1 citations · 3 across the 20 of their papers we have counts for
20 papers
SinoGlyphBench: A Diagnostic Benchmark for Chinese Glyph-Level Obfuscation in Language-Model Moderation
Yifan Wang, Zimu Wang, Suliu Qin +10
Glyph-level obfuscation can leave harmful Chinese content readable to humans while degrading automated moderation. We introduce SinoGlyphBench, a diagnostic benchmark that identifi…
TAVR-VLM: Risk-Conditioned Causal Grounding for Hallucination-Resistant Report Generation
Zhixiang Lu, Xiwei Liu, Sifan Song +5
Transcatheter Aortic Valve Replacement (TAVR) planning requires meticulous multimodal reasoning. However, adapting Multimodal Large Language Models (MLLMs) to this high-stakes doma…
EvidenceLens: A Claim-Evidence Matrix for Auditing Financial Question Answering
Fengchen Gu, Xiaotian Ren, Zhengyong Jiang +6
Large language models are increasingly used to answer questions over annual reports, earnings decks, and analyst notes, yet their outputs remain difficult to verify in high-stakes…
MERIT: Multilingual Expert-Reward Informed Tuning for Chinese-Centric Low-Resource Machine Translation
Zhixiang Lu, Chong Zhang, Chenyu Xue +4
Neural machine translation (NMT) from Chinese to low-resource Southeast Asian languages remains severely constrained by the extreme scarcity of clean parallel corpora and the perva…
SAGE: Sustainable Agent-Guided Expert-tuning for Culturally Attuned Translation in Low-Resource Southeast Asia
Zhixiang Lu, Chong Zhang, Yulong Li +5
The vision of an inclusive World Wide Web is impeded by a severe linguistic divide, particularly for communities in low-resource regions of Southeast Asia. While large language mod…
SAMP-HDRL: Segmented Allocation with Momentum-Adjusted Utility for Multi-agent Portfolio Management via Hierarchical Deep Reinforcement Learning
Xiaotian Ren, Nuerxiati Abudurexiti, Zhengyong Jiang +3
Portfolio optimization in non-stationary markets is challenging due to regime shifts, dynamic correlations, and the limited interpretability of deep reinforcement learning (DRL) po…
Silhouette-to-Contour Registration: Aligning Intraoral Scan Models with Cephalometric Radiographs
Yiyi Miao, Taoyu Wu, Ji Jiang +6
Reliable 3D-2D alignment between intraoral scan (IOS) models and lateral cephalometric radiographs is critical for orthodontic diagnosis, yet conventional intensity-driven registra…
ArchMap: Arch-Flattening and Knowledge-Guided Vision Language Model for Tooth Counting and Structured Dental Understanding
Bohan Zhang, Yiyi Miao, Taoyu Wu +6
A structured understanding of intraoral 3D scans is essential for digital orthodontics. However, existing deep-learning approaches rely heavily on modality-specific training, large…
Dental3R: Geometry-Aware Pairing for Intraoral 3D Reconstruction from Sparse-View Photographs
Yiyi Miao, Taoyu Wu, Tong Chen +6
Intraoral 3D reconstruction is fundamental to digital orthodontics, yet conventional methods like intraoral scanning are inaccessible for remote tele-orthodontics, which typically…
DentalSplat: Dental Occlusion Novel View Synthesis from Sparse Intra-Oral Photographs
Yiyi Miao, Taoyu Wu, Tong Chen +6
In orthodontic treatment, particularly within telemedicine contexts, observing patients' dental occlusion from multiple viewpoints facilitates timely clinical decision-making. Rece…
MedFact: A Large-scale Chinese Dataset for Evidence-based Medical Fact-checking of LLM Responses
Tong Chen, Zimu Wang, Yiyi Miao +6
Medical fact-checking has become increasingly critical as more individuals seek medical information online. However, existing datasets predominantly focus on human-generated conten…
DeepGB-TB: A Risk-Balanced Cross-Attention Gradient-Boosted Convolutional Network for Rapid, Interpretable Tuberculosis Screening
Zhixiang Lu, Yulong Li, Feilong Tang +5
Large-scale tuberculosis (TB) screening is limited by the high cost and operational complexity of traditional diagnostics, creating a need for artificial-intelligence solutions. We…
Factor-MCLS: Multi-agent learning system with reward factor matrix and multi-critic framework for dynamic portfolio optimization
Ruoyu Sun, Angelos Stefanidis, Zhengyong Jiang +1
Typical deep reinforcement learning (DRL) agents for dynamic portfolio optimization learn the factors influencing portfolio return and risk by analyzing the output values of the re…
PG-SAM: Prior-Guided SAM with Medical for Multi-organ Segmentation
Yiheng Zhong, Zihong Luo, Chengzhi Liu +7
Segment Anything Model (SAM) demonstrates powerful zero-shot capabilities; however, its accuracy and robustness significantly decrease when applied to medical image segmentation. E…
MSWAL: 3D Multi-class Segmentation of Whole Abdominal Lesions Dataset
Zhaodong Wu, Qiaochu Zhao, Ming Hu +13
With the significantly increasing incidence and prevalence of abdominal diseases, there is a need to embrace greater use of new innovations and technology for the diagnosis and tre…
MTS: A Deep Reinforcement Learning Portfolio Management Framework with Time-Awareness and Short-Selling
Fengchen Gu, Zhengyong Jiang, Ángel F. García-Fernández +3
Portfolio management remains a crucial challenge in finance, with traditional methods often falling short in complex and volatile market environments. While deep reinforcement appr…
MIGT: Memory Instance Gated Transformer Framework for Financial Portfolio Management
Fengchen Gu, Angelos Stefanidis, Ángel García-Fernández +2
Deep reinforcement learning (DRL) has been applied in financial portfolio management to improve returns in changing market conditions. However, unlike most fields where DRL is wide…
A novel multi-agent dynamic portfolio optimization learning system based on hierarchical deep reinforcement learning
Ruoyu Sun, Yue Xi, Angelos Stefanidis +2
Deep Reinforcement Learning (DRL) has been extensively used to address portfolio optimization problems. The DRL agents acquire knowledge and make decisions through unsupervised int…
Combining Transformer based Deep Reinforcement Learning with Black-Litterman Model for Portfolio Optimization
Ruoyu Sun, Angelos Stefanidis, Zhengyong Jiang +1
As a model-free algorithm, deep reinforcement learning (DRL) agent learns and makes decisions by interacting with the environment in an unsupervised way. In recent years, DRL algor…
CAD: Clustering And Deep Reinforcement Learning Based Multi-Period Portfolio Management Strategy
Zhengyong Jiang, Jeyan Thiayagalingam, Jionglong Su +1
In this paper, we present a novel trading strategy that integrates reinforcement learning methods with clustering techniques for portfolio management in multi-period trading. Speci…