Type
Education
Country
China
CN
Documents
251
through any edge
Papers
251
authorship edges
Awards made
—
Named as funder
—
Identifiers
Papers
Documents in the 44B Library with Duke Kunshan University on the authorship, newest first.
2026-07-09 · 3 authors here
Calibrating Wavelet‐Enhanced <scp>Artificial Intelligence</scp> for <scp>Breast Imaging Reporting and Data System Category</scp> 4 Breast Ultrasound Lesions
2026-07-01 · 5 authors here
Deep memory aided joint estimation network for ego motion and guardrail structure
2026-07-01 · 23 authors here
Ethics of ‘digital duplicates’ or AI simulations of real people: towards an international consensus
2026-06-25 · 10 authors here
X-Pruning: a dual-stream information fusion mammography diagnosis network based on pruned transformer and cross-attention mechanism
2026-06-12 · 9 authors here
Luminara: Transforming Dunhuang Murals into Interactive Narratives Through AI Analysis and Multi-Agent Generation
2026-04-21 · 5 authors here
CompSpoof: A Dataset and Joint Learning Framework for Component-Level Audio Anti-Spoofing Countermeasures
2026-04-21 · 2 authors here
Learning to Rotate Frames for Hyperbolic Graph Feature Extraction
2026-04-21 · 4 authors here
Training Dynamics-Aware Multi-Factor Curriculum Learning for Target Speaker Extraction
2026-04-21 · 5 authors here
MemFormer: Memory-Enhanced Transformer with Multi-Task Learning for Video Anomaly Detection
2026-04-21 · 6 authors here
ARMOR: Agentic Reasoning for Methods Orchestration and Reparameterization for Robust Adversarial Attacks
2026-04-20 · 7 authors here
GCPT: Gradient-aware Clustering Method for Efficient Post-Training Quantization in Large Neural Networks
2026-04-11 · 7 authors here
AI preference prediction beyond substituted judgement: enhancing best interest decision-making
2026-03-16 · 10 authors here
Spectrogram features for audio and speech analysis
2026-03-09 · 4 authors here
Privacy Attacks on Voice Anonymization Systems: Overview and Key Findings from the First VoicePrivacy Attacker Challenge
2026-03-07 · 8 authors here
Artificial intelligence-powered models in predicting mortality in maternal, newborn, and children under five: a systematic review protocol
2026-03-07 · 8 authors here
Additional file 4 of Artificial intelligence-powered models in predicting mortality in maternal, newborn, and children under five: a systematic review protocol
2026-03-07 · 8 authors here
Additional file 5 of Artificial intelligence-powered models in predicting mortality in maternal, newborn, and children under five: a systematic review protocol
2026-03-07 · 8 authors here
Additional file 6 of Artificial intelligence-powered models in predicting mortality in maternal, newborn, and children under five: a systematic review protocol
2026-03-07 · 8 authors here
Additional file 5 of Artificial intelligence-powered models in predicting mortality in maternal, newborn, and children under five: a systematic review protocol
2026-03-07 · 8 authors here
Additional file 3 of Artificial intelligence-powered models in predicting mortality in maternal, newborn, and children under five: a systematic review protocol
2026-03-07 · 8 authors here
Additional file 2 of Artificial intelligence-powered models in predicting mortality in maternal, newborn, and children under five: a systematic review protocol
2026-03-07 · 8 authors here
Additional file 4 of Artificial intelligence-powered models in predicting mortality in maternal, newborn, and children under five: a systematic review protocol
2026-03-07 · 8 authors here
Additional file 2 of Artificial intelligence-powered models in predicting mortality in maternal, newborn, and children under five: a systematic review protocol
2026-03-07 · 8 authors here
Additional file 6 of Artificial intelligence-powered models in predicting mortality in maternal, newborn, and children under five: a systematic review protocol
2026-03-07 · 8 authors here
Additional file 1 of Artificial intelligence-powered models in predicting mortality in maternal, newborn, and children under five: a systematic review protocol
Funding
Read from the funding tables, never from the document count — that count includes authorship, so it ranks institutions, not funders.
Who funds this work
Funders named on the papers above. The record holds no recipient field, so this is reached through the documents themselves — and the two columns are two different claims, kept apart.
Under a named grant
funding_awards — the award carries a grant number
Listed as funder
doc_funder_links — named, with no grant number