GNN-SL: Sequence Labeling Based on Nearest Examples via GNN

To better handle long-tail cases in the sequence labeling (SL) task, in this work, we introduce graph neural networks sequence labeling (GNN-SL), which augments the vanilla SL model output with similar tagging examples retrieved from the whole training set. Since not all the retrieved tagging examples benefit the model prediction, we construct a heterogeneous graph, and leverage graph neural networks (GNNs) to transfer information between the retrieved tagging examples and the input word sequence. The augmented node which aggregates information from neighbors is used to do prediction. This strategy enables the model to directly acquire similar tagging examples and improves the general quality of predictions. We conduct a variety of experiments on three typical sequence labeling tasks: Named Entity Recognition (NER), Part of Speech Tagging (POS), and Chinese Word Segmentation (CWS) to show the significant performance of our GNN-SL. Notably, GNN-SL achieves SOTA results of 96.9 (+0.2) on PKU, 98.3 (+0.4) on CITYU, 98.5 (+0.2) on MSR, and 96.9 (+0.2) on AS for the CWS task, and results comparable to SOTA performances on NER datasets, and POS datasets.Comment: preprin

GPT-NER: Named Entity Recognition via Large Language Models

Despite the fact that large-scale Language Models (LLM) have achieved SOTA performances on a variety of NLP tasks, its performance on NER is still significantly below supervised baselines. This is due to the gap between the two tasks the NER and LLMs: the former is a sequence labeling task in nature while the latter is a text-generation model. In this paper, we propose GPT-NER to resolve this issue. GPT-NER bridges the gap by transforming the sequence labeling task to a generation task that can be easily adapted by LLMs e.g., the task of finding location entities in the input text "Columbus is a city" is transformed to generate the text sequence "@@Columbus## is a city", where special tokens @@## marks the entity to extract. To efficiently address the "hallucination" issue of LLMs, where LLMs have a strong inclination to over-confidently label NULL inputs as entities, we propose a self-verification strategy by prompting LLMs to ask itself whether the extracted entities belong to a labeled entity tag. We conduct experiments on five widely adopted NER datasets, and GPT-NER achieves comparable performances to fully supervised baselines, which is the first time as far as we are concerned. More importantly, we find that GPT-NER exhibits a greater ability in the low-resource and few-shot setups, when the amount of training data is extremely scarce, GPT-NER performs significantly better than supervised models. This demonstrates the capabilities of GPT-NER in real-world NER applications where the number of labeled examples is limited

Paths to light trapping in thin film GaAs solar cells

It is now well established that light trapping is an essential element of thin film solar cell design. Numerous light trapping geometries have already been applied to thin film cells, especially to silicon-based devices. Less attention has been paid to light trapping in GaAs thin film cells, mainly because light trapping is considered less attractive due to the material's direct bandgap and the fact that GaAs suffers from strong surface recombination, which particularly affects etched nanostructures. Here, we study light trapping structures that are implemented in a high-bandgap material on the back of the GaAs active layer, thereby not perturbing the integrity of the GaAs active layer. We study photonic crystal and quasi-random nanostructures both by simulation and by experiment and find that the photonic crystal structures are superior because they exhibit fewer but stronger resonances that are better matched to the narrow wavelength range where GaAs benefits from light trapping. In fact, we show that a 1500 nm thick cell with photonic crystals achieves the same short circuit current as an unpatterned 4000 nm thick cell. These findings are significant because they afford a sizeable reduction in active layer thickness, and therefore a reduction in expensive epitaxial growth time and cost, yet without compromising performance

Spatial resolution effect of light coupling structures

This research project was founded by the National Council for Scientific and Technological Development (CNPq) of Brazil (302397/2014-0), by the National Natural Science Foundation of China (11204386, 11411130117, 11334015), by the Open research project of the State Key Laboratory of Optoelectronic Materials and Technologies, Sun-Yat Sen University of China (OEMT-2015-KF-12, OEMT-2015-KF-13) and by EPSRC of U.K. under grant EP/J01771X/1 (Structured Light). Kezheng Li is also supported by the aboard exchange scholar and international doctoral cooperative project of Sun Yat-sen University.The coupling of light between free space and thin film semiconductors is an essential requirement of modern optoelectronic technology. For monochromatic and single mode devices, high performance grating couplers have been developed that are well understood. For broadband and multimode devices, however, more complex structures, here referred to as "coupling surfaces", are required, which are often difficult to realise technologically. We identify general design rules based on the Fourier properties of the coupling surface and show how they can be used to determine the spatial resolution required for the coupler's fabrication. To our knowledge, this question has not been previously addressed, but it is important for the understanding of diffractive nanostructures and their technological realisation. We exemplify our insights with solar cells and UV photodetectors, where high-performance nanostructures that can be realised cost-effectively are essential.Publisher PDFPeer reviewe

Microstructural Evolution in Chroming Coatings Friction Pairs under Dry Sliding Test Conditions

The microstructures of subsurface layers of 20CrMnTi steel pins against chroming and nonchroming T10 under dry sliding tests were studied by means of OM (optical microscopy), XRD (X-ray diffraction), and SEM (scanning electron microscopy). Results showed that the chroming coating strengthened the disc surface and significantly affected microstructural evolution. Three layers—the matrix, deformation layer (DL), and surface layer (SL)—formed in 20CrMnTi for the chroming T10. The matrix and deformation layer (DL) formed in 20CrMnTi for the nonchroming T10. The formation of the microstructure was considered as a result of the shear deformation

Surrounding Greenness and Biological Aging Based on DNA Methylation: A Twin and Family Study in Australia.

BACKGROUND: High surrounding greenness has many health benefits and might contribute to slower biological aging. However, very few studies have evaluated this from the perspective of epigenetics. OBJECTIVES: We aimed to evaluate the association between surrounding greenness and biological aging based on DNA methylation. METHODS: We derived Horvath's DNA methylation age (DNAmAge), Hannum's DNAmAge, PhenoAge, and GrimAge based on DNA methylation measured in peripheral blood samples from 479 Australian women in 130 families. Measures of DNAmAge acceleration (DNAmAgeAC) were derived from the residuals after regressing each DNAmAge metric on chronological age. Greenness was represented by satellite-derived Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) metrics within 300-, 500-, 1,000-, and 2,000-m buffers surrounding participant addresses. Greenness-DNAmAgeAC associations were estimated using a within-sibship design fitted by linear mixed effect models, adjusting for familial clustering and important covariates. RESULTS: Greenness metrics were associated with significantly lower DNAmAgeAC based on GrimAge acceleration, suggesting slower biological aging with higher greenness based on both NDVI and EVI in 300-2,000m buffer areas. For example, each interquartile range increase in NDVI within 1,000m was associated with a 0.59 (95% CI: 0.18, 1.01)-year decrease in GrimAge acceleration. Greenness was also inversely associated with three of the eight components of GrimAge, specifically, DNA methylation-based surrogates of serum cystatin-C, serum growth differentiation factor 15, and smoking pack years. Associations between greenness and biological aging measured by Horvath's and Hannum's DNAmAgeAC were less consistent, and depended on neighborhood socioeconomic status. No significant associations were estimated for PhenoAge acceleration. DISCUSSION: Higher surrounding greenness was associated with slower biological aging, as indicated by GrimAge age acceleration, in Australian women. Associations were also evident for three individual components of GrimAge, but were inconsistent for other measures of biological aging. Additional studies are needed to confirm our results. https://doi.org/10.1289/EHP8793

Peripheral Dopamine Controlled by Gut Microbes Inhibits Invariant Natural Killer T Cell-Mediated Hepatitis

Neurotransmitters have been shown to regulate immune responses, and thereby are critically related to autoimmune diseases. Here we showed that depletion of dopaminergic neurons significantly promoted activation of hepatic iNKT cells and augmented concanavalin A (Con A)-induced liver injury. The suppressive effect of dopamine on iNKT cells was mediated by D1-like receptor-PKA pathway. Clearance of gut microbiota by antibiotic cocktail reduced synthesis of dopamine in intestines and exacerbated liver damage, and that could be restored by recovery of gut microbiota or replenishment of D1-like receptor agonist. Our results demonstrate that peripheral dopamine controlled by gut microbes inhibits IL4 and IFNγ production in iNKT cells and suppresses iNKT cell-mediated hepatitis. Together, we propose a gut microbe-nervous system-immune system regulatory axis in modulating autoimmune hepatitis

Laser-Like Emission from a Sandwiched MoTe2 Heterostructure on a Silicon Single-Mode Resonator

Molybdenum ditelluride (MoTe2) has recently shown promise as a gain material for silicon photonics. Reliable single-mode operation and material stability remain two of the major issues that need to be addressed to advance this exciting technology, however. Here, laser-like emission from a sandwiched MoTe2 heterostructure on a silicon single-mode resonator is reported. The heterostructure consists of a layer of MoTe2 sandwiched between thin films of hexagonal boron nitride. It is known that tellurium compounds are sensitive to oxygen exposure, which leads to rapid degradation of the exposed layers in air. By encapsulating the MoTe2 gain material, much improved environmental stability is observed. Using a recently introduced single-mode resonator design, better control over the mode spectrum of the cavity is exercised and single-mode operation with a wide free spectral range is demonstrated. At room temperature, a Q-factor of 4500 and a threshold of 4.2 kW cm−2 at 1319 nm wavelength are achieved. These results lend further support to the paradigm of 2D material-based integrated light sources on the silicon platform