arXiv subjects

Wenjie Dong

Publications and source records attributed to Wenjie Dong.

5 recordsLinked to original sources

arXiv2026

Grouping Auction-Consensus Algorithm for Decentralized Task Allocation in Multi-Robot Systems

Decentralized multi-robot task allocation (MRTA) is essential for scalable and resilient autonomous systems. The Consensus-Based Bundle Algorithm (CBBA) is a widely adopted decentralized baseline. However, its individual task-level bidding is poorly aligned with the min-sum objective of minimizing total team travel distance, leading to suboptimal allocations in spatially distributed environments. This paper introduces the Grouping Auction-Consensus Algorithm (GACA). This decentralized MRTA framework adopts the two-phase auction-consensus architecture of CBBA while fundamentally redesigning its bidding mechanism to reason over groups of spatially proximate tasks. A nearest-neighbor preprocessing step partitions tasks into spatially coherent groups before allocation. Agents then iteratively propose structured group-level actions: claiming unassigned groups, acquiring partial groups, or contesting groups held by other agents. Competing actions are resolved through a consensus phase. Operating in the MT-SR-IA problem class, GACA is evaluated against CBBA using a Mixed-Integer Linear Program as the ground-truth optimality reference. Across four swarm sizes and 4,000 test worlds, GACA achieves a median percent optimality of approximately 97% compared to 81--84% for CBBA, while converging in equal or fewer iterations. A scalability evaluation over 3,280 additional problem instances spanning swarm sizes of 5 to 20 agents and task counts of 10 to 50 confirms that these gains generalize robustly across a wide range of problem configurations.

cs.RO↗

arXiv2026

Auction-Consensus Algorithm with Learned Bidding Scheme for Multi-Robot Systems

Multi-Robot Task Allocation (MRTA) is a central challenge in decentralized multi-agent systems, where teams of robots must cooperatively assign and execute tasks under limited communication while optimizing global performance objectives. Auction-consensus algorithms, such as the Consensus-Based Bundle Algorithm (CBBA), provide scalable decentralized coordination with provable convergence, but rely on hand-crafted greedy scoring functions that often lead to suboptimal task allocations. This paper proposes a learning-enhanced auction-consensus framework in which CBBA's deterministic bidding mechanism is replaced by a neural bidding policy trained using reinforcement learning. Under a centralized training and decentralized execution paradigm, agents learn to compute task bids from partial local observations while retaining the standard auction and consensus phases for decentralized coordination. The learned bidding policy is trained using Proximal Policy Optimization with rewards shaped by proximity to globally optimal solutions obtained via mixed-integer linear programming. Multiple neural architectures are evaluated, including a Neural Additive Model, the Long Short-Term Memory (LSTM) model, and the Set Transformer Model. Experimental results across varying swarm sizes demonstrate that learned bidding policies can improve solution quality over classical CBBA while preserving decentralized execution. The proposed approach highlights the effectiveness of integrating reinforcement learning with classical distributed coordination algorithms, offering a scalable pathway toward higher-quality decentralized multi-robot task allocation.

cs.RO↗

arXiv2024

A Survey on Open Information Extraction from Rule-based Model to Large Language Model

Open Information Extraction (OpenIE) represents a crucial NLP task aimed at deriving structured information from unstructured text, unrestricted by relation type or domain. This survey paper provides an overview of OpenIE technologies spanning from 2007 to 2024, emphasizing a chronological perspective absent in prior surveys. It examines the evolution of task settings in OpenIE to align with the advances in recent technologies. The paper categorizes OpenIE approaches into rule-based, neural, and pre-trained large language models, discussing each within a chronological framework. Additionally, it highlights prevalent datasets and evaluation metrics currently in use. Building on this extensive review, the paper outlines potential future directions in terms of datasets, information sources, output formats, methodologies, and evaluation metrics.

cs.CL↗

arXiv2023

Constraining the global mean surface temperature during 1850-1880 with new statistical physical model

As IPCC ARs stated, global warming is estimated based on the average from 1850 to 1900 (global average temperature of preindustrialization estimated from relatively sparse observations). Given the impossibility of massive increasing observation data in the early stages, accurately constraining this baseline has become an unresolved issue. Here we developed a new statistical physical model to quantify the contribution of external forcings to global warming as a "deterministic trend" of the surface temperature series (instead of as non-stationary processes that yield a stochastic trend) and constrained the reconstruction of the early time series (1850-1880). We find that the existing datasets slightly overestimated the temperature anomalies in this period, thus the speed of global warming since pre-industrialization is still underestimated.

physics.ao-ph↗

arXiv2021

Label-free virtual HER2 immunohistochemical staining of breast tissue using deep learning

The immunohistochemical (IHC) staining of the human epidermal growth factor receptor 2 (HER2) biomarker is widely practiced in breast tissue analysis, preclinical studies and diagnostic decisions, guiding cancer treatment and investigation of pathogenesis. HER2 staining demands laborious tissue treatment and chemical processing performed by a histotechnologist, which typically takes one day to prepare in a laboratory, increasing analysis time and associated costs. Here, we describe a deep learning-based virtual HER2 IHC staining method using a conditional generative adversarial network that is trained to rapidly transform autofluorescence microscopic images of unlabeled/label-free breast tissue sections into bright-field equivalent microscopic images, matching the standard HER2 IHC staining that is chemically performed on the same tissue sections. The efficacy of this virtual HER2 staining framework was demonstrated by quantitative analysis, in which three board-certified breast pathologists blindly graded the HER2 scores of virtually stained and immunohistochemically stained HER2 whole slide images (WSIs) to reveal that the HER2 scores determined by inspecting virtual IHC images are as accurate as their immunohistochemically stained counterparts. A second quantitative blinded study performed by the same diagnosticians further revealed that the virtually stained HER2 images exhibit a comparable staining quality in the level of nuclear detail, membrane clearness, and absence of staining artifacts with respect to their immunohistochemically stained counterparts. This virtual HER2 staining framework bypasses the costly, laborious, and time-consuming IHC staining procedures in laboratory, and can be extended to other types of biomarkers to accelerate the IHC tissue staining used in life sciences and biomedical workflow.

q-bio.QM↗