Exploiting global information in complex network repair processes

Robustness of complex networks has been studied for decades,with a particular focus on network attack.Research on network repair,on the other hand,has been conducted only very lately,given the even higher complexity and absence of an effective evaluation metric.A recently proposed network repair strategy is self-healing,which aims to repair networks for larger components at a low cost only with local information.In this paper,we discuss the effectiveness and efficiency of self-healing,which limits network repair to be a multi-objective optimization problem and makes it difficult to measure its optimality.This leads us to a new network repair evaluation metric.Since the time complexity of the computation is very high,we devise a greedy ranking strategy.Evaluations on both real-world and random networks show the effectiveness of our new metric and repair strategy.Our study contributes to optimal network repair algorithms and provides a gold standard for future studies on network repair.

Pre-Train and Learn: Preserving Global Information for Graph Neural Networks

Graph neural networks(GNNs)have shown great power in learning on graphs.However,it is still a challenge for GNNs to model information faraway from the source node.The ability to preserve global information can enhance graph representation and hence improve classification precision.In the paper,we propose a new learning framework named G-GNN(Global information for GNN)to address the challenge.First,the global structure and global attribute features of each node are obtained via unsupervised pre-training,and those global features preserve the global information associated with the node.Then,using the pre-trained global features and the raw attributes of the graph,a set of parallel kernel GNNs is used to learn different aspects from these heterogeneous features.Any general GNN can be used as a kernal and easily obtain the ability of preserving global information,without having to alter their own algorithms.Extensive experiments have shown that state-of-the-art models,e.g.,GCN,GAT,Graphsage and APPNP,can achieve improvement with G-GNN on three standard evaluation datasets.Specially,we establish new benchmark precision records on Cora(84.31%)and Pubmed(80.95%)when learning on attributed graphs.

Global Geology information for contributors

The journal of Global Geology(English Edition) sponsored by the International Center of Geoscience Research and Education in Northeast Asia,Jilin University of China(ISSN 1673-9736,CN22-1371/P).The former name of the journal was the Journal of International Geoscientific Research in Northeast Asia,which was started in 1998 and served the scientists and teachers in geosciences in the world.The Editorial Board is composed of 42 scientists from various countries in the world,including China,German,Japan,Korea,Russia,the UK and USA.

Global Geology information for contributors

The journal of Global Geology (English Edition) sponsored by the International Center of Geoscience Research and Education in Northeast Asia, Jilin University of China(ISSN 1673-9736 CN22-1371/P). The former name of the journal was the Journal of International Geoscientific Research in Northeast Asia, which was started in 1998 and served the scientists and teachers in geosciences in the world. The Editorial Board is composed of 42 scientists from various countries in the world including China, German, Japan, Korea, Russia, the UK and USA. The journal was biannually in publication from 1998 to 2007. It is changed quarterly in publication since 2008. We hope that the readers worldwide will be more interested in and continue to support our journal by papers contributed to the journal. Form of manuscriptManuscripts should be concise and consistent in style, spelling, and the use of abbreviations. Authors should submit their manuscripts in English. The title page should include: the title, the names of the authors and their affiliations. If possible, please use the "Times New Roman" font. References In the text, reference to the literature cited should list author's name, year of publication, title, source, volume (issue) and page.

Self-supervised recalibration network for person re-identification

The attention mechanism can extract salient features in images,which has been proved to be effective in improving the performance of person re-identification(Re-ID).However,most of the existing attention modules have the following two shortcomings:On the one hand,they mostly use global average pooling to generate context descriptors,without highlighting the guiding role of salient information on descriptor generation,resulting in insufficient ability of the final generated attention mask representation;On the other hand,the design of most attention modules is complicated,which greatly increases the computational cost of the model.To solve these problems,this paper proposes an attention module called self-supervised recalibration(SR)block,which introduces both global and local information through adaptive weighted fusion to generate a more refined attention mask.In particular,a special"Squeeze-Excitation"(SE)unit is designed in the SR block to further process the generated intermediate masks,both for nonlinearizations of the features and for constraint of the resulting computation by controlling the number of channels.Furthermore,we combine the most commonly used Res Net-50 to construct the instantiation model of the SR block,and verify its effectiveness on multiple Re-ID datasets,especially the mean Average Precision(m AP)on the Occluded-Duke dataset exceeds the state-of-the-art(SOTA)algorithm by 4.49%.

Monocular Depth Estimation with Sharp Boundary

Monocular depth estimation is the basic task in computer vision.Its accuracy has tremendous improvement in the decade with the development of deep learning.However,the blurry boundary in the depth map is a serious problem.Researchers find that the blurry boundary is mainly caused by two factors.First,the low-level features,containing boundary and structure information,may be lost in deep networks during the convolution process.Second,themodel ignores the errors introduced by the boundary area due to the few portions of the boundary area in the whole area,during the backpropagation.Focusing on the factors mentioned above.Two countermeasures are proposed to mitigate the boundary blur problem.Firstly,we design a scene understanding module and scale transformmodule to build a lightweight fuse feature pyramid,which can deal with low-level feature loss effectively.Secondly,we propose a boundary-aware depth loss function to pay attention to the effects of the boundary’s depth value.Extensive experiments show that our method can predict the depth maps with clearer boundaries,and the performance of the depth accuracy based on NYU-Depth V2,SUN RGB-D,and iBims-1 are competitive.

信息网络视角下的全球城市绿色发展竞合模式研究

在信息网络化推动下,城市绿色发展出现了新的发展特点,全球城市间的竞争与合作也面临新变化。本研究基于“技术-治理-文化”的分析框架,结合信息网络的技术特征,阐释了全球城市绿色发展领域的竞合新模式,并比较了上海与东京绿色发展的信息化现状、政策战略和竞合模式,分析了全球城市在信息网络化推动下竞合特征及其趋势。研究发现,对比于东京的信息化绿色发展,上海市通过对内强化投入技术创新研究、对外联动长三角地区城市和毗邻国家的信息化绿色发展,实现绿色化与全球化双赢。全球城市在信息化促进下,绿色发展领域的竞合模式从传统的“竞合状态”向未来“和合状态”演进,有助于践行“人类命运共同体”理念下的全球生态文明建设倡议。

从Google Maps看我国全球化地理信息服务面临的挑战和对策

以Google Maps为代表的地图服务的推出预示着全球化地理信息服务的开始。IT领域竞争市场的形成、高分辨率卫星影像的应用及Web地理信息服务开发平台的实现是全球化地理信息服务的主要趋势。面对全球化地理信息服务发展机遇,我国面临地理数据库建设、地理信息市场社会化与地理信息区域均衡发展的严峻挑战。在分析我国地理信息服务面临挑战的基础上,从数字化建设、地理信息服务市场培育、卫星遥感技术商业化发展3方面提出我国地理信息服务发展的对策与建议。

新型工业化道路的特征分析

新型工业化道路是具有时代烙印的中国特色的工业化道路。立足于经济全球化和知识经济带来的发展机遇,抓住信息化与工业化发展过程重叠的时机,以信息化促进工业化是核心;新型工业化的过程是城镇化发展的进程,新型工业化成功的关键是大量转移和吸收农村剩余劳动力;新型工业化要实现资源消耗低、环境污染少、人力资源优势得到充分发挥、经济效益好的目标,必须以科技进步和科技创新为发展动力。

Towards No-Reference Image Quality Assessment Based on Multi-Scale Convolutional Neural Network

Image quality assessment has become increasingly important in image quality monitoring and reliability assuring of image processing systems.Most of the existing no-reference image quality assessment methods mainly exploit the global information of image while ignoring vital local information.Actually,the introduced distortion depends on a slight difference in details between the distorted image and the non-distorted reference image.In light of this,we propose a no-reference image quality assessment method based on a multi-scale convolutional neural network,which integrates both global information and local information of an image.We first adopt the image pyramid method to generate four scale images required for network input and then provide two network models by respectively using two fusion strategies to evaluate image quality.In order to better adapt to the quality assessment of the entire image,we use two different loss functions in the training and validation phases.The superiority of the proposed method is verified by several different experiments on the LIVE datasets and TID2008 datasets.

A Novel Attention-based Global and Local Information Fusion Neural Network for Group Recommendation

Due to the popularity of group activities in social media,group recommendation becomes increasingly significant.It aims to pursue a list of preferred items for a target group.Most deep learning-based methods on group recommendation have focused on learning group representations from single interaction between groups and users.However,these methods may suffer from data sparsity problem.Except for the interaction between groups and users,there also exist other interactions that may enrich group representation,such as the interaction between groups and items.Such interactions,which take place in the range of a group,form a local view of a certain group.In addition to local information,groups with common interests may also show similar tastes on items.Therefore,group representation can be conducted according to the similarity among groups,which forms a global view of a certain group.In this paper,we propose a novel global and local information fusion neural network(GLIF)model for group recommendation.In GLIF,an attentive neural network(ANN)activates rich interactions among groups,users and items with respect to forming a group′s local representation.Moreover,our model also leverages ANN to obtain a group′s global representation based on the similarity among different groups.Then,it fuses global and local representations based on attention mechanism to form a group′s comprehensive representation.Finally,group recommendation is conducted under neural collaborative filtering(NCF)framework.Extensive experiments on three public datasets demonstrate its superiority over the state-of-the-art methods for group recommendation.

新世纪全球化和信息化背景下市场营销专业人才培养模式的探讨

新世纪人类进入全球化和信息化时代,新的时代背景带来市场营销实践和理论的创新,相应的市场营销专业人才培养模式也必须与时俱进、不断创新。本文分析了高等农业院校市场营销专业人才培养存在的问题,并从专业定位、人才培养目标、课程设置、实践环节、教学手段等几个方面提出新的见解。

论社会信息化与经济全球化的互动影响

社会信息化和经济全球化是人类社会目前正在持续的两种浪潮。它们互有积极作用和消极影响 ,在一种统一而又矛盾的关系中共同推动着人类社会不断进步。

经济全球化和信息化对可持续发展的影响

经济全球化和信息化通过优化资源配置、提高资源利用效益、转变经济增长方式 ,为可持续发展提供了必要的条件 ;经济全球化和信息化标志着可持续发展进入了以人为本的时代 ;为确保“十五”期间 ,乃至今后更长时期内的可持续发展 ,要抓住经济全球化和信息化机遇 ,切实重视科技创新 ,形成经济发展和生态环境保护的良性循环。

社会的变迁与学校的回应

社会正在发生转型性变化,这些变化主要包括市场经济与全球化的崛起、电脑的普及以及信息化的浪潮和后现代思维的冲击。社会的转型迫使学校教育作出回应,以保证学校的毕业生不被社会拒绝。

入世后外语人才的素质及培养对策

入世后的中国需要大批高素质复合型外语人才 ,这是高等教育面临的重大课题。在这一新形势下 ,本文论述了新时期外语人才的基本素质构成 ,并积极探讨了适应时代发展、培养该类人才的主要对策。

造船现代化的研究

概要叙述了第1次造船现代化的发展历程和理论体系,并进一步地从造船轻量化、绿色化、全球化、信息化方面阐述了向总段巨型化、异地并行、无缝整合生产模式转化的第2次造船现代化理论与体系的未来进程。

经济全球化与企业管理信息化

经济全球化影响着每一个国家,同时对企业传统管理模式产生巨大的冲击。信息技术的迅猛发展促使企业不得不作出回应;面对我国企业信息化的现状,建立以信息化为基础的企业管理新模式以迎接经济全球化与信息化的挑战已迫在眉睫。

新世纪的两大发展趋势——全球化与信息化(续前)

本文主要讨论 2 1世纪的两大发展趋势——全球化与信息化。在全球化的分析中主要说明其出现迹象、发生原因及经济全球化呼唤全球政府 ,全球化的结果将是共产主义的胜利。在信息化的分析中说明了什么是信息化、信息化的内涵要素及信息化对经济和社会的影响。

新世纪中国经济的发展特征与产业竞争战略

This paper analyses the process of China’s melodizing the economic globalization, and points out that company with the imformation industry and knowlage economy development, nowadays global industries are being restructured under the guidance of translational corporations,and the new supplying chains systems is forming, and China is facing the choices of joining in the systems.