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Weightlessness01:01

Weightlessness

6.6K
When an object is dropped, it accelerates toward the center of the Earth. If the net external force on the object is its weight, it is said to be in free fall; that is, the only force acting on the object is gravity. Galileo was instrumental in showing that, in the absence of air resistance, all objects fall with the same acceleration g. However, when objects on the Earth fall downward, they are never truly in free fall, because there is always some upward resistance force from the air acting...
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Mass and Weight01:19

Mass and Weight

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Mass and weight are often used interchangeably in everyday conversation. For example,  medical records often show our weight in kilograms, but never in the correct units of newtons. In physics, however, there is an important distinction. Weight is the pull of the Earth on an object. It depends on the distance from the center of the Earth. Weight dramatically varies if we leave the Earth's surface, unlike mass, which does not vary with location. On the Moon, for example, the...
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Apparent Weight01:09

Apparent Weight

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True weight is the measure of the gravitational force acting on an object. However, if the object accelerates, its measured weight is different from its true weight. Similar observations can be made when the object is submerged in water. An object's weight in water is its apparent weight, which is equal to the difference between its true weight and the buoyant forces.
Consider a person standing on a bathroom scale inside an elevator. If the scale is accurate at rest, its reading equals the...
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Weighted Mean00:57

Weighted Mean

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While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
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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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Imprinting01:22

Imprinting

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Behavioral imprinting is observed in some newborn animals and occurs when they develop strong and specific attachments to another animal (usually a parent) following brief, early-life exposures. Offspring imprint onto parents within a brief period after birth or hatching; this time window is called the critical period. Once imprinting occurs, the bond established between the parents and their offspring is usually long-lasting.
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Related Experiment Video

Updated: Dec 24, 2025

Author Spotlight: Development of an Automated Camera-Based System for Real-Time Blast Overpressure Monitoring and TBI Risk Assessment in Military Training
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Author Spotlight: Development of an Automated Camera-Based System for Real-Time Blast Overpressure Monitoring and TBI Risk Assessment in Military Training

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Hypersphere-Based Weight Imprinting for Few-Shot Learning on Embedded Devices.

Nikolaos Passalis, Alexandros Iosifidis, Moncef Gabbouj

    IEEE Transactions on Neural Networks and Learning Systems
    |April 15, 2020
    PubMed
    Summary

    Weight imprinting (WI) is a gradient descent-free method for few-shot learning. A new hypersphere-based approach enhances WI to overcome limitations like multimodal distributions and overfitting, improving accuracy for novel categories.

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    Area of Science:

    • Machine Learning
    • Computer Vision
    • Deep Learning

    Background:

    • Weight imprinting (WI) enables gradient descent-free few-shot learning, suitable for hardware accelerators without back-propagation.
    • Existing WI methods struggle with multimodal distributions and are prone to overfitting, negatively impacting novel category classification.

    Purpose of the Study:

    • To introduce a novel hypersphere-based Weight Imprinting (WI) approach.
    • To address limitations of existing WI methods, specifically handling multimodal distributions and preventing overfitting.

    Main Methods:

    • Developed a hypersphere-based framework for Weight Imprinting (WI).
    • Implemented regularization techniques within the imprinting process.
    • Evaluated the approach on three diverse image datasets.

    Main Results:

    • The proposed hypersphere-based WI method effectively handles novel categories with multimodal distributions.
    • The approach demonstrates improved regularization, mitigating overfitting issues.
    • Achieved enhanced classification accuracy on novel categories compared to standard WI.

    Conclusions:

    • The hypersphere-based WI method offers a robust solution for gradient descent-free few-shot learning.
    • This approach overcomes key limitations of traditional WI, enhancing its applicability.
    • Validated effectiveness across multiple image datasets, showing significant improvements.