Source author record

Yongqiang Tang

Yongqiang Tang appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

ResearcherUnclaimed source record

Methodology Computer Vision Machine Learning

Catalog footprint

What is connected

5works

3topics

4close collaborators

Actions

Connect this record

Open graph Browse works

Inspect adjacent papers, topics, institutions and collaborators without losing the researcher page.

Building this map preview

BZPEER is loading the nearby papers, people, topics and institutions for this page.

preprint2026arXiv

Text2CAD-Bench: A Benchmark for LLM-based Text-to-Parametric CAD Generation

Text-to-CAD generation aims to create parametric CAD models from natural language, enabling rapid prototyping and intuitive design workflows. However, existing benchmarks focus on basic primitives and simple sketch-extrude sequences, lacking advanced features essential for real-world applications and covering only traditional mechanical parts. We introduce Text2CAD-Bench, the first benchmark systematically evaluating text-to-CAD across geometric complexity and application diversity. Our benchmark comprises 600 human-curated examples spanning four levels: L1-L2 cover fundamental geometry with standard features, L3 introduces complex topology and freeform surfaces, and L4 extends to real-world domains beyond mechanical parts. Each example pairs dual-style prompts -- geometric descriptions mimicking non-expert users, and procedural sequences aligned with expert-level conventions. Evaluating mainstream general LLMs and domain-specific models, we find that current models perform reasonably on basic geometry but degrade substantially on complex topology and advanced features. We release our benchmark to drive progress in text-to-CAD research.

preprint2022arXiv

Attentive pooling for Group Activity Recognition

In group activity recognition, hierarchical framework is widely adopted to represent the relationships between individuals and their corresponding group, and has achieved promising performance. However, the existing methods simply employed max/average pooling in this framework, which ignored the distinct contributions of different individuals to the group activity recognition. In this paper, we propose a new contextual pooling scheme, named attentive pooling, which enables the weighted information transition from individual actions to group activity. By utilizing the attention mechanism, the attentive pooling is intrinsically interpretable and able to embed member context into the existing hierarchical model. In order to verify the effectiveness of the proposed scheme, two specific attentive pooling methods, i.e., global attentive pooling (GAP) and hierarchical attentive pooling (HAP) are designed. GAP rewards the individuals that are significant to group activity, while HAP further considers the hierarchical division by introducing subgroup structure. The experimental results on the benchmark dataset demonstrate that our proposal is significantly superior beyond the baseline and is comparable to the state-of-the-art methods.

preprint2020arXiv

An improved sample size calculation method for score tests in generalized linear models

Self and Mauritsen (1988) developed a sample size determination procedure for score tests in generalized linear models under contiguous alternatives. Its performance may deteriorate when the effect size is large. We propose a modification of the Self-Mauritsen method by taking into account of the variance of the score statistic under both the null and alternative hypotheses, and extend the method to noninferiority trials. The modified approach is employed to calculate the sample size for the logistic regression and negative binomial regression in superiority and noninferiority trials. We further explain why the formulae recently derived by Zhu and Lakkis tend to underestimate the required sample size for the negative binomial regression. Numerical examples are used to demonstrate the accuracy of the proposed method.

preprint2020arXiv

Notes on Exact Power Calculations for t Tests and Analysis of Covariance

Tang derived the exact power formulae for t tests and analysis of covariance (ANCOVA) in superiority, noninferiority and equivalence trials. The power calculation in equivalence trials can be simplified by using Owen's Q function, which is available in standard statistical software. We extend the exact power determination method for ANCOVA to unstratified and stratified multi-arm randomized trials. The method is applied to the design of multi-arm trials and gold standard noninferiority trials.

preprint2016arXiv

An efficient multiple imputation algorithm for control-based and delta-adjusted pattern mixture models using SAS

In clinical trials, mixed effects models for repeated measures (MMRM) and pattern mixture models (PMM) are often used to analyze longitudinal continuous outcomes. We describe a simple missing data imputation algorithm for the MMRM that can be easily implemented in standard statistical software packages such as SAS PROC MI. We explore the relationship of the missing data distribution in the control-based and delta-adjusted PMMs with that in the MMRM, and suggest an efficient imputation algorithm for these PMMs. The unobserved values in PMMs can be imputed by subtracting the mean difference in the posterior predictive distributions of missing data from the imputed values in MMRM. We also suggest a modification of the copy reference imputation procedure to avoid the possibility that after dropout, subjects from the active treatment arm will have better mean response trajectory than subjects who stay on the active treatment. The proposed methods are illustrated by the analysis of an antidepressant trial.