46 citations · 163 across the 24 of their papers we have counts for
25 papers
Probing Late-Stage Hadronic Interactions at High Baryon Density via Production in the RHIC Beam Energy Scan Program
STAR Collaboration, B. E. Aboona, J. Adam +360
A precision measurement of the meson yield is reported in Au+Au collisions at and using the high-statistic…
Carrier Aggregation Enabled Integrated Sensing and Communication Signal Design and Processing
Zhiqing Wei, Haotian Liu, Xinyi Yang +4
The future mobile communication systems will support intelligent applications such as Internet of Vehicles (IoV) and Extended Reality (XR). Integrated Sensing and Communication (IS…
Real-time Monitoring for the Next Core-Collapse Supernova in JUNO
Angel Abusleme, Thomas Adam, Shakeel Ahmad +623
The core-collapse supernova (CCSN) is considered one of the most energetic astrophysical events in the universe. The early and prompt detection of neutrinos before (pre-SN) and dur…
Deep Learning Approaches in Pavement Distress Identification: A Review
Sizhe Guan, Haolan Liu, Hamid R. Pourreza +1
This paper presents a comprehensive review of recent advancements in image processing and deep learning techniques for pavement distress detection and classification, a critical as…
Operator Splitting/Finite Element Methods for the Minkowski Problem
Hao Liu, Shingyu Leung, Jianliang Qian
The classical Minkowski problem for convex bodies has deeply influenced the development of differential geometry. During the past several decades, abundant mathematical theories ha…
Connections between Operator-splitting Methods and Deep Neural Networks with Applications in Image Segmentation
Hao Liu, Xue-Cheng Tai, Raymond Chan
Deep neural network is a powerful tool for many tasks. Understanding why it is so successful and providing a mathematical explanation is an important problem and has been one popul…
PottsMGNet: A Mathematical Explanation of Encoder-Decoder Based Neural Networks
Xue-Cheng Tai, Hao Liu, Raymond Chan
For problems in image processing and many other fields, a large class of effective neural networks has encoder-decoder-based architectures. Although these networks have made impres…
Fine-grained Action Analysis: A Multi-modality and Multi-task Dataset of Figure Skating
Sheng-Lan Liu, Yu-Ning Ding, Gang Yan +4
The fine-grained action analysis of the existing action datasets is challenged by insufficient action categories, low fine granularities, limited modalities, and tasks. In this pap…
Bkd-FedGNN: A Benchmark for Classification Backdoor Attacks on Federated Graph Neural Network
Fan Liu, Siqi Lai, Yansong Ning +1
Federated Graph Neural Network (FedGNN) has recently emerged as a rapidly growing research topic, as it integrates the strengths of graph neural networks and federated learning to…
JUNO sensitivity to the annihilation of MeV dark matter in the galactic halo
JUNO Collaboration, Angel Abusleme, Thomas Adam +596
We discuss JUNO sensitivity to the annihilation of MeV dark matter in the galactic halo via detecting inverse beta decay reactions of electron anti-neutrinos resulting from the ann…
Group projected Subspace Pursuit for Identification of variable coefficient differential equations (GP-IDENT)
Yuchen He, Sung-Ha Kang, Wenjing Liao +2
We propose an effective and robust algorithm for identifying partial differential equations (PDEs) with space-time varying coefficients from a single trajectory of noisy observatio…
On Degeneracy Issues in Multi-parametric Programming and Critical Region Exploration based Distributed Optimization in Smart Grid Operations
Haitian Liu, Ye Guo, Hao Liu
Improving renewable energy resource utilization efficiency is crucial to reducing carbon emissions, and multi-parametric programming has provided a systematic perspective in conduc…
The JUNO experiment Top Tracker
JUNO Collaboration, Angel Abusleme, Thomas Adam +609
The main task of the Top Tracker detector of the neutrino reactor experiment Jiangmen Underground Neutrino Observatory (JUNO) is to reconstruct and extrapolate atmospheric muon tra…
Deep Nonparametric Estimation of Intrinsic Data Structures by Chart Autoencoders: Generalization Error and Robustness
Hao Liu, Alex Havrilla, Rongjie Lai +1
Autoencoders have demonstrated remarkable success in learning low-dimensional latent features of high-dimensional data across various applications. Assuming that data are sampled n…
High Dimensional Binary Classification under Label Shift: Phase Transition and Regularization
Jiahui Cheng, Minshuo Chen, Hao Liu +2
Label Shift has been widely believed to be harmful to the generalization performance of machine learning models. Researchers have proposed many approaches to mitigate the impact of…
Numerical Identification of Nonlocal Potential in Aggregation
Yuchen He, Sung Ha Kang, Wenjing Liao +2
Aggregation equations are broadly used to model population dynamics with nonlocal interactions, characterized by a potential in the equation. This paper considers the inverse probl…
Benefits of Overparameterized Convolutional Residual Networks: Function Approximation under Smoothness Constraint
Hao Liu, Minshuo Chen, Siawpeng Er +3
Overparameterized neural networks enjoy great representation power on complex data, and more importantly yield sufficiently smooth output, which is crucial to their generalization…
An Efficient Operator-Splitting Method for the Eigenvalue Problem of the Monge-Ampère Equation
Hao Liu, Shingyu Leung, Jianliang Qian
We develop an efficient operator-splitting method for the eigenvalue problem of the Monge-Ampère operator in the Aleksandrov sense. The backbone of our method relies on a convergen…
Elastica Models for Color Image Regularization
Hao Liu, Xue-Cheng Tai, Ron Kimmel +1
One classical approach to regularize color is to tream them as two dimensional surfaces embedded in a five dimensional spatial-chromatic space. In this case, a natural regularizati…
Deep Nonparametric Estimation of Operators between Infinite Dimensional Spaces
Hao Liu, Haizhao Yang, Minshuo Chen +2
Learning operators between infinitely dimensional spaces is an important learning task arising in wide applications in machine learning, imaging science, mathematical modeling and…
Statistics of non-polarized points in the CMB polarization maps
Jaan Kasak, James Creswell, Hao Liu +1
The non-polarized points (NPP) of the and Stokes parameters of the CMB can be classified according to the geometry of the polarization field. We describe a procedure to ide…
A Color Elastica Model for Vector-Valued Image Regularization
Hao Liu, Xue-Cheng Tai, Ron Kimmel +1
Models related to the Euler's elastica energy have proven to be useful for many applications including image processing. Extending elastica models to color images and multi-channel…
Curvature Regularized Surface Reconstruction from Point Cloud
Yuchen He, Sung Ha Kang, Hao Liu
We propose a variational functional and fast algorithms to reconstruct implicit surface from point cloud data with a curvature constraint. The minimizing functional balances the di…
Fast Algorithms for Surface Reconstruction from Point Cloud
Yuchen He, Martin Huska, Sung Ha Kang +1
We consider constructing a surface from a given set of point cloud data. We explore two fast algorithms to minimize the weighted minimum surface energy in [Zhao, Osher, Merriman an…