Using anthropometric and performance characteristics to predict selection in junior UK Rugby League players.

Research examining the factors influencing selection within talented junior Rugby League players is limited. The aims of this study were firstly to determine whether differences existed for anthropometric and performance characteristics between regional and national selection in high performance UK junior Rugby League players, and secondly to identify variables that discriminated between these selection levels. Regional representative (n=1172) selected junior players (aged 13-16 years) undertook an anthropometric and fitness testing battery with players split according to selection level (i.e., national, regional). MANCOVA analyses, with age and maturation controlled, identified national players as having lower sum of 4 skinfolds scores compared to regional players, and also performed significantly better on all physical tests. Stepwise discriminant analysis identified that estimated maximum oxygen uptake (VO2max), chronological age, body mass, 20 m sprint, height, sum of 4 skinfolds and sitting height discriminated between selection levels, accounting for 28.7% of the variance. This discriminant analysis corresponded to an overall predictive accuracy of 63.3% for all players. These results indicate that performance characteristics differed between selection levels in junior Rugby League players. However, the small magnitude of difference between selection levels suggests that physical qualities only partially explain higher representative selection. The monitoring and evaluation of such variables, alongside game related performance characteristics, provides greater knowledge and understanding about the processes and consequences of selection, training and performance in youth sport

Tensor Representation in High-Frequency Financial Data for Price Change Prediction

Nowadays, with the availability of massive amount of trade data collected, the dynamics of the financial markets pose both a challenge and an opportunity for high frequency traders. In order to take advantage of the rapid, subtle movement of assets in High Frequency Trading (HFT), an automatic algorithm to analyze and detect patterns of price change based on transaction records must be available. The multichannel, time-series representation of financial data naturally suggests tensor-based learning algorithms. In this work, we investigate the effectiveness of two multilinear methods for the mid-price prediction problem against other existing methods. The experiments in a large scale dataset which contains more than 4 millions limit orders show that by utilizing tensor representation, multilinear models outperform vector-based approaches and other competing ones.Comment: accepted in SSCI 2017, typos fixe

Voice and speech functions (B310-B340)

The International Classification of Functioning, Disability and Health for Children and Youth (ICF-CY) domain ‘voice and speech functions’ (b3) includes production and quality of voice (b310), articulation functions (b320), fluency and rhythm of speech (b330) and alternative vocalizations (b340, such as making musical sounds and crying, which are not reviewed here)

Development of the Motivational Interviewing Supervision and Training Scale

The movement to use empirically supported treatments has increased the need for researchers and supervisors to evaluate therapists’ adherence to and the quality with which they implement those interventions. Few empirically supported approaches exist for providing these types of evaluations. This is also true for motivational interviewing, an empirically supported intervention important in the addictions field. This study describes the development and psychometric evaluation of the Motivational Interviewing Supervision and Training Scale (MISTS), a measure intended for use in training and supervising therapists implementing motivational interviewing. Satisfactory interrater reliability was found (generalizability coefficient p2 = .79), and evidence was found supporting the convergent and discriminant validity of the MISTS. Recommendations for refinement of the measure and future research are discussed

Generative Model with Coordinate Metric Learning for Object Recognition Based on 3D Models

Given large amount of real photos for training, Convolutional neural network shows excellent performance on object recognition tasks. However, the process of collecting data is so tedious and the background are also limited which makes it hard to establish a perfect database. In this paper, our generative model trained with synthetic images rendered from 3D models reduces the workload of data collection and limitation of conditions. Our structure is composed of two sub-networks: semantic foreground object reconstruction network based on Bayesian inference and classification network based on multi-triplet cost function for avoiding over-fitting problem on monotone surface and fully utilizing pose information by establishing sphere-like distribution of descriptors in each category which is helpful for recognition on regular photos according to poses, lighting condition, background and category information of rendered images. Firstly, our conjugate structure called generative model with metric learning utilizing additional foreground object channels generated from Bayesian rendering as the joint of two sub-networks. Multi-triplet cost function based on poses for object recognition are used for metric learning which makes it possible training a category classifier purely based on synthetic data. Secondly, we design a coordinate training strategy with the help of adaptive noises acting as corruption on input images to help both sub-networks benefit from each other and avoid inharmonious parameter tuning due to different convergence speed of two sub-networks. Our structure achieves the state of the art accuracy of over 50\% on ShapeNet database with data migration obstacle from synthetic images to real photos. This pipeline makes it applicable to do recognition on real images only based on 3D models.Comment: 14 page

Classification software technique assessment

A catalog of software options is presented for the use of local user communities to obtain software for analyzing remotely sensed multispectral imagery. The resources required to utilize a particular software program are described. Descriptions of how a particular program analyzes data and the performance of that program for an application and data set provided by the user are shown. An effort is made to establish a statistical performance base for various software programs with regard to different data sets and analysis applications, to determine the status of the state-of-the-art

Novel image descriptors and learning methods for image classification applications

Image classification is an active and rapidly expanding research area in computer vision and machine learning due to its broad applications. With the advent of big data, the need for robust image descriptors and learning methods to process a large number of images for different kinds of visual applications has greatly increased. Towards that end, this dissertation focuses on exploring new image descriptors and learning methods by incorporating important visual aspects and enhancing the feature representation in the discriminative space for advancing image classification. First, an innovative sparse representation model using the complete marginal Fisher analysis (CMFA-SR) framework is proposed for improving the image classification performance. In particular, the complete marginal Fisher analysis method extracts the discriminatory features in both the column space of the local samples based within class scatter matrix and the null space of its transformed matrix. To further improve the classification capability, a discriminative sparse representation model is proposed by integrating a representation criterion such as the sparse representation and a discriminative criterion. Second, the discriminative dictionary distribution based sparse coding (DDSC) method is presented that utilizes both the discriminative and generative information to enhance the feature representation. Specifically, the dictionary distribution criterion reveals the class conditional probability of each dictionary item by using the dictionary distribution coefficients, and the discriminative criterion applies new within-class and between-class scatter matrices for discriminant analysis. Third, a fused color Fisher vector (FCFV) feature is developed by integrating the most expressive features of the DAISY Fisher vector (D-FV) feature, the WLD-SIFT Fisher vector (WS-FV) feature, and the SIFT-FV feature in different color spaces to capture the local, color, spatial, relative intensity, as well as the gradient orientation information. Furthermore, a sparse kernel manifold learner (SKML) method is applied to the FCFV features for learning a discriminative sparse representation by considering the local manifold structure and the label information based on the marginal Fisher criterion. Finally, a novel multiple anthropological Fisher kernel framework (M-AFK) is presented to extract and enhance the facial genetic features for kinship verification. The proposed method is derived by applying a novel similarity enhancement approach based on SIFT flow and learning an inheritable transformation on the multiple Fisher vector features that uses the criterion of minimizing the distance among the kinship samples and maximizing the distance among the non-kinship samples. The effectiveness of the proposed methods is assessed on numerous image classification tasks, such as face recognition, kinship verification, scene classification, object classification, and computational fine art painting categorization. The experimental results on popular image datasets show the feasibility of the proposed methods

The Influence of Rater Training, Scale Format, and Rating Justification on the Quality of Performance Ratings by Three Rater Sources

The primary focus of the present study was to examine systematically the influence of rater training, scale format, and rating justification on the quality (i.e., convergent and discriminant validity, halo, leniency) of ratings exhibited by three rater sources (i.e., self, peer, observer). Ninety-one undergraduate students participated in a videotaped role play exercise and returned at a later time to take part in a three-hour rating session. These individuals provided self- and peer ratings. Forty-five advanced undergraduate students participated in a similar rating session and provided observer ratings. Convergent validity, discriminant validity, and halo were tested with the multitrait-multimethod analysis of variance (MTMM ANOVA) approach. To assess the influence of training, scale format, and rating justification on the quality of performance ratings, each experimental condition was treated as a MTMM design and separate ANOVAs were calculated. A 2 (Training) x 2 (Format) x 2 (Justification) x 3 (Rater Sources) x 4 (Dimensions) ANOVA was computed to test the effects of the experimental conditions on the leniency of performance ratings across rater sources. Mixed support was found for the ability of these variables to influence the quality of performance ratings given by the three rater sources. Specifically, training and the use of the behavioral checklist increased discriminant validity and reduced halo, while raters who had to justify their performance ratings exhibited lower discriminant validity than raters who did not have to justify their ratings. With respect to leniency, the level of ratings across the three rater sources was affected by the variables of interest. Training and the use of the behavioral checklist helped to reduce leniency in self-ratings in those situations when raters had to justify their performance ratings. These results lend support for the use of training and the behavioral checklist to improve the overall quality of performance ratings given by different rater sources. However, future research should assess what specific training program content is needed to improve convergent validity when the behavioral checklist is used. In addition, research must be conducted to identify which rater sources provide high-quality ratings on which performance dimensions if a multiple-method approach to the assessment of job performance is desire

Validade e confiabilidade do Questionário de Adesão às Precauções-Padrão

RESUMO OBJETIVO : Avaliar a validade e a confiabilidade do Questionário de Adesão às Precauções-padrão em enfermeiros. MÉTODOS : Estudo metodológico realizado com 121 enfermeiros de estabelecimentos de saúde do interior de São Paulo, 2012, representados por dois estabelecimentos de alta complexidade e três de média complexidade. A consistência interna foi calculada pelo alfa de Cronbach e a estabilidade pelo coeficiente de correlação intraclasse, por meio de teste-reteste. Foi realizada a validação de construto convergente, divergente e por grupos conhecidos. RESULTADOS: O questionário mostrou-se confiável (alfa de Cronbach: 0,80; coeficiente de correlação intraclasse: 0,97). Quanto à validade de construto convergente e divergente, foi verificada forte correlação entre a adesão às precauções-padrão e a percepção de clima de segurança e a menor percepção de obstáculos para segui-las (r = 0,614 e r = -0,537, respectivamente). Enfermeiros que receberam treinamento sobre as precauções-padrão e atuantes nos estabelecimentos de maior complexidade mostraram-se mais aderentes (p = 0,028 e p = 0,006, respectivamente). CONCLUSÕES : A versão brasileira do questionário de adesão às precauções-padrão mostrou-se válida e confiável. Futuras investigações devem ser realizadas com amostras de enfermeiros mais representativas da realidade brasileira. A utilização do questionário pode auxiliar na proposição de medidas educativas frente às possíveis lacunas identificáveis, com enfoque na saúde do trabalhador e na segurança do paciente.ABSTRACT OBJECTIVE : To evaluate the validity and reliability of the Questionnaire for Compliance with Standard Precaution for nurses. METHODS : This methodological study was conducted with 121 nurses from health care facilities in Sao Paulo's countryside, who were represented by two high-complexity and by three average-complexity health care facilities. Internal consistency was calculated using Cronbach's alpha and stability was calculated by the intraclass correlation coefficient, through test-retest. Convergent, discriminant, and known-groups construct validity techniques were conducted. RESULTS : The questionnaire was found to be reliable (Cronbach's alpha: 0.80; intraclass correlation coefficient: (0.97) In regards to the convergent and discriminant construct validity, strong correlation was found between compliance to standard precautions, the perception of a safe environment, and the smaller perception of obstacles to follow such precautions (r = 0.614 and r = 0.537, respectively). The nurses who were trained on the standard precautions and worked on the health care facilities of higher complexity were shown to comply more (p = 0.028 and p = 0.006, respectively). CONCLUSIONS : The Brazilian version of the Questionnaire for Compliance with Standard Precaution was shown to be valid and reliable. Further investigation must be conducted with nurse samples that are more representative of the Brazilian reality. The use of the questionnaire may support the creation of educational measures considering the possible gaps that can be identified, focusing on the workers' health and on the patients' safety

The minimal important difference in physical activity in patients with COPD

Background Changes in physical activity (PA) are difficult to interpret because no framework of minimal important difference (MID) exists. We aimed to determine the minimal important difference (MID) in physical activity (PA) in patients with Chronic Obstructive Pulmonary Disease and to clinically validate this MID by evaluating its impact on time to first COPD-related hospitalization. Methods PA was objectively measured for one week in 74 patients before and after three months of rehabilitation (rehabilitation sample). In addition the intraclass correlation coefficient was measured in 30 patients (test-retest sample), by measuring PA for two consecutive weeks. Daily number of steps was chosen as outcome measurement. Different distribution and anchor based methods were chosen to calculate the MID. Time to first hospitalization due to an exacerbation was compared between patients exceeding the MID and those who did not. Results Calculation of the MID resulted in 599 (Standard Error of Measurement), 1029 (empirical rule effect size), 1072 (Cohen's effect size) and 1131 (0.5SD) steps.day(-1). An anchor based estimation could not be obtained because of the lack of a sufficiently related anchor. The time to the first hospital admission was significantly different between patients exceeding the MID and patients who did not, using the Standard Error of Measurement as cutoff. Conclusions The MID after pulmonary rehabilitation lies between 600 and 1100 steps.day(-1). The clinical importance of this change is supported by a reduced risk for hospital admission in those patients with more than 600 steps improvement