Developing large-scale neural network models that mimic the brain’s activity is a major goal in the field of computational neuroscience. Existing models that accurately reproduce aspects of brain activity are notoriously complex, and fine-tuning model parameters often requires significant time, intuition, and expertise.Developing large-scale neural network models that mimic the brain’s activity is a major goal in the field of computational neuroscience. Existing models that accurately reproduce aspects of brain activity are notoriously complex, and fine-tuning model parameters often requires significant time, intuition, and expertise.[#item_full_content]

Researchers from the Changchun Institute of Optics, Fine Mechanics and Physics of the Chinese Academy of Sciences have developed a novel autofocus method that harnesses the power of deep learning to dynamically select regions of interest in grayscale images. The study was published in the journal Sensors.Researchers from the Changchun Institute of Optics, Fine Mechanics and Physics of the Chinese Academy of Sciences have developed a novel autofocus method that harnesses the power of deep learning to dynamically select regions of interest in grayscale images. The study was published in the journal Sensors.[#item_full_content]

Generative artificial intelligence (AI) has notoriously struggled to create consistent images, often getting details like fingers and facial symmetry wrong. Moreover, these models can completely fail when prompted to generate images at different image sizes and resolutions.Generative artificial intelligence (AI) has notoriously struggled to create consistent images, often getting details like fingers and facial symmetry wrong. Moreover, these models can completely fail when prompted to generate images at different image sizes and resolutions.[#item_full_content]

Research in the International Journal of Computational Science and Engineering describes a new approach to spotting messages hidden in digital images. The work contributes to the field of steganalysis, which plays a key role in cybersecurity and digital forensics.Research in the International Journal of Computational Science and Engineering describes a new approach to spotting messages hidden in digital images. The work contributes to the field of steganalysis, which plays a key role in cybersecurity and digital forensics.[#item_full_content]

As high-tech companies ramp up construction of massive data centers to meet the business boom in artificial intelligence, one component is becoming an increasingly rare commodity: electricity.As high-tech companies ramp up construction of massive data centers to meet the business boom in artificial intelligence, one component is becoming an increasingly rare commodity: electricity.[#item_full_content]

An algorithm developed by Prakash Vedula, Ph.D., a professor at the University of Oklahoma School of Aerospace and Mechanical Engineering, has been incorporated into advanced computing software developed by Google and IBM. The algorithm is remarkable for its exponential improvement over previous methods.An algorithm developed by Prakash Vedula, Ph.D., a professor at the University of Oklahoma School of Aerospace and Mechanical Engineering, has been incorporated into advanced computing software developed by Google and IBM. The algorithm is remarkable for its exponential improvement over previous methods.[#item_full_content]

Phase separation, when molecules part like oil and water, works alongside oxygen diffusion to help memristors—electrical components that store information using electrical resistance—retain information even after the power is shut off, according to a University of Michigan led study recently published in Matter.Phase separation, when molecules part like oil and water, works alongside oxygen diffusion to help memristors—electrical components that store information using electrical resistance—retain information even after the power is shut off, according to a University of Michigan led study recently published in Matter.[#item_full_content]

Deep learning (DL) has significantly transformed the field of computational imaging, offering powerful solutions to enhance performance and address a variety of challenges. Traditional methods often rely on discrete pixel representations, which limit resolution and fail to capture the continuous and multiscale nature of physical objects. Recent research from Boston University (BU) presents a novel approach to overcome these limitations.Deep learning (DL) has significantly transformed the field of computational imaging, offering powerful solutions to enhance performance and address a variety of challenges. Traditional methods often rely on discrete pixel representations, which limit resolution and fail to capture the continuous and multiscale nature of physical objects. Recent research from Boston University (BU) presents a novel approach to overcome these limitations.[#item_full_content]

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