Composition law and Nodal genus-2 curves in P^2

Recently, there has been great interest in the application of composition laws to problems in enumerative geometry. Using the moduli space of stable maps, we compute the number of irreducible, reduced, nodal, degree-$d$ genus-$2$ plane curves whose normalization has a fixed complex structure and which pass through $3d - 2$ general points in $\Bbb P^2$.Comment: 13 pages, AMS-Te

On-Line Load Balancing with Task Buffer

On-line load balancing is one of the most important problems for applications with resource allocation. It aims to assign tasks to suitable machines and balance the load among all of the machines, where the tasks need to be assigned to a machine upon arrival. In practice, tasks are not always required to be assigned to machines immediately. In this paper, we propose a novel on-line load balancing model with task buffer, where the buffer can temporarily store tasks as many as possible. Three algorithms, namely LPTCP1_α, LPTCP2_α, and LPTCP3_β, are proposed based on the Longest Processing Time (LPT) algorithm and a variety of planarization algorithms. The planarization algorithms are proposed for reducing the difference among each element in a set. Experimental results show that our proposed algorithms can effectively solve the on-line load balancing problem and have good performance in large scale experiments

Intelligent Case Assignment Method Based on the Chain of Criminal Behavior Elements

The assignment of cases means the court assigns cases to specific judges. The traditional case assignment methods, based on the facts of a case, are weak in the analysis of semantic structure of the case not considering the judges\u27 expertise. By analyzing judges\u27 trial logic, we find that the order of criminal behaviors affects the final judgement. To solve these problems, we regard intelligent case assignment as a text-matching problem, and propose an intelligent case assignment method based on the chain of criminal behavior elements. This method introduces the chain of criminal behavior elements to enhance the structured semantic analysis of the case. We build a BCTA (Bert-Cnn-Transformer-Attention) model to achieve intelligent case assignment. This model integrates a judge\u27s expertise in the judge\u27s presentation, thus recommending the most compatible judge for the case. Comparing the traditional case assignment methods, our BCTA model obtains 84% absolutely considerable improvement under P@1. In addition, comparing other classic text matching models, our BCTA model achieves an absolute considerable improvement of 4% under P@1 and 9% under Macro F1. Experiments conducted on real-world data set demonstrate the superiority of our method

Multi-Label Classification Based on the Improved Probabilistic Neural Network

This paper aims to overcome the defects of the existing multi-label classification methods, such as the insufficient use of label correlation and class information. For this purpose, an improved probabilistic neural network for multi-label classification (ML-IPNN) was developed through the following steps. Firstly, the traditional PNN was structurally improved to fit in with multi-label data. Then secondly, a weight matrix was introduced to represent the label correlation and synthetize the information between classes, and the ML-IPNN was trained with the backpropagation mechanism. Finally, the classification results of the ML-IPNN on three common datasets were compared with those of the seven most popular multi-label classification algorithms. The results show that the ML-IPNN outperformed all contrastive algorithms. The research findings brought new light on multi-label classification and the application of artificial neural networks (ANNs)

Named Entity Recognition Based on Multi-scale Attention

The accuracy of named entity recognition (NER) task will promote the research of multiple downstream tasks in natural language field. Due to a large number of nested semantics in text, named entities are recognized difficultly. Recognizing nested semantics becomes a difficulty in natural language processing. Previous studies have single scale of extracting feature and under-utilization of the boundary information. They ignore many details under different scales and then lead to the situation of entity recognition error or omission. Aiming at the above problems, a multi-scale attention method for named entity recognition (MSA-NER) is proposed. Firstly, the BERT model is used to obtain representation vector containing context information, and then the BiLSTM network is used to strengthen the context representation of text. Secondly, the representation vectors are enumerated and concatenated to form span information matrix. The direction information is fused to obtain richer interactive information. Thirdly, multi-head attention is used to construct multiple subspaces. Two-dimensional convolution is used to optionally aggregate text information at different scales in each subspace, so as to implement multi-scale feature fusion in each attention layer. Finally, the fused matrix is used for span classification to identify named entities. Experimental results show that the [F1] score of the proposed method reaches 81.7% and 86.8% on GENIA and ACE2005 English datasets, respectively. The proposed method demonstrates better recognition performance compared with existing mainstream models

SUN: Exploring Intrinsic Uncertainties in Text-to-SQL Parsers

This paper aims to improve the performance of text-to-SQL parsing by exploring the intrinsic uncertainties in the neural network based approaches (called SUN). From the data uncertainty perspective, it is indisputable that a single SQL can be learned from multiple semantically-equivalent questions.Different from previous methods that are limited to one-to-one mapping, we propose a data uncertainty constraint to explore the underlying complementary semantic information among multiple semantically-equivalent questions (many-to-one) and learn the robust feature representations with reduced spurious associations. In this way, we can reduce the sensitivity of the learned representations and improve the robustness of the parser. From the model uncertainty perspective, there is often structural information (dependence) among the weights of neural networks. To improve the generalizability and stability of neural text-to-SQL parsers, we propose a model uncertainty constraint to refine the query representations by enforcing the output representations of different perturbed encoding networks to be consistent with each other. Extensive experiments on five benchmark datasets demonstrate that our method significantly outperforms strong competitors and achieves new state-of-the-art results. For reproducibility, we release our code and data at https://github.com/AlibabaResearch/DAMO-ConvAI/tree/main/sunsql.Comment: Accepted at COLING 202

Social Determinants of Community Health Services Utilization among the Users in China: A 4-Year Cross-Sectional Study

Background To identify social factors determining the frequency of community health service (CHS) utilization among CHS users in China. Methods Nationwide cross-sectional surveys were conducted in 2008, 2009, 2010, and 2011. A total of 86,116 CHS visitors selected from 35 cities were interviewed. Descriptive analysis and multinomial logistic regression analysis were employed to analyze characteristics of CHS users, frequency of CHS utilization, and the socio-demographic and socio-economic factors influencing frequency of CHS utilization. Results Female and senior CHS clients were more likely to make 3–5 and ≥6 CHS visits (as opposed to 1–2 visits) than male and young clients, respectively. CHS clients with higher education were less frequent users than individuals with primary education or less in 2008 and 2009; in later surveys, CHS clients with higher education were the more frequent users. The association between frequent CHS visits and family income has changed significantly between 2008 and 2011. In 2011, income status did not have a discernible effect on the likelihood of making ≥6 CHS visits, and it only had a slight effect on making 3–5 CHS visits. Conclusion CHS may play an important role in providing primary health care to meet the demands of vulnerable populations in China. Over time, individuals with higher education are increasingly likely to make frequent CHS visits than individuals with primary school education or below. The gap in frequency of CHS utilization among different economic income groups decreased from 2008 to 2011