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Gestalt Principles of Perception01:21

Gestalt Principles of Perception

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Gestalt principles provide a framework for understanding how humans perceive objects as unified wholes within their context. These principles are essential in explaining the cognitive processes that make sense of complex visual stimuli by organizing them into coherent groups. One fundamental principle is proximity, which posits that objects located close to each other are perceived as a collective group. For instance, when dots are positioned near one another, the visual system interprets them...
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Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

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A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
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Related Experiment Video

Updated: Aug 3, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Physics-Informed Guided Disentanglement in Generative Networks.

Fabio Pizzati, Pietro Cerri, Raoul de Charette

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |April 7, 2023
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a framework to improve image-to-image translation (i2i) by disentangling visual traits using physics models or neural networks. This enhances translation quality, controllability, and variability in challenging scenarios.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Physics-Informed Machine Learning

    Background:

    • Image-to-image translation (i2i) networks face challenges with entanglement effects, especially when physics-related phenomena like occlusions or fog are present in the target domain.
    • These entanglement effects degrade translation quality, reduce controllability, and limit variability in generated images.

    Purpose of the Study:

    • To propose a general framework for disentangling visual traits in target images for improved i2i.
    • To enhance the quality, controllability, and variability of i2i networks by addressing entanglement effects.

    Main Methods:

    • Developed a framework utilizing simple physics models to guide disentanglement, rendering some target traits physically and learning others.
    • Introduced neural-guided disentanglement as an alternative when physical models are inaccessible, using generative networks.
    • Proposed three disentanglement strategies: fully differentiable physics model, partially non-differentiable physics model, and neural network guidance.

    Main Results:

    • The proposed physical models, optimally regressed on target data, enable controllable generation of unseen scenarios.
    • The framework demonstrates versatility across different guidance strategies (physics-based and neural-guided).
    • Disentanglement strategies significantly improve i2i performance both qualitatively and quantitatively in complex scenarios.

    Conclusions:

    • The framework effectively disentangles visual traits in i2i, overcoming limitations posed by physics-related phenomena.
    • Physics-guided and neural-guided disentanglement offer robust solutions for enhancing i2i translation quality and control.
    • This approach opens new avenues for controllable and high-fidelity image generation.