Quantum correlation enhanced bound of the information exclusion principle

We investigate the information exclusion principle for multiple measurements with assistance of multiple quantum memories that are well bounded by the upper and lower bounds.The lower bound depends on the observables'complementarity and the complementarity of uncertainty whilst the upper bound includes the complementarity of the observables,quantum discord,and quantum condition entropy.In quantum measurement processing,there exists a relationship between the complementarity of uncertainty and the complementarity of information.In addition,based on the information exclusion principle the complementarity of uncertainty and the shareability of quantum discord can exist as an essential factor to enhance the bounds of each other in the presence of quantum memory.

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.

Gate Feature Interaction Network for Relation Prediction in Knowledge Graph

Recently,many knowledge graph embedding models for knowledge graph completion have been proposed,ranging from the initial translation-based model such as TransE to recent CNN-based models such as ConvE.These models fill in the missing relations between entities by focusing on capturing the representation features to further complete the existing knowledge graph(KG).However,the above KG-based relation prediction research ignores the interaction information among entities in KG.To solve this problem,this work proposes a novel model called Gate Feature Interaction Network(GFINet)with a weighted loss function that takes the benefit of interaction information and deep expressive features together.Specifically,the proposed GFINet consists of a gate convolution block and an interaction attention module,corresponding to catching deep expressive features and interaction information based on these valid features respectively.Our method establishes state-of-the-art experimental results on the standard datasets for knowledge graph completion.In addition,we make ablation experiments to verify the effectiveness of the gate convolution block and the interaction attention module.

Grey information coverage interaction relational decision making and its application

This study aims to reflect the information coverage grey number and the interaction between attributes in grey relational decision making. Therefore, a multi-attribute decision method based on the grey information coverage interaction relational degree(GIRD) is proposed. Firstly, this paper defines the information coverage grey number, and establishes the GIRD model by using the Choquet fuzzy integral and grey relational principle. It proves that the proposed model not only is the general and unified form of the point relational degree, interval relational degree, mixed relational degree and grey fuzzy integral relational degree, but also can effectively deal with the interaction between attributes. Further,a decision making example of evaluating the industrial operation quality for 14 cities in Hunan province of China is provided to highlight the implementation, availability, and feasibility of the proposed decision model.

Solution to the Mind-Body Relation Problem: Information

In this paper it is analyzed from the informational perspective the relation between mind and body, an ancientphilosophic issue defined as a problem, which still did not receive up to date an adequate solution. Byintroducing/using the concept of information, it is shown that this concept includes two facets, one of themreferring to the common communications and another one referring to a hidden/structuring matter-relatedinformation, effectively acting in the human body and in the living systems, which determines the dynamicinter-change of information between specific structures of the organism by electric/electronic/chemical agents andgenetic/epigenetic processes. It is shown that the maintenance of body, permanently and obligatory depending onthe external matter (foods, air, water) resources, needed to provide both the structuring/restructuring basic materialand energy, determines the necessary existence of an info-managing system, administrating the internalmechano-chemical/physical processes. As a natural consequence, such a system should organize and assure ownsurvival by an effective informational operability to detect the external food resources, to select the appropriateinterest information and to decide as a function of circumstances. One important component in such aninformational system is memory, allowing to dispose of the reference informational data for analysis/comparisonand the selection between good and bad binary possible decisions. The memory receives and stores thereforesignals from external reality and from the body itself, referring to the emotional reaction, digestion status, creation,and inherited predilections, within specific info-neural communication circuits between the brain and bodyexecution/sensitive organs, the human body appearing as an integrated info-matter self-managed dynamic system.The specific body components memorize information with different degrees of info-integration: short/long-termintegration, emotive/action reaction, info-abilities, culminating with the integration in the chromosomal structuresby epigenetic processes. The new acquired information is transgenerationally transmissible, and is manifested asnew traits, showing the adaptation capability of the human and close relation between mind and body. Analyzingthe results of such a mind-body informational model in comparison with the earlier assumed/proposed/assertedarchaic, Greek and Occidental philosophies, which represent only partial aspects of this relation, it is shown thatthis informational model, elaborated in terms of information on the basis of scientific reasons and arguments,constitutes a general, realist, and coherent model of the mind-body relation, able to integrate and/or explain most ofthe others.

The Relation of Department Components and Activity in Spatial Information Infrastructure

Spatial Information Infrastructure (SII) facilitates the sharing, interoperability and integration of geographical information among department components of a region or a country. The SII is developed and shared by different department components. The relation of department components is a fundament for collaborating tasks and information exchange in SII. There are two kinds of department components, one is the provider which produces geospatial data of SII, other is the consumer which uses geospatial data of SII. The consumer includes two kinds of user, one is the user which only uses and does not produce geospatial data in SII, other is the special user which not only uses but also produces geospatial data in SII. The provider includes different hierarchies corresponding to different kinds of geospatial data in SII. All providers in the hierarchies form provider actor set PA. All consumers also form consumer actor set CA. The sender recipient relation SR and SRI are defined on Cartesian product of PA and CA. Five tasks and information flow in SII demonstrate the geospatial data acquisition, information production, management, and application among department components. The tasks and their sub tasks are subdivided activities. Each activity corresponds to the relation SR or RSI. The activity along with the relation of provider and consumer forms a scheme of geographical information exchange between department components.

On Granularity in Information Systems Based on Binary Relation

In this paper, some important issues of granularity are discussed mainly in information systems (ISs) based on binary relation. Firstly, the vector representation method of knowledge granules is proposed in an infor-mation system based on binary relation to eliminate limitations of set representation method. Secondly, operators among knowledge granularity are introduced and some important properties of them are studied carefully. Thirdly, distance between two knowledge granules is established and granular space is constructed based on it. Fourthly, axiomatic definition of knowledge granularity is investigated, and one can find that some existed knowledge granularities are special cases under the definition. In addition, as an application of knowledge granular space, an example is employed to validate some results in our work.

Information Interpretation of Heisenberg Uncertainty Relation

In this paper the following information interpretation of uncertainty relation is proposed: if one bit of information was extracted from the system as a result of the measurement process, then the measurement itself adds an additional uncertainty (chaos) into the system equaled to one bit. This formulation is developed by calculating of the Shannon information entropy for the classical N-slit interference experiment. This approach allows looking differently at several quantum phenomena. Particularly, the information interpretation is used for explanation of entangled photons diffraction picture compression.

The NGO Relations Section of the UN Department of Public Information

History The importance of working with and through NGOs as an integral part of United Nations information activities was recognized when the Department of Public Information was

The Profound Revelation of Human Social Relations and the Profound Realization of Information Right of Citizens——The social significance of network technique advancement in the perspective of “Entity Cube”

The R＆D group of relationship searching in Microsoft Asia Research Institute explains ＂Entity Cube＂ launched in 2008 as follows：

Two-Layer Information Granulation:Mapping-Equivalence Neighborhood Rough Set and Its Attribute Reduction

Attribute reduction,as one of the essential applications of the rough set,has attracted extensive attention from scholars.Information granulation is a key step of attribute reduction,and its efficiency has a significant impact on the overall efficiency of attribute reduction.The information granulation of the existing neighborhood rough set models is usually a single layer,and the construction of each information granule needs to search all the samples in the universe,which is inefficient.To fill such gap,a new neighborhood rough set model is proposed,which aims to improve the efficiency of attribute reduction by means of two-layer information granulation.The first layer of information granulation constructs a mapping-equivalence relation that divides the universe into multiple mutually independent mapping-equivalence classes.The second layer of information granulation views each mapping-equivalence class as a sub-universe and then performs neighborhood informa-tion granulation.A model named mapping-equivalence neighborhood rough set model is derived from the strategy of two-layer information granulation.Experimental results show that compared with other neighborhood rough set models,this model can effectively improve the efficiency of attribute reduction and reduce the uncertainty of the system.The strategy provides a new thinking for the exploration of neighborhood rough set models and the study of attribute reduction acceleration problems.

A new measure for evaluating spatially related properties of traffic information credibility

With the wide applications of sensor network technology in traffic information acquisition systems,a new measure will be quite necessary to evaluate spatially related properties of traffic information credibility.The heterogeneity of spatial distribution of information credibility from sensor networks is analyzed and a new measure,information credibility function(ICF),is proposed to describe this heterogeneity.Three possible functional forms of sensor ICF and their corresponding expressions are presented.Then,two feasible operations of spatial superposition of sensor ICFs are discussed.Finally,a numerical example is introduced to show the calibration method of sensor ICF and obtain the spatially related properties of expressway in Beijing.The results show that the sensor ICF of expressway in Beijing possesses a negative exponent property.The traffic information is more abundant at or near the locations of sensor,while with the distance away from the sensor increasing,the traffic information credibility will be declined by an exponential trend.The new measure provides theoretical bases for the optimal locations of traffic sensor networks and the mechanism research of spatial distribution of traffic information credibility.

Mutual Information and Relative Entropy of Sequential Effect Algebras

In this paper,we introduce and investigate the mutual information and relative entropy on the sequentialeffect algebra,we also give a comparison of these mutual information and relative entropy with the classical ones by thevenn diagrams.Finally,a nice example shows that the entropies of sequential effect algebra depend extremely on theorder of its sequential product.

Spatial and Dynamic Analysis of Regional Sustainable Development Using Geographic Information System and Relative Carrying Capacity of Resources

Relative carrying capacity of resources is an index to measure sustainable development through carrying capacity. Case studies of eleven cities in Zhejiang (Hangzhou, Ningbo, Wenzhou, Jiaxing, Huzhou, Shaoxing, Jinhua, Quzhou, Zhoushan, Taizhou and Lishui) illustrated regional sustainable development approach. In this study, to provide insight into spatial and dynamic analysis of region sustainable development, we calculated the relative carrying capacity of land resources and economical resources and synthetical carrying capacity of resources in different cities in Zhejiang, and geographic information system was carried out. The results showed that all cities but Hangzhou and Ningbo were ecologically sustainable, and relative carrying capacity of land resources in northern and eastern Zhejiang was larger than those in southern and western Zhejiang. The sampling years of Wenzhou, Hangzhou and Ningbo contribution rates of land resource to synthetic carrying capacity were grouped into three stages, and there were two milestones trends and changes in 1996 and 2004, respectively. This study demonstrated that geographic information system and relative carrying capacity of resources are effective for assessment of region sustainable development, and provide policy guidelines for decision-making.

Parametric message passing-based relative navigation in joint tactical information distribution system

Relative navigation is a key feature in the joint tactical information distribution system（JTIDS）.A parametric message passing algorithm based on factor graph is proposed to perform relative navigation in JTIDS.First of all,the joint posterior distribution of all the terminals＇ positions is represented by factor graph.Because of the nonlinearity between the positions and time-of-arrival（TOA） measurement,messages cannot be obtained in closed forms by directly using the sum-product algorithm on factor graph.To this end,the Euclidean norm is approximated by Taylor expansion.Then,all the messages on the factor graph can be derived in Gaussian forms,which enables the terminals to transmit means and covariances.Finally,the impact of major error sources on the navigation performance are evaluated by Monte Carlo simulations,e.g.,range measurement noise,priors of position uncertainty and velocity noise.Results show that the proposed algorithm outperforms the extended Kalman filter and cooperative extended Kalman filter in both static and mobile scenarios of the JTIDS.

Tighter sum uncertainty relations via(α,β,γ)weighted Wigner–Yanase–Dyson skew information

We establish tighter uncertainty relations for arbitrary finite observables via(α,β,γ)weighted Wigner–Yanase–Dyson((α,β,γ)WWYD)skew information.The results are also applicable to the(α,γ)weighted Wigner–Yanase–Dyson((α,γ)WWYD)skew information and the weighted Wigner–Yanase–Dyson(WWYD)skew information.We also present tighter lower bounds for quantum channels and unitary channels via(α,β,γ)modified weighted Wigner–Yanase–Dyson((α,β,γ)MWWYD)skew information.Detailed examples are provided to illustrate the tightness of our uncertainty relations.

Using Fuzzy Relations and GIS Method to Evaluate Debris Flow Hazard

The study area, located in the southeast of Tibet along the Sichuan-Tibet highway, is a part of Palongzangbu River basin where mountain hazards take place frequently. On the ground of field surveying, historical data and previous research, a total of 31 debris flow gullies are identified in the study area and 5 factors are chosen as main parameters for evaluating the hazard of debris flows in this study. Spatial analyst functions of geographic information system （GIS） are utilized to produce debris flow inventory and parameter maps. All data are built into a spatial database for evaluating debris flow hazard. Integrated with GIS techniques,the fuzzy relation method is used to calculate the strength of relationship between debris flow inventory and parameters of the database. With this methodology,a hazard map of debris flows is produced. According to this map,6.6% of the study area is classified as very high hazard, 7.3% as high hazard,8.4% as moderate hazard,32. 1% as low hazard and 45.6% as very low hazard or non-hazard areas. After validating the results, this methodology is ultimately confirmed to be available.

Comprehensive security risk factor identification for small reservoirs with heterogeneous data based on grey relational analysis model

Identification of security risk factors for small reservoirs is the basis for implementation of early warning systems.The manner of identification of the factors for small reservoirs is of practical significance when data are incomplete.The existing grey relational models have some disadvantages in measuring the correlation between categorical data sequences.To this end,this paper introduces a new grey relational model to analyze heterogeneous data.In this study,a set of security risk factors for small reservoirs was first constructed based on theoretical analysis,and heterogeneous data of these factors were recorded as sequences.The sequences were regarded as random variables,and the information entropy and conditional entropy between sequences were measured to analyze the relational degree between risk factors.Then,a new grey relational analysis model for heterogeneous data was constructed,and a comprehensive security risk factor identification method was developed.A case study of small reservoirs in Guangxi Zhuang Autonomous Region in China shows that the model constructed in this study is applicable to security risk factor identification for small reservoirs with heterogeneous and sparse data.

Event temporal relation computation based on machine learning

Temporal relation computation is one of the tasks of the extraction of temporal arguments from event, and it is also the ultimate goal of temporal information processing. However, temporal relation computation based on machine learning requires a lot of hand-marked work, and exploring more features from discourse. A method of two-stage machine learning based on temporal relation computation （TSMLTRC） is proposed in this paper for the shortcomings of current temporal relation computation between two events. The first stage is to get the main temporal attributes of event based on classification learning. The second stage is to compute the event temporal relation in the discourse through employing the result of the first stage as the basic features, and also employing some new linguistic characteristics. Experiments show that, compared with the artificial golden rule, the computational efficiency in the first stage is much higher, and the F1-Score of event temporal relation which is computed through combining multi-features may be increased at 85.8% in the second stage.