Study on the Transmission of Chinese Culture From the Perspective of Big Translation

This paper analyzes and studies the transmission of Chinese culture from the the perspective of Big Translation. Through analyzing the contents of Big translation   and the practical application of Big Translation in the transmission of Chinese culture, we find intralingual translation, interlingual translation and intersemiotic translation all play important roles in the process of cultural transmission. The purpose of this paper is to study how to spread Chinese culture effectively. With various examples, it is concluded that big translation is an important way to promote the effective spread of Chinese culture. We should attach importance to big translation and use it to promote cross-cultural communication and cultural transmission

Quantifying Cognitive Workload and Mental Capacity from EEG Signals under Complex Cognitive Activities

The objective of the present research is to quantify the changes in cognitive workload and mental capacity from EEG signals when people are conducting complex cognitive activities. Design activities are good examples of complex cognitive activities that require simultaneous involvements of multiple cognitive functions including problem understanding, analyzing, evaluating, and creating. As one of the fundamental human activities, design activities are where a designer’s mental effort is applied to create product descriptions (design solutions) from an initial design problem, which involves looping and jumping among design problems, design knowledge, and design solutions. Using design activities as a starting point, the present study conducted a series of theoretical analyses and literature reviews to identify the opportunities and challenges for applying EEG to quantify designers' cognitive changes, including cognitive workload and mental capacity. The research objectives were formulated based on my pilot studies in applying and extending the stress model, leading to the methodology of the present research. A new framework (tEEG framework) has been proposed to address the identified challenges as a result of our past research attempts and theoretical analyses, which also serves as the foundation for the present research. Afterward, the proposed tEEG framework was applied for quantitatively monitoring changes in people's cognitive workload and mental capacity within and beyond the context of design, where mental capacity was considered as the umbrella of numerous cognitive factors including cognitive control. Finally, my future research goal is to apply the quantification results on cognitive workload and mental capacity to improving human mental effort under complex cognitive activities, which corresponds to the second research objective of the present research. Along this direction, the present research proposes a quantitative approach to elaborate the impact of cognitive workload and mental capacity on mental effort that has been verified by simulation results. My future research will continue to test the approach in cognitive experiments including the ongoing N-back study, aiming to bridge the gap between most existing cognitive studies and their applications in real life

LoG-CAN: local-global Class-aware Network for semantic segmentation of remote sensing images

Remote sensing images are known of having complex backgrounds, high intra-class variance and large variation of scales, which bring challenge to semantic segmentation. We present LoG-CAN, a multi-scale semantic segmentation network with a global class-aware (GCA) module and local class-aware (LCA) modules to remote sensing images. Specifically, the GCA module captures the global representations of class-wise context modeling to circumvent background interference; the LCA modules generate local class representations as intermediate aware elements, indirectly associating pixels with global class representations to reduce variance within a class; and a multi-scale architecture with GCA and LCA modules yields effective segmentation of objects at different scales via cascaded refinement and fusion of features. Through the evaluation on the ISPRS Vaihingen dataset and the ISPRS Potsdam dataset, experimental results indicate that LoG-CAN outperforms the state-of-the-art methods for general semantic segmentation, while significantly reducing network parameters and computation. Code is available at~\href{https://github.com/xwmaxwma/rssegmentation}{https://github.com/xwmaxwma/rssegmentation}.Comment: Accepted at ICASSP 202

Uncertainty-Aware Unlikelihood Learning Improves Generative Aspect Sentiment Quad Prediction

Recently, aspect sentiment quad prediction has received widespread attention in the field of aspect-based sentiment analysis. Existing studies extract quadruplets via pre-trained generative language models to paraphrase the original sentence into a templated target sequence. However, previous works only focus on what to generate but ignore what not to generate. We argue that considering the negative samples also leads to potential benefits. In this work, we propose a template-agnostic method to control the token-level generation, which boosts original learning and reduces mistakes simultaneously. Specifically, we introduce Monte Carlo dropout to understand the built-in uncertainty of pre-trained language models, acquiring the noises and errors. We further propose marginalized unlikelihood learning to suppress the uncertainty-aware mistake tokens. Finally, we introduce minimization entropy to balance the effects of marginalized unlikelihood learning. Extensive experiments on four public datasets demonstrate the effectiveness of our approach on various generation templates1

CRIT:Identifying RNA-binding protein regulator in circRNA life cycle via non-negative matrix factorization

Circular RNAs (circRNAs) are endogenous non-coding RNAs that regulate gene expression and participate in carcinogenesis. However, the RNA-binding proteins (RBPs) involved in circRNAs biogenesis and modulation remain largely unclear. We developed the circRNA regulator identification tool (CRIT), a non-negative matrix-factorization-based pipeline to identify regulating RBPs in cancers. CRIT uncovered 73 novel regulators across thousands of samples by effectively leveraging genomics data and functional annotations. We demonstrated that known RBPs involved in circRNA control are significantly enriched in these predictions. Analysis of circRNA-RBP interactions using two large cross-linking immunoprecipitation (CLIP) databases, we validated the consistency between CRIT prediction and the CLIP experiments. Furthermore, newly discovered RBPs are functionally connected with authentic circRNA regulators by various biological associations, such as physical interaction, similar binding motifs, common transcription factor modulation, and co-expression. When analyzing RNA sequencing (RNA-seq) datasets after short hairpin RNA (shRNA)/small interfering RNA (siRNA) knockdown, we found several novel RBPs that can affect global circRNA expression, which strengthens their role in the circRNA life cycle. The above evidence provided independent confirmation that CRIT is a useful tool to capture RBPs in circRNA processing. Finally, we show that authentic regulators are more likely the core splicing proteins and peripheral factors and usually harbor more alterations in the vast majority of cancers

E-NER: Evidential Deep Learning for Trustworthy Named Entity Recognition

Most named entity recognition (NER) systems focus on improving model performance, ignoring the need to quantify model uncertainty, which is critical to the reliability of NER systems in open environments. Evidential deep learning (EDL) has recently been proposed as a promising solution to explicitly model predictive uncertainty for classification tasks. However, directly applying EDL to NER applications faces two challenges, i.e., the problems of sparse entities and OOV/OOD entities in NER tasks. To address these challenges, we propose a trustworthy NER framework named E-NER by introducing two uncertainty-guided loss terms to the conventional EDL, along with a series of uncertainty-guided training strategies. Experiments show that E-NER can be applied to multiple NER paradigms to obtain accurate uncertainty estimation. Furthermore, compared to state-of-the-art baselines, the proposed method achieves a better OOV/OOD detection performance and better generalization ability on OOV entities.Comment: accepted by ACL Findings (2023

Research trends and hot spots in global nanotechnology applications in liver cancer: a bibliometric and visual analysis (2000-2022)

BackgroundLiver cancer (LC) is one of the most common malignancies. Currently, nanotechnology has made great progress in LC research, and many studies on LC nanotechnology have been published. This study aims to discuss the current status, hot spots, and research trends in this field through bibliometric analysis.MethodsThe Web of Science Core Collection (WoSCC) database was searched for papers related to hepatocellular carcinoma (HCC) included from January 2000 to November 2022, and its research hotspots and trends were visualized and analyzed with the help of VOSviewer. In addition, a search was conducted to find LC papers related to nanotechnology. Then we used the visual analysis software VOSviewer and CiteSpace to evaluate the contributions of countries/regions, authors, and journals related to the topic and analyze keywords to understand the research priorities and hot spots in the field as well as the development direction.ResultsThere are 1908 papers in the highly cited literature on LC, and its research hotspots are pathogenesis, risk factors, and survival rate. The literature on the application of nanotechnology in LC had 921 papers. Among them, China (n=560, 60.8%) and the United States (n=170, 18.5%) were the countries with the highest number of published papers. Wang Yan (n=11) and Llovet JM (n=131) were the first authors and co-cited authors, respectively. The International Journal of Nanomedicine was the most prolific academic journal (n=41). In addition to “hepatocellular carcinoma” and “nanoparticles”, the most frequent keyword was “drug delivery”. In recent years, “metastasis” and “diagnosis” appeared in the keyword bursts. This indicates that the application of nanoparticles in the early diagnosis and drug delivery of LC (including liver metastasis) has a good prospect.ConclusionNanotechnology has received more and more attention in the medical field in recent years. As nanoparticles are easily localized in organelles and cells, they can increase drug permeability in tumor tissues, improve drug delivery efficiency and reduce drug toxicity. Our research results were the first scientific evaluation of the application of nanotechnology in LC, providing scholars with research hotspots and development trends