Natural Language Processing Using Neighbour Entropy-based Segmentation

In natural language processing (NLP) of Chinese hazard text collected in the process of hazard identification, Chinese word segmentation (CWS) is the first step to extracting meaningful information from such semi-structured Chinese texts. This paper proposes a new neighbor entropy-based segmentation (NES) model for CWS. The model considers the segmentation benefits of neighbor entropies, adopting the concept of "neighbor" in optimization research. It is defined by the benefit ratio of text segmentation, including benefits and losses of combining the segmentation unit with more information than other popular statistical models. In the experiments performed, together with the maximum-based segmentation algorithm, the NES model achieves a 99.3% precision, 98.7% recall, and 99.0% f-measure for text segmentation; these performances are higher than those of existing tools based on other seven popular statistical models. Results show that the NES model is a valid CWS, especially for text segmentation requirements necessitating longer-sized characters. The text corpus used comes from the Beijing Municipal Administration of Work Safety, which was recorded in the fourth quarter of 2018

Rapid droplet leads the Liquid-Infused Slippery Surfaces more slippery

The introduction of lubricant between fluid and substrate endows the Liquid-Infused Slippery Surfaces with excellent wetting properties: low contact angle, various liquids repellency, ice-phobic and self-healing. Droplets moving on such surfaces have been widely demonstrated to obey a Landau-Levich-Derjaguin (LLD) friction. Here, we show that this power law is surprisingly decreased with the droplet accelerates: in the rapid droplet regime, the slippery surfaces seem more slippery than LLD friction. Combining experimental and numerical techniques, we find that the meniscus surrounding the droplet exhibits an incompletely developed state. The Incompletely Developed Meniscus possesses shorter shear length and thicker shear thickness than the prediction of Bretherton model and therefore is responsible for the more slippery regime. With an extended Bretherton model, we not only provide an analytical description to the IDM behavior but also the friction when the Capillary Number of the moving droplet is larger than the Critical Capillary Number

Making Language Models Better Tool Learners with Execution Feedback

Tools serve as pivotal interfaces that enable humans to understand and reshape the environment. With the advent of foundation models, AI systems can utilize tools to expand their capabilities and interact with the real world. Existing tool learning methodologies, encompassing supervised fine-tuning and prompt engineering approaches, often induce large language models to utilize tools indiscriminately, as complex tasks often exceed their own competencies. However, introducing tools for simple tasks, which the models themselves can readily resolve, can inadvertently propagate errors rather than enhance performance. This leads to the research question: can we teach language models when and how to use tools? To meet this need, we propose Tool leaRning wIth exeCution fEedback (TRICE), a two-stage end-to-end framework that enables the model to continually learn through feedback derived from tool execution, thereby learning when and how to use tools effectively. Experimental results, backed by further analysis, show that TRICE can make the large language model selectively use tools by improving the accuracy of tool usage while enhancing insufficient tool learning and mitigating excessive reliance on tools. Code is available at https://github.com/zjunlp/TRICE.Comment: NAACL 202

Growth-regulating factor 5 (GRF5)-mediated gene regulatory network promotes leaf growth and expansion in poplar

Although polyploid plants have larger leaves than their diploid counterparts, the molecular mechanisms underlying this difference (or trait) remain elusive. Differentially expressed genes (DEGs) between triploid and full-sib diploid poplar trees were identified from two transcriptomic data sets followed by a gene association study among DEGs to identify key leaf growth regulators. Yeast one-hybrid system, electrophoretic mobility shift assay, and dual-luciferase assay were employed to substantiate that PpnGRF5-1 directly regulated PpnCKX1. The interactions between PpnGRF5-1 and growth-regulating factor (GRF)-interacting factors (GIFs) were experimentally validated and a multilayered hierarchical regulatory network (ML-hGRN)-mediated by PpnGRF5-1 was constructed with top-down graphic Gaussian model (GGM) algorithm by combining RNA-sequencing data from its overexpression lines and DAP-sequencing data. PpnGRF5-1 is a negative regulator of PpnCKX1. Overexpression of PpnGRF5-1 in diploid transgenic lines resulted in larger leaves resembling those of triploids, and significantly increased zeatin and isopentenyladenine in the apical buds and third leaves. PpnGRF5-1 also interacted with GIFs to increase its regulatory diversity and capacity. An ML-hGRN-mediated by PpnGRF5-1 was obtained and could largely elucidate larger leaves. PpnGRF5-1 and the ML-hGRN-mediated by PpnGRF5-1 were underlying the leaf growth and development

Nested sampling statistical errors

Nested sampling (NS) is a popular algorithm for Bayesian computation. We investigate statistical errors in NS both analytically and numerically. We show two analytic results. First, we show that the leading terms in Skilling's expression using information theory match the leading terms in Keeton's expression from an analysis of moments. This approximate agreement was previously only known numerically and was somewhat mysterious. Second, we show that the uncertainty in single NS runs approximately equals the standard deviation in repeated NS runs. Whilst intuitive, this was previously taken for granted. We close by investigating our results and their assumptions in several numerical examples, including cases in which NS uncertainties increase without bound.Comment: 12 pages + appendices, 3 figure

Impact forces of water drops falling on superhydrophobic surfaces

A falling liquid drop, after impact on a rigid substrate, deforms and spreads, owing to the normal reaction force. Subsequently, if the substrate is non-wetting, the drop retracts and then jumps off. As we show here, not only is the impact itself associated with a distinct peak in the temporal evolution of the normal force, but also the jump-off, which was hitherto unknown. We characterize both peaks and elucidate how they relate to the different stages of the drop impact process. The time at which the second peak appears coincides with the formation of a Worthington jet, emerging through flow-focusing, and it is independent of the impact velocity. However, the magnitude of this peak is dictated by the drop's inertia and surface tension. We show that even low-velocity impacts can lead to a surprisingly high peak in the normal force, namely when a more pronounced singular Worthington jet occurs due to the collapse of an air cavity in the drop.Comment: Please find the supplemental movies here: https://youtube.com/playlist?list=PLf5C5HCrvhLGmlYTF1Gg2WviZ-Bkmy2q