67 research outputs found
A sense of unfairness reduces charitable giving to a third -party:Evidence from behavioral and electrophysiological data
Unfairness commonly impacts human economic decision-making. However, whether inequity aversion impairs pro-social decisions and the corresponding neural processes, is poorly understood. Here, we conducted two experiments to investigate whether human gifting behavior and brain activity are affected by inequity aversion. In experiment 1, participants played as a responder in a joint donation game in which they were asked to decide whether or not to accept a donation proposal made by the proposer. In experiment 2, participants played a donation game similar to experiment 1, but the charity projects were classified as high-deservingness and low-deservingness projects. The results in both of two experiments showed that the participants were more likely to reject an unfair donation proposal and the late positivity potential (LPP)/P300 elicited by fair offers was more positive than moderately unfair and highly unfair offers regardless of charity deservingness. Moreover, after principal component analysis, the differences in P300 amplitude between fair and highly unfair conditions were positively correlated with the acceptance rates in experiment 2. Taken together, our study revealed that late positivity (LPP/P300) reflected the evaluation of fairness of proposals, and could predict subsequent pro-social decisions. This study is the first to demonstrate that inequity aversion reduces pro-social motivation to help innocent third party
Equilibrium distribution and diffusion of mixed hydrogen-methane gas in gravity field
Repurposing existing natural gas pipelines is a promising solution for
large-scale transportation of mixed hydrogen-methane gas. However, it remains
debatable whether gravitational stratification can notably affect hydrogen
partial pressure in the gas mixture. To address this issue, we combined
molecular dynamics simulation with thermodynamic and diffusion theories. Our
study systematically examined the equilibrium distribution of hydrogen-methane
mixtures in gravity fields. We demonstrated that partial pressures of both
gases decrease with altitude, with hydrogen showing slower decrease due to its
smaller molar mass. As a result, the volume fraction of hydrogen is maximized
at the top end of pipes. The stratification is more favorable at low
temperature and large altitude drops, with notable gas stratification only
occurring at extremely large drops in altitude, being generally negligible even
at a drop of 1500 m. Furthermore, we showed that the diffusion time required to
achieve the equilibrium distribution is proportional to gas pressure and the
square of pipeline height. This requires approximately 300 years for a 1500 m
pipeline at 1 bar. Therefore, temporary interruptions in pipeline gas
transportation will not cause visible stratification. Our work clarifies the
effect of gravity on hydrogen-methane gas mixtures and provides quantitative
insights into assessing the stratification of gas mixtures in pipelines.Comment: 14 pages, 8 figure
ChatKBQA: A Generate-then-Retrieve Framework for Knowledge Base Question Answering with Fine-tuned Large Language Models
Knowledge Base Question Answering (KBQA) aims to derive answers to natural
language questions over large-scale knowledge bases (KBs), which are generally
divided into two research components: knowledge retrieval and semantic parsing.
However, three core challenges remain, including inefficient knowledge
retrieval, retrieval errors adversely affecting semantic parsing, and the
complexity of previous KBQA methods. In the era of large language models
(LLMs), we introduce ChatKBQA, a novel generate-then-retrieve KBQA framework
built on fine-tuning open-source LLMs such as Llama-2, ChatGLM2 and Baichuan2.
ChatKBQA proposes generating the logical form with fine-tuned LLMs first, then
retrieving and replacing entities and relations through an unsupervised
retrieval method, which improves both generation and retrieval more
straightforwardly. Experimental results reveal that ChatKBQA achieves new
state-of-the-art performance on standard KBQA datasets, WebQSP, and
ComplexWebQuestions (CWQ). This work also provides a new paradigm for combining
LLMs with knowledge graphs (KGs) for interpretable and knowledge-required
question answering. Our code is publicly available.Comment: Preprin
Text2NKG: Fine-Grained N-ary Relation Extraction for N-ary relational Knowledge Graph Construction
Beyond traditional binary relational facts, n-ary relational knowledge graphs
(NKGs) are comprised of n-ary relational facts containing more than two
entities, which are closer to real-world facts with broader applications.
However, the construction of NKGs still significantly relies on manual labor,
and n-ary relation extraction still remains at a course-grained level, which is
always in a single schema and fixed arity of entities. To address these
restrictions, we propose Text2NKG, a novel fine-grained n-ary relation
extraction framework for n-ary relational knowledge graph construction. We
introduce a span-tuple classification approach with hetero-ordered merging to
accomplish fine-grained n-ary relation extraction in different arity.
Furthermore, Text2NKG supports four typical NKG schemas: hyper-relational
schema, event-based schema, role-based schema, and hypergraph-based schema,
with high flexibility and practicality. Experimental results demonstrate that
Text2NKG outperforms the previous state-of-the-art model by nearly 20\% points
in the scores on the fine-grained n-ary relation extraction benchmark in
the hyper-relational schema. Our code and datasets are publicly available.Comment: Preprin
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