The Financial and Macroeconomic Implications of Banking Frictions and Banking Riskiness

This paper develops a model of banking frictions and banking riskiness, the importance of which is highlighted by the recent Global Financial Crisis (GFC). We propose a model-based approach to decompose the effect of a banking riskiness shock into a pure default effect and a risk effect when risk sharing among the depositors is imperfect. Although the default effect is quantitatively more important, the risk effect is not to be neglected. When the shock generates a bank spread similar in value to the peak during the GFC, the overall effect is a decline in employment by 6:57 percent. The pure default effect leads to a 4:76 percent employment decline by a “within-model” measure, and a 5:05 decline by a “between-model” measure. The remaining is attributed to the risk effect.Banking riskiness shocks; two-sided debt contract; default effects; risk effects; financial crisis.

Asset Prices, Monetary Policy, and Aggregate Fluctuations: An Empirical Investigation

This paper studies empirically the dynamic interactions between asset prices, monetary policy, and aggregate fluctuations during the Volcker-Greenspan period. Using a simple structural vector autoregression framework, we investigate the effects of monetary policy on output, inflation and asset prices, the interactions of asset prices with the aggregate economy, as well as the relationship between stock price and house price. Several robust findings emerge. The systematic response of monetary policy to output and inflation is also found to play an important role in stabilizing the aggregate economy. In addition, the results call for special attention to be paid to house price when studying the dynamic relationships between asset prices and macroeconomic fluctuations.House prices; stock prices; systematic monetary policy; structural vector autoregressions.

Enhanced Image Denoising with Diffusion Probability and Dictionary Learning Adaptation

Image denoising is essential for numerous image processing applications, where image noise can profoundly impact processing efficiency and output quality. Addressing the challenge of inflexible reference images in unconditional diffusion probability models and enhancing image denoising performance is of paramount importance. In this research, we propose a novel image denoising model based on component decoupling and introduce sensitivity decoupling operators to prevent entanglement and redundancy among different decoupling models. Additionally, we leverage a model-driven network to fuse image components, resisting noise and model degradation, thereby aiding network convergence. Subsequently, we construct an image adaptive denoising model incorporating diffusion probability and dictionary learning. Experimental results demonstrate the superiority of the proposed approach over other algorithms in grayscale image processing on the Set12 dataset, achieving a peak signal-to-noise ratio (PSNR) of 35.75 dB and an average structural similarity (SSIM) value of 92.68%. Similarly, on the BSD68 dataset, our algorithm outperforms others with a PSNR of 34.35 dB and an average SSIM of 93.89%. Furthermore, for colour image processing, our method yields higher PSNR and average SSIM compared to other approaches. The findings indicate a significant improvement in denoising effectiveness compared to prior methods, highlighting the practical value of the proposed image denoising algorithm

Real Estate, the External Finance Premium and Business Investment: A Quantitative Dynamic General Equilibrium Analysis

This paper studies the connection between the capital market and the real estate market. Empirically, we find that positive real house price shocks lower the external finance premium and stimulate nonresidential investment and real GDP. Our theoretical framework is able to mimic the volatility of the external finance premium, the relative price of real estate and capital, and the investment in real estate and capital. It also captures the cyclicality of the external finance premium and of real estate prices. The contribution of real estate price fluctuations to the variability of the external finance premium and the GDP is confirmed to be significant.External Finance Premium, Residential and Corporate Real Estate, Capital Market Imperfections, Equilibrium Default, Real Estate Price Volatility.

Transformer-based Map Matching Model with Limited Ground-Truth Data using Transfer-Learning Approach

In many spatial trajectory-based applications, it is necessary to map raw trajectory data points onto road networks in digital maps, which is commonly referred to as a map-matching process. While most previous map-matching methods have focused on using rule-based algorithms to deal with the map-matching problems, in this paper, we consider the map-matching task from the data-driven perspective, proposing a deep learning-based map-matching model. We build a Transformer-based map-matching model with a transfer learning approach. We generate trajectory data to pre-train the Transformer model and then fine-tune the model with a limited number of ground-truth data to minimize the model development cost and reduce the real-to-virtual gap. Three metrics (Average Hamming Distance, F-score, and BLEU) at two levels (point and segment level) are used to evaluate the model performance. The results indicate that the proposed model outperforms existing models. Furthermore, we use the attention weights of the Transformer to plot the map-matching process and find how the model matches the road segments correctly.Comment: 25 pages, 9 figures, 4 table

Real Estate, the External Finance Premium and Business Investment: A Quantitative Dynamic General Equilibrium Analysis

This paper studies the connection between the capital market and the real estate market. Empirically, we find that positive real house price shocks lower the external finance premium and stimulate nonresidential investment and real GDP. Our theoretical framework is able to mimic the volatility of the external finance premium, the relative price of real estate and capital, and the investment in real estate and capital. It also captures the cyclicality of the external finance premium and of real estate prices. The contribution of real estate price fluctuations to the variability of the external finance premium and the GDP is confirmed to be significant

An investigation into the risk of population bias in deep learning autocontouring

Background and Purpose: To date, data used in the development of Deep Learning-based automatic contouring (DLC) algorithms have been largely sourced from single geographic populations. This study aimed to evaluate the risk of population-based bias by determining whether the performance of an autocontouring system is impacted by geographic population.Materials and methods: 80 Head Neck CT deidentified scans were collected from four clinics in Europe (n = 2) and Asia (n = 2). A single observer manually delineated 16 organs-at-risk in each. Subsequently, the data was contoured using a DLC solution, and trained using single institution (European) data. Autocontours were compared to manual delineations using quantitative measures. A Kruskal-Wallis test was used to test for any difference between populations. Clinical acceptability of automatic and manual contours to observers from each participating institution was assessed using a blinded subjective evaluation.Results: Seven organs showed a significant difference in volume between groups. Four organs showed statistical differences in quantitative similarity measures. The qualitative test showed greater variation in acceptance of contouring between observers than between data from different origins, with greater acceptance by the South Korean observers.Conclusion: Much of the statistical difference in quantitative performance could be explained by the difference in organ volume impacting the contour similarity measures and the small sample size. However, the qualitative assessment suggests that observer perception bias has a greater impact on the apparent clinical acceptability than quantitatively observed differences. This investigation of potential geographic bias should extend to more patients, populations, and anatomical regions in the future.</p

Recent progress in DNA methyltransferase inhibitors as anticancer agents

DNA methylation mediated by DNA methyltransferase is an important epigenetic process that regulates gene expression in mammals, which plays a key role in silencing certain genes, such as tumor suppressor genes, in cancer, and it has become a promising therapeutic target for cancer treatment. Similar to other epigenetic targets, DNA methyltransferase can also be modulated by chemical agents. Four agents have already been approved to treat hematological cancers. In order to promote the development of a DNA methyltransferase inhibitor as an anti-tumor agent, in the current review, we discuss the relationship between DNA methylation and tumor, the anti-tumor mechanism, the research progress and pharmacological properties of DNA methyltransferase inhibitors, and the future research trend of DNA methyltransferase inhibitors

MONEY, CREDIT, AND BUSINESS CYCLE COMOVEMENT

We developed a two-sector general equilibrium model with money and credit to study cross-sector comovement over the business cycle. Through a working capital channel, both money and productivity shocks can generate procyclicality of sectoral activities and positive cross-sector correlations of output, employment and investment. In our model, firms in each sector borrow in the credit market to finance their purchase of labour inputs, part of which are used in the adjustment process of capital stock. The shocks affect sectoral employment and investment through their impacts on interest rates and external finance premia. Copyright 2009 The Authors. Journal compilation 2009 Blackwell Publishing Asia Pty Ltd