arXiv subjects

Weilin Chen

Publications and source records attributed to Weilin Chen.

22 records · Page 2Linked to original sources

arXiv2023

Patterns Induce Injectivity: A New Thinking in Constructing Injective Local Rules of 1D Cellular Automata over $\mathbb{F}_2$

We discovered that certain patterns called injective patterns remain stable during the revolution process, allowing us to create many reversible CA simply by using them to design the revolution rules. By examining injective patterns, we investigated their structural stability during revolutions. This led us to discover extended patterns and pattern mixtures that can create more reversible cellular automata. Furthermore, our research proposed a new way to study the reversibility of CA by observing the structure of local rule $f$. In this paper, we will explicate our study and propose an efficient method for finding the injective patterns. Our algorithms can find injective rules and generate local rule $f$ by traversing $2^{N}$, instead of $2^{2^{N}}$ to check all injective rules and pick the injective ones.

cs.CC↗

arXiv2023

Generalization bound for estimating causal effects from observational network data

Estimating causal effects from observational network data is a significant but challenging problem. Existing works in causal inference for observational network data lack an analysis of the generalization bound, which can theoretically provide support for alleviating the complex confounding bias and practically guide the design of learning objectives in a principled manner. To fill this gap, we derive a generalization bound for causal effect estimation in network scenarios by exploiting 1) the reweighting schema based on joint propensity score and 2) the representation learning schema based on Integral Probability Metric (IPM). We provide two perspectives on the generalization bound in terms of reweighting and representation learning, respectively. Motivated by the analysis of the bound, we propose a weighting regression method based on the joint propensity score augmented with representation learning. Extensive experimental studies on two real-world networks with semi-synthetic data demonstrate the effectiveness of our algorithm.

cs.LG↗

arXiv2023

Survey of Millimeter Wave Backscatter Communition Systems

Backscatter communication is a burgeoning low-power communication technology that has been introduced into the Internet of Things (IoT) due to its excellent self-sustainability. However, conventional backscatter communication (BackCom) technologies often suffer from insufficient data transmission rates and are difficult to meet the increasing bandwidth demands of Internet of Things applications. In contrast, millimeter wave (mmWave) backscatter communication holds great potential for high-speed data transmission because of its rich spectrum resources. Therefore, this paper presents a comprehensive survey of the existing literature on mmWave backscatter communications. Firstly, we introduce the fundamental principles of mmWave backscatter communication, including its architecture, basic techniques, and primer knowledge. Subsequently, we review the various mmWave backscatter communication techniques proposed in the current literature. We then discuss some applications based on these techniques before finally addressing some challenges and open issues associated with mmWave backscatter.

cs.DC↗

arXiv2022

Rank and Factor Loadings Estimation in Time Series Tensor Factor Model by Pre-averaging

Tensor time series data appears naturally in a lot of fields, including finance and economics. As a major dimension reduction tool, similar to its factor model counterpart, the idiosyncratic components of a tensor time series factor model can exhibit serial correlations, especially in financial and economic applications. This rules out a lot of state-of-the-art methods that assume white idiosyncratic components, or even independent/Gaussian data. While the traditional higher order orthogonal iteration (HOOI) is proved to be convergent to a set of factor loading matrices, the closeness of them to the true underlying factor loading matrices are in general not established, or only under some strict circumstances like having i.i.d. Gaussian noises (Zhang and Xia, 2018). Under the presence of serial and cross-correlations in the idiosyncratic components and time series variables with only bounded fourth order moments, we propose a pre-averaging method that accumulates information from tensor fibres for better estimating all the factor loading spaces. The estimated directions corresponding to the strongest factors are then used for projecting the data for a potentially improved re-estimation of the factor loading spaces themselves, with theoretical guarantees and rate of convergence spelt out. We also propose a new rank estimation method which utilizes correlation information from the projected data, in the same spirit as Fan, Guo and Zheng (2022) for factor models with independent data. Extensive simulation results reveal competitive performance of our rank and factor loading estimators relative to other state-of-the-art or traditional alternatives. A set of matrix-valued portfolio return data is also analyzed.

stat.ME↗