661 citations · 2k across the 35 of their papers we have counts for
35 papers
Abundance Pattern Fitting with Bayesian Inference: Constraining First Stars' Properties and Their Explosion Mechanism with Extremely Metal-poor Stars
Ruizheng Jiang, Haining Li, Gang Zhao +2
The abundance patterns of extremely metal-poor stars preserve a fossil record of the Universe's earliest chemical enrichment by the supernova explosions from the evolution of first…
The Stellar Abundances and Galactic Evolution Survey (SAGES). IV. Surface Gravity Estimation and Giant-Dwarf Separation with the DDO51 Filter
Qiqian Zhang, Zhou Fan, Gang Zhao +16
Reliable estimation of stellar surface gravity (log ) for a large sample is crucial for evaluating stellar evolution models and understanding galactic structure; However, it is…
The Stellar Abundances and Galactic Evolution Survey (SAGES). II. Machine Learning-Based Stellar parameters for 21 million stars from the First Data Release
Hongrui Gu, Zhou Fan, Gang Zhao +14
Stellar parameters for large samples of stars play a crucial role in constraining the nature of stars and stellar populations in the Galaxy. An increasing number of medium-band pho…
The Stellar Abundances and Galactic Evolution Survey (SAGES) III -- The g/r/i-band Data Release
Chun Li, Zhou Fan, Gang Zhao +15
The Stellar Abundances and Galactic Evolution Survey (SAGES) is a multi-band survey that covers the northern sky area of ~12000 deg2. Nanshan One-meter Wide-field Telescope (NOWT)…
The Kinematic and Chemical Properties of the Close-in Planet Host Star 8 UMi
Huiling Chen, Yang Huang, Wei Zhu +11
A recent study by Hon et al. reported that a close-in planet around the red clump star, 8 UMi, should have been engulfed during the expansion phase of its parent star's evolution.…
Beyond spectroscopy. II. Stellar parameters for over twenty million stars in the northern sky from SAGES DR1 and Gaia DR3
Yang Huang, Timothy C. Beers, Hai-Bo Yuan +18
We present precise photometric estimates of stellar parameters, including effective temperature, metallicity, luminosity classification, distance, and stellar age, for nearly 26 mi…
Thermodynamic and Magnetic Topology Evolution of the X1.0 Flare on 2021 October 28 Simulated by a Data-driven Radiative Magnetohydrodynamic Model
Jin-han Guo, Yi-wei Ni, Ze Zhong +6
Solar filament eruptions, flares and coronal mass ejections (CMEs) are manifestations of drastic release of energy in the magnetic field, which are related to many eruptive phenome…
HCE: Improving Performance and Efficiency with Heterogeneously Compressed Neural Network Ensemble
Jingchi Zhang, Huanrui Yang, Hai Li
Ensemble learning has gain attention in resent deep learning research as a way to further boost the accuracy and generalizability of deep neural network (DNN) models. Recent ensemb…
Neutral Atmospheric Density Measurement Using Insight-HXMT Data by Earth Occultation Technique
Wang-Chen Xue, Xiao-Bo Li, Shao-Lin Xiong +12
The Earth occultation technique has broad applications in both astronomy and atmospheric density measurements. We construct the background model during the occultation of the Crab…
Approximate Computing and the Efficient Machine Learning Expedition
Jörg Henkel, Hai Li, Anand Raghunathan +4
Approximate computing (AxC) has been long accepted as a design alternative for efficient system implementation at the cost of relaxed accuracy requirements. Despite the AxC researc…
Joint Optimization of STAR-RIS Assisted UAV Communication Systems
Qin Zhang, Yang Zhao, Hai Li +2
In this letter, we study the simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted unmanned aerial vehicle (UAV) communications. Our goa…
The Chinese Hα Solar Explorer (CHASE) mission: An overview
Chuan Li, Cheng Fang, Zhen Li +32
The Chinese Hα Solar Explorer (CHASE), dubbed "Xihe" - Goddess of the Sun, was launched on October 14, 2021 as the first solar space mission of China National Space Administration…
Privacy Leakage of Adversarial Training Models in Federated Learning Systems
Jingyang Zhang, Yiran Chen, Hai Li
Adversarial Training (AT) is crucial for obtaining deep neural networks that are robust to adversarial attacks, yet recent works found that it could also make models more vulnerabl…
Gaussian Random Number Generator with Reconfigurable Mean and Variance using Stochastic Magnetic Tunnel Junctions
Punyashloka Debashis, Hai Li, Dmitri Nikonov +1
Generating high-quality random numbers with a Gaussian probability distribution function is an important and resource consuming computational task for many applications in the fiel…
Global Vision Transformer Pruning with Hessian-Aware Saliency
Huanrui Yang, Hongxu Yin, Maying Shen +3
Transformers yield state-of-the-art results across many tasks. However, their heuristically designed architecture impose huge computational costs during inference. This work aims o…
Improving Adversarial Robustness in Weight-quantized Neural Networks
Chang Song, Elias Fallon, Hai Li
Neural networks are getting deeper and more computation-intensive nowadays. Quantization is a useful technique in deploying neural networks on hardware platforms and saving computa…
Snooping Attacks on Deep Reinforcement Learning
Matthew Inkawhich, Yiran Chen, Hai Li
Adversarial attacks have exposed a significant security vulnerability in state-of-the-art machine learning models. Among these models include deep reinforcement learning agents. Th…
Observational constraints on the survival of pristine stars
Mattis Magg, Ralf S. Klessen, Simon C. O. Glover +1
There is a longstanding discussion about whether low mass stars can form from pristine gas in the early Universe. A particular point of interest is whether we can find surviving pr…
HyPar: Towards Hybrid Parallelism for Deep Learning Accelerator Array
Linghao Song, Jiachen Mao, Youwei Zhuo +3
With the rise of artificial intelligence in recent years, Deep Neural Networks (DNNs) have been widely used in many domains. To achieve high performance and energy efficiency, hard…
Towards Leveraging the Information of Gradients in Optimization-based Adversarial Attack
Jingyang Zhang, Hsin-Pai Cheng, Chunpeng Wu +2
In recent years, deep neural networks demonstrated state-of-the-art performance in a large variety of tasks and therefore have been adopted in many applications. On the other hand,…
LEASGD: an Efficient and Privacy-Preserving Decentralized Algorithm for Distributed Learning
Hsin-Pai Cheng, Patrick Yu, Haojing Hu +4
Distributed learning systems have enabled training large-scale models over large amount of data in significantly shorter time. In this paper, we focus on decentralized distributed…
Differentiable Fine-grained Quantization for Deep Neural Network Compression
Hsin-Pai Cheng, Yuanjun Huang, Xuyang Guo +4
Neural networks have shown great performance in cognitive tasks. When deploying network models on mobile devices with limited resources, weight quantization has been widely adopted…
Towards Efficient and Secure Delivery of Data for Training and Inference with Privacy-Preserving
Juncheng Shen, Juzheng Liu, Yiran Chen +1
Privacy recently emerges as a severe concern in deep learning, that is, sensitive data must be prohibited from being shared with the third party during deep neural network developm…
DPatch: An Adversarial Patch Attack on Object Detectors
Xin Liu, Huanrui Yang, Ziwei Liu +3
Object detectors have emerged as an indispensable module in modern computer vision systems. In this work, we propose DPatch -- a black-box adversarial-patch-based attack towards ma…