Distribution-Aware Semantics-Oriented Pseudo-label for Imbalanced Semi-Supervised Learning

The capability of the traditional semi-supervised learning (SSL) methods is far from real-world application since they do not consider (1) class imbalance and (2) class distribution mismatch between labeled and unlabeled data. This paper addresses such a relatively under-explored problem, imbalanced semi-supervised learning, where heavily biased pseudo-labels can harm the model performance. Interestingly, we find that the semantic pseudo-labels from a similarity-based classifier in feature space and the traditional pseudo-labels from the linear classifier show the complementary property. To this end, we propose a general pseudo-labeling framework to address the bias motivated by this observation. The key idea is to class-adaptively blend the semantic pseudo-label to the linear one, depending on the current pseudo-label distribution. Thereby, the increased semantic pseudo-label component suppresses the false positives in the majority classes and vice versa. We term the novel pseudo-labeling framework for imbalanced SSL as Distribution-Aware Semantics-Oriented (DASO) Pseudo-label. Extensive evaluation on CIFAR10/100-LT and STL10-LT shows that DASO consistently outperforms both recently proposed re-balancing methods for label and pseudo-label. Moreover, we demonstrate that typical SSL algorithms can effectively benefit from unlabeled data with DASO, especially when (1) class imbalance and (2) class distribution mismatch exist and even on recent real-world Semi-Aves benchmark.Comment: "Code: https://github.com/ytaek-oh/daso

NICE 2023 Zero-shot Image Captioning Challenge

In this report, we introduce NICE project\footnote{\url{https://nice.lgresearch.ai/}} and share the results and outcomes of NICE challenge 2023. This project is designed to challenge the computer vision community to develop robust image captioning models that advance the state-of-the-art both in terms of accuracy and fairness. Through the challenge, the image captioning models were tested using a new evaluation dataset that includes a large variety of visual concepts from many domains. There was no specific training data provided for the challenge, and therefore the challenge entries were required to adapt to new types of image descriptions that had not been seen during training. This report includes information on the newly proposed NICE dataset, evaluation methods, challenge results, and technical details of top-ranking entries. We expect that the outcomes of the challenge will contribute to the improvement of AI models on various vision-language tasks.Comment: Tech report, project page https://nice.lgresearch.ai

Communication and Team Cohesion Moderate the Relationship Between Transformational Leadership and Athletic Performance

This study was grounded on a previous investigation of the mediating effect of teamwork on the relationship between transformational leadership and performance. The purpose of this study is to enhance theoretical knowledge of transformational leadership. This study enrolled 263 athletes registered with the Korea Sports and Olympic Committee. We assessed transformational leadership, communication, team cohesion, and athletic performance. To analyze the collected data, SPSS 24.0, PROCESS macro (V4.1), and Amos 24.0 were used. There was an interaction effect between transformational leadership and communication on athletic performance, mediated by team cohesion. According to the moderated mediating effect index, team cohesion was a significant moderator of athletic performance. It can be concluded that coaches who possess strong coaching abilities and engage in accurate, positive communication with their athletes can enhance team cohesion, organizational behavior, leading to a positive impact on athletic performance

The Relationship between Exercise Re-Participation Intention Based on the Sports-Socialization Process: YouTube Sports Content Intervention

Few studies have used a quantitative research methodology to examine the socialization process model, and such studies were conducted to verify a new model by intervening in the variables of YouTube sports content. To understand this process, we tested the moderated mediating effect by intervening in YouTube sports content based on the sports socialization process model. We recruited 274 participants from the Jeju Residents’ Jeju Sports Festival, Korea. The PROCESS Macro program was performed to test the research hypotheses. The findings indicate that social support had a significant effect on re-participation intention. Social support had a significant mediation effect on exercise interruption intention, re-participation intention, and exercise performance satisfaction. Furthermore, through the relationship between social support and exercise interruption intention, YouTube sports content showed a significant interaction of re-participation intention in exercise. These results extend sports socialization theory by discovering a new model that explains the relationship between the sports socialization process and YouTube sports content. In addition, it will provide a basis for delivering educational information to the public so that they can recognize the importance of physical activity and exercise skills

Recent Trends in Continuum Modeling of Liquid Crystal Networks: A Mini Review

This work aims to provide a comprehensive review of the continuum models of the phase behaviors of liquid crystal networks (LCNs), novel materials with various engineering applications thanks to their unique composition of polymer and liquid crystal. Two distinct behaviors are primarily considered: soft elasticity and spontaneous deformation found in the material. First, we revisit these characteristic phase behaviors, followed by an introduction of various constitutive models with diverse techniques and fidelities in describing the phase behaviors. We also present finite element models that predict these behaviors, emphasizing the importance of such models in predicting the material's behavior. By disseminating various models essential to understanding the underlying physics of the behavior, we hope to help researchers and engineers harness the material's full potential. Finally, we discuss future research directions necessary to advance our understanding of LCNs further and enable more sophisticated and precise control of their properties. Overall, this review provides a comprehensive understanding of the state-of-the-art techniques and models used to analyze the behavior of LCNs and their potential for various engineering applications

Multiscale Phase Behaviors of Nematic Solids: A Short Review

Nematic liquid crystalline solids are novel smart materials of which mesogenic molecules are incorporated within their polymeric chains via crosslinking. The material exhibits many interesting phase behaviors and is envisaged to be harnessed as a key material of soft responsive structures that are adaptive to their surroundings. These behaviors are originated by intricate interactions between diverse phenomena ranging from molecular interactions, mesoscopic phase transition, and elasticity of macroscale. The modeling and analysis of the behavior, therefore, requires the multiscale point of view in that the vast design space of such material cannot be fully exploited otherwise. In this regard, the multiscale behaviors of the nematic solids are first visited, elucidating qualitative behaviors and research of individual physics. Further, the multiscale analysis approach applied to understand and harness the behaviors of the nematic liquid crystalline solids is then reviewed