As the cornerstone of AI generated content, data drives human-machine interaction and is essential for developing sophisticated deep learning agents. Nevertheless, the associated data storage poses a formidable challenge from conventional energy-intensive planar storage, high maintenance cost, and the susceptibility to electromagnetic interference. In this work, we introduce the concept of metasurface disk, meta-disk, to expand the capacity limits of optical holographic storage by leveraging uncorrelated structural twist. We develop a physical twisted neural network to describe the optical behavior of the meta-disk and conduct a comprehensive lateral error analysis, where the meta-disk stores large volumes of information through internal structural multiplexing. Two-layer 640 µm x 640 µm meta-disk is sufficient to store over hundreds of high-fidelity images with SSIM of 0.8. By harnessing advanced three-dimensional (3D) printing technology, optical holographic storage is experimentally demonstrated with Pancharatnam-Berry metasurfaces. Our technology provides essential backing for the next generation of optical storage, display, encryption, and multifunctional optical analog computing.
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http://dx.doi.org/10.1038/s41467-024-53749-6 | DOI Listing |
J Chem Phys
January 2025
Microsoft Research AI for Science, 21 Station Road, Cambridge CB1 2FB, United Kingdom.
Variational ab initio methods in quantum chemistry stand out among other methods in providing direct access to the wave function. This allows, in principle, straightforward extraction of any other observable of interest, besides the energy, but, in practice, this extraction is often technically difficult and computationally impractical. Here, we consider the electron density as a central observable in quantum chemistry and introduce a novel method to obtain accurate densities from real-space many-electron wave functions by representing the density with a neural network that captures known asymptotic properties and is trained from the wave function by score matching and noise-contrastive estimation.
View Article and Find Full Text PDFRadiology
January 2025
Stanford University School of Medicine, Department of Radiation Oncology, Stanford, CA, US.
Background Detection and segmentation of lung tumors on CT scans are critical for monitoring cancer progression, evaluating treatment responses, and planning radiation therapy; however, manual delineation is labor-intensive and subject to physician variability. Purpose To develop and evaluate an ensemble deep learning model for automating identification and segmentation of lung tumors on CT scans. Materials and Methods A retrospective study was conducted between July 2019 and November 2024 using a large dataset of CT simulation scans and clinical lung tumor segmentations from radiotherapy plans.
View Article and Find Full Text PDFElectromagn Biol Med
January 2025
Department of Computer Applications, Kalasalingam Academy of Research and Education - Deemed to be University, Krishnankoil, India.
Brain tumors can cause difficulties in normal brain function and are capable of developing in various regions of the brain. Malignant tumours can develop quickly, pass through neighboring tissues, and extend to further brain regions or the central nervous system. In contrast, healthy tumors typically develop slowly and do not invade surrounding tissues.
View Article and Find Full Text PDFComput Methods Biomech Biomed Engin
January 2025
Department of Clinical Surgery, Cty Clin Emergency Hosp, Sibiu, Romania.
This study examines heat transfer and nanofluid-enhanced blood flow behaviour in stenotic arteries under inflammatory conditions, addressing critical challenges in cardiovascular health. The blood, treated as a Newtonian fluid, is augmented with gold nanoparticles to improve thermal conductivity and support drug delivery applications. A hybrid methodology combining finite element method (FEM) for numerical modelling and artificial neural networks (ANN) for stability prediction provides a robust analytical framework.
View Article and Find Full Text PDFAnal Chem
January 2025
Department of Analytical Chemistry, Faculty of Pharmacy in Hradec Králové, Charles University, 500 05 Hradec Králové, Czechia.
The retention behavior in supercritical fluid chromatography (SFC) remains a complex and poorly understood phenomenon despite the development of various models to explain retention mechanisms. This study aims to deepen the understanding of retention by investigating three distinct stationary phases: high-strength silica octadecyl (HSS C18 SB), charged surface hybrid pentafluorophenyl (CSH PFP), and porous graphitic carbon (PGC) as a nonsilica-based phase. Three mobile phase compositions, i.
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