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相关概念视频

Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

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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

Collisions in Multiple Dimensions: Introduction

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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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Histogram01:05

Histogram

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The histogram is a graphical representation in the x-y form of data distribution in a data set. The horizontal x-axis is labeled with what the data represents (for instance, distance from your home to school). The vertical y-axis is labeled either frequency or relative frequency (or percent frequency or probability).
A histogram graph consists of contiguous (adjoining) boxes. The heights of the bars correspond to frequency values. The graph will have the same shape with respective labels. The...
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X-ray Imaging01:24

X-ray Imaging

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German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with...
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Ultrasonography01:17

Ultrasonography

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Ultrasonography is an imaging technique that uses high-frequency sound waves to visualize the body's internal structures. It is a non-invasive and safe procedure that does not involve the use of ionizing radiation, making it widely used in various medical fields. Ultrasonography is used to study heart function, blood flow in the neck or extremities, certain conditions such as gallbladder disease, and fetal growth and development.
During an ultrasonography procedure, a handheld device called...
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Imaging Biological Samples with Optical Microscopy01:18

Imaging Biological Samples with Optical Microscopy

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Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...
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Updated: Jul 4, 2025

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
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多模组合示例挖掘用于复合查询图像检索的多模组合.

Gangjian Zhang, Shikun Li, Shikui Wei

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    本研究引入了一种新的方法,通过生成硬负面示例来进行复合查询图像检索. 这种方法增强了多式联机嵌入空间,以实现更准确的图像检索.

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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:

    • 复合查询图像检索涉及使用由参考图像和修改文本描述组成的查询来找到目标图像.
    • 现有的方法往往无法充分利用多式联运特征,导致嵌入空间不足,训练数据利用效率低下.
    • 当前的学习目标通常使用查询级负数,忽视了复合查询的微妙多式联络性质.

    研究的目的:

    • 通过专注于多式联接来提高复合查询图像检索的学习目标.
    • 通过挖掘和生成有效的硬负面示例来增强计量空间的构建.
    • 解决多式模式嵌入式学习中传统负采样的局限性.

    主要方法:

    • 提出一个新的学习目标,从多式联络融合的角度构建和挖掘硬负面例子.
    • 通过将参考图像与逻辑未配对的句子配对来创建组件级负面示例.
    • 引入一种新的句子增长技术,以在元素层面上生成不可分辨的多模式负面示例.

    主要成果:

    • 拟议的方法显著提高了构成查询图像检索的有效性.
    • 从组件层面和元素层面的角度挖掘硬负面示例导致更好的度量空间优化.
    • 在四个现实世界数据集上的实验验验证了与现有方法相比,拟议的方法的优越性能.

    结论:

    • 开发的方法有效地解决了构成查询图像检索当前方法的局限性.
    • 生成复杂的负面示例对于学习强大的多式联运嵌入空间至关重要.
    • 这项工作为通过改进学习目标来推进多式联络检索任务提供了一个有希望的方向.