LCT-1 at SemEval-2023 Task 10: Pre-training and Multi-task Learning for Sexism Detection and Classification

Misogyny and sexism are growing problems in social media. Advances have been made in online sexism detection but the systems are often uninterpretable. SemEval-2023 Task 10 on Explainable Detection of Online Sexism aims at increasing explainability of the sexism detection, and our team participated in all the proposed subtasks. Our system is based on further domain-adaptive pre-training (Gururangan et al., 2020). Building on the Transformer-based models with the domain adaptation, we compare fine-tuning with multi-task learning and show that each subtask requires a different system configuration. In our experiments, multi-task learning performs on par with standard fine-tuning for sexism detection and noticeably better for coarse-grained sexism classification, while fine-tuning is preferable for fine-grained classification

Particle dynamics revealed by 210Po/210Pb disequilibria around Prydz Bay, the Southern Ocean in summer

Seawater samples were collected around Prydz Bay in summer of 2014, dissolved and particulate 210Po and 210Pb were measured to reveal the disequilibrium characteristics and particle dynamics. Our results show that the distribution of 210Po and 210Po/210Pb activity ratio in the upper water is mainly affected by biological absorption or particle adsorption. An abnormal excess of 210Po relative to 210Pb was observed in the surface water at stations P1-2 and P2-2, which is likely to be the horizontal transport of water mass with high DPo/DPb)A.R. and TPo/TPb)A.R.. In this study, the removal of particulate 210Po is mainly controlled by the scavenging of dissolved 210Po and the two have a linear positive correlation with the salinity, a negative linear correlation with the content of dissolved oxygen and a reciprocal relationship with the content of POC. The export flux of POC at 100 m is estimated to be 1.8–4.4 mmol·m−2·d−1 (avg. 2.9 mmol·m−2·d−1) based on 210Po/210Pb disequilibria, with the highest value in the shelf, which is consistent with the distribution of biological productivity

Enhanced but highly variable bioturbation around seamounts in the northwest Pacific

Abstract(#br)Seamounts are a unique ecosystem in marine environment, but the relevant understanding is limited. In this study, sedimentation and bioturbation around the Pako Guyot of Magellan, and the Lamont, Scripps, Arnold, and Pot Guyots of Marcus-Wake seamounts in the northwest Pacific were evaluated using 230 Th ex and 210 Pb ex as tracers. Our results showed that the linear sedimentation rate and the mass accumulation rate ranged from 0.12 to 2.50 mm/ka and from 0.06 to 1.14 kg/m 2 /ka with averages of 1.27 ± 0.80 mm/ka and 0.49 ± 0.30 kg/m 2 /ka respectively. The accumulation flux of organic carbon in surface sediments was estimated to be 0.10-4.52 gC/m 2 /ka. The bioturbation coefficients ranged from 1.01 to 27.1 cm 2 /a with an average of 10.8 ± 9.2 cm 2 /a, which is higher than those in abyssal sediments or predicted by traditional empirical equations. The enhanced bioturbation supports the view that seamounts are hotspots for pelagic benthic organisms. The bioturbation intensity showed a great variability with the maximum around 40 km away from the edge of seamount summit. The bioturbation coefficient correlated positively with sedimentation rate and accumulation flux of organic carbon in surface sediments, indicating that the supply of organic matter is a main driving force for enhanced bioturbation around the seamounts. The increase in sedimentary organic matter promotes the activities of benthic organisms. More research is needed to gain a deep understanding of bioturbation in seamounts in the context of future climate change

Highly efficient preparation of Ce0.8Sm0.2O2-δ–SrCo0.9Nb0.1O3-δ dual-phase four-channel hollow fiber membrane via one-step thermal processing approach

Fabricating dual-phase hollow-fiber membranes via a one-step thermal processing (OSTP) approach is challenging, because of complex sintering kinetics and the subsequent impacts on membrane morphology, phase stability, and permeation properties. In this study, we have demonstrated that Ce0.8Sm0.2O2-δ-SrCo0.9Nb0.1O3-δ (SDC-SCN) four-channel hollow fiber membrane can be manufactured via a single high-temperature sintering process, by using metal oxides and carbonates directly as membrane materials (sources of metal ions). It has been found that use of a low ramping rate reduces grain sizes, increases grain and forming cobalt oxide nanoparticles, a key step to promoting surface exchange process followed by enhancing oxygen permeation. While the grain boundary interface region can be limited to approximately 20–30 nm. At 1173 K oxygen permeation of the SDC-SCN four-channel hollow fiber membrane was measured at approximately 1.2 mL cm−2·min−1 using helium as the sweep gas. Meanwhile, the dual-phase membrane shows a good tolerance to carbon dioxide, with the oxygen permeation flux fully recovered after long-term exposure to carbon dioxide (more than 100 h). This will enable further application of the OSTP approach for preparing dual-phase multi-channel hollow fiber membranes for applications of oxyfuel combustion, catalytic membrane reactors and carbon dioxide capture

Comparative analysis of newly identified rodent arteriviruses and porcine reproductive and respiratory syndrome virus to characterize their evolutionary relationships

Porcine reproductive and respiratory syndrome virus (PRRSV) has caused huge economic losses for the global pig industry, but its origins and evolution remain a mystery. In 2018, the genome sequences of seven arteriviruses isolated from rodents were determined, and here we publish new analysis showing that they may be ancestors of PRRSV. The sequence similarity of these viruses to PRRSV was ~60%, with shared genome organization and other characteristics, such as slippery sequences and C-rich motifs in nsp2, and a transactivated protein sequence in nsp1β. Codon usage basis analysis showed that PRRSV was closer to these rodent arteriviruses than lactate dehydrogenase-elevating virus (LDV) and they were both under pressure of natural selection. Evolutionary analysis revealed that four of the rodent arteriviruses shared the same genus with PRRSV, and were more closely related to PRRSV-2 than PRRSV-1. In addition to this, they all appeared earlier than PRRSV according to evolutionary modeling, and we speculate that they represent an intermediate step in the origin of PRRSV by arterivirus transmission from rodents to swine. Our in-depth analysis furthers our understanding of arteriviruses, and will serve as the basis for subsequent exploration of the evolution of PRRSV and other arteriviruses

A Transfer Deep Generative Adversarial Network Model to Synthetic Brain CT Generation from MR Images

Background. The generation of medical images is to convert the existing medical images into one or more required medical images to reduce the time required for sample diagnosis and the radiation to the human body from multiple medical images taken. Therefore, the research on the generation of medical images has important clinical significance. At present, there are many methods in this field. For example, in the image generation process based on the fuzzy C-means (FCM) clustering method, due to the unique clustering idea of FCM, the images generated by this method are uncertain of the attribution of certain organizations. This will cause the details of the image to be unclear, and the resulting image quality is not high. With the development of the generative adversarial network (GAN) model, many improved methods based on the deep GAN model were born. Pix2Pix is a GAN model based on UNet. The core idea of this method is to use paired two types of medical images for deep neural network fitting, thereby generating high-quality images. The disadvantage is that the requirements for data are very strict, and the two types of medical images must be paired one by one. DualGAN model is a network model based on transfer learning. The model cuts the 3D image into multiple 2D slices, simulates each slice, and merges the generated results. The disadvantage is that every time an image is generated, bar-shaped “shadows” will be generated in the three-dimensional image. Method/Material. To solve the above problems and ensure the quality of image generation, this paper proposes a Dual3D&PatchGAN model based on transfer learning. Since Dual3D&PatchGAN is set based on transfer learning, there is no need for one-to-one paired data sets, only two types of medical image data sets are needed, which has important practical significance for applications. This model can eliminate the bar-shaped “shadows” produced by DualGAN’s generated images and can also perform two-way conversion of the two types of images. Results. From the multiple evaluation indicators of the experimental results, it can be analyzed that Dual3D&PatchGAN is more suitable for the generation of medical images than other models, and its generation effect is better

LCT-1 at SemEval-2023 Task 10:Pre-training and Multi-task Learning for Sexism Detection and Classification

Misogyny and sexism are growing problems in social media. Advances have been made in online sexism detection but the systems are often uninterpretable. SemEval-2023 Task 10 on Explainable Detection of Online Sexism aims at increasing explainability of the sexism detection, and our team participated in all the proposed subtasks. Our system is based on further domain-adaptive pre-training (Gururangan et al., 2020). Building on the Transformer-based models with the domain adaptation, we compare fine-tuning with multi-task learning and show that each subtask requires a different system configuration.</p

Characterizing and predicting community members from evolutionary and heterogeneous networks

Mining different types of communities from web data have attracted a lot of research efforts in recent years. However, none of the existing community mining techniques has taken into account both the dynamic as well as heterogeneous nature of web data. In this paper, we propose to characterize and predict community members from the evolution of heterogeneous web data. We first propose a general framework for analyzing the evolution of heterogeneous networks. Then, the academic network, which is extracted from 1 million computer science papers, is used as an example to illustrate the framework. Finally, two example applications of the academic network are presented. Experimental results with a real and very large heterogeneous academic network show that our proposed framework can produce good results in terms of community member recommendation. Also, novel knowledge and insights can be gained by analyzing the community evolution pattern. Copyright 2008 ACM.EI