Modeling kinetic equations is essential for understanding the mechanisms of chemical reactions, yet a complex and time-consuming task. Kinetic equation prediction is formulated as a problem of dynamical symbolic regression (DSR) subject to physical chemistry constraints. Deep learning (DL) holds the potential to capture reaction patterns and predict kinetic equations from data of chemical species, effectively avoiding empirical bias and improving efficiency compared with traditional analytical methods. Despite numerous studies focusing on DSR and the introduction of Transformers to predict ordinary differential equations, the corresponding models lack generalization abilities across diverse categories of reactions. In this study, we propose KinFormer, a generalizable kinetic equation prediction model. KinFormer utilizes a conditional Transformer to model DSR under physical constraints and employs Monte Carlo Tree Search to apply the model to new types of reactions. Experimental results on 20 types of organic reactions demonstrate that KinFormer not only outperforms classical baselines, but also exceeds Transformer baselines in out-of-domain evaluations, thereby proving its generalization ability.

Game theory, as an analytical tool, is frequently utilized to analyze human behavior in social science research. With the high alignment between the behavior of Large Language Models (LLMs) and humans, a promising research direction is to employ LLMs as substitutes for humans in game experiments, enabling social science research. However, despite numerous empirical researches on the combination of LLMs and game theory, the capability boundaries of LLMs in game theory remain unclear. In this research, we endeavor to systematically analyze LLMs in the context of game theory. Specifically, rationality, as the fundamental principle of game theory, serves as the metric for evaluating players' behavior --- building a clear desire, refining belief about uncertainty, and taking optimal actions. Accordingly, we select three classical games (dictator game, Rock-Paper-Scissors, and ring-network game) to analyze to what extent LLMs can achieve rationality in these three aspects. The experimental results indicate that even the current state-of-the-art LLM (GPT-4) exhibits substantial disparities compared to humans in game theory. For instance, LLMs struggle to build desires based on uncommon preferences, fail to refine belief from many simple patterns, and may overlook or modify refined belief when taking actions. Therefore, we consider that introducing LLMs into game experiments in the field of social science should be approached with greater caution.

Large language models (LLMs) have shown incredible performance on many tasks such as dialogue generation, commonsense reasoning and question answering.In-context learning (ICL) is an important paradigm for adapting LLMs to the downstream tasks by prompting few demonstrations.However, the distribution of demonstrations can severely affect the performance, especially for challenging classification tasks.In this paper, we propose the concept of task-level thinking steps that can eliminate bias introduced by demonstrations.Further, to help LLMs distinguish confusing classes, we design a progressive revision framework, which can improve the thinking steps by correcting hard demonstrations.Experimental results prove the superiority of our proposed method, achieving best performance on three kinds of challenging classification tasks in the zero-shot and few-shot settings.Besides, with task-level thinking steps, automatically generated chain-of-thoughts (CoTs) bring more competitive performance.

Permission delegation has become a new way for data sharing by delegating the authorized permission to other users. A flexible authorization model with strict access control policies is promising for electronic health record (EHR) sharing with security. In this paper, a blockchain-based multi-hop permission delegation scheme with controllable delegation depth for EHR sharing has been presented. We use the interplanetary file system (IPFS) for storing the original EHRs. Smart contracts and proxy re-encryption technology are implemented for permission delegation. In order to ensure data security, we use attribute-based encryption to provide fine-grained access control. Additionally, blockchain is used to achieve traceability and immutability. We deploy smart contracts so that the delegation depth can be set by delegators. Security analysis of the proposed protocol shows that our solution meets the designed goals. Finally, we evaluate the proposed algorithm and implement the scheme on the Ethereum test chain. Our scheme outperforms the competition in terms of performance, according to the results of our experiments.