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Researchers applied the mathematical theory of synchronization to clarify how recurrent neural networks (RNNs) generate predictions, revealing a certain map, based on the generalized synchronization, that yields correct target values. They showed that conventional reservoir computing (RC), a type of RNN, can be viewed as a linear approximation, and introduced a 'generalized readout' incorporating further order approximations. Using a chaotic time-series forecasting task, they demonstrated that this approach dramatically enhances both prediction accuracy and robustness.

A team has developed a new method that facilitates and improves predictions of tabular data, especially for small data sets with fewer than 10,000 data points. The new AI model TabPFN is trained on synthetically generated data before it is used and thus learns to evaluate possible causal relationships and use them for predictions.

In the same way that ChatGPT understands human language, a new AI model developed by computational biologists captures the language of cells to accurately predict their activities.

Researchers focus on enhancing the aerodynamic performance of autonomous vehicles by reducing drag induced by externally mounted sensors such as cameras and light detection and ranging instruments. After establishing an automated computational platform, they combined the experimental design with a substitute model and an optimization algorithm to improve the structural shapes of AV sensors. They then performed simulations of both the baseline and optimized models. After optimizing the design, researchers found a 3.44% decrease in the total aerodynamic drag of an AV.

Hula hooping is so commonplace that we may overlook some interesting questions it raises: 'What keeps a hula hoop up against gravity?' and 'Are some body types better for hula hooping than others?' A team of mathematicians explored and answered these questions with findings that also point to new ways to better harness energy and improve robotic positioners.