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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9125570 | PMC |
| http://dx.doi.org/10.1089/cmb.2021.0316 | DOI Listing |
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High-rate quantum LDPC codes for long-range-connected neutral atom registers.
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University of Strasbourg and CNRS, CESQ and ISIS (UMR 7006), aQCess, 67000, Strasbourg, France.
High-rate quantum error correcting (QEC) codes with moderate overheads in qubit number and control complexity are highly desirable for achieving fault-tolerant quantum computing. Recently, quantum error correction has experienced significant progress both in code development and experimental realizations, with neutral atom qubit architecture rapidly establishing itself as a leading platform in the field. Scalable quantum computing will require processing with QEC codes that have low qubit overhead and large error suppression, and while such codes do exist, they involve a degree of non-locality that has yet to be integrated into experimental platforms.
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International Joint Laboratory of Catalytic Chemistry, College of Sciences, Shanghai University, Shanghai 200444, China.
Heterogeneous catalysis at the metal surface generally involves the transport of molecules through the interfacial water layer to access the surface, which is a rate-determining step at the nanoscale. In this study, taking the oxygen reduction reaction on a metal electrode in aqueous solution as an example, using accurate molecular dynamic simulations, we propose a novel long-range regulation strategy in which midinfrared stimulation (MIRS) with a frequency of approximately 1,000 cm is applied to nonthermally induce the structural transition of interfacial water from an ordered to disordered state, facilitating the access of oxygen molecules to metal surfaces at room temperature and increasing the oxygen reduction activity 50-fold. Impressively, the theoretical prediction is confirmed by the experimental observation of a significant discharge voltage increase in zinc-air batteries under MIRS.
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School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA.
Vision transformer (ViT)and convolutional neural networks (CNNs) each possess distinct strengths in medical imaging: ViT excels in capturing long-range dependencies through self-attention, while CNNs are adept at extracting local features via spatial convolution filters. While ViT may struggle with capturing detailed local spatial information, critical for tasks like anomaly detection in medical imaging, shallow CNNs often fail to effectively abstract global context. This study aims to explore and evaluate hybrid architectures that integrate ViT and CNN to leverage their complementary strengths for enhanced performance in medical vision tasks, such as segmentation, classification, reconstruction, and prediction.
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School of Computer Science, University College Dublin (UCD), D04 V1W8 Dublin, Ireland.
Predicting the relative solvent accessibility (RSA) of a protein is critical to understanding its 3D structure and biological function. RSA prediction, especially when homology transfer cannot provide information about a protein's structure, is a significant step toward addressing the protein structure prediction challenge. Today, deep learning is arguably the most powerful method for predicting RSA and other structural features of proteins.
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