High Dynamic Range (HDR) imaging via multi-exposure fusion is an important task for most modern imaging platforms. In spite of recent developments in both hardware and algorithm innovations, challenges remain over content association ambiguities caused by saturation, motion, and various artifacts introduced during multi-exposure fusion such as ghosting, noise, and blur. In this work, we propose an Attention-guided Progressive Neural Texture Fusion (APNT-Fusion) HDR restoration model which aims to address these issues within one framework. An efficient two-stream structure is proposed which separately focuses on texture feature transfer over saturated regions and multi-exposure tonal and texture feature fusion. A neural feature transfer mechanism is proposed which establishes spatial correspondence between different exposures based on multi-scale VGG features in the masked saturated HDR domain for discriminative contextual clues over the ambiguous image areas. A progressive texture blending module is designed to blend the encoded two-stream features in a multi-scale and progressive manner. In addition, we introduce several novel attention mechanisms, i.e., the motion attention module detects and suppresses the content discrepancies among the reference images; the saturation attention module facilitates differentiating the misalignment caused by saturation from those caused by motion; and the scale attention module ensures texture blending consistency between different coder/decoder scales. We carry out comprehensive qualitative and quantitative evaluations and ablation studies, which validate that these novel modules work coherently under the same framework and outperform state-of-the-art methods.

Artificial intelligence (AI) techniques have been spreading in most scientific areas and have become a heated focus in photonics research in recent years. Forward modeling and inverse design using AI can achieve high efficiency and accuracy for photonics components. With AI-assisted electronic circuit design for photonics components, more advanced photonics applications have emerged. Photonics benefit a great deal from AI, and AI, in turn, benefits from photonics by carrying out AI algorithms, such as complicated deep neural networks using photonics components that use photons rather than electrons. Beyond the photonics domain, other related research areas or topics governed by Maxwell’s equations share remarkable similarities in using the help of AI. The studies in computational electromagnetics, the design of microwave devices, as well as their various applications greatly benefit from AI. This article reviews leveraging AI in photonics modeling, simulation, and inverse design; leveraging photonics computing for implementing AI algorithms; and leveraging AI beyond photonics topics, such as microwaves and quantum-related topics.

Existing learning-based hyperspectral reconstruction methods show limitations in fully exploiting the information among the hyperspectral bands. As such, we propose to investigate the chromatic inter-dependencies in their respective hyperspectral embedding space. These embedded features can be fully exploited by querying the inter-channel correlations in a combinatorial manner, with the unique and complementary information efficiently fused into the final prediction. We found such independent modeling and combinatorial excavation mechanisms are extremely beneficial to uncover marginal spectral features, especially in the long wavelength bands. In addition, we have proposed a spatio-spectral attention block and a spectrum-fusion attention module, which greatly facilitates the excavation and fusion of information at both semantically long-range levels and fine-grained pixel levels across all dimensions. Extensive quantitative and qualitative experiments show that our method (dubbed CESST) achieves SOTA performance. Code for this project is at: https://github.com/AlexYangxx/CESST.

This paper presents a new conductive carbon fiber reinforced polymer (CFRP) aircraft composite and its electromagnetic characterization for tackling the lightning direct effect and enhancing the electromagnetic shielding effectiveness (SE). The CFRP composite design is deliberated for ameliorating the electrical conductivity of composite. With the aid of equivalent medium modeling, the inherent electromagnetic properties of the composite have been investigated. The simulated results illustrate that the lightning strike protection (LSP) capability and the electromagnetic shielding behavior of the newly developed aircraft composite have been improved as compared to the original neat CFRP composite, and comparable to that with copper mesh as a protective layer on top. Moreover, an artificial lightning experiment has been carried out in accordance with SAE ARP5416 by mimicking the natural lightning strike attachment. The shielding effectiveness of the proposed composite prototype has also been measured by using electromagnetic waveguide transmission line method. The physical experiments have effectively validated the simulation results and demonstrated the feasibility of the proposed conductive CFRP composite on LSP and SE.

Corneal diseases are the major causes of irreversible blindness in the world. The hydration level of cornea is essential in maintaining the transparency and hydrodynamics function of corneal.¹ It also plays a crucial part in a variety of corneal conditions, such as corneal inflammation, corneal trauma, or corneal edema. Moreover, it is known that corneal hydration can affect the efficacy and safety outcomes of refractive surgery.^{2 – 5} Hence, tools and methods to more precisely evaluate and monitor the hydration level in corneas would be useful. However, there are no available tools to accurately evaluate corneal hydration directly so far. Corneal thickness, measured by either ultrasonic pachymeter or optical coherence tomography (OCT), is used by ophthalmologists as surrogate measures and indirect evaluation of corneal hydration.⁶ Corneal hydration is currently approximated in the clinic by extrapolation using the central corneal thickness (CCT) measurements. Deviations of 20% in CCT or greater are observed in the data, and the extrapolation method is based upon healthy corneas. Therefore, it cannot provide accurate analysis for changes relating to disease states in corneas.^{7 – 9}

Physics-informed neural networks (PINNs) have emerged as a new learning paradigm for solving partial differential equations (PDEs) by enforcing the constraints of physical equations, boundary conditions (BCs), and initial conditions (ICs) into the loss function. Despite their successes, vanilla PINNs still suffer from poor accuracy and slow convergence due to the intractable multi-objective optimization issue. In this paper, we propose a novel Dual-Balanced PINN (DB-PINN), which dynamically adjusts loss weights by integrating inter-balancing and intra-balancing to alleviate two imbalance issues in PINNs. Inter-balancing aims to mitigate the gradient imbalance between PDE residual loss and condition-fitting losses by determining an aggregated weight that offsets their gradient distribution discrepancies. Intra-balancing acts on condition-fitting losses to tackle the imbalance in fitting difficulty across diverse conditions. By evaluating the fitting difficulty based on the loss records, intra-balancing can allocate the aggregated weight proportionally to each condition loss according to its fitting difficulty level. We further introduce a robust weight update strategy to prevent abrupt spikes and arithmetic overflow in instantaneous weight values caused by large loss variances, enabling smooth weight updating and stable training. Extensive experiments demonstrate that DB-PINN achieves significantly superior performance than those popular gradient-based weighting methods in terms of convergence speed and prediction accuracy. Our code and supplementary material are available at https://github.com/chenhong-zhou/DualBalanced-PINNs.