Search Results for author: Yu-Ping Wang

Found 23 papers, 8 papers with code

ImageNomer: developing an fMRI and omics visualization tool to detect racial bias in functional connectivity

no code implementations1 Feb 2023 Anton Orlichenko, Grant Daly, Anqi Liu, Hui Shen, Hong-Wen Deng, Yu-Ping Wang

To remedy this, we develop ImageNomer, a data visualization and analysis tool that allows inspection of both subject-level and cohort-level features.

Data Visualization

KRF: Keypoint Refinement with Fusion Network for 6D Pose Estimation

1 code implementation7 Oct 2022 Irvin Haozhe Zhan, Yiheng Han, Yu-Ping Wang, Long Zeng, Yong-Jin Liu

The CIKP method introduces color information into registration and registers point cloud around each keypoint to increase stability.

6D Pose Estimation

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.

Metric Learning

MotionHint: Self-Supervised Monocular Visual Odometry with Motion Constraints

1 code implementation14 Sep 2021 Cong Wang, Yu-Ping Wang, Dinesh Manocha

A key aspect of our approach is to use an appropriate motion model that can help existing self-supervised monocular VO (SSM-VO) algorithms to overcome issues related to the local minima within their self-supervised loss functions.

Monocular Visual Odometry

A new approach to extracting coronary arteries and detecting stenosis in invasive coronary angiograms

no code implementations25 Jan 2021 Chen Zhao, Haipeng Tang, Daniel McGonigle, Zhuo He, Chaoyang Zhang, Yu-Ping Wang, Hong-Wen Deng, Robert Bober, Weihua Zhou

We aim to develop an automatic algorithm by deep learning to extract coronary arteries from ICAs. In this study, a multi-input and multi-scale (MIMS) U-Net with a two-stage recurrent training strategy was proposed for the automatic vessel segmentation.


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

Xiaomingbot: A Multilingual Robot News Reporter

no code implementations ACL 2020 Runxin Xu, Jun Cao, Mingxuan Wang, Jiaze Chen, Hao Zhou, Ying Zeng, Yu-Ping Wang, Li Chen, Xiang Yin, Xijin Zhang, Songcheng Jiang, Yuxuan Wang, Lei LI

This paper proposes the building of Xiaomingbot, an intelligent, multilingual and multimodal software robot equipped with four integral capabilities: news generation, news translation, news reading and avatar animation.

News Generation Translation +1

Point Set Voting for Partial Point Cloud Analysis

1 code implementation9 Jul 2020 Jun-ming Zhang, Weijia Chen, Yu-Ping Wang, Ram Vasudevan, Matthew Johnson-Roberson

This paper illustrates that this proposed method achieves state-of-the-art performance on shape classification, part segmentation and point cloud completion.

Point Cloud Classification Point Cloud Completion

Interpretable multimodal fusion networks reveal mechanisms of brain cognition

no code implementations16 Jun 2020 Wenxing Hu, Xianghe Meng, Yuntong Bai, Aiying Zhang, Biao Cai, Gemeng Zhang, Tony W. Wilson, Julia M. Stephen, Vince D. Calhoun, Yu-Ping Wang

Moreover, the estimated activation maps are class-specific, and the captured cross-data associations are interest/label related, which further facilitates class-specific analysis and biological mechanism analysis.

Object Recognition

Noise Robust TTS for Low Resource Speakers using Pre-trained Model and Speech Enhancement

no code implementations26 May 2020 Dongyang Dai, Li Chen, Yu-Ping Wang, Mu Wang, Rui Xia, Xuchen Song, Zhiyong Wu, Yuxuan Wang

Firstly, the speech synthesis model is pre-trained with both multi-speaker clean data and noisy augmented data; then the pre-trained model is adapted on noisy low-resource new speaker data; finally, by setting the clean speech condition, the model can synthesize the new speaker's clean voice.

Speech Enhancement Speech Synthesis

A generalized kernel machine approach to identify higher-order composite effects in multi-view datasets

no code implementations29 Apr 2020 Md. Ashad Alam, Chuan Qiu, Hui Shen, Yu-Ping Wang, Hong-Wen Deng

In this paper, we propose a novel generalized kernel machine approach to identify higher-order composite effects in multi-view biomedical datasets.

Multimodal Sparse Classifier for Adolescent Brain Age Prediction

no code implementations1 Apr 2019 Peyman Hosseinzadeh Kassani, Alexej Gossmann, Yu-Ping Wang

The study of healthy brain development helps to better understand the brain transformation and brain connectivity patterns which happen during childhood to adulthood.

Kernel Method for Detecting Higher Order Interactions in multi-view Data: An Application to Imaging, Genetics, and Epigenetics

no code implementations14 Jul 2017 Md. Ashad Alam, Hui-Yi Lin, Vince Calhoun, Yu-Ping Wang

In this study, we tested the interaction effect of multimodal datasets using a novel method called the kernel method for detecting higher order interactions among biologically relevant mulit-view data.

FDR-Corrected Sparse Canonical Correlation Analysis with Applications to Imaging Genomics

1 code implementation11 May 2017 Alexej Gossmann, Pascal Zille, Vince Calhoun, Yu-Ping Wang

Here we propose a way of applying the FDR concept to sparse CCA, and a method to control the FDR.

Influence Function and Robust Variant of Kernel Canonical Correlation Analysis

no code implementations9 May 2017 Md. Ashad Alam, Kenji Fukumizu, Yu-Ping Wang

Many unsupervised kernel methods rely on the estimation of the kernel covariance operator (kernel CO) or kernel cross-covariance operator (kernel CCO).

Learning Schizophrenia Imaging Genetics Data Via Multiple Kernel Canonical Correlation Analysis

no code implementations15 Sep 2016 Owen Richfield, Md. Ashad Alam, Vince Calhoun, Yu-Ping Wang

Kernel and Multiple Kernel Canonical Correlation Analysis (CCA) are employed to classify schizophrenic and healthy patients based on their SNPs, DNA Methylation and fMRI data.

Classification General Classification

Identifying Outliers using Influence Function of Multiple Kernel Canonical Correlation Analysis

no code implementations1 Jun 2016 Md. Ashad Alam, Yu-Ping Wang

Second, we propose an IF of multiple kernel CCA, which can be applied for more than two datasets.


Gene-Gene association for Imaging Genetics Data using Robust Kernel Canonical Correlation Analysis

no code implementations1 Jun 2016 Md. Ashad Alam, Osamu Komori, Yu-Ping Wang

Third, we propose a nonparametric robust KCCU method based on robust kernel CCA, which is designed for contaminated data and less sensitive to noise than classical kernel CCA.


Robust Kernel (Cross-) Covariance Operators in Reproducing Kernel Hilbert Space toward Kernel Methods

no code implementations17 Feb 2016 Md. Ashad Alam, Kenji Fukumizu, Yu-Ping Wang

Finally, we propose a method based on robust kernel CO and robust kernel CCO, called robust kernel CCA, which is designed for contaminated data and less sensitive to noise than classical kernel CCA.

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