Author name cluster

Jiahua Xu

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6 papers

2 author rows

ICRA Conference 2025 Conference Paper

LiDARDustX: A LiDAR Dataset for Dusty Unstructured Road Environments

Chenfeng Wei
Qi Wu
Si Zuo
Jiahua Xu
Boyang Zhao
Zeyu Yang
Guotao Xie
Shenhong Wang

Autonomous driving datasets are essential for validating the progress of intelligent vehicle algorithms, which include localization, perception, and prediction. However, existing datasets are predominantly focused on structured urban environments, which limits the exploration of unstructured and specialized scenarios, particularly those characterized by significant dust levels. This paper introduces the LiDARDustX dataset, which is specifically designed for perception tasks under high-dust conditions, such as those encountered in mining areas. The LiDARDustX dataset consists of 30, 000 LiDAR frames captured by six different LiDAR sensors, each accompanied by 3D bounding box annotations and point cloud semantic segmentation. Notably, over 80% of the dataset comprises dust-affected scenes. By utilizing this dataset, we have established a benchmark for evaluating the performance of state-of-the-art 3D detection and segmentation algorithms. Additionally, we have analyzed the impact of dust on perception accuracy and delved into the causes of these effects. The data and further information can be accessed at: https://github.com/vincentweikey/LiDARDustX.

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AAAI Conference 2025 Conference Paper

Motion Artifact Removal in Pixel-Frequency Domain via Alternate Masks and Diffusion Model

Jiahua Xu
Dawei Zhou
Lei Hu
Jianfeng Guo
Feng Yang
Zaiyi Liu
Nannan Wang
Xinbo Gao

Motion artifacts present in magnetic resonance imaging (MRI) can seriously interfere with clinical diagnosis. Removing motion artifacts is a straightforward solution and has been extensively studied. However, paired data are still heavily relied on in recent works and the perturbations in k-space (frequency domain) are not well considered, which limits their applications in the clinical field. To address these issues, we propose a novel unsupervised purification method which leverages pixel-frequency information of noisy MRI images to guide a pre-trained diffusion model to recover clean MRI images. Specifically, considering that motion artifacts are mainly concentrated in high-frequency components in k-space, we utilize the low-frequency components as the guide to ensure correct tissue textures. Additionally, given that high-frequency and pixel information are helpful for recovering shape and detail textures, we design alternate complementary masks to simultaneously destroy the artifact structure and exploit useful information. Quantitative experiments are performed on datasets from different tissues and show that our method achieves superior performance on several metrics. Qualitative evaluations with radiologists also show that our method provides better clinical feedback.

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I&C Journal 2024 Journal Article

Monomial Boolean functions with large high-order nonlinearities

Jinjie Gao
Haibin Kan
Yuan Li
Jiahua Xu
Qichun Wang

Exhibiting an explicit Boolean function with a large high-order nonlinearity is an important problem in cryptography, coding theory, and computational complexity. We prove lower bounds on the second-order, third-order, and higher order nonlinearities of some monomial Boolean functions. We prove lower bounds on the second-order nonlinearities of functions tr n ( x 7 ) and tr n ( x 2 r + 3 ) where n = 2 r. Among all monomial Boolean functions, our bounds match the best second-order nonlinearity lower bounds by Carlet [IEEE Transactions on Information Theory 54(3), 2008] and Yan and Tang [Discrete Mathematics 343(5), 2020] for odd and even n, respectively. We prove a lower bound on the third-order nonlinearity for functions tr n ( x 15 ), which is the best third-order nonlinearity lower bound. For any r, we prove that the r-th order nonlinearity of tr n ( x 2 r + 1 − 1 ) is at least 2 n − 1 − 2 ( 1 − 2 − r ) n + r 2 r − 1 − 1 − O ( 2 n 2 ). For r ≪ log 2 ⁡ n, this is the best lower bound among all explicit functions.

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JBHI Journal 2024 Journal Article

Protecting Prostate Cancer Classification From Rectal Artifacts via Targeted Adversarial Training

Lei Hu
Dawei Zhou
Jiahua Xu
Cheng Lu
Chu Han
Zhenwei Shi
Qikui Zhu
Xinbo Gao

Magnetic resonance imaging (MRI)-based deep neural networks (DNN) have been widely developed to perform prostate cancer (PCa) classification. However, in real-world clinical situations, prostate MRIs can be easily impacted by rectal artifacts, which have been found to lead to incorrect PCa classification. Existing DNN-based methods typically do not consider the interference of rectal artifacts on PCa classification, and do not design specific strategy to address this problem. In this study, we proposed a novel Targeted adversarial training with Proprietary Adversarial Samples (TPAS) strategy to defend the PCa classification model against the influence of rectal artifacts. Specifically, based on clinical prior knowledge, we generated proprietary adversarial samples with rectal artifact-pattern adversarial noise, which can severely mislead PCa classification models optimized by the ordinary training strategy. We then jointly exploited the generated proprietary adversarial samples and original samples to train the models. To demonstrate the effectiveness of our strategy, we conducted analytical experiments on multiple PCa classification models. Compared with ordinary training strategy, TPAS can effectively improve the single- and multi-parametric PCa classification at patient, slice and lesion level, and bring substantial gains to recent advanced models. In conclusion, TPAS strategy can be identified as a valuable way to mitigate the influence of rectal artifacts on deep learning models for PCa classification.

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YNIMG Journal 2021 Journal Article

Dynamic integration and segregation of amygdala subregional functional circuits linking to physiological arousal

Yimeng Zeng
Fuxiang Tao
Zaixu Cui
Liyun Wu
Jiahua Xu
Wenshan Dong
Chao Liu
Zhi Yang

The dynamical organization of brain networks is essential to support human cognition and emotion for rapid adaption to ever-changing environment. As the core nodes of emotion-related brain circuitry, the basolateral amygdala (BLA) and centromedial amygdala (CMA) as two major amygdalar nuclei, are recognized to play distinct roles in affective functions and internal states, via their unique connections with cortical and subcortical structures in rodents. However, little is known how the dynamical organization of emotion-related brain circuitry reflects internal autonomic responses in humans. Using resting-state functional magnetic resonance imaging (fMRI) with K-means clustering approach in a total of 79 young healthy individuals (cohort 1: 42; cohort 2: 37), we identified two distinct states of BLA- and CMA-based intrinsic connectivity patterns, with one state (integration) showing generally stronger BLA- and CMA-based intrinsic connectivity with multiple brain networks, while the other (segregation) exhibiting weaker yet dissociable connectivity patterns. In an independent cohort 2 of fMRI data with concurrent recording of skin conductance, we replicated two similar dynamic states and further found higher skin conductance level in the integration than segregation state. Moreover, machine learning-based Elastic-net regression analyses revealed that time-varying BLA and CMA intrinsic connectivity with distinct network configurations yield higher predictive values for spontaneous fluctuations of skin conductance level in the integration than segregation state. Our findings highlight dynamic functional organization of emotion-related amygdala nuclei circuits and networks and its links to spontaneous autonomic arousal in humans.

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YNIMG Journal 2020 Journal Article

The function of the hippocampus and middle temporal gyrus in forming new associations and concepts during the processing of novelty and usefulness features in creative designs

Jingyuan Ren
Furong Huang
Ying Zhou
Liping Zhuang
Jiahua Xu
Chuanji Gao
Shaozheng Qin
Jing Luo

Creative thought relies on the reorganization of existing knowledge to generate novel and useful concepts. However, how these new concepts are formed, especially through the processing of novelty and usefulness (which are usually regarded as the key properties of creativity), is not clear. Taking familiar and useful (FU) objects/designs as the starting point or fundamental baseline, we modified them into novel and useless (NS) objects/designs or novel and useful (NU) ones (i. e. , truly creative ones) to investigate how the features of novelty and usefulness are processed (processing of novelty: NU minus FU; processing of usefulness: NU minus NS). Specifically, we predicted that the creative integration of novelty and usefulness entails not only the formation of new associations, which could be critically mediated by the hippocampus and adjacent medial temporal lobe (MTL) areas, but also the formation of new concepts or categories, which is supported by the middle temporal gyrus (MTG). We found that both the MTL and the MTG were involved in the processing of novelty and usefulness. The MTG showed distinctive patterns of information processing, reflected by strengthened functional connectivity with the hippocampus to construct new concepts and strengthened functional connectivity with the executive control system to break the boundaries of old concepts. Additionally, participants’ subjective evaluations of concept distance showed that the distance between the familiar concept (FU) and the successfully constructed concept (NU) was larger than that between the FU and the unsuccessfully constructed concept (NS), and this pattern was found to correspond to the patterns of their neural representations in the MTG. These findings demonstrate the critical mechanism by which new associations and concepts are formed during novelty and usefulness processing in creative design; this mechanism may be critically mediated by the hippocampus-MTG connection.

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