The Effect of Blockholders on Bank Valuation

This paper examines the effect of blockholders on bank valuation. We use two measures of bank valuation, namely Tobin\u27s Q and market to book ratio, and two measures of blockholders, namely  number of blockholders and total ownership of all blockholders. Using a sample of publicly-traded bank holding companies in the U.S. from 1996 to 2001, we find a negative relationship between total ownership of all blockholders and bank valuation, but a positive relationship between number of blockholders and bank valuation

Probabilistic Reduced-Dimensional Vector Autoregressive Modeling for Dynamics Prediction and Reconstruction with Oblique Projections

In this paper, we propose a probabilistic reduced-dimensional vector autoregressive (PredVAR) model with oblique projections. This model partitions the measurement space into a dynamic subspace and a static subspace that do not need to be orthogonal. The partition allows us to apply an oblique projection to extract dynamic latent variables (DLVs) from high-dimensional data with maximized predictability. We develop an alternating iterative PredVAR algorithm that exploits the interaction between updating the latent VAR dynamics and estimating the oblique projection, using expectation maximization (EM) and a statistical constraint. In addition, the noise covariance matrices are estimated as a natural outcome of the EM method. A simulation case study of the nonlinear Lorenz oscillation system illustrates the advantages of the proposed approach over two alternatives

Effect of an equilibrium phase transition on multiphase transport in relativistic heavy ion collisions

The hadronization scheme for parton transport in relativistic heavy ion collisions is considered in detail. It is pointed out that the traditional scheme for particles being freezed out one by one leads to serious problem on unreasonable long lifetime of partons. A collective phase transition following a supercooling is implemented in a simple way. It turns out that the modified model with a sudden phase transition is able to reproduce the experimental longitudinal distributions of final state particles better than the original one does. The encouraging results indicate that equilibrium phase transition should be taken into proper account in parton transport models for relativistic heavy ion collisions

Does the Dirac Cone Exist in Silicene on Metal Substrates?

Absence of the Dirac cone due to a strong band hybridization is revealed to be a common feature for epitaxial silicene on metal substrates according to our first-principles calculations for silicene on Ir, Cu, Mg, Au, Pt, Al, and Ag substrates. The destroyed Dirac cone of silicene, however, can be effectively restored with linear or parabolic dispersion by intercalating alkali metal atoms between silicene and the metal substrates, offering an opportunity to study the intriguing properties of silicene without further transfer of silicene from the metal substrates

Tuning Language Models as Training Data Generators for Augmentation-Enhanced Few-Shot Learning

Recent studies have revealed the intriguing few-shot learning ability of pretrained language models (PLMs): They can quickly adapt to a new task when fine-tuned on a small amount of labeled data formulated as prompts, without requiring abundant task-specific annotations. Despite their promising performance, most existing few-shot approaches that only learn from the small training set still underperform fully supervised training by nontrivial margins. In this work, we study few-shot learning with PLMs from a different perspective: We first tune an autoregressive PLM on the few-shot samples and then use it as a generator to synthesize a large amount of novel training samples which augment the original training set. To encourage the generator to produce label-discriminative samples, we train it via weighted maximum likelihood where the weight of each token is automatically adjusted based on a discriminative meta-learning objective. A classification PLM can then be fine-tuned on both the few-shot and the synthetic samples with regularization for better generalization and stability. Our approach FewGen achieves an overall better result across seven classification tasks of the GLUE benchmark than existing few-shot learning methods, improving no-augmentation methods by 5+ average points, and outperforming augmentation methods by 3+ average points.Comment: Code: https://github.com/yumeng5/FewGe

Hierarchical Topic Mining via Joint Spherical Tree and Text Embedding

Mining a set of meaningful topics organized into a hierarchy is intuitively appealing since topic correlations are ubiquitous in massive text corpora. To account for potential hierarchical topic structures, hierarchical topic models generalize flat topic models by incorporating latent topic hierarchies into their generative modeling process. However, due to their purely unsupervised nature, the learned topic hierarchy often deviates from users' particular needs or interests. To guide the hierarchical topic discovery process with minimal user supervision, we propose a new task, Hierarchical Topic Mining, which takes a category tree described by category names only, and aims to mine a set of representative terms for each category from a text corpus to help a user comprehend his/her interested topics. We develop a novel joint tree and text embedding method along with a principled optimization procedure that allows simultaneous modeling of the category tree structure and the corpus generative process in the spherical space for effective category-representative term discovery. Our comprehensive experiments show that our model, named JoSH, mines a high-quality set of hierarchical topics with high efficiency and benefits weakly-supervised hierarchical text classification tasks.Comment: KDD 2020 Research Track. (Code: https://github.com/yumeng5/JoSH