Led by Dr. Jue Ruan and Dr. Weihua Pan, a study published in the journal National Science Review delves into the realm of DNA digital storage (DDS), a technology acclaimed for its high-density (EB/g), long-term (million years) and low maintenance costs, offering a promising solution for the ever-growing demands of big data storage.Led by Dr. Jue Ruan and Dr. Weihua Pan, a study published in the journal National Science Review delves into the realm of DNA digital storage (DDS), a technology acclaimed for its high-density (EB/g), long-term (million years) and low maintenance costs, offering a promising solution for the ever-growing demands of big data storage.[#item_full_content]

Researchers from EPFL have just released Meditron, the world’s best performing open source large language model tailored to the medical field designed to help guide clinical decision-making.Researchers from EPFL have just released Meditron, the world’s best performing open source large language model tailored to the medical field designed to help guide clinical decision-making.[#item_full_content]

Deep machine learning has achieved remarkable success in various fields of artificial intelligence, but achieving both high interpretability and high efficiency simultaneously remains a critical challenge. Shi-Ju Ran of Capital Normal University and Gang Su of the University of the Chinese Academy of Sciences have reviewed an innovative approach based on tensor networks, drawing inspiration from quantum mechanics. This approach offers a promising solution to the long-standing challenge of reconciling interpretability and efficiency in deep machine learning.Deep machine learning has achieved remarkable success in various fields of artificial intelligence, but achieving both high interpretability and high efficiency simultaneously remains a critical challenge. Shi-Ju Ran of Capital Normal University and Gang Su of the University of the Chinese Academy of Sciences have reviewed an innovative approach based on tensor networks, drawing inspiration from quantum mechanics. This approach offers a promising solution to the long-standing challenge of reconciling interpretability and efficiency in deep machine learning.[#item_full_content]

As the high-speed railway network in China extends beyond 40,000 kilometers, maintaining seamless internet connectivity for passengers is becoming increasingly challenging. The demand for consistent and reliable online access is particularly crucial for travelers who spend extended hours on trains, relying on the expectation of undisturbed work, study, or entertainment. Addressing this need, a team of researchers from the School of Computer Science at Peking University has developed “HiMoDiag”—an innovative tool designed to enhance the understanding and management of network performance in extremely high-mobility scenarios.As the high-speed railway network in China extends beyond 40,000 kilometers, maintaining seamless internet connectivity for passengers is becoming increasingly challenging. The demand for consistent and reliable online access is particularly crucial for travelers who spend extended hours on trains, relying on the expectation of undisturbed work, study, or entertainment. Addressing this need, a team of researchers from the School of Computer Science at Peking University has developed “HiMoDiag”—an innovative tool designed to enhance the understanding and management of network performance in extremely high-mobility scenarios.[#item_full_content]

Image recognition technology has come a long way since 2012 when a group of computer scientists at the University of Toronto created a convolutional neural network (CNN)—dubbed “AlexNet” after its creator Alex Krizhevsky—that correctly identified images much better than others. Its findings have propelled successful use of CNNs in related fields such as video analysis and pattern recognition, and now researchers are now focusing on 3D deep learning networks.Image recognition technology has come a long way since 2012 when a group of computer scientists at the University of Toronto created a convolutional neural network (CNN)—dubbed “AlexNet” after its creator Alex Krizhevsky—that correctly identified images much better than others. Its findings have propelled successful use of CNNs in related fields such as video analysis and pattern recognition, and now researchers are now focusing on 3D deep learning networks.[#item_full_content]

In the real world/digital world cross-over of mixed reality, a user’s immersive engagement with the program is called presence. Now, UMass Amherst researchers are the first to identify reaction time as a potential presence measurement tool. Their findings, published in IEEE Transactions on Visualization and Computer Graphics, have implications for calibrating mixed reality to the user in real time.In the real world/digital world cross-over of mixed reality, a user’s immersive engagement with the program is called presence. Now, UMass Amherst researchers are the first to identify reaction time as a potential presence measurement tool. Their findings, published in IEEE Transactions on Visualization and Computer Graphics, have implications for calibrating mixed reality to the user in real time.[#item_full_content]

In a study published in Scientific Reports, a research team from the University of Passau compared the quality of machine-generated content with essays written by secondary school students. The upshot: The AI-based chatbot performed better across all criteria, especially when it came to language mastery.In a study published in Scientific Reports, a research team from the University of Passau compared the quality of machine-generated content with essays written by secondary school students. The upshot: The AI-based chatbot performed better across all criteria, especially when it came to language mastery.[#item_full_content]

Researchers at Seoul National University have recently tried to train an artificial intelligence (AI) agent to create collages (i.e., artworks created by sticking various pieces of materials together), reproducing renowned artworks and other images. Their proposed model was introduced in a paper pre-printed on arXiv and presented at ICCV 2023 in October.Researchers at Seoul National University have recently tried to train an artificial intelligence (AI) agent to create collages (i.e., artworks created by sticking various pieces of materials together), reproducing renowned artworks and other images. Their proposed model was introduced in a paper pre-printed on arXiv and presented at ICCV 2023 in October.[#item_full_content]

Multi-focus image fusion (MFIF) is an image enhancement technology that helps to solve the depth-of-field problem and capture all-in-focus images. It has broad application prospects that can effectively extend the depth of field of optical lenses.Multi-focus image fusion (MFIF) is an image enhancement technology that helps to solve the depth-of-field problem and capture all-in-focus images. It has broad application prospects that can effectively extend the depth of field of optical lenses.[#item_full_content]

With the emergence of huge amounts of heterogeneous multi-modal data—including images, videos, texts/languages, audios, and multi-sensor data—deep learning-based methods have shown promising performance for various computer vision and machine learning tasks, such as visual comprehension, video understanding, visual-linguistic analysis, and multi-modal fusion.With the emergence of huge amounts of heterogeneous multi-modal data—including images, videos, texts/languages, audios, and multi-sensor data—deep learning-based methods have shown promising performance for various computer vision and machine learning tasks, such as visual comprehension, video understanding, visual-linguistic analysis, and multi-modal fusion.[#item_full_content]