arXiv subjects

Chengxin Zhang

Publications and source records attributed to Chengxin Zhang.

5 recordsLinked to original sources

arXiv2025

Assessing the Effectiveness of Driver Training Interventions in Improving Safe Engagement with Vehicle Automation Systems

This study investigates how targeted training interventions can improve safe driver interaction with vehicle automation (VA) systems, focusing on Adaptive Cruise Control (ACC) and Lane Keeping Assist (LKA), both safety-critical advanced driver assistance systems (ADAS). Effective training reduces misuse and enhances road safety by promoting correct knowledge and application. A review of multiple automakers' owners' manuals revealed inconsistencies in describing ACC and LKA functions. Three training formats were compared: (1) owners' manual (OM), (2) knowledge-based (KB) with summarized operational guidelines and visual aids, and (3) skill-based hands-on practice in a driving simulator (SIM). Thirty-six participants with no prior VA experience were randomly assigned to one group. Safety-relevant outcomes - system comprehension (quiz scores) and real-world engagement (frequency and duration of activations) - were analyzed using mixed-effects and negative binomial models. KB training produced the greatest improvements in comprehension of system limitations, as well as safer engagement patterns. Compared with OM participants, KB participants achieved significantly higher quiz scores and engaged LKA and ACC more often (1.4 and 1.45 times, respectively); they also demonstrated greater awareness of scenarios requiring manual control, indicating reduced risk of inappropriate reliance. Older drivers exhibited longer activations overall, highlighting age-related differences in reliance and potential safety implications. Short, targeted training can significantly improve safe and effective VA system use, particularly for senior drivers. These results highlight training as a proactive safety intervention to reduce human-automation mismatch and enhance system reliability in real-world driving.

cs.HC↗

arXiv2025

Neural Network Surrogate Model for Junction Temperature and Hotspot Position in $3$D Multi-Layer High Bandwidth Memory (HBM) Chiplets under Varying Thermal Conditions

As the demand for computational power increases, high-bandwidth memory (HBM) has become a critical technology for next-generation computing systems. However, the widespread adoption of HBM presents significant thermal management challenges, particularly in multilayer through-silicon-via (TSV) stacked structures under varying thermal conditions, where accurate prediction of junction temperature and hotspot position is essential during the early design. This work develops a data-driven neural network model for the fast prediction of junction temperature and hotspot position in 3D HBM chiplets. The model, trained with a data set of $13,494$ different combinations of thermal condition parameters, sampled from a vast parameter space characterized by high-dimensional combination (up to $3^{27}$), can accurately and quickly infer the junction temperature and hotspot position for any thermal conditions in the parameter space. Moreover, it shows good generalizability for other thermal conditions not considered in the parameter space. The data set is constructed using accurate finite element solvers. This method not only minimizes the reliance on costly experimental tests and extensive computational resources for finite element analysis but also accelerates the design and optimization of complex HBM systems, making it a valuable tool for improving thermal management and performance in high-performance computing applications.

cs.LG↗

arXiv2024

Kirigami: large convolutional kernels improve deep learning-based RNA secondary structure prediction

We introduce a novel fully convolutional neural network (FCN) architecture for predicting the secondary structure of ribonucleic acid (RNA) molecules. Interpreting RNA structures as weighted graphs, we employ deep learning to estimate the probability of base pairing between nucleotide residues. Unique to our model are its massive 11-pixel kernels, which we argue provide a distinct advantage for FCNs on the specialized domain of RNA secondary structures. On a widely adopted, standardized test set comprised of 1,305 molecules, the accuracy of our method exceeds that of current state-of-the-art (SOTA) secondary structure prediction software, achieving a Matthews Correlation Coefficient (MCC) over 11-40% higher than that of other leading methods on overall structures and 58-400% higher on pseudoknots specifically.

q-bio.BM↗

arXiv2022

Design a Sustainable Micro-mobility Future: Trends and Challenges in the United States and European Union Using Natural Language Processing Techniques

Micro-mobility is promising to contribute to sustainable cities in the future with its efficiency and low cost. To better design such a sustainable future, it is necessary to understand the trends and challenges. Thus, we examined people's opinions on micro-mobility in the US and the EU using Tweets. We used topic modeling based on advanced natural language processing techniques and categorized the data into seven topics: promotion and service, mobility, technical features, acceptance, recreation, infrastructure and regulations. Furthermore, using sentiment analysis, we investigated people's positive and negative attitudes towards specific aspects of these topics and compared the patterns of the trends and challenges in the US and the EU. We found that 1) promotion and service included the majority of Twitter discussions in the both regions, 2) the EU had more positive opinions than the US, 3) micro-mobility devices were more widely used for utilitarian mobility and recreational purposes in the EU than in the US, and 4) compared to the EU, people in the US had many more concerns related to infrastructure and regulation issues. These findings help us understand the trends and challenges and prioritize different aspects in micro-mobility to improve their safety and experience across the two areas for designing a more sustainable micro-mobility future.

cs.CL↗

arXiv2020

Protein structure and sequence re-analysis of 2019-nCoV genome does not indicate snakes as its intermediate host or the unique similarity between its spike protein insertions and HIV-1

As the infection of 2019-nCoV coronavirus is quickly developing into a global pneumonia epidemic, careful analysis of its transmission and cellular mechanisms is sorely needed. In this report, we re-analyzed the computational approaches and findings presented in two recent manuscripts by Ji et al. (https://doi.org/10.1002/jmv.25682) and by Pradhan et al. (https://doi.org/10.1101/2020.01.30.927871), which concluded that snakes are the intermediate hosts of 2019-nCoV and that the 2019-nCoV spike protein insertions shared a unique similarity to HIV-1. Results from our re-implementation of the analyses, built on larger-scale datasets using state-of-the-art bioinformatics methods and databases, do not support the conclusions proposed by these manuscripts. Based on our analyses and existing data of coronaviruses, we concluded that the intermediate hosts of 2019-nCoV are more likely to be mammals and birds than snakes, and that the "novel insertions" observed in the spike protein are naturally evolved from bat coronaviruses.

q-bio.GN↗