Search Results for author: Gang Qu

Found 10 papers, 4 papers with code

LLM4SecHW: Leveraging Domain Specific Large Language Model for Hardware Debugging

no code implementations28 Jan 2024 Weimin Fu, Kaichen Yang, Raj Gautam Dutta, Xiaolong Guo, Gang Qu

To address these challenges, we propose a unique approach to compile a dataset of open source hardware design defects and their remediation steps, utilizing version control data.

Language Modelling Large Language Model

Exploring General Intelligence via Gated Graph Transformer in Functional Connectivity Studies

no code implementations18 Jan 2024 Gang Qu, Anton Orlichenko, Junqi Wang, Gemeng Zhang, Li Xiao, Aiying Zhang, Zhengming Ding, Yu-Ping Wang

Functional connectivity (FC) as derived from fMRI has emerged as a pivotal tool in elucidating the intricacies of various psychiatric disorders and delineating the neural pathways that underpin cognitive and behavioral dynamics inherent to the human brain.

Identifiability in Functional Connectivity May Unintentionally Inflate Prediction Results

1 code implementation2 Aug 2023 Anton Orlichenko, Gang Qu, Kuan-Jui Su, Anqi Liu, Hui Shen, Hong-Wen Deng, Yu-Ping Wang

Using the UK Biobank dataset, we find one can achieve the same level of variance explained with 50 training subjects by exploiting identifiability as with 10, 000 training subjects without double-dipping.

Angle Basis: a Generative Model and Decomposition for Functional Connectivity

1 code implementation17 May 2023 Anton Orlichenko, Gang Qu, Ziyu Zhou, Zhengming Ding, Yu-Ping Wang

We also find that both the decomposition and its residual have approximately equal predictive value, and when combined into an ensemble, exceed the AUC of FC-based prediction by up to 5%.

Latent Similarity Identifies Important Functional Connections for Phenotype Prediction

1 code implementation30 Aug 2022 Anton Orlichenko, Gang Qu, Gemeng Zhang, Binish Patel, Tony W. Wilson, Julia M. Stephen, Vince D. Calhoun, Yu-Ping Wang

Significance: We propose a novel algorithm for small sample, high feature dimension datasets and use it to identify connections in task fMRI data.

Computational Efficiency Metric Learning

Fooling the Eyes of Autonomous Vehicles: Robust Physical Adversarial Examples Against Traffic Sign Recognition Systems

no code implementations17 Jan 2022 Wei Jia, Zhaojun Lu, Haichun Zhang, Zhenglin Liu, Jie Wang, Gang Qu

From the view of object detectors, the traffic sign`s position and quality of the video are continuously changing, rendering the digital AEs ineffective in the physical world.

Object Traffic Sign Recognition

Don't Forget to Sign the Gradients!

1 code implementation5 Mar 2021 Omid Aramoon, Pin-Yu Chen, Gang Qu

Engineering a top-notch deep learning model is an expensive procedure that involves collecting data, hiring human resources with expertise in machine learning, and providing high computational resources.

Image Classification

Meta Federated Learning

no code implementations10 Feb 2021 Omid Aramoon, Pin-Yu Chen, Gang Qu, Yuan Tian

Due to its distributed methodology alongside its privacy-preserving features, Federated Learning (FL) is vulnerable to training time adversarial attacks.

Federated Learning Privacy Preserving

Ensemble manifold based regularized multi-modal graph convolutional network for cognitive ability prediction

no code implementations20 Jan 2021 Gang Qu, Li Xiao, Wenxing Hu, Kun Zhang, Vince D. Calhoun, Yu-Ping Wang

Methods: To take advantage of complementary information from multi-modal fMRI, we propose an interpretable multi-modal graph convolutional network (MGCN) model, incorporating the fMRI time series and the functional connectivity (FC) between each pair of brain regions.

Graph Embedding Time Series Analysis

Distance Correlation Based Brain Functional Connectivity Estimation and Non-Convex Multi-Task Learning for Developmental fMRI Studies

no code implementations30 Sep 2020 Li Xiao, Biao Cai, Gang Qu, Julia M. Stephen, Tony W. Wilson, Vince D. Calhoun, Yu-Ping Wang

Resting-state functional magnetic resonance imaging (rs-fMRI)-derived functional connectivity patterns have been extensively utilized to delineate global functional organization of the human brain in health, development, and neuropsychiatric disorders.

Connectivity Estimation Multi-Task Learning

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