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Categories: Mathematics: Statistics

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A team of researchers has created a digital pathology platform based on artificial intelligence. The platform uses new algorithms developed by the team and enables fully automated analysis of tissue sections from lung cancer patients. The platform makes it possible to analyze digitized tissue samples on the computer for lung tumors more quickly and accurately than before.

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Research revealed how the use of ChatGPT to cheat on general chemistry multiple-choice exams can be detected through specific statistical methods.

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What if the 'Market Economy' always existed? Archaeologists tried to answer this question by researching how much Bronze Age people used to spend to sustain their daily lives. Their results show that, starting at least 3,500 years ago, the spending habits of prehistoric Europeans were not substantially different from what they are today.

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Researchers argue that, in some situations where machine-learning models are used to allocate scarce resources or opportunities, randomizing decisions in a structured way may lead to fairer outcomes.

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A new technique enables huge machine-learning models to efficiently generate more accurate quantifications of their uncertainty about certain predictions. This could help practitioners determine whether to trust the model when it is deployed in real-world settings.

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Ten million new cases of dementia are diagnosed each year but the presence of different dementia forms and overlapping symptoms can complicate diagnosis and delivery of effective treatments. Now researchers have developed an AI tool that can diagnose ten different types of dementia such as vascular dementia, Lewy body dementia, and frontotemporal dementia, even if they co-occur.

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Traditional methods to assess balance often suffer from subjectivity, aren't comprehensive enough and can't be administered remotely. They also are expensive and require specialized equipment and clinical expertise. Using wearable sensors and advanced machine learning algorithms, researchers offer a practical and cost-effective solution for capturing detailed movement data, essential for balance analysis. This approach is more accessible and can be administered remotely, which could have significant implications for health care, rehabilitation, sports science or other fields where balance assessment is important.

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Researchers combine AI and mobile health to predict recovery from lumbar spine surgery.

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Team utilizes advanced statistical techniques to project the future groundwater depletion risk.

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In human populations, it is relatively easy to calculate demographic trends and make projections of the future if data on basic processes such as births and immigration is known. The data, given by individuals, can be also death and emigration, which subtract. In the wild, on the other hand, understanding the processes that determine wildlife demographic patterns is a highly complex challenge for the scientific community. Although a wide range of methods are now available to estimate births and deaths in wildlife, quantifying emigration and immigration has historically been difficult or impossible in many populations of interest, particularly in the case of threatened species.

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Researchers used generative AI to develop a physics-informed technique to classify phase transitions in materials or physical systems that is much more efficient than existing machine-learning approaches.

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Researchers have recast diffusion in multicomponent alloys as a sum of individual contributions, called 'kinosons.' Using machine learning to compute the statistical distribution of the individual contributions, they were able to model the alloy and calculate its diffusivity orders of magnitude more efficiently than computing whole trajectories.

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Artificial intelligence (AI) computer programs that process MRI results show differences in how the brains of men and women are organized at a cellular level, a new study shows. These variations were spotted in white matter, tissue primarily located in the human brain's innermost layer, which fosters communication between regions.

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A special review examines highly-cited papers in statistical ecology. The review, which covers a century of research, details how models and concepts have evolved alongside increasing computational power.

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Physicists and computer scientists have recently expanded the modern theory of the thermodynamics of computation. By combining approaches from statistical physics and computer science, the researchers introduce mathematical equations that reveal the minimum and maximum predicted energy cost of computational processes that depend on randomness, which is a powerful tool in modern computers.

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Elucidating the relationship between the sequences of non-coding regulatory elements and their target genes is key to understanding gene regulation and its variation between plant species and ecotypes. Now, an international research team developed deep learning models that link gene sequence data with mRNA copy number for several plant species and predicted the regulatory effect of gene sequence variation.

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Researchers have developed a new method to predict the financial impacts climate change will have on agriculture, which can help support food security and financial stability for countries increasingly prone to climate catastrophes.

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Scientists have developed an automated workflow that could accelerate the discovery of new pharmaceutical drugs and other useful products. The new automated approach could analyze chemical reactions in real time and identify new chemical-reaction products much faster than current laboratory methods.

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Neural networks have been powering breakthroughs in artificial intelligence, including the large language models that are now being used in a wide range of applications, from finance, to human resources to healthcare. But these networks remain a black box whose inner workings engineers and scientists struggle to understand. Now, a team has given neural networks the equivalent of an X-ray to uncover how they actually learn.

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Quantum sensor technology promises even more precise measurements of physical quantities. A team has now compared the signals of up to 91 quantum sensors with each other and thus successfully eliminated the noise caused by interactions with the environment. Correlation spectroscopy can be used to increase the precision of sensor networks.