Search Results for author: Shuai Huang

Found 29 papers, 8 papers with code

Solving Quadratic Systems with Full-Rank Matrices Using Sparse or Generative Priors

no code implementations16 Sep 2023 Junren Chen, Shuai Huang, Michael K. Ng, Zhaoqiang Liu

The problem of recovering a signal $\boldsymbol{x} \in \mathbb{R}^n$ from a quadratic system $\{y_i=\boldsymbol{x}^\top\boldsymbol{A}_i\boldsymbol{x},\ i=1,\ldots, m\}$ with full-rank matrices $\boldsymbol{A}_i$ frequently arises in applications such as unassigned distance geometry and sub-wavelength imaging.

Model-based T1, T2* and Proton Density Mapping Using a Bayesian Approach with Parameter Estimation and Complementary Undersampling Patterns

no code implementations5 Jul 2023 Shuai Huang, James J. Lah, Jason W. Allen, Deqiang Qiu

Purpose: To achieve automatic hyperparameter estimation for the joint recovery of quantitative MR images, we propose a Bayesian formulation of the reconstruction problem that incorporates the signal model.

Context De-confounded Emotion Recognition

1 code implementation CVPR 2023 Dingkang Yang, Zhaoyu Chen, Yuzheng Wang, Shunli Wang, Mingcheng Li, Siao Liu, Xiao Zhao, Shuai Huang, Zhiyan Dong, Peng Zhai, Lihua Zhang

However, a long-overlooked issue is that a context bias in existing datasets leads to a significantly unbalanced distribution of emotional states among different context scenarios.

Emotion Recognition

DynImp: Dynamic Imputation for Wearable Sensing Data Through Sensory and Temporal Relatedness

no code implementations26 Sep 2022 Zepeng Huo, Taowei Ji, Yifei Liang, Shuai Huang, Zhangyang Wang, Xiaoning Qian, Bobak Mortazavi

We argue that traditional methods have rarely made use of both times-series dynamics of the data as well as the relatedness of the features from different sensors.

Activity Recognition Denoising +3

Robust Quantitative Susceptibility Mapping via Approximate Message Passing with Parameter Estimation

1 code implementation29 Jul 2022 Shuai Huang, James J. Lah, Jason W. Allen, Deqiang Qiu

Purpose: For quantitative susceptibility mapping (QSM), the lack of ground-truth in clinical settings makes it challenging to determine suitable parameters for the dipole inversion.


Density-Aware Personalized Training for Risk Prediction in Imbalanced Medical Data

no code implementations23 Jul 2022 Zepeng Huo, Xiaoning Qian, Shuai Huang, Zhangyang Wang, Bobak J. Mortazavi

Medical events of interest, such as mortality, often happen at a low rate in electronic medical records, as most admitted patients survive.

Orthogonal Matrix Retrieval with Spatial Consensus for 3D Unknown-View Tomography

1 code implementation6 Jul 2022 Shuai Huang, Mona Zehni, Ivan Dokmanić, Zhizhen Zhao

Unknown-view tomography (UVT) reconstructs a 3D density map from its 2D projections at unknown, random orientations.


VFDS: Variational Foresight Dynamic Selection in Bayesian Neural Networks for Efficient Human Activity Recognition

no code implementations31 Mar 2022 Randy Ardywibowo, Shahin Boluki, Zhangyang Wang, Bobak Mortazavi, Shuai Huang, Xiaoning Qian

At its core is an implicit variational distribution on binary gates that are dependent on previous observations, which will select the next subset of features to observe.

Human Activity Recognition

A Probabilistic Bayesian Approach to Recover $R_2^*$ map and Phase Images for Quantitative Susceptibility Mapping

no code implementations9 Mar 2021 Shuai Huang, James J. Lah, Jason W. Allen, Deqiang Qiu

In order to achieve better image quality and avoid manual parameter tuning, we propose a probabilistic Bayesian approach to recover $R_2^*$ map and phase images for quantitative susceptibility mapping (QSM), while allowing automatic parameter estimation from undersampled data.

Compressive Sensing

Fast Nonconvex $T_2^*$ Mapping Using ADMM

no code implementations4 Aug 2020 Shuai Huang, James J. Lah, Jason W. Allen, Deqiang Qiu

Magnetic resonance (MR)-$T_2^*$ mapping is widely used to study hemorrhage, calcification and iron deposition in various clinical applications, it provides a direct and precise mapping of desired contrast in the tissue.

Compressive Sensing

Bayesian Massive MIMO Channel Estimation with Parameter Estimation Using Low-Resolution ADCs

no code implementations29 Jul 2020 Shuai Huang, Deqiang Qiu, Trac D. Tran

The proposed approach leads to a much simpler parameter estimation method, allowing us to work with the quantization noise model directly.

Quantization Information Theory Signal Processing Information Theory

Uncertainty Quantification for Deep Context-Aware Mobile Activity Recognition and Unknown Context Discovery

no code implementations3 Mar 2020 Zepeng Huo, Arash Pakbin, Xiaohan Chen, Nathan Hurley, Ye Yuan, Xiaoning Qian, Zhangyang Wang, Shuai Huang, Bobak Mortazavi

Activity recognition in wearable computing faces two key challenges: i) activity characteristics may be context-dependent and change under different contexts or situations; ii) unknown contexts and activities may occur from time to time, requiring flexibility and adaptability of the algorithm.

Clustering Human Activity Recognition +1

UQ-CHI: An Uncertainty Quantification-Based Contemporaneous Health Index for Degenerative Disease Monitoring

no code implementations21 Feb 2019 Aven Samareh, Shuai Huang

In this paper, we aim at filling this gap by developing an uncertainty quantification based contemporaneous longitudinal index, named UQ-CHI, with a particular focus on continuous patient monitoring of degenerative conditions.

Decision Making Management +1

Solving Complex Quadratic Systems with Full-Rank Random Matrices

1 code implementation14 Feb 2019 Shuai Huang, Sidharth Gupta, Ivan Dokmanić

We tackle the problem of recovering a complex signal $\boldsymbol x\in\mathbb{C}^n$ from quadratic measurements of the form $y_i=\boldsymbol x^*\boldsymbol A_i\boldsymbol x$, where $\boldsymbol A_i$ is a full-rank, complex random measurement matrix whose entries are generated from a rotation-invariant sub-Gaussian distribution.

Information Theory Information Theory

Adaptive Activity Monitoring with Uncertainty Quantification in Switching Gaussian Process Models

no code implementations8 Jan 2019 Randy Ardywibowo, Guang Zhao, Zhangyang Wang, Bobak Mortazavi, Shuai Huang, Xiaoning Qian

This power-efficient sensing scheme can be achieved by deciding which group of sensors to use at a given time, requiring an accurate characterization of the trade-off between sensor energy usage and the uncertainty in ignoring certain sensor signals while monitoring.

Gaussian Processes Human Activity Recognition +1

Geometric Invariants for Sparse Unknown View Tomography

1 code implementation25 Nov 2018 Mona Zehni, Shuai Huang, Ivan Dokmanić, Zhizhen Zhao

For a point source model, we show that these features reveal geometric information about the model such as the radial and pairwise distances.

Safe Active Feature Selection for Sparse Learning

no code implementations15 Jun 2018 Shaogang Ren, Jianhua Z. Huang, Shuai Huang, Xiaoning Qian

More critically, SAIF has the safe guarantee as it has the convergence guarantee to the optimal solution to the original full LASSO problem.

feature selection Sparse Learning

Reconstructing Point Sets from Distance Distributions

1 code implementation6 Apr 2018 Shuai Huang, Ivan Dokmanić

Our method is the first practical approach to solve the large-scale noisy beltway problem where the points lie on a loop.

A Robust AUC Maximization Framework with Simultaneous Outlier Detection and Feature Selection for Positive-Unlabeled Classification

no code implementations18 Mar 2018 Ke Ren, Haichuan Yang, Yu Zhao, Mingshan Xue, Hongyu Miao, Shuai Huang, Ji Liu

The positive-unlabeled (PU) classification is a common scenario in real-world applications such as healthcare, text classification, and bioinformatics, in which we only observe a few samples labeled as "positive" together with a large volume of "unlabeled" samples that may contain both positive and negative samples.

EEG feature selection +5

Predicting Depression Severity by Multi-Modal Feature Engineering and Fusion

no code implementations29 Nov 2017 Aven Samareh, Yan Jin, Zhangyang Wang, Xiangyu Chang, Shuai Huang

We present our preliminary work to determine if patient's vocal acoustic, linguistic, and facial patterns could predict clinical ratings of depression severity, namely Patient Health Questionnaire depression scale (PHQ-8).

Feature Engineering

Robust Emotion Recognition from Low Quality and Low Bit Rate Video: A Deep Learning Approach

no code implementations10 Sep 2017 Bowen Cheng, Zhangyang Wang, Zhaobin Zhang, Zhu Li, Ding Liu, Jianchao Yang, Shuai Huang, Thomas S. Huang

Emotion recognition from facial expressions is tremendously useful, especially when coupled with smart devices and wireless multimedia applications.

Decoder Emotion Recognition +1

Prognostics of Surgical Site Infections using Dynamic Health Data

no code implementations12 Nov 2016 Chuyang Ke, Yan Jin, Heather Evans, Bill Lober, Xiaoning Qian, Ji Liu, Shuai Huang

Since existing prediction models of SSI have quite limited capacity to utilize the evolving clinical data, we develop the corresponding solution to equip these mHealth tools with decision-making capabilities for SSI prediction with a seamless assembly of several machine learning models to tackle the analytic challenges arising from the spatial-temporal data.

Decision Making Imputation +1

Stacked Approximated Regression Machine: A Simple Deep Learning Approach

no code implementations14 Aug 2016 Zhangyang Wang, Shiyu Chang, Qing Ling, Shuai Huang, Xia Hu, Honghui Shi, Thomas S. Huang

With the agreement of my coauthors, I Zhangyang Wang would like to withdraw the manuscript "Stacked Approximated Regression Machine: A Simple Deep Learning Approach".


On Benefits of Selection Diversity via Bilevel Exclusive Sparsity

no code implementations CVPR 2016 Haichuan Yang, Yijun Huang, Lam Tran, Ji Liu, Shuai Huang

In this paper, we proposed a general bilevel exclusive sparsity formulation to pursue the diversity by restricting the overall sparsity and the sparsity in each group.

feature selection Image Classification

Identifying Alzheimer's Disease-Related Brain Regions from Multi-Modality Neuroimaging Data using Sparse Composite Linear Discrimination Analysis

no code implementations NeurIPS 2011 Shuai Huang, Jing Li, Jieping Ye, Teresa Wu, Kewei Chen, Adam Fleisher, Eric Reiman

This is especially true for early AD, at which stage the disease-related regions are most likely to be weak-effect regions that are difficult to be detected from a single modality alone.

feature selection Specificity

Learning Brain Connectivity of Alzheimer's Disease from Neuroimaging Data

no code implementations NeurIPS 2009 Shuai Huang, Jing Li, Liang Sun, Jun Liu, Teresa Wu, Kewei Chen, Adam Fleisher, Eric Reiman, Jieping Ye

Recent advances in neuroimaging techniques provide great potentials for effective diagnosis of Alzheimer’s disease (AD), the most common form of dementia.

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