Two Essays on the Relationship Between Financial Development and Income Distribution

This dissertation consists of two chapters on the relation between financial development, income inequality and poverty. The first chapter examines the relationship between financial deepening and income inequality for 16 countries based on a time series approach. Unlike previous work that focuses on testing the credit channel (private credit and bank asset) through which finance improves inequality, this chapter also investigates the deposit channel (liquid liabilities and bank deposit). The results suggest that the finance-inequality relationships behave differently across countries. Five countries support the reducing-inequality function through the credit channel, whereas three countries are found to support the deposit channel. In India, Japan, Bolivia, Malta and the United States, financial deepening is actually harming the income distribution. By implementing instrumental variable regressions on a sample of 144 countries from 1961 to 2011, the second chapter extends the examination from financial deepening to multiple dimensions of financial development--financial access, efficiency, stability and openness. Evidence shows that, except for financial openness, the development of the financial system can significantly improve the income inequality and poverty in an economy

A Classification Supervised Auto-Encoder Based on Predefined Evenly-Distributed Class Centroids

Classic Autoencoders and variational autoencoders are used to learn complex data distributions, that are built on standard function approximators, such as neural networks, which can be trained by stochastic gradient descent methods. Especially, VAE has shown promise on a lot of complex task. In this paper, a new autoencoder model - classification supervised autoencoder (CSAE) based on predefined evenly-distributed class centroids (PEDCC) is proposed. To carry out the supervised learning for autoencoder, we use PEDCC of latent variables to train the network to ensure the maximization of inter-class distance and the minimization of inner-class distance. Instead of learning mean/variance of latent variables distribution and taking reparameterization of VAE, latent variables of CSAE are directly used to classify and as input of decoder. In addition, a new loss function is proposed to combine the loss function of classification, the loss function of image codec error and the loss function for enhancing subjective quality of decoded image. Based on the basic structure of the universal autoencoder, we realized the comprehensive optimal results of encoding, decoding and classification, and good model generalization performance at the same time. Theoretical advantages are reflected in experimental results.Comment: 17 pages,9 figures, 5 table

Identification of Structural Parameters Based on HHT and NExT

Signal processing approaches are widely used in the field of earthquake engineering, especially in the identification of structural modal parameters. Hilbert-Huang Transformation (HHT) is one new signal processing approach, which can be used to identify the modal frequency, damping ratio, mode shape, even the interlayer stiffness of the shear-type structure, incorporating with Natural Excitation Technique (NExT) method to take information from the response records of the structure. The stiffness of the structure is of great importance to judge the loss of its bearing capacity after earthquake. However, all of modal parameters are required to calculate the stiffness of the structure by use of HHT and NExT, which means that the response records shall contain all of modal information. However, it has been found that the responses of the structure recorded only contain the former order modal information; even it is excited by earthquake. Therefore, it is necessary to found a formula (formulas) to calculate the stiffness only using limited modal parameters. In this paper, the calculation formulas of the interlayer stiffness of shear-type structure are derived by using of the flexibility method, which indicate that all of interlayer stiffnesses could be worked out as long as any one set of modal parameters is obtained. After that, Taking Sheraton-Universal Hotel subjected to North Bridge earthquake in 1994 as an example, HHT and NExT are used to identify its modal parameters, the derived formulas are used to calculate the interlayer stiffnesses, and their applicability and accuracy are verified

A Closer Look at the Self-Verification Abilities of Large Language Models in Logical Reasoning

Logical reasoning has been an ongoing pursuit in the field of AI. Despite significant advancements made by large language models (LLMs), they still struggle with complex logical reasoning problems. To enhance reasoning performance, one promising direction is scalable oversight, which requires LLMs to identify their own errors and then improve by themselves. Various self-verification methods have been proposed in pursuit of this goal. Nevertheless, whether existing models understand their own errors well is still under investigation. In this paper, we take a closer look at the self-verification abilities of LLMs in the context of logical reasoning, focusing on their ability to identify logical fallacies accurately. We introduce a dataset, FALLACIES, containing 232 types of reasoning fallacies categorized in a hierarchical taxonomy. By conducting exhaustive experiments on FALLACIES, we obtain comprehensive and detailed analyses of a series of models on their verification abilities. Our main findings suggest that existing LLMs could struggle to identify fallacious reasoning steps accurately and may fall short of guaranteeing the validity of self-verification methods. Drawing from these observations, we offer suggestions for future research and practical applications of self-verification methods.Comment: work in progres

Acupuncture Treatment for Bortezomib-Induced Peripheral Neuropathy: A Case Report

Peripheral neuropathy is a common and severe dose-limiting side effect of the chemotherapy agent, bortezomib, in multiple myeloma patients. Treatment with narcotics, antidepressants, and anticonvulsants has limited response and potential significant side effects. Acupuncture has been reported to be effective in treating diabetic neuropathy and chemo-induced peripheral neuropathy. There has not been report on the effect of acupuncture in treating bortezomib-induced peripheral neuropathy specifically. Here, we report a successful case of using acupuncture to relieve bortezomib-induced peripheral neuropathy symptoms

A Numerical Study on the Temperature Field of a R290 Hermetic Reciprocating Compressor with Experimental Validation

A numerical model to predict the temperature field in a R290 hermetic reciprocating compressor is presented in this work. The control volume method and the lumped parameter method are used in the simulation. The compressor is divided into 6 control volumes, including the suction muffler, the cylinder, the discharge chamber, the discharge muffler, the discharge pipe and the shell. The system of non-linear equations is formed of the energy balance equations of every control column. The temperature field is derived by solving the equations. To valid the numerical model accurately, temperature experiment has been carried out in 3 same-type hermetic reciprocating compressors using R290 as working fluid. The simulation result shows a good agreement compared with the experiment

RPG-Palm: Realistic Pseudo-data Generation for Palmprint Recognition

Palmprint recently shows great potential in recognition applications as it is a privacy-friendly and stable biometric. However, the lack of large-scale public palmprint datasets limits further research and development of palmprint recognition. In this paper, we propose a novel realistic pseudo-palmprint generation (RPG) model to synthesize palmprints with massive identities. We first introduce a conditional modulation generator to improve the intra-class diversity. Then an identity-aware loss is proposed to ensure identity consistency against unpaired training. We further improve the B\'ezier palm creases generation strategy to guarantee identity independence. Extensive experimental results demonstrate that synthetic pretraining significantly boosts the recognition model performance. For example, our model improves the state-of-the-art B\'ezierPalm by more than $5\%$ and $14\%$ in terms of TAR@FAR=1e-6 under the $1:1$ and $1:3$ Open-set protocol. When accessing only $10\%$ of the real training data, our method still outperforms ArcFace with $100\%$ real training data, indicating that we are closer to real-data-free palmprint recognition.Comment: 12 pages,8 figure