Flat optics for augmented-reality headsets
Related Articles
High-performance achromatic flat lens by multiplexing meta-atoms on a stepwise phase dispersion compensation layer
Flat optics have attracted interest for decades due to their flexibility in manipulating optical wave properties, which allows the miniaturization of bulky optical assemblies into integrated planar components. Recent advances in achromatic flat lenses have shown promising applications in various fields. However, it is a significant challenge for achromatic flat lenses with a high numerical aperture to simultaneously achieve broad bandwidth and expand the aperture sizes. Here, we present the zone division multiplex of the meta-atoms on a stepwise phase dispersion compensation (SPDC) layer to address the above challenge. In principle, the aperture size can be freely enlarged by increasing the optical thickness difference between the central and marginal zones of the SPDC layer, without the limit of the achromatic bandwidth. The SPDC layer also serves as the substrate, making the device thinner. Two achromatic flat lenses of 500 nm thickness with a bandwidth of 650–1000 nm are experimentally achieved: one with a numerical aperture of 0.9 and a radius of 20.1 µm, and another with a numerical aperture of 0.7 and a radius of 30.0 µm. To the best of our knowledge, they are the broadband achromatic flat lenses with highest numerical apertures, the largest aperture sizes and thinnest thickness reported so far. Microscopic imaging with a 1.10 µm resolution has also been demonstrated by white light illumination, surpassing any previously reported resolution attained by achromatic metalenses and multi-level diffractive lenses. These unprecedented performances mark a substantial step toward practical applications of flat lenses.
Fast adaptive optics for high-dimensional quantum communications in turbulent channels
Quantum Key Distribution (QKD) promises a provably secure method to transmit information from one party to another. Free-space QKD allows for this information to be sent over great distances and in places where fibre-based communications cannot be implemented, such as ground-satellite. The primary limiting factor for free-space links is the effect of atmospheric turbulence, which can result in significant error rates and increased losses in QKD channels. Here, we employ the use of a high-speed Adaptive Optics (AO) system to make real-time corrections to the wavefront distortions on spatial modes that are used for high-dimensional QKD in our turbulent channel. First, we demonstrate the effectiveness of the AO system in improving the coupling efficiency of a Gaussian mode that has propagated through turbulence. Through process tomography, we show that our system is capable of significantly reducing the crosstalk of spatial modes in the channel. Finally, we show that employing AO reduces the quantum dit error rate for a high-dimensional orbital angular momentum-based QKD protocol, allowing for secure communication in a channel where it would otherwise be impossible. These results are promising for establishing long-distance free-space QKD systems.
To choose or not to choose? A study on decision-making for virtual reality intervention in children with ADHD
Digital health interventions (DHI) using virtual reality (VR) technologies have been developed to treat attention deficit hyperactivity disorder (ADHD). While previous studies have mainly evaluated the feasibility of VR as an ADHD intervention, there is a dearth of research examining the decision-making psychology and influencing factors among parents of ADHD patients regarding the adoption of such emerging VR intervention techniques, which carry inherent risks. Building on the principles of Prospect Theory, this study highlights preference structures, belief characteristics, and community participation. The study selected 23 explanatory variables, including parents’ comprehension of VR treatment, level of trust, information sources, time and financial costs. A self-designed questionnaire was used to collect data on the willingness of parents of ADHD children to opt for VR treatment. By constructing a binary logistic regression model, we examine the preference structure, belief characteristics and decision readiness of parents of children with ADHD when choosing a virtual reality intervention policy. Parents’ choices of VR interventions for their children are complex. While parents consider the therapeutic benefits of VR, the time investment required for children’s treatment, and knowledge on VR interventions from online communities, their decisions are not always made objectively like an agent would. Instead, they frequently make choices based on a willingness to take risks, placing greater emphasis on relative rather than absolute values. Their decision-making is often swayed by online community information, resulting in choices that may not optimize benefits and sometimes disregarding financial and time costs related to their children’s health. Overall, parents of children with ADHD have demonstrated acceptance of the innovative VR intervention technique. Through examining the factors that impact preference selection, the implementation and promotion of VR intervention in ADHD treatments can be facilitated, thereby advancing the development of Digital Health Interventions (DHI). This can provide valuable insights for developing effective ADHD intervention strategies.
Narrowing the gap between machine learning scoring functions and free energy perturbation using augmented data
Machine learning offers great promise for fast and accurate binding affinity predictions. However, current models lack robust evaluation and fail on tasks encountered in (hit-to-) lead optimisation, such as ranking the binding affinity of a congeneric series of ligands, thereby limiting their application in drug discovery. Here, we address these issues by first introducing a novel attention-based graph neural network model called AEV-PLIG (atomic environment vector–protein ligand interaction graph). Second, we introduce a new and more realistic out-of-distribution test set called the OOD Test. We benchmark our model on this set, CASF-2016, and a test set used for free energy perturbation (FEP) calculations, that not only highlights the competitive performance of AEV-PLIG, but provides a realistic assessment of machine learning models with rigorous physics-based approaches. Moreover, we demonstrate how leveraging augmented data (generated using template-based modelling or molecular docking) can significantly improve binding affinity prediction correlation and ranking on the FEP benchmark (weighted mean PCC and Kendall’s τ increases from 0.41 and 0.26 to 0.59 and 0.42). These strategies together are closing the performance gap with FEP calculations (FEP+ achieves weighted mean PCC and Kendall’s τ of 0.68 and 0.49 on the FEP benchmark) while being ~400,000 times faster.
Optical sorting: past, present and future
Optical sorting combines optical tweezers with diverse techniques, including optical spectrum, artificial intelligence (AI) and immunoassay, to endow unprecedented capabilities in particle sorting. In comparison to other methods such as microfluidics, acoustics and electrophoresis, optical sorting offers appreciable advantages in nanoscale precision, high resolution, non-invasiveness, and is becoming increasingly indispensable in fields of biophysics, chemistry, and materials science. This review aims to offer a comprehensive overview of the history, development, and perspectives of various optical sorting techniques, categorised as passive and active sorting methods. To begin, we elucidate the fundamental physics and attributes of both conventional and exotic optical forces. We then explore sorting capabilities of active optical sorting, which fuses optical tweezers with a diversity of techniques, including Raman spectroscopy and machine learning. Afterwards, we reveal the essential roles played by deterministic light fields, configured with lens systems or metasurfaces, in the passive sorting of particles based on their varying sizes and shapes, sorting resolutions and speeds. We conclude with our vision of the most promising and futuristic directions, including AI-facilitated ultrafast and bio-morphology-selective sorting. It can be envisioned that optical sorting will inevitably become a revolutionary tool in scientific research and practical biomedical applications.
Responses