802 research outputs found
The Very Dark Side of Internal Capital Markets: Evidence from Diversified Business Groups in Korea
This paper examines the capital allocation within Korean chaebol firms during the period from 1991 to 2000. We find strong evidence that, during the pre-Asian financial crisis period in the early 1990's, poorly performing firms with less investment opportunities invest more than well-performing firms with better growth opportunities. We also find the evidence of cross-subsidization among firms in the same chaebol group during the pre-crisis period. It appears that the existence of the "dark" side of internal capital markets explains most part of this striking phenomenon where "tunneling" practice has been common during the pre-crisis period. However, the inefficient capital allocation seems to disappear after the crisis as banks gain more power and market disciplines inefficient chaebol firms.
Families in the household registers of Seventeenth-century Korea : capital, urban and rural areas
Altres ajuts: NRF-2019S1A5B5A07106883Because of the Japanese (1592-1598), and Manchu (1627, 1636-1637), invasions, the seventeenth century was a turning point in the Neo-Confucian transformation of Chosลn dynasty. Changes and continuities in Korean society and families can be seen in household registers published in the seventeenth century. Occupational records and family structures from the top to the bottom of society show that social hierarchies and governmental systems were well preserved even after the invasions. This study also highlights the value of household registers as a primary historical source for the study of Korean social and family histor
BioCAD: an information fusion platform for bio-network inference and analysis
Background : As systems biology has begun to draw growing attention, bio-network inference and analysis have become more and more important. Though there have been many efforts for bio-network inference, they are still far from practical applications due to too many false inferences and lack of comprehensible interpretation in the biological viewpoints. In order for applying to real problems, they should provide effective inference, reliable validation, rational elucidation, and sufficient extensibility to incorporate various relevant information sources.
Results : We have been developing an information fusion software platform called BioCAD. It is utilizing both of local and global optimization for bio-network inference, text mining techniques for network validation and annotation, and Web services-based workflow techniques. In addition, it includes an effective technique to elucidate network edges by integrating various information sources. This paper presents the architecture of BioCAD and essential modules for bio-network inference and analysis.
Conclusion : BioCAD provides a convenient infrastructure for network inference and network analysis. It automates series of users' processes by providing data preprocessing tools for various formats of data. It also helps inferring more accurate and reliable bio-networks by providing network inference tools which utilize information from distinct sources. And it can be used to analyze and validate the inferred bio-networks using information fusion tools.ope
Breaking the Spurious Causality of Conditional Generation via Fairness Intervention with Corrective Sampling
To capture the relationship between samples and labels, conditional
generative models often inherit spurious correlations from the training
dataset. This can result in label-conditional distributions that are imbalanced
with respect to another latent attribute. To mitigate this issue, which we call
spurious causality of conditional generation, we propose a general two-step
strategy. (a) Fairness Intervention (FI): emphasize the minority samples that
are hard to generate due to the spurious correlation in the training dataset.
(b) Corrective Sampling (CS): explicitly filter the generated samples and
ensure that they follow the desired latent attribute distribution. We have
designed the fairness intervention to work for various degrees of supervision
on the spurious attribute, including unsupervised, weakly-supervised, and
semi-supervised scenarios. Our experimental results demonstrate that FICS can
effectively resolve spurious causality of conditional generation across various
datasets.Comment: TMLR 202
S-CLIP: Semi-supervised Vision-Language Learning using Few Specialist Captions
Vision-language models, such as contrastive language-image pre-training
(CLIP), have demonstrated impressive results in natural image domains. However,
these models often struggle when applied to specialized domains like remote
sensing, and adapting to such domains is challenging due to the limited number
of image-text pairs available for training. To address this, we propose S-CLIP,
a semi-supervised learning method for training CLIP that utilizes additional
unpaired images. S-CLIP employs two pseudo-labeling strategies specifically
designed for contrastive learning and the language modality. The caption-level
pseudo-label is given by a combination of captions of paired images, obtained
by solving an optimal transport problem between unpaired and paired images. The
keyword-level pseudo-label is given by a keyword in the caption of the nearest
paired image, trained through partial label learning that assumes a candidate
set of labels for supervision instead of the exact one. By combining these
objectives, S-CLIP significantly enhances the training of CLIP using only a few
image-text pairs, as demonstrated in various specialist domains, including
remote sensing, fashion, scientific figures, and comics. For instance, S-CLIP
improves CLIP by 10% for zero-shot classification and 4% for image-text
retrieval on the remote sensing benchmark, matching the performance of
supervised CLIP while using three times fewer image-text pairs.Comment: NeurIPS 202
MASKER: Masked Keyword Regularization for Reliable Text Classification
Pre-trained language models have achieved state-of-the-art accuracies on
various text classification tasks, e.g., sentiment analysis, natural language
inference, and semantic textual similarity. However, the reliability of the
fine-tuned text classifiers is an often underlooked performance criterion. For
instance, one may desire a model that can detect out-of-distribution (OOD)
samples (drawn far from training distribution) or be robust against domain
shifts. We claim that one central obstacle to the reliability is the
over-reliance of the model on a limited number of keywords, instead of looking
at the whole context. In particular, we find that (a) OOD samples often contain
in-distribution keywords, while (b) cross-domain samples may not always contain
keywords; over-relying on the keywords can be problematic for both cases. In
light of this observation, we propose a simple yet effective fine-tuning
method, coined masked keyword regularization (MASKER), that facilitates
context-based prediction. MASKER regularizes the model to reconstruct the
keywords from the rest of the words and make low-confidence predictions without
enough context. When applied to various pre-trained language models (e.g.,
BERT, RoBERTa, and ALBERT), we demonstrate that MASKER improves OOD detection
and cross-domain generalization without degrading classification accuracy. Code
is available at https://github.com/alinlab/MASKER.Comment: AAAI 2021. First two authors contributed equall
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