Zhanfeng Wang

Due to the massive scale and distributed sparsity of the existing IPv6 address space and the complexity of address allocation patterns, a generative active probing model called Hierarchical Mixture‐of‐Experts Scanner (Hi‐MoE‐Scan) is proposed. It combines structured knowledge guidance and sparse expert modeling, which constructs a basic database through the fusion of multi‐source heterogeneous data, parses IPv6 addresses into five‐level prefixes, and embeds contextual features. A gated sparse expert mechanism is adopted to dynamically select experts for participation in computations, combined with a dynamic capacity adjustment strategy to optimize computational load. Additionally, a cross‐layer self‐distillation mechanism is introduced to enhance the consistency of hierarchical gating distributions. Experimental results show that the candidate address hit rate of Hi‐MoE‐Scan reaches 12.5%, an increase of 28% compared with the existing state‐of‐the‐art methods. The valid rate after deduplication reaches 8.4%, covering more independent active network segments. The average generation time is only 245 milliseconds per 10,000 entries, which is significantly better than mainstream baseline models.

Gaussian processes (GPs) are powerful tools to nonparameterically model stochastic functions. With regard to computation load, especially for massive data with large sample size, recent researches have applied spectral representations with stationary kernels to build low‐rank approaches to approximate GPs. However, these low‐rank methods may struggle to capture the spatially varying properties of the data in reality due to their finite representations. In this paper, we propose a novel framework for the low‐rank GPs, where a normal‐gamma prior is applied instead of the standard Gaussian prior for the coefficients in the spectral representation. The normal‐gamma prior promotes flexibility, robustness and sparsity in the low‐rank representations of GPs. An efficient hierarchical likelihood approach is also developed for estimation, which maintains the computational benefits of the low‐rank approximation. To handle the multidimensional covariates, we extend this approach to additive GPs. Numerical studies and real data examples demonstrate the performance and applicability of the proposed approaches.

Due to the huge network address space of IPv6, the overhead of IPv6 space detection is very large. Thus, how to improve the detection efficiency is a hot research topic. Firstly, the basic process of IPv6 network space detection is illustrated, and then the detection algorithms are categorized into two primary categories: optimization scanning-based detection algorithms and address generation-based detection algorithms. According to the timelines proposed by the algorithms, the ideas of these algorithms are discussed separately. Finally, a comprehensive comparison and analysis of these detection algorithms are conducted from multiple dimensions. Future research directions are discussed, including: collection of active IPv6 addresses and allocation patterns, discovery of IPv6 address detection patterns based on machine learning, improvement of the detection response rate, and moral issues caused by network detection.

Longitudinal proportional data, which are restricted in a closed interval, are frequently observed together with survival data in clinical trials and other medical studies. In this paper, we propose a new model for the joint analysis of longitudinal proportional and survival data. This model uses generalized mean‐variance mixed model for longitudinal outcomes, which avoids parametric assumption for their distribution, and Cox proportional hazards model for survival times. A procedure is developed to estimate the parameters in the proposed model based on quasi‐likelihood for longitudinal data and partial likelihood for survival data with a Laplace approximation for the joint likelihood. A random weighting method is proposed to calculate the variance of these parameter estimators. The performance of the proposed model and estimation procedures are assessed through simulation studies and the application to the analysis of data from a randomized clinical trial on early breast cancer.

International Press of Boston - publishers of scholarly mathematical and scientific journals and books

Data collected from distributed sources or sites commonly have different distributions or contaminated observations. Active learning procedures allow us to assess data when recruiting new data into model building. Thus, combining several active learning procedures together is a promising idea, even when the collected data set is contaminated. Here, we study how to conduct and integrate several adaptive sequential procedures at a time to produce a valid result via several machines or a parallel‐computing framework. To avoid distraction by complicated modelling processes, we use confidence set estimation for linear models to illustrate the proposed method and discuss the approach's statistical properties. We then evaluate its performance using both synthetic and real data. We have implemented our method using Python and made it available through Github at https://github.com/zhuojianc/dsep.