Media And Government Relations In Papua New Guinea

All is not well with news media in Papua New Guinea. Media and government relations are stressed, a situation adverse to the country's development. Media organisations have to deal with operational difficulties, threats against editorial freedom, and harassment or physical danger experienced by journalists. Yet there are positive factors providing hope for the future, especially that key element, freedom to publish, which goes together with a habit of openess in public life as part of the national culture. That is the main finding of a study made during a working visit to Papua New Guinea

Tubes Containing String Modules in Symmetric Special Multiserial Algebras

We provide a method for determining the existence and ranks of tubes in the stable Auslander-Reiten quiver of symmetric special multiserial algebras using only the information from the underlying Brauer configuration. Essentially, we generalise the notion of a Green walk around a Brauer graph to the notion of a Green `hyperwalk' around a Brauer configuration, and show that these walks determine the number and rank of some of the stable tubes of the corresponding algebra. This description includes both tame and wild symmetric special multiserial algebras. We also provide a description of additional rank two tubes in both tame and wild algebras that do not arise from Green hyperwalks, but which nevertheless contain string modules at the mouth.Comment: 45 page

Rich environments for active learning in action: Problem‐based learning

Rich Environments for Active Learning (REALs) are comprehensive instructional systems that are consistent with constructivist theories. They promote study and investigation within authentic contexts; encourage the growth of student responsibility, initiative, decision making and intentional learning; cultivate collaboration among students and teachers; utilize dynamic, interdisciplinary, generative learning activities that promote higher‐order thinking processes to help students develop rich and complex knowledge structures; and assess student progress in content and learning‐to‐learn within authentic contexts using realistic tasks and performances. Problem‐Based Learning (PBL) is an instructional methodology that can be used to create REALs. PBL's student‐centred approach engages students in a continuous collaborative process of building and reshaping understanding as a natural consequence of their experiences and interactions within learning environments that authentically reflect the world around them. In this way, PBL and REALs are a response to teacher‐centred educational practices that promote the development of inert knowledge, such as conventional teacher‐to‐student knowledge dissemination activities. In this article, we compare existing assumptions underlying teacher‐directed educational practice with new assumptions that promote problem solving and higher‐level thinking by putting students at the centre of learning activities. We also examine the theoretical foundation that supports these new assumptions and the need for REALs. Finally, we describe each REAL characteristic and provide supporting examples of REALs in action using PB

Structure-Aware Sampling: Flexible and Accurate Summarization

In processing large quantities of data, a fundamental problem is to obtain a summary which supports approximate query answering. Random sampling yields flexible summaries which naturally support subset-sum queries with unbiased estimators and well-understood confidence bounds. Classic sample-based summaries, however, are designed for arbitrary subset queries and are oblivious to the structure in the set of keys. The particular structure, such as hierarchy, order, or product space (multi-dimensional), makes range queries much more relevant for most analysis of the data. Dedicated summarization algorithms for range-sum queries have also been extensively studied. They can outperform existing sampling schemes in terms of accuracy on range queries per summary size. Their accuracy, however, rapidly degrades when, as is often the case, the query spans multiple ranges. They are also less flexible - being targeted for range sum queries alone - and are often quite costly to build and use. In this paper we propose and evaluate variance optimal sampling schemes that are structure-aware. These summaries improve over the accuracy of existing structure-oblivious sampling schemes on range queries while retaining the benefits of sample-based summaries: flexible summaries, with high accuracy on both range queries and arbitrary subset queries