activity
20032023
most citedThe DArk Matter Particle Explorer mission

322 citations · 1.9k across the 97 of their papers we have counts for

collaborators

97 papers

cs.LG2023★ 34 cited

Anonymous Jamming Detection in 5G with Bayesian Network Model Based Inference Analysis

Ying Wang, Shashank Jere, Soumya Banerjee +3

Jamming and intrusion detection are critical in 5G research, aiming to maintain reliability, prevent user experience degradation, and avoid infrastructure failure. This paper intro…

hep-ex2023

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…

hep-ex2023★ 3 cited

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…

cs.CL2023★ 21 cited

A Survey on Fairness in Large Language Models

Yingji Li, Mengnan Du, Rui Song +2

Large Language Models (LLMs) have shown powerful performance and development prospects and are widely deployed in the real world. However, LLMs can capture social biases from unpro…

cs.CR2023★ 5 cited

Formal and Fuzzing Amplification: Targeting Vulnerability Detection in 5G and Beyond

Jingda Yang, Ying Wang

Softwarization and virtualization in 5G and beyond require rigorous testing against vulnerabilities and unintended emergent behaviors for critical infrastructure and network securi…

cs.CL2023

Prompt Tuning Pushes Farther, Contrastive Learning Pulls Closer: A Two-Stage Approach to Mitigate Social Biases

Yingji Li, Mengnan Du, Xin Wang +1

As the representation capability of Pre-trained Language Models (PLMs) improve, there is growing concern that they will inherit social biases from unprocessed corpora. Most previou…