Essays on Macroeconomics

University of Minnesota Ph.D. dissertation. August 2015. Major: Economics. Advisor: Varadarajan Chari. 1 computer file (PDF); ix, 81 pages.Productivity Uncertainty, Learning, and Firm Leverage: This paper studies the role of learning about firm productivity in accounting for the variation of firm leverage and capital growth rate with firm age. Using comprehensive firm-level data in China, I find average firm leverage increases then decreases with firm age. Also, I find that new entrants' capital stock grows significantly higher than that of incumbents. I analyze whether information frictions and learning, in addition to financial frictions, are important in rationalizing these two features in the data. My answer is: yes. In the theoretical part, I first present a model with financial frictions that arise from costly equity issuance and default risks in a full information environment. I then extend the model to include information frictions and learning where firms learn about their productivity through production. I calibrate both models to match salient features in the China data, including the difference in capital growth rate between new entrants and incumbents. I find that the model with learning accounts well for the hump-pattern of the age-leverage profile in the data, whereas the model with full information overpredicts the leverage of young firms. When parametrized to match the leverage-age pattern, the model with full information underestimates the capital growth rate of new entrants relative to the incumbents. Lastly I show in a counterfactual exercise that equity issuance costs are a key source of financial friction that shapes the hump-shaped age-leverage pattern in the model with learning

Robust nonparametric quantification of clustering density of molecules in single-molecule localization microscopy

We report a robust nonparametric descriptor,&nbsp;J&prime;(r), for quantifying the density of clustering molecules in single-molecule localization microscopy.&nbsp;J&prime;(r), based on nearest neighbor distribution functions, does not require any parameter as an input for analyzing point patterns. We show that&nbsp;J&prime;(r) displays a valley shape in the presence of clusters of molecules, and the characteristics of the valley reliably report the clustering features in the data. Most importantly, the position of the&nbsp;J&prime;(r) valley () depends exclusively on the density of clustering molecules (&rho;c). Therefore, it is ideal for direct estimation of the clustering density of molecules in single-molecule localization microscopy. As an example, this descriptor was applied to estimate the clustering density of&nbsp;ptsG&nbsp;mRNA in&nbsp;E. coli&nbsp;bacteria.</p

A Study on the Practice of Folk Dance Culture in Kindergarten

China is characterized by a long history of development and a rich and colorful national culture. Folk dance culture shines in the field of excellent traditional culture. However, the progress of social development has led to the gradual extinction of folk dance. Preschool education, as the beginning stage of education in a person’s life, is a favorable time to embrace folk dance culture. By integrating folk dance into the stage of preschool education, it is conducive to carry forward the traditional culture of the nation and fostering national self-confidence and national pride in children while passing on the excellent traditional culture. Therefore, it is urgent to explore the path of combining folk dance culture with early childhood education

MotionGPT: Human Motion as a Foreign Language

Though the advancement of pre-trained large language models unfolds, the exploration of building a unified model for language and other multi-modal data, such as motion, remains challenging and untouched so far. Fortunately, human motion displays a semantic coupling akin to human language, often perceived as a form of body language. By fusing language data with large-scale motion models, motion-language pre-training that can enhance the performance of motion-related tasks becomes feasible. Driven by this insight, we propose MotionGPT, a unified, versatile, and user-friendly motion-language model to handle multiple motion-relevant tasks. Specifically, we employ the discrete vector quantization for human motion and transfer 3D motion into motion tokens, similar to the generation process of word tokens. Building upon this "motion vocabulary", we perform language modeling on both motion and text in a unified manner, treating human motion as a specific language. Moreover, inspired by prompt learning, we pre-train MotionGPT with a mixture of motion-language data and fine-tune it on prompt-based question-and-answer tasks. Extensive experiments demonstrate that MotionGPT achieves state-of-the-art performances on multiple motion tasks including text-driven motion generation, motion captioning, motion prediction, and motion in-between.Comment: Project Page: https://github.com/OpenMotionLab/MotionGP

Mechanism of Qingchang Suppository on repairing the intestinal mucosal barrier in ulcerative colitis

Ulcerative colitis (UC) is a refractory inflammatory bowel disease, and the outcomes of conventional therapies of UC, including 5-aminosalicylic acid, glucocorticoids, immunosuppressants, and biological agents, are not satisfied with patients and physicians with regard to adverse reactions and financial burden. The abnormality of the intestinal mucosal barrier in the pathogenesis of UC was verified. Qingchang Suppository (QCS) is an herbal preparation and is effective in treating ulcerative proctitis. The mechanism of QCS and its active ingredients have not been concluded especially in mucosal healing. This review elucidated the potential mechanism of QCS from the intestinal mucosal barrier perspective to help exploring future QCS research directions

Stoichiometry and stable isotopes of plants and their response to environmental factors in boreal peatland, Northeast China

The alterations of plant composition and diversity pose a threat to the stability of the carbon pool in boreal peatland under climate change. We collected the samples of three plant functional types (deciduous shrubs, evergreen shrubs, and sedge) in seven permafrost peatlands of the Great Hing’an Mountains, China, and measured the properties of total carbon (TC), nitrogen (TN), and phosphorus (TP), their stoichiometric ratios (C:N, C:P, and N:P), and the stable isotope values (δ13C and δ15N) of six tissues (ranging from leaves to roots). For TC, TN, and TP, the contents had an average of 470.69 ± 1.56, 8.03 ± 0.23, and 1.71 ± 0.61 mg·g−1, respectively. TC contents of sedge were lower than those of shrubs for the whole plant. The allocations of N and P to shrub leaves were higher than to stems and roots. There was a similar trend of TN and TP contents, and stoichiometric ratios from leaves to roots between deciduous shrubs and evergreen shrubs. Shrubs and sedge have similar C: N in leaves and fine roots, while leaves of sedge C:P and N:P ratios were higher than shrubs, mainly showed that sedge is N and P co-limitation and shrubs are N limitation. The values of δ13C and δ15N were significantly higher in leaves and roots of sedge than those of shrubs, which means shrubs have higher nutrient acquisition strategies. These results support the shrubs are expanding in the boreal peatland under climate warming through nutrient competition. TC contents of all deciduous shrubs and sedge tissues were positively linear correlated to MAT and the values of δ13C and δ15N in sedge had significant relationships with MAT and MAP. Our results imply warming can increase plant photosynthesis in boreal peatland, and sedge was more sensitive to climate change. These findings would be helpful to understanding the responses of different plant tissues to climate changes in permafrost peatland

Executing your Commands via Motion Diffusion in Latent Space

We study a challenging task, conditional human motion generation, which produces plausible human motion sequences according to various conditional inputs, such as action classes or textual descriptors. Since human motions are highly diverse and have a property of quite different distribution from conditional modalities, such as textual descriptors in natural languages, it is hard to learn a probabilistic mapping from the desired conditional modality to the human motion sequences. Besides, the raw motion data from the motion capture system might be redundant in sequences and contain noises; directly modeling the joint distribution over the raw motion sequences and conditional modalities would need a heavy computational overhead and might result in artifacts introduced by the captured noises. To learn a better representation of the various human motion sequences, we first design a powerful Variational AutoEncoder (VAE) and arrive at a representative and low-dimensional latent code for a human motion sequence. Then, instead of using a diffusion model to establish the connections between the raw motion sequences and the conditional inputs, we perform a diffusion process on the motion latent space. Our proposed Motion Latent-based Diffusion model (MLD) could produce vivid motion sequences conforming to the given conditional inputs and substantially reduce the computational overhead in both the training and inference stages. Extensive experiments on various human motion generation tasks demonstrate that our MLD achieves significant improvements over the state-of-the-art methods among extensive human motion generation tasks, with two orders of magnitude faster than previous diffusion models on raw motion sequences.Comment: 18 pages, 11 figures, conferenc

Salient Region Detection by UFO: Uniqueness, Focusness and Objectness

The goal of saliency detection is to locate important pix-els or regions in an image which attract humans ’ visual at-tention the most. This is a fundamental task whose output may serve as the basis for further computer vision tasks like segmentation, resizing, tracking and so forth. In this paper we propose a novel salient region detec-tion algorithm by integrating three important visual cues namely uniqueness, focusness and objectness (UFO). In particular, uniqueness captures the appearance-derived vi-sual contrast; focusness reflects the fact that salient regions are often photographed in focus; and objectness helps keep completeness of detected salient regions. While uniqueness has been used for saliency detection for long, it is new to integrate focusness and objectness for this purpose. In fac-t, focusness and objectness both provide important salien-cy information complementary of uniqueness. In our ex-periments using public benchmark datasets, we show that, even with a simple pixel level combination of the three com-ponents, the proposed approach yields significant improve-ment compared with previously reported methods. 1