Investigating Multilingual Coreference Resolution by Universal Annotations

Multilingual coreference resolution (MCR) has been a long-standing and challenging task. With the newly proposed multilingual coreference dataset, CorefUD (Nedoluzhko et al., 2022), we conduct an investigation into the task by using its harmonized universal morphosyntactic and coreference annotations. First, we study coreference by examining the ground truth data at different linguistic levels, namely mention, entity and document levels, and across different genres, to gain insights into the characteristics of coreference across multiple languages. Second, we perform an error analysis of the most challenging cases that the SotA system fails to resolve in the CRAC 2022 shared task using the universal annotations. Last, based on this analysis, we extract features from universal morphosyntactic annotations and integrate these features into a baseline system to assess their potential benefits for the MCR task. Our results show that our best configuration of features improves the baseline by 0.9% F1 score.Comment: Accepted at Findings of EMNLP202

Single cell atlas for 11 non-model mammals, reptiles and birds.

The availability of viral entry factors is a prerequisite for the cross-species transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Large-scale single-cell screening of animal cells could reveal the expression patterns of viral entry genes in different hosts. However, such exploration for SARS-CoV-2 remains limited. Here, we perform single-nucleus RNA sequencing for 11 non-model species, including pets (cat, dog, hamster, and lizard), livestock (goat and rabbit), poultry (duck and pigeon), and wildlife (pangolin, tiger, and deer), and investigated the co-expression of ACE2 and TMPRSS2. Furthermore, cross-species analysis of the lung cell atlas of the studied mammals, reptiles, and birds reveals core developmental programs, critical connectomes, and conserved regulatory circuits among these evolutionarily distant species. Overall, our work provides a compendium of gene expression profiles for non-model animals, which could be employed to identify potential SARS-CoV-2 target cells and putative zoonotic reservoirs

Identification of pressed and extracted vegetable oils by headspace GC-MS

Edible vegetable oils are produced either by mechanical pressing or extraction. Although pressing retains the inherent flavor and nutritional value of the oil, the oil yield is low and the process expensive. Extraction methods have high oil yields, low processing costs, and economic benefits; however, No. 6 solvent, which may pose potential risks to human health, is commonly used in the extraction and cleaning process. Differentiating extracted oil containing these solvents from pressed oil, for quality control, based on visual appearance is difficult. Hence, in this study, an identification method using the characteristic components of solvent No. 6 under optimized headspace Gas chromatography-mass spectrometry (GC-MS) conditions was established. It also provided a reference for quality control of industrial production by estimating the amount of solvent present in the oil. Results showed that, in addition to five main components (2-methylpentane, 3-methylpentane, and n-hexane, Methylcyclopentane, Cyclohexane), accounting for 97% of the solvent, No. 6 solvent also contains 16 types of organic substances, such as olefins, aromatic hydrocarbons, and polycyclic aromatic hydrocarbons. Under optimized headspace GC-MS conditions (headspace sampler equilibrium temperature = 150 °C), the No. 6 solvent exhibits high linearity over a concentration range of 0.05–1 mg/kg with a correlation coefficient of 0.999 and a detection limit of 0.01 mg/kg. Pressed and extracted oils can be determined as follows: If three or fewer main components of the No. 6 solvent are detected, and the total content of No. 6 solvent is less than 0.5 mg/kg, it is a pressed oil; if four or more main components of No. 6 solvent are detected, or the total content of No. 6 solvent is ≥0.5 mg/kg, it is confirmed as an extracted oil

Evaluating Coreference Resolvers on Community-based Question Answering: From Rule-based to State of the Art

Coreference resolution is a key step in natural language understanding. Developments in coreference resolution are mainly focused on improving the performance on standard datasets annotated for coreference resolution. However, coreference resolution is an intermediate step for text understanding and it is not clear how these improvements translate into downstream task performance. In this paper, we perform a thorough investigation on the impact of coreference resolvers in multiple settings of community-based question answering task, i.e., answer selection with long answers. Our settings cover multiple text domains and encompass several answer selection methods. We first inspect extrinsic evaluation of coreference resolvers on answer selection by using coreference relations to decontextualize individual sentences of candidate answers, and then annotate a subset of answers with coreference information for intrinsic evaluation. The results of our extrinsic evaluation show that while there is a significant difference between the performance of the rule-based system vs. state-of-the-art neural model on coreference resolution datasets, we do not observe a considerable difference on their impact on downstream models. Our intrinsic evaluation shows that (i) resolving coreference relations on less-formal text genres is more difficult even for trained annotators, and (ii) the values of linguistic-agnostic coreference evaluation metrics do not correlate with the impact on downstream data

Learning Document Embeddings with Crossword Prediction

In this paper, we propose a Document Embedding Network (DEN) to learn document embeddings in an unsupervised manner. Our model uses the encoder-decoder architecture as its backbone, which tries to reconstruct the input document from an encoded document embedding. Unlike the standard decoder for text reconstruction, we randomly block some words in the input document, and use the incomplete context information and the encoded document embedding to predict the blocked words in the document, inspired by the crossword game. Thus, our decoder can keep the balance between the known and unknown information, and consider both global and partial information when decoding the missing words. We evaluate the learned document embeddings on two tasks: document classification and document retrieval. The experimental results show that our model substantially outperforms the compared methods.1

Influence of O

The release behavior of sulfur during coal gasification was studied in a bench-scale self-heated circulating fluidized bed gasifier. With the increase of the O2/C molar ratio, gasification temperature increases, which promotes sulfur release rate and the formation of H2S. The conversion reaction between H2S and COS is far from equilibrium and the yield of COS is excessive. Under the same molar ratio of O2/C, the increase of coal feeding rate can elevate the gasification temperature, promote the release of sulfur and the transformation of gaseous sulfur to H2S

The Odor Release Regularity of Livestock and Poultry Manure and the Screening of Deodorizing Strains

Human living environments and health are seriously affected by the odor produced from fermentation of livestock and poultry manure. In order to reduce the odor pollution caused by livestock and poultry manure, efficient strains were screened and two methods were tried in this study. The orthogonal test design was used to analyze the gas produced by pig manure under different conditions of temperature, time, wheat straw doping amount and calcium carbonate doping amount. Then, according to ammonia, hydrogen sulfide and comprehensive odor removal effects, the high efficiency of deodorizing strains were screened. The results showed that pig manure produced the least odor when the temperature was 20 °C, added 0% calcium carbonate, 20% wheat straw and waited for 48 h. Three strains were screened to inhibit the odor production of pig manure: Paracoccus denitrificans, Bacillus licheniformis and Saccharomyces cerevisiae, showed that their highest removal rate of ammonia and hydrogen sulfide gas could reach 96.58% and 99.74% among them; while for three strains of end-control pig manure stench: Pichia kudriavzevii, P. denitrificans and Bacillus subtilis, the highest removal rate of ammonia and hydrogen sulfide gas reached 85.91% and 90.80% among them. This research provides bacteria resources as the high-efficiency deodorizing function for the source suppression and the end treatment of the odor gas of pig manure, which has high application value for the control of odor pollution

CRISPR/Cas9 Technology and Its Utility for Crop Improvement

The rapid growth of the global population has resulted in a considerable increase in the demand for food crops. However, traditional crop breeding methods will not be able to satisfy the worldwide demand for food in the future. New gene-editing technologies, the most widely used of which is CRISPR/Cas9, may enable the rapid improvement of crop traits. Specifically, CRISPR/Cas9 genome-editing technology involves the use of a guide RNA and a Cas9 protein that can cleave the genome at specific loci. Due to its simplicity and efficiency, the CRISPR/Cas9 system has rapidly become the most widely used tool for editing animal and plant genomes. It is ideal for modifying the traits of many plants, including food crops, and for creating new germplasm materials. In this review, the development of the CRISPR/Cas9 system, the underlying mechanism, and examples of its use for editing genes in important crops are discussed. Furthermore, certain limitations of the CRISPR/Cas9 system and potential solutions are described. This article will provide researchers with important information regarding the use of CRISPR/Cas9 gene-editing technology for crop improvement, plant breeding, and gene functional analyses

Efficient and Secure Pairing Protocol for Devices with Unbalanced Computational Capabilities

Wearable devices that collect data about human beings are widely used in healthcare applications. Once collected, the health data will be securely transmitted to smartphones in most scenarios. Authenticated Key Exchange (AKE) can protect wireless communications between wearables and smartphones, and a typical solution is the Bluetooth Secure Simple Pairing (SSP) protocol with numeric comparison. However, this protocol requires equivalent computation on both devices, even though their computational capabilities are significantly different. This paper proposes a lightweight numeric comparison protocol for communications in which two parties have unbalanced computational capabilities, e.g., a wearable sensor and a smartphone, named UnBalanced secure Pairing using numeric comparison (UB-Pairing for short). The security of UB-Pairing is analyzed using the modified Bellare–Rogaway model (mBR). The analysis results show that UB-Pairing achieves the security goals. We also carry out a number of experiments to evaluate the performance of UB-Pairing. The results show that UB-Pairing is friendly to wearable devices, and more efficient than standard protocols when the computation capabilities of the two communication parties are highly unbalanced