Parkinson’s disease and affective disorder: The temporal relationship

In relationship between the affective disorders and Parkinson’s disease (PD) it was found that comorbidity was higher than expected in the majority of the studies. Patients with PD are at increased risk of developing depression and, conversely, recent studies have shown that patients with depressive disorders have increased risk of incident PD. However, the temporal associations between the disorders are not fully elucidated. From this review it could be learned that the temporal aspects strongly suggest that a neurobiological association exists between affective disorder and PD. This is illustrated with hitherto unpublished data. Some of these issues may be investigated in case register studies, e.g. by linkage of registers of somatic and psychiatric illness, and suggestions for future research are given. For GP’s, psychiatrists, geriatricians, and neurologists these new findings will lead to a better understanding and better treatment for patients with complicated comorbid conditions. Here timing is important!

Exploring and visualizing temporal relations in multivariate time series

This paper introduces an approach to analyzing multivariate time series(MVTS)data through progressive temporal abstraction of the data into patterns characterizing the behavior of the studied dynamic phenomenon.The paper focuses on two core challenges:identifying basic behavior patterns of individual attributes and examining the temporal relations between these patterns across the range of attributes to derive higher-level abstractions of multi-attribute behavior.The proposed approach combines existing methods for univariate pattern extraction,computation of temporal relations according to the Allen’s time interval algebra,visual displays of the temporal relations,and interactive query operations into a cohesive visual analytics workflow.The paper describes the application of the approach to real-world examples of population mobility data during the COVID-19 pandemic and characteristics of episodes in a football match,illustrating its versatility and effectiveness in understanding composite patterns of interrelated attribute behaviors in MVTS data.

A Relation Matrix Approach to Labelling Temporal Relations in Scheduling

In this paper,we present a relation matrix description of temporal relations.Based on this modela new algorithm for labelling temporal relations is proposed.Under certain conditions the algorithm iscompleted and has a polynomial complexity.In general cases it is still an efficient algorithm comparedto some known algorithms.

Using Syntactic-Based Kernels for Classifying Temporal Relations

Temporal relation classification is one of contemporary demanding tasks of natural language processing. This task can be used in various applications such as question answering, summarization, and language specific information retrieval. In this paper, we propose an improved algorithm for classifying temporal relations, between events or between events and time, using support vector machines （SVM）. Along with gold-standard corpus features, the proposed method aims at exploiting some useful automatically generated syntactic features to improve the accuracy of classification. Accordingly, a number of novel kernel functions are introduced and evaluated. Our evaluations clearly demonstrate that adding syntactic features results in a considerable improvement over the state-of-the-art method of classifying temporal relations.

Nondeterministic Temporal Relations in Multimedia Data

Synchronization is an important issue in multimedia systems which integrate a variety of temporally related media objects. One part of synchronization is the representation of temporal information. With the emerging illteractive multimedia, deterministic temporal models are replaced by nondeterministic ones with more expressiveness. This paper classifies temporal models by their expressiveness, and evaluates relevant nondeterministic temporal relations in multimedia data. Additionally, an intervalbased nondeterndnistic model based on a complete temporal operator set is proposed providing highlevel abstractions and a high degree of expressiveness for interactive multimedia systems.

Processing of Uncertainty Temporal Relations

A kind of classification on temporal relations of propositions is presented.By introducing temporal approaching relation, a new temporal logic based ontime-point and time-interval is proposed, which can describe uncertain temporalrelations. Finally some properties of temporal proposition under.uncertainrelations are proposed.

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.

Research of storage technique for C-Temporal Relation Data Model

As there is datum redundancy in tradition database and temporal database in existence and the quantities of temporal database are increasing fleetly. We put forward compress storage tactics for temporal datum which combine compress technology in existence in order to settle datum redundancy in the course of temporal datum storage and temporal datum of slow acting domain and momentary acting domain are accessed by using each from independence clock method and mutual clock method .We also bring forward strategy of gridding storage to resolve the problems of temporal datum rising rapidly.

Fuzz-TIME: an intelligent system for managing fuzzy temporal information

Purpose-Time modeling is a crucial feature in many application domains.However,temporal information often is not crisp,but is subjective and fuzzy.The purpose of this paper is to address the issue related to the modeling and handling of imperfection inherent to both temporal relations and intervals.Design/methodology/approach-On the one hand,fuzzy extensions of Allen temporal relations are investigated and,on the other hand,extended temporal relations to define the positions of two fuzzy time intervals are introduced.Then,a database system,called Fuzzy Temporal Information Management and Exploitation(Fuzz-TIME),is developed for the purpose of processing fuzzy temporal queries.Findings-To evaluate the proposal,the authors have implemented a Fuzz-TIME system and created a fuzzy historical database for the querying purpose.Some demonstrative scenarios from history domain are proposed and discussed.Research limitations/implications-The authors have conducted some experiments on archaeological data to show the effectiveness of the Fuzz-TIME system.However,thorough experiments on large-scale databases are highly desirable to show the behavior of the tool with respect to the performance and time execution criteria.Practical implications-The tool developed(Fuzz-TIME)can have many practical applications where time information has to be dealt with.In particular,in several real-world applications like history,medicine,criminal and financial domains,where time is often perceived or expressed in an imprecise/fuzzy manner.Social implications-The social implications of this work can be expected,more particularly,in two domains:in the museum to manage,exploit and analysis the piece of information related to archives and historic data;and in the hospitals/medical organizations to deal with time information inherent to data about patients and diseases.Originality/value-This paper presents the design and characterization of a novel and intelligent database system to process and manage the imperfection inherent to both temporal relations and intervals.

Study and application of temporal index technology

This paper addresses the mathematical relation on a set of periods and temporal indexing construc- tions as well as their applications.First we introduce two concepts, i.e.the temporal connection and temporal inclusion, which are equivalence relation and preorder relation respectively.Second, by study- ing some basic topics such as the division of ＂large＂ equivalence classes and the overlaps of preorder relational sets, we propose a temporal data index model （TDIM） with a tree-structure consisting of a root node, equivalence class nodes and linearly ordered branch nodes.Third, we study algorithms for the temporal querying and incremental updating as well as dynamical management within the framework of TDIM.Based on a proper mathematical supporting, TDIM can be applied to researching some significant practical cases such as temporal relational and temporal XML data and so on.

Event Temporal Relation Extraction with Attention Mechanism and Graph Neural Network

Event temporal relation extraction is an important part of natural language processing.Many models are being used in this task with the development of deep learning.However,most of the existing methods cannot accurately obtain the degree of association between different tokens and events,and event-related information cannot be effectively integrated.In this paper,we propose an event information integration model that integrates event information through multilayer bidirectional long short-term memory(Bi-LSTM)and attention mechanism.Although the above scheme can improve the extraction performance,it can still be further optimized.To further improve the performance of the previous scheme,we propose a novel relational graph attention network that incorporates edge attributes.In this approach,we first build a semantic dependency graph through dependency parsing,model a semantic graph that considers the edges’attributes by using top-k attention mechanisms to learn hidden semantic contextual representations,and finally predict event temporal relations.We evaluate proposed models on the TimeBank-Dense dataset.Compared to previous baselines,the Micro-F1 scores obtained by our models improve by 3.9%and 14.5%,respectively.

Knowledge Representation and Reasoning for Complex Time Expression in Clinical Text

Temporal information is pervasive and crucial in medical records and other clinical text,as it formulates the development process of medical conditions and is vital for clinical decision making.However,providing a holistic knowledge representation and reasoning framework for various time expressions in the clinical text is challenging.In order to capture complex temporal semantics in clinical text,we propose a novel Clinical Time Ontology(CTO)as an extension from OWL framework.More specifically,we identified eight timerelated problems in clinical text and created 11 core temporal classes to conceptualize the fuzzy time,cyclic time,irregular time,negations and other complex aspects of clinical time.Then,we extended Allen’s and TEO’s temporal relations and defined the relation concept description between complex and simple time.Simultaneously,we provided a formulaic and graphical presentation of complex time and complex time relationships.We carried out empirical study on the expressiveness and usability of CTO using real-world healthcare datasets.Finally,experiment results demonstrate that CTO could faithfully represent and reason over 93%of the temporal expressions,and it can cover a wider range of time-related classes in clinical domain.

A formal model of fair exchange protocols

Based on the study of existing fair exchange protocols, this paper sets up an accurate formal model by stepwise refinement. In the process of refinement an unreliable channel is employed to simulate an attack behavior. The model provides a novel formal definition of exchanged items, and presents the formal goals for fairness, accountability, etc., reflecting the inherent requirements for fair exchange protocols across-the-board. In order to check, prove, and design fair exchange protocols effectively and efficiently, the model puts forward a novel property of abuse-freeness which applies to all fair exchange protocols, gives a formal definition for trust strand of the third party, and presents general criteria of designing a secure and effective fair exchange protocol. Taking a typical fair exchange protocol as an example, this paper presents the analysis steps of fair exchange protocols appealing to our model. An unknown attack is uncovered. The analysis reveals the process of a complete attack, discovering deeper reasons for causing an attack. Finally, we modify the flawed protocol and the revised protocol ensures the desirable properties.

O2iJoin： An Efficient Index-Based Algorithm for Overlap Interval Join

Time intervals are often associated with tuples to represent their valid time in temporal relations, where overlap join is crucial for various kinds of queries. Many existing overlap join algorithms use indices based on tree structures such as quad-tree, B＋-tree and interval tree. These algorithms usually have high CPU cost since deep path traversals are unavoidable, which makes them not so competitive as data-partition or plane-sweep based algorithms. This paper proposes an efficient overlap join algorithm based on a new two-layer flat index named as Overlap Interval Inverted Index （i.e., O2i Index）. It uses an array to record the end points of intervals and approximates the nesting structures of intervals via two functions in the first layer, and the second layer uses inverted lists to trace all intervals satisfying the approximated nesting structures. With the help of the new index, the join algorithm only visits the must-be-scanned lists and skips all others. Analyses and experiments on both real and synthetic datasets show that the proposed algorithm is as competitive as the state-of-the-art algorithms.