Finitary Abstract Elementary Classes

The research in model theory has extended from the study of elementary classes to non-elementary classes, i.e. to classes which are not completely axiomatizable in elementary logic. The main theme has been the attempt to generalize tools from elementary stability theory to cover more applications arising in other branches of mathematics. In this doctoral thesis we introduce finitary abstract elementary classes, a non-elementary framework of model theory. These classes are a special case of abstract elementary classes (AEC), introduced by Saharon Shelah in the 1980's. We have collected a set of properties for classes of structures, which enable us to develop a 'geometric' approach to stability theory, including an independence calculus, in a very general framework. The thesis studies AEC's with amalgamation, joint embedding, arbitrarily large models, countable Löwenheim-Skolem number and finite character. The novel idea is the property of finite character, which enables the use of a notion of a weak type instead of the usual Galois type. Notions of simplicity, superstability, Lascar strong type, primary model and U-rank are inroduced for finitary classes. A categoricity transfer result is proved for simple, tame finitary classes: categoricity in any uncountable cardinal transfers upwards and to all cardinals above the Hanf number. Unlike the previous categoricity transfer results of equal generality the theorem does not assume the categoricity cardinal being a successor. The thesis consists of three independent papers. All three papers are joint work with Tapani Hyttinen.

Whiteness scholarship in early childhood education

This paper explores how whiteness scholarship can support deep engagement with both historical and contemporary forms of whiteness and racism in early childhood education. To this point, the uptake of whiteness scholarship in the field of early childhood has focused predominantly on autobiographical narratives. These narratives recount white educators’ stories of ‘becoming aware’ or ‘unmasking’ their whiteness. In colonising contexts including Australia, New Zealand and Canada, understanding how whiteness operates in different ways and what this means for educational research and practice, can support researchers and educators to identify and describe more fully the impacts of subtle forms of racism in their everyday practices. In this paper, whiteness is explored in a broader sense as: a form of property; an organising principle for institutional behaviours and practices; and as a fluid identity or subject position. These three intersecting elements of whiteness are drawn on to analyse data from a doctoral study about embedding Aboriginal and Torres Strait Islander perspectives in early childhood education curricula in two Australian urban childcare settings. Analysis is focused on how whiteness operated within the research site and research processes, along with the actions, inaction and talk of two educators engaged in embedding work. Findings show that both the researcher and educators reinforced, rather than reduced the impacts of whiteness and racism, despite the best of intentions.

On the Rademacher maximal function

Tools known as maximal functions are frequently used in harmonic analysis when studying local behaviour of functions. Typically they measure the suprema of local averages of non-negative functions. It is essential that the size (more precisely, the L^p-norm) of the maximal function is comparable to the size of the original function. When dealing with families of operators between Banach spaces we are often forced to replace the uniform bound with the larger R-bound. Hence such a replacement is also needed in the maximal function for functions taking values in spaces of operators. More specifically, the suprema of norms of local averages (i.e. their uniform bound in the operator norm) has to be replaced by their R-bound. This procedure gives us the Rademacher maximal function, which was introduced by Hytönen, McIntosh and Portal in order to prove a certain vector-valued Carleson's embedding theorem. They noticed that the sizes of an operator-valued function and its Rademacher maximal function are comparable for many common range spaces, but not for all. Certain requirements on the type and cotype of the spaces involved are necessary for this comparability, henceforth referred to as the “RMF-property”. It was shown, that other objects and parameters appearing in the definition, such as the domain of functions and the exponent p of the norm, make no difference to this. After a short introduction to randomized norms and geometry in Banach spaces we study the Rademacher maximal function on Euclidean spaces. The requirements on the type and cotype are considered, providing examples of spaces without RMF. L^p-spaces are shown to have RMF not only for p greater or equal to 2 (when it is trivial) but also for 1 < p < 2. A dyadic version of Carleson's embedding theorem is proven for scalar- and operator-valued functions. As the analysis with dyadic cubes can be generalized to filtrations on sigma-finite measure spaces, we consider the Rademacher maximal function in this case as well. It turns out that the RMF-property is independent of the filtration and the underlying measure space and that it is enough to consider very simple ones known as Haar filtrations. Scalar- and operator-valued analogues of Carleson's embedding theorem are also provided. With the RMF-property proven independent of the underlying measure space, we can use probabilistic notions and formulate it for martingales. Following a similar result for UMD-spaces, a weak type inequality is shown to be (necessary and) sufficient for the RMF-property. The RMF-property is also studied using concave functions giving yet another proof of its independence from various parameters.

The Politics of Pulp Investment and the Brazilian Landless Movement (MST)

The Politics of Pulp Investment and the Brazilian Landless Movement (MST) The paper industry has been moving more heavily to the global South at the beginning of the 21st century. In a number of cases the rural populations of the global South have engaged in increasingly important resistance in their scuffle with the large-scale tree plantation-relying pulp investment model. The resistance had generally not yet managed to slow down Southern industrial tree plantation expansion until 2004. After all, even the MST, perhaps the strongest of the Southern movements, has limited power in comparison to the corporations pushing for plantation expansion. This thesis shows how, even against these odds, depending on the mechanisms of contention and case-specific conflict dynamics, in some cases the movements have managed to slow and even reverse plantation expansion. The thesis is based on extensive field research in the Brazilian countryside. It outlines a new theory of contentious agency promotion, emphasizing its importance in the shaping of corporate resource exploitation. The thesis includes a Qualitative Comparative Analysis of resistance influence on the economic outcomes of all (14) Brazilian large-scale pulp projects between 2004-2008. The central hypothesis of the thesis is that corporate resource exploitation can be slowed down more effectively and likely when the resistance is based on contentious agency. Contentious agency is created by the concatenation of five mutually supporting mechanisms of contention: organizing and politicizing a social movement; heterodox framing of pulp projects; protesting; networking; and embedding whilst maintaining autonomy. The findings suggest that contentious agency can slow or even reverse the expansion of industrial plantations, whereas when contentious agency promotion was inactive, fast or even unchecked plantation expansion was always the outcome. The rule applied to all the assessed 14 pulp conflict cases. The hypothesis gained strong support even in situations where corporate agency promotion was simultaneously active. In previous studies on social movements, there has been a lack of contributions that help us understand the causal mechanisms of contention influencing economic outcomes. The thesis answers to the call by merging a Polanyian analysis of the political economy with the Dynamics of Contention research program and making a case for the impact of contentious agency on capital accumulation. The research concludes that an efficient social movement can utilize mechanisms of contention to promote the potential of activism among its members and influence investment outcomes. Protesting, for example via pioneering land occupations, seemed to be particularly important. Until now, there has been no comprehensive theory on when and how contentious agency can slow down or reverse the expansion of corporate resource exploitation. The original contribution of this research is to provide such a theory, and utilize it to offer an extensive explanation on the conflicts over pulp investment in Brazil, the globalization of the paper industry, and slowing of industrial plantation expansion in the global South.

VIBRANTe 2.0

ARTIST STATEMENT VIBRANTe 2.0 was inspired by a research project for Parkinson’s disease patients aimed at developing a wearable device to collect relevant data for patients and medical health professionals. Vibrante is a Spanish word that translates to vibrant; literally meaning shaking or vibrations. Vibrante also has a dual meaning including vibrancy, energy, activity, and liveliness. Parkinson’s can be a debilitating disease, but it does not mean the person has to lose energy, activeness or vibrancy. As technology moves from being worn to becoming implantable and completely hidden within the body, the very notion of its physicality becomes difficult to grasp. While the human body hides implantable technology, VIBRANTe 2.0 intentionally hides the human body by making it invisible to reveal the technology stitched within. Wires become veins, delivering lifeblood to the technology inside, allowing it to pulsate and exist, while motherboards become networked hubs by which information is transferred through and within the body, performing functions that mirror and often surpass human performance capabilities. Ultimately, VIBRANTe 2.0 seeks to prompt the viewer to reflect on the potential ramifications of the complete immersion of technology into the human body. CONTEXT Technology is increasingly penetrating all aspects of our environment, and the rapid uptake of devices that live near, on or in our bodies is facilitating radical new ways of working, relating and socialising. Such technology, with its capacity to generate previously unimaginable levels of data, offers the potential to provide life-augmenting levels of interactivity. However, the absorption of technology into the very fabric of clothes, accessories and even bodies begins to dilute boundaries between physical, technological and social spheres, generating genuine ethical and privacy concerns and potentially having implications for human evolution. Embedding technology into the fabric of our clothes, accessories, and even the body enable the acquisition of and the connection to vast amounts of data about people and environments in order to provide life-augmenting levels of interactivity. Wearable sensors for example, offer the potential for significant benefits in the future management of our wellbeing. Fitness trackers such as ‘Fitbit’ and ‘Garmen’ provide wearers with the ability to monitor their personal fitness indicators while other wearables provide healthcare professionals with information that improves diagnosis and observation of medical conditions. This exhibition aimed to illustrate this shifting landscape through a selection of experimental wearable and interactive works by local, national and international artists and designers. The exhibition will also provide a platform for broader debate around wearable technology, our mediated future-selves and human interactions in this future landscape. EXHIBITION As part of Artisan’s Wearnext exhibition, the work was on public display from 25 July to 7 November 2015 and received the following media coverage: [Please refer to Additional URLs]

Wear Next: An Exploration into the Future of Wearable Technology

Technology is increasingly infiltrating all aspects of our lives and the rapid uptake of devices that live near, on or in our bodies are facilitating radical new ways of working, relating and socialising. This distribution of technology into the very fabric of our everyday life creates new possibilities, but also raises questions regarding our future relationship with data and the quantified self. By embedding technology into the fabric of our clothes and accessories, it becomes ‘wearable’. Such ‘wearables’ enable the acquisition of and the connection to vast amounts of data about people and environments in order to provide life-augmenting levels of interactivity. Wearable sensors for example, offer the potential for significant benefits in the future management of our wellbeing. Fitness trackers such as ‘Fitbit’ and ‘Garmen’ provide wearers with the ability to monitor their personal fitness indicators while other wearables provide healthcare professionals with information that improves diagnosis. While the rapid uptake of wearables may offer unique and innovative opportunities, there are also concerns surrounding the high levels of data sharing that come as a consequence of these technologies. As more ‘smart’ devices connect to the Internet, and as technology becomes increasingly available (e.g. via Wi-Fi, Bluetooth), more products, artefacts and things are becoming interconnected. This digital connection of devices is called The ‘Internet of Things’ (IoT). IoT is spreading rapidly, with many traditionally non-online devices becoming increasingly connected; products such as mobile phones, fridges, pedometers, coffee machines, video cameras, cars and clothing. The IoT is growing at a rapid rate with estimates indicating that by 2020 there will be over 25 billion connected things globally. As the number of devices connected to the Internet increases, so too does the amount of data collected and type of information that is stored and potentially shared. The ability to collect massive amounts of data - known as ‘big data’ - can be used to better understand and predict behaviours across all areas of research from societal and economic to environmental and biological. With this kind of information at our disposal, we have a more powerful lens with which to perceive the world, and the resulting insights can be used to design more appropriate products, services and systems. It can however, also be used as a method of surveillance, suppression and coercion by governments or large organisations. This is becoming particularly apparent in advertising that targets audiences based on the individual preferences revealed by the data collected from social media and online devices such as GPS systems or pedometers. This type of technology also provides fertile ground for public debates around future fashion, identity and broader social issues such as culture, politics and the environment. The potential implications of these type of technological interactions via wearables, through and with the IoT, have never been more real or more accessible. But, as highlighted, this interconnectedness also brings with it complex technical, ethical and moral challenges. Data security and the protection of privacy and personal information will become ever more present in current and future ethical and moral debates of the 21st century. This type of technology is also a stepping-stone to a future that includes implantable technology, biotechnologies, interspecies communication and augmented humans (cyborgs). Technologies that live symbiotically and perpetually in our bodies, the built environment and the natural environment are no longer the stuff of science fiction; it is in fact a reality. So, where next?... The works exhibited in Wear Next_ provide a snapshot into the broad spectrum of wearables in design and in development internationally. This exhibition has been curated to serve as a platform for enhanced broader debate around future technology, our mediated future-selves and the evolution of human interactions. As you explore the exhibition, may we ask that you pause and think to yourself, what might we... Wear Next_? WEARNEXT ONLINE LISTINGS AND MEDIA COVERAGE: http://indulgemagazine.net/wear-next/ http://www.weekendnotes.com/wear-next-exhibition-gallery-artisan/ http://concreteplayground.com/brisbane/event/wear-next_/ http://www.nationalcraftinitiative.com.au/news_and_events/event/48/wear-next http://bneart.com/whats-on/wear-next_/ http://creativelysould.tumblr.com/post/124899079611/creative-weekend-art-edition http://www.abc.net.au/radionational/programs/breakfast/smartly-dressed-the-future-of-wearable-technology/6744374 http://couriermail.newspaperdirect.com/epaper/viewer.aspx RADIO COVERAGE http://www.abc.net.au/radionational/programs/breakfast/wear-next-exhibition-whats-next-for-wearable-technology/6745986 TELEVISION COVERAGE http://www.abc.net.au/radionational/programs/breakfast/wear-next-exhibition-whats-next-for-wearable-technology/6745986 https://au.news.yahoo.com/video/watch/29439742/how-you-could-soon-be-wearing-smart-clothes/#page1

Place categorization and semantic mapping on a mobile robot

In this paper we focus on the challenging problem of place categorization and semantic mapping on a robot with-out environment-specific training. Motivated by their ongoing success in various visual recognition tasks, we build our system upon a state-of-the-art convolutional network. We overcome its closed-set limitations by complementing the network with a series of one-vs-all classifiers that can learn to recognize new semantic classes online. Prior domain knowledge is incorporated by embedding the classification system into a Bayesian filter framework that also ensures temporal coherence. We evaluate the classification accuracy of the system on a robot that maps a variety of places on our campus in real-time. We show how semantic information can boost robotic object detection performance and how the semantic map can be used to modulate the robot’s behaviour during navigation tasks. The system is made available to the community as a ROS module.

Nonlinear ensemble prediction of chaotic daily rainfall

The significance of treating rainfall as a chaotic system instead of a stochastic system for a better understanding of the underlying dynamics has been taken up by various studies recently. However, an important limitation of all these approaches is the dependence on a single method for identifying the chaotic nature and the parameters involved. Many of these approaches aim at only analyzing the chaotic nature and not its prediction. In the present study, an attempt is made to identify chaos using various techniques and prediction is also done by generating ensembles in order to quantify the uncertainty involved. Daily rainfall data of three regions with contrasting characteristics (mainly in the spatial area covered), Malaprabha, Mahanadi and All-India for the period 1955-2000 are used for the study. Auto-correlation and mutual information methods are used to determine the delay time for the phase space reconstruction. Optimum embedding dimension is determined using correlation dimension, false nearest neighbour algorithm and also nonlinear prediction methods. The low embedding dimensions obtained from these methods indicate the existence of low dimensional chaos in the three rainfall series. Correlation dimension method is done on th phase randomized and first derivative of the data series to check whether the saturation of the dimension is due to the inherent linear correlation structure or due to low dimensional dynamics. Positive Lyapunov exponents obtained prove the exponential divergence of the trajectories and hence the unpredictability. Surrogate data test is also done to further confirm the nonlinear structure of the rainfall series. A range of plausible parameters is used for generating an ensemble of predictions of rainfall for each year separately for the period 1996-2000 using the data till the preceding year. For analyzing the sensitiveness to initial conditions, predictions are done from two different months in a year viz., from the beginning of January and June. The reasonably good predictions obtained indicate the efficiency of the nonlinear prediction method for predicting the rainfall series. Also, the rank probability skill score and the rank histograms show that the ensembles generated are reliable with a good spread and skill. A comparison of results of the three regions indicates that although they are chaotic in nature, the spatial averaging over a large area can increase the dimension and improve the predictability, thus destroying the chaotic nature. (C) 2010 Elsevier Ltd. All rights reserved.

Semi-supervised learning of WLAN radio maps

In this thesis a manifold learning method is applied to the problem of WLAN positioning and automatic radio map creation. Due to the nature of WLAN signal strength measurements, a signal map created from raw measurements results in non-linear distance relations between measurement points. These signal strength vectors reside in a high-dimensioned coordinate system. With the help of the so called Isomap-algorithm the dimensionality of this map can be reduced, and thus more easily processed. By embedding position-labeled strategic key points, we can automatically adjust the mapping to match the surveyed environment. The environment is thus learned in a semi-supervised way; gathering training points and embedding them in a two-dimensional manifold gives us a rough mapping of the measured environment. After a calibration phase, where the labeled key points in the training data are used to associate coordinates in the manifold representation with geographical locations, we can perform positioning using the adjusted map. This can be achieved through a traditional supervised learning process, which in our case is a simple nearest neighbors matching of a sampled signal strength vector. We deployed this system in two locations in the Kumpula campus in Helsinki, Finland. Results indicate that positioning based on the learned radio map can achieve good accuracy, especially in hallways or other areas in the environment where the WLAN signal is constrained by obstacles such as walls.

Two bracketing schemes for the Penn Treebank

The trees in the Penn Treebank have a standard representation that involves complete balanced bracketing. In this article, an alternative for this standard representation of the tree bank is proposed. The proposed representation for the trees is loss-less, but it reduces the total number of brackets by 28%. This is possible by omitting the redundant pairs of special brackets that encode initial and final embedding, using a technique proposed by Krauwer and des Tombe (1981). In terms of the paired brackets, the maximum nesting depth in sentences decreases by 78%. The 99.9% coverage is achieved with only five non-top levels of paired brackets. The observed shallowness of the reduced bracketing suggests that finite-state based methods for parsing and searching could be a feasible option for tree bank processing.

Two bracketing schemes for the Penn Treebank

The trees in the Penn Treebank have a standard representation that involves complete balanced bracketing. In this article, an alternative for this standard representation of the tree bank is proposed. The proposed representation for the trees is loss-less, but it reduces the total number of brackets by 28%. This is possible by omitting the redundant pairs of special brackets that encode initial and final embedding, using a technique proposed by Krauwer and des Tombe (1981). In terms of the paired brackets, the maximum nesting depth in sentences decreases by 78%. The 99.9% coverage is achieved with only five non-top levels of paired brackets. The observed shallowness of the reduced bracketing suggests that finite-state based methods for parsing and searching could be a feasible option for tree bank processing.

Nonlinear measures of heart rate time series: influence of posture and controlled breathing

In this study, we investigated measures of nonlinear dynamics and chaos theory in regards to heart rate variability in 27 normal control subjects in supine and standing postures, and 14 subjects in spontaneous and controlled breathing conditions. We examined minimum embedding dimension (MED), largest Lyapunov exponent (LLE) and measures of nonlinearity (NL) of heart rate time series. MED quantifies the system's complexity, LLE predictability and NL, a measure of deviation from linear processes. There was a significant decrease in complexity (P<0.00001), a decrease in predictability (P<0.00001) and an increase in nonlinearity (P=0.00001) during the change from supine to standing posture. Decrease in MED, and increases in NL score and LLE in standing posture appear to be partly due to an increase in sympathetic activity of the autonomous nervous system in standing posture. An improvement in predictability during controlled breathing appears to be due to the introduction of a periodic component. (C) 2000 published by Elsevier Science B.V.

Planar subgraphs without low-degree nodes

We study the following problem: given a geometric graph G and an integer k, determine if G has a planar spanning subgraph (with the original embedding and straight-line edges) such that all nodes have degree at least k. If G is a unit disk graph, the problem is trivial to solve for k = 1. We show that even the slightest deviation from the trivial case (e.g., quasi unit disk graphs or k = 1) leads to NP-hard problems.

Predictive uncertainty of chaotic daily streamflow using ensemble wavelet networks approach

Perfect or even mediocre weather predictions over a long period are almost impossible because of the ultimate growth of a small initial error into a significant one. Even though the sensitivity of initial conditions limits the predictability in chaotic systems, an ensemble of prediction from different possible initial conditions and also a prediction algorithm capable of resolving the fine structure of the chaotic attractor can reduce the prediction uncertainty to some extent. All of the traditional chaotic prediction methods in hydrology are based on single optimum initial condition local models which can model the sudden divergence of the trajectories with different local functions. Conceptually, global models are ineffective in modeling the highly unstable structure of the chaotic attractor. This paper focuses on an ensemble prediction approach by reconstructing the phase space using different combinations of chaotic parameters, i.e., embedding dimension and delay time to quantify the uncertainty in initial conditions. The ensemble approach is implemented through a local learning wavelet network model with a global feed-forward neural network structure for the phase space prediction of chaotic streamflow series. Quantification of uncertainties in future predictions are done by creating an ensemble of predictions with wavelet network using a range of plausible embedding dimensions and delay times. The ensemble approach is proved to be 50% more efficient than the single prediction for both local approximation and wavelet network approaches. The wavelet network approach has proved to be 30%-50% more superior to the local approximation approach. Compared to the traditional local approximation approach with single initial condition, the total predictive uncertainty in the streamflow is reduced when modeled with ensemble wavelet networks for different lead times. Localization property of wavelets, utilizing different dilation and translation parameters, helps in capturing most of the statistical properties of the observed data. The need for taking into account all plausible initial conditions and also bringing together the characteristics of both local and global approaches to model the unstable yet ordered chaotic attractor of a hydrologic series is clearly demonstrated.

Multivariate nonlinear ensemble prediction of daily chaotic rainfall with climate inputs

The basic characteristic of a chaotic system is its sensitivity to the infinitesimal changes in its initial conditions. A limit to predictability in chaotic system arises mainly due to this sensitivity and also due to the ineffectiveness of the model to reveal the underlying dynamics of the system. In the present study, an attempt is made to quantify these uncertainties involved and thereby improve the predictability by adopting a multivariate nonlinear ensemble prediction. Daily rainfall data of Malaprabha basin, India for the period 1955-2000 is used for the study. It is found to exhibit a low dimensional chaotic nature with the dimension varying from 5 to 7. A multivariate phase space is generated, considering a climate data set of 16 variables. The chaotic nature of each of these variables is confirmed using false nearest neighbor method. The redundancy, if any, of this atmospheric data set is further removed by employing principal component analysis (PCA) method and thereby reducing it to eight principal components (PCs). This multivariate series (rainfall along with eight PCs) is found to exhibit a low dimensional chaotic nature with dimension 10. Nonlinear prediction employing local approximation method is done using univariate series (rainfall alone) and multivariate series for different combinations of embedding dimensions and delay times. The uncertainty in initial conditions is thus addressed by reconstructing the phase space using different combinations of parameters. The ensembles generated from multivariate predictions are found to be better than those from univariate predictions. The uncertainty in predictions is decreased or in other words predictability is increased by adopting multivariate nonlinear ensemble prediction. The restriction on predictability of a chaotic series can thus be altered by quantifying the uncertainty in the initial conditions and also by including other possible variables, which may influence the system. (C) 2011 Elsevier B.V. All rights reserved.