arXiv subjects

He Yong

Publications and source records attributed to He Yong.

3 recordsLinked to original sources

arXiv2025

An Extensive Study on Text Serialization Formats and Methods

Text serialization is a fundamental concept in modern computing, enabling the conversion of complex data structures into a format that can be easily stored, transmitted, and reconstructed. This paper provides an extensive overview of text serialization, exploring its importance, prevalent formats, underlying methods, and comparative performance characteristics. We dive into the advantages and disadvantages of various text-based serialization formats, including JSON, XML, YAML, and CSV, examining their structure, readability, verbosity, and suitability for different applications. The paper also discusses the common methods involved in the serialization and deserialization processes, such as parsing techniques and the role of schemas. To illustrate the practical implications of choosing a serialization format, we present hypothetical performance results in the form of tables, comparing formats based on metrics like serialization deserialization speed and resulting data size. The discussion analyzes these results, highlighting the trade offs involved in selecting a text serialization format for specific use cases. This work aims to provide a comprehensive resource for understanding and applying text serialization in various computational domains.

cs.PL↗

arXiv2020

Quantile factor analysis for large-dimensional time series with statistical guarantee

Quantile is an important measure in finance and quality assessment in service industry. In this paper, we model the temporal and cross-sectional interactive effect of the quantiles of large-dimensional time series by a latent quantile factor model. The factor loadings and scores are learnt with statistical guarantee via an iterative check-loss-minimization procedure. Without any moment constraint on the idiosyncratic errors, we correctly identify the common and idiosyncratic components for each variable. We obtained the statistical convergence rates of the minimization estimators. Bahardur representations for the estimated factor loadings and scores are provided under some mild conditions. Moreover, a robust method is proposed to select the number of factors consistently. Simulation experiments checked the validity of the theory. Our analysis on a financial data set shows the superiority of learning quantile factors in portfolio allocation over other state-of-the-art methods that learn mean factors.

stat.ME↗

arXiv2020

Simultaneous Differential Network Analysis and Classification for High-dimensional Matrix-variate Data, with application to Brain Connectivity Alteration Detection and fMRI-guided Medical Diagnoses of Alzheimer's Disease

Alzheimer's disease (AD) is the most common form of dementia, which causes problems with memory, thinking and behavior. Growing evidence has shown that the brain connectivity network experiences alterations for such a complex disease. Network comparison, also known as differential network analysis, is thus particularly powerful to reveal the disease pathologies and identify clinical biomarkers for medical diagnoses (classification). Data from neurophysiological measurements are multi-dimensional and in matrix-form, which poses major challenges in brain connectivity analysis and medical diagnoses. Naive vectorization method is not sufficient as it ignores the structural information within the matrix. In the article, we adopt the Kronecker product covariance matrix framework to capture both spatial and temporal correlations of the matrix-variate data while the temporal covariance matrix is treated as a nuisance parameter. By recognizing that the strengths of network connections may vary across subjects, we develop an ensemble-learning procedure, which identifies the differential interaction patterns of brain regions between the AD group and the control group and conducts medical diagnosis (classification) of AD simultaneously. We applied the proposed procedure to functional connectivity analysis of fMRI dataset related with Alzheimer's disease. The hub nodes and differential interaction patterns identified are consistent with existing experimental studies, and satisfactory out-of-sample classification performance is achieved for medical diagnosis of Alzheimer's disease. An R package \SDNCMV" for implementation is available at https://github.com/heyongstat/SDNCMV.

stat.ME↗