Study on Development Strategies of Chinese Library Based on Ubiquitous Knowledge Environment

Ubiquitous knowledge environment with five salient features is transferring from concept to reality. The library based on ubiquitous knowledge environment not only faces the rare development opportunities with three levels, but also encounters unprecedented challenges and crises. Building a system with characteristics of high-quality mass knowledge resources, creating a personalized intelligent service system and establishing human values, achieving transfer from the information services to knowledge services, and constructing a highly collaborative library alliance are the only ways for the healthy development of Chinese libraries under ubiquitous knowledge environment

CpG oligonucleotides suppress HepG2 cells-induced Jurkat cell apoptosis via the Fas-FasL-mediated pathway

<p>Abstract</p> <p>Objective</p> <p>To explore the potential role of CpG motif-containing oligonucleotides (CpG-ODN) in modulating the expression of FasL in HepG2 and Fas in Jurkat cells <it>in vitro</it>, and to examine the effect of CpG-ODN treatment on the HepG2 cells-mediated Jurkat cell apoptosis <it>in vitro</it>.</p> <p>Methods</p> <p>The expressions of FasL in HepG2 and Fas in Jurkat cells were examined by real time PCR and flow cytometry (FCM). HepG2 and Jurkat cells were co-cultured, and the frequency of apoptotic Jurkat cells and levels of activated caspase-3 were determined by FCM.</p> <p>Results</p> <p>Treatment with CpG-ODN down-regulated the expression of FasL in HepG2 cells in a dose- and time-dependent manner. In addition, treatment with CpG-ODN down-regulated the Fas mRNA transcription and protein expression in Jurkat cells. Treatment of HepG2 cells or Jurkat cells with FasL-neutralizing antibody NOK-2 remarkably inhibited the HepG2-medaited Jurkat cell apoptosis. Pre-treatment of HepG2 or Jurkat cells with CpG-ODN significantly reduced the frequency of HepG2-mediated apoptotic Jurkat cells and inhibited the activation of caspase-3 in Jurkat cells <it>in vitro</it>.</p> <p>Conclusions</p> <p>Our data indicated that treatment with CpG-ODN inhibited the HepG2 cells-mediated Jurkat cell apoptosis by modulating the Fas/FasL pathway. Apparently, CpG-ODN treatment may be a potential therapeutic reagent for HCC.</p

Mathematical Model of Helical Gear Topography Measurements and Tooth Flank Errors Separation

During large-size gear topological modification by form grinding, the helical gear tooth surface geometrical shape will be complex and it is difficult for the traditional scanning measurement to characterize the whole tooth surface. Therefore, in order to characterize the actual tooth surfaces, an on-machine topography measurement approach is proposed for topological modification helical gears on the five-axis CNC gear form grinding machine that can measure the modified gear tooth deviations on the machine immediately after grinding. Combined with gear form grinding kinematics principles, the mathematical model of topography measurements is established based on the polar coordinate method. The mathematical models include calculating trajectory of the centre of measuring probe, defining gear flanks by grid of points, and solving coordinate values of topology measurement. Finally, a numerical example of on-machine topography measurement is presented. By establishing the topography diagram and the contour map of tooth error, the tooth surface modification amount and the tooth flank errors are separated, respectively. Research results can serve as foundation for topological modification and tooth surface errors closed-loop feedback correction

Meta-augmented Prompt Tuning for Better Few-shot Learning

Prompt tuning is a parameter-efficient method, which freezes all PLM parameters and only prepends some additional tunable tokens called soft prompts to the input text. However, soft prompts heavily rely on a better initialization and may easily result in overfitting under few-shot settings, which causes prompt-tuning performing much worse than fine-tuning. To address the above issues, this paper proposes a novel Self-sUpervised Meta-prompt learning framework with MEtagradient Regularization for few shot generalization (SUMMER). We leverage self-supervised meta-learning to better initialize soft prompts and curriculum-based task augmentation is further proposed to enrich the meta-task distribution. Besides, a novel meta-gradient regularization method is integrated into the meta-prompt learning framework, which meta-learns to transform the raw gradient during few-shot learning into a domain-generalizable direction, thus alleviating the problem of overfitting. Extensive experiments show that SUMMER achieves better performance for different few-shot downstream tasks, and also exhibits a stronger domain generalization ability

Cross-Lingual Low-Resource Set-to-Description Retrieval for Global E-Commerce

With the prosperous of cross-border e-commerce, there is an urgent demand for designing intelligent approaches for assisting e-commerce sellers to offer local products for consumers from all over the world. In this paper, we explore a new task of cross-lingual information retrieval, i.e., cross-lingual set-to-description retrieval in cross-border e-commerce, which involves matching product attribute sets in the source language with persuasive product descriptions in the target language. We manually collect a new and high-quality paired dataset, where each pair contains an unordered product attribute set in the source language and an informative product description in the target language. As the dataset construction process is both time-consuming and costly, the new dataset only comprises of 13.5k pairs, which is a low-resource setting and can be viewed as a challenging testbed for model development and evaluation in cross-border e-commerce. To tackle this cross-lingual set-to-description retrieval task, we propose a novel cross-lingual matching network (CLMN) with the enhancement of context-dependent cross-lingual mapping upon the pre-trained monolingual BERT representations. Experimental results indicate that our proposed CLMN yields impressive results on the challenging task and the context-dependent cross-lingual mapping on BERT yields noticeable improvement over the pre-trained multi-lingual BERT model.Comment: AAAI 202