Physics - Sep 30
Physics
Neural networks are learning algorithms that approximate the solution to a task by training with available data. However, it is usually unclear how exactly they accomplish this. Two young Basel physicists have now derived mathematical expressions that allow one to calculate the optimal solution without training a network. Their results not only give insight into how those learning algorithms work, but could also help to detect unknown phase transitions in physical systems in the future.
Environment - Sep 29
Environment

Since Darwin, we have known that evolutionary adaptation is reflected in the appearance and function of species' bodies under environmental changes. One of the questions commonly asked by evolutionary biologists is how body shape relates to a specific ecological feature, such as diet. In a recent study published in the journal Evolution

Life Sciences - Sep 28
Life Sciences

Metabolism is essential to all living organisms, and modeling the chemical reactions that sustain life is no easy task. Now, scientists have released REKINDLE, paving the way for more efficient and accurate modeling of metabolic processes thanks to deep-learning.

Environment - Sep 28
Environment

Good news from Antarctica: researchers have examined emperor penguins and found no evidence of microplastics in their stomachs. The study, conducted by the University of Basel and the Alfred-Wegener Institute, is an important assessment of environmental pollution at the South Pole.

Materials Science - Sep 27
Materials Science

To reduce rail noise for residents, noise barriers or quieter wheel systems and brakes are not the only options. An inconspicuous component under the railway tracks is a beacon of hope for quieter rail traffic for a team of researchers with Empa involvement - and first tests on passing trains show a positive effect.

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