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相关概念视频

Attribution Theory00:56

Attribution Theory

13.8K
Behavior is a product of both the situation (e.g., cultural influences, social roles, and the presence of bystanders) and of the person (e.g., personality characteristics). Subfields of psychology tend to focus on one influence or behavior over others. Situationism is the view that our behavior and actions are determined by our immediate environment and surroundings. In contrast, dispositionism holds that our behavior is determined by internal factors (Heider, 1958).
13.8K
Fundamental Attribution Error01:14

Fundamental Attribution Error

13.8K
According to some social psychologists, people tend to overemphasize internal factors as explanations—or attributions—for the behavior of other people. They tend to assume that the behavior of another person is a trait of that person, and to underestimate the power of the situation on the behavior of others. They tend to fail to recognize when the behavior of another is due to situational variables, and thus to the person’s state. This erroneous assumption is...
13.8K
Attribution01:26

Attribution

295
In social interactions, individuals frequently seek to understand the motivations and causes behind others' behaviors. This fundamental aspect of social perception, known as attribution, plays a crucial role in shaping interpersonal relationships and guiding future actions. Attribution refers to the cognitive process through which people infer the reasons behind others' behaviors, allowing them to assess character traits, intentions, and situational influences.Attribution Theory and Its...
295
Language01:16

Language

917
Language is a unique communication system that uses words and systematic rules to organize and transmit information. Unlike other forms of communication, which may involve postures, movements, odors, or vocalizations, language relies on symbols and grammar. This makes human communication distinct from that of other species, who also communicate but do not use language in the same way humans do.
Corballis and Suddendorf (2007) and Tomasello and Rakoczy (2003) highlight the role of language in...
917
Fineness of Cement01:15

Fineness of Cement

520
The fineness of cement directly influences the rate of hydration, as the hydration begins at the surface of the cement particles. In addition to hydration, the fineness of cement is vital for various properties of concrete including workability, gypsum requirement, and long-term behavior. The fineness of cement is represented in terms of the specific surface of cement which is typically measured in square meters per kilogram, with several methods available for this determination.
Direct...
520
Fineness Modulus01:19

Fineness Modulus

1.5K
The fineness modulus (FM) of aggregate is a numerical index that measures the coarseness or fineness of the particles. It is calculated by adding the cumulative percentages of aggregate retained on each of a specified series of sieves and dividing the sum by 100.
Consider performing sieve analysis on sand through a set of ASTM sieves. The weight of aggregate retained in each sieve and pan placed at the bottom is recorded, as given in Column B of Table 1.
To determine the fineness modulus of...
1.5K

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相关实验视频

Updated: Feb 5, 2026

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language
09:27

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Published on: October 13, 2018

10.7K

通过微调大型语言模型和归因图神经网络来探索金融情绪分析.

Zongshen Mu1, Yujie Wan2, Yueting Zhuang3

  • 1School of Big Data and Software Engineering, Chongqing University, Chongqing, China; Southwest Securities Co., Ltd, Chongqing, China.

Neural networks : the official journal of the International Neural Network Society
|February 3, 2026
PubMed
概括

本研究引入了一个新的框架,将LLM和GNN结合起来进行金融情绪分析,通过考虑跨资产影响来改善股票预测. 该模型在中国A股市场上实现了夏普比率50%的提高.

关键词:
归因图形神经网络的神经网络微调的大型语言模型.情绪分析是一种情绪分析.

更多相关视频

Deep Neural Networks for Image-Based Dietary Assessment
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Deep Neural Networks for Image-Based Dietary Assessment

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Exploring Infant Sensitivity to Visual Language using Eye Tracking and the Preferential Looking Paradigm
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Exploring Infant Sensitivity to Visual Language using Eye Tracking and the Preferential Looking Paradigm

Published on: May 15, 2019

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相关实验视频

Last Updated: Feb 5, 2026

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language
09:27

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Published on: October 13, 2018

10.7K
Deep Neural Networks for Image-Based Dietary Assessment
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Deep Neural Networks for Image-Based Dietary Assessment

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Exploring Infant Sensitivity to Visual Language using Eye Tracking and the Preferential Looking Paradigm
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Exploring Infant Sensitivity to Visual Language using Eye Tracking and the Preferential Looking Paradigm

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科学领域:

  • 计算金融是指计算金融.
  • 自然语言处理自然语言处理.
  • 机器学习 机器学习

背景情况:

  • 金融情绪分析 (FSA) 面临的挑战是预先训练有素的LLM由于领域的特异性和数据模式的适应.
  • 现有的LLM经常忽视跨资产影响,仅专注于价格预测的个人股票信息.

研究的目的:

  • 开发一种新的框架,将LLM和GNN结合起来,以利用金融新闻情绪来改进股价动态建模.
  • 提高LLM对金融情绪的敏感性,并有效地模拟跨资产依赖.

主要方法:

  • 使用了Llama-3-8B模型,对金融情绪敏感性进行了SFT和DPO的微调.
  • 开发了一个GNN来处理LLM情绪输出,创建文本属性图表来建模跨资产依赖和时间变化的相关性.

主要成果:

  • 证明金融情绪显著影响中国A股市场的股价波动.
  • 拟议的框架表现优于现有基线,平均夏普比率提高了50%.

结论:

  • 集成的LLM-GNN框架有效地捕捉了金融情绪和跨资产动态,用于股票价格预测.
  • 这种方法在应用NLP和基于图形的方法来分析金融市场方面取得了重大进展.