基于禁忌搜索的晶体位置谱分割与校准方法

为满足不同规格晶体探测器对分割校准晶体条响应事件位置数据的需求，开发了一种以禁忌搜索算法为核心，结合UDP数据帧、二维高斯模型及光导折射率的位置信息分割校准方法。实验结果表明，本方法在溢出率为5%时，峰谷比可达1218，空间分辨率达17 mm，晶体条响应位置识别准确度高达99%，滤除了引发伪峰的噪声，避免了过分割问题。在面对多种规格晶体阵列的位置谱时，本方法能依照阵列规格将晶体条区域的响应自动校准编号并输出晶体位置查找表，实现响应事件的精确校准。     

From text to graph: a general transition-based AMR parsing using neural network

Neural Computing and Applications - Semantic understanding is an essential research issue for many applications, such as social network analysis, collective intelligence and content computing,...     

RLINK: Deep reinforcement learning for user identity linkage

World Wide Web - User identity linkage is a task of recognizing the identities of the same user across different social networks (SN). Previous works tackle this problem via estimating the pairwise...     

Random walk based rank aggregation to improving web search

In Web search, with the aid of related query recommendation, Web users can revise their initial queries in several serial rounds in pursuit of finding needed Web pages. In this paper, we address the Web search problem on aggregating search results of related queries to improve the retrieval quality. Given an initial query and the suggested related queries, our search system concurrently processes their search result lists from an existing search engine and then forms a single list aggregated by all the retrieved lists. We specifically propose a generic rank aggregation framework which consists of three steps. First we build a so-called Win/Loss graph of Web pages according to a competition rule, and then apply the random walk mechanism on the Win/Loss graph. Last we sort these Web pages by their ranks using a PageRank-like rank mechanism. The proposed framework considers not only the number of wins that an item won in competitions, but also the quality of its competitor items in calculating the ranking of Web page items. Experimental results show that our search system can clearly improve the retrieval quality in a parallel manner over the traditional search strategy that serially returns result lists. Moreover, we also provide empirical evidences as to demonstrate how different rank aggregation methods affect the retrieval quality.     

Toward breast cancer survivability prediction models through improving training space

Due to the difficulties of outlier and skewed data, the prediction of breast cancer survivability has presented many challenges in the field of data mining and pattern precognition, especially in medical research. To solve these problems, we have proposed a hybrid approach to generating higher quality data sets in the creation of improved breast cancer survival prediction models. This approach comprises two main steps: (1) utilization of an outlier filtering approach based on C-Support Vector Classification (C-SVC) to identify and eliminate outlier instances; and (2) application of an over-sampling approach using over-sampling with replacement to increase the number of instances in the minority class. In order to assess the capability and effectiveness of the proposed approach, several measurement methods including basic performance (e.g., accuracy, sensitivity, and specificity), Area Under the receiver operating characteristic Curve (AUC) and F-measure were utilized. Moreover, a 10-fold cross-validation method was used to reduce the bias and variance of the results of breast cancer survivability prediction models. Results have indicated that the proposed approach leads to improving the performance of breast cancer survivability prediction models by up to 28.34% due to the improved training data space.     

Recommendation using DMF-based fine tuning method

Recommender Systems (RS) have been comprehensively analyzed in the past decade, Matrix Factorization (MF)-based Collaborative Filtering (CF) method has been proved to be an useful model to improve the performance of recommendation. Factors that inferred from item rating patterns shows the vectors which are useful for MF to characterize both items and users. A recommendation can concluded from good correspondence between item and user factors. A basic MF model starts with an object function, which is consisted of the squared error between original training matrix and predicted matrix as well as the regularization term (regularization parameters). To learn the predicted matrix, recommender systems minimize the squared error which has been regularized. However, two important details have been ignored: (1) the predicted matrix will be more and more accuracy as the iterations carried out, then a fix value of regularization parameters may not be the most suitable choice. (2) the final distribution trend of ratings of predicted matrix is not similar with the original training matrix. Therefore, we propose a Dynamic-MF algorithm and fine tuning method which is quite general to overcome the mentioned detail problems. Some other information, such as social relations, etc, can be easily incorporated into this method (model). The experimental analysis on two large datasets demonstrates that our approaches outperform the basic MF-based method.     

镜面式与阻容式露点仪测试SF6气体微水时的对比及分析

通过现场的实例,说明镜面式露点仪及阻容式露点仪由于试验方法的不同,会在SF6气体存在某些杂质的情况下,使测得的露点值相差很大,从而导致由露点换算出来的SF6气体微水含最结果也相差很大.由此引发了对试验方法、监督指标、设备的安全运行等问题的讨论,并提出了一些建议.     

Mechanical and dielectric properties of functionalized boron nitride nanosheets/silicon nitride composites

Uniformly dispersed boron nitride nanosheets (BNNSs) reinforced silicon nitride (Si₃N₄) composites were prepared by surface modification assisted flocculation combined with SPS sintering. In order to improve the dispersibility of the BNNSs in the composites, the liquid phase stripped BNNSs are surface functionalized by a two-step covalently modification. The amino-modified BNNSs (NH₂-BNNSs) and Si₃N₄ powders have opposite surface potential, mixed evenly by electrostatic interaction during flocculation. The results showed that mechanical properties of Si₃N₄ composites were obviously enhanced by adding NH₂-BNNSs. The fracture toughness and bending strength of Si₃N₄ composites added 0.75 wt% NH₂-BNNSs were increased by 34% and 28%, respectively, compared with monolithic Si₃N₄. Toughening mechanisms are synergistic action of the torn, pull-out or bridging of BNNSs and crack deflection mechanisms with microstructural analyzes. The dielectric properties of the Si₃N₄ ceramics are also improved after the addition of NH₂-BNNSs.     

Exploration on efficient similar sentences extraction

Measuring the semantic similarity between sentences is an essential issue for many applications, such as text summarization, Web page retrieval, question-answer model, image extraction, and so forth. A few studies have explored on this issue by several techniques, e.g., knowledge-based strategies, corpus-based strategies, hybrid strategies, etc. Most of these studies focus on how to improve the effectiveness of the problem. In this paper, we address the efficiency issue, i.e., for a given sentence collection, how to efficiently discover the top-k semantic similar sentences to a query. The previous methods cannot handle the big data efficiently, i.e., applying such strategies directly is time consuming because every candidate sentence needs to be tested. In this paper, we propose efficient strategies to tackle such problem based on a general framework. The basic idea is that for each similarity, we build a corresponding index in the preprocessing. Traversing these indices in the querying process can avoid to test many candidates, so as to improve the efficiency. Moreover, an optimal aggregation algorithm is introduced to assemble these similarities. Our framework is general enough that many similarity metrics can be incorporated, as will be discussed in the paper. We conduct extensive experimental evaluation on three real datasets to evaluate the efficiency of our proposal. In addition, we illustrate the trade-off between the effectiveness and efficiency. The experimental results demonstrate that the performance of our proposal outperforms the state-of-the-art techniques on efficiency while keeping the same high precision as them.     

煤矿瓦斯地质特征及瓦斯类型评价

柳江盆地位于河北省秦皇岛市石门寨地区,其地质研究程度较高,煤炭开采历史悠久,区内出露地层丰富,构造复杂,燕山期岩浆活动强烈。矿区瓦斯分布规律的研究十分必要,总结了矿区瓦斯成分含量、各地质因素对瓦斯分布的影响,并对其进行了评价。