Visualization of Photonic Band Structures via Far-field Measurements in SiNx Photonic Crystal Slabs

The band structures of the photonic crystal slabs play a significant role in manipulating the flow of light and pre-dicting exotic physics in photonics. In this letter, we show that the key features of photonic band structures can be achieved experimentally by the polarization- and momentum-resolved photoluminescence spectroscopy utilizing the light emission properties of SiNx. The two-dimensional spectra clearly reveal the energy-momentum dispersion of band structures which is in perfect agreement with the simulation results. The isofrequency contours can be measured easily by adding a bandpass filter with a desired photon energy. Furthermore, it is convenient to observe clearly and directly the optical singularity -- the optical bound states in the continuum featured by dark point in three-dimensional photoluminescence spectra. The polarization-resolved isofrequency contours clearly show that this dark point is the center of an azimuthally polarized vortex. Finally, the helical topological edge states can be easily observed in photonic topological insulators with deformed hexagonal lattices. Our work provides a simple and effective approach for exploring topological photonics and other intriguing phenomena hidden in the photonic crystal slabs.Comment: 6 pages, 5 figure

OphGLM: Training an Ophthalmology Large Language-and-Vision Assistant based on Instructions and Dialogue

Large multimodal language models (LMMs) have achieved significant success in general domains. However, due to the significant differences between medical images and text and general web content, the performance of LMMs in medical scenarios is limited. In ophthalmology, clinical diagnosis relies on multiple modalities of medical images, but unfortunately, multimodal ophthalmic large language models have not been explored to date. In this paper, we study and construct an ophthalmic large multimodal model. Firstly, we use fundus images as an entry point to build a disease assessment and diagnosis pipeline to achieve common ophthalmic disease diagnosis and lesion segmentation. Then, we establish a new ophthalmic multimodal instruction-following and dialogue fine-tuning dataset based on disease-related knowledge data and publicly available real-world medical dialogue. We introduce visual ability into the large language model to complete the ophthalmic large language and vision assistant (OphGLM). Our experimental results demonstrate that the OphGLM model performs exceptionally well, and it has the potential to revolutionize clinical applications in ophthalmology. The dataset, code, and models will be made publicly available at https://github.com/ML-AILab/OphGLM.Comment: OphGLM:The first ophthalmology large language-and-vision assistant based on instructions and dialogu

Establishment and validation of a 3-month prediction model for poor functional outcomes in patients with acute cardiogenic cerebral embolism related to non-valvular atrial fibrillation

ObjectivesCardiogenic cerebral embolism (CCE) poses a significant health risk; however, there is a dearth of published prognostic prediction models addressing this issue. Our objective is to establish prognostic prediction models (PM) for predicting poor functional outcomes at 3 months in patients with acute CCE associated with non-valvular atrial fibrillation (NVAF) and perform both internal and external validations.MethodsWe included a total of 730 CCE patients in the development cohort. The external regional validation cohort comprised 118 patients, while the external time-sequential validation cohort included 63 patients. Multiple imputation by chained equations (MICE) was utilized to address missing values and the least absolute shrink and selection operator (LASSO) regression was implemented through the glmnet package, to screen variables.ResultsThe 3-month prediction model for poor functional outcomes, denoted as N-ABCD2, was established using the following variables: NIHSS score at admission (N), Age (A), Brain natriuretic peptide (BNP), C-reactive protein (CRP), D-dimer polymers (D), and discharge with antithrombotic medication (D). The model’s Akaike information criterion (AIC) was 637.98, and the area under Curve (AUC) for the development cohort, external regional, and time-sequential cohorts were 0.878 (95% CI, 0.854–0.902), 0.918 (95% CI, 0.857–0.979), and 0.839 (95% CI, 0.744–0.934), respectively.ConclusionThe N-ABCD2 model can accurately predict poor outcomes at 3 months for CCE patients with NVAF, demonstrating strong prediction abilities. Moreover, the model relies on objective variables that are readily obtainable in clinical practice, enhancing its convenience and applicability in clinical settings

Deep learning enabled topological design of exceptional points for multi-optical-parameter control

Abstract Metasurfaces are 2D artificial nanostructures that exhibit fascinating optical phenomena and flexible capabilities. Multi-optical-parameter metasurfaces have advantages over single-function or single-dimensional metasurfaces, especially in practical applications like holography, sub-diffraction imaging, and vectorial fields. However, achieving multi-optical-parameter control is challenging due to a lack of design strategy, limited manipulation channels, and signal-to-noise ratio problems. Exceptional points (EPs) possess inherent polarization decoupling properties and allow for amplitude and wavelength modulation, opening up research prospects for multi-optical-parameter electromagnetic field modulation and developing compact integrated devices. Leveraging deep learning, we observe topological charge conservation and utilize the topologically protected optical parameter distribution around scattered EPs. Based on these, we introduce amplitude-phase multiplexing and wavelength division multiplexing devices. Our work allows rapid and precise discovery of EPs topology, offers a powerful tool for digging related physics, and provides a paradigm for multi-optical parametric manipulation with high performance and less crosstalk, which is critical for imaging, encryption, and information storage applications