Increased risk for neurodegenerative diseases in professional athletes

BACKGROUND: Although concussion and sport-related traumatic brain injury is being acknowledged as a major public issue, especially in professional football players, current study is mostly limited to retrospective studies and post-mortem autopsies. The purpose of this study is to identify a potential association between concussion and neurodegenerative disease in athletes, and propose a prospective approach of studying concussion and its effect. METHODS: A total of 26 studies related to concussion in athletes and published after January 2000 were collected from PubMed and Google Scholar. More recent papers with higher citation counts were given the priority. RESULTS: Retired professional football players showed five times greater risk for mild cognitive impairment, three times greater risk for memory loss, and four times greater risk for amyotrophic lateral sclerosis and Alzheimer disease. Autopsy results from football players also revealed findings consistent with chronic traumatic encephalopathy. Population with the Apolipoprotein E (APOE) promoter G-219T TT (Thymine-Thymine) genotype showed increased susceptibility for concussion. CONCLUSION: This study revealed that a history of concussion has statistically significant associations with high incidence of neurodegenerative diseases in professional athletes. In addition, the results suggest the 2-(1-{6-[(2-[F-18]fluoroethyl)(methyl)amino]-2-naphthyl}ethylidene)malononitril(FDDNP)-positron emission tomography tau binding patterns and the APOE promoter G-219T TT genotype provide a new approach to study and monitor the progression of neurodegenerative conditions in athletes

The Meaning of Fashion: Implicit and Explicit Self-esteem and Depression

This study investigates the relationship between the implicit self-esteem and the depression to fill the gap. In psychological field, the therapy is considered to be effective as both external and internal selves are healed. Hence, this study employed implicit self-reported method to examine the genuine therapeutic effect of fashion. This study is significant as it facilitated the implicit association test (IAT) in first place in fashion field. The purpose of the study is to develop the foundation of positive effect of fashion by revealing the relationship between the fashion and the substantial self

Window Axial Vision Transformer for Image Classification

Currently, a popular approach to image classification uses the deep Transformer architecture. In a Transformer, the attention mechanism enables the model to learn efficiently with fewer computational resources than the convolutional neural networks (CNNs). In this thesis, we study the sparse attention mechanism widely used in the Transformers developed specifically for natural language processing (NLP). We generalize these models to enable the processing of 2D images. The resulting new models specified in this thesis have fewer parameters, and as we show experimentally give good results on image classification. In particular, from our experiments, the well-known problem that the vision Transformers (ViT) lack the capability to model prior knowledge is compensated in our new models by adopting a local attention estimation. This helps our models to perform well even on small datasets. Evaluation is presented on the benchmark datasets for image classification including CIFAR-10, CIFAR-100, and ImageNet-1K. A comparison with ViT--a popular Transformer in computer vision--shows that our new models outperform ViT on all three datasets

Social Network Analysis of Global Value Chain: Focused on Fabric Cotton

Companies try to establishing optimal production networks that can perform all stages of production activities at competitive cost and quality around the world. In this study, we try to determine the structure of the global value chain in the apparel industry using social network analysis. Data for analysis were created a matrix using 2005 and 2015 trade data about the top 10 trading partners in the import and export countries of cotton fabric (hs code 5208, 5209). China was the largest exporting country. Comparing the betweenness centrality and closeness centrality of exports of cotton fabrics, India and China are playing their role as mediating countries. Vietnamese cotton imports have increased significantly. The role of mediators in the importation of cotton fiber was continued by China, the United States, and European countries. We identified the part of the fashion industry structure by investigating the international trade patterns of cotton fabric

Tell Me What They're Holding: Weakly-supervised Object Detection with Transferable Knowledge from Human-object Interaction

In this work, we introduce a novel weakly supervised object detection (WSOD) paradigm to detect objects belonging to rare classes that have not many examples using transferable knowledge from human-object interactions (HOI). While WSOD shows lower performance than full supervision, we mainly focus on HOI as the main context which can strongly supervise complex semantics in images. Therefore, we propose a novel module called RRPN (relational region proposal network) which outputs an object-localizing attention map only with human poses and action verbs. In the source domain, we fully train an object detector and the RRPN with full supervision of HOI. With transferred knowledge about localization map from the trained RRPN, a new object detector can learn unseen objects with weak verbal supervision of HOI without bounding box annotations in the target domain. Because the RRPN is designed as an add-on type, we can apply it not only to the object detection but also to other domains such as semantic segmentation. The experimental results on HICO-DET dataset show the possibility that the proposed method can be a cheap alternative for the current supervised object detection paradigm. Moreover, qualitative results demonstrate that our model can properly localize unseen objects on HICO-DET and V-COCO datasets.Comment: AAAI 2020 Oral Camera Read

On the Powerfulness of Textual Outlier Exposure for Visual OoD Detection

Successful detection of Out-of-Distribution (OoD) data is becoming increasingly important to ensure safe deployment of neural networks. One of the main challenges in OoD detection is that neural networks output overconfident predictions on OoD data, make it difficult to determine OoD-ness of data solely based on their predictions. Outlier exposure addresses this issue by introducing an additional loss that encourages low-confidence predictions on OoD data during training. While outlier exposure has shown promising potential in improving OoD detection performance, all previous studies on outlier exposure have been limited to utilizing visual outliers. Drawing inspiration from the recent advancements in vision-language pre-training, this paper venture out to the uncharted territory of textual outlier exposure. First, we uncover the benefits of using textual outliers by replacing real or virtual outliers in the image-domain with textual equivalents. Then, we propose various ways of generating preferable textual outliers. Our extensive experiments demonstrate that generated textual outliers achieve competitive performance on large-scale OoD and hard OoD benchmarks. Furthermore, we conduct empirical analyses of textual outliers to provide primary criteria for designing advantageous textual outliers: near-distribution, descriptiveness, and inclusion of visual semantics.Comment: Accepted by NeurIPS 202