The impacts of new technologies on physical activities: Based on fitness app use and fitness social media postings

Focusing on fitness app use and social context of fitness postings on social media, this study examined the implications of mHealth technologies use for fitness. This study explored descriptive information about respondents’ use of fitness apps such as self-monitoring, self-regulation, social facilitators, and rewards. Furthermore, respondents’ fitness posting experience was also explored. For respondents who saw others’ fitness posts, this study examined how viewers’ social comparison on fitness postings (upward and downward) related to their physical activity (PA) self-efficacy, motivation, and participation. For those who posted about their fitness information on social media, this study investigated fitness posters’ ways of self-presentation related to receiving supportive feedback, and how supportive feedback related to fitness posters’ PA motivation and participation. This study recruited fitness app users from a crowdsourcing internet marketplace. Quantitative data analysis examined the role of social comparison, self-presentation, and supportive feedback in respondents’ PA self-efficacy, motivation, and participation. The results revealed that people mostly used the fitness apps for physical activity-related self-monitoring and self-regulation. For those who engaged in upward social comparison tended to have more self-efficacy for PA, PA motivation, and therefore participated more in PA. Both positive and negative self-presenters received more supportive feedback from others. The more supportive feedback fitness posters received, the more self-efficacy for PA they had. The more self-efficacy for PA fitness posters had, the more PA motivation they had. The results also showed that people received more esteem support and emotional support from others when they positively presented their fitness on social media. Fitness posters with negative self-presentation received more emotional support and informational support

Sustainability of the PyeongChang 2018 Winter Olympics

The main aim of this study is to investigate how to deliver a sustainable legacy from the PyeongChang 2018 Winter Olympic Games. The research sets forth to look into the case of the 2018 Olympics compared to SMEs held in other states. The overall goal of this research is to find the ideal model for the sustainable legacy, which could be adopted after hosting the SME in Korea. The three research questions of the study are: 1) What legacy strategies did the two previous Olympics in Vancouver and London use to develop sustainability?; 2) What are the discrepancies in the plan for a sustainable legacy of the PyeongChang Olympics between the bid proposal and actual realisation? Why? and 3) What are the factors to consider for sustainable post-SME legacy in Korea?. To answer the research questions, the specific methods used to collect the data are semistructure interviews and document analysis; 10 interviews were conducted with various stakeholder of PyeongChang Olympic Games. In addition, multiple case studies are employed as a triangulation technique to enhance the reliability and validation of this study. There are three cases: the 2010 Vancouver Winter Olympic Games, the 2012 London Summer Olympic Games and the 2018 PyeongChang Winter Olympic Games. The data collection identified all factors of the sustainability of the last three Olympics were aggregated to establish a new sustainable legacy strategy for potential sports megaevents in Korea in terms of Triple Bottom Line framework: 1) definite plans with stakeholder consultation in advance for economic, social and environmental sustainability; 2) active communication among stakeholders related to sports mega-events for economic and social sustainability; 3) efficient governance for sports events for economic, social and environmental sustainability and 4) strict management and regulation for environmental legacy for environmental sustainability

Exploration of microtopia based on issues of surveillance in the Kamppi shopping center

This thesis involves four main categories: an exploration of the term ‘microtopia’ and discussion of related works, research on Kamppi Shopping Center as a specific spot for this thesis, research into surveillance issues as a means of exploring microtopia, and, lastly, the installation work I planed, TWO BOXES. ‘Microtopia’ is primarily a term mentioned in a book, “Relational Aesthetics,” written by French art critic Nicolas Bourriaud. The main meaning of the word “microtopia” is that an artist should arrange ideal but realistic moments instead of seeking imaginary and remote utopian realities, which is the strongest notion in this thesis. In addition, because microtopia is based on ‘the public sphere,’ I focused on the public context by studying popular spots in Helsinki. Through this field work, I be- came very interested in ‘Surveillance issues in public space’ because of its ironic characteristics: although the word ‘public’ means ‘general and for all people,’ in reality there are many guards who monitor the flow of people in metro, bus, and train stations, and even in public squares. In that sense, I wish to address the question, ‘Who has the access to enter and who does not?’ I aimed to explore microtopia based on surveillance issues; that is to say, in this thesis, I attempt to identify the ideal level of surveillance. Based on the two keywords ‘microtopia’ and ‘surveillance,’ a partici- patory installation, TWO BOXES, was planned in Kamppi Shopping Center in order to explore the concept of microtopia. I will illustrate how the concept and details of TWO BOXES arose and dis- cuss in detail its implementation in Kamppi Shopping center. In the conclusion, I share the feed- back that I received from participants, discuss the drawbacks of the project, and give ideas for future work based on the findings. In order to avoid confusion on the part of readers, I should mention that, although my major is Spatial Design, which is largely related to architecture and interior design, I am actually more in- terested in conceptual and intangible spaces than physically touchable and logical ones. For this reason, this thesis is written in a conceptual and artistic way. Furthermore, for the same reason, I want to present the ‘moving and changing’ characteristics of space in terms of social interventions

Proxy Anchor-based Unsupervised Learning for Continuous Generalized Category Discovery

Recent advances in deep learning have significantly improved the performance of various computer vision applications. However, discovering novel categories in an incremental learning scenario remains a challenging problem due to the lack of prior knowledge about the number and nature of new categories. Existing methods for novel category discovery are limited by their reliance on labeled datasets and prior knowledge about the number of novel categories and the proportion of novel samples in the batch. To address the limitations and more accurately reflect real-world scenarios, in this paper, we propose a novel unsupervised class incremental learning approach for discovering novel categories on unlabeled sets without prior knowledge. The proposed method fine-tunes the feature extractor and proxy anchors on labeled sets, then splits samples into old and novel categories and clusters on the unlabeled dataset. Furthermore, the proxy anchors-based exemplar generates representative category vectors to mitigate catastrophic forgetting. Experimental results demonstrate that our proposed approach outperforms the state-of-the-art methods on fine-grained datasets under real-world scenarios.Comment: Accepted to ICCV 202

ContextMix: A context-aware data augmentation method for industrial visual inspection systems

While deep neural networks have achieved remarkable performance, data augmentation has emerged as a crucial strategy to mitigate overfitting and enhance network performance. These techniques hold particular significance in industrial manufacturing contexts. Recently, image mixing-based methods have been introduced, exhibiting improved performance on public benchmark datasets. However, their application to industrial tasks remains challenging. The manufacturing environment generates massive amounts of unlabeled data on a daily basis, with only a few instances of abnormal data occurrences. This leads to severe data imbalance. Thus, creating well-balanced datasets is not straightforward due to the high costs associated with labeling. Nonetheless, this is a crucial step for enhancing productivity. For this reason, we introduce ContextMix, a method tailored for industrial applications and benchmark datasets. ContextMix generates novel data by resizing entire images and integrating them into other images within the batch. This approach enables our method to learn discriminative features based on varying sizes from resized images and train informative secondary features for object recognition using occluded images. With the minimal additional computation cost of image resizing, ContextMix enhances performance compared to existing augmentation techniques. We evaluate its effectiveness across classification, detection, and segmentation tasks using various network architectures on public benchmark datasets. Our proposed method demonstrates improved results across a range of robustness tasks. Its efficacy in real industrial environments is particularly noteworthy, as demonstrated using the passive component dataset.Comment: Accepted to EAA

The impact of South Korea’s new drug-pricing policy on market competition among off-patent drugs

A new pricing policy was introduced in Korea in April 2012 with the aim of strengthening competition among off-patent drugs by eliminating price gaps between originators and generics. Examine the effect of newly implemented pricing policy. Retrospectively examining the effects through extracting from the National Health Insurance claims data a 30-month panel dataset (January 2011 - June 2013) containing consumption data in four major therapeutic classes (antihypertensives, lipid-lowering drugs, antiulcerants and antidepressants). Proxies for market competition were examined before and after the policy. The new pricing policy didn’t enhance competition among off-patent drugs. In fact, price dispersion significantly decreased as opposed to the expected change. Originator-to-generic utilization increased to 6.12 times (p=0.000) after the new policy. The new pricing policy made no impact on competition among off-patent drugs. Competition in the off-patent market cannot be enhanced unless both supply and demand-side measures are coordinated

AI-KD: Adversarial learning and Implicit regularization for self-Knowledge Distillation

We present a novel adversarial penalized self-knowledge distillation method, named adversarial learning and implicit regularization for self-knowledge distillation (AI-KD), which regularizes the training procedure by adversarial learning and implicit distillations. Our model not only distills the deterministic and progressive knowledge which are from the pre-trained and previous epoch predictive probabilities but also transfers the knowledge of the deterministic predictive distributions using adversarial learning. The motivation is that the self-knowledge distillation methods regularize the predictive probabilities with soft targets, but the exact distributions may be hard to predict. Our method deploys a discriminator to distinguish the distributions between the pre-trained and student models while the student model is trained to fool the discriminator in the trained procedure. Thus, the student model not only can learn the pre-trained model's predictive probabilities but also align the distributions between the pre-trained and student models. We demonstrate the effectiveness of the proposed method with network architectures on multiple datasets and show the proposed method achieves better performance than state-of-the-art methods.Comment: 12 pages, 7 figure

Estimation of the spatial profile of neuromodulation and the temporal latency in motor responses induced by focused ultrasound brain stimulation

This study investigates the spatial profile and the temporal latency of the brain stimulation induced by the transcranial application of pulsed focused ultrasound (FUS). The site of neuromodulation was detected using 2-deoxy-2-[18F]fluoro-d-glucose PET immediately after FUS sonication on the unilateral thalamic area of Sprague–Dawley rats. The latency of the stimulation was estimated by measuring the time taken from the onset of the stimulation of the appropriate brain motor area to the corresponding tail motor response. The brain area showing elevated glucose uptake from the PET image was much smaller (56±10% in diameter, 24±6% in length) than the size of the acoustic focus, which is conventionally defined by the full-width at half-maximum of the acoustic intensity field. The spatial dimension of the FUS-mediated neuromodulatory area was more localized, approximated to be full-width at 90%-maximum of the acoustic intensity field. In addition, the time delay of motor responses elicited by the FUS sonication was 171±63 (SD) ms from the onset of sonication. When compared with latencies of other nonultrasonic neurostimulation techniques, the longer time delay associated with FUS-mediated motor responses is suggestive of the nonelectrical modes of neuromodulation, making it a distinctive brain stimulation method

High-throughput preparation of complex multi-scale patterns from block copolymer/homopolymer blend films

A simple, straightforward process for fabricating multi-scale micro-and nanostructured patterns from polystyrene-block-poly(2-vinylpyridine) (PS-b-P2VP)/poly(methyl methacrylate) (PMMA) homopolymer in a preferential solvent for PS and PMMA is demonstrated. When the PS-b-P2VP/PMMA blend films were spin-coated onto a silicon wafer, PS-b-P2VP micellar arrays consisting of a PS corona and a P2VP core were formed, while the PMMA macrodomains were isolated, due to the macrophase separation caused by the incompatibility between block copolymer micelles and PMMA homopolymer during the spin-coating process. With an increase of PMMA composition, the size of PMMA macrodomains increased. Moreover, the P2VP blocks have a strong interaction with a native oxide of the surface of the silicon wafer, so that the P2VP wetting layer was first formed during spin-coating, and PS nanoclusters were observed on the PMMA macrodomains beneath. Whereas when a silicon surface was modified with a PS brush layer, the PS nanoclusters underlying PMMA domains were not formed. The multi-scale patterns prepared from copolymer micelle/homopolymer blend films are used as templates for the fabrication of gold nanoparticle arrays by incorporating the gold precursor into the P2VP chains. The combination of nanostructures prepared from block copolymer micellar arrays and macrostructures induced by incompatibility between the copolymer and the homopolymer leads to the formation of complex, multi-scale surface patterns by a simple casting process.close2