East Asia's dynamic development model and the Republic of Korea's experiences

No region has been more dynamic in recent years than East Asia. Despite its successful economic development, evaluations of the East Asian development model have often been capricious, shifting from"miracle"to"cronyism."How can we explain East Asia's ups and downs consistently? To respond to this challenge, it is necessary to study the progress of East Asian development and to trace the influence of Asian cultural values. This study mainly focuses on cultural aspects of economic progress and analyzes East Asia's philosophical and historical backgrounds to explain the dynamic process. East Asians believe that balance between opposite but complementary forces, Yin and Yang, will ensure social stability and progress. Through repeated rebalancing to maintain harmony, the society comes to maturity. In traditional East Asian societies, a balance was maintained between Confucianism (Yang) and Taoism, Buddhism, and other philosophies (Yin). In modern societies, the challenge is to balance traditional systems (Yang) and Western style capitalism (Yin). This East Asian development model explains the Republic of Korea's rise, fall, and recovery. Korea was a poor country until the early 1960s, during the time when spiritualism (Yang) dominated. From the 1960s through the 1980s, Korea achieved rapid growth by finding a new balance and moving toward materialism (Yin) from spiritualism (Yang). But the failure to maintain a harmonious balance between cooperative systems and collectivism (Yang) and individualism (Yin) led to major weaknesses in labor and financial markets that contributed significantly to the financial crisis in 1997. As Korea arrived at a new balance by instituting reform programs, the venture-oriented information and communication technology (ICT) industry blossomed and led to a rapid economic recovery. Since 2000, domestic financial scandals and political corruption have emerged as new social issues. Korea's next challenge is to find a new harmonization between morality (Yang) and legal frameworks (Yin).Environmental Economics&Policies,Public Health Promotion,Ethics&Belief Systems,Earth Sciences&GIS,Decentralization,Earth Sciences&GIS,Ethics&Belief Systems,Economic Theory&Research,Environmental Economics&Policies,Health Economics&Finance

SMA observations of the proto brown dwarf candidate SSTB213 J041757

Context. The previously identified source SSTB213 J041757 is a proto brown dwarf candidate in Taurus, which has two possible components A and B. It was found that component B is probably a class 0/I proto brown dwarf associated with an extended envelope. Aims. Studying molecular outflows from young brown dwarfs provides important insight into brown dwarf formation mechanisms, particularly brown dwarfs at the earliest stages such as class 0, I. We therefore conducted a search for molecular outflows from SSTB213 J041757. Methods. We observed SSTB213 J041757 with the Submillimeter Array to search for CO molecular outflow emission from the source. Results. Our CO maps do not show any outflow emission from the proto brown dwarf candidate. Conclusions. The non-detection implies that the molecular outflows from the source are weak; deeper observations are therefore needed to probe the outflows from the source.Comment: 7 pages, 4 figures, accepted for publication in A&

Structure-Aware Dynamic Scheduler for Parallel Machine Learning

Training large machine learning (ML) models with many variables or parameters can take a long time if one employs sequential procedures even with stochastic updates. A natural solution is to turn to distributed computing on a cluster; however, naive, unstructured parallelization of ML algorithms does not usually lead to a proportional speedup and can even result in divergence, because dependencies between model elements can attenuate the computational gains from parallelization and compromise correctness of inference. Recent efforts toward this issue have benefited from exploiting the static, a priori block structures residing in ML algorithms. In this paper, we take this path further by exploring the dynamic block structures and workloads therein present during ML program execution, which offers new opportunities for improving convergence, correctness, and load balancing in distributed ML. We propose and showcase a general-purpose scheduler, STRADS, for coordinating distributed updates in ML algorithms, which harnesses the aforementioned opportunities in a systematic way. We provide theoretical guarantees for our scheduler, and demonstrate its efficacy versus static block structures on Lasso and Matrix Factorization