Independent thinking: Cross-cultural possibilities

This performative vignette represents a snapshot taken from a recent cross-disciplinary Action Research/Action Learning project at Curtin University of Technology. The cameo conveys the nature of our participation as staff, students and facilitator in addressing the development and enhancement of the FLOTE (first language other than English) students' communication skills in the context of their postgraduate studies. You will hear the multiple voices of the supervisors from Pharmacy, Social Work and Teacher Education, together with those of their respective PhD and Masters students, and that of the project facilitator. We focus upon the elusive nature of the concept of independent thinking in advanced scholarship. This has necessitated that we become mindful of the students' 'totality', power relations and interpersonal communication within the supervisory process, and the necessity for reflective practice. In representing how the different stakeholders dialogued on these complex issues, we hope to convey some of the process and outcomes attached to this Action Research Project

Decision-Theoretic Planning with non-Markovian Rewards

A decision process in which rewards depend on history rather than merely on the current state is called a decision process with non-Markovian rewards (NMRDP). In decision-theoretic planning, where many desirable behaviours are more naturally expressed as properties of execution sequences rather than as properties of states, NMRDPs form a more natural model than the commonly adopted fully Markovian decision process (MDP) model. While the more tractable solution methods developed for MDPs do not directly apply in the presence of non-Markovian rewards, a number of solution methods for NMRDPs have been proposed in the literature. These all exploit a compact specification of the non-Markovian reward function in temporal logic, to automatically translate the NMRDP into an equivalent MDP which is solved using efficient MDP solution methods. This paper presents NMRDPP (Non-Markovian Reward Decision Process Planner), a software platform for the development and experimentation of methods for decision-theoretic planning with non-Markovian rewards. The current version of NMRDPP implements, under a single interface, a family of methods based on existing as well as new approaches which we describe in detail. These include dynamic programming, heuristic search, and structured methods. Using NMRDPP, we compare the methods and identify certain problem features that affect their performance. NMRDPPs treatment of non-Markovian rewards is inspired by the treatment of domain-specific search control knowledge in the TLPlan planner, which it incorporates as a special case. In the First International Probabilistic Planning Competition, NMRDPP was able to compete and perform well in both the domain-independent and hand-coded tracks, using search control knowledge in the latter

A short empirical note on perfectionism and flourishing

Flourishing describes an optimal state of mental health characterized by emotional, psychological, and social well-being. In a recent publication, Flett and Hewitt (2015) suggested that perfectionism prevents people from flourishing. Perfectionism, however, is a multidimensional personality characteristic, and its various dimensions show different relationships with indicators of subjective well-being. In the first empirical study of perfectionism and flourishing, we examined the relationships of multidimensional perfectionism (self-oriented, other-oriented, and socially prescribed perfectionism) and self-reported flourishing in the past two weeks. Results from the sample of 388 university students revealed that only socially prescribed perfectionism showed a negative relationship with flourishing, whereas self-oriented perfectionism showed a positive relationship. These results were unchanged when positive and negative affect were controlled statistically. Our findings indicate that not all dimensions of perfectionism undermine flourishing and that it is important to differentiate perfectionistic strivings and concerns when regarding the perfectionism–flourishing relationship

CNN Architectures for Large-Scale Audio Classification

Convolutional Neural Networks (CNNs) have proven very effective in image classification and show promise for audio. We use various CNN architectures to classify the soundtracks of a dataset of 70M training videos (5.24 million hours) with 30,871 video-level labels. We examine fully connected Deep Neural Networks (DNNs), AlexNet [1], VGG [2], Inception [3], and ResNet [4]. We investigate varying the size of both training set and label vocabulary, finding that analogs of the CNNs used in image classification do well on our audio classification task, and larger training and label sets help up to a point. A model using embeddings from these classifiers does much better than raw features on the Audio Set [5] Acoustic Event Detection (AED) classification task.Comment: Accepted for publication at ICASSP 2017 Changes: Added definitions of mAP, AUC, and d-prime. Updated mAP/AUC/d-prime numbers for Audio Set based on changes of latest Audio Set revision. Changed wording to fit 4 page limit with new addition

Network conduciveness with application to the graph-coloring and independent-set optimization transitions

We introduce the notion of a network's conduciveness, a probabilistically interpretable measure of how the network's structure allows it to be conducive to roaming agents, in certain conditions, from one portion of the network to another. We exemplify its use through an application to the two problems in combinatorial optimization that, given an undirected graph, ask that its so-called chromatic and independence numbers be found. Though NP-hard, when solved on sequences of expanding random graphs there appear marked transitions at which optimal solutions can be obtained substantially more easily than right before them. We demonstrate that these phenomena can be understood by resorting to the network that represents the solution space of the problems for each graph and examining its conduciveness between the non-optimal solutions and the optimal ones. At the said transitions, this network becomes strikingly more conducive in the direction of the optimal solutions than it was just before them, while at the same time becoming less conducive in the opposite direction. We believe that, besides becoming useful also in other areas in which network theory has a role to play, network conduciveness may become instrumental in helping clarify further issues related to NP-hardness that remain poorly understood

On the Expressivity and Applicability of Model Representation Formalisms

A number of first-order calculi employ an explicit model representation formalism for automated reasoning and for detecting satisfiability. Many of these formalisms can represent infinite Herbrand models. The first-order fragment of monadic, shallow, linear, Horn (MSLH) clauses, is such a formalism used in the approximation refinement calculus. Our first result is a finite model property for MSLH clause sets. Therefore, MSLH clause sets cannot represent models of clause sets with inherently infinite models. Through a translation to tree automata, we further show that this limitation also applies to the linear fragments of implicit generalizations, which is the formalism used in the model-evolution calculus, to atoms with disequality constraints, the formalisms used in the non-redundant clause learning calculus (NRCL), and to atoms with membership constraints, a formalism used for example in decision procedures for algebraic data types. Although these formalisms cannot represent models of clause sets with inherently infinite models, through an additional approximation step they can. This is our second main result. For clause sets including the definition of an equivalence relation with the help of an additional, novel approximation, called reflexive relation splitting, the approximation refinement calculus can automatically show satisfiability through the MSLH clause set formalism.Comment: 15 page

Perfectionism and self-conscious emotions in British and Japanese students: Predicting pride and embarrassment after success and failure

Regarding self-conscious emotions, studies have shown that different forms of perfectionism show different relationships with pride, shame, and embarrassment depending on success and failure. What is unknown is whether these relationships also show cultural variations. Therefore, we conducted a study investigating how self-oriented and socially prescribed perfectionism predicted pride and embarrassment after success and failure comparing 363 British and 352 Japanese students. Students were asked to respond to a set of scenarios where they imagined achieving either perfect (success) or flawed results (failure). In both British and Japanese students, self-oriented perfectionism positively predicted pride after success and embarrassment after failure whereas socially prescribed perfectionism predicted embarrassment after success and failure. Moreover, in Japanese students, socially prescribed perfectionism positively predicted pride after success and self-oriented perfectionism negatively predicted pride after failure. The findings have implications for our understanding of perfectionism indicating that the perfectionism–pride relationship not only varies between perfectionism dimensions, but may also show cultural variations

Automatically generating streamlined constraint models with ESSENCE and CONJURE

Streamlined constraint reasoning is the addition of uninferred constraints to a constraint model to reduce the search space, while retaining at least one solution. Previously, effective streamlined models have been constructed by hand, requiring an expert to examine closely solutions to small instances of a problem class and identify regularities. We present a system that automatically generates many conjectured regularities for a given Essence specification of a problem class by examining the domains of decision variables present in the problem specification. These conjectures are evaluated independently and in conjunction with one another on a set of instances from the specified class via an automated modelling tool-chain comprising of Conjure, Savile Row and Minion. Once the system has identified effective conjectures they are used to generate streamlined models that allow instances of much larger scale to be solved. Our results demonstrate good models can be identified for problems in combinatorial design, Ramsey theory, graph theory and group theory - often resulting in order of magnitude speed-ups.Postprin