PARTICIPATION AND PERFORMANCE DURING THE EXTREME OPEN-WATER ‘FREEDOM SWIM’ RACE FROM 2001 TO 2018

Introduction: Ultra-endurance and extreme open water present a unique exercise environment that stresses both the physiological and psychological characteristics of a swimmer. In recent years, a number of studies have specifically focused on Northern Hemisphere races. The aim of this study to analyse the participation and performance trends of the Freedom Swim extreme open water swim from 2001-2018. Methods: A retrospective analysis of publicly available data was used to assess participation and performance. Results: Participation did not significantly increase over the period of interest in either gender. However, for every year there were significantly more men than women taking part. Mean finishing time was not significantly different between genders, however, in 2008, men were significantly faster but in 2010, women were significantly faster. Conclusion: the present study provides evidence that women's participation is open water sea swimming is significantly less than that of men, in accordance with previous research. Further, there was no significant difference in performance between men and women. These findings suggest that men and women achieve similar swimming performances in cold water long distance.  Article visualizations

A confidence level algorithm for the determination of absolute configuration using vibrational circular dichroism or raman optical activity

Spectral comparison is an important part of the assignment of the absolute configuration (AC) by vibrational circular dichroism (VCD), or equally by Raman optical activity (ROA). In order to avoid bias caused by personal interpretation, numerical methods have been developed to compare measured and calculated spectra. Using a neighbourhood similarity measure, the agreement between a computed and measured VCD or ROA spectrum is expressed numerically to introduce a novel confidence level measure. This allows users of vibrational optical activity (VOA) techniques (VCD and ROA) to assess the reliability of their assignment of the AC of a compound. To that end, a database of successful AC determinations is compiled along with neighbourhood similarity values between the experimental spectrum and computed spectra for both enantiomers. For any new AC determination, the neighbourhood similarities between the experimental spectrum and the computed spectra for both enantiomers are projected on the database allowing an interpretation of the reliability of their assignment

Data augmentation and semi-supervised learning for deep neural networks-based text classifier

User feedback is essential for understanding user needs. In this paper, we use free-text obtained from a survey on sleep-related issues to build a deep neural networks-based text classifier. However, to train the deep neural networks model, a lot of labelled data is needed. To reduce manual data labelling, we propose a method which is a combination of data augmentation and pseudo-labelling: data augmentation is applied to labelled data to increase the size of the initial train set and then the trained model is used to annotate unlabelled data with pseudo-labels. The result shows that the model with the data augmentation achieves macro-averaged f1 score of 65.2% while using 4,300 training data, whereas the model without data augmentation achieves macro-averaged f1 score of 68.2% with around 14,000 training data. Furthermore, with the combination of pseudo-labelling, the model achieves macro-averaged f1 score of 62.7% with only using 1,400 training data with labels. In other words, with the proposed method we can reduce the amount of labelled data for training while achieving relatively good performance

Efficient Image Gallery Representations at Scale Through Multi-Task Learning

Image galleries provide a rich source of diverse information about a product which can be leveraged across many recommendation and retrieval applications. We study the problem of building a universal image gallery encoder through multi-task learning (MTL) approach and demonstrate that it is indeed a practical way to achieve generalizability of learned representations to new downstream tasks. Additionally, we analyze the relative predictive performance of MTL-trained solutions against optimal and substantially more expensive solutions, and find signals that MTL can be a useful mechanism to address sparsity in low-resource binary tasks.Comment: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieva

Secular Changes in the Postcranial Skeleton of American Whites

Secular change in height has been extensively investigated, but size and shape of the postcranial skeleton much less so. The availability of large, documented collections of nineteenth- and twentieth-century skeletons makes it possible to examine changes in skeletal structure over the past 150 years. We examined secular changes in long bone lengths and proportions, their allometric relationship to stature, and cross- sectional properties of long bone shafts. Bone measurements and stature were organized into 10-year birth cohorts, ranging from 1840 to 1989. Variation among cohorts was tested by one-way ANOVA, and secular trend was examined visually by plotting mean measurements by birth decade. Allometry was examined by regressing log bone lengths onto log stature, using least squares regression. Allometry was also examined using the geometric mean of log bone lengths as the size variable. All bone lengths and stature showed positive secular change. Stature and the distal long bones showed the most pronounced changes. Proportions also changed, as revealed by the brachial and crural indices. Both indices increased, but the brachial index change was the most pronounced. Allometric relationships suggest that brachial index changes result from positive allometry of the radius and negative allometry of the humerus. Similar but less marked allometric relationships were found in the tibia and femur. Long bone shaft properties changed in the following ways: femur midshafts and tibia shafts at the nutrient foramen became more mediolaterally narrowed, and the femur became more mediolaterally thickened at the subtrochanteric level, approaching platymeria. All major long bones became more gracile. These remarkable changes in the postcranial skeleton are a response to the unparalleled changes in the environment in which modern Americans now live. Changes in growth resulting from plentiful and secure nutrition, reduced disease load, and marked reduction in bone loading from reduced activity levels are mainly responsible

Refining genetically inferred relationships using treelet covariance smoothing

Recent technological advances coupled with large sample sets have uncovered many factors underlying the genetic basis of traits and the predisposition to complex disease, but much is left to discover. A common thread to most genetic investigations is familial relationships. Close relatives can be identified from family records, and more distant relatives can be inferred from large panels of genetic markers. Unfortunately these empirical estimates can be noisy, especially regarding distant relatives. We propose a new method for denoising genetically - inferred relationship matrices by exploiting the underlying structure due to hierarchical groupings of correlated individuals. The approach, which we call Treelet Covariance Smoothing, employs a multiscale decomposition of covariance matrices to improve estimates of pairwise relationships. On both simulated and real data, we show that smoothing leads to better estimates of the relatedness amongst distantly related individuals. We illustrate our method with a large genome-wide association study and estimate the "heritability" of body mass index quite accurately. Traditionally heritability, defined as the fraction of the total trait variance attributable to additive genetic effects, is estimated from samples of closely related individuals using random effects models. We show that by using smoothed relationship matrices we can estimate heritability using population-based samples. Finally, while our methods have been developed for refining genetic relationship matrices and improving estimates of heritability, they have much broader potential application in statistics. Most notably, for error-in-variables random effects models and settings that require regularization of matrices with block or hierarchical structure.Comment: Published in at http://dx.doi.org/10.1214/12-AOAS598 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org