Active drag of front crawl swimmers: estimation, measurement and analysis

Researchers’ methods of estimating water’s resistance (drag) to a swimmer include the Assisted Towing Method (ATM) with fluctuating speed. This thesis aimed to assess the ATM method’s reliability with fluctuating speed, using it to examine the estimation of active drag’s validity. Chapter 3 investigated its reliability using Intra-class Correlation Coefficients (ICC) within-subject for one day and over two other days. The ICCs within-subject were moderately reliable for Day 1 (0.82) and Day 2 (0.85) but there was high reliability (0.92) when averaged active drag values were used. In chapter 4, mean active drag values resulted from two assisted and resisted methods compared to see if they measured the same values for active drag: both methods showed large differences in active drag with some swimmers. The ATM method calculates active drag from a function of three measured variables (swim speed, tow speed belt force) with two assumptions about power output between trials and the square relationship between drag force and swim speed. In chapter 5, each variable’s uncertainty and its contribution to active drag value were calculated. Results showed that a power change of 7.5% between trials meant about 30% error in calculated drag, showing that uncertainty in a range exponent of 1.8–2.6 would mean about 5% error in active drag value. The measured variables’ contributions to active drag were approximately 6–7% error for free and tow swim speeds and 2–3% error for belt force. Previous ATM method studies have presented an active drag profile of front crawl swimmers calculated from instantaneous values of three variables: free swim speed, tow speed and tow force. In chapter 6, comparison of the free swim profile with the two methods’ tow speed profiles, to see if these fluctuations are as large as in free swimming, showed the difference between maximum and minimum speeds was approximately 36%, 25.3% and 12.7% for the free, assisted and resisted swimming respectively

Segregation of chain ends to the surface of a polymer melt: effect of surface profile versus chain discreteness

Silberberg has argued that the surface of a polymer melt behaves like a reflecting boundary on the random-walk statistics of the polymers. Although this is approximately true, independent studies have shown that violations occur due to the finite width of the surface profile and to the discreteness of the polymer molecule, resulting in an excess of chain ends at the surface and a reduction in surface tension inversely proportional to the chain length, N. Using self-consistent field theory (SCFT), we compare the magnitude of these two effects by examining a melt of discrete polymers modeled as N monomers connected by Hookean springs of average length, a, next to a polymer surface of width, xi. The effects of the surface width and the chain discreteness are found to be comparable for realistic profiles of xi ~ a. A semi-analytical approximation is developed to help explain the behavior. The relative excess of ends at the surface is dependent on the details of the model, but in general it decreases for shorter polymers. The excess is balanced by a long-range depletion that has a universal shape independent of the molecular details. Furthermore, the approximation predicts that the reduction in surface energy equals one unit of kT for every extra chain end at the surfaceUniversity of Waterlo

Entropic segregation of short polymers to the surface of a polydisperse melt

Natural Sciences and Engineering Research Council of CanadaChain ends are known to have an entropic preference for the surface of a polymer melt, which in turn is expected to cause the short chains of a polydisperse melt to segregate to the surface. Here, we examine this entropic segregation for a bidisperse melt of short and long polymers, using self-consistent field theory (SCFT). The individual polymers are modeled by discrete monomers connected by freely-jointed bonds of statistical length a, and the field is adjusted so as to produce a specified surface profile of width xi. Semi-analytical expressions for the excess concentration of short polymers, the integrated excess, and the entropic effect on the surface tension are derived and tested against the numerical SCFT. The expressions exhibit universal dependences on the molecular-weight distribution with model-dependent coefficients. In general, the coefficients have to be evaluated numerically, but they can be approximated analytically once xi > a. We illustrate how this can be used to derive a simple expression for the interfacial tension between immiscible A- and B-type polydisperse homopolymers

Chasing a Better Decision Margin for Discriminative Histopathological Breast Cancer Image Classification

When considering a large dataset of histopathologic breast images captured at various magnification levels, the process of distinguishing between benign and malignant cancer from these images can be time-intensive. The automation of histopathological breast cancer image classification holds significant promise for expediting pathology diagnoses and reducing the analysis time. Convolutional neural networks (CNNs) have recently gained traction for their ability to more accurately classify histopathological breast cancer images. CNNs excel at extracting distinctive features that emphasize semantic information. However, traditional CNNs employing the softmax loss function often struggle to achieve the necessary discriminatory power for this task. To address this challenge, a set of angular margin-based softmax loss functions have emerged, including angular softmax (A-Softmax), large margin cosine loss (CosFace), and additive angular margin (ArcFace), each sharing a common objective: maximizing inter-class variation while minimizing intra-class variation. This study delves into these three loss functions and their potential to extract distinguishing features while expanding the decision boundary between classes. Rigorous experimentation on a well-established histopathological breast cancer image dataset, BreakHis, has been conducted. As per the results, it is evident that CosFace focuses on augmenting the differences between classes, while A-Softmax and ArcFace tend to emphasize augmenting within-class variations. These observations underscore the efficacy of margin penalties on angular softmax losses in enhancing feature discrimination within the embedding space. These loss functions consistently outperform softmax-based techniques, either by widening the gaps among classes or enhancing the compactness of individual classes

Optimierung der auf Deep Learning basierenden Klassifizierung von Pflanzenkrankheiten mit CBAM

In recent years, deep learning-based plant disease classification has been widely developed. However, it is challenging to collect sufficient annotated image data to effectively train deep learning models for plant disease recognition. The attention mechanism in deep learning assists the model to focus on the informative data segments and extract the discriminative features of inputs to enhance training performance. This paper investigates the Convolutional Block Attention Module (CBAM) to improve classification with CNNs, which is a lightweight attention module that can be plugged into any CNN architecture with negligible overhead. Specifically, CBAM is applied to the output feature map of CNNs to highlight important local regions and extract more discriminative features. Well-known CNN models (i.e. EfficientNetB0, MobileNetV2, ResNet50, InceptionV3, and VGG19) were applied to do transfer learning for plant disease classification and then fine-tuned by a publicly available plant disease dataset of foliar diseases in pear trees called DiaMOS Plant. Amongst others, this dataset contains 3006 images of leaves affected by different stress symptoms. Among the tested CNNs, EfficientNetB0 has shown the best performance. EfficientNetB0+CBAM has outperformed EfficientNetB0 and obtained 86.89% classification accuracy. Experimental results show the effectiveness of the attention mechanism to improve the recognition accuracy of pre-trained CNNs when there are few training data

Chasing a Better Decision Margin for Discriminative Histopathological Breast Cancer Image Classification

When considering a large dataset of histopathologic breast images captured at various magnification levels, the process of distinguishing between benign and malignant cancer from these images can be time-intensive. The automation of histopathological breast cancer image classification holds significant promise for expediting pathology diagnoses and reducing the analysis time. Convolutional neural networks (CNNs) have recently gained traction for their ability to more accurately classify histopathological breast cancer images. CNNs excel at extracting distinctive features that emphasize semantic information. However, traditional CNNs employing the softmax loss function often struggle to achieve the necessary discriminatory power for this task. To address this challenge, a set of angular margin-based softmax loss functions have emerged, including angular softmax (A-Softmax), large margin cosine loss (CosFace), and additive angular margin (ArcFace), each sharing a common objective: maximizing inter-class variation while minimizing intra-class variation. This study delves into these three loss functions and their potential to extract distinguishing features while expanding the decision boundary between classes. Rigorous experimentation on a well-established histopathological breast cancer image dataset, BreakHis, has been conducted. As per the results, it is evident that CosFace focuses on augmenting the differences between classes, while A-Softmax and ArcFace tend to emphasize augmenting within-class variations. These observations underscore the efficacy of margin penalties on angular softmax losses in enhancing feature discrimination within the embedding space. These loss functions consistently outperform softmax-based techniques, either by widening the gaps among classes or enhancing the compactness of individual classes.This work is partially supported by the project GUI19/027 and by the grant PID2021-126701OB-I00 funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe”

Deep Learning with Discriminative Margin Loss for Cross-Domain Consumer-to-Shop Clothes Retrieval

Consumer-to-shop clothes retrieval refers to the problem of matching photos taken by customers with their counterparts in the shop. Due to some problems, such as a large number of clothing categories, different appearances of clothing items due to different camera angles and shooting conditions, different background environments, and different body postures, the retrieval accuracy of traditional consumer-to-shop models is always low. With advances in convolutional neural networks (CNNs), the accuracy of garment retrieval has been significantly improved. Most approaches addressing this problem use single CNNs in conjunction with a softmax loss function to extract discriminative features. In the fashion domain, negative pairs can have small or large visual differences that make it difficult to minimize intraclass variance and maximize interclass variance with softmax. Margin-based softmax losses such as Additive Margin-Softmax (aka CosFace) improve the discriminative power of the original softmax loss, but since they consider the same margin for the positive and negative pairs, they are not suitable for cross-domain fashion search. In this work, we introduce the cross-domain discriminative margin loss (DML) to deal with the large variability of negative pairs in fashion. DML learns two different margins for positive and negative pairs such that the negative margin is larger than the positive margin, which provides stronger intraclass reduction for negative pairs. The experiments conducted on publicly available fashion datasets DARN and two benchmarks of the DeepFashion dataset—(1) Consumer-to-Shop Clothes Retrieval and (2) InShop Clothes Retrieval—confirm that the proposed loss function not only outperforms the existing loss functions but also achieves the best performance

Entropic Segregation at Surfaces of Polymer Melts

Throughout recent years, polymers have been one of the most widely used materials in industry due to their suitability to a vast variety of fields from construction to biomedical and technological utilities. Their extensive and broad range of applications, demands the ever increasing necessity for extensive insight into the behavior and properties of this group of materials. Whilst some aspects of this demand can be addressed through experiments, the inherent difficulties and restrictions of studying polymeric systems with greater degrees of complexity, have motivated researchers to discover alternative means of examining these cases of interest effectively. The advent of polymer theory as well as the development of appropriate computer simulation techniques have proven to be invaluable sources of new insight. With the ever increasing use of polymeric materials in industrial applications, such as highly efficient coatings and adhesives, an in-depth knowledge of their behavior close to surfaces is needed. Moreover, for the development of effective materials, an accurate understanding of their physical properties such as their surface tension is also required. In this light, our goal is to study the surface behavior of polymers melts, whether they be monodisperse or polydisperse. The study of the former system, which is a melt composed of chains of a single length, is more attractive theoretically as a basis for a comprehensive study of influential parameters. However, in reality, most polymeric materials are polydisperse, hence motivating the detailed assessment of both. Surfaces behave as reflecting boundaries for the most part but violations are seen to occur due to a number of parameters such as the finite width of surface profiles, discreteness of chains as well as excluded volume effects. These result in an excess of end monomers being observed at the surfaces of monodisperse melts and shorter chains segregating to the surfaces of polydisperse ones, from which the surface tension is seen to be affected as well. The source of these migrations could either be enthalpic, with a preference for ends to be closer to surfaces, but also purely entropic which is the case studied here. With the inherent difficulties of experimentally isolating these entropic phenomena, a more successful outcome is obtained through their theoretical study. Hence, for both of the aforementioned systems, we shall be performing mean-field calculations as well as Monte Carlo simulations, in addition to comparing them with universal predicted forms to test their accuracy