Contentious politics in protracted transition and the dynamics of actors: an analysis of South Korean movement history and party politics

Twentieth century has seen a significant number of social changes, taking in different forms of revolution, revolts and protests. Nevertheless, as the world stabilized with the termination of Cold War, contention also seemed to have died down. Dominating theories concluded with generalizations that contentions are inevitable process of social change; it comes and goes. South Korea, on the other hand, remains an anomaly due to contentious actors’ persisting influence in the society. In reality, contention does not exist in isolation from the society, but arises from the very soil of it. South Korea actors, the institutions and parties reflecting contentious identity attests its protracted existence beyond the contentious episodes. I argue that contentious politics is not an isolated event that belongs in the transitionary period, but is capable of creating a continuously interacting variable in the society. Thus, in the case of South Korea and its protracted democratization, contention needs to be understood as an organic product of South Korean history that continues to influence the contentious identity to fulfill their self-perceived historical duty of achieving a legitimate government

BayesDLL: Bayesian Deep Learning Library

We release a new Bayesian neural network library for PyTorch for large-scale deep networks. Our library implements mainstream approximate Bayesian inference algorithms: variational inference, MC-dropout, stochastic-gradient MCMC, and Laplace approximation. The main differences from other existing Bayesian neural network libraries are as follows: 1) Our library can deal with very large-scale deep networks including Vision Transformers (ViTs). 2) We need virtually zero code modifications for users (e.g., the backbone network definition codes do not neet to be modified at all). 3) Our library also allows the pre-trained model weights to serve as a prior mean, which is very useful for performing Bayesian inference with the large-scale foundation models like ViTs that are hard to optimise from scratch with the downstream data alone. Our code is publicly available at: \url{https://github.com/SamsungLabs/BayesDLL}\footnote{A mirror repository is also available at: \url{https://github.com/minyoungkim21/BayesDLL}.}

First record of the family Prodoxidae (Lepidoptera: Adeloidea), Lampronia flavimitrella (Hübner), reported from Korea

AbstractThe family Prodoxidae is recorded for the first time from Korea, reporting Lampronia flavimitrella (Hübner) which was collected at Jeju-do Island. Redescription of the adult is given, with images of adult and male genitalia

Artificial Neural Network estimation of soil erosion and nutrient concentrations in runoff from land application areas

The transport of sediment and nutrients from land application areas is an environmental concern. New methods are needed for estimating soil and nutrient concentrations of runoff from cropland areas on which manure is applied. Artificial Neural Networks (ANNs) trained with a backpropagation (BP) algorithm were used to estimate soil erosion, dissolved P (DP) and NH4–N concentrations of runoff from a land application site near Lincoln, Nebraska, USA. Simulation results from ANN-derived models showed that the amount of soil eroded is positively correlated with rainfall and runoff. In addition, concentrations of DP and NH4–N in overland flow were related to measurements of runoff, EC and pH. Coefficient of determination values (R2) relating predicted versus measured estimates of soil erosion, DP, and NH4–N were 0.62, 0.72 and 0.92, respectively. The ANN models derived from measurements of runoff, electrical conductivity (EC) and pH provided reliable estimates of DP and NH4–N concentrations in runoff

Vulnerability Clustering and other Machine Learning Applications of Semantic Vulnerability Embeddings

Cyber-security vulnerabilities are usually published in form of short natural language descriptions (e.g., in form of MITRE's CVE list) that over time are further manually enriched with labels such as those defined by the Common Vulnerability Scoring System (CVSS). In the Vulnerability AI (Analytics and Intelligence) project, we investigated different types of semantic vulnerability embeddings based on natural language processing (NLP) techniques to obtain a concise representation of the vulnerability space. We also evaluated their use as a foundation for machine learning applications that can support cyber-security researchers and analysts in risk assessment and other related activities. The particular applications we explored and briefly summarize in this report are clustering, classification, and visualization, as well as a new logic-based approach to evaluate theories about the vulnerability space.Comment: 27 pages, 13 figure