An Study on the Annotation of the Translation of Cultural-Loaded Words in Chinese Idiom Stories

Chinese Idiom Stories is concerning traditional Chinese literature, which consists of plenty of cultural-loaded words. Regarding the relevant classification, the culture-loaded words in source text are grouped into six types. By exploring the annotation of cultural-loaded words in translation, this paper presents three types of methods and three principles of annotation. To begin with, the author provides an overview of domestic and foreign research on annotation. In addition, the author analyses various definitions of cultural-loaded words and classification standards, and outlines his own classification standard. Last but not least, the author cites some examples for analysis and draws out the methods and principles of annotation

LLMaAA: Making Large Language Models as Active Annotators

Prevalent supervised learning methods in natural language processing (NLP) are notoriously data-hungry, which demand large amounts of high-quality annotated data. In practice, acquiring such data is a costly endeavor. Recently, the superior few-shot performance of large language models (LLMs) has propelled the development of dataset generation, where the training data are solely synthesized from LLMs. However, such an approach usually suffers from low-quality issues, and requires orders of magnitude more labeled data to achieve satisfactory performance. To fully exploit the potential of LLMs and make use of massive unlabeled data, we propose LLMaAA, which takes LLMs as annotators and puts them into an active learning loop to determine what to annotate efficiently. To learn robustly with pseudo labels, we optimize both the annotation and training processes: (1) we draw k-NN examples from a small demonstration pool as in-context examples, and (2) we adopt the example reweighting technique to assign training samples with learnable weights. Compared with previous approaches, LLMaAA features both efficiency and reliability. We conduct experiments and analysis on two classic NLP tasks, named entity recognition and relation extraction. With LLMaAA, task-specific models trained from LLM-generated labels can outperform the teacher within only hundreds of annotated examples, which is much more cost-effective than other baselines.Comment: Findings of EMNLP 2023 camera read

Ordered GeSi nanorings grown on patterned Si (001) substrates

An easy approach to fabricate ordered pattern using nanosphere lithography and reactive iron etching technology was demonstrated. Long-range ordered GeSi nanorings with 430 nm period were grown on patterned Si (001) substrates by molecular beam epitaxy. The size and shape of rings were closely associated with the size of capped GeSi quantum dots and the Si capping processes. Statistical analysis on the lateral size distribution shows that the high growth temperature and the long-term annealing can improve the uniformity of nanorings

An Expression Tree Decoding Strategy for Mathematical Equation Generation

Generating mathematical equations from natural language requires an accurate understanding of the relations among math expressions. Existing approaches can be broadly categorized into token-level and expression-level generation. The former treats equations as a mathematical language, sequentially generating math tokens. Expression-level methods generate each expression one by one. However, each expression represents a solving step, and there naturally exist parallel or dependent relations between these steps, which are ignored by current sequential methods. Therefore, we integrate tree structure into the expression-level generation and advocate an expression tree decoding strategy. To generate a tree with expression as its node, we employ a layer-wise parallel decoding strategy: we decode multiple independent expressions (leaf nodes) in parallel at each layer and repeat parallel decoding layer by layer to sequentially generate these parent node expressions that depend on others. Besides, a bipartite matching algorithm is adopted to align multiple predictions with annotations for each layer. Experiments show our method outperforms other baselines, especially for these equations with complex structures.Comment: Accepted to EMNLP-2023, camera-ready versio

Two is Better Than One: Answering Complex Questions by Multiple Knowledge Sources with Generalized Links

Incorporating multiple knowledge sources is proven to be beneficial for answering complex factoid questions. To utilize multiple knowledge bases (KB), previous works merge all KBs into a single graph via entity alignment and reduce the problem to question-answering (QA) over the fused KB. In reality, various link relations between KBs might be adopted in QA over multi-KBs. In addition to the identity between the alignable entities (i.e. full link), unalignable entities expressing the different aspects or types of an abstract concept may also be treated identical in a question (i.e. partial link). Hence, the KB fusion in prior works fails to represent all types of links, restricting their ability to comprehend multi-KBs for QA. In this work, we formulate the novel Multi-KB-QA task that leverages the full and partial links among multiple KBs to derive correct answers, a benchmark with diversified link and query types is also constructed to efficiently evaluate Multi-KB-QA performance. Finally, we propose a method for Multi-KB-QA that encodes all link relations in the KB embedding to score and rank candidate answers. Experiments show that our method markedly surpasses conventional KB-QA systems in Multi-KB-QA, justifying the necessity of devising this task

Plasmon-gating photoluminescence in graphene/GeSi quantum dots hybrid structures

The ability to control light-matter interaction is central to several potential applications in lasing, sensing, and communication. Graphene plasmons provide a way of strongly enhancing the interaction and realizing ultrathin optoelectronic devices. Here, we find that photoluminescence (PL) intensities of the graphene/GeSi quantum dots hybrid structures are saturated and quenched under positive and negative voltages at the excitation of 325 nm, respectively. A mechanism called plasmon-gating effect is proposed to reveal the PL dependence of the hybrid structures on the external electric field. On the contrary, the PL intensities at the excitation of 405 and 795 nm of the hybrid structures are quenched due to the charge transfer by tuning the Fermi level of graphene or the blocking of the excitons recombination by excitons separation effect. The results also provide an evidence for the charge transfer mechanism. The plasmon gating effect on the PL provides a new way to control the optical properties of graphene/QD hybrid structures

Target Enzyme-Activated Two-Photon Fluorescent Probes:A Case Study of CYP3A4 Using a Two-Dimensional Design Strategy

The rapid development of fluorescent probes for monitoring target enzymes is still a great challenge owing to the lack of efficient ways to optimize a specific fluorophore. Herein, a practical two-dimensional strategy was designed for the development of an isoform-specific probe for CYP3A4, a key cytochrome P450 isoform responsible for the oxidation of most clinical drugs. In first dimension of the design strategy, a potential two-photon fluorescent substrate (NN) for CYP3A4 was effectively selected using ensemble-based virtual screening. In the second dimension, various substituent groups were introduced into NN to optimize the isoform-selectivity and reactivity. Finally, with ideal selectivity and sensitivity, NEN was successfully applied to the real-time detection of CYP3A4 in living cells and zebrafish. These findings suggested that our strategy is practical for developing an isoform-specific probe for a target enzyme.</p

Serum neurofilament light chain: a predictive marker for outcomes following mild-to-moderate ischemic stroke

BackgroundBiomarkers that reflect brain damage or predict functional outcomes may aid in guiding personalized stroke treatments. Serum neurofilament light chain (sNfL) emerges as a promising candidate for fulfilling this role.MethodsThis prospective, observational cohort investigation included 319 acute ischemic stroke (IS) patients. The endpoints were the incidence of early neurological deterioration (END, an elevation of two or more points in the National Institute of Health stroke scale score within a week of hospitalization compared with the baseline) and functional outcome at 3 months (an mRS score of &gt;2 at 3 months was categorized as an unfavorable/poor functional outcome). The association of sNfL, which was assessed within 24 h of admission, with END and unfavorable functional outcomes at follow-up was assessed via multivariate logistic regression, whereas the predictive value of sNfL for unfavorable functional outcomes and END was elucidated by the receiver operating characteristic curve (ROC).ResultsOf 319 IS individuals, 89 (27.90%) suffered from END. sNfL not only reflects the severity of stroke measured by NIHSS score (p &lt; 0.05) but also closely related to the severity of age-related white matter changes. Higher initial NIHSS score, severe white matter lesions, diabetes mellitus, and upregulated sNfL were significant predictors of END. Similarly, the multivariate logistic regression analysis results showed that elevated sNfL, a higher baseline NIHSS score, and severe white matter lesions were substantially linked with unfavorable outcomes for 3 months. Similarly, sNfL was valuable for the prediction of the 3 months of poor outcome (95%CI, 0.504–0.642, p = 0.044). Kaplan–Meier analysis shows that patients with elevated sNfL levels are more likely to reach combined cerebrovascular endpoints (log-rank test p &lt; 0.05).ConclusionThis investigation suggests that sNfL can serve as a valuable biomarker for predicting END and 3-month poor functional outcomes after an IS and has the potential to forecast long-term cardiovascular outcomes