Spectral-domain optical coherence tomography (SDOCT) is the gold standard of imaging the eye in clinics. Penetration depth with such devices is, however, limited and visualization of the choroid, which is essential for diagnosing chorioretinal disease, remains limited. Whereas swept-source OCT (SSOCT) devices allow for visualization of the choroid these instruments are expensive and availability in praxis is limited. We present an artificial intelligence (AI)-based solution to enhance the visualization of the choroid in OCT scans and allow for quantitative measurements of choroidal metrics using generative deep learning (DL). Synthetically enhanced SDOCT B-scans with improved choroidal visibility were generated, leveraging matching images to learn deep anatomical features during the training. Using a single-center tertiary eye care institution cohort comprising a total of 362 SDOCT-SSOCT paired subjects, we trained our model with 150,784 images from 410 healthy, 192 glaucoma, and 133 diabetic retinopathy eyes. An independent external test dataset of 37,376 images from 146 eyes was deployed to assess the authenticity and quality of the synthetically enhanced SDOCT images. Experts’ ability to differentiate real versus synthetic images was poor (47.5% accuracy). Measurements of choroidal thickness, area, volume, and vascularity index, from the reference SSOCT and synthetically enhanced SDOCT, showed high Pearson’s correlations of 0.97 [95% CI: 0.96–0.98], 0.97 [0.95–0.98], 0.95 [0.92–0.98], and 0.87 [0.83–0.91], with intra-class correlation values of 0.99 [0.98–0.99], 0.98 [0.98–0.99], and 0.95 [0.96–0.98], 0.93 [0.91–0.95], respectively. Thus, our DL generative model successfully generated realistic enhanced SDOCT data that is indistinguishable from SSOCT images providing improved visualization of the choroid. This technology enabled accurate measurements of choroidal metrics previously limited by the imaging depth constraints of SDOCT. The findings open new possibilities for utilizing affordable SDOCT devices in studying the choroid in both healthy and pathological conditions.

β-Aryl ketones are present in various natural products and bioactive molecules. Till now, -aryl ketone has been synthesized through the directing group (DG) approach or Mizoroki-Heck coupling. Herein, we reported a palladium(II) catalyzed dual ligand-enabled non-directed C−H alkylation with arene and heteroarene as a limiting reagent for the synthesis of -(hetero)aryl ketones. The combined influence of 2-methyl quinoxaline and N-acetyl phenylalanine ligands imparts complementary selectivity, facilitating the diversification of drugs and natural products through C−H alkylation. Integrated experimental and computational mechanistic studies demonstrate the C−H activation as both the regio- and rate-determining step. Interestingly, while the Pd−Ag heterobimetallic species is not directly involved in the 1,2-migratory insertion step, it is proposed to play a vital role during the product release phase of the catalytic cycle.

AbstractThe study of topological phases with unconventional bulk‐boundary correspondences and nontrivial real Chern number has garnered significant attention in the topological states of matter. Using the first‐principle calculations and theoretical analysis, a high‐throughput material screening of the 3D obstructed atomic insulators (OAIs) and 3D real Chern insulators (RCIs) based on the Samara Carbon Allotrope Database (SACADA) are performed. Results show that 422 out of 703 3D carbon allotropes are 3D OAIs with multiple bulk‐boundary correspondences, including 2D obstructed surface states (OSSs) and 1D hinge states, which are in 1D and 2Ds lower than the 3D bulk, respectively. The 2D OSSs in these OAIs can be modified when subjected to appropriate boundaries, which benefits the investigation of surface engineering and the development of efficient topological catalysts. These 422 OAIs, which have 2D and 1D boundary states, are excellent platforms for multi‐dimensional topological boundaries research. Remarkably, 138 of 422 OAIs are also 3D RCIs, which show a nontrivial real topology in the protection of spacetime inversion symmetry. This work not only provides a comprehensive list of 3D carbon‐based OAIs and RCIs, but also guides their application in various aspects based on multiple bulk‐boundary correspondences and real topological phases.

AbstractTo keep pace with the expanding data capacity in modern communications and increasing on‐chip integration density, it is highly desirable to have electro‐optic modulators featured with compact footprint, large modulation bandwidth, low energy consumption, and full complementary metal‐oxide‐semiconductor (CMOS) compatibility. Here, this work experimentally demonstrates a high‐speed electro‐optic modulator in the telecommunication wavelength range made of a 1D chain resonator of Mie‐resonant silicon nanoparticles. The modulator exploits the plasma dispersion effect and works in the carrier depletion configuration. The resulting large modulation bandwidth over 40 GHz and low energy consumption of 23 fJ bit−1 are attributed to its ultra‐compact footprint of 10 µm2. The open eye diagrams are measured up to 40 Gbits s−1. The modulators are fabricated at wafer level by a silicon photonic foundry using CMOS‐compatible process. This modulator shows promise for achieving higher integration density and larger modulation bandwidth. It can be added to the existing on‐chip device building blocks library and used for a wide range of applications, including data communication, quantum photonics, and non‐reciprocal optics.

Medical imaging provides many valuable clues involving anatomical structure and pathological characteristics. However, image degradation is a common issue in clinical practice, which can adversely impact the observation and diagnosis by physicians and algorithms. Although extensive enhancement models have been developed, these models require a well pre-training before deployment, while failing to take advantage of the potential value of inference data after deployment. In this paper, we raise an algorithm for source-free unsupervised domain adaptive medical image enhancement (SAME), which adapts and optimizes enhancement models using test data in the inference phase. A structure-preserving enhancement network is first constructed to learn a robust source model from synthesized training data. Then a teacher-student model is initialized with the source model and conducts source-free unsupervised domain adaptation (SFUDA) by knowledge distillation with the test data. Additionally, a pseudo-label picker is developed to boost the knowledge distillation of enhancement tasks. Experiments were implemented on ten datasets from three medical image modalities to validate the advantage of the proposed algorithm, and setting analysis and ablation studies were also carried out to interpret the effectiveness of SAME. The remarkable enhancement performance and benefits for downstream tasks demonstrate the potential and generalizability of SAME. The code is available at https://github.com/liamheng/Annotation-free-Medical-Image-Enhancement.

Ship Time Headway (STH) is used in maritime navigation to describe the time interval between the arrivals of two consecutive ships in the same water area. This measurement may offer a straightforward way to gauge the frequency of ship traffic and the likelihood of congestion in a particular area. STH is an important factor in understanding and managing the dynamics of ship movements in busy waterways. This paper introduces a hybrid deep learning method for predicting STH in time domain. The method integrates the Seasonal-Trend Decomposition using Loess (STL), Multi-head Self-Attention (MSA) mechanism into Long Short-Term Memory (LSTM) neural network. The STH dataset was extracted from the Automatic Identification System (AIS) through ship trajectory spatial motion, and the seasonal, trend and residual components of the decomposition were then determined from the STH dataset using the STL algorithms. MSA-LSTM is adopted to comprehensively capture the evolving patterns of STH from the sequence. Comparison studies with existing methods demonstrate the accuracy and robustness of the predictions provided by this method, indicating that the proposed method outperforms other models in terms of prediction performance and learning capabilities. By predicting STH, the method offers potential to assist maritime traffic managers and navigators in assessing ship flow, thereby enabling them to make informed decisions on navigation safety and efficiency.

Abstract From Physics and Biology to Seismology and Economics, the behaviour of countless systems is determined by impactful yet unlikely transitions between metastable states known as rare events, the study of which is essential for understanding and controlling these systems’ properties. Classical computational methods to sample rare events remain prohibitively inefficient and are bottlenecks for enhanced samplers requiring prior data. Here, we introduce a novel physics-informed machine learning framework, FlowRES, that uses unsupervised normalising flow neural networks to enhance Monte Carlo sampling of rare events by generating high-quality nonlocal Monte Carlo proposals. We validate FlowRES by sampling the transition path ensembles of equilibrium and non-equilibrium systems of Brownian particles exploring increasingly complex potentials. Beyond eliminating requirements for prior data, FlowRES features key advantages over established samplers: no collective variables need defining, efficiency remains constant even as events become increasingly rare, and systems with multiple routes between states can be straightforwardly simulated.

A physics-informed neural network (PINN) uses physics-augmented loss functions, e.g., incorporating the residual term from governing partial differential equations (PDEs), to ensure its output is consistent with fundamental physics laws. However, it turns out to be difficult to train an accurate PINN model for many problems in practice. In this paper, we present a novel perspective of the merits of learning in sinusoidal spaces with PINNs. By analyzing behavior at model initialization, we first show that a PINN of increasing expressiveness induces an initial bias around flat output functions. Notably, this initial solution can be very close to satisfying many physics PDEs, i.e., falling into a local minimum of the PINN loss that only minimizes PDE residuals, while still being far from the true solution that jointly minimizes PDE residuals and the initial and/or boundary conditions. It is difficult for gradient descent optimization to escape from such a local minimum trap, often causing the training to stall. We then prove that the sinusoidal mapping of inputs, in an architecture we label as sf-PINN, is effective to increase input gradient variability, thus avoiding being trapped in such deceptive local minimum. The level of variability can be effectively modulated to match high-frequency patterns in the problem at hand. A key facet of this paper is the comprehensive empirical study that demonstrates the efficacy of learning in sinusoidal spaces with PINNs for a wide range of forward and inverse modelling problems spanning multiple physics domains.