الباحثون

Ming Li

المنشورات 13

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Joint EM-BB Beamforming Design for ISAC Systems With Reconfigurable Antenna Arrays

Jvxiang Hu, Mengzhen Liu, Rang Liu وآخرون · 2026

This paper investigates the joint electromagnetic (EM)-domain and baseband (BB)-domain beamforming for integrated sensing and communication (ISAC) systems enabled by pattern- and polarization-reconfigurable pixel-antenna arrays. A multiport-network-based antenna model is developed to map each binary switch configuratio …

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Mutual-Coupling-Aware Electromagnetic and Signal Processing Co-Design for Pixel Antenna Arrays

Ping Ma, Mengzhen Liu, Rang Liu وآخرون · 2026

Reconfigurable pixel antenna arrays provide additional electromagnetic (EM) degrees of freedom for wireless transmission by enabling hardware-level adaptation of radiation responses. In practical arrays, inter-element mutual coupling (MC) alters the switch-state-dependent responses, causing MC-unaware designs to be mis …

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Exploiting Acoustic and Content-Oriented Speaker Verification Attacks Against Multilingual Voice Anonymization

Ridwan Arefeen, Ze Li, Rong Tong وآخرون · 2026

Attacker ASV systems for voice anonymization have been studied primarily in English, leaving their behavior in multilingual settings largely unexplored. Conventional ASV has shown that both acoustic and contextual information are important for multilingual speaker verification. Inspired by this, we investigate whether …

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From Access to Realized Affordances: University Students' Generative AI Engagement across Linguistic and Sociotechnical Contexts

Ming Li, Qin Xie, Ariunaa Enkhtur وآخرون · 2026

Generative artificial intelligence (GenAI) is increasingly embedded in university students' academic work, yet student engagement is often examined through adoption, frequency of use, or general perceptions, with less attention to how it is shaped by linguistic and sociotechnical conditions. This comparative qualitativ …

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A Missing Piece for Trustworthy AI Reviewers: From Benchmarking Rhetorical Robustness to SciCore Review

Chenguang Wang, Ming Li, Chengrui Fan وآخرون · 2026

AI reviewers can assign different judgments to manuscripts that report the same science in different wording, potentially rewarding rhetorical optimization over scientific improvement. We formulate Rhetorical Robustness as the joint requirement of stability across content-preserving rewrites and discrimination across p …

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Candidate Retention for Abductive Learning

Abductive learning combines neural perception with symbolic reasoning, using explanations generated by abduction to supervise the perception model. Multiple valid explanations of the same symbolic target can assign conflicting labels to the same inputs. Common policies select a single candidate as a pseudo-label, which …

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Can Vision-Language Models Stay Helpful When Facing Implicit Risks? Intent-Privilege OPSD for Efficient Safety-Helpfulness Alignment

Haotian Deng, Wenbin Xing, Gang Xu وآخرون · 2026

Vision-Language Models (VLMs) remain vulnerable to cross-modal implicit risks: visual and textual inputs that appear benign in isolation can jointly elicit unsafe responses. Existing safety methods often require large preference datasets, costly multi-rollout training, or additional safeguards at inference time. They m …

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DualTrack: Synchronized speech-gesture generation via symmetric coupling of pretrained priors

Yuanzhuo Hu, Zehan Liu, Xiaoyi Qin وآخرون · 2026

Joint speech-gesture synthesis must coordinate two modalities despite limited paired data. Existing approaches often lack bidirectional interaction, have limited language coverage, or simplify body and finger representations. We present DualTrack, which couples pretrained speech and motion priors on a shared 12.5 Hz ti …

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On the Lexical Superstition of Large Language Models for Code Comprehension: Re-evaluation on Code of Low Lexical Quality

Xin Shen, San-Zhuo Xi, Yali Du وآخرون · 2026

Recent advances in large language models (LLMs) have made them widely used for code-related tasks. Identifier names are statistically informative in naturally occurring code, but their information is not always reliable. We investigate whether current LLMs assign disproportionate weight to lexical cues when renaming pr …

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