Less is More: A Lightweight and Robust Neural Architecture for Discourse Parsing

Complex feature extractors are widely employed for text representation building. However, these complex feature extractors make the NLP systems prone to overfitting especially when the downstream training datasets are relatively small, which is the case for several discourse parsing tasks. Thus, we propose an alternative lightweight neural architecture that removes multiple complex feature extractors and only utilizes learnable self-attention modules to indirectly exploit pretrained neural language models, in order to maximally preserve the generalizability of pre-trained language models. Experiments on three common discourse parsing tasks show that powered by recent pretrained language models, the lightweight architecture consisting of only two self-attention layers obtains much better generalizability and robustness. Meanwhile, it achieves comparable or even better system performance with fewer learnable parameters and less processing time

RST-style Discourse Parsing Guided by Document-level Content Structures

Rhetorical Structure Theory based Discourse Parsing (RST-DP) explores how clauses, sentences, and large text spans compose a whole discourse and presents the rhetorical structure as a hierarchical tree. Existing RST parsing pipelines construct rhetorical structures without the knowledge of document-level content structures, which causes relatively low performance when predicting the discourse relations for large text spans. Recognizing the value of high-level content-related information in facilitating discourse relation recognition, we propose a novel pipeline for RST-DP that incorporates structure-aware news content sentence representations derived from the task of News Discourse Profiling. By incorporating only a few additional layers, this enhanced pipeline exhibits promising performance across various RST parsing metrics

Prompt-based Effective Input Reformulation for Legal Case Retrieval

Legal case retrieval plays an important role for legal practitioners to effectively retrieve relevant cases given a query case. Most existing neural legal case retrieval models directly encode the whole legal text of a case to generate a case representation, which is then utilised to conduct a nearest neighbour search for retrieval. Although these straightforward methods have achieved improvement over conventional statistical methods in retrieval accuracy, two significant challenges are identified in this paper: (1) Legal feature alignment: the usage of the whole case text as the input will generally incorporate redundant and noisy information because, from the legal perspective, the determining factor of relevant cases is the alignment of key legal features instead of whole text matching; (2) Legal context preservation: furthermore, since the existing text encoding models usually have an input length limit shorter than the case, the whole case text needs to be truncated or divided into paragraphs, which leads to the loss of the global context of legal information. In this paper, a novel legal case retrieval framework, PromptCase, is proposed to tackle these challenges. Firstly, legal facts and legal issues are identified and formally defined as the key features facilitating legal case retrieval based on a thorough study of the definition of relevant cases from a legal perspective. Secondly, with the determining legal features, a prompt-based encoding scheme is designed to conduct an effective encoding with language models. Extensive zero-shot experiments have been conducted on two benchmark datasets in legal case retrieval, which demonstrate the superior retrieval effectiveness of the proposed PromptCase. The code has been released on https://github.com/yanran-tang/PromptCase

Ammonia and Carbon Dioxide Emissions vs. Feeding and Defecation Activities of Laying Hens

This study characterizes dynamic ammonia (NH3) and carbon dioxide (CO2) emissions associated with feeding and defecation activities of laying hens. Manure handling scheme used was reflective of commercial manure-belt house operation. Four dynamic emission chambers and measurement system was developed, featuring continuous measurement of the following variables for each chamber: (a) NH3 concentrations of inlet and outlet air, (b) air temperature and relative humidity, (c) airflow rate, (d) feeder weight and thus feeding activity, and (e) manure pan weight and thus defecation activity. Daily feed consumption of the hens averaged 103 g/hen-d and fresh manure production averaged 125 g/hen-d. Ammonia emission rate ranged from 1.26 mg/hen-hr on the first day of manure accumulation to 9.26 mg/hen-hr after 7 d of manure accumulation. CO2 emission rate averaged 3.41 and 2.47 g/hen-hr during light and dark hours of the day, respectively. Dynamic NH3 emissions tend to be inversely related to defecation events as manure accumulates. Results from this study will contribute to the development and/or validation of process-based farm emission model for predicting NH3 emissions from laying-hen houses. The dynamic nature of NH3 emissions vs. defecation may also provide insight concerning application timing of manure treatment agents to mitigate NH3 emissions from laying-hen houses

Update on poly(ADP-ribose) polymerase inhibitors resistance in ovarian cancer

Ovarian cancer is one of the most common reproductive system tumors. The incidence of ovarian cancer in China is on the rise. Poly(ADP-ribose) polymerase (PARP) inhibitor (PARPi) is a DNA repair enzyme associated with DNA damage repair. PARPi takes PARP as a target to kill tumor cells, especially for tumors with homologous recombination (HR) dysfunction. Currently, PARPi has been widely used in clinical practice, mainly for the maintenance of advanced ovarian epithelial cancer. The intrinsic or acquired drug resistance of PARPi has gradually become an important clinical problem with the wide application of PARPi. This review summarizes the mechanisms of PARPi resistance and the current progress on PARPi-based combination strategies

Research Progress in Anaerobic Digestion of High Moisture

High moisture organic waste constitutes a large fraction of municipal solid waste and caused a nuisance. Anaerobic digestion of this high degradable fraction has been developed during the past 20 years. Parameters such as characteristic of substrates, temperature, organic loading rate and hydraulic retention time were studied. The most important conversion of intermediate of volatile fatty acid was observed as a indicator of digestion efficiency. One stage and two stage system are based on the stage separated into acidogenic phase and methnogenis phase. Two stage digestion of this kind of wastes were proved a better efficiency than single stage digestion. Batch system and continuous system are conducted in single stage and two-stage system. One stage system are split between wet system(Total solid less than 15%) and dry system( total solid higher than 15%) according to the characteristics of feedstock. Two-stage solid bed system are observed more and more popular in the digestion of solid state VFW and food waste experimental studies, however the large majority of industrial application use single stage systems. Two stage digestion of HMOW will be applied to industrial scale due to its larger resistance to high loading rate, high and stable gas production

Effects of Qijin granules on high glucose-induced proliferation, apoptosis and expression of nuclear factor- κB and MCP-1 in rat glomerular mesangial cells

Purpose: To investigate the effects of Qijin granules on high glucose-induced proliferation and apoptosis in rat glomerular mesangial cells (MC).Methods: MC cells from rats were passaged and cultured, and randomly divided into control group (CNG), high glucose group (HGG), Western medicine group (WMG, high glucose + Benazepril + Gliquidone), and Qijin granules 1/2/3 group (high glucose + different doses of Qijin granules). Mesangial cells proliferation was measured using MTT assay. The NF-κB, MCP-1 and inflammatory factors in supernatant were determined by ELISA. Apoptosis rate and cell cycle were assessed by flow cytometry. The apoptosis-related TGF-β1/Smad signaling pathway-related protein expressions were measured by Western blot.Results: The A-value and early apoptosis rate, apoptosis rate and S-phase percentage, and protein expressions of NF-κB, MCP-1, IL-6, IL-2, TNF-ɑ, Bax, Cyt-C, caspase-3, TGF-β1, and p-Smad3 of MC cells in the HGG at 12 h, 24 h and 48 h were higher than those in the CNG. The above indices were lower in the WMG, and Qijin granules 1/2/3 groups than in the HGG. The Bcl-2, Smad7 protein expression level and the percentage of G1 and G2/M phase were lower in the HGG than in the CNG, and the above indeices were higher in the WMG and Qijin granules 1/2/3 group than in HGG.Conclusion: Qijin granules can dose-dependently inhibit high glucose-induced proliferation and apoptosis in rat MC cells, block the cell cycle and reduce inflammatory responses. This may be related to the regulation of NF-κB, MCP-1 and TGF-β1/Smad signaling pathways. These findings provide theoretical and experimental basis for the clinical treatment of early diabetic nephropathy

Multichannel Seismic Deconvolution Using Bayesian Method

In this paper, we propose an algorithm for multichannel blind deconvolution of seismic signals, which exploits variational Bayesian method. It is related to the Kullback-Leibler divergence, which measures the independence degree of deconvolved data sequence. We assume that the reflectivity sequence is almost the same for each receiver while the noise level may differ at each channel. Compared to blind deconvolution of a single seismic trace, multichannel blind deconvolution provides an accurate convergence of the estimated parameters and reflectivity sequence

The divided brain : Functional brain asymmetry underlying self-construal

Acknowledgments This research is partly supported by the National Natural Science Foundation of China (62071049, 61801026) & Capital Medical University Advanced Innovation Center for Big Data-Based Precision Medicine Plan (BHME-201907), and the Leverhulme Trust (RPG-2019-010).Peer reviewedPublisher PD