, and social work in the United States. Responders included training directors (or their designates) from 221 programs (73 in psychiatry, 63 in PhD clinical psychology, 21 in PsyD psychology, and 64 in master's-level social work). The overall response rate was 73.7%. Main Outcome Measure Requiring both a didactic and clinical supervision in an EBT. Although programs offered electives in EBT and non-EBT, few required both a didactic and clinical supervision in EBT, and most required training was non-EBT. Psychiatry required coursework and clinical supervision in the largest percentage of EBT (28.1%). Cognitive behavioral therapy was the EBT most frequently offered and required as a didactic in all 3 disciplines. More than 90% of the psychiatry training programs were complying with the new cognitive behavior therapy requirement. The 2 disciplines with the largest number of students and emphasis on clinical training-professional clinical psychology (PsyD) and social work-had the largest percentage of programs (67.3% and 61.7%, respectively) not requiring a didactic and clinical supervision in any EBT. There is a

Semi-supervised morphosyntactic classification of Old Icelandic.

Science.gov (United States)

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.

Public Supervision over Private Relationships : Towards European Supervision Private Law?

NARCIS (Netherlands)

Cherednychenko, O.O.

2014-01-01

The rise of public supervision over private relationships in many areas of private law has led to the development of what, in the author’s view, could be called ‘European supervision private law’. This emerging body of law forms part of European regulatory private law and is made up of

Improving supervised classification accuracy using non-rigid multimodal image registration: detecting prostate cancer

Science.gov (United States)

Chappelow, Jonathan; Viswanath, Satish; Monaco, James; Rosen, Mark; Tomaszewski, John; Feldman, Michael; Madabhushi, Anant

2008-03-01

Computer-aided diagnosis (CAD) systems for the detection of cancer in medical images require precise labeling of training data. For magnetic resonance (MR) imaging (MRI) of the prostate, training labels define the spatial extent of prostate cancer (CaP); the most common source for these labels is expert segmentations. When ancillary data such as whole mount histology (WMH) sections, which provide the gold standard for cancer ground truth, are available, the manual labeling of CaP can be improved by referencing WMH. However, manual segmentation is error prone, time consuming and not reproducible. Therefore, we present the use of multimodal image registration to automatically and accurately transcribe CaP from histology onto MRI following alignment of the two modalities, in order to improve the quality of training data and hence classifier performance. We quantitatively demonstrate the superiority of this registration-based methodology by comparing its results to the manual CaP annotation of expert radiologists. Five supervised CAD classifiers were trained using the labels for CaP extent on MRI obtained by the expert and 4 different registration techniques. Two of the registration methods were affi;ne schemes; one based on maximization of mutual information (MI) and the other method that we previously developed, Combined Feature Ensemble Mutual Information (COFEMI), which incorporates high-order statistical features for robust multimodal registration. Two non-rigid schemes were obtained by succeeding the two affine registration methods with an elastic deformation step using thin-plate splines (TPS). In the absence of definitive ground truth for CaP extent on MRI, classifier accuracy was evaluated against 7 ground truth surrogates obtained by different combinations of the expert and registration segmentations. For 26 multimodal MRI-WMH image pairs, all four registration methods produced a higher area under the receiver operating characteristic curve compared to that

Technical Training for Managers.

Science.gov (United States)

Haverland, Edgar M.

The question has arisen as to what kind of information a manager without extensive technical training needs to learn to supervise effectively. For example, the Nike Hercules fire control platoon leader, usually an officer in his first active duty assignment, seldom has had extensive technical training. Yet he is responsibile for the…

Forskellighed i supervision

DEFF Research Database (Denmark)

Petersen, Birgitte; Beck, Emma

2009-01-01

Indtryk og tendenser fra den anden danske konference om supervision, som blev holdt på Københavns Universitet i oktober 2008......Indtryk og tendenser fra den anden danske konference om supervision, som blev holdt på Københavns Universitet i oktober 2008...

Flexible manifold embedding: a framework for semi-supervised and unsupervised dimension reduction.

Science.gov (United States)

Nie, Feiping; Xu, Dong; Tsang, Ivor Wai-Hung; Zhang, Changshui

2010-07-01

We propose a unified manifold learning framework for semi-supervised and unsupervised dimension reduction by employing a simple but effective linear regression function to map the new data points. For semi-supervised dimension reduction, we aim to find the optimal prediction labels F for all the training samples X, the linear regression function h(X) and the regression residue F(0) = F - h(X) simultaneously. Our new objective function integrates two terms related to label fitness and manifold smoothness as well as a flexible penalty term defined on the residue F(0). Our Semi-Supervised learning framework, referred to as flexible manifold embedding (FME), can effectively utilize label information from labeled data as well as a manifold structure from both labeled and unlabeled data. By modeling the mismatch between h(X) and F, we show that FME relaxes the hard linear constraint F = h(X) in manifold regularization (MR), making it better cope with the data sampled from a nonlinear manifold. In addition, we propose a simplified version (referred to as FME/U) for unsupervised dimension reduction. We also show that our proposed framework provides a unified view to explain and understand many semi-supervised, supervised and unsupervised dimension reduction techniques. Comprehensive experiments on several benchmark databases demonstrate the significant improvement over existing dimension reduction algorithms.

Supervision af psykoterapi

DEFF Research Database (Denmark)

SUPERVISION AF PSYKOTERAPI indtager en central position i uddannelsen og udviklingen af psykoterapeuter. Trods flere lighedspunkter med psykoterapi, undervisning og konsultation er psykoterapisupervision et selvstændigt virksomhedsområde. Supervisor må foruden at være en trænet psykoterapeut kende...... supervisionens rammer og indplacering i forhold til organisation og samfund. En række kapitler drejer sig om supervisors opgaver, roller og kontrolfunktion, supervision set fra supervisandens perspektiv samt betragtninger over relationer og processer i supervision. Der drøftes fordele og ulemper ved de...... forskellige måder, hvorpå en sag kan fremlægges. Bogens første del afsluttes med refleksioner over de etiske aspekter ved psykoterapisupervision. Bogens anden del handler om de særlige forhold, der gør sig gældende ved supervision af en række specialiserede behandlingsformer eller af psykoterapi med bestemte...

Intelligent physical exercise at work: effect of supervision on motivation and reduction in neck-shoulder pain. Result from VIMS-study

DEFF Research Database (Denmark)

Gram, Bibi; Zebis, Mette Kreutzfeldt; Pedersen, Mogens Theisen

INTELLIGENT PHYSICAL EXERCISE AT WORK: EFFECT OF SUPERVISION ON MOTIVATION AND REDUCTION IN NECK-SHOULDER PAIN? RESULT FROM VIMS-STUDY. Gram B1,Zebis MK1, Pedersen MT2, Andersen LL3, Sjøgaard G1 1: Inst. of Sports Science and Clinical Biomechanics, University of Southern Denmark, Odense, Denmark 2....... Inst. of Exercise and Sport Sciences, University of Copenhagen, Denmark 3: National Research Centre for the Working Environment, Denmark Introduction It is well known that sedentary occupation with computer work is associated with development of pain in neck and shoulder. Studies have shown...... that physical exercise at work is effective in managing musculoskeletal pain (1,2). However, the effect of supervision during training sessions in workplace interventions needs to be clarified. Thus, the aim of this study was to evaluate the effect of different amount of supervision on training motivation...

Supervised and Unsupervised Classification for Pattern Recognition Purposes

Directory of Open Access Journals (Sweden)

Catalina COCIANU

2006-01-01

Full Text Available A cluster analysis task has to identify the grouping trends of data, to decide on the sound clusters as well as to validate somehow the resulted structure. The identification of the grouping tendency existing in a data collection assumes the selection of a framework stated in terms of a mathematical model allowing to express the similarity degree between couples of particular objects, quasi-metrics expressing the similarity between an object an a cluster and between clusters, respectively. In supervised classification, we are provided with a collection of preclassified patterns, and the problem is to label a newly encountered pattern. Typically, the given training patterns are used to learn the descriptions of classes which in turn are used to label a new pattern. The final section of the paper presents a new methodology for supervised learning based on PCA. The classes are represented in the measurement/feature space by a continuous repartitions

Novel maximum-margin training algorithms for supervised neural networks.

Science.gov (United States)

Ludwig, Oswaldo; Nunes, Urbano

2010-06-01

This paper proposes three novel training methods, two of them based on the backpropagation approach and a third one based on information theory for multilayer perceptron (MLP) binary classifiers. Both backpropagation methods are based on the maximal-margin (MM) principle. The first one, based on the gradient descent with adaptive learning rate algorithm (GDX) and named maximum-margin GDX (MMGDX), directly increases the margin of the MLP output-layer hyperplane. The proposed method jointly optimizes both MLP layers in a single process, backpropagating the gradient of an MM-based objective function, through the output and hidden layers, in order to create a hidden-layer space that enables a higher margin for the output-layer hyperplane, avoiding the testing of many arbitrary kernels, as occurs in case of support vector machine (SVM) training. The proposed MM-based objective function aims to stretch out the margin to its limit. An objective function based on Lp-norm is also proposed in order to take into account the idea of support vectors, however, overcoming the complexity involved in solving a constrained optimization problem, usually in SVM training. In fact, all the training methods proposed in this paper have time and space complexities O(N) while usual SVM training methods have time complexity O(N (3)) and space complexity O(N (2)) , where N is the training-data-set size. The second approach, named minimization of interclass interference (MICI), has an objective function inspired on the Fisher discriminant analysis. Such algorithm aims to create an MLP hidden output where the patterns have a desirable statistical distribution. In both training methods, the maximum area under ROC curve (AUC) is applied as stop criterion. The third approach offers a robust training framework able to take the best of each proposed training method. The main idea is to compose a neural model by using neurons extracted from three other neural networks, each one previously trained by

The research of automatic speed control algorithm based on Green CBTC

Science.gov (United States)

Lin, Ying; Xiong, Hui; Wang, Xiaoliang; Wu, Youyou; Zhang, Chuanqi

2017-06-01

Automatic speed control algorithm is one of the core technologies of train operation control system. It’s a typical multi-objective optimization control algorithm, which achieve the train speed control for timing, comfort, energy-saving and precise parking. At present, the train speed automatic control technology is widely used in metro and inter-city railways. It has been found that the automatic speed control technology can effectively reduce the driver’s intensity, and improve the operation quality. However, the current used algorithm is poor at energy-saving, even not as good as manual driving. In order to solve the problem of energy-saving, this paper proposes an automatic speed control algorithm based on Green CBTC system. Based on the Green CBTC system, the algorithm can adjust the operation status of the train to improve the efficient using rate of regenerative braking feedback energy while ensuring the timing, comfort and precise parking targets. Due to the reason, the energy-using of Green CBTC system is lower than traditional CBTC system. The simulation results show that the algorithm based on Green CBTC system can effectively reduce the energy-using due to the improvement of the using rate of regenerative braking feedback energy.

Reflective Process in Play Therapy: A Practical Model for Supervising Counseling Students

Science.gov (United States)

Allen, Virginia B.; Folger, Wendy A.; Pehrsson, Dale-Elizabeth

2007-01-01

Counselor educators and other supervisors, who work with graduate student counseling interns utilizing Play Therapy, should be educated, grounded, and trained in theory, supervision, and techniques specific to Play Therapy. Unfortunately, this is often not the case. Therefore, a three step model was created to assist those who do not have specific…

Practicum Training for Teachers of Struggling Readers

Science.gov (United States)

Morris, Darrell

2011-01-01

Teachers who work with struggling beginning readers need a supervised training experience that leads them to understand both how reading ability develops and how to adapt instruction to meet the needs of individual children. The practicum, in which a teacher works with one struggling reader under the supervision of an experienced and expert…

Researching online supervision

DEFF Research Database (Denmark)

Bengtsen, Søren S. E.; Mathiasen, Helle

2014-01-01

Online supervision and the use of digital media in supervisory dialogues is a fast increasing practice in higher education today. However, the concepts in our pedagogical repertoire often reflect the digital tools used for supervision purposes as either a prolongation of the face-to-face contact...

Implementation of Instructional Supervision in Secondary School ...

African Journals Online (AJOL)

Science, Technology and Arts Research Journal ... Supervision is critical in the development of any educational program in both developed and ... Clinical Supervision, Collegial Supervision, Self-directive supervision, Informal Supervision etc.

Multi-level deep supervised networks for retinal vessel segmentation.

Science.gov (United States)

Mo, Juan; Zhang, Lei

2017-12-01

Changes in the appearance of retinal blood vessels are an important indicator for various ophthalmologic and cardiovascular diseases, including diabetes, hypertension, arteriosclerosis, and choroidal neovascularization. Vessel segmentation from retinal images is very challenging because of low blood vessel contrast, intricate vessel topology, and the presence of pathologies such as microaneurysms and hemorrhages. To overcome these challenges, we propose a neural network-based method for vessel segmentation. A deep supervised fully convolutional network is developed by leveraging multi-level hierarchical features of the deep networks. To improve the discriminative capability of features in lower layers of the deep network and guide the gradient back propagation to overcome gradient vanishing, deep supervision with auxiliary classifiers is incorporated in some intermediate layers of the network. Moreover, the transferred knowledge learned from other domains is used to alleviate the issue of insufficient medical training data. The proposed approach does not rely on hand-crafted features and needs no problem-specific preprocessing or postprocessing, which reduces the impact of subjective factors. We evaluate the proposed method on three publicly available databases, the DRIVE, STARE, and CHASE_DB1 databases. Extensive experiments demonstrate that our approach achieves better or comparable performance to state-of-the-art methods with a much faster processing speed, making it suitable for real-world clinical applications. The results of cross-training experiments demonstrate its robustness with respect to the training set. The proposed approach segments retinal vessels accurately with a much faster processing speed and can be easily applied to other biomedical segmentation tasks.

Automatic Segmentation of Dermoscopic Images by Iterative Classification

Directory of Open Access Journals (Sweden)

Maciel Zortea

2011-01-01

Full Text Available Accurate detection of the borders of skin lesions is a vital first step for computer aided diagnostic systems. This paper presents a novel automatic approach to segmentation of skin lesions that is particularly suitable for analysis of dermoscopic images. Assumptions about the image acquisition, in particular, the approximate location and color, are used to derive an automatic rule to select small seed regions, likely to correspond to samples of skin and the lesion of interest. The seed regions are used as initial training samples, and the lesion segmentation problem is treated as binary classification problem. An iterative hybrid classification strategy, based on a weighted combination of estimated posteriors of a linear and quadratic classifier, is used to update both the automatically selected training samples and the segmentation, increasing reliability and final accuracy, especially for those challenging images, where the contrast between the background skin and lesion is low.

Observations on Current Practices in Preceptor Training

Science.gov (United States)

Volberding, Jennifer L.; Richardson, Lawrence

2015-01-01

Preceptor education is a major focus for all athletic training programs. Clinical education is a required and fundamental component of an athletic training student's education, so it is imperative the preceptors delivering and supervising clinical experiences have the highest level of training. The purpose of this exploratory qualitative…

20 CFR 656.21 - Supervised recruitment.

Science.gov (United States)

2010-04-01

... 20 Employees' Benefits 3 2010-04-01 2010-04-01 false Supervised recruitment. 656.21 Section 656.21... Supervised recruitment. (a) Supervised recruitment. Where the Certifying Officer determines it appropriate, post-filing supervised recruitment may be required of the employer for the pending application or...

Best practices in nursing homes. Clinical supervision, management, and human resource practices.

Science.gov (United States)

Dellefield, Mary Ellen

2008-07-01

Human resource practices including supervision and management are associated with organizational performance. Evidence supportive of such an association in nursing homes is found in the results of numerous research studies conducted during the past 17 years. In this article, best practices related to this topic have been culled from descriptive, explanatory, and intervention studies in a range of interdisciplinary research journals published between 1990 and 2007. Identified best practices include implementation of training programs on supervision and management for licensed nurses, certified nursing assistant job enrichment programs, implementation of consistent nursing assignments, and the use of electronic documentation. Organizational barriers and facilitators of these best practices are described. Copyright 2009, SLACK Incorporated.

Supervision Duty of School Principals

Directory of Open Access Journals (Sweden)

Kürşat YILMAZ

2009-04-01

Full Text Available Supervision by school administrators is becoming more and more important. The change in the roles ofschool administrators has a great effect on that increase. At present, school administrators are consideredmore than as technical directors, but as instructional leaders. This increased the importance of schooladministrators’ expected supervision acts. In this respect, the aim of this study is to make a conceptualanalysis about school administrators’ supervision duties. For this reason, a literature review related withsupervision and contemporary supervision approaches was done, and the official documents concerningsupervision were examined. As a result, it can be said that school administrators’ supervision duties havebecome very important. And these duties must certainly be carried out by school administrators.

Automatic Classification of High Resolution Satellite Imagery - a Case Study for Urban Areas in the Kingdom of Saudi Arabia

Science.gov (United States)

Maas, A.; Alrajhi, M.; Alobeid, A.; Heipke, C.

2017-05-01

Updating topographic geospatial databases is often performed based on current remotely sensed images. To automatically extract the object information (labels) from the images, supervised classifiers are being employed. Decisions to be taken in this process concern the definition of the classes which should be recognised, the features to describe each class and the training data necessary in the learning part of classification. With a view to large scale topographic databases for fast developing urban areas in the Kingdom of Saudi Arabia we conducted a case study, which investigated the following two questions: (a) which set of features is best suitable for the classification?; (b) what is the added value of height information, e.g. derived from stereo imagery? Using stereoscopic GeoEye and Ikonos satellite data we investigate these two questions based on our research on label tolerant classification using logistic regression and partly incorrect training data. We show that in between five and ten features can be recommended to obtain a stable solution, that height information consistently yields an improved overall classification accuracy of about 5%, and that label noise can be successfully modelled and thus only marginally influences the classification results.

Moment constrained semi-supervised LDA

DEFF Research Database (Denmark)

Loog, Marco

2012-01-01

This BNAIC compressed contribution provides a summary of the work originally presented at the First IAPR Workshop on Partially Supervised Learning and published in [5]. It outlines the idea behind supervised and semi-supervised learning and highlights the major shortcoming of many current methods...

Influence of a 12-year supervised physical activity program for the elderly

Directory of Open Access Journals (Sweden)

José Rodrigo Pauli

2009-06-01

Full Text Available Aging is an inevitable process and is associated with declining physiological and functional capacity in humans. The objective of this study was to determine the effects of a 12-yearsupervised physical training program on functional fitness in the elderly. Ten women (mean age: 65 years participated in the study. The subjects were divided into two groups: a a trained group consisting of women who had been attending a supervised program including different types of physical activities of moderate intensity over the last 12 years; b an untrained group consisting of women who were not engaged in any supervised physical activity program over the last 12years. Functional fitness was assessed using the AAHPERD field-test battery which comprises five single motor tests: coordination, flexibility, strength endurance, agility and dynamic balance, and overall aerobic endurance. The results showed a better performance of elderly women whoparticipated in a physical activity program over the last 12 years. Thus, whereas elderly women who perform regular physical activities in a supervised program tend to show improvement of all functional fitness components even after a period of 12 years, a tendency towards a reduction in most of these components is observed in their non-active peers. These findings seem to predict an increasing gap in functional fitness between these two groups as they grow older, with opposite effects on the quality of life of these subjects.

Snorkel: Rapid Training Data Creation with Weak Supervision.

Science.gov (United States)

Ratner, Alexander; Bach, Stephen H; Ehrenberg, Henry; Fries, Jason; Wu, Sen; Ré, Christopher

2017-11-01

Labeling training data is increasingly the largest bottleneck in deploying machine learning systems. We present Snorkel, a first-of-its-kind system that enables users to train state-of- the-art models without hand labeling any training data. Instead, users write labeling functions that express arbitrary heuristics, which can have unknown accuracies and correlations. Snorkel denoises their outputs without access to ground truth by incorporating the first end-to-end implementation of our recently proposed machine learning paradigm, data programming. We present a flexible interface layer for writing labeling functions based on our experience over the past year collaborating with companies, agencies, and research labs. In a user study, subject matter experts build models 2.8× faster and increase predictive performance an average 45.5% versus seven hours of hand labeling. We study the modeling tradeoffs in this new setting and propose an optimizer for automating tradeoff decisions that gives up to 1.8× speedup per pipeline execution. In two collaborations, with the U.S. Department of Veterans Affairs and the U.S. Food and Drug Administration, and on four open-source text and image data sets representative of other deployments, Snorkel provides 132% average improvements to predictive performance over prior heuristic approaches and comes within an average 3.60% of the predictive performance of large hand-curated training sets.

Broad Absorption Line Quasar catalogues with Supervised Neural Networks

International Nuclear Information System (INIS)

Scaringi, Simone; Knigge, Christian; Cottis, Christopher E.; Goad, Michael R.

2008-01-01

We have applied a Learning Vector Quantization (LVQ) algorithm to SDSS DR5 quasar spectra in order to create a large catalogue of broad absorption line quasars (BALQSOs). We first discuss the problems with BALQSO catalogues constructed using the conventional balnicity and/or absorption indices (BI and AI), and then describe the supervised LVQ network we have trained to recognise BALQSOs. The resulting BALQSO catalogue should be substantially more robust and complete than BI-or AI-based ones.

Asco 2044 nuclear power plant: supervision; Central nuclear Asco 2044: supervision

Energy Technology Data Exchange (ETDEWEB)

Sabartes, J.

2010-07-01

Good supervision constitutes an efficient barrier to avoid the errors caused by inadequate work practices. In this sense, it is necessary to strengthen supervision to make sure that the work is carried out with adequate human performance, tending to avoid error ande provinding safety quality and efficiency at work. (Author).

Whither Supervision?

OpenAIRE

Duncan Waite

2006-01-01

This paper inquires if the school supervision is in decadence. Dr. Waite responds that the answer will depend on which perspective you look at it. Dr. Waite suggests taking in consideration three elements that are related: the field itself, the expert in the field (the professor, the theorist, the student and the administrator), and the context. When these three elements are revised, it emphasizes that there is not a consensus about the field of supervision, but there are coincidences related...

Effects of coaching supervision, mentoring supervision and abusive supervision on talent development among trainee doctors in public hospitals: moderating role of clinical learning environment.

Science.gov (United States)

Subramaniam, Anusuiya; Silong, Abu Daud; Uli, Jegak; Ismail, Ismi Arif

2015-08-13

Effective talent development requires robust supervision. However, the effects of supervisory styles (coaching, mentoring and abusive supervision) on talent development and the moderating effects of clinical learning environment in the relationship between supervisory styles and talent development among public hospital trainee doctors have not been thoroughly researched. In this study, we aim to achieve the following, (1) identify the extent to which supervisory styles (coaching, mentoring and abusive supervision) can facilitate talent development among trainee doctors in public hospital and (2) examine whether coaching, mentoring and abusive supervision are moderated by clinical learning environment in predicting talent development among trainee doctors in public hospital. A questionnaire-based critical survey was conducted among trainee doctors undergoing housemanship at six public hospitals in the Klang Valley, Malaysia. Prior permission was obtained from the Ministry of Health Malaysia to conduct the research in the identified public hospitals. The survey yielded 355 responses. The results were analysed using SPSS 20.0 and SEM with AMOS 20.0. The findings of this research indicate that coaching and mentoring supervision are positively associated with talent development, and that there is no significant relationship between abusive supervision and talent development. The findings also support the moderating role of clinical learning environment on the relationships between coaching supervision-talent development, mentoring supervision-talent development and abusive supervision-talent development among public hospital trainee doctors. Overall, the proposed model indicates a 26 % variance in talent development. This study provides an improved understanding on the role of the supervisory styles (coaching and mentoring supervision) on facilitating talent development among public hospital trainee doctors. Furthermore, this study extends the literature to better

Maximum margin semi-supervised learning with irrelevant data.

Science.gov (United States)

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 �

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

Science.gov (United States)

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.

GRAPH-BASED SEMI-SUPERVISED HYPERSPECTRAL IMAGE CLASSIFICATION USING SPATIAL INFORMATION

Directory of Open Access Journals (Sweden)

N. Jamshidpour

2017-09-01

Full Text Available 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.

Perceptions of balance and falls following a supervised training intervention - a qualitative study of people with Parkinson's disease.

Science.gov (United States)

Leavy, Breiffni; Berntsson, Johan; Franzén, Erika; Skavberg Roaldsen, Kirsti

2017-12-21

To explore perceptions of balance and falls among people with mild to moderate Parkinson's disease 3 - 12 months following participation in supervised balance training. This qualitative study used in-depth individual interviews for data collection among 13 people with Parkinson's disease. Interviews were systematically analyzed using qualitative content analysis with an inductive approach. Three main themes arose: Falls - avoided and intended highlights the wide spectrum of fall perceptions, ranging from worse-case scenario to undramatized events; Balance identity incorporates how gradual deterioration in balance served as a reminder of disease progression and how identifying themselves as "aware not afraid" helped certain participants to maintain balance confidence despite everyday activity restriction; Training as treatment recounts how participants used exercise as disease self-management with the aim to maintain independence in daily life. Interpretation of the underlying patterns of these main themes resulted in the overarching theme Training as treatment when battling problems with balance and falls. Whereas certain participants expressed a fear of falling which they managed by activity restriction, others described being confident in their balance despite avoidance of balance-challenging activities. Training was used as treatment to self-manage disease-related balance impairments in order to maintain independence in daily life. Implication for Rehabilitation People with Parkinson's disease require early advice about the positive effects of physical activity as well as strategies for self-management in order to ease the psychological and physical burden of progressive balance impairment. Fear of falling should be investigated alongside activity avoidance in this group in order to provide a more accurate insight into the scope of psychological concerns regarding balance and falls in everyday life. Certain people with Parkinson's disease define their

Human semi-supervised learning.

Science.gov (United States)

Gibson, Bryan R; Rogers, Timothy T; Zhu, Xiaojin

2013-01-01

Most empirical work in human categorization has studied learning in either fully supervised or fully unsupervised scenarios. Most real-world learning scenarios, however, are semi-supervised: Learners receive a great deal of unlabeled information from the world, coupled with occasional experiences in which items are directly labeled by a knowledgeable source. A large body of work in machine learning has investigated how learning can exploit both labeled and unlabeled data provided to a learner. Using equivalences between models found in human categorization and machine learning research, we explain how these semi-supervised techniques can be applied to human learning. A series of experiments are described which show that semi-supervised learning models prove useful for explaining human behavior when exposed to both labeled and unlabeled data. We then discuss some machine learning models that do not have familiar human categorization counterparts. Finally, we discuss some challenges yet to be addressed in the use of semi-supervised models for modeling human categorization. Copyright © 2013 Cognitive Science Society, Inc.

Quality assurance in postgraduate pathology training the Dutch way: regular assessment, monitoring of training programs but no end of training examination.

Science.gov (United States)

van der Valk, Paul

2016-01-01

It might seem self-evident that in the transition from a supervised trainee to an independent professional who is no longer supervised, formal assessment of whether the trainee knows his/her trade well enough to function independently is necessary. This would then constitute an end of training examination. Such examinations are practiced in several countries but a rather heterogeneous situation exists in the EU countries. In the Netherlands, the training program is not concluded by a summative examination and reasons behind this situation are discussed. Quality assurance of postgraduate medical training in the Netherlands has been developed along two tracks: (1) not a single testing moment but continuous evaluation of the performance of the trainee in 'real time' situations and (2) monitoring of the quality of the offered training program through regular site-visits. Regular (monthly and/or yearly) evaluations should be part of every self-respecting training program. In the Netherlands, these evaluations are formative only: their intention is to provide the trainee a tool by which he or she can see whether they are on track with their training schedule. In the system in the Netherlands, regular site-visits to training programs constitute a crucial element of quality assurance of postgraduate training. During the site-visit, the position and perceptions of the trainee are key elements. The perception by the trainee of the training program, the institution (or department) offering the training program, and the professionals involved in the training program is explicitly solicited and systematically assessed. With this two-tiered approach high-quality postgraduate training is assured without the need for an end of training examination.

Semi-Supervised Multi-View Ensemble Learning Based On Extracting Cross-View Correlation

Directory of Open Access Journals (Sweden)

ZALL, R.

2016-05-01

Full Text Available Correlated information between different views incorporate useful for learning in multi view data. Canonical correlation analysis (CCA plays important role to extract these information. However, CCA only extracts the correlated information between paired data and cannot preserve correlated information between within-class samples. In this paper, we propose a two-view semi-supervised learning method called semi-supervised random correlation ensemble base on spectral clustering (SS_RCE. SS_RCE uses a multi-view method based on spectral clustering which takes advantage of discriminative information in multiple views to estimate labeling information of unlabeled samples. In order to enhance discriminative power of CCA features, we incorporate the labeling information of both unlabeled and labeled samples into CCA. Then, we use random correlation between within-class samples from cross view to extract diverse correlated features for training component classifiers. Furthermore, we extend a general model namely SSMV_RCE to construct ensemble method to tackle semi-supervised learning in the presence of multiple views. Finally, we compare the proposed methods with existing multi-view feature extraction methods using multi-view semi-supervised ensembles. Experimental results on various multi-view data sets are presented to demonstrate the effectiveness of the proposed methods.

Using a Mixed Model to Explore Evaluation Criteria for Bank Supervision: A Banking Supervision Law Perspective.

Directory of Open Access Journals (Sweden)

Sang-Bing Tsai

Full Text Available Financial supervision means that monetary authorities have the power to supervise and manage financial institutions according to laws. Monetary authorities have this power because of the requirements of improving financial services, protecting the rights of depositors, adapting to industrial development, ensuring financial fair trade, and maintaining stable financial order. To establish evaluation criteria for bank supervision in China, this study integrated fuzzy theory and the decision making trial and evaluation laboratory (DEMATEL and proposes a fuzzy-DEMATEL model. First, fuzzy theory was applied to examine bank supervision criteria and analyze fuzzy semantics. Second, the fuzzy-DEMATEL model was used to calculate the degree to which financial supervision criteria mutually influenced one another and their causal relationship. Finally, an evaluation criteria model for evaluating bank and financial supervision was established.

Using a Mixed Model to Explore Evaluation Criteria for Bank Supervision: A Banking Supervision Law Perspective

Science.gov (United States)

Tsai, Sang-Bing; Chen, Kuan-Yu; Zhao, Hongrui; Wei, Yu-Min; Wang, Cheng-Kuang; Zheng, Yuxiang; Chang, Li-Chung; Wang, Jiangtao

2016-01-01

Financial supervision means that monetary authorities have the power to supervise and manage financial institutions according to laws. Monetary authorities have this power because of the requirements of improving financial services, protecting the rights of depositors, adapting to industrial development, ensuring financial fair trade, and maintaining stable financial order. To establish evaluation criteria for bank supervision in China, this study integrated fuzzy theory and the decision making trial and evaluation laboratory (DEMATEL) and proposes a fuzzy-DEMATEL model. First, fuzzy theory was applied to examine bank supervision criteria and analyze fuzzy semantics. Second, the fuzzy-DEMATEL model was used to calculate the degree to which financial supervision criteria mutually influenced one another and their causal relationship. Finally, an evaluation criteria model for evaluating bank and financial supervision was established. PMID:27992449

Supervision in banking industry

OpenAIRE

Šmída, David

2012-01-01

The aim of submitted thesis Supervision in banking is to define the nature and the importance of banking supervision, to justify its existence and to analyze the applicable mechanisms while the system of banking regulation and supervision in this thesis is primarily examined in the European context, with a focus on the Czech Republic. The thesis is divided into five main chapters. The first chapter is devoted to the financial system and the importance of banks in this system, it defines the c...

Variations on a Theme by Kolb: A New Perspective for Understanding Counseling and Supervision.

Science.gov (United States)

Abbey, David S.; And Others

1985-01-01

An analysis of clinical dialogue between client and counselor and between counselor-in-training and supervisor is used to demonstrate that effective counseling and supervision demands that Kolb's four modes of experience (concrete experience, reflective observation, abstract conceptualization, and active experimentation) must be available to the…

17 CFR 166.3 - Supervision.

Science.gov (United States)

2010-04-01

... 17 Commodity and Securities Exchanges 1 2010-04-01 2010-04-01 false Supervision. 166.3 Section 166.3 Commodity and Securities Exchanges COMMODITY FUTURES TRADING COMMISSION CUSTOMER PROTECTION RULES § 166.3 Supervision. Each Commission registrant, except an associated person who has no supervisory duties, must diligently supervise the handling b...

28 CFR 810.1 - Supervision contact requirements.

Science.gov (United States)

2010-07-01

... 28 Judicial Administration 2 2010-07-01 2010-07-01 false Supervision contact requirements. 810.1 Section 810.1 Judicial Administration COURT SERVICES AND OFFENDER SUPERVISION AGENCY FOR THE DISTRICT OF COLUMBIA COMMUNITY SUPERVISION: ADMINISTRATIVE SANCTIONS § 810.1 Supervision contact requirements. If you are an offender under supervision by th...

Supervised learning in spiking neural networks with FORCE training.

Science.gov (United States)

Nicola, Wilten; Clopath, Claudia

2017-12-20

Populations of neurons display an extraordinary diversity in the behaviors they affect and display. Machine learning techniques have recently emerged that allow us to create networks of model neurons that display behaviors of similar complexity. Here we demonstrate the direct applicability of one such technique, the FORCE method, to spiking neural networks. We train these networks to mimic dynamical systems, classify inputs, and store discrete sequences that correspond to the notes of a song. Finally, we use FORCE training to create two biologically motivated model circuits. One is inspired by the zebra finch and successfully reproduces songbird singing. The second network is motivated by the hippocampus and is trained to store and replay a movie scene. FORCE trained networks reproduce behaviors comparable in complexity to their inspired circuits and yield information not easily obtainable with other techniques, such as behavioral responses to pharmacological manipulations and spike timing statistics.

Mentoring, coaching and supervision

OpenAIRE

McMahon, Samantha; Dyer, Mary; Barker, Catherine

2016-01-01

This chapter considers the purpose of coaching, mentoring and supervision in early childhood eduaction and care. It examines a number of different approaches and considers the key skills required for effective coaching, mentoring and supervision.

Empirical study of supervised gene screening

Directory of Open Access Journals (Sweden)

Ma Shuangge

2006-12-01

Full Text Available Abstract Background Microarray studies provide a way of linking variations of phenotypes with their genetic causations. Constructing predictive models using high dimensional microarray measurements usually consists of three steps: (1 unsupervised gene screening; (2 supervised gene screening; and (3 statistical model building. Supervised gene screening based on marginal gene ranking is commonly used to reduce the number of genes in the model building. Various simple statistics, such as t-statistic or signal to noise ratio, have been used to rank genes in the supervised screening. Despite of its extensive usage, statistical study of supervised gene screening remains scarce. Our study is partly motivated by the differences in gene discovery results caused by using different supervised gene screening methods. Results We investigate concordance and reproducibility of supervised gene screening based on eight commonly used marginal statistics. Concordance is assessed by the relative fractions of overlaps between top ranked genes screened using different marginal statistics. We propose a Bootstrap Reproducibility Index, which measures reproducibility of individual genes under the supervised screening. Empirical studies are based on four public microarray data. We consider the cases where the top 20%, 40% and 60% genes are screened. Conclusion From a gene discovery point of view, the effect of supervised gene screening based on different marginal statistics cannot be ignored. Empirical studies show that (1 genes passed different supervised screenings may be considerably different; (2 concordance may vary, depending on the underlying data structure and percentage of selected genes; (3 evaluated with the Bootstrap Reproducibility Index, genes passed supervised screenings are only moderately reproducible; and (4 concordance cannot be improved by supervised screening based on reproducibility.

32 CFR 727.11 - Supervision.

Science.gov (United States)

2010-07-01

... 32 National Defense 5 2010-07-01 2010-07-01 false Supervision. 727.11 Section 727.11 National Defense Department of Defense (Continued) DEPARTMENT OF THE NAVY PERSONNEL LEGAL ASSISTANCE § 727.11 Supervision. The Judge Advocate General will exercise supervision over all legal assistance activities in the Department of the Navy. Subject to the...

Ranking Medical Terms to Support Expansion of Lay Language Resources for Patient Comprehension of Electronic Health Record Notes: Adapted Distant Supervision Approach.

Science.gov (United States)

Chen, Jinying; Jagannatha, Abhyuday N; Fodeh, Samah J; Yu, Hong

2017-10-31

Medical terms are a major obstacle for patients to comprehend their electronic health record (EHR) notes. Clinical natural language processing (NLP) systems that link EHR terms to lay terms or definitions allow patients to easily access helpful information when reading through their EHR notes, and have shown to improve patient EHR comprehension. However, high-quality lay language resources for EHR terms are very limited in the public domain. Because expanding and curating such a resource is a costly process, it is beneficial and even necessary to identify terms important for patient EHR comprehension first. We aimed to develop an NLP system, called adapted distant supervision (ADS), to rank candidate terms mined from EHR corpora. We will give EHR terms ranked as high by ADS a higher priority for lay language annotation-that is, creating lay definitions for these terms. Adapted distant supervision uses distant supervision from consumer health vocabulary and transfer learning to adapt itself to solve the problem of ranking EHR terms in the target domain. We investigated 2 state-of-the-art transfer learning algorithms (ie, feature space augmentation and supervised distant supervision) and designed 5 types of learning features, including distributed word representations learned from large EHR data for ADS. For evaluating ADS, we asked domain experts to annotate 6038 candidate terms as important or nonimportant for EHR comprehension. We then randomly divided these data into the target-domain training data (1000 examples) and the evaluation data (5038 examples). We compared ADS with 2 strong baselines, including standard supervised learning, on the evaluation data. The ADS system using feature space augmentation achieved the best average precision, 0.850, on the evaluation set when using 1000 target-domain training examples. The ADS system using supervised distant supervision achieved the best average precision, 0.819, on the evaluation set when using only 100 target

Artificial feedback for remotely supervised training of motor skills

NARCIS (Netherlands)

van Dijk, Henk; Hermens, Hermanus J.

Electromyographic (EMG) biofeedback can be used to train motor functions at a distance, which makes therapy at home a possibility. To enable patients to train properly without the presence of a therapist, artificial feedback is considered essential. We studied the combined effect of age and timing

A neurocomputational model of automatic sequence production.

Science.gov (United States)

Helie, Sebastien; Roeder, Jessica L; Vucovich, Lauren; Rünger, Dennis; Ashby, F Gregory

2015-07-01

Most behaviors unfold in time and include a sequence of submovements or cognitive activities. In addition, most behaviors are automatic and repeated daily throughout life. Yet, relatively little is known about the neurobiology of automatic sequence production. Past research suggests a gradual transfer from the associative striatum to the sensorimotor striatum, but a number of more recent studies challenge this role of the BG in automatic sequence production. In this article, we propose a new neurocomputational model of automatic sequence production in which the main role of the BG is to train cortical-cortical connections within the premotor areas that are responsible for automatic sequence production. The new model is used to simulate four different data sets from human and nonhuman animals, including (1) behavioral data (e.g., RTs), (2) electrophysiology data (e.g., single-neuron recordings), (3) macrostructure data (e.g., TMS), and (4) neurological circuit data (e.g., inactivation studies). We conclude with a comparison of the new model with existing models of automatic sequence production and discuss a possible new role for the BG in automaticity and its implication for Parkinson's disease.

The vision in supervision: transference-countertransference dynamics and disclosure in the supervision relationship.

Science.gov (United States)

Coburn, W J

1997-01-01

The centrality of the supervision experience in the development of the supervisee's personal and professional capacities is addressed. The supervision relationship and process are explored in light of the potential effects of transference-countertransference configurations of supervisor and supervisee. Parallels between supervision and treatment are highlighted. The importance of developing and utilizing the capacity for reflectivity is reviewed, as is the impact of supervisee nondisclosure to supervisor. The direct use of countertransference experiences in the context of supervision is explored, and the centrality of self-disclosure is highlighted. It is recommended that supervisor and supervisee remain receptive to exploring these experiences in the service of developing a shared subjective sense of the patient, of increasing the supervisee's capacity to treat his or her patient, and of providing the supervisee with a novel, growth-enhancing relationship.

Automatic phase aberration compensation for digital holographic microscopy based on deep learning background detection.

Science.gov (United States)

Nguyen, Thanh; Bui, Vy; Lam, Van; Raub, Christopher B; Chang, Lin-Ching; Nehmetallah, George

2017-06-26

We propose a fully automatic technique to obtain aberration free quantitative phase imaging in digital holographic microscopy (DHM) based on deep learning. The traditional DHM solves the phase aberration compensation problem by manually detecting the background for quantitative measurement. This would be a drawback in real time implementation and for dynamic processes such as cell migration phenomena. A recent automatic aberration compensation approach using principle component analysis (PCA) in DHM avoids human intervention regardless of the cells' motion. However, it corrects spherical/elliptical aberration only and disregards the higher order aberrations. Traditional image segmentation techniques can be employed to spatially detect cell locations. Ideally, automatic image segmentation techniques make real time measurement possible. However, existing automatic unsupervised segmentation techniques have poor performance when applied to DHM phase images because of aberrations and speckle noise. In this paper, we propose a novel method that combines a supervised deep learning technique with convolutional neural network (CNN) and Zernike polynomial fitting (ZPF). The deep learning CNN is implemented to perform automatic background region detection that allows for ZPF to compute the self-conjugated phase to compensate for most aberrations.

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

Science.gov (United States)

Miao, Zelang

2017-04-01

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

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

Science.gov (United States)

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.

Semi-supervised learning of hyperspectral image segmentation applied to vine tomatoes and table grapes

Directory of Open Access Journals (Sweden)

Jeroen van Roy

2018-03-01

Full Text Available Nowadays, quality inspection of fruit and vegetables is typically accomplished through visual inspection. Automation of this inspection is desirable to make it more objective. For this, hyperspectral imaging has been identified as a promising technique. When the field of view includes multiple objects, hypercubes should be segmented to assign individual pixels to different objects. Unsupervised and supervised methods have been proposed. While the latter are labour intensive as they require masking of the training images, the former are too computationally intensive for in-line use and may provide different results for different hypercubes. Therefore, a semi-supervised method is proposed to train a computationally efficient segmentation algorithm with minimal human interaction. As a first step, an unsupervised classification model is used to cluster spectra in similar groups. In the second step, a pixel selection algorithm applied to the output of the unsupervised classification is used to build a supervised model which is fast enough for in-line use. To evaluate this approach, it is applied to hypercubes of vine tomatoes and table grapes. After first derivative spectral preprocessing to remove intensity variation due to curvature and gloss effects, the unsupervised models segmented 86.11% of the vine tomato images correctly. Considering overall accuracy, sensitivity, specificity and time needed to segment one hypercube, partial least squares discriminant analysis (PLS-DA was found to be the best choice for in-line use, when using one training image. By adding a second image, the segmentation results improved considerably, yielding an overall accuracy of 96.95% for segmentation of vine tomatoes and 98.52% for segmentation of table grapes, demonstrating the added value of the learning phase in the algorithm.

Minimal hardware Bluetooth tracking for long-term at-home elder supervision.

Science.gov (United States)

Kelly, Damian; McLoone, Sean; Farrell, Ronan

2010-01-01

The ability to automatically detect the location of an elder within their own home is a significant enabler of remote elder supervision and interaction applications. This location information is typically generated via a myriad of sensors throughout the home environment. Even with high sensor redundancy, there are still situations where traditional elder monitoring systems are unable to resolve the location of the elder. This work develops a minimal infrastructure radio-frequency localisation system for long-term elder location tracking. An RFID room-labelling technique is employed and with it, the localisation system developed in this work is shown to exhibit superior performance to more traditional localisation systems in realistic long-term deployments.

Supervision som undervisningsform i voksenspecialundervisningen

DEFF Research Database (Denmark)

Kristensen, René

2000-01-01

Supervision som undervisningsform i voksenspecialundervisningen. Procesarbejde i undervisning af voksne.......Supervision som undervisningsform i voksenspecialundervisningen. Procesarbejde i undervisning af voksne....

Evaluation of the process of recording patient education, consistency of record-keeping with perception, and patient satisfaction after implementing clinical supervision: An embedded evaluation

Directory of Open Access Journals (Sweden)

Khorasani Parvaneh

2016-08-01

Full Text Available Background and Objective: Currently, patient education has been considered in medical centers. Clinical provision, which is one of the legal tools with training-support dimensions, can evaluate the consistency between the implemented procedures and the planned ones. This study aimed to evaluate the process of recording patient education, consistency of record-keeping with perception, and patient satisfaction after implementing clinical supervision. Materials and Methods: This longitudinal, embedded study was conducted during 2013-2015 in three stages of designing, implementation, and evaluation of the supervision program using randomized convenience sampling on 786 monitoring units (medical records of patients being discharged at Alzahra University Hospital, Isfahan University of Medical Sciences, Isfahan, Iran. In the designing stage, the checklists for supervision of recording patient education and consistency of patient perception with the recorded trainings and the patient satisfaction questionnaire were designed and their valididty and reliability were established. In the implementation stage, structure of the monitoring program was designed with the cooperation of eight supervisors. During 12 months, 2333 checklists and questionnaires were completed at the time of hospital discharge in the evaluation stage. Data analysis was performed in SPSS, version 18, using One-way ANOVA. Results: After 12 months of embedded evaluation, the mean score of recording patient education was 88.5±21.75, and the mean scores of patient satisfaction with the training process and consistency between patients’ perception and the recorded trainings were 47.17±21.48 and 73±25.13, respectively. The mean scores of recording patient training and consistency between patients’ perception and the recorded trainings had an increasing trend (P<0.001, while the mean score of patient satisfaction reduced (P<0.001. Conclusion: The results of clinical supervision during

Mapping Typical Urban LULC from Landsat Imagery without Training Samples or Self-Defined Parameters

Directory of Open Access Journals (Sweden)

Hui Li

2017-07-01

Full Text Available Land use/land cover (LULC change is one of the most important indicators in understanding the interactions between humans and the environment. Traditionally, when LULC maps are produced yearly, most existing remote-sensing methods have to collect ground reference data annually, as the classifiers have to be trained individually in each corresponding year. This study presented a novel strategy to map LULC classes without training samples or assigning parameters. First of all, several novel indices were carefully selected from the index pool, which were able to highlight certain LULC very well. Following this, a common unsupervised classifier was employed to extract the LULC from the associated index image without assigning thresholds. Finally, a supervised classification was implemented with samples automatically collected from the unsupervised classification outputs. Results illustrated that the proposed method could achieve satisfactory performance, reaching similar accuracies to traditional approaches. Findings of this study demonstrate that the proposed strategy is a simple and effective alternative to mapping urban LULC. With the proposed strategy, the budget and time required for remote-sensing data processing could be reduced dramatically.

Requirements for nurse supervisor training: A qualitative content analysis.

Science.gov (United States)

Dehghani, Khadijeh; Nasiriani, Khadijeh; Salimi, Tahere

2016-01-01

Supervisors should have certain characteristics and adequate preparation for their roles. Yet, there are no well-educated experts knowing about the supervisor's role and responsibilities and how to train them. So, this research was conducted with the purpose of finding the factors affecting nursing supervisor training. This research is an inductive content analysis. Participants were 25 in number, consisting of nurses and supervisors in Shahid Sadoughi University hospitals. The participants were chosen by a purposive sampling method. Data collection was done by semi-structured interviews and reviewing documents. Data were analyzed using conventional content analysis. Findings included two main themes: Firstly, establishment of a supervisory infrastructure that includes "making the appointments and retention of supervisors, clarifying the duties and authority of supervisor, developing supervisory culture, specializing supervision, and conducting practice-based training" and secondly, comprehensive supervisory competencies that include "acquiring scientific, managing, communicative, professional, ethical, pedagogical, and supporting adequacy." Clinical supervisor has a major role in ensuring the quality of nursing care. This leads to improvements in patient care and nurses' personal and professional development. So, it is necessary that for effective supervision in nursing, first an infrastructure is provided for supervision and then the comprehensive competency of a supervisor is enhanced to apply effective supervision.

Novice supervisors' tasks and training

DEFF Research Database (Denmark)

Nielsen, Jan; Jacobsen, Claus Haugaard; Mathiesen, Birgit Bork

2012-01-01

were confronted with complicated jobs, e.g., group, internal and interdisciplinary supervision, but were not prepared, i.e. trained, prior to these tasks. These findings imply that more training is needed for novice supervisors. Preferably, this training should be introduced before, or at least...... Questionnaire covering a wide range of items on professional development, experience, and practice. In this paper we focus on background data (experience, training and practice), specifically the tasks and training of the respondents as novice supervisors. The results show, that a majority of novice supervisors...

Supervised Kernel Optimized Locality Preserving Projection with Its Application to Face Recognition and Palm Biometrics

Directory of Open Access Journals (Sweden)

Chuang Lin

2015-01-01

Full Text Available Kernel Locality Preserving Projection (KLPP algorithm can effectively preserve the neighborhood structure of the database using the kernel trick. We have known that supervised KLPP (SKLPP can preserve within-class geometric structures by using label information. However, the conventional SKLPP algorithm endures the kernel selection which has significant impact on the performances of SKLPP. In order to overcome this limitation, a method named supervised kernel optimized LPP (SKOLPP is proposed in this paper, which can maximize the class separability in kernel learning. The proposed method maps the data from the original space to a higher dimensional kernel space using a data-dependent kernel. The adaptive parameters of the data-dependent kernel are automatically calculated through optimizing an objective function. Consequently, the nonlinear features extracted by SKOLPP have larger discriminative ability compared with SKLPP and are more adaptive to the input data. Experimental results on ORL, Yale, AR, and Palmprint databases showed the effectiveness of the proposed method.

29 CFR 1926.21 - Safety training and education.

Science.gov (United States)

2010-07-01

... 29 Labor 8 2010-07-01 2010-07-01 false Safety training and education. 1926.21 Section 1926.21... Provisions § 1926.21 Safety training and education. (a) General requirements. The Secretary shall, pursuant to section 107(f) of the Act, establish and supervise programs for the education and training of...

Multi combined Adlerian supervision in Counseling

OpenAIRE

Gungor, Abdi

2017-01-01

For counselor professional and counselor education, supervision is an important process, in which more experienced professional helps and guides less experienced professional. To provide an effective and beneficial supervision, various therapy, development, or process based approaches and models have been developed. In addition, different eclectic models integrating more than one model have been developed. In this paper, as a supervision model, multi combined Adlerian supervision model is pro...

The pedagogical effectiveness of ASR-based computer assisted pronunciation training

NARCIS (Netherlands)

Neri, A.

2007-01-01

Computer Assisted Pronunciation Training (CAPT) systems with Automatic Speech Recognition (ASR) technology have become increasingly popular to train pronunciation in the second language (L2). The advantage of these systems is the provision of a self-paced, stress-free type of training with automatic

Implementing the 2009 Institute of Medicine recommendations on resident physician work hours, supervision, and safety.

Science.gov (United States)

Blum, Alexander B; Shea, Sandra; Czeisler, Charles A; Landrigan, Christopher P; Leape, Lucian

2011-01-01

Long working hours and sleep deprivation have been a facet of physician training in the US since the advent of the modern residency system. However, the scientific evidence linking fatigue with deficits in human performance, accidents and errors in industries from aeronautics to medicine, nuclear power, and transportation has mounted over the last 40 years. This evidence has also spawned regulations to help ensure public safety across safety-sensitive industries, with the notable exception of medicine. In late 2007, at the behest of the US Congress, the Institute of Medicine embarked on a year-long examination of the scientific evidence linking resident physician sleep deprivation with clinical performance deficits and medical errors. The Institute of Medicine's report, entitled "Resident duty hours: Enhancing sleep, supervision and safety", published in January 2009, recommended new limits on resident physician work hours and workload, increased supervision, a heightened focus on resident physician safety, training in structured handovers and quality improvement, more rigorous external oversight of work hours and other aspects of residency training, and the identification of expanded funding sources necessary to implement the recommended reforms successfully and protect the public and resident physicians themselves from preventable harm. Given that resident physicians comprise almost a quarter of all physicians who work in hospitals, and that taxpayers, through Medicare and Medicaid, fund graduate medical education, the public has a deep investment in physician training. Patients expect to receive safe, high-quality care in the nation's teaching hospitals. Because it is their safety that is at issue, their voices should be central in policy decisions affecting patient safety. It is likewise important to integrate the perspectives of resident physicians, policy makers, and other constituencies in designing new policies. However, since its release, discussion of the

Learning Dynamics in Doctoral Supervision

DEFF Research Database (Denmark)

Kobayashi, Sofie

investigates learning opportunities in supervision with multiple supervisors. This was investigated through observations and recording of supervision, and subsequent analysis of transcripts. The analyses used different perspectives on learning; learning as participation, positioning theory and variation theory....... The research illuminates how learning opportunities are created in the interaction through the scientific discussions. It also shows how multiple supervisors can contribute to supervision by providing new perspectives and opinions that have a potential for creating new understandings. The combination...... of different theoretical frameworks from the perspectives of learning as individual acquisition and a sociocultural perspective on learning contributed to a nuanced illustration of the otherwise implicit practices of supervision....

Acquiring and refining CBT skills and competencies: which training methods are perceived to be most effective?

Science.gov (United States)

Bennett-Levy, James; McManus, Freda; Westling, Bengt E; Fennell, Melanie

2009-10-01

A theoretical and empirical base for CBT training and supervision has started to emerge. Increasingly sophisticated maps of CBT therapist competencies have recently been developed, and there is evidence that CBT training and supervision can produce enhancement of CBT skills. However, the evidence base suggesting which specific training techniques are most effective for the development of CBT competencies is lacking. This paper addresses the question: What training or supervision methods are perceived by experienced therapists to be most effective for training CBT competencies? 120 experienced CBT therapists rated which training or supervision methods in their experience had been most effective in enhancing different types of therapy-relevant knowledge or skills. In line with the main prediction, it was found that different training methods were perceived to be differentially effective. For instance, reading, lectures/talks and modelling were perceived to be most useful for the acquisition of declarative knowledge, while enactive learning strategies (role-play, self-experiential work), together with modelling and reflective practice, were perceived to be most effective in enhancing procedural skills. Self-experiential work and reflective practice were seen as particularly helpful in improving reflective capability and interpersonal skills. The study provides a framework for thinking about the acquisition and refinement of therapist skills that may help trainers, supervisors and clinicians target their learning objectives with the most effective training strategies.

Training needs of recreation staff at recreation centres: Supervising ...

African Journals Online (AJOL)

A study in 2008 revealed that 44% of municipal sport and recreation facilities in South Africa were reported to be poorly maintained because of the lack of necessary skills and poorly trained staff. It seems that training could be a major contributor to solving this problem. The aim of this qualitative research was to determine ...

Supervision and group dynamics

DEFF Research Database (Denmark)

Hansen, Søren; Jensen, Lars Peter

2004-01-01

 An important aspect of the problem based and project organized study at Aalborg University is the supervision of the project groups. At the basic education (first year) it is stated in the curriculum that part of the supervisors' job is to deal with group dynamics. This is due to the experience...... that many students are having difficulties with practical issues such as collaboration, communication, and project management. Most supervisors either ignore this demand, because they do not find it important or they find it frustrating, because they do not know, how to supervise group dynamics...... as well as at Aalborg University. The first visible result has been participating supervisors telling us that the course has inspired them to try supervising group dynamics in the future. This paper will explore some aspects of supervising group dynamics as well as, how to develop the Aalborg model...

Automatic detection of spiculation of pulmonary nodules in computed tomography images

DEFF Research Database (Denmark)

Ciompi, F; Jacobs, C; Scholten, E.T.

2015-01-01

to classify spiculated nodules via supervised learning. We tested our approach on a set of nodules from the Danish Lung Cancer Screening Trial (DLCST) dataset. Our results show that the proposed method outperforms other 3-D descriptors of morphology in the automatic assessment of spiculation. © (2015......-up procedure. For this reason, lung cancer screening scenario would benefit from the presence of a fully automatic system for the assessment of spiculation. The presented framework relies on the fact that spiculated nodules mainly differ from non-spiculated ones in their morphology. In order to discriminate....... A library of spectra is created by clustering data via unsupervised learning. The centroids of the clusters are used to label back each spectrum in the sampling pattern. A compact descriptor encoding the nodule morphology is obtained as the histogram of labels along all the spherical surfaces and used...

A review of supervised machine learning applied to ageing research.

Science.gov (United States)

Fabris, Fabio; Magalhães, João Pedro de; Freitas, Alex A

2017-04-01

Broadly speaking, supervised machine learning is the computational task of learning correlations between variables in annotated data (the training set), and using this information to create a predictive model capable of inferring annotations for new data, whose annotations are not known. Ageing is a complex process that affects nearly all animal species. This process can be studied at several levels of abstraction, in different organisms and with different objectives in mind. Not surprisingly, the diversity of the supervised machine learning algorithms applied to answer biological questions reflects the complexities of the underlying ageing processes being studied. Many works using supervised machine learning to study the ageing process have been recently published, so it is timely to review these works, to discuss their main findings and weaknesses. In summary, the main findings of the reviewed papers are: the link between specific types of DNA repair and ageing; ageing-related proteins tend to be highly connected and seem to play a central role in molecular pathways; ageing/longevity is linked with autophagy and apoptosis, nutrient receptor genes, and copper and iron ion transport. Additionally, several biomarkers of ageing were found by machine learning. Despite some interesting machine learning results, we also identified a weakness of current works on this topic: only one of the reviewed papers has corroborated the computational results of machine learning algorithms through wet-lab experiments. In conclusion, supervised machine learning has contributed to advance our knowledge and has provided novel insights on ageing, yet future work should have a greater emphasis in validating the predictions.

Does compliance with amblyopia management improve following supervised occlusion treatment?

Science.gov (United States)

El-Ghrably, I A; Longville, D; Gnanaraj, L

2007-01-01

To demonstrate improvement in compliance following supervised occlusion therapy for amblyopia in children who had failed to respond to outpatient treatment. Retrospective review of the visual outcome of 30 children who were admitted to an ophthalmology ward for 1-day intensive supervised occlusion. These children had documented poor compliance and previously failed to respond to the outpatient occlusion treatment. During their stay a trained ophthalmology nurse educated parents regarding amblyopia and the benefits of occlusion therapy. Visual acuity (VA) of the amblyopic and fellow eyes was recorded on admission, discharge, and at each subsequent visit. The compliance was recorded from parent's history and also indirectly by noticing improvement in vision. The mean supervised occlusion was 7.4 hours (range 4-12 hours). The compliance with occlusion therapy improved in 23 children (77%) after discharge. The mean duration of occlusion after discharge improved to 4 hours (range 1-12 hours). The mean follow-up was 18 months (range 4-24 months). Though there was no dramatic improvement in VA at discharge there was a statistically significant improvement in VA between admission and last recorded VA (pocclusion following discharge, 21 (91%) gained at least one line of acuity in their amblyopic eye on the last assessment of their VA and five of them achieved 6/12. Of the seven children who did not comply with occlusion following discharge, only one patient gained one line improvement in his amblyopic eye. This study shows that supervised occlusion treatment and parental education was effective in children who had initially failed traditional outpatient treatment.

Networks of Professional Supervision

Science.gov (United States)

Annan, Jean; Ryba, Ken

2013-01-01

An ecological analysis of the supervisory activity of 31 New Zealand school psychologists examined simultaneously the theories of school psychology, supervision practices, and the contextual qualities that mediated participants' supervisory actions. The findings indicated that the school psychologists worked to achieve the supervision goals of…

Supervised Convolutional Sparse Coding

KAUST Repository

Affara, Lama Ahmed

2018-04-08

Convolutional Sparse Coding (CSC) is a well-established image representation model especially suited for image restoration tasks. In this work, we extend the applicability of this model by proposing a supervised approach to convolutional sparse coding, which aims at learning discriminative dictionaries instead of purely reconstructive ones. We incorporate a supervised regularization term into the traditional unsupervised CSC objective to encourage the final dictionary elements to be discriminative. Experimental results show that using supervised convolutional learning results in two key advantages. First, we learn more semantically relevant filters in the dictionary and second, we achieve improved image reconstruction on unseen data.

Semi-Supervised Learning to Identify UMLS Semantic Relations.

Science.gov (United States)

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-automatic supervised classification of minerals from x-ray mapping images

DEFF Research Database (Denmark)

Nielsen, Allan Aasbjerg; Flesche, Harald; Larsen, Rasmus

1998-01-01

to a small area in order to allow for the estimation of a variance-covariance matrix. This expansion is controlled by upper limits for the spatial and Euclidean spectral distances from the seed point. Second, after this initial expansion the growing of the training set is controlled by an upper limit...... is obtained by excluding observations that have high Mahalanobis distances to the training class mean. Spatial closeness is obtained by requiring connectivity. The marginal effects of changes in the parameters that are input to the seed growing algorithm are evaluated. Initially, the seed is expanded...... for the Mahalanobis distance to the current estimate of the class centre. Also, the estimates of class centres and covariance matrices may be continuously updated or the initial estimates may be used. Finally, the effect of the operator's choice of seed among a number of potential seeding points is evaluated. After...

Tværfaglig supervision

DEFF Research Database (Denmark)

Tværfaglig supervision dækker over supervision af forskellige faggrupper. Det er en kompleks disciplin der stiller store krav tl supervisor. Bogens første del præsenterer fire faglige supervisionsmodeller: En almen, en psykodynamisk, en kognitiv adfærdsterapeutisk og en narrativ. Anden del...

Methods of Feminist Family Therapy Supervision.

Science.gov (United States)

Prouty, Anne M.; Thomas, Volker; Johnson, Scott; Long, Janie K.

2001-01-01

Presents three supervision methods which emerged from a qualitative study of the experiences of feminist family therapy supervisors and the therapists they supervised: the supervision contract, collaborative methods, and hierarchical methods. Provides a description of the participants' experiences of these methods and discusses their fit with…

Supervision and feedback for junior medical staff in Australian emergency departments: findings from the emergency medicine capacity assessment study

Directory of Open Access Journals (Sweden)

Weiland Tracey J

2010-11-01

Full Text Available Abstract Background Clinical supervision and feedback are important for the development of competency in junior doctors. This study aimed to determine the adequacy of supervision of junior medical staff in Australian emergency departments (EDs and perceived feedback provided. Methods Semi-structured telephone surveys sought quantitative and qualitative data from ED Directors, Directors of Emergency Medicine Training, registrars and interns in 37 representative Australian hospitals; quantitative data were analysed with SPSS 15.0 and qualitative data subjected to content analysis identifying themes. Results Thirty six of 37 hospitals took part. Of 233 potential interviewees, 95 (40.1% granted interviews including 100% (36/36 of ED Directors, and 96.2% (25/26 of eligible DEMTs, 24% (19/81 of advanced trainee/registrars, and 17% (15/90 of interns. Most participants (61% felt the ED was adequately supervised in general and (64.2% that medical staff were adequately supervised. Consultants and registrars were felt to provide most intern supervision, but this varied depending on shift times, with registrars more likely to provide supervision on night shift and at weekends. Senior ED medical staff (64% and junior staff (79% agreed that interns received adequate clinical supervision. Qualitative analysis revealed that good processes were in place to ensure adequate supervision, but that service demands, particularly related to access block and overcrowding, had detrimental effects on both supervision and feedback. Conclusions Consultants appear to provide the majority of supervision of junior medical staff in Australian EDs. Supervision and feedback are generally felt to be adequate, but are threatened by service demands, particularly related to access block and ED overcrowding.

Information Extraction with Character-level Neural Networks and Free Noisy Supervision

OpenAIRE

Meerkamp, Philipp; Zhou, Zhengyi

2016-01-01

We present an architecture for information extraction from text that augments an existing parser with a character-level neural network. The network is trained using a measure of consistency of extracted data with existing databases as a form of noisy supervision. Our architecture combines the ability of constraint-based information extraction systems to easily incorporate domain knowledge and constraints with the ability of deep neural networks to leverage large amounts of data to learn compl...

28 CFR 2.91 - Supervision responsibility.

Science.gov (United States)

2010-07-01

... 28 Judicial Administration 1 2010-07-01 2010-07-01 false Supervision responsibility. 2.91 Section 2.91 Judicial Administration DEPARTMENT OF JUSTICE PAROLE, RELEASE, SUPERVISION AND RECOMMITMENT OF PRISONERS, YOUTH OFFENDERS, AND JUVENILE DELINQUENTS District of Columbia Code: Prisoners and Parolees § 2.91 Supervision responsibility. (a) Pursuan...

Winter is Coming: Doctoral Supervision in the Neoliberal University

Directory of Open Access Journals (Sweden)

Tara Brabazon

2017-01-01

Full Text Available : Doctoral Education Studies, particularly in its North American manifestations, emphasizes quantitative methods. The resulting research is empirical and occasionally empiricist. The challenges revealed through this mode of research is that the highly ideological, volatile environment of higher education is flattened, framed and justified. My research offers an alternative view and perspective of doctoral education through a post-empirical, theoretical article. Within my piece, the PhD and doctoral supervision are framed by the post-Global Financial Crisis to understand the very specific – and volatile – context for research and research training.

Deep Web Search Interface Identification: A Semi-Supervised Ensemble Approach

OpenAIRE

Hong Wang; Qingsong Xu; Lifeng Zhou

2014-01-01

To surface the Deep Web, one crucial task is to predict whether a given web page has a search interface (searchable HyperText Markup Language (HTML) form) or not. Previous studies have focused on supervised classification with labeled examples. However, labeled data are scarce, hard to get and requires tediousmanual work, while unlabeled HTML forms are abundant and easy to obtain. In this research, we consider the plausibility of using both labeled and unlabeled data to train better models to...

Significant Tasks in Training of Job-Shop Supervisors

Science.gov (United States)

Pederson, Leonard S.; Dresdow, Sally; Benson, Joy

2013-01-01

Purpose: The need for effective training of first-line supervisors is well established. Well-trained supervision is essential to our future as a country. A fundamental step in developing effective training is to develop a jobs needs assessment. In order to develop an effective needs assessment, it is necessary to know what the tasks are of…

Modeling and monitoring of pipelines and networks advanced tools for automatic monitoring and supervision of pipelines

CERN Document Server

Torres, Lizeth

2017-01-01

This book focuses on the analysis and design of advanced techniques for on-line automatic computational monitoring of pipelines and pipe networks. It discusses how to improve the systems’ security considering mathematical models of the flow, historical flow rate and pressure data, with the main goal of reducing the number of sensors installed along a pipeline. The techniques presented in the book have been implemented in digital systems to enhance the abilities of the pipeline network’s operators in recognizing anomalies. A real leak scenario in a Mexican water pipeline is used to illustrate the benefits of these techniques in locating the position of a leak. Intended for an interdisciplinary audience, the book addresses researchers and professionals in the areas of mechanical, civil and control engineering. It covers topics on fluid mechanics, instrumentation, automatic control, signal processing, computing, construction and diagnostic technologies.

20 CFR 655.30 - Supervised recruitment.

Science.gov (United States)

2010-04-01

... 20 Employees' Benefits 3 2010-04-01 2010-04-01 false Supervised recruitment. 655.30 Section 655.30... Workers) § 655.30 Supervised recruitment. (a) Supervised recruitment. Where an employer is found to have... failed to adequately conduct recruitment activities or failed in any obligation of this part, the CO may...

A New Approach for Education and Training of Medical Physicists in Cuba: From University to Clinical Training

International Nuclear Information System (INIS)

Alfonso-Laguardia, R.; Rivero Blanco, J.M.

2016-01-01

Full text: According to the international recommendations of IAEA and the International Organization for Medical Physics (IOMP), the education and training of clinically qualified medical physicists (CQMP) should include three main academic and professional elements: a university level education, a postgraduate education specific in medical physics (MP) and a supervised clinical training. In Cuba, most of the medical physicists working in radiation oncology (RO) or nuclear medicine (NM) services have graduated from nuclear related programmes of the High Institute on Applied Technologies and Sciences (InSTEC), who further perform a postgraduate study in medical physics (MP), at the level of a so-called Diploma course or a Master in Sciences. Nevertheless, the third level of education, namely the supervised clinical training has not yet been established, due to the lack of official recognition of the profession of MP by the health authorities. A new approach for comprehensive training of CQMP is presented, where, by maintaining the three elements of education, the process is optimized so that a medical physicist is prepared with the highest level of theoretical and clinical training, in agreement with the current demand of the advanced technologies put in service in Cuban hospitals. (author

Effects of increased overnight supervision on resident education, decision-making, and autonomy.

Science.gov (United States)

Haber, Lawrence A; Lau, Catherine Y; Sharpe, Bradley A; Arora, Vineet M; Farnan, Jeanne M; Ranji, Sumant R

2012-10-01

New supervisory regulations highlight the challenge of balancing housestaff supervision and autonomy. To better understand the impact of increased supervision on residency training, we investigated housestaff perceptions of education, autonomy, and clinical decision-making before and after implementation of an in-hospital, overnight attending physician (nocturnist). We established a nocturnist program in July 2010 at our academic, tertiary care medical center. We administered pre-surveys and post-surveys of internal medicine residents on night float rotation during the 2010-2011 academic year. We surveyed residents before and after experiencing the nocturnist program. Housestaff reported an increase in the clinical value of the night float rotation (3.95 vs 4.27, P = 0.01) and the adequacy of overnight supervision (3.65 vs 4.30, P autonomy (4.35 vs 4.45, P = 0.44). Trainees agreed that nocturnist supervision positively impacted patient outcomes (3.79 vs 4.30, P = 0.002). Housestaff contacted attendings more frequently for transfers from outside facilities (2.00 vs 3.20, P = 0.006), during adverse events (2.51 vs 3.25, P = 0.04), prior to ordering invasive diagnostics (1.75 vs 2.76, P = 0.004), and prior to vasopressor use (1.52 vs 2.40, P = 0.004). Residents' fear of revealing knowledge gaps and desire to make decisions independently did not change. Increased overnight supervision enhanced the clinical value of the night float rotation, increased rates of attending contact during critical clinical decision-making, and improved perception of patient care. These changes occurred without a decrease in housestaff's perceived decision-making autonomy. Copyright © 2012 Society of Hospital Medicine.

Man-machine supervision

International Nuclear Information System (INIS)

Montmain, J.

2005-01-01

Today's complexity of systems where man is involved has led to the development of more and more sophisticated information processing systems where decision making has become more and more difficult. The operator task has moved from operation to supervision and the production tool has become indissociable from its numerical instrumentation and control system. The integration of more and more numerous and sophisticated control indicators in the control room does not necessary fulfill the expectations of the operation team. It is preferable to develop cooperative information systems which are real situation understanding aids. The stake is not the automation of operators' cognitive tasks but the supply of a reasoning help. One of the challenges of interactive information systems is the selection, organisation and dynamical display of information. The efficiency of the whole man-machine system depends on the communication interface efficiency. This article presents the principles and specificities of man-machine supervision systems: 1 - principle: operator's role in control room, operator and automation, monitoring and diagnosis, characteristics of useful models for supervision; 2 - qualitative reasoning: origin, trends, evolutions; 3 - causal reasoning: causality, causal graph representation, causal and diagnostic graph; 4 - multi-points of view reasoning: multi flow modeling method, Sagace method; 5 - approximate reasoning: the symbolic numerical interface, the multi-criteria decision; 6 - example of application: supervision in a spent-fuel reprocessing facility. (J.S.)

Evolution in banking supervision

OpenAIRE

Edward J. Stevens

2000-01-01

Banking supervision must keep pace with technical innovations in the banking industry. The international Basel Committee on Banking Supervision currently is reviewing public comments on its proposed new method for judging whether a bank maintains enough capital to absorb unexpected losses. This Economic Commentary explains how existing standards became obsolete and describes the new plan.

Supervising Model of Independent Enterprise Group (Study of Community Development PT Badak NGL

Directory of Open Access Journals (Sweden)

Hermansyah Hermansyah

2016-06-01

Full Text Available This research aims to arrange an empowerment model of enterprise group through the program of Community Development in order to be independent and ready to compete, which is begun from the empirical study of the success of Cipta Busana Cooperative.. This research uses the descriptive analysis by using a case study on one enterprise supervised by PT Badak NGL that is Koperasi Cipta Busana (Kocibu. Kocibu is chosen to be the object of research due to its success to achieve the target to be the independent supervised enterprise in the fourth year. The data analysis method used in this research is the explorative analysis. Based on the research, there are some results such as that Kocibu is one of the supervised Micro, Small and Medium Enterprises of PT Badak NGL that could develop and be independent through several supporting programs. Some of key successes of Kocibu are as follows: a high commitment, a good leader, and intensive supervising programs. Besides, a good marketing system also contributes to the key of success. There are some aspects that naturally contribute to the Kocibu improvement and emerge naturally as follows: the leader figure and the high commitment from the stakeholders. While, the aspects emerged by design are: the supervising and training programs, the evaluation, the determination of rules, and the business targets. Hopefully, after this research has been conducted, the aspects appeared naturaly would be realized so early that the success of the public empowerment program will be able to increase. 

Changes in default mode network as automaticity develops in a categorization task.

Science.gov (United States)

Shamloo, Farzin; Helie, Sebastien

2016-10-15

The default mode network (DMN) is a set of brain regions in which blood oxygen level dependent signal is suppressed during attentional focus on the external environment. Because automatic task processing requires less attention, development of automaticity in a rule-based categorization task may result in less deactivation and altered functional connectivity of the DMN when compared to the initial learning stage. We tested this hypothesis by re-analyzing functional magnetic resonance imaging data of participants trained in rule-based categorization for over 10,000 trials (Helie et al., 2010) [12,13]. The results show that some DMN regions are deactivated in initial training but not after automaticity has developed. There is also a significant decrease in DMN deactivation after extensive practice. Seed-based functional connectivity analyses with the precuneus, medial prefrontal cortex (two important DMN regions) and Brodmann area 6 (an important region in automatic categorization) were also performed. The results show increased functional connectivity with both DMN and non-DMN regions after the development of automaticity, and a decrease in functional connectivity between the medial prefrontal cortex and ventromedial orbitofrontal cortex. Together, these results further support the hypothesis of a strategy shift in automatic categorization and bridge the cognitive and neuroscientific conceptions of automaticity in showing that the reduced need for cognitive resources in automatic processing is accompanied by a disinhibition of the DMN and stronger functional connectivity between DMN and task-related brain regions. Copyright © 2016 Elsevier B.V. All rights reserved.

An integrated system for land resources supervision based on the IoT and cloud computing

Science.gov (United States)

Fang, Shifeng; Zhu, Yunqiang; Xu, Lida; Zhang, Jinqu; Zhou, Peiji; Luo, Kan; Yang, Jie

2017-01-01

Integrated information systems are important safeguards for the utilisation and development of land resources. Information technologies, including the Internet of Things (IoT) and cloud computing, are inevitable requirements for the quality and efficiency of land resources supervision tasks. In this study, an economical and highly efficient supervision system for land resources has been established based on IoT and cloud computing technologies; a novel online and offline integrated system with synchronised internal and field data that includes the entire process of 'discovering breaches, analysing problems, verifying fieldwork and investigating cases' was constructed. The system integrates key technologies, such as the automatic extraction of high-precision information based on remote sensing, semantic ontology-based technology to excavate and discriminate public sentiment on the Internet that is related to illegal incidents, high-performance parallel computing based on MapReduce, uniform storing and compressing (bitwise) technology, global positioning system data communication and data synchronisation mode, intelligent recognition and four-level ('device, transfer, system and data') safety control technology. The integrated system based on a 'One Map' platform has been officially implemented by the Department of Land and Resources of Guizhou Province, China, and was found to significantly increase the efficiency and level of land resources supervision. The system promoted the overall development of informatisation in fields related to land resource management.

Automatic methods for processing track-detector data at the PAVICOM facility

International Nuclear Information System (INIS)

Aleksandrov, A.B.; Goncharova, L.A.; Polukhina, N.G.; Fejnberg, E.L.; Davydov, D.A.; Publichenko, P.A.; Roganova, T.M.

2007-01-01

New automatic methods essentially simplify and hasten the data treatment of tracking detectors. It allows handling big data files and appreciably improves their statistics; this fact predetermines an elaboration of new experiments, which suppose to use large volume targets, emulsive and solid-state large square tracking detectors. Thereupon the problem of training competent physicists able to work on modern automatic equipment is very relevant. About ten Moscow students working in LPI at PAVICOM facility master new methods every year. Most of the students working in high-energy physics take the print only about archaic hand methods of data handling from tracking detectors. In 2005 on the base of the PAVICOM facility and physics training of the MSU a new educational work for determination of the energy of neutrons passing through nuclear emulsion, which lets students acquire a base habit of data handling from tracking detectors using an automatic facility, was prepared; it can be included in the training process for students of any physical faculty. Specialists mastering methods of an automatic handling by the simple and obvious example of tracking detectors will be able to use their knowledge in various areas of science and techniques. The organization of upper division courses is a new additional aspect of using the PAVICOM facility described in an earlier paper [4

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

Science.gov (United States)

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.

Automatic classification of MR scans in Alzheimer's disease

OpenAIRE

García, Fernando Pérez; uk, fernando perezgarcia ucl ac

2018-01-01

Presentation of the paper "Automatic classification of MR scans in Alzheimer's disease" by Klöppel et al. for the journal club of the Centre for Doctoral Training in Medical Image Computing at University College London.

A Study of Supervision of China's Commercial Banks from the Perspective of the Trinity-Characteristics of Bank Supervision System

Institute of Scientific and Technical Information of China (English)

LV Jianglin; HUANG Guang

2015-01-01

Based on the theoretical analysis,this paper applies the entropy method to establish a comprehensive index system for the evaluation of the overall level of risk control and comprehensive efficiency of the supervision of China's commercial banks.Considering the trinity-characteristics of bank supervision system consisting of the People's Bank of China(PBC),the CBRC and the financial offices of local governments,the following conclusions have been drawn:the amount of penalties on banking illegal transactions is not correlated with the supervision efficiency of China's commercial banks;the capital adequacy ratio,the loan to deposit ratio,the percentage point of the non-performing loan rate of urban commercial banks higher than that of the national joint-stock banks are negatively correlated with the supervision efficiency of China 's commercial banks;the total asset variation of the PBC and the different loan balance in local and foreign currency of the banks are positively correlated with the supervision efficiency of China's commercial banks,but the effect is minor.Therefore,China should give the capital adequacy ratio a full play in the bank supervision,accelerate the construction of supervision information system and improve the supervision function of the local governments.

Supervision Experiences of Professional Counselors Providing Crisis Counseling

Science.gov (United States)

Dupre, Madeleine; Echterling, Lennis G.; Meixner, Cara; Anderson, Robin; Kielty, Michele

2014-01-01

In this phenomenological study, the authors explored supervision experiences of 13 licensed professional counselors in situations requiring crisis counseling. Five themes concerning crisis and supervision were identified from individual interviews. Findings support intensive, immediate crisis supervision and postlicensure clinical supervision.

Randomized, Controlled Trial of CBT Training for PTSD Providers

Science.gov (United States)

2016-10-29

clinician applicants occurred. b. SP baseline interviews with eligible clinicians occurred. c. Automated random assignment of participants with Completed SP...intervention without web-centered supervision and a wait-list control with regard to improvements in two CBT-based skill areas (behavioral task...Secondary Aim #1: To compare improvements in knowledge and attitudes following internet- based training with or without web-centered supervision and

Automatic Task Classification via Support Vector Machine and Crowdsourcing

Directory of Open Access Journals (Sweden)

Hyungsik Shin

2018-01-01

Full Text Available Automatic task classification is a core part of personal assistant systems that are widely used in mobile devices such as smartphones and tablets. Even though many industry leaders are providing their own personal assistant services, their proprietary internals and implementations are not well known to the public. In this work, we show through real implementation and evaluation that automatic task classification can be implemented for mobile devices by using the support vector machine algorithm and crowdsourcing. To train our task classifier, we collected our training data set via crowdsourcing using the Amazon Mechanical Turk platform. Our classifier can classify a short English sentence into one of the thirty-two predefined tasks that are frequently requested while using personal mobile devices. Evaluation results show high prediction accuracy of our classifier ranging from 82% to 99%. By using large amount of crowdsourced data, we also illustrate the relationship between training data size and the prediction accuracy of our task classifier.

The critical role of supervision in retaining staff in obstetric services: a three country study.

Directory of Open Access Journals (Sweden)

Eilish McAuliffe

Full Text Available 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

On the automaticity of response inhibition in individuals with alcoholism.

Science.gov (United States)

Noël, Xavier; Brevers, Damien; Hanak, Catherine; Kornreich, Charles; Verbanck, Paul; Verbruggen, Frederick

2016-06-01

Response inhibition is usually considered a hallmark of executive control. However, recent work indicates that stop performance can become associatively mediated ('automatic') over practice. This study investigated automatic response inhibition in sober and recently detoxified individuals with alcoholism.. We administered to forty recently detoxified alcoholics and forty healthy participants a modified stop-signal task that consisted of a training phase in which a subset of the stimuli was consistently associated with stopping or going, and a test phase in which this mapping was reversed. In the training phase, stop performance improved for the consistent stop stimuli, compared with control stimuli that were not associated with going or stopping. In the test phase, go performance tended to be impaired for old stop stimuli. Combined, these findings support the automatic inhibition hypothesis. Importantly, performance was similar in both groups, which indicates that automatic inhibitory control develops normally in individuals with alcoholism.. This finding is specific to individuals with alcoholism without other psychiatric disorders, which is rather atypical and prevents generalization. Personalized stimuli with a stronger affective content should be used in future studies. These results advance our understanding of behavioral inhibition in individuals with alcoholism. Furthermore, intact automatic inhibitory control may be an important element of successful cognitive remediation of addictive behaviors.. Copyright © 2016 Elsevier Ltd. All rights reserved.

A comparison of automatic histogram constructions

NARCIS (Netherlands)

Davies, P.L.; Gather, U.; Nordman, D.J.; Weinert, H.

2009-01-01

Even for a well-trained statistician the construction of a histogram for a given real-valued data set is a difficult problem. It is even more difficult to construct a fully automatic procedure which specifies the number and widths of the bins in a satisfactory manner for a wide range of data sets.

Pediatric Anesthesiology Fellows' Perception of Quality of Attending Supervision and Medical Errors.

Science.gov (United States)

Benzon, Hubert A; Hajduk, John; De Oliveira, Gildasio; Suresh, Santhanam; Nizamuddin, Sarah L; McCarthy, Robert; Jagannathan, Narasimhan

2018-02-01

Appropriate supervision has been shown to reduce medical errors in anesthesiology residents and other trainees across various specialties. Nonetheless, supervision of pediatric anesthesiology fellows has yet to be evaluated. The main objective of this survey investigation was to evaluate supervision of pediatric anesthesiology fellows in the United States. We hypothesized that there was an indirect association between perceived quality of faculty supervision of pediatric anesthesiology fellow trainees and the frequency of medical errors reported. A survey of pediatric fellows from 53 pediatric anesthesiology fellowship programs in the United States was performed. The primary outcome was the frequency of self-reported errors by fellows, and the primary independent variable was supervision scores. Questions also assessed barriers for effective faculty supervision. One hundred seventy-six pediatric anesthesiology fellows were invited to participate, and 104 (59%) responded to the survey. Nine of 103 (9%, 95% confidence interval [CI], 4%-16%) respondents reported performing procedures, on >1 occasion, for which they were not properly trained for. Thirteen of 101 (13%, 95% CI, 7%-21%) reported making >1 mistake with negative consequence to patients, and 23 of 104 (22%, 95% CI, 15%-31%) reported >1 medication error in the last year. There were no differences in median (interquartile range) supervision scores between fellows who reported >1 medication error compared to those reporting ≤1 errors (3.4 [3.0-3.7] vs 3.4 [3.1-3.7]; median difference, 0; 99% CI, -0.3 to 0.3; P = .96). Similarly, there were no differences in those who reported >1 mistake with negative patient consequences, 3.3 (3.0-3.7), compared with those who did not report mistakes with negative patient consequences (3.4 [3.3-3.7]; median difference, 0.1; 99% CI, -0.2 to 0.6; P = .35). We detected a high rate of self-reported medication errors in pediatric anesthesiology fellows in the United States

Intelligent multivariate process supervision

International Nuclear Information System (INIS)

Visuri, Pertti.

1986-01-01

This thesis addresses the difficulties encountered in managing large amounts of data in supervisory control of complex systems. Some previous alarm and disturbance analysis concepts are reviewed and a method for improving the supervision of complex systems is presented. The method, called multivariate supervision, is based on adding low level intelligence to the process control system. By using several measured variables linked together by means of deductive logic, the system can take into account the overall state of the supervised system. Thus, it can present to the operators fewer messages with higher information content than the conventional control systems which are based on independent processing of each variable. In addition, the multivariate method contains a special information presentation concept for improving the man-machine interface. (author)

Automatic Shadow Detection and Removal from a Single Image.

Science.gov (United States)

Khan, Salman H; Bennamoun, Mohammed; Sohel, Ferdous; Togneri, Roberto

2016-03-01

We present a framework to automatically detect and remove shadows in real world scenes from a single image. Previous works on shadow detection put a lot of effort in designing shadow variant and invariant hand-crafted features. In contrast, our framework automatically learns the most relevant features in a supervised manner using multiple convolutional deep neural networks (ConvNets). The features are learned at the super-pixel level and along the dominant boundaries in the image. The predicted posteriors based on the learned features are fed to a conditional random field model to generate smooth shadow masks. Using the detected shadow masks, we propose a Bayesian formulation to accurately extract shadow matte and subsequently remove shadows. The Bayesian formulation is based on a novel model which accurately models the shadow generation process in the umbra and penumbra regions. The model parameters are efficiently estimated using an iterative optimization procedure. Our proposed framework consistently performed better than the state-of-the-art on all major shadow databases collected under a variety of conditions.

On psychoanalytic supervision as signature pedagogy.

Science.gov (United States)

Watkins, C Edward

2014-04-01

What is signature pedagogy in psychoanalytic education? This paper examines that question, considering why psychoanalytic supervision best deserves that designation. In focusing on supervision as signature pedagogy, I accentuate its role in building psychoanalytic habits of mind, habits of hand, and habits of heart, and transforming theory and self-knowledge into practical product. Other facets of supervision as signature pedagogy addressed in this paper include its features of engagement, uncertainty, formation, and pervasiveness, as well as levels of surface, deep, and implicit structure. Epistemological, ontological, and axiological in nature, psychoanalytic supervision engages trainees in learning to do, think, and value what psychoanalytic practitioners in the field do, think, and value: It is, most fundamentally, professional preparation for competent, "good work." In this paper, effort is made to shine a light on and celebrate the pivotal role of supervision in "making" or developing budding psychoanalysts and psychoanalytic psychotherapists. Now over a century old, psychoanalytic supervision remains unparalleled in (1) connecting and integrating conceptualization and practice, (2) transforming psychoanalytic theory and self-knowledge into an informed analyzing instrument, and (3) teaching, transmitting, and perpetuating the traditions, practice, and culture of psychoanalytic treatment.

A hierarchical causal modeling for large industrial plants supervision

International Nuclear Information System (INIS)

Dziopa, P.; Leyval, L.

1994-01-01

A supervision system has to analyse the process current state and the way it will evolve after a modification of the inputs or disturbance. It is proposed to base this analysis on a hierarchy of models, witch differ by the number of involved variables and the abstraction level used to describe their temporal evolution. In a first step, special attention is paid to causal models building, from the most abstract one. Once the hierarchy of models has been build, the most detailed model parameters are estimated. Several models of different abstraction levels can be used for on line prediction. These methods have been applied to a nuclear reprocessing plant. The abstraction level could be chosen on line by the operator. Moreover when an abnormal process behaviour is detected a more detailed model is automatically triggered in order to focus the operator attention on the suspected subsystem. (authors). 11 refs., 11 figs

Abusive Supervision and Subordinate Performance : Instrumentality Considerations in the Emergence and Consequences of Abusive Supervision

NARCIS (Netherlands)

Walter, Frank; Lam, Catherine K.; van der Vegt, Geert; Huang, X.; Miao, Q.

Drawing from moral exclusion theory, this article examines outcome dependence and interpersonal liking as key boundary conditions for the linkage between perceived subordinate performance and abusive supervision. Moreover, it investigates the role of abusive supervision for subordinates' subsequent,

What's in It for Me? Maintenance of Certification as an Incentive for Faculty Supervision of Resident Quality Improvement Projects.

Science.gov (United States)

Rosenbluth, Glenn; Tabas, Jeffrey A; Baron, Robert B

2016-01-01

Residents are required to engage in quality improvement (QI) activities, which requires faculty engagement. Because of increasing program requirements and clinical demands, faculty may be resistant to taking on additional teaching and supervisory responsibilities without incentives. The authors sought to create an authentic benefit for University of California, San Francisco (UCSF) Pediatrics Residency Training Program faculty who supervise pediatrics residents' QI projects by offering maintenance of certification (MOC) Part 4 (Performance in Practice) credit. The authors identified MOC as an ideal framework to both more actively engage faculty who were supervising QI projects and provide incentives for doing so. To this end, in 2011, the authors designed an MOC portfolio program which included faculty development, active supervision of residents, and QI projects designed to improve patient care. The UCSF Pediatrics Residency Training Program's Portfolio Sponsor application was approved by the American Board of Pediatrics (ABP) in 2012, and faculty whose projects were included in the application were granted MOC Part 4 credit. As of December 2013, six faculty had received MOC Part 4 credit for their supervision of residents' QI projects. Based largely on the success of this program, UCSF has transitioned to the MOC portfolio program administered through the American Board of Medical Specialties, which allows the organization to offer MOC Part 4 credit from multiple specialty boards including the ABP. This may require refinements to screening, over sight, and reporting structures to ensure the MOC standards are met. Ongoing faculty development will be essential.

Supervision of radiation environment management of nuclear facilities

International Nuclear Information System (INIS)

Luo Mingyan

2013-01-01

Through literature and documents, the basis, content and implementation of the supervision of radiation environment management of nuclear facilities were defined. Such supervision was extensive and complicated with various tasks and overlapping duties, and had large social impact. Therefore, it was recommend to make further research on this supervision should be done, clarify and specify responsibilities of the executor of the supervision so as to achieve institutionalization, standardization and routinization of the supervision. (author)

Automatic generation of gene finders for eukaryotic species

DEFF Research Database (Denmark)

Terkelsen, Kasper Munch; Krogh, A.

2006-01-01

and quality of reliable gene annotation grows. Results We present a procedure, Agene, that automatically generates a species-specific gene predictor from a set of reliable mRNA sequences and a genome. We apply a Hidden Markov model (HMM) that implements explicit length distribution modelling for all gene......Background The number of sequenced eukaryotic genomes is rapidly increasing. This means that over time it will be hard to keep supplying customised gene finders for each genome. This calls for procedures to automatically generate species-specific gene finders and to re-train them as the quantity...... structure blocks using acyclic discrete phase type distributions. The state structure of the each HMM is generated dynamically from an array of sub-models to include only gene features represented in the training set. Conclusion Acyclic discrete phase type distributions are well suited to model sequence...

Human Supervision of Multiple Autonomous Vehicles

Science.gov (United States)

2013-03-22

AFRL-RH-WP-TR-2013-0143 HUMAN SUPERVISION OF MULTIPLE AUTONOMOUS VEHICLES Heath A. Ruff Ball...REPORT TYPE Interim 3. DATES COVERED (From – To) 09-16-08 – 03-22-13 4. TITLE AND SUBTITLE HUMAN SUPERVISION OF MULTIPLE AUTONOMOUS VEHICLES 5a...Supervision of Multiple Autonomous Vehicles To support the vision of a system that enables a single operator to control multiple next-generation

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

Science.gov (United States)

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