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相关概念视频

Heuristics01:21

Heuristics

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Heuristics are problem-solving strategies that use mental shortcuts to simplify decision-making. Unlike algorithms, which must be followed precisely to achieve a correct result, heuristics offer a general problem-solving framework. They save time and energy but can sometimes lead to less rational decisions.
People often rely on heuristics when faced with an overload of information, limited time, low importance of the decision, limited information, or when a heuristic readily comes to mind. For...
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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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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.
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Design Consideration01:22

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Designing a structure involves a series of considerations, primarily the material's ultimate strength, calculated through tests that measure changes under increased force until the material reaches its breaking point or limit. The ultimate load, where the material breaks, is divided by its original cross-sectional area, resulting in the ultimate normal stress or strength. The ultimate shearing stress is another significant factor taken into account.
The factor of safety is another key...
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Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

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Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
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未来的布局导向多模式在线散列.

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    此摘要是机器生成的。

    本研究介绍了前景布局指导的多模式在线哈希 (PLG-MOH) 来增强数据检索. 在流动场景中,PLG-MOH有效地处理新数据类,提高检索性能并适应新出现的数据模式.

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    科学领域:

    • 计算机科学 计算机科学
    • 机器学习 机器学习
    • 数据科学数据科学数据科学

    背景情况:

    • 现实世界的数据经常流动,需要高效的在线多模式散列,以快速检索和低存储.
    • 现有的方法在流动场景中与新数据类作斗争,限制了检索性能.
    • 应对不断变化的数据类的挑战对于强大的在线散列系统至关重要.

    研究的目的:

    • 提出一种新的在线多式联络哈希方法,即前景布局指导的多式联络哈希 (PLG-MOH).
    • 在流式情景中提高检索性能,特别是在新数据类出现时.
    • 开发一种有效地适应新数据的哈希技术,同时保留旧数据的信息.

    主要方法:

    • PLG-MOH使用散列中心建立了一个哈明空间布局,每个中心被分配给一个类.
    • 引入了潜在的布局引导损失函数,利用所有散列中心 (分配和未分配) 进行训练.
    • 该方法通过将未分配的中心纳入培训过程,主动考虑未来的数据类.

    主要成果:

    • 与在线场景中最先进的基线相比,PLG-MOH表现出优越的检索性能.
    • 该方法有效地将哈希函数适应到来的新样本,保持语义相似性.
    • PLG-MOH成功地保留了从以前遇到的数据中学到的信息.

    结论:

    • 拟议的PLG-MOH方法在具有挑战性的在线流数据场景中显著提高了检索性能.
    • 当面对新出现的数据类别时,PLG-MOH为多模式哈希提供了一个强大的解决方案.
    • 这种方法通过结合前性学习来推进在线多模式哈希领域.