Hierarchical topological network analysis of anatomical human brain connectivity and differences related to sex and kinship

Modern non-invasive brain imaging technologies, such as diffusion weighted magnetic resonance imaging (DWI), enable the mapping of neural fiber tracts in the white matter, providing a basis to reconstruct a detailed map of brain structural connectivity networks. Brain connectivity networks differ from random networks in their topology, which can be measured using small worldness, modularity, and high-degree nodes (hubs). Still, little is known about how individual differences in structural brain network properties relate to age, sex, or genetic differences. Recently, some groups have reported brain network biomarkers that enable differentiation among individuals, pairs of individuals, and groups of individuals. In addition to studying new topological features, here we provide a unifying general method to investigate topological brain networks and connectivity differences between individuals, pairs of individuals, and groups of individuals at several levels of the data hierarchy, while appropriately controlling false discovery rate (FDR) errors. We apply our new method to a large dataset of high quality brain connectivity networks obtained from High Angular Resolution Diffusion Imaging (HARDI) tractography in 303 young adult twins, siblings, and unrelated people. Our proposed approach can accurately classify brain connectivity networks based on sex (93% accuracy) and kinship (88.5% accuracy). We find statistically significant differences associated with sex and kinship both in the brain connectivity networks and in derived topological metrics, such as the clustering coefficient and the communicability matrix.

Neural activity associated with semantic versus phonological anomia treatments in aphasia

Naming impairments in aphasia are typically targeted using semantic and/or phonologically based tasks. However, it is not known whether these treatments have different neural mechanisms. Eight participants with aphasia received twelve treatment sessions using an alternating treatment design, with fMRI scans pre- and post-treatment. Half the sessions employed Phonological Components Analysis (PCA), and half the sessions employed Semantic Feature Analysis (SFA). Pre-treatment activity in the left caudate correlated with greater immediate treatment success for items treated with SFA, whereas recruitment of the left supramarginal gyrus and right precuneus post-treatment correlated with greater immediate treatment success for items treated with PCA. The results support previous studies that have found greater treatment outcome to be associated with activity in predominantly left hemisphere regions, and suggest that different mechanisms may be engaged dependent on the type of treatment employed.

Intellectual property and climate change: Inventing clean technologies

In the wake of the international summits in Copenhagen and Cancún, there is an urgent need to consider the role of intellectual property law in encouraging research, development, and diffusion of clean technologies to mitigate and adapt to the effects of climate change. This book charts the patent landscapes and legal conflicts emerging in a range of fields of innovation – including renewable forms of energy, such as solar power, wind power, and geothermal energy; as well as biofuels, green chemistry, green vehicles, energy efficiency, and smart grids. As well as reviewing key international treaties, this book provides a detailed analysis of current trends in patent policy and administration in key nation states, and offers clear recommendations for law reform. It considers such options as technology transfer, compulsory licensing, public sector licensing, and patent pools; and analyses the development of Climate Innovation Centres, the Eco-Patent Commons, and environmental prizes, such as the L-Prize, the H-Prize, and the X-Prizes. This book will have particular appeal to policy-makers given its focus upon recent legislative developments and reform proposals, as well as legal practitioners by developing a better understanding of recent legal, scientific, and business developments, and how they affect their practice. Innovators, scientists and researchers will also benefit from reading this book.

Intellectual property and emerging technologies: The new biology

This unique and comprehensive collection investigates the challenges posed to intellectual property by recent paradigm shifts in biology. It explores the legal ramifications of emerging technologies, such as genomics, synthetic biology, stem cell research, nanotechnology, and biodiscovery. Extensive contributions examine recent controversial court decisions in patent law – such as Bilski v. Kappos, and the litigation over Myriad’s patents in respect of BRCA1 and BRCA2 – while other papers explore sui generis fields, such as access to genetic resources, plant breeders' rights, and traditional knowledge. The collection considers the potential and the risks of the new biology for global challenges – such as access to health-care, the protection of the environment and biodiversity, climate change, and food security. It also considers Big Science projects – such as biobanks, the 1000 Genomes Project, and the Doomsday Vault. The inter-disciplinary research brings together the work of scholars from Australia, Canada, Europe, the UK and the US and involves not only legal analysis of case law and policy developments, but also historical, comparative, sociological, and ethical methodologies. Intellectual Property and Emerging Technologies will appeal to policy-makers, legal practitioners, business managers, inventors, scientists and researchers.

Re-theorizing the “digital divide”: Identifying dimensions of social exclusion in contemporary media technologies

The digital divide is the disparancy in access to information, in the ability to communicate, and in the capacity to make information and communication serve full participation in the information society. Indeed, the conversation about the digital divide has developed over the last decade from a focus on connectivity and access to information and communication technologies, to a conversation that encompasses the ability to use them and to the utility that usage provides (Wei et al., 2011). However, this conversation, while transitioning from technology to the skills of the people that use them and to the fruits of their use is limited in its ability to take into account the social role of information and communication technologies (ICTs). One successful attempt in conceptualizing the social impact of the differences in access to and utilization of digital communication technologies, was developed by van Dijk (2005) whose sequential model for analyzing the divide states that: 1. Categorical inequalities in society produce an unequal distribution of resources; 2. An unequal distribution of resources causes unequal access to digital technologies; 3. Unequal access to digital technologies also depends on the characteristics of these technologies; 4. Unequal access to digital technologies brings about unequal participation in society; 5. Unequal participation in society reinforces categorical inequalities and unequal distributions of resources.” (p. 15) As van Dijk’s model demonstrates, the divide’s impact is the exclusion of individuals from participation. Still left to be defined are the “categorical inequalities,” the “resources,” the “characteristics of digital technologies,” and the different levels of “access” that result in differentiated levels of participation, as these change over time due to the evolving nature of technology and the dynamics of society. And most importantly, the meaning of “participation” in contemporary society needs to be determined as it is differentiated levels of participation that are the result of the divide and the engine of the ever-growing disparities. Our argument is structured in the following manner: We first claim that contemporary digital media differ from the previous generation of ICTs along four dimensions: They offer an abundance of information resources and communication channels when compared to the relative paucity of both in the past; they offer mobility as opposed to the stationary nature of their predecessors; they are interactive in that they provide users with the capability to design their own media environments in contrast to the dictated environs of previous architectures; and, they allow users to communicate utilizing multi forms of mediation, unlike the uniformity of sound or word that limited users in the past. We then submit that involvement in the information society calls for egalitarian access to all four dimensions of the user experience that make contemporary media different from their predecessors and that the ability to experience all four affects the levels in which humans partake in the shaping of society. The model being cyclical, we then discuss how lower levels of participation contribute to the enhancement of social inequalities. Finally, we discuss why participation is needed in order to achieve full membership in the information society and what political philosophy should govern policy solutions targeting the re-inclusion of those digitally excluded.

Improving Near-Miss Event Detection Rate at Railway Level Crossings

Even though crashes between trains and road users are rare events at railway level crossings, they are one of the major safety concerns for the Australian railway industry. Nearmiss events at level crossings occur more frequently, and can provide more information about factors leading to level crossing incidents. In this paper we introduce a video analytic approach for automatically detecting and localizing vehicles from cameras mounted on trains for detecting near-miss events. To detect and localize vehicles at level crossings we extract patches from an image and classify each patch for detecting vehicles. We developed a region proposals algorithm for generating patches, and we use a Convolutional Neural Network (CNN) for classifying each patch. To localize vehicles in images we combine the patches that are classified as vehicles according to their CNN scores and positions. We compared our system with the Deformable Part Models (DPM) and Regions with CNN features (R-CNN) object detectors. Experimental results on a railway dataset show that the recall rate of our proposed system is 29% higher than what can be achieved with DPM or R-CNN detectors.

Application of simulated annealing to data distribution for all-to-all comparison problems in homogeneous systems

Distributed systems are widely used for solving large-scale and data-intensive computing problems, including all-to-all comparison (ATAC) problems. However, when used for ATAC problems, existing computational frameworks such as Hadoop focus on load balancing for allocating comparison tasks, without careful consideration of data distribution and storage usage. While Hadoop-based solutions provide users with simplicity of implementation, their inherent MapReduce computing pattern does not match the ATAC pattern. This leads to load imbalances and poor data locality when Hadoop's data distribution strategy is used for ATAC problems. Here we present a data distribution strategy which considers data locality, load balancing and storage savings for ATAC computing problems in homogeneous distributed systems. A simulated annealing algorithm is developed for data distribution and task scheduling. Experimental results show a significant performance improvement for our approach over Hadoop-based solutions.

Distributed computing of all-to-all comparison problems in heterogeneous systems

The requirement of distributed computing of all-to-all comparison (ATAC) problems in heterogeneous systems is increasingly important in various domains. Though Hadoop-based solutions are widely used, they are inefficient for the ATAC pattern, which is fundamentally different from the MapReduce pattern for which Hadoop is designed. They exhibit poor data locality and unbalanced allocation of comparison tasks, particularly in heterogeneous systems. The results in massive data movement at runtime and ineffective utilization of computing resources, affecting the overall computing performance significantly. To address these problems, a scalable and efficient data and task distribution strategy is presented in this paper for processing large-scale ATAC problems in heterogeneous systems. It not only saves storage space but also achieves load balancing and good data locality for all comparison tasks. Experiments of bioinformatics examples show that about 89\% of the ideal performance capacity of the multiple machines have be achieved through using the approach presented in this paper.

Using genetic algorithm in profile-based assignment of applications to virtual machines for greener data centers

The increase in data center dependent services has made energy optimization of data centers one of the most exigent challenges in today's Information Age. The necessity of green and energy-efficient measures is very high for reducing carbon footprint and exorbitant energy costs. However, inefficient application management of data centers results in high energy consumption and low resource utilization efficiency. Unfortunately, in most cases, deploying an energy-efficient application management solution inevitably degrades the resource utilization efficiency of the data centers. To address this problem, a Penalty-based Genetic Algorithm (GA) is presented in this paper to solve a defined profile-based application assignment problem whilst maintaining a trade-off between the power consumption performance and resource utilization performance. Case studies show that the penalty-based GA is highly scalable and provides 16% to 32% better solutions than a greedy algorithm.

Improving deep convultional neural networks with unsupervised feature learning

The latest generation of Deep Convolutional Neural Networks (DCNN) have dramatically advanced challenging computer vision tasks, especially in object detection and object classification, achieving state-of-the-art performance in several computer vision tasks including text recognition, sign recognition, face recognition and scene understanding. The depth of these supervised networks has enabled learning deeper and hierarchical representation of features. In parallel, unsupervised deep learning such as Convolutional Deep Belief Network (CDBN) has also achieved state-of-the-art in many computer vision tasks. However, there is very limited research on jointly exploiting the strength of these two approaches. In this paper, we investigate the learning capability of both methods. We compare the output of individual layers and show that many learnt filters and outputs of the corresponding level layer are almost similar for both approaches. Stacking the DCNN on top of unsupervised layers or replacing layers in the DCNN with the corresponding learnt layers in the CDBN can improve the recognition/classification accuracy and training computational expense. We demonstrate the validity of the proposal on ImageNet dataset.

Digital design interventions for creating new presentations of self in public urban places

During everyday urban life, people spend time in public urban places waiting for specific events to occur. During these times, people sometimes tend to engage with their information and communication technology (ICT) devices in a way that shuts off interactions with collocated people. These devices could also be used to better connect with the urban space and collocated people within. This chapter presents and discusses the impact of three design interventions on the urban user experience enabling collocated people to share lightweight, non-privacy-sensitive data in the urban space. We investigate and discuss the impact on the urban experience under the notions of people, place, and technology with an emphasis on how the sharing of non-privacy-sensitive data can positively transform anonymous public urban places in various ways through anonymous digital augmentations.

The role of institutional pressures and top management support in the intention to adopt cloud computing solutions

Purpose – While many studies have predominantly looked at the benefits and risks of cloud computing, little is known whether and to what extent institutional forces play a role in cloud computing adoption. The purpose of this paper is to explore the role of institutional factors in top management team’s (TMT’s) decision to adopt cloud computing services. Design/methodology/approach – A model is developed and tested with data from an Australian survey using the partial least squares modeling technique. Findings – The results suggest that mimetic and coercive pressures influence TMT’s beliefs in the benefits of cloud computing. The results also show that TMT’s beliefs drive TMT’s participation, which in turn affects the intention to increase the adoption of cloud computing solutions. Research limitations/implications – Future studies could incorporate the influences of local actors who might also press for innovation. Practical implications – Given the influence of institutional forces and the plethora of cloud-based solutions on the market, it is recommended that TMTs exercise a high degree of caution when deciding for the types of applications to be outsourced as organizational requirements in terms of performance and security will differ. Originality/value – The paper contributes to the growing empirical literature on cloud computing adoption and offers the institutional framework as an alternative lens with which to interpret cloud-based information technology outsourcing.

Storytelling and new media technologies: Investigating the potential of the ABC's Heywire for regional youth

This thesis investigates an instance of audience participation within the Australian public service media institution, the ABC. The case study for this research – the ABC’s ‘Heywire’ project – is a regional youth storytelling project, an online platform, and an example of the potentials as well as the limitations of personal narrative and digital technologies for incorporating the voices and self-representations of audiences. By investigating 'Heywire', this thesis illuminates the exciting opportunities and also some profound challenges that arise when public service media institutions invite their audiences to participate as content creators and storytellers.

Advances in solid-catalytic and non-catalytic technologies for biodiesel production

The insecure supply of fossil fuel coerces the scientific society to keep a vision to boost investments in the renewable energy sector. Among the many renewable fuels currently available around the world, biodiesel offers an immediate impact in our energy. In fact, a huge interest in related research indicates a promising future for the biodiesel technology. Heterogeneous catalyzed production of biodiesel has emerged as a preferred route as it is environmentally benign needs no water washing and product separation is much easier. The number of well-defined catalyst complexes that are able to catalyze transesterification reactions efficiently has been significantly expanded in recent years. The activity of catalysts, specifically in application to solid acid/base catalyst in transesterification reaction depends on their structure, strength of basicity/acidity, surface area as well as the stability of catalyst. There are various process intensification technologies based on the use of alternate energy sources such as ultrasound and microwave. The latest advances in research and development related to biodiesel production is represented by non-catalytic supercritical method and focussed exclusively on these processes as forthcoming transesterification processes. The latest developments in this field featuring highly active catalyst complexes are outlined in this review. The knowledge of more extensive research on advances in biofuels will allow a deeper insight into the mechanism of these technologies toward meeting the critical energy challenges in future.