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Instinctive Drift01:05

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Instinctive drift refers to the tendency of animals to revert to their innate behaviors despite repeated reinforcement. Breland and Breland demonstrated this concept in an experiment with a raccoon. The raccoon was trained to pick up two coins and place them in a container in exchange for food. Initially, the raccoon learned to associate the coins with food, making them a conditioned stimulus or a substitute for food. However, over time, the raccoon became less willing to put the coins into the...
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The high speed of electrical signals results from the fact that the force between charges acts rapidly at a distance. Thus, when a free charge is forced into a wire, the incoming charge pushes other charges ahead due to the repulsive force between like charges. These moving charges move the charges farther down the line. The density of charge in a system cannot easily be increased, so the signal is passed on rapidly. The resulting electrical shock wave moves through the system at nearly the...
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Natural selection—probably the most well-known evolutionary mechanism—increases the prevalence of traits that enhance survival and reproduction. However, evolution does not merely propagate favorable traits, nor does it always benefit populations.
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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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Multi-input and Multi-variable systems01:22

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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Masking and Demasking Agents01:19

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EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
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Related Experiment Video

Updated: Nov 3, 2025

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
07:05

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

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Driftage: a multi-agent system framework for concept drift detection.

Diogo Munaro Vieira1, Chrystinne Fernandes1, Carlos Lucena1

  • 1Informatics Department, Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Marques de São Vicente, 225, Gávea, Rio de Janeiro, RJ 22451-900, Brazil.

Gigascience
|June 1, 2021
PubMed
Summary
This summary is machine-generated.

Concept drift, a challenge in machine learning due to changing data patterns, is addressed by Driftage. This new framework uses multi-agent systems to simplify drift detection, improve interpretability, and enhance algorithm adaptability.

Keywords:
anomaly detectionconcept driftdata driftdata miningmachine learning explainabilitymachine learning interpretabilitymulti-agent systemstime series

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Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Data Science

Background:

  • Machine learning algorithms degrade in accuracy due to rapid societal data and behavior changes.
  • This degradation, known as concept drift, necessitates complex and costly detection and maintenance strategies.
  • Existing methods often require specialized knowledge in drift detection algorithms and software engineering.

Purpose of the Study:

  • To introduce Driftage, a novel framework designed to simplify the implementation of concept drift detectors.
  • To enhance the explainability of concept drift detection by dividing responsibilities among agents.
  • To enable more dynamic adaptation of machine learning algorithms to evolving data patterns.

Main Methods:

  • Development of Driftage, a new framework utilizing multi-agent systems.
  • Implementation of a case study using electromyography muscle activity monitoring.
  • Demonstration of agent-based responsibility division for concept drift detection.

Main Results:

  • Significant simplification in implementing concept drift detection.
  • Enhanced interpretability of drift detection processes.
  • Reduction in false-positive drift detections.
  • Improved interactivity of drift detectors with external knowledge bases.

Conclusions:

  • Driftage establishes a new paradigm for implementing concept drift algorithms.
  • The multi-agent architecture facilitates distributed drift detection responsibilities.
  • This approach leads to more interpretable and dynamically adaptable algorithms.