Interactions between Fiscal and Monetary Policy: A New Keynesian Model with Regime Switching Process

This paper examines the interactions between traditional fiscal and monetary policy tools: government spending and the interest rate. Two models are used: a baseline linear model, and a Markov switching model with active/passive fiscal and monetary policy combinations. The linear model is estimated and the posterior mean parameterization is used to calibrate the regime-switching model. Sims (2002) algorithm and policy function iteration are used to solve the models, and a particle filter is used to evaluate the likelihood functions. The results show that government spending alone cannot raise inflation despite the positive effect on output. The duration of the stimulus effect in output increases significantly under active fiscal regime. The strongest effect occurs when both monetary and fiscal policy are active

Understanding the limitation of Total Correlation Estimation Based on Mutual Information Bounds

The total correlation(TC) is a crucial index to measure the correlation between marginal distribution in multidimensional random variables, and it is frequently applied as an inductive bias in representation learning. Previous research has shown that the TC value can be estimated using mutual information boundaries through decomposition. However, we found through theoretical derivation and qualitative experiments that due to the use of importance sampling in the decomposition process, the bias of TC value estimated based on MI bounds will be amplified when the proposal distribution in the sampling differs significantly from the target distribution. To reduce estimation bias issues, we propose a TC estimation correction model based on supervised learning, which uses the training iteration loss sequence of the TC estimator based on MI bounds as input features to output the true TC value. Experiments show that our proposed method can improve the accuracy of TC estimation and eliminate the variance generated by the TC estimation process

Magnitude-image based data-consistent deep learning method for MRI super resolution

Magnetic Resonance Imaging (MRI) is important in clinic to produce high resolution images for diagnosis, but its acquisition time is long for high resolution images. Deep learning based MRI super resolution methods can reduce scan time without complicated sequence programming, but may create additional artifacts due to the discrepancy between training data and testing data. Data consistency layer can improve the deep learning results but needs raw k-space data. In this work, we propose a magnitude-image based data consistency deep learning MRI super resolution method to improve super resolution images' quality without raw k-space data. Our experiments show that the proposed method can improve NRMSE and SSIM of super resolution images compared to the same Convolutional Neural Network (CNN) block without data consistency module.Comment: Accepted by IEEE CBMS 202

Exploration of eco-environment and urbanization changes in coastal zones: A case study in China over the past 20 years

Abstract With the rapid development of urbanization and population migration, since the 20th century, the natural and eco-environment of coastal areas have been under tremendous pressure due to the strong interference of human response. To objectively evaluate the coastal eco-environment condition and explore the impact from the urbanization process, this paper, by integrating daytime remote sensing and nighttime remote sensing, carried out a quantitative assessment of the coastal zone of China in 2000–2019 based on Remote Sensing Ecological Index (RSEI) and Comprehensive Nighttime Light Index (CNLI) respectively. The results showed that: 1) the overall eco-environmental conditions in China's coastal zone have shown a trend of improvement, but regional differences still exist; 2) during the study period, the urbanization process of cities continued to advance, especially in seaside cities and prefecture-level cities in Jiangsu and Shandong, which were much higher than the average growth rate; 3) the Coupling Coordination Degree (CCD) between the urbanization and eco-environment in coastal cities is constantly increasing, but the main contribution of environmental improvement comes from non-urbanized areas, and the eco-environment pressure in urbanized areas is still not optimistic. As a large-scale, long-term series of eco-environment and urbanization process change analysis, this study can provide theoretical support for mesoscale development planning, eco-environment condition monitoring and environmental protection policies from decision-makers

Feature screening for clustering analysis

In this paper, we consider feature screening for ultrahigh dimensional clustering analyses. Based on the observation that the marginal distribution of any given feature is a mixture of its conditional distributions in different clusters, we propose to screen clustering features by independently evaluating the homogeneity of each feature's mixture distribution. Important cluster-relevant features have heterogeneous components in their mixture distributions and unimportant features have homogeneous components. The well-known EM-test statistic is used to evaluate the homogeneity. Under general parametric settings, we establish the tail probability bounds of the EM-test statistic for the homogeneous and heterogeneous features, and further show that the proposed screening procedure can achieve the sure independent screening and even the consistency in selection properties. Limiting distribution of the EM-test statistic is also obtained for general parametric distributions. The proposed method is computationally efficient, can accurately screen for important cluster-relevant features and help to significantly improve clustering, as demonstrated in our extensive simulation and real data analyses

Break The Spell Of Total Correlation In betaTCVAE

In the absence of artificial labels, the independent and dependent features in the data are cluttered. How to construct the inductive biases of the model to flexibly divide and effectively contain features with different complexity is the main focal point of unsupervised disentangled representation learning. This paper proposes a new iterative decomposition path of total correlation and explains the disentangled representation ability of VAE from the perspective of model capacity allocation. The newly developed objective function combines latent variable dimensions into joint distribution while relieving the independence constraints of marginal distributions in combination, leading to latent variables with a more manipulable prior distribution. The novel model enables VAE to adjust the parameter capacity to divide dependent and independent data features flexibly. Experimental results on various datasets show an interesting relevance between model capacity and the latent variable grouping size, called the "V"-shaped best ELBO trajectory. Additionally, we empirically demonstrate that the proposed method obtains better disentangling performance with reasonable parameter capacity allocation

A Reconfigurable FPGA Overlay Architecture for Matrix-Matrix Multiplication

The increasing popularity of deep learning in workloads across vision, speech, and language has inspired many attempts to develop hardware accelerators for matrix-matrix multiplication. Both application-specific integrated circuits (ASICs), and field-programmable arrays (FPGAs) are used for this purpose. However, a trade-off between the two platforms is that ASICs provide little flexibility after they are manufactured while designs on FPGAs are flexible but application development on FPGAs is more time-consuming. In this work, we aim to find the balance between reconfigurability and development efficiency by designing a reconfigurable systolic architecture as an overlay on the FPGA. Our contribution to the reconfigurable systolic architectures is a multiplexer-based crossbar network that interconnects every processing element in the network. The crossbar network grants user run-time reconfigurability of the topology of the systolic array, enabling the user to specify the shape and size of the systolic architecture on-the-fly. The proposed overlay architecture achieves similar computational hardware resource usage and maximum clock frequency compared to the baseline designs