Zero-Label Prompt Selection

Natural language prompts have been shown to facilitate cross-task generalization for large language models. However, with no or limited labeled examples, the cross-task performance is highly sensitive to the choice of prompts, while selecting a high-performing prompt is challenging given the scarcity of labels. To address the issue, we propose a Zero-Label Prompt Selection (ZPS) method that selects prompts without any labeled data or gradient update. Specifically, given the candidate human-written prompts for a task, ZPS labels a set of unlabeled data with a prompt ensemble and uses the pseudo-labels for prompt selection. Experiments show that ZPS improves over prior methods by a sizeable margin in zero-label performance. We also extend ZPS to a few-shot setting and show its advantages over strong baselines such as prompt tuning and model tuning

Prompt-Based Metric Learning for Few-Shot NER

Few-shot named entity recognition (NER) targets generalizing to unseen labels and/or domains with few labeled examples. Existing metric learning methods compute token-level similarities between query and support sets, but are not able to fully incorporate label semantics into modeling. To address this issue, we propose a simple method to largely improve metric learning for NER: 1) multiple prompt schemas are designed to enhance label semantics; 2) we propose a novel architecture to effectively combine multiple prompt-based representations. Empirically, our method achieves new state-of-the-art (SOTA) results under 16 of the 18 considered settings, substantially outperforming the previous SOTA by an average of 8.84% and a maximum of 34.51% in relative gains of micro F1. Our code is available at https://github.com/AChen-qaq/ProML

The impact of COVID-19 pandemic on emotional and behavioral problems of children with autism spectrum disorder and developmental delay aged 1–6 years in China

IntroductionThe COVID-19 pandemic outbreak have caused increased levels of emotional and behavioral problems, particularly among people with pre-existing mental health conditions. Young individuals with autism spectrum disorders (ASD) and developmental delay (DD) are particularly at risk due to their vulnerability. The purpose of this study was to look into the different effects of the COVID-19 pandemic on 1–6-year-old children with ASD and DD.MethodsParents and guardians of children with ASD completed an online survey that included questions about their children’s socio-demographics characteristics, the effects of the COVID-19 outbreak on their health, and what they needed in order to deal with the conditions of the pandemic.ResultsThis study compared 4,138 children with ASD to 711 children with DD. Children with ASD had a higher risk of having more emotional and behavioral problems than children with DD (OR 1.38, 95% CI 1.12–1.70). Compared to parent-oriented rehabilitation at home, discontinuing rehabilitation had a higher likelihood of negative emotional and behavioral change (OR 1.67, 95% CI 1.41–1.98). Having teachers’ online support had a higher likelihood of negative emotional and behavioral change for ASD children (OR 1.26, 95% CI 1.03–1.54).ConclusionsThis article provided evidence that children with developmental disabilities, particularly ASD, were at risk for a variety of challenges to their emotional functioning during the COVID-19 period, and that online support was not an ideal way for children with ASD to receive effective educational intervention in China

Determinants of the competitive advantage of dairy supply chains: Evidence from the Chinese dairy industry

In this study, we use an evidence-based approach to examine the factors that determine the competitive advantage of dairy supply chains using evidence from the Chinese dairy industry. We focus on the quality assurance of dairy products, which is considered one of the fundamental influential factors. We investigate interrelationships among the identified determinants, which include dairy production behavior, dairy cow culture model, government regulations, corporate social responsibility, and quality assurance, and examine how these determinants influence the competitive advantage of dairy supply chains. We employ the structural equation modeling approach in which grouped observable variables that represent the identified determinants are extrapolated from primary data collected through a questionnaire survey. Our key findings show that by mediating the effects of dairy production behavior and the dairy cow culture model, government regulation and corporate social responsibility significantly affect the quality assurance of dairy products. In turn, dairy production behavior and the dairy cow culture model significantly affect the competitive advantage of the dairy supply chain via the fully mediated effects of the quality assurance of dairy products. Specifically, the dairy cow culture model helps ensure the safety and quality of milk supply, allowing core dairy firms to control product quality throughout the dairy supply chain. Our empirical study shows that the identified determinants interact to assure the quality of dairy products and enhance the competitive advantage of the dairy supply chain in China

NLP From Scratch Without Large-Scale Pretraining: A Simple and Efficient Framework

Pretrained language models have become the standard approach for many NLP tasks due to strong performance, but they are very expensive to train. We propose a simple and efficient learning framework, TLM, that does not rely on large-scale pretraining. Given some labeled task data and a large general corpus, TLM uses task data as queries to retrieve a tiny subset of the general corpus and jointly optimizes the task objective and the language modeling objective from scratch. On eight classification datasets in four domains, TLM achieves results better than or similar to pretrained language models (e.g., RoBERTa-Large) while reducing the training FLOPs by two orders of magnitude. With high accuracy and efficiency, we hope TLM will contribute to democratizing NLP and expediting its development.Comment: 14 pages, 5 figure

SBML-SAT: a systems biology markup language (SBML) based sensitivity analysis tool

<p>Abstract</p> <p>Background</p> <p>It has long been recognized that sensitivity analysis plays a key role in modeling and analyzing cellular and biochemical processes. Systems biology markup language (SBML) has become a well-known platform for coding and sharing mathematical models of such processes. However, current SBML compatible software tools are limited in their ability to perform global sensitivity analyses of these models.</p> <p>Results</p> <p>This work introduces a freely downloadable, software package, SBML-SAT, which implements algorithms for simulation, steady state analysis, robustness analysis and local and global sensitivity analysis for SBML models. This software tool extends current capabilities through its execution of global sensitivity analyses using multi-parametric sensitivity analysis, partial rank correlation coefficient, SOBOL's method, and weighted average of local sensitivity analyses in addition to its ability to handle systems with discontinuous events and intuitive graphical user interface.</p> <p>Conclusion</p> <p>SBML-SAT provides the community of systems biologists a new tool for the analysis of their SBML models of biochemical and cellular processes.</p

Overexpression of a Water-Forming NADH Oxidase Improves the Metabolism and Stress Tolerance of Saccharomyces cerevisiae in Aerobic Fermentation

Recognising that the world into which students emerge upon graduation is characterised by constant change, we embrace a critical pedagogy that can be implemented in the classroom through the use of freehand drawing. Freehand drawing is a technique that can stimulate a critical stance, as visual representations allow us to comprehend the world differently, while permitting us see how others understand the world. First year students, in their first lecture, were asked to draw their interpretations of Irish politics and to explain in writing what they had drawn. The students were then placed in groups and asked to note what they saw in each other’s drawings, allowing for the identification of general patterns and themes. In this context, freehand drawing facilitates our ability to: ‘see’ how we understand a topic and that there are multiple ways of understanding; test theories, orthodoxies and accepted truths; scrutinise tacit assumptions; and ponder other possibilities. In employing freehand drawing in this manner, our aim is to create a learning environment where students develop their capacity for critical self-reflection

Strontium chloride improves bone mass by affecting the gut microbiota in young male rats

IntroductionBone mass accumulated in early adulthood is an important determinant of bone mass throughout the lifespan, and inadequate bone deposition may lead to associated skeletal diseases. Recent studies suggest that gut bacteria may be potential factors in boosting bone mass. Strontium (Sr) as a key bioactive element has been shown to improve bone quality, but the precise way that maintains the equilibrium of the gut microbiome and bone health is still not well understood.MethodsWe explored the capacity of SrCl2 solutions of varying concentrations (0, 100, 200 and 400 mg/kg BW) on bone quality in 7-week-old male Wistar rats and attempted to elucidate the mechanism through gut microbes.ResultsThe results showed that in a Wistar rat model under normal growth conditions, serum Ca levels increased after Sr-treatment and showed a dose-dependent increase with Sr concentration. Three-point mechanics and Micro-CT results showed that Sr exposure enhanced bone biomechanical properties and improved bone microarchitecture. In addition, the osteoblast gene markers BMP, BGP, RUNX2, OPG and ALP mRNA levels were significantly increased to varying degrees after Sr treatment, and the osteoclast markers RANKL and TRAP were accompanied by varying degrees of reduction. These experimental results show that Sr improves bones from multiple angles. Further investigation of the microbial population revealed that the composition of the gut microbiome was changed due to Sr, with the abundance of 6 of the bacteria showing a different dose dependence with Sr concentration than the control group. To investigate whether alterations in bacterial flora were responsible for the effects of Sr on bone remodeling, a further pearson correlation analysis was done, 4 types of bacteria (Ruminococcaceae_UCG-014, Lachnospiraceae_NK4A136_group, Alistipes and Weissella) were deduced to be the primary contributors to Sr-relieved bone loss. Of these, we focused our analysis on the most firmly associated Ruminococcaceae_UCG-014.DiscussionTo summarize, our current research explores changes in bone mass following Sr intervention in young individuals, and the connection between Sr-altered intestinal flora and potentially beneficial bacteria in the attenuation of bone loss. These discoveries underscore the importance of the “gut-bone” axis, contributing to an understanding of how Sr affects bone quality, and providing a fresh idea for bone mass accumulation in young individuals and thereby preventing disease due to acquired bone mass deficiency

Fusion of block and keypoints based approaches for effective copy-move image forgery detection

Keypoint-based and block-based methods are two main categories of techniques for detecting copy-move forged images, one of the most common digital image forgery schemes. In general, block-based methods suffer from high computational cost due to the large number of image blocks used and fail to handle geometric transformations. On the contrary, keypoint-based approaches can overcome these two drawbacks yet are found difficult to deal with smooth regions. As a result, fusion of these two approaches is proposed for effective copy-move forgery detection. First, our scheme adaptively determines an appropriate initial size of regions to segment the image into non-overlapped regions. Feature points are extracted as keypoints using the scale invariant feature transform (SIFT) from the image. The ratio between the number of keypoints and the total number of pixels in that region is used to classify the region into smooth or non-smooth (keypoints) regions. Accordingly, block based approach using Zernike moments and keypoint based approach using SIFT along with filtering and post-processing are respectively applied to these two kinds of regions for effective forgery detection. Experimental results show that the proposed fusion scheme outperforms the keypoint-based method in reliability of detection and the block-based method in efficiency