Multi-Dimensional Inheritance

In this paper, we present an alternative approach to multiple inheritance for typed feature structures. In our approach, a feature structure can be associated with several types coming from different hierarchies (dimensions). In case of multiple inheritance, a type has supertypes from different hierarchies. We contrast this approach with approaches based on a single type hierarchy where a feature structure has only one unique most general type, and multiple inheritance involves computation of greatest lower bounds in the hierarchy. The proposed approach supports current linguistic analyses in constraint-based formalisms like HPSG, inheritance in the lexicon, and knowledge representation for NLP systems. Finally, we show that multi-dimensional inheritance hierarchies can be compiled into a Prolog term representation, which allows to compute the conjunction of two types efficiently by Prolog term unification.Comment: 9 pages, styles: a4,figfont,eepic,eps

Predicting object mass nouns across languages

The hypothesis explored in this paper is that the amount of object mass nouns (e.g., furniture, jewelry) in a given language is related to the amount of morphosyntax that indicates the countability of nouns (e.g., many, much) in that language. This hypothesis, together with the analysis of Sutton & Filip (2016) best captures the occurrence of object mass nouns across languages. The analysis of Sutton & Filip (2016) accurately predicts which class of nouns will have object mass nouns across languages – collective artifacts – and the novel hypothesis provides a means of predicting the amount of object mass nouns in a given language: languages with many morphosyntactic reflexes of the mass/count distinction will likewise have many object mass nouns – e.g. English – and languages with few morphosyntactic reflexes of the mass/count distinction will likewise have few object mass nouns – e.g., Greek, Hungarian, and Japanese

On the interaction of adaptive timescales on networks

The dynamics of real-world systems often involve multiple processes that influence system state. The timescales that these processes operate on may be separated by orders of magnitude or may coincide closely. Where timescales are not separable, the way that they relate to each other will be important for understanding system dynamics. In this paper, we present a short overview of how modellers have dealt with multiple timescales and introduce a definition to formalise conditions under which timescales are separable. We investigate timescale separation in a simple model, consisting of a network of nodes on which two processes act. The first process updates the values taken by the network’s nodes, tending to move a node’s value towards that of its neighbours. The second process influences the topology of the network, by rewiring edges such that they tend to more often lie between similar individuals. We show that the behaviour of the system when timescales are separated is very different from the case where they are mixed. When the timescales of the two processes are mixed, the ratio of the rates of the two processes determines the systems equilibrium state. We go on to explore the impact of heterogeneity in the system’s timescales, i.e., where some nodes may update their value and/or neighbourhood faster than others, demonstrating that it can have a significant impact on the equilibrium behaviour of the model

The attitudes of principals of Catholic secondary schools toward teacher empowerment: A study of principals in the Western region of the national Catholic Education Association

The purpose of this study was to examine the attitudes of principals of selected Catholic secondary schools toward aspects of teacher empowerment. The Attitudes Toward Teacher Empowerment Survey was developed to measure attitudes toward empowering teachers in certain decision areas and to examine attitudes about the effects of teacher empowerment; The instrument was reviewed by two separate expert panels and sent, in final form, to 201 secondary school principals in the western region of the National Catholic Educational Association (NCEA). The selected principals returned 161 (80.1%) usable surveys; The first part of the survey, items 1 through 29, measured principal attitudes toward empowering teachers in certain key decision areas in schools. Factor analysis reduced the data into three decision domains: (a) the manager-controlled decision domain which dealt with decisions made primarily by administrators, (b) the teacher-controlled decision domain which dealt with decisions made primarily by teachers, and (c) the collaborative decision domain which included decisions for which administrators wanted to share responsibility with teachers; The principals surveyed in this study had highly positive views toward empowering teachers in the teacher-controlled decisions and the collaborative decisions, and they were mildly positive toward increasing teachers\u27 influence in manager-controlled decisions; The second part of the survey, items 30-43, measured the principals\u27 views about the effects of empowering teachers. Factor analysis reduced the data into two groupings: the positive effects of empowerment grouping and the problems with empowerment grouping. The principals\u27 views about these two groups of questions were consistent with their attitudes toward the first section of the survey. The principals surveyed were optimistic about the positive benefits of empowering teachers, and they did not exhibit great concern about the problems that empowering teachers might create; Finally, characteristics such as a principal\u27s lay or clerical status, gender, or years of experience as a principal and the size of the school were evaluated. These data had no meaningful effect on the principals\u27 view of empowerment

Dynamics and stability of small social networks

The choices and behaviours of individuals in social systems combine in unpredictable ways to create complex, often surprising, social outcomes. The structure of these behaviours, or interactions between individuals, can be represented as a social network. These networks are not static but vary over time as connections are made and broken or change in intensity. Generally these changes are gradual, but in some cases individuals disagree and as a result "fall out" with each other, i.e. , actively end their relationship by ceasing all contact. These "fallouts" have been shown to be capable of fragmenting the social network into disconnected parts. Fragmentation can impair the functioning of social networks and it is thus important to better understand the social processes that have such consequences. In this thesis we investigate the question of how networks fragment: what mechanism drives the changes that ultimately result in fragmentation? To do so, we also aim to understand the necessary conditions for fragmentation to be possible and identify the connections that are most important for the cohesion of the network. To answer these questions, we need a model of social network dynamics that is stable enough such that fragmentation does not occur spontaneously, but is simultaneously dynamic enough to allow the system to react to perturbations (i.e. , disagreements). We present such a model and show that it is able to grow and maintain networks exhibiting the characteristic properties of social networks, and does so using local behavioural rules inspired by sociological theory. We then provide a detailed investigation of fragmentation and confirm basic intuitions on the importance of bridges for network cohesion. Furthermore, we show that this topological feature alone does not explain which points of the network are most vulnerable to fragmentation. Rather, we find that dependencies between edges are crucial for understanding subtle differences between stable and vulnerable bridges. This understandingof the vulnerability of different network components is likely to be valuable for preventing fragmentation and limiting the impact of social fallou