Diffractive optical neural networks (DONNs) have emerged as a promising optical hardware platform for ultra-fast and energy-efficient signal processing for machine learning tasks, particularly in computer vision. Previous experimental demonstrations of DONNs have only been performed using coherent light. However, many real-world DONN applications require consideration of the spatial coherence properties of the optical signals. Here, we study the role of spatial coherence in DONN operation and performance. We propose a numerical approach to efficiently simulate DONNs under incoherent and partially coherent input illumination and discuss the corresponding computational complexity. As a demonstration, we train and evaluate simulated DONNs on the MNIST dataset of handwritten digits to process light with varying spatial coherence.
Electronic coherences are key to understanding and controlling photoinduced molecular transformations. We identify a crucial quantum-mechanical feature of electron-nuclear correlation, the projected nuclear quantum momenta, essential to capture the correct coherence behavior. For simulations, we show that, unlike traditional trajectory-based schemes, exact-factorization-based methods approximate these correlation terms and correctly capture electronic coherences in a range of situations, including their spatial dependence, an important aspect that influences subsequent electron dynamics and that is becoming accessible in more experiments.
National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, 710049 China.
Magnetoencephalography (MEG) records the extremely weak magnetic fields on the surface of the scalp through highly sensitive sensors. Multi-channel MEG data provide higher spatial and temporal resolution when measuring brain activities, and can be applied for brain-computer interfaces as well. However, a large number of channels leads to high computational complexity and can potentially impact decoding accuracy.
Modern pulmonary imaging can reveal underlying pathological and pathophysiological changes in the lungs of people with asthma, with important clinical implications. A multitude of imaging modalities are now used to examine underlying structure/function relationships including computed tomography, magnetic resonance imaging, optical coherence tomography, and endobronchial ultrasound. Imaging-based biomarkers from these techniques, including airway dimensions, blood vessel volumes, mucus scores, ventilation defect extent and air trapping extent, often have increased sensitivity compared to traditional lung function measurements, and are increasingly used as endpoints in clinical trials.
We describe the design and performance of a magnetic bottle electron spectrometer (MBES) for high-energy electron spectroscopy. Our design features a 2 m long electron drift tube and electrostatic retardation lens, achieving sub-electronvolt (eV) electron kinetic energy resolution for high energy (several hundred eV) electrons with a close to 4π collection solid angle. A segmented anode electron detector enables the simultaneous collection of photoelectron spectra in high resolution and high collection efficiency modes.
Background: To investigate the spatial and morphologic features of lenses with different axial length (ALs) in cataract patients using swept-source optical coherence tomography (SS-OCT).
Methods: Totally 105 eyes of 105 patients scheduled to have cataract surgery were included. Eyes were divided into the control (AL < 24.
