151 citations · 650 across the 34 of their papers we have counts for
34 papers
Observation of decay
M. Ablikim
Using a sample of events collected with the BESIII detector, the decay with subsequently decaying into $γπ^+…
Investigation of the rule and test of CP violation through the measurement of decay asymmetry parameters in decays
BESIII Collaboration, M. Ablikim, M. N. Achasov +615
Using events collected with the BESIII detector, numerous and decay asymmetry parameters are simultaneously determined from the process…
Measurement of the cross sections from 2.000 to 3.080 GeV
BESIII Collaboration, M. Ablikim, M. N. Achasov +615
Based on collision data collected at center-of-mass energies from 2.000 to 3.080 GeV by the BESIII detector at the BEPCII collider, a partial wave analysis is performe…
FilterFL: Knowledge Filtering-based Data-Free Backdoor Defense for Federated Learning
Yanxin Yang, Ming Hu, Xiaofei Xie +4
As a distributed machine learning paradigm, Federated Learning (FL) enables large-scale clients to collaboratively train a model without sharing their raw data. However, due to the…
AIDE: A Vision-Driven Multi-View, Multi-Modal, Multi-Tasking Dataset for Assistive Driving Perception
Dingkang Yang, Shuai Huang, Zhi Xu +12
Driver distraction has become a significant cause of severe traffic accidents over the past decade. Despite the growing development of vision-driven driver monitoring systems, the…
Sampling to Distill: Knowledge Transfer from Open-World Data
Yuzheng Wang, Zhaoyu Chen, Jie Zhang +7
Data-Free Knowledge Distillation (DFKD) is a novel task that aims to train high-performance student models using only the pre-trained teacher network without original training data…
The mechanism of the irradiation synergistic effect of Silicon bipolar junction transistors explained by multiscale simulations of Monte Carlo and excited-state first-principle calculations
Zeng-hui Yang, Yang Liu, Ning An +1
Neutron and -ray irradiation damages to transistors are found to be non-additive, and this is denoted as the irradiation synergistic effect (ISE). Its mechanism is not well-unde…
Is Aggregation the Only Choice? Federated Learning via Layer-wise Model Recombination
Ming Hu, Zhihao Yue, Xiaofei Xie +6
Although Federated Learning (FL) enables global model training across clients without compromising their raw data, due to the unevenly distributed data among clients, existing Fede…
Cost-efficient Crowdsourcing for Span-based Sequence Labeling: Worker Selection and Data Augmentation
Yujie Wang, Chao Huang, Liner Yang +5
This paper introduces a novel crowdsourcing worker selection algorithm, enhancing annotation quality and reducing costs. Unlike previous studies targeting simpler tasks, this study…
Open problems in causal structure learning: A case study of COVID-19 in the UK
Anthony Constantinou, Neville K. Kitson, Yang Liu +5
Causal machine learning (ML) algorithms recover graphical structures that tell us something about cause-and-effect relationships. The causal representation praovided by these algor…
Mechanical cooling in the bistable regime of a dissipative optomechanical cavity with a Kerr medium
Ye Liu, Yang Liu, Chang-Sheng Hu +3
In this paper, we study static bistability and mechanical cooling of a dissipative optomechanical cavity filled with a Kerr medium. The system exhibits optical bistability for a wi…
ContraBERT: Enhancing Code Pre-trained Models via Contrastive Learning
Shangqing Liu, Bozhi Wu, Xiaofei Xie +2
Large-scale pre-trained models such as CodeBERT, GraphCodeBERT have earned widespread attention from both academia and industry. Attributed to the superior ability in code represen…
Unveiling Code Pre-Trained Models: Investigating Syntax and Semantics Capacities
Wei Ma, Shangqing Liu, Mengjie Zhao +5
Past research has examined how well these models grasp code syntax, yet their understanding of code semantics still needs to be explored. We extensively analyze seven code models t…
Learning to Learn Domain-invariant Parameters for Domain Generalization
Feng Hou, Yao Zhang, Yang Liu +6
Due to domain shift, deep neural networks (DNNs) usually fail to generalize well on unknown test data in practice. Domain generalization (DG) aims to overcome this issue by capturi…
SAP-DETR: Bridging the Gap Between Salient Points and Queries-Based Transformer Detector for Fast Model Convergency
Yang Liu, Yao Zhang, Yixin Wang +5
Recently, the dominant DETR-based approaches apply central-concept spatial prior to accelerate Transformer detector convergency. These methods gradually refine the reference points…
Improving Radiology Summarization with Radiograph and Anatomy Prompts
Jinpeng Hu, Zhihong Chen, Yang Liu +2
The impression is crucial for the referring physicians to grasp key information since it is concluded from the findings and reasoning of radiologists. To alleviate the workload of…
Multi-Modal Masked Autoencoders for Medical Vision-and-Language Pre-Training
Zhihong Chen, Yuhao Du, Jinpeng Hu +4
Medical vision-and-language pre-training provides a feasible solution to extract effective vision-and-language representations from medical images and texts. However, few studies h…
CommitBART: A Large Pre-trained Model for GitHub Commits
Shangqing Liu, Yanzhou Li, Xiaofei Xie +1
GitHub commits, which record the code changes with natural language messages for description, play a critical role for software developers to comprehend the software evolution. To…
Decoupled Pyramid Correlation Network for Liver Tumor Segmentation from CT images
Yao Zhang, Jiawei Yang, Yang Liu +6
Purpose: Automated liver tumor segmentation from Computed Tomography (CT) images is a necessary prerequisite in the interventions of hepatic abnormalities and surgery planning. How…
Hero-Gang Neural Model For Named Entity Recognition
Jinpeng Hu, Yaling Shen, Yang Liu +2
Named entity recognition (NER) is a fundamental and important task in NLP, aiming at identifying named entities (NEs) from free text. Recently, since the multi-head attention mecha…
Self-supervised Contrastive Learning for Audio-Visual Action Recognition
Yang Liu, Ying Tan, Haoyuan Lan
The underlying correlation between audio and visual modalities can be utilized to learn supervised information for unlabeled videos. In this paper, we propose an end-to-end self-su…
Learning Program Semantics with Code Representations: An Empirical Study
Jing Kai Siow, Shangqing Liu, Xiaofei Xie +2
Program semantics learning is the core and fundamental for various code intelligent tasks e.g., vulnerability detection, clone detection. A considerable amount of existing works pr…
Domain Adaptation in LiDAR Semantic Segmentation via Alternating Skip Connections and Hybrid Learning
Eduardo R. Corral-Soto, Mrigank Rochan, Yannis Y. He +3
In this paper we address the challenging problem of domain adaptation in LiDAR semantic segmentation. We consider the setting where we have a fully-labeled data set from source dom…
Cross-Lingual Transfer Learning for Statistical Type Inference
Zhiming Li, Xiaofei Xie, Haoliang Li +3
Hitherto statistical type inference systems rely thoroughly on supervised learning approaches, which require laborious manual effort to collect and label large amounts of data. Mos…