Empirical Risk Minimization over Artificial Neural Networks Overcomes the Curse of Dimensionality in the Numerical Approximation of Linear Kolmogorov Partial Differential Equations with Unbounded Initial Functions

Deep learning algorithms have been successfully applied to numerically solve linear Kolmogorov partial differential equations (PDEs). A recent research shows that the empirical risk minimization~(ERM) over deep artificial neural networks overcomes the curse of dimensionality in the numerical approximation of linear Kolmogorov PDEs with bounded initial functions. However, the initial functions may be unbounded in many applications such as the Black Scholes PDEs in pricing call options. In this paper, we extend this result to the cases involving unbounded initial functions. We prove that for $d$-dimensional linear Kolmogorov PDEs with unbounded initial functions, under suitable assumptions, the number of training data and the size of the artificial neural network required to achieve an accuracy $\varepsilon$ for the ERM grow polynomially in both $d$ and $\varepsilon^{-1}$. Moreover, we verify that the required assumptions hold for Black-Scholes PDEs and heat equations which are two important cases of linear Kolmogorov PDEs

Space-Invariant Projection in Streaming Network Embedding

Newly arriving nodes in dynamics networks would gradually make the node embedding space drifted and the retraining of node embedding and downstream models indispensable. An exact threshold size of these new nodes, below which the node embedding space will be predicatively maintained, however, is rarely considered in either theory or experiment. From the view of matrix perturbation theory, a threshold of the maximum number of new nodes that keep the node embedding space approximately equivalent is analytically provided and empirically validated. It is therefore theoretically guaranteed that as the size of newly arriving nodes is below this threshold, embeddings of these new nodes can be quickly derived from embeddings of original nodes. A generation framework, Space-Invariant Projection (SIP), is accordingly proposed to enables arbitrary static MF-based embedding schemes to embed new nodes in dynamics networks fast. The time complexity of SIP is linear with the network size. By combining SIP with four state-of-the-art MF-based schemes, we show that SIP exhibits not only wide adaptability but also strong empirical performance in terms of efficiency and efficacy on the node classification task in three real datasets

Analysis of the Generalization Error of deep learning based on Randomized Quasi-Monte Carlo for Solving Linear Kolmogorov PDEs

Deep learning algorithms have been widely used to solve linear Kolmogorov partial differential equations~(PDEs) in high dimensions, where the loss function is defined as a mathematical expectation. We propose to use the randomized quasi-Monte Carlo (RQMC) method instead of the Monte Carlo (MC) method for computing the loss function. In theory, we decompose the error from empirical risk minimization~(ERM) into the generalization error and the approximation error. Notably, the approximation error is independent of the sampling methods. We prove that the convergence order of the mean generalization error for the RQMC method is $O(n^{-1+\epsilon})$ for arbitrarily small $\epsilon>0$, while for the MC method it is $O(n^{-1/2+\epsilon})$ for arbitrarily small $\epsilon>0$. Consequently, we find that the overall error for the RQMC method is asymptotically smaller than that for the MC method as $n$ increases. Our numerical experiments show that the algorithm based on the RQMC method consistently achieves smaller relative $L^{2}$ error than that based on the MC method

Real estate rental market: a 10-year bibliometricbased review

The real estate rental market (RERM) is considered to have an important role in the entire real estate market. It refers to a property composed of land and its buildings, including the natural resources that can be rented or leased. Previous researches show that most developed countries have experienced the historical process of passively renting, actively buying, and actively renting. Moreover, academic interest in the impact of different sectors of the RERM has been reviewed increasingly over the past decade. However, previous studies provide limited insights into a comprehensive review of the RERM. Based on a 10-year database of 790 articles collected from the Web of Science, a comprehensive literature review is presented to discover the knowledge structure of RERM using CiteSpace software. First, this study recognizes the cluster of the articles, and discusses six major clusters in detail. Next, this study has identified four research trends that emerged during the past decade. To reveal the differences between the studies in the United States (US), China and the United Kingdom (UK), this study compares their publication scales and co-word networks. Finally, this study suggests six meaningful future research directions

Coherent structure analysis of spatiotemporal chaos

We introduce a measure to quantify spatiotemporal turbulence in extended systems. It is based on the statistical analysis of a coherent structure decomposition of the evolving system. Applied to a cellular excitable medium and a reaction-diffusion model describing the oxidation of CO on Pt(100), it reveals power-law scaling of the size distribution of coherent space-time structures for the state of spiral turbulence. The coherent structure decomposition is also used to define an entropy measure, which sharply increases in these systems at the transition to turbulence