Efficient Volumetric Method of Moments for Modeling Plasmonic Thin-Film Solar Cells with Periodic Structures

Metallic nanoparticles (NPs) support localized surface plasmon resonances (LSPRs), which enable to concentrate sunlight at the active layer of solar cells. However, full-wave modeling of the plasmonic solar cells faces great challenges in terms of huge computational workload and bad matrix condition. It is tremendously difficult to accurately and efficiently simulate near-field multiple scattering effects from plasmonic NPs embedded into solar cells. In this work, a preconditioned volume integral equation (VIE) is proposed to model plasmonic organic solar cells (OSCs). The diagonal block preconditioner is applied to different material domains of the device structure. As a result, better convergence and higher computing efficiency are achieved. Moreover, the calculation is further accelerated by two-dimensional periodic Green's functions. Using the proposed method, the dependences of optical absorption on the wavelengths and incident angles are investigated. Angular responses of the plasmonic OSCs show the super-Lambertian absorption on the plasmon resonance but near-Lambertian absorption off the plasmon resonance. The volumetric method of moments and explored physical understanding are of great help to investigate the optical responses of OSCs.Comment: 11 pages, 6 figure

Exploring Visualization Methods for Complex Variables

Applications of complex variables and related manifolds appear throughout mathematics and science. Here we review a family of basic methods for applying visualization concepts to the study of complex variables and the properties of specific complex manifolds. We begin with an outline of the methods we can employ to directly visualize poles and branch cuts as complex functions of one complex variable. $CP^2$ polynomial methods and their higher analogs can then be exploited to produce visualizations of Calabi-Yau spaces such as those modeling the hypothesized hidden dimensions of string theory. Finally, we show how the study of N-boson scattering in dual model/string theory leads to novel cross-ratio-space methods for the treatment of analysis in two or more complex variables

Dynamic Global Memory for Document-level Argument Extraction

Extracting informative arguments of events from news articles is a challenging problem in information extraction, which requires a global contextual understanding of each document. While recent work on document-level extraction has gone beyond single-sentence and increased the cross-sentence inference capability of end-to-end models, they are still restricted by certain input sequence length constraints and usually ignore the global context between events. To tackle this issue, we introduce a new global neural generation-based framework for document-level event argument extraction by constructing a document memory store to record the contextual event information and leveraging it to implicitly and explicitly help with decoding of arguments for later events. Empirical results show that our framework outperforms prior methods substantially and it is more robust to adversarially annotated examples with our constrained decoding design. (Our code and resources are available at https://github.com/xinyadu/memory_docie for research purpose.)Comment: ACL 2022 main conference (12 pages

PLA2G6-Associated Neurodegeneration (PLAN): Review of Clinical Phenotypes and Genotypes

Phospholipase A2 group VI (PLA2G6)-associated neurodegeneration (PLAN) includes a series of neurodegenerative diseases that result from the mutations in PLA2G6. PLAN has genetic and clinical heterogeneity, with different mutation sites, mutation types and ethnicities and its clinical phenotype is different. The clinical phenotypes and genotypes of PLAN are closely intertwined and vary widely. PLA2G6 encodes a group of VIA calcium-independent phospholipase A2 proteins (iPLA2β), an enzyme involved in lipid metabolism. According to the age of onset and progressive clinical features, PLAN can be classified into the following subtypes: infantile neuroaxonal dystrophy (INAD), atypical neuroaxonal dystrophy (ANAD) and parkinsonian syndrome which contains adult onset dystonia parkinsonism (DP) and autosomal recessive early-onset parkinsonism (AREP). In this review, we present an overview of PLA2G6-associated neurodegeneration in the context of current research

2-Chloro­methyl-2,3-dihydro­thieno[3,4-b][1,4]dioxine

In the mol­ecule of the title compound, C7H7ClO2S, the six-membered ring adopts a twisted conformation. In the crystal structure, weak inter­molecular C—H⋯O hydrogen bonds link the mol­ecules. There is also a weak C—H⋯π inter­action

GLEN: General-Purpose Event Detection for Thousands of Types

The progress of event extraction research has been hindered by the absence of wide-coverage, large-scale datasets. To make event extraction systems more accessible, we build a general-purpose event detection dataset GLEN, which covers 205K event mentions with 3,465 different types, making it more than 20x larger in ontology than today's largest event dataset. GLEN is created by utilizing the DWD Overlay, which provides a mapping between Wikidata Qnodes and PropBank rolesets. This enables us to use the abundant existing annotation for PropBank as distant supervision. In addition, we also propose a new multi-stage event detection model CEDAR specifically designed to handle the large ontology size in GLEN. We show that our model exhibits superior performance compared to a range of baselines including InstructGPT. Finally, we perform error analysis and show that label noise is still the largest challenge for improving performance for this new dataset. Our dataset, code, and models are released at \url{https://github.com/ZQS1943/GLEN}.}Comment: Accepted to EMNLP 2023. The first two authors contributed equally. (16 pages