403 research outputs found
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
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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
A Cross-cultural Study of Proximity of Clothing to Self between South Korea and Mongolia
South Korea and Mongolia which countries experienced totally different social and cultural background. The current study, explored how the level of the cultural dimensions, affects the human psychology in relation to using fashion as a tool of representing ˜self\u27, and examined how the level of self-expression through clothing affects the quality of life. The questionnaire was composed based on the Hofstede\u27s Cultural Variability Dimension Scale (Hofstede and Minkov, 2013), the Proximity of Clothing to Self Scale developed by Sontag and Lee (2004), and Quality of Life Scale from study of Lee, et al (2002) for the study. Through exploratory factor analysis, four original factors of PCS were identified. To compare the level of cultural dimensions, each cultural index was calculated using formula suggested by Hofstede and Minkov (2013) and then independent T-test was performed to confirm significance. Multiple regression analysis identified negative relationship between the level of IDV and PCS2 (β=-.28, p\u3c.001) and PCS3 (β=-.19, p\u3c.05) as well as MAS and PCS4 (β=-.15, p\u3c.05) in South Korea; while it found positive relationship between IVR and PCS2 (β=.15, p\u3c.05) and PCS3(β=.21, p\u3c.001) in Mongolia. Further, regression analysis results revealed that PCS1 (β=.28, p\u3c.001), PCS2 (β=.27, p\u3c.001), and PCS4 (β=.20, p\u3c.01) are positively related to QOL in South Korea; and PCS1 (β=.16, p\u3c.01), PCS2 (β=.30, p\u3c.001), PCS3 (β=.22, p\u3c.001) and PCS4 (β=.22, p\u3c.001) are positively related to QOL in Mongolia. The findings of this exploratory study helps explain differences in fashion psychology in relation to the cultural value and the important role of clothing in the quality of human life. These findings together suggest that specific cultural values of a country can affect motives for choosing certain product or brand to express self through fashion. Thus marketers need to be considerate in communicating advertisement message, as self-enhancement through fashion can be motivated by different cultural values
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
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