This paper presents a novel Retweet Model for online social networks.The objective is to model retweeting patterns based on historical data of user interactions, inherent topical similarity between tweets reaching current user and tweets of his top K friends, and the nature of interactions of a user with his neighbours in the social network. Point Processes have recently received significant at...

Analysis of spatio-temporal point patterns plays an important role in several disciplines, yet inference in these systems remains computationally challenging due to the high resolution modelling generally required by large data sets and the analytically intractable likelihood function. Here, we exploit the sparsity structure of a fully-discretised log-Gaussian Cox process model by using expecta...

Spatio-temporal point process models play a central role in the analysis of spatially distributed systems in several disciplines. Yet, scalable inference remains computationally challenging both due to the high resolution modelling generally required and the analytically intractable likelihood function. Here, we exploit the sparsity structure typical of (spatially) discretised log-Gaussian Cox ...

The present thesis addresses the role of structures in the human medial temporal lobe (MTL) in memory formation and retrieval. More specifically, guided by a detailed consideration of the anatomical connections both amongst the subregions of the MTL and between the MTL and regions of the neocortex, two principles of the functional organization of the MTL are explored: domain-specificity and pro...

Physics-based modeling techniques have been shown to be advanced methods for creating high quality surfaces for application in CAGD. In this paper we propose a physics-based Temporal Computational Object (TCO) as a modeling process that simulates a liquid membrane to generate organic freeform models. This process compliments existing solid and elastic physics based modeling processes by simulat...

(ABSTRACT) This dissertation investigates algorithmic techniques for temporal process discovery in many domains. Many different formalisms have been proposed for modeling temporal processes such as motifs, dynamic Bayesian networks and partial orders, but the direct inference of such models from data has been computationally intensive or even intractable. In this work, we propose the mining of ...

Computer-supported treatment planning is an important task to support the medical staff in their daily routine. Several (semi-)formal languages to represent the medical treatment processes are available to support the treatment planning. Due to the complexity of most of these languages, assistance for developing the often very sophisticated processes is necessary. This paper defines a basis for...

Business process management (BPM) has emerged as a dominant technology in current enterprise systems and business solutions. However, the technology continues to face challenges in coping with dynamic business environments where requirements and goals are constantly changing. In this paper, we present a modelling framework for business processes that is conducive to dynamic change and the need ...

A different type of spatial-temporal correlation in the process approaching the self-organized criticality is investigated for the two simple models for biological evolution. The changed behaviors of the position with minimum barrier are shown to be quantitatively different in the two models. Different results of the correlation are given for the two models. We argue that the correlation can be...

Based on our previous work on the conformance verification of service compositions, in this paper we present a framework and associated techniques to generate the process models of a service composition from a set of temporal business rules. Dedicated techniques including path-finding, branch structure introduction, and parallel structure introduction are used to semiautomatically synthesize th...