Corporate Social Responsibility and Corporate Financial Performance: Evidence from Korea

This paper studies the empirical relation between corporate social responsibility (CSR) and corporate financial performance in Korea using a sample of 1122 firm-years during 2002-2008. We measure corporate social responsibility by both an equal-weighted CSR index and a stakeholder-weighted CSR index suggested by Akpinar et al. (2008). Corporate financial performance is measured by ROE, ROA and Tobin’s Q. We find a positive and significant relation between corporate financial performance and the stakeholder-weighted CSR index, but not the equal-weighted CSR index. This finding is robust to alternative model specifications and several additional tests, providing evidence in support of instrumental stakeholder theory.corporate social responsibility; corporate financial performance; KEJI index; instrumental stakeholder theory

Incremental Few-Shot Object Detection via Simple Fine-Tuning Approach

In this paper, we explore incremental few-shot object detection (iFSD), which incrementally learns novel classes using only a few examples without revisiting base classes. Previous iFSD works achieved the desired results by applying meta-learning. However, meta-learning approaches show insufficient performance that is difficult to apply to practical problems. In this light, we propose a simple fine-tuning-based approach, the Incremental Two-stage Fine-tuning Approach (iTFA) for iFSD, which contains three steps: 1) base training using abundant base classes with the class-agnostic box regressor, 2) separation of the RoI feature extractor and classifier into the base and novel class branches for preserving base knowledge, and 3) fine-tuning the novel branch using only a few novel class examples. We evaluate our iTFA on the real-world datasets PASCAL VOC, COCO, and LVIS. iTFA achieves competitive performance in COCO and shows a 30% higher AP accuracy than meta-learning methods in the LVIS dataset. Experimental results show the effectiveness and applicability of our proposed method.Comment: Accepted to ICRA 202

COMPARISON OF THE RISK FACTORS OF KOREAN ADOLESCENT SUICIDE RESIDING IN HIGH SUICIDAL REGIONS VERSUS THOSE IN LOW SUICIDAL REGIONS

Background: The suicide rate of the youth in South Korea has been increasing, and suicide of the youth still has been the most common cause of death since 2007. We aimed to determine the trends and the regional risk factors of youth suicide in South Korea from 2001 to 2010. Subjects and Methods: We used the data from the National Statistical Office to calculate the standardized suicide rates and various regional data including population census, employment, and labor. To calculate the effect of individual risk factors, we used the data from the fourth Korean Youth Risk Behavior Web-based Survey (KYRBWS-VI). Conditional autoregressive model for regional standardized mortality ratio (SMR) using inter-regional spatial information was fitted. Results: Suicide rates of adolescents aged 12 to 18 was from 3.5 per 100,000 people in 2001 and 5.3 per 100,000 in 2010. There were no significant gender difference in suicide rates, however, the number of suicides among adolescents aged 15-18 accounted for four times than those of adolescents ages 12-14. High proportion of late adolescents, higher number of recipients of national basic livelihood, and higher number of adolescents who treated with depression were related to elevated suicide rate of adolescent. Total sleep time of adolescents and regional unemployment rate were negatively associated with the suicide risk of respective regions. Conclusions: Age distribution, economic status, total sleep time, and the number of adolescent patients with depression were different between those in low and in high adolescent suicidal regions in Korea. Our findings suggest that preferential appliance of adolescent suicide prevention program for regions by considering those factors may be important steps to reduce adolescent suicide in Korea

Description of the Diadegma fenestrale (Hymenoptera: Ichneumonidae: Campopleginae) Attacking the Potato Tuber Moth, Phthorimaea operculella (Lep.: Gelechiidae) New to Korea

Diadegma fenestrale is known as a parasitoid of the potato tuber moth, Phthorimaea operculella. The potato tuber moth, Phthorimaea operculella (Zeller) is one of the most destructive pest of potatoes. Also, we found this species attacking the diamondback moth, Plutella xylostella (Lepidoptera: Plutellidae). Ratio of parasitism is 20-30% and cocoon of lepidopteran was parasitic ichneumonid species after 3 days. This species and the genus Diadegma are recorded for the first time from Korea. In this paper, description of the parasitoid and photographs of the diagnostic characteristics are provided

Observation and analysis of low temperature leak characteristics of the O-ring for hydrogen electric vehicles

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Image-Object-Specific Prompt Learning for Few-Shot Class-Incremental Learning

While many FSCIL studies have been undertaken, achieving satisfactory performance, especially during incremental sessions, has remained challenging. One prominent challenge is that the encoder, trained with an ample base session training set, often underperforms in incremental sessions. In this study, we introduce a novel training framework for FSCIL, capitalizing on the generalizability of the Contrastive Language-Image Pre-training (CLIP) model to unseen classes. We achieve this by formulating image-object-specific (IOS) classifiers for the input images. Here, an IOS classifier refers to one that targets specific attributes (like wings or wheels) of class objects rather than the image's background. To create these IOS classifiers, we encode a bias prompt into the classifiers using our specially designed module, which harnesses key-prompt pairs to pinpoint the IOS features of classes in each session. From an FSCIL standpoint, our framework is structured to retain previous knowledge and swiftly adapt to new sessions without forgetting or overfitting. This considers the updatability of modules in each session and some tricks empirically found for fast convergence. Our approach consistently demonstrates superior performance compared to state-of-the-art methods across the miniImageNet, CIFAR100, and CUB200 datasets. Further, we provide additional experiments to validate our learned model's ability to achieve IOS classifiers. We also conduct ablation studies to analyze the impact of each module within the architecture.Comment: 8 pages, 4 figures, 4 table