28 citations · 28 across the 28 of their papers we have counts for
34 papers
Deep learning driven framework for optimization of polycrystalline microstructures under competing strength requirements
Ashwini Gupta, Indrashish Saha, Lori Graham-Brady +1
Computational design of polycrystalline microstructures for enhanced mechanical performance requires repeated high-fidelity simulations over stochastic morphologies, rendering conv…
CtrlCache: Accelerating Interactive Video World Models with Control-Aware Caching
Shangye Song, Dong Gong, Hong Jia +2
Interactive video world models need to generate each video chunk efficiently while responding faithfully to user controls. Many systems use chunk-wise autoregressive generation wit…
Frame-Level Temporal Alignment for Human-to-Robot Visual Adaptation
Xizhe Zhang, Jingfeng Zhang, Zirun Zhou +1
Transferring visual representations pretrained on human videos to robot manipulation requires learning reliable correspondences between human and robot demonstrations. However, pai…
Not Too Hard, Not Too Easy: Learning from Intermediate States for LLM Structured Reasoning
Hongbo Chen, Guohua Lu, Ting Dang +1
A common principle of effective learning is to practice material that is neither already mastered nor too difficult to permit progress. We ask how to apply this principle to struct…
Listen Then Reason: Perception-Grounded Test-Time Reinforcement Learning for Large Audio-Language Models
Jiaheng Dong, Xiaofeng Yu, Jean Honorio +3
Large audio-language models (LALMs) are increasingly used for a broader range of audio reasoning tasks. These models typically incorporate audio representations into a large langua…
Flower Hub: A Reproducible Benchmarking Platform for Federated Learning in Simulation and Deployment
Yan Gao, Mohammad Naseri, Javier Fernandez-Marques +19
Federated learning (FL) has emerged as a key approach for training models across decentralized data, yet benchmarking in FL remains difficult to reproduce, compare, and extend. Exi…
MADBench: A Benchmark for Modality-Aware Audio Deepfake Detection
Yanqiu Li, Yang Xiao, Jisheng Bai +3
Recent advances in speech synthesis and audio generation have made high-fidelity acoustic forgery low-cost and difficult to attribute, enabling a realistic attack scenario in which…
LIRA: Local Cross-Layer Information Routing for Vision-Language-Action Decoding
Zhewei Zhang, Puyue Wang, Guanren Qiao +10
Vision-Language-Action (VLA) models transform representations from pretrained vision-language models (VLMs) into robot actions, yet the interface that routes intermediate VLM featu…
Efficient Multimodal Clinical Question Answering for Pulmonary Embolism Risk Assessment
Xiangyuan Xue, Yang Yu, Yan Gao +5
Pulmonary embolism (PE) is a high risk cardiopulmonary condition whose management requires both timely diagnosis and reliable assessment of future clinical risk. Because PE care ro…
Pocket-Dentist: On-Device Dental Image Understanding via Efficient Multimodal Large Language Models
Kai Bian, Xucheng Guo, Bin Chen +4
Evaluations of dental vision-language models remain fragmented across datasets, task definitions and metrics, and often ignore their computational cost. This limits their widesprea…
VitalAgent: A Tool-Augmented Agent for Reactive and Proactive Physiological Monitoring over Wearable Health Data
Di Zhu, Yu Yvonne Wu, Hong Jia +3
Wearable devices enable continuous monitoring of physiological signals such as ECG and PPG, but existing mHealth systems are largely limited to task-specific prediction pipelines o…
RAIL: Rethinking Auditory Intelligence in Large Audio-Language Models with a CHC-Grounded Benchmark
Hongyu Jin, Siyi Wang, Yang Xiao +11
Humans process rich auditory environments through various cognitive capabilities such as audio perception, audio reasoning and memory. Despite recent progress in large audio-langua…
Titans-as-a-Layer: Test-Time Memory for Conversational Speech Emotion Recognition
Daniel Chen, Qicong Hu, Yang Xiao +2
Speech emotion recognition (SER) is commonly formulated as utterance-level classification, although conversational emotion depends on a speaker's usual vocal range and the emotiona…
Why Can't They Remember? Uncovering Representation and Retrieval Bottlenecks in Multi-Turn Acoustic Memory
Yang Xiao, Siyi Wang, Han Yin +4
Large audio language models (LALMs) process both speech and environmental acoustic cues, yet struggle to retain non-speech information across multi-turn interactions. The performan…
Adaptive Federated Fine-Tuning of Self-Supervised Speech Representations
Xin Guo, Chunrui Zhao, Hong Jia +4
Integrating Federated Learning (FL) with self-supervised learning (SSL) enables privacy-preserving fine-tuning for speech tasks. However, federated environments exhibit significant…
HoRD: Robust Humanoid Control via History-Conditioned Reinforcement Learning and Online Distillation
Puyue Wang, Jiawei Hu, Yan Gao +7
Humanoid robots can suffer significant performance drops under small changes in dynamics, task specifications, or environment setup. We propose HoRD, a two-stage learning framework…
Edge-Cloud Collaborative Speech Emotion Captioning via Token-Level Speculative Decoding in Audio-Language Models
Xiangyuan Xue, Jiajun Lu, Yan Gao +3
Speech Emotion Captioning (SEC) leverages large audio-language models to generate rich, context-aware affective descriptions from speech. However, real-world deployment remains cha…
Disentangling Reasoning in Large Audio-Language Models for Ambiguous Emotion Prediction
Xiaofeng Yu, Jiaheng Dong, Jean Honorio +3
Speech emotion recognition plays an important role in various applications. However, most existing approaches predict a single emotion label, oversimplifying the inherently ambiguo…
Localizing and Editing Knowledge in Large Audio-Language Models
Sung Kyun Chung, Jiaheng Dong, Qiuchi Hu +3
Large Audio-Language Models (LALMs) have shown strong performance in speech understanding, making speech a natural interface for accessing factual information. Yet they are trained…
LQA: A Lightweight Quantized-Adaptive Framework for Vision-Language Models on the Edge
Xin Wang, Hong Jia, Hualin Zhou +4
Deploying Vision-Language Models (VLMs) on edge devices is challenged by resource constraints and performance degradation under distribution shifts. While test-time adaptation (TTA…
Rethinking Perplexity: Revealing the Impact of Input Length on Perplexity Evaluation in LLMs
Letian Cheng, Junyan Wang, Yan Gao +3
Perplexity is a widely adopted metric for assessing the predictive quality of large language models (LLMs) and often serves as a reference metric for downstream evaluations. Howeve…
Decoding Ambiguous Emotions with Test-Time Scaling in Audio-Language Models
Hong Jia, Weibin Li, Jingyao Wu +6
Emotion recognition from human speech is a critical enabler for socially aware conversational AI. However, while most prior work frames emotion recognition as a categorical classif…
CoCoEmo: Composable and Controllable Human-Like Emotional TTS via Activation Steering
Siyi Wang, Shihong Tan, Siyi Liu +4
Emotional expression in human speech is nuanced and compositional, often involving multiple, sometimes conflicting, affective cues that may diverge from linguistic content. In cont…
Test-Time Adaptation for Speech Emotion Recognition
Jiaheng Dong, Hong Jia, Ting Dang
The practical utility of Speech Emotion Recognition (SER) systems is undermined by their fragility to domain shifts, such as speaker variability, the distinction between acted and…