Quan Yuan

CauseCollab: Causal Unified and Modality-Agnostic Network for Heterogeneous Collaborative Perception

Collaborative perception enhances environment understanding through multi-agent information sharing, but its performance in real-world scenarios is constrained by heterogeneous sensor modalities and model architectures. Recent protocol-based two-stage methods alleviate this problem by mapping hetero…

From Discriminative to Generative: A Diffusion-Based Paradigm for Multi-Agent Collaborative Perception

Collaborative perception leveraging intermediate feature fusion has emerged as a leading paradigm to significantly enhance the environmental perception capabilities of autonomous driving systems. However, existing methods typically rely on discriminative supervision guided by downstream tasks. This

Increasing the Stiffness of Tendon-Driven Continuum Robots Via Multi-Constraints

One Model to Translate Them All: Universal Any-to-Any Translation for Heterogeneous Collaborative Perception

By sharing intermediate features, collaborative perception extends each agent's sensing beyond standalone limits, but real-world feature modality heterogeneity remains a key barrier to effective fusion. Most existing methods, including direct adaption and protocol-based transforma-tion, typically re…

MARK: Multi-agent Collaboration with Ranking Guidance for Text-attributed Graph Clustering

This paper studies the problem of text-attributed graph clustering, which aims to cluster each node into different groups using both textual attributes and structural information. Although graph neural networks (GNNs) have been proposed to solve this problem, their performance is usually limited whe…

NegoCollab: A Common Representation Negotiation Approach for Heterogeneous Collaborative Perception

Collaborative perception expands the perception range by sharing information among agents, effectively improving task performance. Immutable heterogeneity poses a significant challenge in collaborative perception, as participating agents may employ different and fixed perception models. This leads t…

One is Plenty: A Polymorphic Feature Interpreter for Immutable Heterogeneous Collaborative Perception

Collaborative perception in autonomous driving significantly enhances the perception capabilities of individual agents. Immutable heterogeneity in collaborative perception, where agents have different and fixed perception networks, presents a major challenge due to the semantic gap in their exchange…

Development of a Novel Redundant Parallel Mechanism with Enlarged Workspace and Enhanced Dexterity for Fracture Reduction Surgery

The limited workspace and complex singularity issues are predominant factors impeding the clinical applicability of fracture reduction parallel robots. To address these challenges, this paper proposes a novel redundant parallel mechanism (NRPM) for robotic-assisted fracture reduction with an enlarge…

TaskLAMA: Probing the Complex Task Understanding of Language Models

Structured Complex Task Decomposition (SCTD) is the problem of breaking down a complex real-world task (such as planning a wedding) into a directed acyclic graph over individual steps that contribute to achieving the task, with edges specifying temporal dependencies between steps. SCTD is an i…

AlphaRoute: Large-Scale Coordinated Route Planning via Monte Carlo Tree Search

This paper proposes AlphaRoute, an AlphaGo inspired algorithm for coordinating large-scale routes, built upon graph attention reinforcement learning and Monte Carlo Tree Search (MCTS). We first partition the road network into regions and model large-scale coordinated route planning as a Markov game,…

Aperture Diffraction for Compact Snapshot Spectral Imaging

We demonstrate a compact, cost-effective snapshot spectral imaging system named Aperture Diffraction Imaging Spectrometer (ADIS), which consists only of an imaging lens with an ultra-thin orthogonal aperture mask and a mosaic filter sensor, requiring no additional physical footprint compared to comm…

BoardgameQA: A Dataset for Natural Language Reasoning with Contradictory Information

Automated reasoning with unstructured natural text is a key requirement for many potential applications of NLP and for developing robust AI systems. Recently, Language Models (LMs) have demonstrated complex reasoning capacities even without any finetuning. However, existing evaluation for automated…

GPLight: Grouped Multi-agent Reinforcement Learning for Large-scale Traffic Signal Control

The use of multi-agent reinforcement learning (MARL) methods in coordinating traffic lights (CTL) has become increasingly popular, treating each intersection as an agent. However, existing MARL approaches either treat each agent absolutely homogeneous, i.e., same network and parameter for each agent…

Pushing the Accuracy-Group Robustness Frontier with Introspective Self-play

Standard empirical risk minimization (ERM) training can produce deep neural network (DNN) models that are accurate on average but under-perform in under-represented population subgroups, especially when there are imbalanced group distributions in the long-tailed training data. Therefore, approaches…

Retrieval-Augmented Parsing for Complex Graphs by Exploiting Structure and Uncertainty

Retrieval augmentation enhances generative language models by retrieving informative exemplars relevant for output prediction. However, in realistic graph parsing problems where the output space is large and complex, classic retrieval methods based on input-sentence similarity can fail to identify t…

Using Domain Knowledge to Guide Dialog Structure Induction via Neural Probabilistic Soft Logic

Dialog Structure Induction (DSI) is the task of inferring the latent dialog structure (i.e., a set of dialog states and their temporal transitions) of a given goal-oriented dialog. It is a critical component for modern dialog system design and discourse analysis. Existing DSI approaches are often pu…

A Novel Sequence-to-Subgraph Framework for Diagnosis Classification

Text-based diagnosis classification is a critical problem in AI-enabled healthcare studies, which assists clinicians in making correct decision and lowering the rate of diagnostic errors. Previous studies follow the routine of sequence based deep learning models in NLP literature to deal with clinic…

SCC: an efficient deep reinforcement learning agent mastering the game of StarCraft II

AlphaStar, the AI that reaches GrandMaster level in StarCraft II, is a remarkable milestone demonstrating what deep reinforcement learning can achieve in complex Real-Time Strategy (RTS) games. However, the complexities of the game, algorithms and systems, and especially the tremendous amount of com…

The Graph-based Mutual Attentive Network for Automatic Diagnosis

The automatic diagnosis has been suffering from the problem of inadequate reliable corpus to train a trustworthy predictive model. Besides, most of the previous deep learning based diagnosis models adopt the sequence learning techniques (CNN or RNN), which is difficult to extract the complex structu…