A Global Context Mechanism for Sequence Labeling

Sequential labeling tasks necessitate the computation of sentence representations for each word within a given sentence. With the advent of advanced pretrained language models; one common approach involves incorporating a BiLSTM layer to bolster the sequence structure information at the output level. Nevertheless, it has been empirically demonstrated (P.-H. Li et al., 2020) that the potential of BiLSTM for generating sentence representations for sequence labeling tasks is constrained, primarily due to the amalgamation of fragments form past and future sentence representations to form a complete sentence representation. In this study, we discovered that strategically integrating the whole sentence representation, which existing in the first cell and last cell of BiLSTM, into sentence representation of ecah cell, could markedly enhance the F1 score and accuracy. Using BERT embedded within BiLSTM as illustration, we conducted exhaustive experiments on nine datasets for sequence labeling tasks, encompassing named entity recognition (NER), part of speech (POS) tagging and End-to-End Aspect-Based sentiment analysis (E2E-ABSA). We noted significant improvements in F1 scores and accuracy across all examined datasets

Explicit design optimization of air rudders for maximizing stiffness and fundamental frequency

In aerospace engineering, there is a growing demand for lightweight design through topology optimization. This paper presents a novel design optimization method for stiffened air rudders, commonly used for aircraft attitude control, based on the Moving Morphable Components (MMC) method. The stiffeners within the irregular enclosed design domain are modeled as MMCs and discretized by shell elements, accurately capturing their geometry and evolution during optimization process using explicit parameters. In order to maximize the stiffness and fundamental frequency of the rudder structures, numerical analysis algorithms were developed with shape sensitivity analysis conducted. To comply with the manufacturing requirement, a minimum thickness is prescribed for the stiffeners. Penalty strategies were developed for the thickness and density of stiffeners with thickness smaller than the threshold to meet the thickness requirement and suppress spurious modes. The method's effectiveness was demonstrated through optimization examples of two typical air rudders, illustrating the significance of stiffener's distribution on design objectives. The explicit modeling characteristics allow for directly importing the optimization results into CAD systems, significantly enhancing the engineering applicability

Crosstalk between Platelets and SARS-CoV-2: Implications in Thrombo-Inflammatory Complications in COVID-19

The SARS-CoV-2 virus, causing the devastating COVID-19 pandemic, has been reported to affect platelets and cause increased thrombotic events, hinting at the possible bidirectional interactions between platelets and the virus. In this review, we discuss the potential mechanisms underlying the increased thrombotic events as well as altered platelet count and activity in COVID-19. Inspired by existing knowledge on platelet–pathogen interactions, we propose several potential antiviral strategies that platelets might undertake to combat SARS-CoV-2, including their abilities to internalize the virus, release bioactive molecules to interfere with viral infection, and modulate the functions of immune cells. Moreover, we discuss current and potential platelet-targeted therapeutic strategies in controlling COVID-19, including antiplatelet drugs, anticoagulants, and inflammation-targeting treatments. These strategies have shown promise in clinical settings to alleviate the severity of thrombo-inflammatory complications and reduce the mortality rate among COVID-19 patients. In conclusion, an in-depth understanding of platelet–SARS-CoV-2 interactions may uncover novel mechanisms underlying severe COVID-19 complications and could provide new therapeutic avenues for managing this disease

Visual Analysis of Set Relations in a Graph

Many applications can be modeled as a graph with additional attributes attached to the nodes. For example, a graph can be used to model the relationship of people in a social media website or a bibliographical dataset. Meanwhile, additional information is often available, such as the topics people are interested in and the music they listen to. Based on this additional information, different set relationships may exist among people. Revealing the set relationships in a network can help people gain social insight and better understand their roles within a community. In this paper, we present a visualization system for exploring set relations in a graph. Our system is designed to reveal three different relationships simultaneously: the social relationship of people, the set relationship among people's items of interest, and the similarity relationship of the items. We propose two novel visualization designs: a) a glyph-based visualization to reveal people's set relationships in the context of their social networks; b) an integration of visual links and a contour map to show people and their items of interest which are clustered into different groups. The effectiveness of the designs has been demonstrated by the case studies on two representative datasets including one from a social music service website and another from an academic collaboration network. Categories and Subject Descriptors (according to ACM CCS): H. 5.2 [Information Interfaces and Presentations]: User Interfaces-Graphical user interfaces (GUI