Self-Contrastive Learning: Single-viewed Supervised Contrastive Framework using Sub-network

Contrastive loss has significantly improved performance in supervised classification tasks by using a multi-viewed framework that leverages augmentation and label information. The augmentation enables contrast with another view of a single image but enlarges training time and memory usage. To exploit the strength of multi-views while avoiding the high computation cost, we introduce a multi-exit architecture that outputs multiple features of a single image in a single-viewed framework. To this end, we propose Self-Contrastive (SelfCon) learning, which self-contrasts within multiple outputs from the different levels of a single network. The multi-exit architecture efficiently replaces multi-augmented images and leverages various information from different layers of a network. We demonstrate that SelfCon learning improves the classification performance of the encoder network, and empirically analyze its advantages in terms of the single-view and the sub-network. Furthermore, we provide theoretical evidence of the performance increase based on the mutual information bound. For ImageNet classification on ResNet-50, SelfCon improves accuracy by +0.6% with 59% memory and 48% time of Supervised Contrastive learning, and a simple ensemble of multi-exit outputs boosts performance up to +1.5%. Our code is available at https://github.com/raymin0223/self-contrastive-learning.Comment: AAAI 202

The Impact of Impervious Surface on Water Quality and Its Threshold in Korea

The change in the impervious-pervious balance has significantly altered the stream water quality, and thus the threshold of the impervious surface area in the watershed has been an active research topic for many years. The objective of this study is to verify the correlation between impervious surfaces and water quality and to determine the threshold of the percentage of the impervious surface area (PISA) for diagnosing the severity of future stream water quality problems in the watershed as well as regulating the PISA in Korea. Statistical results indicated that the PISA is a suitable indicator of water quality at the watershed scale and can illustrate the water quality problems caused by the impervious surface. In addition, the results from this study suggest that controlling the PISA within about 10% in watersheds is a fundamental strategy to mitigate the degradation of water quality

Analysis of Drought Intensity and Trends Using the Modified SPEI in South Korea from 1981 to 2010

The aim of this study is to analyze the characteristics of drought, such as intensity and trends, based on SPEI (Standardized Precipitation Evapotranspiration Index) at 8 stations in South Korea from 1981 to 2010. The traditional SPEI is based on the Thornthwaite equation for estimating evapotranspiration; SPEI_th. However, a standard of agricultural water management in Korea suggests the FAO Penman-Monteith equation; SPEI_pm. Therefore, we analyzed the intensity, variability, and trends of drought using SPEI_th and SPEI_pm, respectively, and compared the results. SPEI_pm showed slightly more intensive drought rather than SPEI_th except for Chuncheon and Gwangju. In 5 stations—excluding Cheoncheon, Gwangju and Jinju—the cumulative probability that SPEI_pm was below −1.5 was significantly increased from 1981–1995 to 1996–2010. In addition, the northwest and southwest regions had higher intensity of 1-month droughts, and the central and southwest regions had a higher intensity of 3-month droughts. According to the Mann–Kendall test, there was a decreasing trend of 1-month SPEI during the fall season and 3-month SPEI during winter season

Assessment of the Impact of Climate Change on Drought Characteristics in the Hwanghae Plain, North Korea Using Time Series SPI and SPEI: 1981–2100

North Korea is a food-deficit nation in which climate change could have a significant impact on drought. We analyzed drought characteristics in the Hwanghae Plain, North Korea using both the multiple timescales of the standardized precipitation index (SPI) and the standardized precipitation evapotranspiration index (SPEI) from 1981 to 2100. The probability of non-exceedance for a one-month SPEI below −1.0 was only 1.1% in the spring season of 1995 but increased to 24.4% in 2085. The SPEI for a ten-year return period varied from −0.6 to −0.9 in 1995 and decreased to −1.18 in 2025. The results indicate that severe drought is more likely to occur in future as a result of climate change. The seasonal drought conditions were also significantly influenced by climate change. The largest decrease in the SPEI occurred in late spring and early summer, both of which are important for rice growth. Drought characteristics include severity, duration, and intensity. Therefore, we applied the time series of SPIs and SPEIs to the runs theory and found that the drought intensity identified by one-month SPEIs in 1995 was at a level of 1.21, which reached 1.39 in 2085, implying that climate change will intensify drought in the future

Assessing Sensitivity of Paddy Rice to Climate Change in South Korea

Paddy rice constitutes a staple crop in Korea. This study conducted sensitivity analysis to evaluate the vulnerability of paddy rice to future climate change, and compared temporal and regional characteristics to classify regions with unfavorable water balances. Rainfall Effectiveness Index for Paddy fields (REIP), the ratio of effective rainfall and consumptive use, was used as a sensitivity index. Weather data from 1971 to 2010 and future climate change scenarios Representative Concentration Pathways (RCP) 4.5 and 8.5 were used to evaluate the sensitivity. Results showed an overall increase in water requirements and consumptive use. The REIP values were small for every period, except the 2040s, 2060s, and 2080s under scenario RCP 4.5, and the 2040s and 2080s under scenario RCP 8.5. Both climate change scenarios showed high sensitivity in regions Jeollabuk-do, Jeollanam-do, and Gyeongnam-do. However, regions Gyeonggi-do, Gangwon-do, and Chungcheongbuk-do had low sensitivity compared to other regions. The REIPs were used to categorize sensitivity into four types: low consumption–water rich, low consumption–water poor, high consumption–water rich, and high consumption–water poor. The Gangwon-do region had the highest number of regions that changed from the low consumption–water rich category to the high consumption-water poor category, making it a priority for measures to improve its adaptive capacity for climate change