Representing and discovering adversarial team behaviors using player roles

In this paper, we describe a method to represent and discover adversarial group behavior in a continuous domain. In comparison to other types of behavior, adversarial behavior is heavily structured as the location of a player (or agent) is dependent both on their teammates and adversaries, in addition to the tactics or strategies of the team. We present a method which can exploit this relationship through the use of a spatiotemporal basis model. As players constantly change roles during a match, we show that employing a "role-based" representation instead of one based on player "identity" can best exploit the playing structure. As vision-based systems currently do not provide perfect detection/tracking (e.g. missed or false detections), we show that our compact representation can effectively "denoise" erroneous detections as well as enabe temporal analysis, which was previously prohibitive due to the dimensionality of the signal. To evaluate our approach, we used a fully instrumented field-hockey pitch with 8 fixed high-definition (HD) cameras and evaluated our approach on approximately 200,000 frames of data from a state-of-the-art real-time player detector and compare it to manually labelled data.

Large-scale analysis of formations in soccer

Due to the demand for better and deeper analysis in sports, organizations (both professional teams and broadcasters) are looking to use spatiotemporal data in the form of player tracking information to obtain an advantage over their competitors. However, due to the large volume of data, its unstructured nature, and lack of associated team activity labels (e.g. strategic/tactical), effective and efficient strategies to deal with such data have yet to be deployed. A bottleneck restricting such solutions is the lack of a suitable representation (i.e. ordering of players) which is immune to the potentially infinite number of possible permutations of player orderings, in addition to the high dimensionality of temporal signal (e.g. a game of soccer last for 90 mins). Leveraging a recent method which utilizes a "role-representation", as well as a feature reduction strategy that uses a spatiotemporal bilinear basis model to form a compact spatiotemporal representation. Using this representation, we find the most likely formation patterns of a team associated with match events across nearly 14 hours of continuous player and ball tracking data in soccer. Additionally, we show that we can accurately segment a match into distinct game phases and detect highlights. (i.e. shots, corners, free-kicks, etc) completely automatically using a decision-tree formulation.

The Empty City (production)

This creative work is the production of the live and animated performance of The Empty City. With a significant period of creative development and script work behind it, the team engaged in a range of innovative performance-making practices in order to realise the work onstage as a non-verbal live and animated theatre work. This intermedial process was often led by music, and involved the creation and convergence of non-verbal action, virtual performers, performing objects and two simultaneous projections of animated images. The production opened at the Brisbane Powerhouse on June 27 2013, with a subsequent tour to Perth’s Awesome Festival in October 2013. Its technical achievements were noted in the critical responses. "The story is told on a striking set of two huge screens, the front one transparent, upon which still and moving images are projected, and between which Oliver performs and occasional “real” objects are placed. The effect is startling, and creates a cartoon three dimensionality like those old Viewmaster slide shows. The live action… and soundscape sync perfectly with the projected imagery to complete a dense, intricately devised and technically brilliant whole." (The West Australian 14.10.13)

Optical coupling of Au nanoparticles on vertical graphenes to maximize SERS response

Gold particle interaction with few-layer graphenes is of interest for the development of numerous optical nanodevices. The results of numerical studies of the coupling of gold nanoparticles with few-layer vertical graphene sheets are presented. The field strengths are computed and the optimum nanoparticle configurations for the formation of SERS hotpots are obtained. The nanoparticles are modeled as 8 nm diameter spheres atop 1.5 nm (5 layers) graphene sheet. The vertical orientation is of particular interest as it is possible to use both sides of the graphene structure and potentially double the number of particles in the system. Our results show that with the addition of an opposing particle a much stronger signal can be obtained as well as the particle separation can be controlled by the number of atomic carbon layers. These results provide further insights and contribute to the development of next-generation plasmonic devices based on nanostructures with hybrid dimensionality.

A bayesian approach for place recognition

This paper presents a robust place recognition algorithm for mobile robots that can be used for planning and navigation tasks. The proposed framework combines nonlinear dimensionality reduction, nonlinear regression under noise, and Bayesian learning to create consistent probabilistic representations of places from images. These generative models are incrementally learnt from very small training sets and used for multi-class place recognition. Recognition can be performed in near real-time and accounts for complexity such as changes in illumination, occlusions, blurring and moving objects. The algorithm was tested with a mobile robot in indoor and outdoor environments with sequences of 1579 and 3820 images, respectively. This framework has several potential applications such as map building, autonomous navigation, search-rescue tasks and context recognition.

Fault diagnosis of low speed bearing based on acoustic emission signal and multi-class relevance vector machine

This study presents an acoustic emission (AE) based fault diagnosis for low speed bearing using multi-class relevance vector machine (RVM). A low speed test rig was developed to simulate the various defects with shaft speeds as low as 10 rpm under several loading conditions. The data was acquired using anAEsensor with the test bearing operating at a constant loading (5 kN) andwith a speed range from20 to 80 rpm. This study is aimed at finding a reliable method/tool for low speed machines fault diagnosis based on AE signal. In the present study, component analysis was performed to extract the bearing feature and to reduce the dimensionality of original data feature. The result shows that multi-class RVM offers a promising approach for fault diagnosis of low speed machines.

Paracrine interactions between LNCaP prostate cancer cells and bioengineered bone in 3D in vitro culture reflect molecular changes during bone metastasis

As microenvironmental factors such as three-dimensionality and cell–matrix interactions are increasingly being acknowledged by cancer biologists, more complex 3D in vitro models are being developed to study tumorigenesis and cancer progression. To better understand the pathophysiology of bone metastasis, we have established and validated a 3D indirect co-culture model to investigate the paracrine interactions between prostate cancer (PCa) cells and human osteoblasts. Co-culture of the human PCa, LNCaP cells embedded within polyethylene glycol hydrogels with human osteoblasts in the form of a tissue engineered bone construct (TEB), resulted in reduced proliferation of LNCaP cells. LNCaP cells in both monoculture and co-culture were responsive to the androgen analog, R1881, as indicated by an increase in the expression (mRNA and/or protein induction) of androgen-regulated genes including prostate specific antigen and fatty acid synthase. Microarray gene expression analysis further revealed an up-regulation of bone markers and other genes associated with skeletal and vasculature development and a significant activation of transforming growth factor β1 downstream genes in LNCaP cells after co-culture with TEB. LNCaP cells co-cultured with TEB also unexpectedly showed similar changes in classical androgen-responsive genes under androgen-deprived conditions not seen in LNCaP monocultures. The molecular changes of LNCaP cells after co-culturing with TEBs suggest that osteoblasts exert a paracrine effect that may promote osteomimicry and modulate the expression of androgen-responsive genes in LNCaP cells. Taken together, we have presented a novel 3D in vitro model that allows the study of cellular and molecular changes occurring in PCa cells and osteoblasts that are relevant to metastatic colonization of bone. This unique in vitro model could also facilitate cancer biologists to dissect specific biological hypotheses via extensive genomic or proteomic assessments to further our understanding of the PCa-bone crosstalk.

The analysis of large scale data taken from the world groundnut (Arachis hypogaea L.) germplasm collection I. Two-way quantitative data

Data associated with germplasm collections are typically large and multivariate with a considerable number of descriptors measured on each of many accessions. Pattern analysis methods of clustering and ordination have been identified as techniques for statistically evaluating the available diversity in germplasm data. While used in many studies, the approaches have not dealt explicitly with the computational consequences of large data sets (i.e. greater than 5000 accessions). To consider the application of these techniques to germplasm evaluation data, 11328 accessions of groundnut (Arachis hypogaea L) from the International Research Institute for the Semi-Arid Tropics, Andhra Pradesh, India were examined. Data for nine quantitative descriptors measured in the rainy and post-rainy growing seasons were used. The ordination technique of principal component analysis was used to reduce the dimensionality of the germplasm data. The identification of phenotypically similar groups of accessions within large scale data via the computationally intensive hierarchical clustering techniques was not feasible and non-hierarchical techniques had to be used. Finite mixture models that maximise the likelihood of an accession belonging to a cluster were used to cluster the accessions in this collection. The patterns of response for the different growing seasons were found to be highly correlated. However, in relating the results to passport and other characterisation and evaluation descriptors, the observed patterns did not appear to be related to taxonomy or any other well known characteristics of groundnut.

Plasma nanoscience : from nano-solids in plasmas to nano-plasmas in solids

The unique plasma-specific features and physical phenomena in the organization of nanoscale soild-state systems in a broad range of elemental composition, structure, and dimensionality are critically reviewed. These effects lead to the possibility to localize and control energy and matter at nanoscales and to produce self-organized nano-solids with highly unusual and superior properties. A unifying conceptual framework based on the control of production, transport, and self-organization of precursor species is introduced and a variety of plasma-specific non-equilibrium and kinetics-driven phenomena across the many temporal and spatial scales is explained. When the plasma is localized to micrometer and nanometer dimensions, new emergent phenomena arise. The examples range from semiconducting quantum dots and nanowires, chirality control of single-walled carbon nanotubes, ultra-fine manipulation of graphenes, nano-diamond, and organic matter to nano-plasma effects and nano-plasmas of different states of matter. © 2013 Taylor and Francis Group, LLC.

Engineered microenvironments provide new insights into ovarian and prostate cancer progression and drug responses

Tissue engineering technologies, which have originally been designed to reconstitute damaged tissue structure and function, can mimic not only tissue regeneration processes but also cancer development and progression. Bioengineered approaches allow cell biologists to develop sophisticated experimentally and physiologically relevant cancer models to recapitulate the complexity of the disease seen in patients. Tissue engineering tools enable three-dimensionality based on the design of biomaterials and scaffolds that re-create the geometry, chemistry, function and signalling milieu of the native tumour microenvironment. Three-dimensional (3D) microenvironments, including cell-derived matrices, biomaterial-based cell culture models and integrated co-cultures with engineered stromal components, are powerful tools to study dynamic processes like proteolytic functions associated with cancer progression, metastasis and resistance to therapeutics. In this review, we discuss how biomimetic strategies can reproduce a humanised niche for human cancer cells, such as peritoneal or bone-like microenvironments, addressing specific aspects of ovarian and prostate cancer progression and therapy response.

Deriving a preference-based utility measure for cancer patients from the European Organisation for the Research and Treatment of Cancer's Quality of Life Questionnaire C30 : a confirmatory versus exploratory approach

Background Multi attribute utility instruments (MAUIs) are preference-based measures that comprise a health state classification system (HSCS) and a scoring algorithm that assigns a utility value to each health state in the HSCS. When developing a MAUI from a health-related quality of life (HRQOL) questionnaire, first a HSCS must be derived. This typically involves selecting a subset of domains and items because HRQOL questionnaires typically have too many items to be amendable to the valuation task required to develop the scoring algorithm for a MAUI. Currently, exploratory factor analysis (EFA) followed by Rasch analysis is recommended for deriving a MAUI from a HRQOL measure. Aim To determine whether confirmatory factor analysis (CFA) is more appropriate and efficient than EFA to derive a HSCS from the European Organisation for the Research and Treatment of Cancer’s core HRQOL questionnaire, Quality of Life Questionnaire (QLQ-C30), given its well-established domain structure. Methods QLQ-C30 (Version 3) data were collected from 356 patients receiving palliative radiotherapy for recurrent/metastatic cancer (various primary sites). The dimensional structure of the QLQ-C30 was tested with EFA and CFA, the latter informed by the established QLQ-C30 structure and views of both patients and clinicians on which are the most relevant items. Dimensions determined by EFA or CFA were then subjected to Rasch analysis. Results CFA results generally supported the proposed QLQ-C30 structure (comparative fit index =0.99, Tucker–Lewis index =0.99, root mean square error of approximation =0.04). EFA revealed fewer factors and some items cross-loaded on multiple factors. Further assessment of dimensionality with Rasch analysis allowed better alignment of the EFA dimensions with those detected by CFA. Conclusion CFA was more appropriate and efficient than EFA in producing clinically interpretable results for the HSCS for a proposed new cancer-specific MAUI. Our findings suggest that CFA should be recommended generally when deriving a preference-based measure from a HRQOL measure that has an established domain structure.

Signaling pathway genes for blood pressure, folate and cholesterol levels among hypertensives : an epistasis analysis

Irregular atrial pressure, defective folate and cholesterol metabolism contribute to the pathogenesis of hypertension. However, little is known about the combined roles of the methylenetetrahydrofolate reductase (MTHFR), apolipoprotein-E (ApoE) and angiotensin-converting enzyme (ACE) genes, which are involved in metabolism and homeostasis. The objective of this study is to investigate the association of the MTHFR 677 C>T and 1298A>C, ACE insertion–deletion (I/D) and ApoE genetic polymorphisms with hypertension and to further explore the epistasis interactions that are involved in these mechanisms. A total of 594 subjects, including 348 normotensive and 246 hypertensive ischemic stroke subjects were recruited. The MTHFR 677 C>T and 1298A>C, ACE I/D and ApoEpolymorphisms were genotyped and the epistasis interaction were analyzed. The MTHFR 677 C>T and ApoE polymorphisms demonstrated significant associations with susceptibility to hypertension in multiple logistic regression models, multifactor dimensionality reduction and a classification and regression tree. In addition, the logistic regression model demonstrated that significant interactions between the ApoE E3E3, E2E4, E2E2 and MTHFR 677 C>T polymorphisms existed. In conclusion, the results of this epistasis study indicated significant association between the ApoE and MTHFR polymorphisms and hypertension.

Refining user and item profiles based on multidimensional data for top-n item recommendation

In recommender systems based on multidimensional data, additional metadata provides algorithms with more information for better understanding the interaction between users and items. However, most of the profiling approaches in neighbourhood-based recommendation approaches for multidimensional data merely split or project the dimensional data and lack the consideration of latent interaction between the dimensions of the data. In this paper, we propose a novel user/item profiling approach for Collaborative Filtering (CF) item recommendation on multidimensional data. We further present incremental profiling method for updating the profiles. For item recommendation, we seek to delve into different types of relations in data to understand the interaction between users and items more fully, and propose three multidimensional CF recommendation approaches for top-N item recommendations based on the proposed user/item profiles. The proposed multidimensional CF approaches are capable of incorporating not only localized relations of user-user and/or item-item neighbourhoods but also latent interaction between all dimensions of the data. Experimental results show significant improvements in terms of recommendation accuracy.

Reliable fault diagnosis for incipient low-speed bearings using fault feature analysis based on a binary bat algorithm

In this paper, we propose a highly reliable fault diagnosis scheme for incipient low-speed rolling element bearing failures. The scheme consists of fault feature calculation, discriminative fault feature analysis, and fault classification. The proposed approach first computes wavelet-based fault features, including the respective relative wavelet packet node energy and entropy, by applying a wavelet packet transform to an incoming acoustic emission signal. The most discriminative fault features are then filtered from the originally produced feature vector by using discriminative fault feature analysis based on a binary bat algorithm (BBA). Finally, the proposed approach employs one-against-all multiclass support vector machines to identify multiple low-speed rolling element bearing defects. This study compares the proposed BBA-based dimensionality reduction scheme with four other dimensionality reduction methodologies in terms of classification performance. Experimental results show that the proposed methodology is superior to other dimensionality reduction approaches, yielding an average classification accuracy of 94.9%, 95.8%, and 98.4% under bearing rotational speeds at 20 revolutions-per-minute (RPM), 80 RPM, and 140 RPM, respectively.

An Intelligent Driver Warning System

This chapter is focussed on the research and development of an intelligent driver warning system (IDWS) as a means to improve road safety and driving comfort. Two independent IDWS case studies are presented. The first study examines the methodology and implementation for attentive visual tracking and trajectory estimation for dynamic scene segmentation problems. In the second case study, the concept of driver modelling is evaluated which can be used to provide useful feedback to drivers. In both case studies, the quality of IDWS is largely determined by the modelling capability for estimating multiple vehicle trajectories and modelling driving behaviour. A class of modelling techniques based on neural-fuzzy systems, which exhibits provable learning and modelling capability, is proposed. For complex modelling problems where the curse of dimensionality becomes an issue, a network construction algorithm based on Adaptive Spline Modelling of Observation Data (ASMOD) is also proposed.