Improved multi-beam neural network scatterometer forward models

Current methods for retrieving near surface winds from scatterometer observations over the ocean surface require a foward sensor model which maps the wind vector to the measured backscatter. This paper develops a hybrid neural network forward model, which retains the physical understanding embodied in ¸mod, but incorporates greater flexibility, allowing a better fit to the observations. By introducing a separate model for the mid-beam and using a common model for the fore- and aft-beams, we show a significant improvement in local wind vector retrieval. The hybrid model also fits the scatterometer observations more closely. The model is trained in a Bayesian framework, accounting for the noise on the wind vector inputs. We show that adding more high wind speed observations in the training set improves wind vector retrieval at high wind speeds without compromising performance at medium or low wind speeds.

Improved neural network scatterometer forward models

Current methods for retrieving near-surface winds from scatterometer observations over the ocean surface require a forward sensor model which maps the wind vector to the measured backscatter. This paper develops a hybrid neural network forward model, which retains the physical understanding embodied in CMOD4, but incorporates greater flexibility, allowing a better fit to the observations. By introducing a separate model for the midbeam and using a common model for the fore and aft beams, we show a significant improvement in local wind vector retrieval. The hybrid model also fits the scatterometer observations more closely. The model is trained in a Bayesian framework, accounting for the noise on the wind vector inputs. We show that adding more high wind speed observations in the training set improves wind vector retrieval at high wind speeds without compromising performance at medium or low wind speeds. Copyright 2001 by the American Geophysical Union.

Semi-supervised learning of statistical models for natural language understanding

Natural language understanding is to specify a computational model that maps sentences to their semantic mean representation. In this paper, we propose a novel framework to train the statistical models without using expensive fully annotated data. In particular, the input of our framework is a set of sentences labeled with abstract semantic annotations. These annotations encode the underlying embedded semantic structural relations without explicit word/semantic tag alignment. The proposed framework can automatically induce derivation rules that map sentences to their semantic meaning representations. The learning framework is applied on two statistical models, the conditional random fields (CRFs) and the hidden Markov support vector machines (HM-SVMs). Our experimental results on the DARPA communicator data show that both CRFs and HM-SVMs outperform the baseline approach, previously proposed hidden vector state (HVS) model which is also trained on abstract semantic annotations. In addition, the proposed framework shows superior performance than two other baseline approaches, a hybrid framework combining HVS and HM-SVMs and discriminative training of HVS, with a relative error reduction rate of about 25% and 15% being achieved in F-measure.

Adaptation of a Task-oriented Training Environment to its Users

The problem of adapting teaching systems to the teacher has not been extensively covered in the specialised literature. The authors present the server-client architecture of a Task-Oriented Environment for Design of Virtual Labs (TOEDVL). The paper focuses on the computational models supporting its base of tasks (BT) and on two groups of behavioural tutor’s models for planning training sessions. Detailed examples are presented.

Business Models for Open Access. Copyright and Licensing under Open Access

The lecture analyses the traditional business model in scientific communication and describes the new emerging models in the context of Open Access. Copyright and licensing part provides an overview of the legal issues and copyright at the heart of Open Access.

Management Training Theories: Tools for Hospitality Managers and Trainers

Management training in the hospitality industry is as important as employee training. There are a number of effective models and approaches for training effective managers. The author reviews these models and offers guidelines for maximizing the results from each of these approaches.

The impact of cross -training on team processes and performance

Recently, researchers have begun to investigate the benefits of cross-training teams. It has been hypothesized that cross-training should help improve team processes and team performance (Cannon-Bowers, Salas, Blickensderfer, & Bowers, 1998; Travillian, Volpe, Cannon-Bowers, & Salas, 1993). The current study extends previous research by examining different methods of cross-training (positional clarification and positional modeling) and the impact they have on team process and performance in both more complex and less complex environments. One hundred and thirty-five psychology undergraduates were placed in 45 three-person teams. Participants were randomly assigned to roles within teams. Teams were asked to “fly” a series of missions on a PC-based helicopter flight simulation. ^ Results suggest that cross-training improves team mental model accuracy and similarity. Accuracy of team mental models was found to be a predictor of coordination quality, but similarity of team mental models was not. Neither similarity nor accuracy of team mental models was found to be a predictor of backup behavior (quality and quantity). As expected, both team coordination (quality) and backup behaviors (quantity and quality) were significant predictors of overall team performance. Contrary to expectations, there was no interaction between cross-training and environmental complexity. Results from this study further cross-training research by establishing positional clarification and positional modeling as training strategies for improving team performance. ^

Concept maps as a tool to analyze the ideas of teacher training students regarding what to teach on human nutrition in Primary Education

Concept maps are a technique used to obtain a visual representation of a person's ideas about a concept or a set of related concepts. Specifically, in this paper, through a qualitative methodology, we analyze the concept maps proposed by 52 groups of teacher training students in order to find out the characteristics of the maps and the degree of adequacy of the contents with regard to the teaching of human nutrition in the 3rd cycle of primary education. The participants were enrolled in the Teacher Training Degree majoring in Primary Education, and the data collection was carried out through a training activity under the theme of what to teach about Science in Primary School? The results show that the maps are a useful tool for working in teacher education as they allow organizing, synthesizing, and communicating what students know. Moreover, through this work, it has been possible to see that future teachers have acceptable skills for representing the concepts/ideas in a concept map, although the level of adequacy of concepts/ideas about human nutrition and its relations is usually medium or low. These results are a wake-up call for teacher training, both initial and ongoing, because they shows the inability to change priorities as far as the selection of content is concerned.

Paradigms that guide the models of attention to the health in Brazil: an analytic essay

SILVA, Flávio César Bezerra da ; COSTA, Francisca Marta de Lima; ANDRADE, Hamilton Leandro Pinto de; FREIRE, Lúcia de Fátima; MACIEL, Patrícia Suerda de Oliveira; ENDERS, Bertha Cruz ; MENEZES, Rejane Maria Paiva de. Paradigms that guide the models of attention to the health in Brazil: an analytic essay. Revista de Enfermagem UFPE On Line., Recife, v.3,n.4, p.460-65. out/dez. 2009. Disponível em < http://www.ufpe.br/revistaenfermagem/index.php/revista/search/results >.

A Video Analysis of Intra- and Inter-professional Leadership Behaviours Within “The Burns Suite”: Identifying Key Leadership Models

Objective Leadership is particularly important in complex highly interprofessional health care contexts involving a number of staff, some from the same specialty (intraprofessional), and others from different specialties (interprofessional). The authors recently published the concept of “The Burns Suite” (TBS) as a novel simulation tool to deliver interprofessional and teamwork training. It is unclear which leadership behaviors are the most important in an interprofessional burns resuscitation scenario, and whether they can be modeled on to current leadership theory. The purpose of this study was to perform a comprehensive video analysis of leadership behaviors within TBS. Methods A total of 3 burns resuscitation simulations within TBS were recorded. The video analysis was grounded-theory inspired. Using predefined criteria, actions/interactions deemed as leadership behaviors were identified. Using an inductive iterative process, 8 main leadership behaviors were identified. Cohen’s κ coefficient was used to measure inter-rater agreement and calculated as κ = 0.7 (substantial agreement). Each video was watched 4 times, focusing on 1 of the 4 team members per viewing (senior surgeon, senior nurse, trainee surgeon, and trainee nurse). The frequency and types of leadership behavior of each of the 4 team members were recorded. Statistical significance to assess any differences was assessed using analysis of variance, whereby a p < 0.05 was taken to be significant. Leadership behaviors were triangulated with verbal cues and actions from the videos. Results All 3 scenarios were successfully completed. The mean scenario length was 22 minutes. A total of 362 leadership behaviors were recorded from the 12 participants. The most evident leadership behaviors of all team members were adhering to guidelines (which effectively equates to following Advanced Trauma and Life Support/Emergency Management of Severe Burns resuscitation guidelines and hence “maintaining standards”), followed by making decisions. Although in terms of total frequency the senior surgeon engaged in more leadership behaviors compared with the entire team, statistically there was no significant difference between all 4 members within the 8 leadership categories. This analysis highlights that “distributed leadership” was predominant, whereby leadership was “distributed” or “shared” among team members. The leadership behaviors within TBS also seemed to fall in line with the “direction, alignment, and commitment” ontology. Conclusions Effective leadership is essential for successful functioning of work teams and accomplishment of task goals. As the resuscitation of a patient with major burns is a dynamic event, team leaders require flexibility in their leadership behaviors to effectively adapt to changing situations. Understanding leadership behaviors of different team members within an authentic simulation can identify important behaviors required to optimize nontechnical skills in a major resuscitation. Furthermore, attempting to map these behaviors on to leadership models can help further our understanding of leadership theory. Collectively this can aid the development of refined simulation scenarios for team members, and can be extrapolated into other areas of simulation-based team training and interprofessional education.

Decision making models embedded into a web-based tool for assessing pest infestation risk

Current practices in agricultural management involve the application of rules and techniques to ensure high quality and environmentally friendly production. Based on their experience, agricultural technicians and farmers make critical decisions affecting crop growth while considering several interwoven agricultural, technological, environmental, legal and economic factors. In this context, decision support systems and the knowledge models that support them, enable the incorporation of valuable experience into software systems providing support to agricultural technicians to make rapid and effective decisions for efficient crop growth. Pest control is an important issue in agricultural management due to crop yield reductions caused by pests and it involves expert knowledge. This paper presents a formalisation of the pest control problem and the workflow followed by agricultural technicians and farmers in integrated pest management, the crop production strategy that combines different practices for growing healthy crops whilst minimising pesticide use. A generic decision schema for estimating infestation risk of a given pest on a given crop is defined and it acts as a metamodel for the maintenance and extension of the knowledge embedded in a pest management decision support system which is also presented. This software tool has been implemented by integrating a rule-based tool into web-based architecture. Evaluation from validity and usability perspectives concluded that both agricultural technicians and farmers considered it a useful tool in pest control, particularly for training new technicians and inexperienced farmers.

Paradigms that guide the models of attention to the health in Brazil: an analytic essay

SILVA, Flávio César Bezerra da ; COSTA, Francisca Marta de Lima; ANDRADE, Hamilton Leandro Pinto de; FREIRE, Lúcia de Fátima; MACIEL, Patrícia Suerda de Oliveira; ENDERS, Bertha Cruz ; MENEZES, Rejane Maria Paiva de. Paradigms that guide the models of attention to the health in Brazil: an analytic essay. Revista de Enfermagem UFPE On Line., Recife, v.3,n.4, p.460-65. out/dez. 2009. Disponível em < http://www.ufpe.br/revistaenfermagem/index.php/revista/search/results >.

Datasets, features, learning, and models in visual recognition

Visual recognition is a fundamental research topic in computer vision. This dissertation explores datasets, features, learning, and models used for visual recognition. In order to train visual models and evaluate different recognition algorithms, this dissertation develops an approach to collect object image datasets on web pages using an analysis of text around the image and of image appearance. This method exploits established online knowledge resources (Wikipedia pages for text; Flickr and Caltech data sets for images). The resources provide rich text and object appearance information. This dissertation describes results on two datasets. The first is Berg’s collection of 10 animal categories; on this dataset, we significantly outperform previous approaches. On an additional set of 5 categories, experimental results show the effectiveness of the method. Images are represented as features for visual recognition. This dissertation introduces a text-based image feature and demonstrates that it consistently improves performance on hard object classification problems. The feature is built using an auxiliary dataset of images annotated with tags, downloaded from the Internet. Image tags are noisy. The method obtains the text features of an unannotated image from the tags of its k-nearest neighbors in this auxiliary collection. A visual classifier presented with an object viewed under novel circumstances (say, a new viewing direction) must rely on its visual examples. This text feature may not change, because the auxiliary dataset likely contains a similar picture. While the tags associated with images are noisy, they are more stable when appearance changes. The performance of this feature is tested using PASCAL VOC 2006 and 2007 datasets. This feature performs well; it consistently improves the performance of visual object classifiers, and is particularly effective when the training dataset is small. With more and more collected training data, computational cost becomes a bottleneck, especially when training sophisticated classifiers such as kernelized SVM. This dissertation proposes a fast training algorithm called Stochastic Intersection Kernel Machine (SIKMA). This proposed training method will be useful for many vision problems, as it can produce a kernel classifier that is more accurate than a linear classifier, and can be trained on tens of thousands of examples in two minutes. It processes training examples one by one in a sequence, so memory cost is no longer the bottleneck to process large scale datasets. This dissertation applies this approach to train classifiers of Flickr groups with many group training examples. The resulting Flickr group prediction scores can be used to measure image similarity between two images. Experimental results on the Corel dataset and a PASCAL VOC dataset show the learned Flickr features perform better on image matching, retrieval, and classification than conventional visual features. Visual models are usually trained to best separate positive and negative training examples. However, when recognizing a large number of object categories, there may not be enough training examples for most objects, due to the intrinsic long-tailed distribution of objects in the real world. This dissertation proposes an approach to use comparative object similarity. The key insight is that, given a set of object categories which are similar and a set of categories which are dissimilar, a good object model should respond more strongly to examples from similar categories than to examples from dissimilar categories. This dissertation develops a regularized kernel machine algorithm to use this category dependent similarity regularization. Experiments on hundreds of categories show that our method can make significant improvement for categories with few or even no positive examples.

Scoping review of adherence promotion theories in pelvic floor muscle training : 2011 ics state-of-the-science seminar research paper I of IV

Aims This paper, the first of four emanating from the International Continence Society's 2011 State-of-the-Science Seminar on pelvic-floor-muscle training (PFMT) adherence, aimed to summarize the literature on theoretical models to promote PFMT adherence, as identified in the research, or suggested by the seminar's expert panel, and recommends future directions for clinical practice and research. Methods Existing literature on theories of health behavior were identified through a conventional subject search of electronic databases, reference-list checking, and input from the expert panel. A core eligibility criterion was that the study included a theoretical model to underpin adherence strategies used in an intervention to promote PFM training/exercise. Results A brief critique of 12 theoretical models/theories is provided and, were appropriate, their use in PFMT adherence strategies identified or examples of possible uses in future studies outlined. Conclusion A better theoretical-based understanding of interventions to promote PFMT adherence through changes in health behaviors is required. The results of this scoping review and expert opinions identified several promising models. Future research should explicitly map the theories behind interventions that are thought to improve adherence in various populations (e.g., perinatal women to prevent or lessen urinary incontinence). In addition, identified behavioral theories applied to PFMT require a process whereby their impact can be evaluated.

Scoping review of adherence promotion theories in pelvic floor muscle training : 2011 ics state-of-the-science seminar research paper I of IV

Aims This paper, the first of four emanating from the International Continence Society's 2011 State-of-the-Science Seminar on pelvic-floor-muscle training (PFMT) adherence, aimed to summarize the literature on theoretical models to promote PFMT adherence, as identified in the research, or suggested by the seminar's expert panel, and recommends future directions for clinical practice and research. Methods Existing literature on theories of health behavior were identified through a conventional subject search of electronic databases, reference-list checking, and input from the expert panel. A core eligibility criterion was that the study included a theoretical model to underpin adherence strategies used in an intervention to promote PFM training/exercise. Results A brief critique of 12 theoretical models/theories is provided and, were appropriate, their use in PFMT adherence strategies identified or examples of possible uses in future studies outlined. Conclusion A better theoretical-based understanding of interventions to promote PFMT adherence through changes in health behaviors is required. The results of this scoping review and expert opinions identified several promising models. Future research should explicitly map the theories behind interventions that are thought to improve adherence in various populations (e.g., perinatal women to prevent or lessen urinary incontinence). In addition, identified behavioral theories applied to PFMT require a process whereby their impact can be evaluated.