Neural Semantic Parsing in Low-Resource Settings with Back-Translation and Meta-Learning

Neural semantic parsing has achieved impressive results in recent years, yet its success relies on the availability of large amounts of supervised data. Our goal is to learn a neural semantic parser when only prior knowledge about a limited number of simple rules is available, without access to either annotated programs or execution results. Our approach is initialized by rules, and improved in a back-translation paradigm using generated question-program pairs from the semantic parser and the question generator. A phrase table with frequent mapping patterns is automatically derived, also updated as training progresses, to measure the quality of generated instances. We train the model with model-agnostic meta-learning to guarantee the accuracy and stability on examples covered by rules, and meanwhile acquire the versatility to generalize well on examples uncovered by rules. Results on three benchmark datasets with different domains and programs show that our approach incrementally improves the accuracy. On WikiSQL, our best model is comparable to the SOTA system learned from denotations

WizardLM: Empowering Large Language Models to Follow Complex Instructions

Training large language models (LLM) with open-domain instruction following data brings colossal success. However, manually creating such instruction data is very time-consuming and labor-intensive. Moreover, humans may struggle to produce high-complexity instructions. In this paper, we show an avenue for creating large amounts of instruction data with varying levels of complexity using LLM instead of humans. Starting with an initial set of instructions, we use our proposed Evol-Instruct to rewrite them step by step into more complex instructions. Then, we mix all generated instruction data to fine-tune LLaMA. We call the resulting model WizardLM. Human evaluations on a complexity-balanced test bed show that instructions from Evol-Instruct are superior to human-created ones. By analyzing the human evaluation results of the high complexity part, we demonstrate that outputs from our WizardLM model are preferred to outputs from OpenAI ChatGPT. Even though WizardLM still lags behind ChatGPT in some aspects, our findings suggest that fine-tuning with AI-evolved instructions is a promising direction for enhancing large language models. Our codes and generated data are public at https://github.com/nlpxucan/WizardLMComment: large language model, instruction fine-tun

A study of Multistage/Multifunction Column for Fine Coal Cleaning CRADA PC93-005, Final Report

The overall objective of the this research project is to explore the potential applicability of a multistage column for fine coal cleaning and other applications in fluid particle separation. The research work identifies the design parameters and their effects on the performance of the separation device. The results of this study provide an engineering data basis for further development of this technology in coal cleaning and in general areas of fluid and particle separations

MiR-641 participates in the progression of breast cancer by modulation of RELN expression

Purpose: To examine the role of micro-ribonucleic acid 641 (miR-641) in breast cancer, and to uncover its possible molecular mechanism. Methods: MiR-641 expressions in breast cancer cell lines and tissues were determined using Real-time fluorescence quantitative reverse transcription-polymerase chain reaction (qRT-PCR), and the diagnostic potential value of miR-641 was assessed using receiver operating characteristic (ROC) curves. The survival of the patients was analyzed using Kaplan-Meier, and the cell viability and migration capacity were evaluated using Transwell and cell counting kit-8 (CCK-8) assay, and the downstream target gene of miR-641 was confirmed via dual-luciferase reporter gene assay. Finally, reversal assay was employed to corroborate the molecular mechanism that affects cell proliferation and migration via modulation of RELN. Results: MiR-641 was lowly expressed in breast cancer cell lines and tissues, and its expression in the metastasizing group was lower than that in the matched group (p < 0.05). It was also observed that miR-641 expression gradually decreased as the breast cancer advanced. Moreover, lower miR-641 expression revealed a poor prognosis, and up-regulating miR-641 suppressed the proliferative and migrative capacities of breast cancer cells. It was proven that RELN is a target gene of miR-641. RELN expression rose in breast cancer, and it was evidently and negatively correlated with that of miR-641. Finally, miR-641 regulated RELN, and it affected the proliferation and migration of cells. Conclusion: MiR-641 has an obviously decreased expression level in breast cancer, and facilitates the proliferative and migrative capacity of breast cancer cells probably by modulating the RELN expression. This study may provide new targets for the treatment of breast cancer

Can magmatic zircon be distinguished from hydrothermal zircon by trace element composition? The effect of mineral inclusions on zircon trace element composition

Mineral inclusions, e.g., apatite, titanite, monazite, K-feldspar, are common in magmatic zircons. Although many studies mention that light rare earth element (LREE) contents of zircons could be compromised by an analytical artefact of the accidental sampling of mineral inclusions, how and to what degree these inclusions influence analysis of zircon composition is still not well constrained. Here we report U–Pb ages and trace element abundances for zircon crystals, where apatite and K-feldspar inclusions are observed, from diorite porphyry in the Weibao deposit, East Kunlun Mountains, Northern Tibetan Plateau. Although zircon morphological and chronological evidence consistently advocates a magmatic origin without undergoing significant hydrothermal alteration, 7 of 15 analytical spots show LREE-enriched patterns and low Ce/Ce* ratios which are comparable to those for published “hydrothermal” zircon. Quantitative modelling in this study manifests that these LREE-enriched patterns and low Ce/Ce* ratios can be achieved with only 0.1 to 2 vol% contamination from sub-micrometer apatite inclusions, which in practice are hard to monitor under the LA–ICP–MS (normally with large pit diameter and depth) and conventional microscopes. Titanite, monazite, xenotime, and allanite have similar roles to apatite, and LREE contents of zircon can be significantly elevated with only 0.05 vol% contamination from these inclusions. We therefore suggest that the widely used geochemical discrimination criteria for magmatic and hydrothermal zircon, e.g., (Sm/La)N vs. La and Ce/Ce* vs. (Sm/La)N diagrams and the degree of Ce anomalies, are ambiguous since they are extremely susceptible to contamination by mineral inclusions, and that within single samples only Ce4+/Ce3+ values calculated from zircons of low LREE values probably represent the oxidation state of magmas