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Categories: Mathematics: General, Mathematics: Statistics

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Using artificial intelligence (AI), researchers can now follow cell movement across time and space. The method could be very helpful for developing more effective cancer medications.

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A new study finds that a 200-year-old technique called Fourier analysis can reveal crucial information about how the form of artificial intelligence called deep neural networks (DNN) learn to perform tasks involving complex physics. Researchers discovered the technique can directly connect what a DNN has learned to the physics of the complex system the DNN is modeling.

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If scaled up successfully, the team's new system could help answer questions about certain kinds of superconductors and other unusual states of matter.

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Researchers tested a large sample of the major AI technologies available today and found that not only did they reproduce human biases in facial age recognition, but they exaggerated those biases.

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Synthetic populations are computer-generated models that mimic real-world populations in terms of characteristics such as age, gender, and occupation; they are useful when conducting social simulations. In a recent study, researchers developed a new approach to assign workplaces to individuals in a synthetic Japanese population with household information, based on ODI (Origin-Destination-Industry) data. Their efforts will enable more accurate, realistic simulations of the day-time distribution of workers in Japan, helping to improve decision-making and planning.

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A mathematician was keen to forecast the evolution of the COVID epidemic. Instead, he ended up solving a problem which had troubled computer scientists for decades.

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A new paper presents a mathematical model for the motion of bacteria that includes cell division and death, the basic ingredients of the cell cycle.

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A new study shows machine-learning models trained using simple symptoms, demographic features are effective in predicting COVID-19 infections.

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Mathematical analysis identifies a vortex structure that is impervious to decay.

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The reliability of data storage and writing speed in advanced magnetic devices depend on drastic, complex changes in microscopic magnetic domain structures. However, it is extremely challenging to quantify these changes, limiting our understanding of magnetic phenomena. To tackle this, researchers developed, using machine learning and topology, an analysis method that quantifies the complexity of the magnetic domain structures, revealing hidden features of magnetization reversal that are hardly seen by human eyes.

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With the theory that for every action, even those seemingly complex and random, there is a math problem that describes it, a researcher is publishing a new formula that helps find that equation quickly. Yes, he's speeding up science.

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Researchers have devised a computer-based platform for drug discovery that could make the process more effective, more efficient and less costly.

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Researchers have identified a new method for scheduling radiation therapy that could be as much as 22 percent more effective at killing cancer cells than current standard radiation treatment regimens.

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Coupling two approaches of game theory can shed light on how moral norms evolve.

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A new statistical method provides a more efficient way to uncover biologically meaningful changes in genomic data that span multiple conditions -- such as cell types or tissues.

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A new study by mathematicians shows that math borrowed from neuroscience can describe how swarms of these unique insects coordinate their light show, capturing key details about how they behave in the wild.

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Reducing redundant information to find simplifying patterns in data sets and complex networks is a scientific challenge in many knowledge fields. Moreover, detecting the dimensionality of the data is still a hard-to-solve problem. A new article presents a method to infer the dimensionality of complex networks through the application of hyperbolic geometrics, which capture the complexity of relational structures of the real world in many diverse domains.

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America was unprepared for the magnitude of the pandemic, which overwhelmed many counties and filled some hospitals to capacity. A new study suggests there may have been a mathematical method, of sorts, to the madness of those early COVID days.

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Music and arts classes are often first on the chopping block when schools face tight budgets and pressure to achieve high scores on standardized tests. But it's precisely those classes that can increase student interest in school and even benefit their math achievement, according to a new study.

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Policy makers assume that adding more detail to mathematical models will produce more accurate predictions, but that's not always the case, a new study shows.