Experimental Study of Ultralight (<300 kg/m 3

A type of ultralight (<300 kg/m3) foamed concrete (FC), which can be used as a new energy-conservation and environmental-protection building material and is particularly suitable for the thermal-insulation engineering of building external walls, was produced. The influences of different mixing amounts of fly ash, fly ash activator, WC (WC) ratio, and foaming agent (FA) on the compressive strength of FC were reported. The experimental study indicated that (1) the addition of fly ash reduced the strength of the FC and that the appropriate mixing amount of fly ash in this ultralight FC system should not exceed 45%; (2) with the increasing of fly ash activator, the strength of the FC sample is notably enhanced and the appropriate mixing amount of fly ash activator is 2.5%; (3) the optimized proportion of WC ratio is 0.45, and the FC that was produced according to this proportion has relatively high compressive strength; (4) by increasing the mixing amount of FA, the compressive strength of the FC notably decreases, and the optimal mixing amount of FA in this experiment is 3.5%

Exploring the Limits of ChatGPT for Query or Aspect-based Text Summarization

Text summarization has been a crucial problem in natural language processing (NLP) for several decades. It aims to condense lengthy documents into shorter versions while retaining the most critical information. Various methods have been proposed for text summarization, including extractive and abstractive summarization. The emergence of large language models (LLMs) like GPT3 and ChatGPT has recently created significant interest in using these models for text summarization tasks. Recent studies \cite{goyal2022news, zhang2023benchmarking} have shown that LLMs-generated news summaries are already on par with humans. However, the performance of LLMs for more practical applications like aspect or query-based summaries is underexplored. To fill this gap, we conducted an evaluation of ChatGPT's performance on four widely used benchmark datasets, encompassing diverse summaries from Reddit posts, news articles, dialogue meetings, and stories. Our experiments reveal that ChatGPT's performance is comparable to traditional fine-tuning methods in terms of Rouge scores. Moreover, we highlight some unique differences between ChatGPT-generated summaries and human references, providing valuable insights into the superpower of ChatGPT for diverse text summarization tasks. Our findings call for new directions in this area, and we plan to conduct further research to systematically examine the characteristics of ChatGPT-generated summaries through extensive human evaluation.Comment: Work in progres

DNA-GPT: Divergent N-Gram Analysis for Training-Free Detection of GPT-Generated Text

Large language models (LLMs) have notably enhanced the fluency and diversity of machine-generated text. However, this progress also presents a significant challenge in detecting the origin of a given text, and current research on detection methods lags behind the rapid evolution of LLMs. Conventional training-based methods have limitations in flexibility, particularly when adapting to new domains, and they often lack explanatory power. To address this gap, we propose a novel training-free detection strategy called Divergent N-Gram Analysis (DNA-GPT). Given a text, we first truncate it in the middle and then use only the preceding portion as input to the LLMs to regenerate the new remaining parts. By analyzing the differences between the original and new remaining parts through N-gram analysis in black-box or probability divergence in white-box, we can clearly illustrate significant discrepancies between machine-generated and human-written text. We conducted extensive experiments on the most advanced LLMs from OpenAI, including text-davinci-003, GPT-3.5-turbo, and GPT-4, as well as open-source models such as GPT-NeoX-20B and LLaMa-13B. Results show that our zero-shot approach exhibits state-of-the-art performance in distinguishing between human and GPT-generated text on four English and one German dataset, outperforming OpenAI's own classifier, which is trained on millions of text. Additionally, our methods provide reasonable explanations and evidence to support our claim, which is a unique feature of explainable detection. Our method is also robust under the revised text attack and can additionally solve model sourcing. Codes are available at https://github.com/Xianjun-Yang/DNA-GPT

Dynamic Prompting: A Unified Framework for Prompt Tuning

It has been demonstrated that the art of prompt tuning is highly effective in efficiently extracting knowledge from pretrained foundation models, encompassing pretrained language models (PLMs), vision pretrained models, and vision-language (V-L) models. However, the efficacy of employing fixed soft prompts with a predetermined position for concatenation with inputs for all instances, irrespective of their inherent disparities, remains uncertain. Variables such as the position, length, and representations of prompts across diverse instances and tasks can substantially influence the performance of prompt tuning. In this context, we provide a theoretical analysis, which reveals that optimizing the position of the prompt to encompass the input can capture additional semantic information that traditional prefix or postfix prompt tuning methods fail to capture. Building upon our analysis, we present a unified dynamic prompt (DP) tuning strategy that dynamically determines different factors of prompts based on specific tasks and instances. To accomplish this, we employ a lightweight learning network with Gumble-Softmax, allowing us to learn instance-dependent guidance. Experimental results underscore the significant performance improvement achieved by dynamic prompt tuning across a wide range of tasks, including NLP tasks, vision recognition tasks, and vision-language tasks. Furthermore, we establish the universal applicability of our approach under full-data, few-shot, and multitask scenarios. Codes are available at https://github.com/Xianjun-Yang/DPT.Comment: updat

Bovine serum albumin in saliva mediates grazing response in Leymus chinensis revealed by RNA sequencing

BACKGROUND: Sheepgrass (Leymus chinensis) is an important perennial forage grass across the Eurasian Steppe and is adaptable to various environmental conditions, but little is known about its molecular mechanism responding to grazing and BSA deposition. Because it has a large genome, RNA sequencing is expensive and impractical except for the next-generation sequencing (NGS) technology. RESULTS: In this study, NGS technology was employed to characterize de novo the transcriptome of sheepgrass after defoliation and grazing treatments and to identify differentially expressed genes (DEGs) responding to grazing and BSA deposition. We assembled more than 47 M high-quality reads into 120,426 contigs from seven sequenced libraries. Based on the assembled transcriptome, we detected 2,002 DEGs responding to BSA deposition during grazing. Enrichment analysis of Gene ontology (GO), EuKaryotic Orthologous Groups (KOG) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways revealed that the effects of grazing and BSA deposition involved more apoptosis and cell oxidative changes compared to defoliation. Analysis of DNA fragments, cell oxidative factors and the lengths of leaf scars after grazing provided physiological and morphological evidence that BSA deposition during grazing alters the oxidative and apoptotic status of cells. CONCLUSIONS: This research greatly enriches sheepgrass transcriptome resources and grazing-stress-related genes, helping us to better understand the molecular mechanism of grazing in sheepgrass. The grazing-stress-related genes and pathways will be a valuable resource for further gene-phenotype studies. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2164-15-1126) contains supplementary material, which is available to authorized users

Effects of Short-term Feeding Magnesium before Slaughter on Blood Metabolites and Postmortem Muscle Traits of Halothane-carrier Pigs

Fifty-four, mixed-sex, halothane-carrier crossbred (Yorkshire×Landrace) pigs with an average initial BW of 108.2±0.8 kg were randomly allotted to one of three dietary treatments for 5 d before slaughter: i) a control corn-soybean meal finisher diet devoid of supplemental magnesium; ii) a diet supplemented with 1.5 g/kg of elemental Mg from magnesium acetate; and iii) a diet supplemented with 1.5 g/kg of elemental Mg from magnesium sulfate heptahydrate. Serum creatine kinase (CK), lactate and glucose were analyzed at slaughter. Muscles from longissimus (LM) were packaged and stored to simulate display storage for muscle lactate and glycogen determinations at 0, 1, 2, 3, and 4 d. Mg supplementation reduced (p0.05) on serum glucose. Daily change of muscle lactate concentration linearly increased (p<0.01), while glucose concentration linearly decreased (p<0.05) as storage time increased in all treatments. However, dietary Mg acetate and Mg sulfate supplementation in pigs elevated (p<0.05) muscle glycogen and reduced (p<0.05) muscle lactate concentrations, especially during the first 2 d of display, compared with pigs fed the control diet. This study suggests that short-term feeding of magnesium acetate and magnesium sulfate to heterozygous carriers of the halothane gene has beneficial effects on stress response and pork quality by improving blood and muscle biochemical indexes

AlpaCare:Instruction-tuned Large Language Models for Medical Application

Large Language Models (LLMs) have demonstrated significant enhancements in instruction-following abilities through instruction tuning, achieving notable performances across various tasks. Previous research has focused on fine-tuning medical domain-specific LLMs using an extensive array of medical-specific data, incorporating millions of pieces of biomedical literature to augment their medical capabilities. However, existing medical instruction-tuned LLMs have been constrained by the limited scope of tasks and instructions available, restricting the efficacy of instruction tuning and adversely affecting performance in the general domain. In this paper, we fine-tune LLaMA-series models using 52k diverse, machine-generated, medical instruction-following data, MedInstruct-52k, resulting in the model AlpaCare. Comprehensive experimental results on both general and medical-specific domain free-form instruction evaluations showcase AlpaCare's strong medical proficiency and generalizability compared to previous instruction-tuned models in both medical and general domains. We provide public access to our MedInstruct-52k dataset and a clinician-crafted free-form instruction test set, MedInstruct-test, along with our codebase, to foster further research and development. Our project page is available at https://github.com/XZhang97666/AlpaCare

Germplasm Evaluation of an Eurasia Steppe Native Specie--Sheepgrass (\u3cem\u3eLeymus chinensis\u3c/em\u3e)

Sheepgrass (Leymus chinensis (Trin.) Tzvel) is an advantageous perennial native grass in China and other northern Eurasian countries having steppe. As an important forage grass of great value in animal husbandry, sheepgrass is well known for its abundant foliage, high palatability and high nutritive content. Sheepgrass is also valuable in grassland restoration and conservation since it is a perennial grass with a rhizome network to fix the soil and can survive well in stressful environments. Terefore, the collection, evaluation and utilization of sheepgrass are necessary for protecting grassland biodiversity, for establishing artificial pasture, restoring degraded grassland, and the development of forage industry and animal husbandry in Eurasia’s native steppe. Here, we reviewed our previous studies on the collection, evaluation of phenotypic diversity for germplasm resources, distribution and domestication of wild sheepgrass, and application of sheepgrass new varieties