Researchers build powerful model for discovering new drugs

Researchers have developed a new computer framework that holds promise in the work to discover new drugs. Their framework uses an artificial intelligence method called a convolutional neural network to provide global information ...

Quantum computers in action in chemistry

Quantum computers are one of the key future technologies of the 21st century. Their potential surpasses even the best supercomputers. They have proven to be a powerful tool, in particular for solving complex computational ...

Trio of tuning tools for modeling large spatial datasets

Predictive modeling of very large datasets, such as environmental measurements, across a wide area can be a highly computationally intensive exercise. These computational demands can be significantly reduced by applying various ...

Why animals recognise numbers but only humans can do math

Counting feels utterly effortless to adults, who are unlikely to even remember when or how they picked up this useful, apparently automatic skill. Yet when you think about it, counting is a remarkable invention. It helped ...

How to make computers faster and climate friendly

Your smartphone is far more powerful than the NASA computers that put Neil Armstrong and Buzz Aldrin on the moon in 1969, but it is also an energy hog. In computing, energy use is often considered a secondary problem to speed ...

Solving real-world problems

Tools developed by HÃ¥vard Rue have transformed data analysis, interpretation and communication, and are applied broadly: from modeling the spread of infectious diseases to mapping fish stocks.

Less is more when choosing between groups of assorted items

When making decisions about the value of an assortment of different objects, people approximate an average overall value, which though frequently useful can lead to apparently irrational decision-making. A new study published ...

Explained: Linear and nonlinear systems

Much scientific research across a range of disciplines tries to find linear approximations of nonlinear behaviors. But what does that mean?

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