Empirical ResearchonTeaching KnowledgeSharingin University Townand Its Influential Factors

The implement of knowledge sharing in University Town facilitates to aggregate education resource and improve overall strength of University Town. According to factors and performance of teaching knowledge sharing in University Town, the model and theoretical hypothesis of teaching knowledge sharing in University Town are proposed. Questionnaire and structural equation model are used to empirically study teaching knowledge sharing model in University Town. The results indicate that three factors including the characteristics of knowledge, the cluster of University Town and the system and mechanism for University Town have a significant correlation with teaching knowledge sharing in University Town, while teaching knowledge sharing in University Town has a significant correlation with Knowledge Innovation, comprehensive strength and education quality of University Town. By analysis results, effective strategies are designed for knowledge sharing mechanism in University Town

Waiting but not Aging: Optimizing Information Freshness Under the Pull Model

The Age-of-Information is an important metric for investigating the timeliness performance in information-update systems. In this paper, we study the AoI minimization problem under a new Pull model with replication schemes, where a user proactively sends a replicated request to multiple servers to "pull" the information of interest. Interestingly, we find that under this new Pull model, replication schemes capture a novel tradeoff between different values of the AoI across the servers (due to the random updating processes) and different response times across the servers, which can be exploited to minimize the expected AoI at the user's side. Specifically, assuming Poisson updating process for the servers and exponentially distributed response time, we derive a closed-form formula for computing the expected AoI and obtain the optimal number of responses to wait for to minimize the expected AoI. Then, we extend our analysis to the setting where the user aims to maximize the AoI-based utility, which represents the user's satisfaction level with respect to freshness of the received information. Furthermore, we consider a more realistic scenario where the user has no prior knowledge of the system. In this case, we reformulate the utility maximization problem as a stochastic Multi-Armed Bandit problem with side observations and leverage a special linear structure of side observations to design learning algorithms with improved performance guarantees. Finally, we conduct extensive simulations to elucidate our theoretical results and compare the performance of different algorithms. Our findings reveal that under the Pull model, waiting does not necessarily lead to aging; waiting for more than one response can often significantly reduce the AoI and improve the AoI-based utility in most scenarios.Comment: 15 pages. arXiv admin note: substantial text overlap with arXiv:1704.0484

Experiments and simulations of hollow cylinders falling through quiescent liquids

Acknowledgment This work was supported by the National Natural Science Foundation of China (grant numbers: 22078191, 21978165, 22081340412 and 92156020).Peer reviewe

Meta-Learning Triplet Network with Adaptive Margins for Few-Shot Named Entity Recognition

Meta-learning methods have been widely used in few-shot named entity recognition (NER), especially prototype-based methods. However, the Other(O) class is difficult to be represented by a prototype vector because there are generally a large number of samples in the class that have miscellaneous semantics. To solve the problem, we propose MeTNet, which generates prototype vectors for entity types only but not O-class. We design an improved triplet network to map samples and prototype vectors into a low-dimensional space that is easier to be classified and propose an adaptive margin for each entity type. The margin plays as a radius and controls a region with adaptive size in the low-dimensional space. Based on the regions, we propose a new inference procedure to predict the label of a query instance. We conduct extensive experiments in both in-domain and cross-domain settings to show the superiority of MeTNet over other state-of-the-art methods. In particular, we release a Chinese few-shot NER dataset FEW-COMM extracted from a well-known e-commerce platform. To the best of our knowledge, this is the first Chinese few-shot NER dataset. All the datasets and codes are provided at https://github.com/hccngu/MeTNet

Use of redundant exclusion PCR to identify a novel Bacillus thuringiensis Cry8 toxin gene from pooled genomic DNA

With the aim of optimizing the cloning of novel genes from a genomic pool containing many previously identified, homologous, genes we designed a redundant exclusion PCR technique. In RE-PCR a pair of generic amplification primers are combined with additional primers that are designed to specifically bind to redundant, unwanted genes that are a subset of those copied by the amplification primers. During RE-PCR the specific primer blocks amplification of the full length redundant gene. Using this method we managed to clone a number of cry8 or cry9 toxin genes from a pool of Bacillus thuringiensis genomic DNA while excluding amplicons for cry9Da, cry9Ea and cry9Eb. The method proved very efficient at increasing the number of rare genes in the resulting library. One such rare, and novel, cry8-like gene was expressed and the encoded toxin was shown to be toxic to Anomola corpulenta

Tripping Friction Model for Multi-Stage Fracturing and Completion String in Horizontal Well

The structure of multi-stage fracturing completion string in horizontal well is complicated. The downhole tools such as packers and sliding sleeves whose dimensions are very close to the size of the borehole, and the completion string has strong stiffness as well. Thus, it leads to larger frictional restriction when running string. Based on the above reasons, it is essential to calculate the tripping capacity before the strings running into the well in case of sticking off. However, calculation errors of conventional string tripping models are relatively larger. This paper took the structure of multi-stage fracturing completion string into consideration, divided completion string by contact points between string and borehole to establish the stress and bending model of the string between two contact points, and established the tripping friction and hookload model for multi-stage fracturing completion string. An applied example of multi-stage fracturing horizontal well in Hong 90-1 block of Jilin Oil Field shows that the created model in the paper is more accurate. The accuracy of hookload while the string running in form curved section to bottom is 95.80%. The established model is more accurate and reliable. It can be used to estimate the tripping ability of the multi-stage fracturing completion string.Key words: Multistage fracturing; Tripping; Tripping friction; Mechanical mode