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相关概念视频

Lewis Symbols and the Octet Rule02:36

Lewis Symbols and the Octet Rule

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Chemical bonds are complex interactions between two or more atoms or ions, which reduce the potential energy of the molecule. Gilbert N. Lewis developed a model called the Lewis model that simplified the depiction of chemical bond formation and provided straightforward explanations for the chemical bonds seen in most common compounds.
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Elements: Chemical Symbols and Isotopes02:31

Elements: Chemical Symbols and Isotopes

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A chemical symbol is an abbreviation used to indicate an element or an atom of an element. For example, the symbol for mercury is Hg. The same symbol is used to indicate one atom of mercury (microscopic domain) or to label a container of many atoms of the element mercury (macroscopic domain).
Some symbols are derived from the common English name of the element; others are abbreviations of the name in another language — Latin, Greek or German. For example, the symbol for aluminum (common name)...
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Chemical Symbols01:09

Chemical Symbols

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A chemical symbol is an abbreviation that is used to indicate an element or an atom of an element. For example, the symbol for mercury is Hg. We use the same symbol to indicate one atom of mercury (microscopic domain) or to label a container of many atoms of the element mercury (macroscopic domain).
Some symbols are derived from the common name of the element; others are abbreviations of the name in another language. Most symbols have one or two letters, but three-letter symbols have been used...
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Encoding01:19

Encoding

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Information enters the brain through encoding, which is the input of information into the memory system. Once sensory information is received from the environment, the brain labels or codes it. The information is then organized with similar information and connected to existing concepts. Encoding occurs through automatic processing and effortful processing.
Automatic processing involves the encoding of details like time, space, frequency, and the meaning of words, usually done without conscious...
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Protein Networks02:26

Protein Networks

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
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Network Covalent Solids02:18

Network Covalent Solids

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Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
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Monitoring Dynamic Changes In Mitochondrial Calcium Levels During Apoptosis Using A Genetically Encoded Calcium Sensor
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UniSymNet:一个统一的符号网络,具有稀疏编码和双层优化.

Xinxin Li1, Juan Zhang2, Da Li3

  • 1School of Mathematical Sciences, East China Normal University, Shang Hai, 200241, China.

Neural networks : the official journal of the International Neural Network Society
|February 2, 2026
PubMed
概括
此摘要是机器生成的。

本研究介绍了一个统一的符号网络 (UniSymNet),以改进数学表达式的发现. UniSymNet 统一了运营商,使得自然现象的建模复杂度降低,性能更好.

关键词:
符号网络是一个象征性网络.象征性回归是一种象征性回归.变压器模型模型

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科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习
  • 计算数学 计算数学 计算数学

背景情况:

  • 符号回归 (SR) 是发现数学表达式以建模自然现象的关键.
  • 传统的SR方法与复杂的树结构作斗争,限制了性能.
  • 现有的符号网络面临着多变量运营商和固定架构的挑战,导致过度装配.

研究的目的:

  • 提出一个新的统一符号网络 (UniSymNet),克服现有符号网络的局限性.
  • 为了增强表达力,并减少发现的数学表达式的复杂性.
  • 开发一个灵活的培训框架,适应各种数据集.

主要方法:

  • 将非线性二进制运算符统一为嵌套的单元运算符,以创建多变量运算符.
  • 开发一个双层优化框架:用于结构选择和目标特定参数优化的变压器预训练.
  • 严格的理论证明,以确定UniSymNet的能力.

主要成果:

  • UniSymNet 在复杂性和表现性方面展示了理论上的优势.
  • 双层优化框架使灵活的结构适应和减少表达式的复杂性.
  • 标准基准和SRBench的评估显示了高的符号解决率,合适的准确性和低复杂性.

结论:

  • UniSymNet提供了一个有前途的新范式,用于具有增强功能的符号回归.
  • 拟议的培训框架有效指导结构选择和参数优化.
  • 在发现准确而简洁的数学表达式方面,UniSymNet 实现了卓越的性能.