Multitask Learning Deep Neural Networks to Combine Revealed and Stated Preference Data

It is an enduring question how to combine revealed preference (RP) and stated preference (SP) data to analyze travel behavior. This study presents a framework of multitask learning deep neural networks (MTLDNNs) for this question, and demonstrates that MTLDNNs are more generic than the traditional nested logit (NL) method, due to its capacity of automatic feature learning and soft constraints. About 1,500 MTLDNN models are designed and applied to the survey data that was collected in Singapore and focused on the RP of four current travel modes and the SP with autonomous vehicles (AV) as the one new travel mode in addition to those in RP. We found that MTLDNNs consistently outperform six benchmark models and particularly the classical NL models by about 5% prediction accuracy in both RP and SP datasets. This performance improvement can be mainly attributed to the soft constraints specific to MTLDNNs, including its innovative architectural design and regularization methods, but not much to the generic capacity of automatic feature learning endowed by a standard feedforward DNN architecture. Besides prediction, MTLDNNs are also interpretable. The empirical results show that AV is mainly the substitute of driving and AV alternative-specific variables are more important than the socio-economic variables in determining AV adoption. Overall, this study introduces a new MTLDNN framework to combine RP and SP, and demonstrates its theoretical flexibility and empirical power for prediction and interpretation. Future studies can design new MTLDNN architectures to reflect the speciality of RP and SP and extend this work to other behavioral analysis

SME financing in Zhejiang province

This thesis laid the emphasis on Zhejiang Province which SMEs play a dominant role and is characterized with informal finance. The current financial system provided little opportunity for the SMEs to raise fund; companies in temporary illiquidity or facing solvency crisis obviously could not possibly rely upon internal capital for financing. Then, informal finance should be deeply studied. Guiding the informal finance to alleviate the SMEs financing difficulties could make contribution the financial and social stability at large. Therefore, it is a meaningful topic to be studied, both in theoretical and practical perspective. In chapter 2, the focus is mainly laid on studying the status quo of the SME financing in Zhejiang Province and exploring the role formal and informal financing play. Through literatures consulting, a tailor made questionnaire has been designed to learn the basic information, business establishment, business growth and funding sources of the firms. The copies of the questionnaire have been disseminated to the sampled entrepreneurs of SMEs across Zhejiang Province. Through the data collected from the valid copies, we could gain a brief understanding of these firms and its financing situation through basic descriptive statistics. With the data collected from the 150 valid copies, we have gained a brief understanding of these firms and their financing situation through basic descriptive statistics. In chapter 3, we tend to adopt various empirical methods to analyze the relationship of usage of formal (informal) lending and other factors. Correlation analysis, binary regression model and ordered logistic regression model is applied on the collected questionnaire data. With this empirical investigation, we try to further explore what impact these reputation and relationship variables may have on the financing practices they try to employ. From the empirical results, we find that firms with strong political ties, higher education, larger turnover and having received credit rating are more likely to employ formal financing practices. No consistent results have been found for informal financing practices. Moreover, we find that more factors work in the case of global financial crisis while only political ties and credit rating status work in the tightened monetary background like the period after year 2010. Combining these results, we conclude that reputation and relationship are vital in obtaining funds from formal financing channels in China. By contrast, all kinds of SMEs’ entrepreneurs are likely to tap the informal financing market. The finding is critical: on the one hand, the criteria necessary to obtain formal loans are quite stringent; on the other hand, the informal market seems to set no threshold for financing. In the light of these considerations, informal financing will inevitably play a dominant role within the financial system. In chapter 4, we firstly consult the extant literatures to learn the SMEs practice around the world. We then hold interviews with five managers from a commercial bank to learn their mindset towards SMEs business. Then, through the combination of what we find from the literature and the interviews, as well as the empirical results from the previous chapters, we propose specific policy suggestions. Policy suggestions are proposed from three different dimensions: the supply side of funds for SMEs financing (including both the formal and informal financial institutions); and the demand side. Such grand view will offer more insightful understanding in SMEs financing. The policy suggestions proposed are explicit, specific and practical

Molecular Image Segmentation Based on Improved Fuzzy Clustering

Segmentation of molecular images is a difficult task due to the low signal-to-noise ratio of images. A novel two-dimensional fuzzy C-means (2DFCM) algorithm is proposed for the molecular image segmentation. The 2DFCM algorithm is composed of three stages. The first stage is the noise suppression by utilizing a method combining a Gaussian noise filter and anisotropic diffusion techniques. The second stage is the texture energy characterization using a Gabor wavelet method. The third stage is introducing spatial constraints provided by the denoising data and the textural information into the two-dimensional fuzzy clustering. The incorporation of intensity and textural information allows the 2DFCM algorithm to produce satisfactory segmentation results for images corrupted by noise (outliers) and intensity variations. The 2DFCM can achieve 0.96 ± 0.03 segmentation accuracy for synthetic images under different imaging conditions. Experimental results on a real molecular image also show the effectiveness of the proposed algorithm

bbl: Boltzmann Bayes Learner for High-Dimensional Inference with Discrete Predictors in R

Non-regression-based inferences, such as discriminant analysis, can account for the effect of predictor distributions that may be significant in big data modeling. We describe bbl, an R package for Boltzmann Bayes learning, which enables a comprehensive supervised learning of the association between a large number of categorical predictors and multi-level response variables. Its basic underlying statistical model is a collection of (fully visible) Boltzmann machines inferred for each distinct response level. The algorithm reduces to the naive Bayes learner when interaction is ignored. We illustrate example use cases for various scenarios, ranging from modeling of a relatively small set of factors with heterogeneous levels to those with hundreds or more predictors with uniform levels such as image or genomic data. We show how bbl explicitly quantifies the extra power provided by interactions via higher predictive performance of the model. In comparison to deep learning-based methods such as restricted Boltzmann machines, bbl-trained models can be interpreted directly via their bias and interaction parameters