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Optic nerve head (ONH) detection has been a crucial area of study in ophthalmology for years. However, the significant discrepancy between fundus image datasets, each generated using a single type of fundus camera, poses challenges to the generalizability of ONH detection approaches developed based on semantic segmentation networks. Despite the numerous recent advancements in general-purpose semantic segmentation methods using convolutional neural networks (CNNs) and Transformers, there is currently a lack of benchmarks for these state-of-the-art (SoTA) networks specifically trained for ONH detection. Therefore, in this article, we make contributions from three key aspects: network design, the publication of a dataset, and the establishment of a comprehensive benchmark. Our newly developed ONH detection network, referred to as ODFormer, is based upon the Swin Transformer architecture and incorporates two novel components: a multi-scale context aggregator and a lightweight bidirectional feature recalibrator. Our published large-scale dataset, known as TongjiU-DROD, provides multi-resolution fundus images for each participant, captured using two distinct types of cameras. Our established benchmark involves three datasets: DRIONS-DB, DRISHTI-GS1, and TongjiU-DROD, created by researchers from different countries and containing fundus images captured from participants of diverse races and ages. Extensive experimental results demonstrate that our proposed ODFormer outperforms other state-of-the-art (SoTA) networks in terms of performance and generalizability. Our dataset and source code are publicly available at mias.group/ODFormer.

Momentum-matched type II van der Waals heterostructures (vdWHs) have been designed by assembling layered two-dimensional semiconductors (2DSs) with special band-structure combinations - that is, the valence band edge at the Gamma point (the Brillouin-zone center) for one 2DS and the conduction band edge at the Gamma point for the other [Ubrig et al., Nat. Mater. 19, 299 (2020)]. However, the band offset sizes, band-alignment types, and whether momentum matched or not, all are affected by the interfacial effects between the component 2DSs, such as the quasichemical-bonding (QB) interaction between layers and the electrical dipole moment formed around the vdW interface. Here, based on density-functional theory calculations, first we probe the interfacial effects (including different QBs for valence and conduction bands, interface dipole, and, the synergistic effects of these two aspects) on band-edge evolution in energy and valley (location in the Brillouin zone) and the resulting changes in band alignment and momentum matching for a typical vdWH of monolayer InSe and bilayer WS2, in which the band edges of subsystems satisfy the special band-structure combination for a momentum-matched type II vdWH. Then, based on the conclusions of the studied interfacial effects, we propose a practical screening method for robust momentum-matched type II vdWHs. This practical screening method can also be applied to other band alignment types. Our current study opens a way for practical screening and designing of vdWHs with robust momentum-matching and band alignment type.

Tabular data analysis is performed every day across various domains. It requires an accurate understanding of field semantics to correctly operate on table fields and find common patterns in daily analysis. In this paper, we introduce the AnaMeta dataset, a collection of 467k tables with derived supervision labels for four types of commonly used field metadata: measure/dimension dichotomy, common field roles, semantic field type, and default aggregation function. We evaluate a wide range of models for inferring metadata as the benchmark. We also propose a multi-encoder framework, called KDF, which improves the metadata understanding capability of tabular models by incorporating distribution and knowledge information. Furthermore, we propose four interfaces for incorporating field metadata into downstream analysis tasks., Comment: Published in Findings of ACL 2023

The phase transitions among polymorphic two-dimensional (2D) transition metal dichalcogenides (TMDs) have attracted increasing attention for their potential in enabling distinct functionalities in the same material for making integrated devices. Electron-injection to TMDs has been proved to be a feasible way to drive structural phase transition from the semiconducting H-phase to the semimetal dT-phase. In this contribution, based on density-functional theory (DFT) calculations, firstly we demonstrate that hole-injection drives the transition of the H-phase more efficiently to the metallic T-phase than to the semimetallic dT-phase for group VI-B TMDs (MoS2, WS2, and MoSe2, etc.). The origin can be attributed to the smaller work function of the T-phase than that of the dT-phase. Our work function analysis can distinguish the T and dT phases quantitatively while it is challenging for the commonly used crystal field splitting analysis. In addition, our analysis provides a unified understanding for both hole- and electron-injection induced phase transitions for 2D materials beyond TMDs, such as the newly synthesized MoSi2N4 family. Moreover, the hole-driven T-phase transition mechanism can explain the recent experiment of WS2 phase transition by hole-doping with yttrium (Y) atoms. Using 1/3 Y-doped WS2 and MoSe2 as examples, we show that the Mo and W valency increases to 5+. These above findings open up an avenue to obtain the metallic T-phase, which expands the possible stable phases of 2D materials.