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相关概念视频

Collisions in Multiple Dimensions: Problem Solving01:06

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In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
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Collisions in Multiple Dimensions: Introduction01:05

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It is far more common for collisions to occur in two dimensions; that is, the initial velocity vectors are neither parallel nor antiparallel to each other. Let's see what complications arise from this. The first idea is that momentum is a vector. Like all vectors, it can be expressed as a sum of perpendicular components (usually, though not always, an x-component and a y-component, and a z-component if necessary). Thus, when the statement of conservation of momentum is written for a...
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While Mendel’s Law of Segregation states that the two alleles for one gene are separated into different gametes, a different question of how different genes are inherited remains. For example, is the gene for tall plants inherited with the gene for green peas? Mendel asked this question by experimenting with a dihybrid cross; a cross in which both parents are homozygous for two distinct traits resulting in an F1 generation that are heterozygous for both traits.
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Cluster Sampling Method01:20

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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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Elastic Collisions: Case Study01:15

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Elastic collision of a system demands conservation of both momentum and kinetic energy. To solve problems involving one-dimensional elastic collisions between two objects, the equations for conservation of momentum and conservation of internal kinetic energy can be used. For the two objects, the sum of momentum before the collision equals the total momentum after the collision. An elastic collision conserves internal kinetic energy, and so the sum of kinetic energies before the collision equals...
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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
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相关实验视频

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Cross-Modal Multivariate Pattern Analysis
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交叉模式的哈希与缺失的标签.

Haomin Ni1, Jianjun Zhang2, Peipei Kang2

  • 1School of Automation, Guangdong University of Technology, No. 100 Waihuan Xi Road, Guangzhou, 510006, Guangdong, China; Guangdong Key Laboratory of IoT Information Technology (GDUT), No. 100 Waihuan Xi Road, Guangzhou, 510006, Guangdong, China.

Neural networks : the official journal of the International Neural Network Society
|June 5, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了Cross-Modal Hashing with Missing Labels (CMHML),这是一种跨模式检索的新方法,可以有效地处理不完整或缺失的标签和标签相似性,提高检索准确性.

关键词:
交叉模式的检索检索哈希化方法 哈希化方法没有标签的标签.监督的弱点 监督的弱点

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科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 基于哈希的交叉模式检索提供了存储和速度的好处.
  • 现有的方法往往假设完整的标签,这是不现实的.
  • 缺失的标签和忽视的标签相似性是现实应用中的关键挑战.

研究的目的:

  • 开发一种用于跨模式检索的新方法,以解决缺失的标签和标签相关性.
  • 为了提高基于哈希的跨模式检索系统的准确性和稳定性.

主要方法:

  • 建议使用缺失标签 (CMHML) 的交叉模式哈希方法.
  • 引入可靠的标签 学习利用观察到的标签.
  • 将标签分解为隐藏的表示,以推断缺少的信息.
  • 包含标签相关性 保存以捕捉语义关系.
  • 雇员全球近似学习用于哈希代码生成.
  • 从预测的标签构建了一个相似性矩阵,以指导哈希代码学习.
  • 训练有素的线性分类器用于将样本映射到低维的哈明空间.

主要成果:

  • 在CMHML中,CMHML表现出与最先进的方法相比具有竞争力的性能.
  • 该模型即使缺少很大一部分标签,也有效.
  • 在四个公共数据集上进行了广泛的实验,验证了拟议的方法.

结论:

  • CMHML成功地解决了现有的交叉模式哈希方法的局限性.
  • 处理缺失标签和标签相关性的建议技术提高了检索性能.
  • 对于具有不完美的数据的实际交叉模式检索场景,CMHML提供了一个强大的解决方案.