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In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. However, sometimes, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the...
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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.
Classical conditioning, also known...
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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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Anchoring junctions are multiprotein complexes that help cells connect to other cells and the extracellular matrix. Anchoring junctions are present on the lateral and basal surfaces of cells, providing strong and flexible connections. Focal adhesions are often formed due to cell interactions with the ECM substrata, which initiate signal transduction via kinase cascades and other mechanisms. Together, they provide stability and tissue integrity. There are three types of anchoring junctions:...
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After budding out from the ER membrane, some COPII vesicles lose their coat and fuse with one another to form larger vesicles and interconnected tubules called vesicular tubular clusters or VTCs. These clusters constitute a compartment at the ER-Golgi interface known as ERGIC (Endoplasmic Reticulum Golgi Intermediate Compartment). The ERGIC is a mobile membrane-bound cargo transport system that sorts proteins secreted from ER and delivers them to the Golgi.
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Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
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相关实验视频

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Cross-Modal Multivariate Pattern Analysis
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张量学习与动态学习相遇:从完整到不完整的多视图集群.

Yongyong Chen, Xiaojia Zhao, Zheng Zhang

    IEEE transactions on neural networks and learning systems
    |June 28, 2023
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    概括

    本研究介绍了多视图集群 (MVC) 的统一框架,可以有效处理完整和不完整的数据. 这种新的方法,TDASC,使用张量和动态学习进行可扩展的,准确的集群跨多样化的数据集.

    科学领域:

    • 机器学习 机器学习
    • 数据科学数据科学数据科学
    • 人工智能的人工智能

    背景情况:

    • 多视图集群 (MVC) 有效地揭示了内在的数据结构.
    • 现有的MVC方法仅限于完整或不完整的数据集.
    • 这两种情况都缺乏统一的框架.

    研究的目的:

    • 为多视图集群 (MVC) 提出一个统一的框架,同时处理完整和不完整的数据.
    • 开发一种可扩展和高效的MVC方法,其复杂度大约为线性.
    • 为了提高集群性能,利用张量学习和动态学习.

    主要方法:

    • 开发了TDASC (可扩展集群的Tensor和动态学习).
    • 集成的张量学习来捕捉面试的低级别和高级别的相关性.
    • 采用动态学习用于内部视图低级别和高效的图形构建.

    主要成果:

    • 对于可扩展的集群,TDASC实现了大约线性复杂度.
    • 该框架有效地模拟了跨多个观点的高阶相关性.
    • 实验表明,与最先进的方法相比,TDASC对完整和不完整的数据集具有更高的有效性和效率.

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    结论:

    • TDASC为完整和不完整的多视图集群提供了统一和高效的解决方案.
    • 张量和学习的整合显著提高了集群精度和可扩展性.
    • TDASC代表了多视图集群研究的重大进展.