A scenario–task–role–resource based structured framework for developing emergency response checklists for rainstorm-induced disaster chains

Natural Hazards · 2026-02-01

A Necessary and Sufficient Condition for the Unique Solution of the Bellman Equation for LTL Surrogate Rewards

Research Directions Cyber-Physical Systems · 2025-11-04

Abstract Linear Temporal Logic (LTL) offers a formal way of specifying complex objectives for Cyber-Physical Systems (CPS). In the presence of uncertain dynamics, the planning for an LTL objective can be solved by model-free reinforcement learning (RL). Surrogate rewards for LTL objectives are commonly utilized in model-free RL for LTL objectives. In a widely adopted surrogate reward approach, two discount factors are used to ensure that the expected return (i.e., the cumulative reward) approximates the satisfaction probability of the LTL objective. The expected return then can be estimated by methods using the Bellman updates such as RL. However, the uniqueness of the solution to the Bellman equation with two discount factors has not been explicitly discussed. We demonstrate, through an example, that when one of the discount factors is set to one, as allowed in many previous works, the Bellman equation may have multiple solutions, leading to an inaccurate evaluation of the expected return. To address this issue, we propose a condition that ensures the Bellman equation has the expected return as its unique solution. Specifically, we require that the solutions for states within rejecting bottom strongly connected components (BSCCs) be zero. We prove that this condition guarantees the uniqueness of the solution, first for recurrent states (i.e., states within a BSCC) and then for transient states. Finally, we numerically validate our results through case studies.

The n-butanol extract of Polygonatum sibiricum improves spleen aging via p53 pathway

Phytomedicine · 2025-06-01 · 7 citations

Multisource data-driven method for product innovation design based on knowledge graph

Advanced Engineering Informatics · 2025-07-28 · 4 citations

Entity-Level Cross-Modal Fusion For Multimodal Chinese Agricultural Diseases and Pests Named Entity Recognition

SSRN Electronic Journal · 2025-01-01

Contrastive Trajectory Learning for Multi-Agent Reinforcement Learning Policy Transfer

2025-07-11

Cooperative multi-agent reinforcement learning (MARL) has achieved significant success in various applications. While parameter sharing is commonly used in existing methods to improve training efficiency and learn task-specific policies, it often leads to homogeneous behaviors among agents. This homogeneity severely limits the policy's exploration capability within the task's state space. Consequently, when these policies are transferred to new tasks, this poor exploration frequently results in suboptimal learning performance. To address this challenge, we propose a Contrastive Trajectory Learning (CTL) method to enhance policy generalization across different tasks. Specifically, we use an attention-based entity disentanglement mechanism to accurately identify key entities within an agent's observations, preventing irrelevant entity information from being embedded into the trajectory representation. In addition, we incorporate a permutation-equivariant network to adapt to the dynamic mapping relationship between observation entities and the action space across tasks. This decouples the policy distribution and improves the sample efficiency of policy transfer. Moreover, to boost policy exploration over different task space, we introduce a contrastive disagreement (CD) loss between the trajectory representations of different agents to learn discriminative and diverse trajectory representations. Experimental results on the RealSim Empowered Learning Arena (RELA) demonstrate that CTL achieves efficient transfer across different tasks.

Delayed Interactions in Active Agents: Stability and Formations

ArXiv.org · 2025-08-30

Active agents with time-delayed interactions arise naturally in various real-world systems, such as biological systems, transportation networks and robotic swarms. Such systems are typically modeled as Delay Differential Equations (DDEs) that incorporate inertial effects. In this paper, we investigate the stability of pattern formation of active agents with inertia and time delays, in both uncoupled and coupled scenarios. We derive and analyze a high-dimensional linear DDE model that characterizes the stability of such formations. Starting with the uncoupled scenario, where agents are driven only by a virtual leader, we describe the stability spectrum and provide conditions for the delay-independent (absolute) stability of the formations, as well as delay-dependent stability and unstable hyperbolic behavior. Different cases correspond to distinct universality classes of the corresponding spectrum. For the coupled scenario, where agents are driven by both the virtual leader and inter-agent interactions, we consider both symmetric and non-symmetric coupling topologies. Here we also provide an explicit spectrum classification, including the absolute stability criterion. Additionally, we investigate interactions in the large-delay limit, where delays affect inter-agent coupling, while local feedback remains instantaneous. In this limit, we prove rigorously that the stability region in the complex plane of the eigenvalues of the Laplacian matrix converges to a circle centered at the origin, a phenomenon previously observed in delay-coupled networks. Our findings provide a universal framework for understanding stable formations and motions of active agents with delayed interactions.

Entity-level cross-modal fusion for multimodal chinese agricultural diseases and pests named entity recognition

Smart Agricultural Technology · 2025-07-16 · 1 citations

• A new dataset for multimodal agricultural name entity recognition dataset was collected and constructed. • A novel entity-level cross-modal fusion architecture (AgriFuseNER)was proposed . • Experiments demonstrated its superiority in multimodal name entity recognition and ​5.96-11.41% performance gains over text, multimodal baselines and LLMs. Named Entity Recognition 1 1 To improve clarity and accessibility for readers unfamiliar with the topic, we provide definitions of key terms used throughout the paper, along with relevant references for further reading, as shown in Table 5 in Appendix A . (NER), as one of the popular directions in natural language processing, plays a critical role in fields such as information extraction and agricultural knowledge graph construction. However, traditional single modal methods based on pure text often face limitations in agricultural entity recognition, such as text description ambiguity, contextual limitations, and a lack of information fusion capabilities. This paper overcomes those limitations by introducing an agricultural multimodal NER model that uses entity-level cross-modal alignment. First, we propose a Dual-Stream Entity-Level Feature Encoder. The text stream employs a Boundary-Middle (B-M) classification strategy to achieve fine-grained semantic unit segmentation, effectively addressing long-entity boundary ambiguity and parallel computing challenges. The visual stream focuses on interesting region detection to enhance multi-scale visual entity feature extraction capabilities. Secondly, we introduce a Dynamic Cross-modal Gated Attention (DCGA) mechanism that adaptively adjusts visual feature contributions through gating weights. This approach integrates cross-modal contrastive learning to strengthen semantic connections at the entity level between images and text. To validate the model's effectiveness, we constructed a multimodal NER dataset containing 12,074 sample pairs across 10 entity categories, covering 10 crops, 82 typical diseases/pests, and related agrochemical data. The proposed method achieves a macro-average F1 score of 90.73% across 10 agricultural entity types, outperforming single-modal baselines by 5.96%, mainstream multimodal NER models by +3.06%, zero-shot GPT models by +11.41%, and fine-tuned multimodal large models by +2.1%. Comprehensive experimental results indicated that our multimodal collaborative learning framework could effectively enhance agricultural entity recognition accuracy, providing reliable technical support for downstream applications such as agricultural knowledge graph construction and intelligent question answering.

Topology-aware planning under linear temporal logic constraints

Journal of Applied and Computational Topology · 2025-06-23

Epoxy resin coatings with the competitive relationship of hydrophobic and aerophilic towards anticorrosion application

SSRN Electronic Journal · 2025-01-01