Friendship prediction model based on factor graphs integrating geographical location

With the development of network services and location-based systems,many mobile applications begin to use users’geographical location to provide better services.In terms of social networks,geographical location is actively shared by users.In some applications with recommendation services,before the geographical location recommendation is provided,the authors have to obtain user’s permission.This kind of social network integrated with geographical location information is called location-based social networks(abbreviate for LBSNs).In the LBSN,each user has location information when he or she checked in hotels or feature spots.Based on this information,they can identify user’s trajectory of movement behaviour and activity patterns.In general,if there is friendship between two users,their trajectories in reality are likely to be similar.In this study,according to user’s geographical location information over a period of time,they explore whether there exists friendly relationship between two users based on trajectory similarity and the structure theory of graphs.In particular,they propose a new factor function and a factor graph model based on user’s geographical location to predict the friendship between two users in the real LBSN.

Survey on vehicle map matching techniques

With the development of location‐based services and Big data technology,vehicle map matching techniques are growing rapidly,which is the fundamental techniques in the study of exploring global positioning system(GPS)data.The pre‐processed GPS data can provide the guarantee of high‐quality data for the research of mining passenger’s points of interest and urban computing services.The existing surveys mainly focus on map‐matching algorithms,but there are few descriptions on the key phases of the acquisition of sampling data,floating car and road data preprocessing in vehicle map matching systems.To address these limitations,the contribution of this survey on map matching techniques lies in the following aspects:(i)the background knowledge,function and system framework of vehicle map matching techniques;(ii)description of floating car data and road network structure to understand the detailed phase of map matching;(iii)data preprocessing rules,specific methodologies,and significance of floating car and road data;(iv)map matching algorithms are classified by the sampling frequency and data information.The authors give the introduction of open‐source GPS sampling data sets,and the evaluation measurements of map‐matching approaches;(v)the suggestions on data preprocessing and map matching algorithms in the future work.

城乡融合背景下相对贫困问题的表现、原因及其治理

相对于以往解决农业、农村和农民问题的有关制度安排,乡村振兴战略最大的特点之一就是将乡村振兴战略置于城乡融合架构中推进,并以新型城市化战略来引领。乡村振兴战略的实施将会给相对贫困治理带来新的机遇与挑战。在城乡融合发展背景下,相对贫困治理应考虑城乡人口资源要素聚合所带来的新变化。城乡相对贫困的表征变化,城乡致贫因素、所面临的生计风险、贫困脆弱性同质性增强,迫切需要党和政府构建相对贫困治理体系,完善相对贫困治理政策,以实现对城乡相对贫困问题的精准治理。

Sn and Y co-doped BaCo_(0.6)Fe_(0.4)O_(3)-δcathodes with enhanced oxygen reduction activity and CO_(2) tolerance for solid oxide fuel cells

Applying mixed oxygen ionic and electronic conducting(MIEC)oxides as the cathode offers a promis-ing solution to enhance the performance of solid oxide fuel cells(SOFCs).However,the phase instability in CO_(2)-containing air and sluggish oxygen reduction activity of MIEC cathodes remain a long-term chal-lenge for optimizing the electrochemical performance of SOFCs.Herein,a heterovalent co-doping strategy is proposed to enhance the oxygen reduction activity and CO_(2)tolerance of SOFCs cathodes,which can be demonstrated by developing a novel BaCo_(0.6)Fe_(0.4)O_(3)-δ(BCF)-based MIEC oxide,BaCo_(0.6)Fe_(0.2)Sn_(0.1) Y_(0.1)O_(3-δ)(BCFSY).In addition to improving the stability of BCF-based perovskites,this strategy achieves an opti-mized balance of ionic mobility and oxygen vacancies due to the synergies between the effects of the co-dopants.Compared with single-doped materials,BCFSY exhibits improved CO_(2)tolerance and consider-ably higher ORR activity,which is reflected in a significantly lower polarization resistance of 0.15Ωcm^(2) at 600℃.The results of this work provide an efficient tactic for designing electrode materials for SOFCs.

Neoproterozoic Diamictite of the Luoquan Formation from the North China Block and Their Implications

Glacial diamictite may provide important information on paleoenvironment and average composition of the upper continental crust(UCC).In this study,we report sedimentary facies,petrological and geochemical characteristics of Neoproterozoic diamictite from a profile of the Luoquan Formation on the southern margin of the North China Block(NCB).Upwards the sampling profile,lithostratigraphic strata vary from massive diamictite with poorly sorted carbonate gravels to laminated diamictite with small gravels of terrestrial detrital materials.Along the profile,CaO-MgO-LOI-Sr values decrease with the increase of SiO_(2)-Al2O_(3)-K2O contents.All these petrological and geochemical variations indicate a change from lodgement till deposition in the proximal of ice sheet to ice-rafting deposition in glacial-marine environment with less dolomite to supply their source.Together with previous studies on diamictite from other outcrops on the NCB,the deposition of Luoquan diamictite reflects that the glaciation on the NCB vanished and the ice-rafting effect weakened with glacial transgression process.In addition,significant co-variations of various elements with La and Al2O_(3)confirm the significant conservation of most analyzed elements during the sedimentary processes to produce diamictite.

多颗微小卫星接管失效航天器姿态运动的微分博弈学习控制

对空间中由于燃料耗尽或执行机构故障而失效的航天器进行姿态接管控制能够实现其上高价值有效载荷的再利用,多个微小卫星通过协同,能够以较低的成本对其实施姿态接管控制.微分博弈研究了多个体的最优决策问题,其中各个体通过局部性能指标函数的优化获得控制策略,并实现博弈事先设定的全局目标.本文将失效航天器的姿态接管控制问题转换为多微小卫星的微分博弈问题进行研究.首先建立了多微小卫星的微分博弈模型,设计了微小卫星的性能指标函数,实现了对微小卫星微分博弈问题的数学描述.之后给出了微小卫星微分博弈的HJ (Hamilton-Jacobi)方程,并通过基于单神经网络的策略迭代算法进行了耦合HJ方程解的学习,实现了对微小卫星博弈均衡策略的逼近.最后进行了微小卫星微分博弈学习控制方法有效性的数值仿真验证,仿真结果表明,微小卫星通过学习到的近似博弈均衡控制策略,实现了失效航天器姿态接管控制的全局目标.

Cardinality Estimator:Processing SQL with a Vertical Scanning Convolutional Neural Network

Although the popular database systems perform well on query optimization,they still face poor query execution plans when the join operations across multiple tables are complex.Bad execution planning usually results in bad cardinality estimations.The cardinality estimation models in traditional databases cannot provide high-quality estimation,because they are not capable of capturing the correlation between multiple tables in an effective fashion.Recently,the state-of-the-art learning-based cardinality estimation is estimated to work better than the traditional empirical methods.Basically,they used deep neural networks to compute the relationships and correlations of tables.In this paper,we propose a vertical scanning convolutional neural network(abbreviated as VSCNN)to capture the relationships between words in the word vector in order to generate a feature map.The proposed learning-based cardinality estimator converts Structured Query Language(SQL)queries from a sentence to a word vector and we encode table names in the one-hot encoding method and the samples into bitmaps,separately,and then merge them to obtain enough semantic information from data samples.In particular,the feature map obtained by VSCNN contains semantic information including tables,joins,and predicates about SQL queries.Importantly,in order to improve the accuracy of cardinality estimation,we propose the negative sampling method for training the word vector by gradient descent from the base table and compress it into a bitmap.Extensive experiments are conducted and the results show that the estimation quality of q-error of the proposed vertical scanning convolutional neural network based model is reduced by at least 14.6%when compared with the estimators in traditional databases.