Versatile interactions at interfaces for SPH-based simulations

The realistic capture of various interactions at interfaces is a challenging problem for SPH-based simulation. Previous works have mainly considered a single type of interaction, while real-world phenomena typically exhibit multiple interactions at different interfaces. For instance, when cracking an egg, there are simultaneous interactions between air, egg white, egg yolk, and the shell. To conveniently handle all interactions simultaneously in a single simulation, a versatile approach is critical. In this paper, we present a new approach to the surface tension model based on pairwise interaction forces; its basis is to use a larger number of neighboring particles. Our model is stable, conserves momentum, and furthermore, prevents the particle clustering problem which commonly occurs at the free surface. It can be applied to simultaneous interactions at multiple interfaces (e.g. fluid-solid and fluid-fluid). Our method is versatile, physically plausible and easy-to-implement. We also consider the close connection between droplets and bubbles, and show how to animate bubbles in air as droplets, with the help of a new surface particle detection method. Examples are provided to demonstrate the capabilities and effectiveness of our approach

Collective Human Opinions in Semantic Textual Similarity

Despite the subjective nature of semantic textual similarity (STS) and pervasive disagreements in STS annotation, existing benchmarks have used averaged human ratings as the gold standard. Averaging masks the true distribution of human opinions on examples of low agreement, and prevents models from capturing the semantic vagueness that the individual ratings represent. In this work, we introduce USTS, the first Uncertainty-aware STS dataset with ~15,000 Chinese sentence pairs and 150,000 labels, to study collective human opinions in STS. Analysis reveals that neither a scalar nor a single Gaussian fits a set of observed judgements adequately. We further show that current STS models cannot capture the variance caused by human disagreement on individual instances, but rather reflect the predictive confidence over the aggregate dataset.Comment: 16 pages, 7 figure

Interferon regulatory factor 7- (IRF7-) mediated immune response affects Newcastle disease virus replication in chicken embryo fibroblasts

Interferon regulatory factor 7 (IRF7) is essential for the induction of an antiviral response. Previous studies have shown that virus replication causes the activation or expression of Type I interferon (IFN) in cells, which further activates IFN-stimulated genes (ISGs) to retard virus growth. In this study, after infection of chicken embryo fibroblasts (CEFs) with the lentogenic Newcastle disease virus (NDV) strain LaSota or the velogenic NDV strain GM, the mRNA and protein levels of IRF7 showed a significant increase, and part of the IRF7 protein was translocated from the cytoplasm to the nucleus. In order to further explore the effect of IRF7-mediated innate immune response on the replication of NDV in CEFs, the mRNA levels of IFN-α, IFN-β and STAT1 were measured and the replication kinetics of NDV determined. The results showed that specific siRNA could inhibit the expression of IRF7 and limit the mRNA level of IFN-α, IFN-β and STAT1 and, accordingly, the replication kinetics of both NDVs were enhanced after the inhibition of IRF7. In conclusion, IRF7 is an important nuclear transcription factor for the induction of Type I IFNs during the antiviral response, which can affect the replication of NDV and spread to CEFs in the early phase of viral infection

NJUNLP's Participation for the WMT2023 Quality Estimation Shared Task

We introduce the submissions of the NJUNLP team to the WMT 2023 Quality Estimation (QE) shared task. Our team submitted predictions for the English-German language pair on all two sub-tasks: (i) sentence- and word-level quality prediction; and (ii) fine-grained error span detection. This year, we further explore pseudo data methods for QE based on NJUQE framework (https://github.com/NJUNLP/njuqe). We generate pseudo MQM data using parallel data from the WMT translation task. We pre-train the XLMR large model on pseudo QE data, then fine-tune it on real QE data. At both stages, we jointly learn sentence-level scores and word-level tags. Empirically, we conduct experiments to find the key hyper-parameters that improve the performance. Technically, we propose a simple method that covert the word-level outputs to fine-grained error span results. Overall, our models achieved the best results in English-German for both word-level and fine-grained error span detection sub-tasks by a considerable margin