83 citations · 220 across the 26 of their papers we have counts for
28 papers
Controllable Lyrics-to-Melody Generation
Zhe Zhang, Yi Yu, Atsuhiro Takasu
Lyrics-to-melody generation is an interesting and challenging topic in AI music research field. Due to the difficulty of learning the correlations between lyrics and melody, previo…
Event-Centric Query Expansion in Web Search
Yanan Zhang, Weijie Cui, Yangfan Zhang +5
In search engines, query expansion (QE) is a crucial technique to improve search experience. Previous studies often rely on long-term search log mining, which leads to slow updates…
Energy loss enhancement of very intense proton beams in dense matter due to the beam-density effect
Benzheng Chen, Jieru Ren, Zhigang Deng +27
Thoroughly understanding the transport and energy loss of intense ion beams in dense matter is essential for high-energy-density physics and inertial confinement fusion. Here, we r…
SE-ORNet: Self-Ensembling Orientation-aware Network for Unsupervised Point Cloud Shape Correspondence
Jiacheng Deng, Chuxin Wang, Jiahao Lu +4
Unsupervised point cloud shape correspondence aims to obtain dense point-to-point correspondences between point clouds without manually annotated pairs. However, humans and some an…
CM-CASL: Comparison-based Performance Modeling of Software Systems via Collaborative Active and Semisupervised Learning
Rong Cao, Liang Bao, Chase Wu +3
Configuration tuning for large software systems is generally challenging due to the complex configuration space and expensive performance evaluation. Most existing approaches follo…
Efficient Gridless DoA Estimation Method of Non-uniform Linear Arrays with Applications in Automotive Radars
Silin Gao, Zhe Zhang, Muhan Wang +5
This paper focuses on the gridless direction-of-arrival (DoA) estimation for data acquired by non-uniform linear arrays (NLAs) in automotive applications. Atomic norm minimization…
Coincident Learning for Unsupervised Anomaly Detection
Ryan Humble, Zhe Zhang, Finn O'Shea +2
Anomaly detection is an important task for complex systems (e.g., industrial facilities, manufacturing, large-scale science experiments), where failures in a sub-system can lead to…
ATASI-Net: An Efficient Sparse Reconstruction Network for Tomographic SAR Imaging with Adaptive Threshold
Muhan Wang, Zhe Zhang, Xiaolan Qiu +2
Tomographic SAR technique has attracted remarkable interest for its ability of three-dimensional resolving along the elevation direction via a stack of SAR images collected from di…
Imputation Strategies Under Clinical Presence: Impact on Algorithmic Fairness
Vincent Jeanselme, Maria De-Arteaga, Zhe Zhang +2
Machine learning risks reinforcing biases present in data and, as we argue in this work, in what is absent from data. In healthcare, societal and decision biases shape patterns in…
Efficient Federated Learning with Spike Neural Networks for Traffic Sign Recognition
Kan Xie, Zhe Zhang, Bo Li +4
With the gradual popularization of self-driving, it is becoming increasingly important for vehicles to smartly make the right driving decisions and autonomously obey traffic rules…
TomoSAR-ALISTA: Efficient TomoSAR Imaging via Deep Unfolded Network
Muhan Wang, Zhe Zhang, Yue Wang +2
Synthetic aperture radar (SAR) tomography (TomoSAR) has attracted remarkable interest for its ability in achieving three-dimensional reconstruction along the elevation direction fr…
A Novel Gradient Descent Least Squares (GDLS) Algorithm for Efficient SMV Gridless Line Spectrum Estimation with Applications in Tomographic SAR Imaging
Ruizhe Shi, Zhe Zhang, Xiaolan Qiu +1
This paper presents a novel efficient method for gridless line spectrum estimation problem with single snapshot, namely the gradient descent least squares (GDLS) method. Convention…
BlockGNN: Towards Efficient GNN Acceleration Using Block-Circulant Weight Matrices
Zhe Zhou, Bizhao Shi, Zhe Zhang +3
In recent years, Graph Neural Networks (GNNs) appear to be state-of-the-art algorithms for analyzing non-euclidean graph data. By applying deep-learning to extract high-level repre…
MöbiusE: Knowledge Graph Embedding on Möbius Ring
Yao Chen, Jiangang Liu, Zhe Zhang +2
In this work, we propose a novel Knowledge Graph Embedding (KGE) strategy, called MöbiusE, in which the entities and relations are embedded to the surface of a Möbius ring. The pro…
New-Generation Design-Technology Co-Optimization (DTCO): Machine-Learning Assisted Modeling Framework
Zhe Zhang, Runsheng Wang, Cheng Chen +6
In this paper, we propose a machine-learning assisted modeling framework in design-technology co-optimization (DTCO) flow. Neural network (NN) based surrogate model is used as an a…
ANM-PhaseLift: Structured Line Spectrum Estimation from Quadratic Measurements
Zhe Zhang, Zhi Tian
PhaseLift is a noted convex optimization technique for phase retrieval that can recover a signal exactly from amplitude measurements only, with high probability. Conventional Phase…
Low-complexity optimization for Two-Dimensional Direction-of-arrival Estimation via Decoupled Atomic Norm Minimization
Zhi Tian, Zhe Zhang, Yue Wang
This paper presents an efficient optimization technique for super-resolution two-dimensional (2D) direction of arrival (DOA) estimation by introducing a new formulation of atomic n…
Discovering Job Preemptions in the Open Science Grid
Zhe Zhang, Brian Bockelman, Derek Weitzel +1
The Open Science Grid(OSG) is a world-wide computing system which facilitates distributed computing for scientific research. It can distribute a computationally intensive job to ge…
Increasing Parallelism in the ROOT I/O Subsystem
Guilherme Amadio, Brian Bockelman, Philippe Canal +3
When processing large amounts of data, the rate at which reading and writing can take place is a critical factor. High energy physics data processing relying on ROOT is no exceptio…
Trifo-VIO: Robust and Efficient Stereo Visual Inertial Odometry using Points and Lines
Feng Zheng, Grace Tsai, Zhe Zhang +3
In this paper, we present the Trifo Visual Inertial Odometry (Trifo-VIO), a tightly-coupled filtering-based stereo VIO system using both points and lines. Line features help improv…
PIRT: A Runtime Framework to Enable Energy-Efficient Real-Time Robotic Applications on Heterogeneous Architectures
Liu Liu, Shaoshan Liu, Zhe Zhang +3
Enabling full robotic workloads with diverse behaviors on mobile systems with stringent resource and energy constraints remains a challenge. In recent years, attempts have been mad…
Optimizing ROOT IO For Analysis
Brian Bockelman, Zhe Zhang, Jim Pivarski
The ROOT I/O (RIO) subsystem is foundational to most HEP experiments - it provides a file format, a set of APIs/semantics, and a reference implementation in C++. It is often found…
PIRVS: An Advanced Visual-Inertial SLAM System with Flexible Sensor Fusion and Hardware Co-Design
Zhe Zhang, Shaoshan Liu, Grace Tsai +3
In this paper, we present the PerceptIn Robotics Vision System (PIRVS) system, a visual-inertial computing hardware with embedded simultaneous localization and mapping (SLAM) algor…
Exploring compression techniques for ROOT IO
Zhe Zhang, Brian Bockelman
ROOT provides an flexible format used throughout the HEP community. The number of use cases - from an archival data format to end-stage analysis - has required a number of tradeoff…