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相关概念视频

Improving Translational Accuracy02:07

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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
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Protein glycosylation starts in the ER lumen and continues in the Golgi apparatus. Glycosyltransferases catalyze the addition of sugar molecules or glycosylation of proteins. Usually, these enzymes add sugars to the hydroxyl groups of selected serine or threonine residues to form O-linked glycans or the amino groups of asparagine residues to form N-linked glycans. Different positions on the same polypeptide chain can contain differently linked glycans.
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Multiprotein signaling complexes are formed in a dynamic process involving protein-protein interactions at the cytoplasmic domain of transmembrane receptors or enzymatic and non-enzymatic proteins associated with the receptor. These complexes ensure the activation and propagation of intracellular signals that regulate cell functions.
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The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
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Ex2Vec:通过端到端的执行意识嵌入来增强汇编代码语义.

Xingyu Gong1, Yang Xu1, Sicong Zhang1

  • 1School of Cyber Science and Technology, Guizhou Normal University, Guiyang 550001, China; Guizhou Key Laboratory of NewGen Cyberspace Security, Guiyang 550001, China.

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

Ex2vec通过学习指令执行语义来增强二进制代码相似性检测,优于现有的方法. 这种方法可以提高识别类似代码和检测漏洞的准确性.

关键词:
二进制码相似性检测检测二进制码相似性检测二元相似性分析二元相似性分析函数的语义是函数的语义.图形匹配网络 (GMN) 是指图形匹配网络.变压器 变压器 变压器漏洞检测 发现漏洞的检测

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科学领域:

  • 计算机科学 计算机科学
  • 网络安全 网络安全
  • 机器学习 机器学习

背景情况:

  • 二进制代码相似性检测 (BSCD) 对于计算机安全任务至关重要.
  • 目前的深度神经网络 (DNN) 方法经常使用掩盖语言建模 (MLM),限制执行语义捕获.
  • 现有的技术很难完全代表代码指令的功能意义.

研究的目的:

  • 引入Ex2vec,一种端到端编码方法,用于BSCD的高质量,执行语义丰富的嵌入.
  • 开发一种新的预培训策略,以学习指导对注册状态的影响.
  • 提高二进制代码分析的准确性和有效性.

主要方法:

  • Ex2vec模拟汇编指令执行以捕获语义特征.
  • 一个新的预培训策略侧重于指令对注册状态的影响,而不仅仅是同时发生.
  • 主要组件分析 (PCA) 用于可视化和验证指令的语义聚类.

主要成果:

  • Ex2vec生成了富含执行语义的嵌入.
  • 功能上相似的指令在嵌入空间中明显聚集在一起.
  • 在BSCD性能方面,Ex2vec显著超过现有的最先进的方法.

结论:

  • 通过有效地捕捉执行语义,Ex2vec为BSCD提供了一种优越的方法.
  • 该方法在大型数据集上取得了最先进的结果.
  • 在真实世界的漏洞检测场景中,Ex2vec表现出高准确度.