Koizumi's Yasukuni Visits,China-Japan Relations And Japan's International Image

Early on the morning of August 15, 2006, which was the 61st anniversary of Japan’s surrender in the Second World War, Junichiro Koizumi dressed himself up in a formal tuxedo jacket with coattails and paid his sixth and also the last visit to the Yasukuni Shrine in his official capacity as Japan’s prime minister. Koizumi’s deliberate action taken amidst the strong protests from people both in and outside Japan has

Reflections on the Return of the China-Japan Relationship to the Normal Track

The China-Japan relationship has swung between deterioration and improvement in the 21st century while Japanese leaders'cognition and Japanese public opinion remain operative at a deep level.The return of the bilateral relationship in 2018 to the normal development track was aided by Japan's shift toward coordination,China's re-shaped policy,and a boost by the Trump administration.Though the China-Japan relationship will continue improving in the short run,it is unlikely“deterioratiomimprovcment”cycles will end since too many glaring structural contradictions exist.While memories of their intense history and today's territorial issues linger,the fluctuation of Japan*s diplomatic options and weak Japanese public opinion for improved relations with China will continue the same cyclical,but not forward,momentum.

CHINA-JAPAN RELATIONS AFTER PRIME MINISTER ABE'S VISIT TO CHINA

2018 marked the 40th anniversary of the signing of China:Japan Peace land Friendship Treaty and alsd the 4oth anniversary of China's reform and opening-up.China-Japan relations entered a new stage,a major mark of which was the resumption of exchange of official leadership visits between the two countries that had been interrupted for years.Following Chinese Premier Li Keqiang's successful visit to Japan in May 2018.

China-Japan Security Relations and China＇s Countermeasures

Security relations are crucial in China-Japan relations. Since the"nationalization"of the Diaoyu Islands by Japan in 2012, mutual trust between China and Japan has been declining, and bilateral security relations have reached a low point. China-Japan security relations are typified by ongoing dispute over the Diaoyu Islands, lack of mutual trust exacerbated by various actions by Japan, and the delay of China-Japan security dialogues. The evolution of China-Japan security relations derived from the Diaoyu Islands dispute is clearly beyond the scope of the dispute itself, with deep-rooted internal and external factors. In the near future,China-Japan security relations will face more challenges than opportunities. China should take measures, including enhancing mutual trust in politics and security between the two countries, to impose necessary pressure on Japan to change course, to win over neighboring countries so as to avoid pretext for Japan's intervention in regional issues,and to strengthen interpersonal communication and understanding between people of the two countries in order to stabilize security relations.

The Middle East in China-Japan Relations: Shared Interests, Competition, and Rivalry Spillover

Since the early twenty-first century,interaction between East Asia and the Middle East has grown considerably.An expected,though understudied,consequence of this development is a gradual spillover of the intra-East Asian political process to the Middle East.Attempting to enhance the understanding of this understudied aspect of East-West Asia relations,as well as new aspects of the China-Japan dynamic,this article explores the competition and possible expansion of the Sino-Japanese rivalry into the Middle East.To examine more concretely we focus on two areas of activities that exceed China’s and Japan’s traditional interests in the Middle East:the utilisation of Middle East events by Japan to loosen its half-century restrictions on the use of military force,and China’s respective response;and Japan’s reaction to China’s Belt and Road Initiative.

Tunable dispersion relations manipulated by strain in skyrmion-based magnonic crystals

We theoretically investigate the propagation characteristics of spin waves in skyrmion-based magnonic crystals. It is found that the dispersion relation can be manipulated by strains through magneto-elastic coupling. Especially, the allowed bands and forbidden bands in dispersion relations shift to higher frequency with strain changing from compressive to tensile,while shifting to lower frequency with strain changing from tensile to compressive. We also confirm that the spin wave with specific frequency can pass the magnonic crystal or be blocked by tuning the strains. The result provides an advanced platform for studying the tunable skyrmion-based spin wave devices.

Understanding the Relationship Between Shrinking Cities and Land Prices:Spatial Pattern,Effectiveness,and Policy Implications

Urban shrinkage has emerged as a widespread phenomenon globally and has a significant impact on land,particularly in terms of land use and price.This study focuses on 2851 county-level cities in China in 2005–2018(excluding Hong Kong,Macao,Taiwan,and‘no data’areas in Qinhai-Tibet Plateau)as the fundamental units of analysis.By employing nighttime light(NTL)data to identify shrinking cities,the propensity score matching(PSM)model was used to quantitatively examine the impact of shrinking cities on land prices,and evaluate the magnitude of this influence.The findings demonstrate the following:1)there were 613 shrinking cities in China,with moderate shrinkage being the most prevalent and severe shrinkage being the least.2)Regional disparities are evident in the spatial distribution of shrinking cities,especially in areas with diverse terrain.3)The spatial pattern of land price exhibits a significant correlated to the economic and administrative levels.4)Shrinking cities significantly negatively impact on the overall land price(ATT=–0.1241,P<0.05).However,the extent of the effect varies significantly among different spatial regions.This study contributes novel insights into the investigation of land prices and shrinking cities,ultimately serving as a foundation for government efforts to promote the sustainable development of urban areas.

Attention-relation network for mobile phone screen defect classification via a few samples

How to use a few defect samples to complete the defect classification is a key challenge in the production of mobile phone screens.An attention-relation network for the mobile phone screen defect classification is proposed in this paper.The architecture of the attention-relation network contains two modules:a feature extract module and a feature metric module.Different from other few-shot models,an attention mechanism is applied to metric learning in our model to measure the distance between features,so as to pay attention to the correlation between features and suppress unwanted information.Besides,we combine dilated convolution and skip connection to extract more feature information for follow-up processing.We validate attention-relation network on the mobile phone screen defect dataset.The experimental results show that the classification accuracy of the attentionrelation network is 0.9486 under the 5-way 1-shot training strategy and 0.9039 under the 5-way 5-shot setting.It achieves the excellent effect of classification for mobile phone screen defects and outperforms with dominant advantages.

A Joint Entity Relation Extraction Model Based on Relation Semantic Template Automatically Constructed

The joint entity relation extraction model which integrates the semantic information of relation is favored by relevant researchers because of its effectiveness in solving the overlapping of entities,and the method of defining the semantic template of relation manually is particularly prominent in the extraction effect because it can obtain the deep semantic information of relation.However,this method has some problems,such as relying on expert experience and poor portability.Inspired by the rule-based entity relation extraction method,this paper proposes a joint entity relation extraction model based on a relation semantic template automatically constructed,which is abbreviated as RSTAC.This model refines the extraction rules of relation semantic templates from relation corpus through dependency parsing and realizes the automatic construction of relation semantic templates.Based on the relation semantic template,the process of relation classification and triplet extraction is constrained,and finally,the entity relation triplet is obtained.The experimental results on the three major Chinese datasets of DuIE,SanWen,and FinRE showthat the RSTAC model successfully obtains rich deep semantics of relation,improves the extraction effect of entity relation triples,and the F1 scores are increased by an average of 0.96% compared with classical joint extraction models such as CasRel,TPLinker,and RFBFN.

Generating Sets of the Complete Semigroup of Binary Relations Defined by Semilattices of the Class Σ8(X, 5)

In this article, we study generating sets of the complete semigroups of binary relations defined by X-semilattices of unions of the class Σ8(X, 5). Found uniquely irreducible generating set for the given semigroups and when X is finite set formulas for calculating the number of elements in generating sets are derived.

Precise Determination of the Bottom-Quark On-Shell Mass Using Its Four-Loop Relation to the MS-Scheme Running Mass

We explore the properties of the bottom-quark on-shell mass(M_(b))by using its relation to the MS mass(m_(b)).At present,this MS-on-shell relation has been known up to four-loop QCD corrections,which however still has a~2%scale uncertainty by taking the renormalization scale as m_(b)(m_(b))and varying it within the usual range of [m_(b)(m_(b))/2,2m_(b)(m_(b))].

Dispersion Relations in Diffraction in Time

In agreement with Titchmarsh’s theorem, we prove that dispersion relations are just the Fourier-transform of the identity, g(x′)=±Sgn(x′)g(x′), which defines the property of being a truncated functions at the origin. On the other hand, we prove that the wave-function of a generalized diffraction in time problem is just the Fourier-transform of a truncated function. Consequently, the existence of dispersion relations for the diffraction in time wave-function follows. We derive these explicit dispersion relations.

The Impact of Adolescent and Young Adult Cancer-AJA-on Family Functioning.Study of Family Relationships in Terms of Adaptability and Cohesion

Objectives:The arrival of cancer in adolescents and young adults(aged 15 to 24 years)-Adolescents and young adults(AJA)-corresponds to a fragile period during which the adulthood of the young person and the evolution of family ties mobilize the family as a whole.Therefore,cancer,beyond its individual traumatic dimension,affects the whole family,which can modify family ties and family functioning.Our objective is to evaluate family functioning from the complex model evaluating cohesion and adaptability when an adolescent or young adult has cancer.Methods:Adolescents and young adults with cancer(n=41),mothers(n=41),and fathers(n=13)participated in this study.They completed the Family Adaptation and Cohesion Scales(FACES Ⅲ)questionnaire.Family functioning when an aya is ill has been compared to that of families without any disease.Results:a comparison of the mean scores of perceived cohesion and adaptability of face Ⅲ indicates no significant difference for cohesion.In contrast,the averages of the adaptability scores of our sample with those of the general population indicate that families with cancer hais generally feel more“adaptable”than the non-clinical population.These results are statistically significant for AJA,but also for mothers and fathers.Regarding the mean scores of ideal cohesion and ideal adaptability,there are no significant differences between fathers in our sample and fathers in the general population.In contrast,mothers in our sample had less ideal adaptability than those in the general population.In aya patients with cancer,the scores of both adaptability and cohesion were significantly different from those of non-diseased adolescents.Conclusion:Cancer leads to many changes in family relationships,making it difficult to empower the young patient and latent the evolution of the relationship.

Relationship of Ambient Humidity with Cardiovascular Diseases:A Prospective Study of 24,510 Adults in a General Population

Objective This study aimed to explore the association between humidity exposure and the risk of cardiovascular disease(CVD),utilizing follow-up data and relative humidity(RH)metric assessments.Methods We extracted the baseline data from the China Hypertension Survey(CHS)of 24,510 enrolled participants aged≥35 years without a history of CVD between 2012 and 2015 and followed them up from 2018 to 2019.The National Meteorological Information Center(NMIC)of the China Meteorological Administration(CMA)provided the quality-controlled relative humidity(RH)datasets.Cox proportional hazards models were used to estimate hazard ratios(HRs)for CVD in relation to RH.Results During the follow-up period(2018-2019),973 patients with CVD were identified.The HR of CVD risk was 1.17(95%CI:1.04-1.31)per 10%increase in summer mean RH.Compared with participants in the 3rd quintile group,those in the 1st and 5th quintiles of RH had a higher risk of CVD.For summer mean RH,the HRs(95%CIs)for the 1st and 5th quintiles were 1.34(1.04-1.71)and 1.44(1.14-1.83),respectively.The relationship(“U”shape)between summer mean RH and the risk of CVD was nonlinear.Stratified analyses indicated that the risk of CVD was substantially influenced by the summer mean RH in female,older individuals,and those in southern China.Conclusion Unsuitable(too high or low)humidity environments affect the risk of CVD.Our study highlights those future policies for adapting to climate change should consider the humidity-CVD relationship.

Case Retrieval Strategy of Turning Process Based on Grey Relational Analysis

To solve the problem of long response time when users obtain suitable cutting parameters through the Internet based platform,a case-based reasoning framework is proposed.Specifically,a Hamming distance and Euclidean distance combined method is designed to measure the similarity of case features which have both numeric and category properties.In addition,AHP(Analytic Hierarchy Process)and entropy weight method are integrated to provide features weight,where both user preferences and comprehensive impact of the index have been concerned.Grey relation analysis is used to obtain the similarity of a new problem and alternative cases.Finally,a platform is also developed on Visual Studio 2015,and a case study is demonstrated to verify the practicality and efficiency of the proposed method.This method can obtain cutting parameters which is suitable without iterative calculation.Compared with the traditional PSO(Particle swarm optimization algorithm)and GA(Genetic algorithm),it can obtain faster response speed.This method can provide ideas for selecting processing parameters in industrial production.While guaranteeing the characteristic information is similar,this approach can select processing parameters which is the most appropriate for the production process and a lot of time can be saved.

Enhancing Data Forwarding Efficiency in SIoT with Multidimensional Social Relations

Effective data communication is a crucial aspect of the Social Internet of Things(SIoT)and continues to be a significant research focus.This paper proposes a data forwarding algorithm based on Multidimensional Social Relations(MSRR)in SIoT to solve this problem.The proposed algorithm separates message forwarding into intra-and cross-community forwarding by analyzing interest traits and social connections among nodes.Three new metrics are defined:the intensity of node social relationships,node activity,and community connectivity.Within the community,messages are sent by determining which node is most similar to the sender by weighing the strength of social connections and node activity.When a node performs cross-community forwarding,the message is forwarded to the most reasonable relay community by measuring the node activity and the connection between communities.The proposed algorithm was compared to three existing routing algorithms in simulation experiments.Results indicate that the proposed algorithmsubstantially improves message delivery efficiency while lessening network overhead and enhancing connectivity and coordination in the SIoT context.

Defining the We-map Reference Frames and Providing Mathematical Expressions for Transformation Relations

We-map is an interactive mobile map that can be easily communicated and applied on personal electronic devices,such as personal computers and mobile phones.Therefore,the study of direction systems and coordinate systems is critical,and exploring reference frames is essential in direction and coordinate systems.Despite its significance,existing research on We-map lacks specific solutions for the exploration of reference frames is indispensable for the establishment of accurate direction and coordinate systems.In this paper,we endeavor to address this gap by elucidating the significance of We-map reference frames,defining them with mathematical constraints,summarizing their nature and characteristics,deriving their transformation relationships and representing them through mathematical formulars and equations.Our work contributes to the fundamental theory of We-map and provides valuable systems and support for the mathematical foundation of We-map,map production,and platform development.Ultimately,this research serves to advance the development of We-map.

Relational Turkish Text Classification Using Distant Supervised Entities and Relations

Text classification,by automatically categorizing texts,is one of the foundational elements of natural language processing applications.This study investigates how text classification performance can be improved through the integration of entity-relation information obtained from the Wikidata(Wikipedia database)database and BERTbased pre-trained Named Entity Recognition(NER)models.Focusing on a significant challenge in the field of natural language processing(NLP),the research evaluates the potential of using entity and relational information to extract deeper meaning from texts.The adopted methodology encompasses a comprehensive approach that includes text preprocessing,entity detection,and the integration of relational information.Experiments conducted on text datasets in both Turkish and English assess the performance of various classification algorithms,such as Support Vector Machine,Logistic Regression,Deep Neural Network,and Convolutional Neural Network.The results indicate that the integration of entity-relation information can significantly enhance algorithmperformance in text classification tasks and offer new perspectives for information extraction and semantic analysis in NLP applications.Contributions of this work include the utilization of distant supervised entity-relation information in Turkish text classification,the development of a Turkish relational text classification approach,and the creation of a relational database.By demonstrating potential performance improvements through the integration of distant supervised entity-relation information into Turkish text classification,this research aims to support the effectiveness of text-based artificial intelligence(AI)tools.Additionally,it makes significant contributions to the development ofmultilingual text classification systems by adding deeper meaning to text content,thereby providing a valuable addition to current NLP studies and setting an important reference point for future research.

A Graph with Adaptive AdjacencyMatrix for Relation Extraction

The relation is a semantic expression relevant to two named entities in a sentence.Since a sentence usually contains several named entities,it is essential to learn a structured sentence representation that encodes dependency information specific to the two named entities.In related work,graph convolutional neural networks are widely adopted to learn semantic dependencies,where a dependency tree initializes the adjacency matrix.However,this approach has two main issues.First,parsing a sentence heavily relies on external toolkits,which can be errorprone.Second,the dependency tree only encodes the syntactical structure of a sentence,which may not align with the relational semantic expression.In this paper,we propose an automatic graph learningmethod to autonomously learn a sentence’s structural information.Instead of using a fixed adjacency matrix initialized by a dependency tree,we introduce an Adaptive Adjacency Matrix to encode the semantic dependency between tokens.The elements of thismatrix are dynamically learned during the training process and optimized by task-relevant learning objectives,enabling the construction of task-relevant semantic dependencies within a sentence.Our model demonstrates superior performance on the TACRED and SemEval 2010 datasets,surpassing previous works by 1.3%and 0.8%,respectively.These experimental results show that our model excels in the relation extraction task,outperforming prior models.

Graph Convolutional Networks Embedding Textual Structure Information for Relation Extraction

Deep neural network-based relational extraction research has made significant progress in recent years,andit provides data support for many natural language processing downstream tasks such as building knowledgegraph,sentiment analysis and question-answering systems.However,previous studies ignored much unusedstructural information in sentences that could enhance the performance of the relation extraction task.Moreover,most existing dependency-based models utilize self-attention to distinguish the importance of context,whichhardly deals withmultiple-structure information.To efficiently leverage multiple structure information,this paperproposes a dynamic structure attention mechanism model based on textual structure information,which deeplyintegrates word embedding,named entity recognition labels,part of speech,dependency tree and dependency typeinto a graph convolutional network.Specifically,our model extracts text features of different structures from theinput sentence.Textual Structure information Graph Convolutional Networks employs the dynamic structureattention mechanism to learn multi-structure attention,effectively distinguishing important contextual features invarious structural information.In addition,multi-structure weights are carefully designed as amergingmechanismin the different structure attention to dynamically adjust the final attention.This paper combines these featuresand trains a graph convolutional network for relation extraction.We experiment on supervised relation extractiondatasets including SemEval 2010 Task 8,TACRED,TACREV,and Re-TACED,the result significantly outperformsthe previous.