arXiv subjects

Qingzhao Zhang

Publications and source records attributed to Qingzhao Zhang.

34 records · Page 2Linked to original sources

arXiv2023

Exploring the Limits of ChatGPT in Software Security Applications

Large language models (LLMs) have undergone rapid evolution and achieved remarkable results in recent times. OpenAI's ChatGPT, backed by GPT-3.5 or GPT-4, has gained instant popularity due to its strong capability across a wide range of tasks, including natural language tasks, coding, mathematics, and engaging conversations. However, the impacts and limits of such LLMs in system security domain are less explored. In this paper, we delve into the limits of LLMs (i.e., ChatGPT) in seven software security applications including vulnerability detection/repair, debugging, debloating, decompilation, patching, root cause analysis, symbolic execution, and fuzzing. Our exploration reveals that ChatGPT not only excels at generating code, which is the conventional application of language models, but also demonstrates strong capability in understanding user-provided commands in natural languages, reasoning about control and data flows within programs, generating complex data structures, and even decompiling assembly code. Notably, GPT-4 showcases significant improvements over GPT-3.5 in most security tasks. Also, certain limitations of ChatGPT in security-related tasks are identified, such as its constrained ability to process long code contexts.

cs.CR↗

arXiv2023

CALICO: Self-Supervised Camera-LiDAR Contrastive Pre-training for BEV Perception

Perception is crucial in the realm of autonomous driving systems, where bird's eye view (BEV)-based architectures have recently reached state-of-the-art performance. The desirability of self-supervised representation learning stems from the expensive and laborious process of annotating 2D and 3D data. Although previous research has investigated pretraining methods for both LiDAR and camera-based 3D object detection, a unified pretraining framework for multimodal BEV perception is missing. In this study, we introduce CALICO, a novel framework that applies contrastive objectives to both LiDAR and camera backbones. Specifically, CALICO incorporates two stages: point-region contrast (PRC) and region-aware distillation (RAD). PRC better balances the region- and scene-level representation learning on the LiDAR modality and offers significant performance improvement compared to existing methods. RAD effectively achieves contrastive distillation on our self-trained teacher model. CALICO's efficacy is substantiated by extensive evaluations on 3D object detection and BEV map segmentation tasks, where it delivers significant performance improvements. Notably, CALICO outperforms the baseline method by 10.5% and 8.6% on NDS and mAP. Moreover, CALICO boosts the robustness of multimodal 3D object detection against adversarial attacks and corruption. Additionally, our framework can be tailored to different backbones and heads, positioning it as a promising approach for multimodal BEV perception.

cs.CV↗

arXiv2023

Hierarchical False Discovery Rate Control for High-dimensional Survival Analysis with Interactions

With the development of data collection techniques, analysis with a survival response and high-dimensional covariates has become routine. Here we consider an interaction model, which includes a set of low-dimensional covariates, a set of high-dimensional covariates, and their interactions. This model has been motivated by gene-environment (G-E) interaction analysis, where the E variables have a low dimension, and the G variables have a high dimension. For such a model, there has been extensive research on estimation and variable selection. Comparatively, inference studies with a valid false discovery rate (FDR) control have been very limited. The existing high-dimensional inference tools cannot be directly applied to interaction models, as interactions and main effects are not ``equal". In this article, for high-dimensional survival analysis with interactions, we model survival using the Accelerated Failure Time (AFT) model and adopt a ``weighted least squares + debiased Lasso'' approach for estimation and selection. A hierarchical FDR control approach is developed for inference and respect of the ``main effects, interactions'' hierarchy. { The asymptotic distribution properties of the debiased Lasso estimators} are rigorously established. Simulation demonstrates the satisfactory performance of the proposed approach, and the analysis of a breast cancer dataset further establishes its practical utility.

stat.ME↗

arXiv2023

On Data Fabrication in Collaborative Vehicular Perception: Attacks and Countermeasures

Collaborative perception, which greatly enhances the sensing capability of connected and autonomous vehicles (CAVs) by incorporating data from external resources, also brings forth potential security risks. CAVs' driving decisions rely on remote untrusted data, making them susceptible to attacks carried out by malicious participants in the collaborative perception system. However, security analysis and countermeasures for such threats are absent. To understand the impact of the vulnerability, we break the ground by proposing various real-time data fabrication attacks in which the attacker delivers crafted malicious data to victims in order to perturb their perception results, leading to hard brakes or increased collision risks. Our attacks demonstrate a high success rate of over 86% on high-fidelity simulated scenarios and are realizable in real-world experiments. To mitigate the vulnerability, we present a systematic anomaly detection approach that enables benign vehicles to jointly reveal malicious fabrication. It detects 91.5% of attacks with a false positive rate of 3% in simulated scenarios and significantly mitigates attack impacts in real-world scenarios.

cs.CR↗

arXiv2023

Regulation-incorporated Gene Expression Network-based Heterogeneity Analysis

Gene expression-based heterogeneity analysis has been extensively conducted. In recent studies, it has been shown that network-based analysis, which takes a system perspective and accommodates the interconnections among genes, can be more informative than that based on simpler statistics. Gene expressions are highly regulated. Incorporating regulations in analysis can better delineate the "sources" of gene expression effects. Although conditional network analysis can somewhat serve this purpose, it does render enough attention to the regulation relationships. In this article, significantly advancing from the existing heterogeneity analyses based only on gene expression networks, conditional gene expression network analyses, and regression-based heterogeneity analyses, we propose heterogeneity analysis based on gene expression networks (after accounting for or "removing" regulation effects) as well as regulations of gene expressions. A high-dimensional penalized fusion approach is proposed, which can determine the number of sample groups and parameter values in a single step. An effective computational algorithm is proposed. It is rigorously proved that the proposed approach enjoys the estimation, selection, and grouping consistency properties. Extensive simulations demonstrate its practical superiority over closely related alternatives. In the analysis of two breast cancer datasets, the proposed approach identifies heterogeneity and gene network structures different from the alternatives and with sound biological implications.

stat.ME↗

arXiv2023

Locally sparse quantile estimation for a partially functional interaction model

Functional data analysis has been extensively conducted. In this study, we consider a partially functional model, under which some covariates are scalars and have linear effects, while some other variables are functional and have unspecified nonlinear effects. Significantly advancing from the existing literature, we consider a model with interactions between the functional and scalar covariates. To accommodate long-tailed error distributions which are not uncommon in data analysis, we adopt the quantile technique for estimation. To achieve more interpretable estimation, and to accommodate many practical settings, we assume that the functional covariate effects are locally sparse (that is, there exist subregions on which the effects are exactly zero), which naturally leads to a variable/model selection problem. We propose respecting the "main effect, interaction" hierarchy, which postulates that if a subregion has a nonzero effect in an interaction term, then its effect has to be nonzero in the corresponding main functional effect. For estimation, identification of local sparsity, and respect of the hierarchy, we propose a penalization approach. An effective computational algorithm is developed, and the consistency properties are rigorously established under mild regularity conditions. Simulation shows the practical effectiveness of the proposed approach. The analysis of the Tecator data further demonstrates its practical applicability. Overall, this study can deliver a novel and practically useful model and a statistically and numerically satisfactory estimation approach.

stat.ME↗

arXiv2022

Heterogeneity-aware Clustered Distributed Learning for Multi-source Data Analysis

In diverse fields ranging from finance to omics, it is increasingly common that data is distributed and with multiple individual sources (referred to as ``clients'' in some studies). Integrating raw data, although powerful, is often not feasible, for example, when there are considerations on privacy protection. Distributed learning techniques have been developed to integrate summary statistics as opposed to raw data. In many of the existing distributed learning studies, it is stringently assumed that all the clients have the same model. To accommodate data heterogeneity, some federated learning methods allow for client-specific models. In this article, we consider the scenario that clients form clusters, those in the same cluster have the same model, and different clusters have different models. Further considering the clustering structure can lead to a better understanding of the ``interconnections'' among clients and reduce the number of parameters. To this end, we develop a novel penalization approach. Specifically, group penalization is imposed for regularized estimation and selection of important variables, and fusion penalization is imposed to automatically cluster clients. An effective ADMM algorithm is developed, and the estimation, selection, and clustering consistency properties are established under mild conditions. Simulation and data analysis further demonstrate the practical utility and superiority of the proposed approach.

stat.ME↗

arXiv2022

On Adversarial Robustness of Trajectory Prediction for Autonomous Vehicles

Trajectory prediction is a critical component for autonomous vehicles (AVs) to perform safe planning and navigation. However, few studies have analyzed the adversarial robustness of trajectory prediction or investigated whether the worst-case prediction can still lead to safe planning. To bridge this gap, we study the adversarial robustness of trajectory prediction models by proposing a new adversarial attack that perturbs normal vehicle trajectories to maximize the prediction error. Our experiments on three models and three datasets show that the adversarial prediction increases the prediction error by more than 150%. Our case studies show that if an adversary drives a vehicle close to the target AV following the adversarial trajectory, the AV may make an inaccurate prediction and even make unsafe driving decisions. We also explore possible mitigation techniques via data augmentation and trajectory smoothing. The implementation is open source at https://github.com/zqzqz/AdvTrajectoryPrediction.

cs.CV↗

arXiv2022

Benchmarking Robustness of 3D Point Cloud Recognition Against Common Corruptions

Deep neural networks on 3D point cloud data have been widely used in the real world, especially in safety-critical applications. However, their robustness against corruptions is less studied. In this paper, we present ModelNet40-C, the first comprehensive benchmark on 3D point cloud corruption robustness, consisting of 15 common and realistic corruptions. Our evaluation shows a significant gap between the performances on ModelNet40 and ModelNet40-C for state-of-the-art (SOTA) models. To reduce the gap, we propose a simple but effective method by combining PointCutMix-R and TENT after evaluating a wide range of augmentation and test-time adaptation strategies. We identify a number of critical insights for future studies on corruption robustness in point cloud recognition. For instance, we unveil that Transformer-based architectures with proper training recipes achieve the strongest robustness. We hope our in-depth analysis will motivate the development of robust training strategies or architecture designs in the 3D point cloud domain. Our codebase and dataset are included in https://github.com/jiachens/ModelNet40-C

cs.LG↗

arXiv2020

Integrative Sparse Partial Least Squares

Partial least squares, as a dimension reduction method, has become increasingly important for its ability to deal with problems with a large number of variables. Since noisy variables may weaken the performance of the model, the sparse partial least squares (SPLS) technique has been proposed to identify important variables and generate more interpretable results. However, the small sample size of a single dataset limits the performance of conventional methods. An effective solution comes from gathering information from multiple comparable studies. The integrative analysis holds an important status among multi-datasets analyses. The main idea is to improve estimation results by assembling raw datasets and analyzing them jointly. In this paper, we develop an integrative SPLS (iSPLS) method using penalization based on the SPLS technique. The proposed approach consists of two penalties. The first penalty conducts variable selection under the context of integrative analysis; The second penalty, a contrasted one, is imposed to encourage the similarity of estimates across datasets and generate more reasonable and accurate results. Computational algorithms are provided. Simulation experiments are conducted to compare iSPLS with alternative approaches. The practical utility of iSPLS is shown in the analysis of two TCGA gene expression data.

stat.ME↗

arXiv2020

Robust Identification of Gene-Environment Interactions under High-Dimensional Accelerated Failure Time Models

For complex diseases, beyond the main effects of genetic (G) and environmental (E) factors, gene-environment (G-E) interactions also play an important role. Many of the existing G-E interaction methods conduct marginal analysis, which may not appropriately describe disease biology. Joint analysis methods have been developed, with most of the existing loss functions constructed based on likelihood. In practice, data contamination is not uncommon. Development of robust methods for interaction analysis that can accommodate data contamination is very limited. In this study, we consider censored survival data and adopt an accelerated failure time (AFT) model. An exponential squared loss is adopted to achieve robustness. A sparse group penalization approach, which respects the "main effects, interactions" hierarchy, is adopted for estimation and identification. Consistency properties are rigorously established. Simulation shows that the proposed method outperforms direct competitors. In data analysis, the proposed method makes biologically sensible findings.

stat.ME↗

arXiv2018

Structured gene-environment interaction analysis

For the etiology, progression, and treatment of complex diseases, gene-environment (G-E) interactions have important implications beyond the main G and E effects. G-E interaction analysis can be more challenging with the higher dimensionality and need for accommodating the "main effects, interactions" hierarchy. In the recent literature, an array of novel methods, many of which are based on the penalization technique, have been developed. In most of these studies, however, the structures of G measurements, for example the adjacency structure of SNPs (attributable to their physical adjacency on the chromosomes) and network structure of gene expressions (attributable to their coordinated biological functions and correlated measurements), have not been well accommodated. In this study, we develop the structured G-E interaction analysis, where such structures are accommodated using penalization for both the main G effects and interactions. Penalization is also applied for regularized estimation and selection. The proposed structured interaction analysis can be effectively realized. It is shown to have the consistency properties under high dimensional settings. Simulations and the analysis of GENEVA diabetes data with SNP measurements and TCGA melanoma data with gene expression measurements demonstrate its competitive practical performance.

stat.ME↗

arXiv2016

Identifying Gene-Environment Interactions with A Least Relative Error Approach

For complex diseases, the interactions between genetic and environmental risk factors can have important implications beyond the main effects. Many of the existing interaction analyses conduct marginal analysis and cannot accommodate the joint effects of multiple main effects and interactions. In this study, we conduct joint analysis which can simultaneously accommodate a large number of effects. Significantly different from the existing studies, we adopt loss functions based on relative errors, which offer a useful alternative to the "classic" methods such as the least squares and least absolute deviation. Further to accommodate censoring in the response variable, we adopt a weighted approach. Penalization is used for identification and regularized estimation. Computationally, we develop an effective algorithm which combines the majorize-minimization and coordinate descent. Simulation shows that the proposed approach has satisfactory performance. We also analyze lung cancer prognosis data with gene expression measurements.

stat.ME↗

arXiv2015

A Robust Approach for Identifying Gene-Environment Interactions for Prognosis

For many complex diseases, prognosis is of essential importance. It has been shown that, beyond the main effects of genetic (G) and environmental (E) risk factors, the gene-environment (G$\times$E) interactions also play a critical role. In practice, the prognosis outcome data can be contaminated, and most of the existing methods are not robust to data contamination. In the literature, it has been shown that even a single contaminated observation can lead to severely biased model estimation. In this study, we describe prognosis using an accelerated failure time (AFT) model. An exponential squared loss is proposed to accommodate possible data contamination. A penalization approach is adopted for regularized estimation and marker selection. The proposed method is realized using an effective coordinate descent (CD) and minorization maximization (MM) algorithm. Simulation shows that without contamination, the proposed method has performance comparable to or better than the unrobust alternative. With contamination, it outperforms the unrobust alternative and, under certain scenarios, can be superior to the robust method based on quantile regression. The proposed method is applied to the analysis of TCGA (The Cancer Genome Atlas) lung cancer data. It identifies interactions different from those using the alternatives. The identified marker have important implications and satisfactory stability.

stat.AP↗

arXiv2015

Promoting Similarity of Sparsity Structures in Integrative Analysis with Penalization

For data with high-dimensional covariates but small to moderate sample sizes, the analysis of single datasets often generates unsatisfactory results. The integrative analysis of multiple independent datasets provides an effective way of pooling information and outperforms single-dataset analysis and some alternative multi-datasets approaches including meta-analysis. Under certain scenarios, multiple datasets are expected to share common important covariates, that is, the multiple models have similarity in sparsity structures. However, the existing methods do not have a mechanism to {\it promote} the similarity of sparsity structures in integrative analysis. In this study, we consider penalized variable selection and estimation in integrative analysis. We develop an $L_0$-penalty based approach, which is the first to explicitly promote the similarity of sparsity structures. Computationally it is realized using a coordinate descent algorithm. Theoretically it has the much desired consistency properties. In simulation, it significantly outperforms the competing alternative when the models in multiple datasets share common important covariates. It has better or similar performance as the alternative when the sparsity structures share no similarity. Thus it provides a "safe" choice for data analysis. Applying the proposed method to three lung cancer datasets with gene expression measurements leads to models with significantly more similar sparsity structures and better prediction performance.

stat.ME↗

arXiv2015

Penalized integrative analysis under the accelerated failure time model

For survival data with high-dimensional covariates, results generated in the analysis of a single dataset are often unsatisfactory because of the small sample size. Integrative analysis pools raw data from multiple independent studies with comparable designs, effectively increases sample size, and has better performance than meta-analysis and single-dataset analysis. In this study, we conduct integrative analysis of survival data under the accelerated failure time (AFT) model. The sparsity structures of multiple datasets are described using the homogeneity and heterogeneity models. For variable selection under the homogeneity model, we adopt group penalization approaches. For variable selection under the heterogeneity model, we use composite penalization and sparse group penalization approaches. As a major advancement from the existing studies, the asymptotic selection and estimation properties are rigorously established. Simulation study is conducted to compare different penalization methods and against alternatives. We also analyze four lung cancer prognosis datasets with gene expression measurements.

stat.ME↗