arXiv subjects

Changchun Liu

Publications and source records attributed to Changchun Liu.

6 recordsLinked to original sources

arXiv2026

Boundedness and blow-up for a quasilinear Keller-Segel system with flux limitation and indirect signal production

The quasilinear Keller-Segel system with flux limitation and indirect signal production u_t=\nabla\cdot\left(D(u)\nabla u\right) -\nabla\cdot\left(u(1+\left|\nabla v\right|^2)^\sigma\nabla v\right), &x\in\Omega, t>0, \\ 0=\Delta v-v+w,x\in\Omega, t>0, \\ w_t=-w+u,x\in\Omega, t>0, under homogeneous Neumann boundary conditions in a smooth bounded domain \Omega\subsetR^N is considered, where D(u)\simeq u^{m-1} as u\simeq\infty. We conclude that For N=1 and any \sigma\in\mathbb{R}, if m\geq0, the classical solution exists globally, and it is moreover bounded if m>0. However, if m<0 and \Omega is a ball, there exist radially symmetric initial data such that the classical solution exhibits finite-time blow-up. For any N\geq2 and m>1-\frac{1}{N}, if \sigma\leq \frac{mN+2-2N}{2N-2}, the classical solution is global. Furthermore, if \sigma<\frac{mN+2-2N}{2N-2}, the corresponding solution is uniformly bounded. For any N\geq2 and m<2-\frac{2}{N}, if \sigma>\max\left\{\frac{N}{2-2N},\frac{mN+2-2N}{2N-2}\right\} and \Omega is a ball, there exist radially symmetric initial data such that the classical solution blows up in finite time.

math.AP↗

arXiv2025

Chinese Spelling Correction: A Comprehensive Survey of Progress, Challenges, and Opportunities

Chinese Spelling Correction (CSC) is a critical task in natural language processing, aimed at detecting and correcting spelling errors in Chinese text. This survey provides a comprehensive overview of CSC, tracing its evolution from pre-trained language models to large language models, and critically analyzing their respective strengths and weaknesses in this domain. Moreover, we further present a detailed examination of existing benchmark datasets, highlighting their inherent challenges and limitations. Finally, we propose promising future research directions, particularly focusing on leveraging the potential of LLMs and their reasoning capabilities for improved CSC performance. To the best of our knowledge, this is the first comprehensive survey dedicated to the field of CSC. We believe this work will serve as a valuable resource for researchers, fostering a deeper understanding of the field and inspiring future advancements.

cs.CL↗

arXiv2024

A Large Language Model-based multi-agent manufacturing system for intelligent shopfloor

As customer demand for multi-variety and small-batch production increases, dynamic disturbances place greater demands on manufacturing systems. To address such challenges, researchers proposed the multi-agent manufacturing system. However, conventional agent negotiation typically relies on pre-defined and fixed heuristic rules, which are ill-suited to managing complex and fluctuating disturbances. In current implementations, mainstream approaches based on reinforcement learning require the development of simulators and training models specific to a given shopfloor, necessitating substantial computational resources and lacking scalability. To overcome this limitation, the present study proposes a Large Language Model-based (LLM-based) multi-agent manufacturing system for intelligent shopfloor management. By defining the diverse modules of agents and their collaborative methods, this system facilitates the processing of all workpieces with minimal human intervention. The agents in this system consist of the Machine Server Module (MSM), Bid Inviter Module (BIM), Bidder Module (BM), Thinking Module (TM), and Decision Module (DM). By harnessing the reasoning capabilities of LLMs, these modules enable agents to dynamically analyze shopfloor information and select appropriate processing machines. The LLM-based modules, predefined by system prompts, provide dynamic functionality for the system without the need for pre-training. Extensive experiments were conducted in physical shopfloor settings. The results demonstrate that the proposed system exhibits strong adaptability, and achieves superior performance (makespan) and stability (as measured by sample standard deviation) compared to other approaches without requiring pre-training.

cs.AI↗

arXiv2023

Explainable Data-driven Share-of-choice Product Line Design Optimization

The share-of-choice (SOC) problem is a widely studied problem for product line design (PLD) where representative customers are sampled from a target population and the percentage of the ones who choose the offered products over outside options over the sample is maximized. The utility maximization framework captures individual choices. A significant challenge is that these utilities are not directly observable and must be estimated from other primitive data. Conjoint analysis is a commonly applied technique for generating such data, where sampled customers rate, rank, or choose between different product alternatives. With the responses, various methods, such as the hierarchical Bayesian method and polyhedral estimation, are employed to estimate the utilities. However, this " estimate-then-optimize" procedure disconnects the decision-making process from the primitive data and thus cannot attribute the PLD decision to the unresolved survey data. To fill the gap, we integrate the polyhedral estimation method into the PLD optimization, which constructs a polyhedron set containing all utilities consistent with the responses. Specifically, we proposed a robust model that maximizes the share-of-choice calculated by the worst-case utility over the polyhedron. The model brings threefold explainability to the PLD problem. First, it enables us to explore the influence of both estimation error and the number of sampled customers through an out-of-sample performance guarantee of the proposed model. Secondly, the linearly tractable equivalent reformulation constructed by the duality allows us to attribute the optimal product line to the survey data through the dual variables. Third, inspired by the fact that the survey questions are associated with columns in the dual reformulation, we propose a column generation approach to identify new questions that most effectively improve the model's objective.

math.OC↗

arXiv2018

An Auto-tuning Framework for Autonomous Vehicles

Many autonomous driving motion planners generate trajectories by optimizing a reward/cost functional. Designing and tuning a high-performance reward/cost functional for Level-4 autonomous driving vehicles with exposure to different driving conditions is challenging. Traditionally, reward/cost functional tuning involves substantial human effort and time spent on both simulations and road tests. As the scenario becomes more complicated, tuning to improve the motion planner performance becomes increasingly difficult. To systematically solve this issue, we develop a data-driven auto-tuning framework based on the Apollo autonomous driving framework. The framework includes a novel rank-based conditional inverse reinforcement learning algorithm, an offline training strategy and an automatic method of collecting and labeling data. Our auto-tuning framework has the following advantages that make it suitable for tuning an autonomous driving motion planner. First, compared to that of most inverse reinforcement learning algorithms, our algorithm training is efficient and capable of being applied to different scenarios. Second, the offline training strategy offers a safe way to adjust the parameters before public road testing. Third, the expert driving data and information about the surrounding environment are collected and automatically labeled, which considerably reduces the manual effort. Finally, the motion planner tuned by the framework is examined via both simulation and public road testing and is shown to achieve good performance.

cs.RO↗

arXiv2018

Baidu Apollo EM Motion Planner

In this manuscript, we introduce a real-time motion planning system based on the Baidu Apollo (open source) autonomous driving platform. The developed system aims to address the industrial level-4 motion planning problem while considering safety, comfort and scalability. The system covers multilane and single-lane autonomous driving in a hierarchical manner: (1) The top layer of the system is a multilane strategy that handles lane-change scenarios by comparing lane-level trajectories computed in parallel. (2) Inside the lane-level trajectory generator, it iteratively solves path and speed optimization based on a Frenet frame. (3) For path and speed optimization, a combination of dynamic programming and spline-based quadratic programming is proposed to construct a scalable and easy-to-tune framework to handle traffic rules, obstacle decisions and smoothness simultaneously. The planner is scalable to both highway and lower-speed city driving scenarios. We also demonstrate the algorithm through scenario illustrations and on-road test results. The system described in this manuscript has been deployed to dozens of Baidu Apollo autonomous driving vehicles since Apollo v1.5 was announced in September 2017. As of May 16th, 2018, the system has been tested under 3,380 hours and approximately 68,000 kilometers (42,253 miles) of closed-loop autonomous driving under various urban scenarios. The algorithm described in this manuscript is available at https://github.com/ApolloAuto/apollo/tree/master/modules/planning.

cs.RO↗