Standby Lovers: A Typology and Theoretical Investigation of Back Burner Relational Maintenance

The purpose of this dissertation was to investigate the connections between relational maintenance behaviors, theoretical factors, and relational characteristics in back burner relationships. A back burner relationship involves at least one individual who is romantically or sexually interested in a target, but they are not currently involved with the target. Given that back burners maintain communication with each other with the possibility of becoming romantically or sexually involved in the future, Study 1 was concerned with inductively identifying the relational maintenance behaviors used in back burner relationships. Following prior typology methods, participants (N = 86) in Study 1 were currently involved in at least one back burner relationship and responded to an open-ended question. The findings revealed that individuals use 10 back burner maintenance behaviors (i.e., Flirting & Humor, Minimize Intimacy, Openness, Positivity & Support, Regular Contact, Relationship Talk, Shared Activities, Social Networks, Special Occasions & Gifts, and Strategic Deceit). Study 2 questioned the behavioral predictability of theoretical factors (i.e., attachment style, relationship uncertainty, and self-expansion) and hypothesized that the use of maintenance behaviors would be positively associated with relational characteristics (i.e., commitment, liking, control mutuality, and relationship satisfaction). Participants (N = 187) were currently involved in at least one back burner relationship and completed an online questionnaire. The results indicated that individuals’ preoccupied attachment, secure attachment, behavioral uncertainty, future uncertainty, and experienced self-expansion each uniquely predicted the use of various back burner maintenance behaviors. The hypothesis was partially supported. Six maintenance behaviors (i.e., Flirting & Humor, Openness, Positivity & Support, Regular Contact, Shared Activities, and Special Occasions & Gifts) were positively associated with commitment, liking, control mutuality, and relationships. The results also revealed several unique associations for the Relationship Talk, Social Networks, Minimize Intimacy, and Strategic Deceit back burner maintenance behaviors

Relational Reasoning Network (RRN) for Anatomical Landmarking

Accurately identifying anatomical landmarks is a crucial step in deformation analysis and surgical planning for craniomaxillofacial (CMF) bones. Available methods require segmentation of the object of interest for precise landmarking. Unlike those, our purpose in this study is to perform anatomical landmarking using the inherent relation of CMF bones without explicitly segmenting them. We propose a new deep network architecture, called relational reasoning network (RRN), to accurately learn the local and the global relations of the landmarks. Specifically, we are interested in learning landmarks in CMF region: mandible, maxilla, and nasal bones. The proposed RRN works in an end-to-end manner, utilizing learned relations of the landmarks based on dense-block units and without the need for segmentation. For a given a few landmarks as input, the proposed system accurately and efficiently localizes the remaining landmarks on the aforementioned bones. For a comprehensive evaluation of RRN, we used cone-beam computed tomography (CBCT) scans of 250 patients. The proposed system identifies the landmark locations very accurately even when there are severe pathologies or deformations in the bones. The proposed RRN has also revealed unique relationships among the landmarks that help us infer several reasoning about informativeness of the landmark points. RRN is invariant to order of landmarks and it allowed us to discover the optimal configurations (number and location) for landmarks to be localized within the object of interest (mandible) or nearby objects (maxilla and nasal). To the best of our knowledge, this is the first of its kind algorithm finding anatomical relations of the objects using deep learning.Comment: 10 pages, 6 Figures, 3 Table

Reasoning about Independence in Probabilistic Models of Relational Data

We extend the theory of d-separation to cases in which data instances are not independent and identically distributed. We show that applying the rules of d-separation directly to the structure of probabilistic models of relational data inaccurately infers conditional independence. We introduce relational d-separation, a theory for deriving conditional independence facts from relational models. We provide a new representation, the abstract ground graph, that enables a sound, complete, and computationally efficient method for answering d-separation queries about relational models, and we present empirical results that demonstrate effectiveness.Comment: 61 pages, substantial revisions to formalisms, theory, and related wor

Analysis of Neighbourhoods in Multi-layered Dynamic Social Networks

Social networks existing among employees, customers or users of various IT systems have become one of the research areas of growing importance. A social network consists of nodes - social entities and edges linking pairs of nodes. In regular, one-layered social networks, two nodes - i.e. people are connected with a single edge whereas in the multi-layered social networks, there may be many links of different types for a pair of nodes. Nowadays data about people and their interactions, which exists in all social media, provides information about many different types of relationships within one network. Analysing this data one can obtain knowledge not only about the structure and characteristics of the network but also gain understanding about semantic of human relations. Are they direct or not? Do people tend to sustain single or multiple relations with a given person? What types of communication is the most important for them? Answers to these and more questions enable us to draw conclusions about semantic of human interactions. Unfortunately, most of the methods used for social network analysis (SNA) may be applied only to one-layered social networks. Thus, some new structural measures for multi-layered social networks are proposed in the paper, in particular: cross-layer clustering coefficient, cross-layer degree centrality and various versions of multi-layered degree centralities. Authors also investigated the dynamics of multi-layered neighbourhood for five different layers within the social network. The evaluation of the presented concepts on the real-world dataset is presented. The measures proposed in the paper may directly be used to various methods for collective classification, in which nodes are assigned to labels according to their structural input features.Comment: 16 pages, International Journal of Computational Intelligence System

Exploiting sparsity and sharing in probabilistic sensor data models

Probabilistic sensor models defined as dynamic Bayesian networks can possess an inherent sparsity that is not reflected in the structure of the network. Classical inference algorithms like variable elimination and junction tree propagation cannot exploit this sparsity. Also, they do not exploit the opportunities for sharing calculations among different time slices of the model. We show that, using a relational representation, inference expressions for these sensor models can be rewritten to make efficient use of sparsity and sharing