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Evaluating the performance of Q-learning-based AI in auctions

Liu et al. | Nov 09, 2025

Evaluating the performance of Q-learning-based AI in auctions
Image credit: Liu and Liu

Advertising platforms like Google Ads use AI to drive the algorithms used to maximize advertisers benefits. This study shows that AI does not adjust it strategy based on auction type and highlights the limitations of AI running without explicit guidance.

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Quantifying natural recovery of dopamine deficits induced by chronic stress

Josyula et al. | Oct 27, 2025

Quantifying natural recovery of dopamine deficits induced by chronic stress
Image credit: Shawn Day

Here the authors investigated the natural recovery of stress-induced dopamine-related gene deficits in C. elegans by measuring the expression of cat-2 (dopamine biosynthesis) and sod-2 (oxidative stress) following exposure to starvation or hydrocortisone. They found that the reversibility of sod-2 and the expression of cat-2 were highly dependent on the type and severity of the stressor, suggesting that the body's natural ability to recover from dopamine dysfunction has biological limitations.

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Optimizing AI-generated image detection using a Convolutional Neural Network model with Fast Fourier Transform

Gupta et al. | Oct 24, 2025

Optimizing AI-generated image detection using a Convolutional Neural Network model with Fast Fourier Transform

Recent advances in generative AI have made it increasingly hard to distinguish real images from AI-generated ones. Traditional detection models using CNNs or U-net architectures lack precision because they overlook key spatial and frequency domain details. This study introduced a hybrid model combining Convolutional Neural Networks (CNN) with Fast Fourier Transform (FFT) to better capture subtle edge and texture patterns.

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