Bubbles and Experience: An Experiment with a Steady Inflow of New Traders

We revisit the effect of traders' experience on price bubbles by introducing either one-third or two-thirds steady inflow of new traders in the repeated experimental asset markets. We find that bubbles are not significantly abated by the third repetition of the market with the inflow of new traders. The relative importance of experience to the formation of bubbles depends on the proportion of new traders in the market. Our findings identify a market environment where increased experience is not sufficient to eliminate price bubbles.Bubbles; Asset Markets; Experience; Inflow of Traders

Bubbles and Experience: An Experiment with a Steady Inflow of New Traders

We revisit the effect of traders' experience on price bubbles by introducing either one-third or two-thirds steady inflow of new traders in the repeated experimental asset markets. We find that bubbles are not significantly abated by the third repetition of the market with the inflow of new traders. The relative importance of experience to the formation of bubbles depends on the proportion of new traders in the market. Our findings identify a market environment where experience is not sufficient to eliminate price bubbles.Price bubbles, experience, inflow of new traders, experiments,

Essays in Applied Microeconomics

In this dissertation, I develop empirical methods, built on the recent advances in industrial organization, to study charitable giving and fundraising in the charity market. In the first essay, we propose a multiple discrete choice model with differentiated charitable products and estimate the model using a unique data set of donor lists for the ten largest charitable organizations in Pittsburgh. We find that some private benefits such as invitations to private dinner parties and special events are effective tools for fundraising. Our policy simulations suggest that the composition of private benefits has a potentially large impact on donor behavior. In the second essay, I investigate the determinants of donations to charitable organizations by incorporating their managerial capacity and fundraising productivity. Using data from environmental charities, I find that managerial capacity has a significantly positive impact on raising donations, which demonstrates the long-run benefits of managerial expenses. Fundraising productivity is a charity-specific and serially-correlated unobserved variable that causes an endogeneity problem in the estimation of the donation function. After controlling for the fundraising productivity, the estimated impact from managerial capacity on donations is significantly increased, while the impact from fundraising expenditure is significantly decreased. Finally, after estimating the donation function, I construct a measure of fundraising productivity and show that it is a key factor in explaining the variation of donations, suggesting that policy discussions should account for charities' differences in fundraising productivity and the causes of such differences

PerceptionGPT: Effectively Fusing Visual Perception into LLM

The integration of visual inputs with large language models (LLMs) has led to remarkable advancements in multi-modal capabilities, giving rise to visual large language models (VLLMs). However, effectively harnessing VLLMs for intricate visual perception tasks remains a challenge. In this paper, we present a novel end-to-end framework named PerceptionGPT, which efficiently and effectively equips the VLLMs with visual perception abilities by leveraging the representation power of LLMs' token embedding. Our proposed method treats the token embedding of the LLM as the carrier of spatial information, then leverage lightweight visual task encoders and decoders to perform visual perception tasks (e.g., detection, segmentation). Our approach significantly alleviates the training difficulty suffered by previous approaches that formulate the visual outputs as discrete tokens, and enables achieving superior performance with fewer trainable parameters, less training data and shorted training time. Moreover, as only one token embedding is required to decode the visual outputs, the resulting sequence length during inference is significantly reduced. Consequently, our approach enables accurate and flexible representations, seamless integration of visual perception tasks, and efficient handling of a multiple of visual outputs. We validate the effectiveness and efficiency of our approach through extensive experiments. The results demonstrate significant improvements over previous methods with much fewer trainable parameters and GPU hours, which facilitates future research in enabling LLMs with visual perception abilities

tdCoxSNN: Time-Dependent Cox Survival Neural Network for Continuous-time Dynamic Prediction

The aim of dynamic prediction is to provide individualized risk predictions over time, which are updated as new data become available. In pursuit of constructing a dynamic prediction model for a progressive eye disorder, age-related macular degeneration (AMD), we propose a time-dependent Cox survival neural network (tdCoxSNN) to predict its progression using longitudinal fundus images. tdCoxSNN builds upon the time-dependent Cox model by utilizing a neural network to capture the non-linear effect of time-dependent covariates on the survival outcome. Moreover, by concurrently integrating a convolutional neural network (CNN) with the survival network, tdCoxSNN can directly take longitudinal images as input. We evaluate and compare our proposed method with joint modeling and landmarking approaches through extensive simulations. We applied the proposed approach to two real datasets. One is a large AMD study, the Age-Related Eye Disease Study (AREDS), in which more than 50,000 fundus images were captured over a period of 12 years for more than 4,000 participants. Another is a public dataset of the primary biliary cirrhosis (PBC) disease, where multiple lab tests were longitudinally collected to predict the time-to-liver transplant. Our approach demonstrates commendable predictive performance in both simulation studies and the analysis of the two real datasets

X2^2-VLM: All-In-One Pre-trained Model For Vision-Language Tasks

Vision language pre-training aims to learn alignments between vision and language from a large amount of data. We proposed multi-grained vision language pre-training, a unified approach which can learn vision language alignments in multiple granularity. This paper advances the proposed method by unifying image and video encoding in one model and scaling up the model with large-scale data. We present X$^2$-VLM, a pre-trained VLM with a modular architecture for both image-text tasks and video-text tasks. Experiment results show that X$^2$-VLM performs the best on base and large scale for both image-text and video-text tasks, making a good trade-off between performance and model scale. Moreover, we show that the modular design of X$^2$-VLM results in high transferability for X$^2$-VLM to be utilized in any language or domain. For example, by simply replacing the text encoder with XLM-R, X$^2$-VLM outperforms state-of-the-art multilingual multi-modal pre-trained models without any multilingual pre-training. The code and pre-trained models will be available at github.com/zengyan-97/X2-VLM.Comment: 21 pages, 8 figure

Intelligent Scoliosis Screening and Diagnosis: A Survey

Scoliosis is a three-dimensional spinal deformity, which may lead to abnormal morphologies, such as thoracic deformity, and pelvic tilt. Severe patients may suffer from nerve damage and urinary abnormalities. At present, the number of scoliosis patients in primary and secondary schools has exceeded five million in China, the incidence rate is about 3% to 5% which is growing every year. The research on scoliosis, therefore, has important clinical value. This paper systematically introduces computer-assisted scoliosis screening and diagnosis as well as analyzes the advantages and limitations of different algorithm models in the current issue field. Moreover, the paper also discusses the current development bottlenecks in this field and looks forward to future development trends.Comment: in Chinese languag

The Effect on Long-Chain Fatty Acids in Lucerne Silage with Jujube Powder and \u3cem\u3eLactobacillus plantarum\u3c/em\u3e

The major nutrients of lucerne silage are well documented. However, forages are also an important dietary source of α-linolenic acid (C18:3n-3) and linoleicacid (C18:2n-6) that are biohydrogenated in the rumen, originating a complex pattern of C18 fatty acids (Jenkins et al. 2008). Studies have reported slight effects on the fatty acid (FA) composition of grass silages by the use of additives like formalin, formic acid, or enzymes (Alves et al. 2011) However, there are no studies on the addition of jujube powder in lucerne silage, which has a high sugar content. The effect of Lactobacillus plantarum (LA) on the silage fermentation quality has been frequently observed. Few studies have focussed on long-chain fatty acids in lucerne silage with jujube powder and Lactobacillus plantarum. The objective of this study was to evaluate the effect of the addition of jujube powder and the Lactobacillus plantarum on the long-chain fatty acids (mainly C16-C18) in lucerne silage

A Lactic Acid Bacterium Isolated from Grass in Native Grassland in Northern China

The epiphytic LAB converts sugar into lactic acid during the ensiling process. As a result, the pH is reduced, and the forage is preserved. Therefore, further study of epiphytic LAB species is required, especially the screening of excellent LAB. However, to our knowledge, limited information is available on the epiphytic microflora on grass in native grassland. The present study set out to screen, isolate and identify the LAB from grass silages made in native grass-land in northern China