Automatic calibration of a spiking head-direction network for representing robot orientation

Calibration of movement tracking systems is a difficult problem faced by both animals and robots. The ability to continuously calibrate changing systems is essential for animals as they grow or are injured, and highly desirable for robot control or mapping systems due to the possibility of component wear, modification, damage and their deployment on varied robotic platforms. In this paper we use inspiration from the animal head direction tracking system to implement a self-calibrating, neurally-based robot orientation tracking system. Using real robot data we demonstrate how the system can remove tracking drift and learn to consistently track rotation over a large range of velocities. The neural tracking system provides the first steps towards a fully neural SLAM system with improved practical applicability through selftuning and adaptation.

Single camera vision-only SLAM on a suburban road network

Simultaneous Localization And Mapping (SLAM) is one of the major challenges in mobile robotics. Probabilistic techniques using high-end range finding devices are well established in the field, but recent work has investigated vision only approaches. This paper presents a method for generating approximate rotational and translation velocity information from a single vehicle-mounted consumer camera, without the computationally expensive process of tracking landmarks. The method is tested by employing it to provide the odometric and visual information for the RatSLAM system while mapping a complex suburban road network. RatSLAM generates a coherent map of the environment during an 18 km long trip through suburban traffic at speeds of up to 60 km/hr. This result demonstrates the potential of ground based vision-only SLAM using low cost sensing and computational hardware.

RatSLAM : a hippocampal model for simultaneous localization and mapping

The work presents a new approach to the problem of simultaneous localization and mapping - SLAM - inspired by computational models of the hippocampus of rodents. The rodent hippocampus has been extensively studied with respect to navigation tasks, and displays many of the properties of a desirable SLAM solution. RatSLAM is an implementation of a hippocampal model that can perform SLAM in real time on a real robot. It uses a competitive attractor network to integrate odometric information with landmark sensing to form a consistent representation of the environment. Experimental results show that RatSLAM can operate with ambiguous landmark information and recover from both minor and major path integration errors.

How long is a tweet? Mapping dynamic conversation networks on Twitter using Gawk and Gephi

Twitter is now well established as the world’s second most important social media platform, after Facebook. Its 140-character updates are designed for brief messaging, and its network structures are kept relatively flat and simple: messages from users are either public and visible to all (even to unregistered visitors using the Twitter website), or private and visible only to approved ‘followers’ of the sender; there are no more complex definitions of degrees of connection (family, friends, friends of friends) as they are available in other social networks. Over time, Twitter users have developed simple, but effective mechanisms for working around these limitations: ‘#hashtags’, which enable the manual or automatic collation of all tweets containing the same #hashtag, as well allowing users to subscribe to content feeds that contain only those tweets which feature specific #hashtags; and ‘@replies’, which allow senders to direct public messages even to users whom they do not already follow. This paper documents a methodology for extracting public Twitter activity data around specific #hashtags, and for processing these data in order to analyse and visualize the @reply networks existing between participating users – both overall, as a static network, and over time, to highlight the dynamic structure of @reply conversations. Such visualizations enable us to highlight the shifting roles played by individual participants, as well as the response of the overall #hashtag community to new stimuli – such as the entry of new participants or the availability of new information. Over longer timeframes, it is also possible to identify different phases in the overall discussion, or the formation of distinct clusters of preferentially interacting participants.

Mapping Australia's philanthropic investment in women and girls

The Australian Women Donors Network (Women Donors) partnered the Australian Centre for Philanthropy and Nonprofit Studies (ACPNS) at QUT to conduct this research. No studies exist on the size or sources of philanthropic giving in Australia directed intentionally towards the needs of women and girls. The survey aims to fill this knowledge gap and create a baseline for understanding trends and views in this area. Because the survey treads some new ground, its findings raise questions as well as giving answers. Encouragingly, 100 people from across the philanthropy spectrum completed the survey, embracing 41 individual donors and other respondents from various foundation types. Given the population difference, this response compares well with the 145 respondents to the USA-based Foundation Center’s 2009 European study (reported in 2011). The survey was designed for givers generally, not just those involved in giving to women and girls specifically. It is possible, though, that people with an interest in funding this area were more likely to participate. This potential oversampling may inflate the figures on funding women and girls to some degree. Also, because the population size of Australian philanthropists is unknown, no claims can be made that this information is generalizable to all Australian funders. Nonetheless, some patterns and themes emerge from the 100 responses.

Mapping the dynamics of the project management field : project management in action (part 6)

This is the sixth part of a Letter from the Editor series where the results are presented of an ongoing research undertaken in order to investigate the dynamic of the evolution of the field of project management and the key trends. Dynamics of networks is a key feature in strategic diagrams analysis. The radical change in the configuration of a network between two periods, or the change at subnetwork level reflects the dynamic of science. I present here an example of subnetwork comparison over the four periods of time considered in this study. I will develop and discuss an example of subnetwork transformation in future Letter from the Editor article..

The Chemistry Discipline Network: One Year On

The Chemistry Discipline Network was funded in mid-2011, with the aim of improving communication between chemistry academics in Australia. In our first year of operation, we have grown to over 100 members, established a web presence, and produced substantial mapping reports on chemistry teaching in Australia. We are now working on the definition of standards for a chemistry degree based on the Threshold Learning Outcomes published by the Learning and Teaching Academic Standards Project.

Phosphoprotein pathway mapping: Akt/Mammalian target of rapamycin activation is negatively associated with childhood rhabdomyosarcoma survival

Mapping of protein signaling networks within tumors can identify new targets for therapy and provide a means to stratify patients for individualized therapy. Despite advances in combination chemotherapy, the overall survival for childhood rhabdomyosarcoma remains ∼60%. A critical goal is to identify functionally important protein signaling defects associated with treatment failure for the 40% nonresponder cohort. Here, we show, by phosphoproteomic network analysis of microdissected tumor cells, that interlinked components of the Akt/mammalian target of rapamycin (mTOR) pathway exhibited increased levels of phosphorylation for tumors of patients with short-term survival. Specimens (n = 59) were obtained from the Children's Oncology Group Intergroup Rhabdomyosarcoma Study (IRS) IV, D9502 and D9803, with 12-year follow-up. High phosphorylation levels were associated with poor overall and poor disease-free survival: Akt Ser473 (overall survival P < 0.001, recurrence-free survival P < 0.0009), 4EBP1 Thr37/46 (overall survival P < 0.0110, recurrence-free survival P < 0.0106), eIF4G Ser1108 (overall survival P < 0.0017, recurrence-free survival P < 0.0072), and p70S6 Thr389 (overall survival P < 0.0085, recurrence-free survival P < 0.0296). Moreover, the findings support an altered interrelationship between the insulin receptor substrate (IRS-1) and Akt/mTOR pathway proteins (P < 0.0027) for tumors from patients with poor survival. The functional significance of this pathway was tested using CCI-779 in a mouse xenograft model. CCI-779 suppressed phosphorylation of mTOR downstream proteins and greatly reduced the growth of two different rhabdomyosarcoma (RD embryonal P = 0.00008; Rh30 alveolar P = 0.0002) cell lines compared with controls. These results suggest that phosphoprotein mapping of the Akt/mTOR pathway should be studied further as a means to select patients to receive mTOR/IRS pathway inhibitors before administration of chemotherapy.

Use of reverse phase protein microarrays and reference standard development for molecular network analysis of metastatic ovarian carcinoma

Cancer can be defined as a deregulation or hyperactivity in the ongoing network of intracellular and extracellular signaling events. Reverse phase protein microarray technology may offer a new opportunity to measure and profile these signaling pathways, providing data on post-translational phosphorylation events not obtainable by gene microarray analysis. Treatment of ovarian epithelial carcinoma almost always takes place in a metastatic setting since unfortunately the disease is often not detected until later stages. Thus, in addition to elucidation of the molecular network within a tumor specimen, critical questions are to what extent do signaling changes occur upon metastasis and are there common pathway elements that arise in the metastatic microenvironment. For individualized combinatorial therapy, ideal therapeutic selection based on proteomic mapping of phosphorylation end points may require evaluation of the patient's metastatic tissue. Extending these findings to the bedside will require the development of optimized protocols and reference standards. We have developed a reference standard based on a mixture of phosphorylated peptides to begin to address this challenge.

Development and integration of a solar powered unmanned aerial vehicle and a wireless sensor network to monitor greenhouse gases

Measuring gases for environmental monitoring is a demanding task that requires long periods of observation and large numbers of sensors. Wireless Sensor Networks (WSNs) and Unmanned Aerial Vehicles (UAVs) currently represent the best alternative to monitor large, remote, and difficult access areas, as these technologies have the possibility of carrying specialized gas sensing systems. This paper presents the development and integration of a WSN and an UAV powered by solar energy in order to enhance their functionality and broader their applications. A gas sensing system implementing nanostructured metal oxide (MOX) and non-dispersive infrared sensors was developed to measure concentrations of CH4 and CO2. Laboratory, bench and field testing results demonstrate the capability of UAV to capture, analyze and geo-locate a gas sample during flight operations. The field testing integrated ground sensor nodes and the UAV to measure CO2 concentration at ground and low aerial altitudes, simultaneously. Data collected during the mission was transmitted in real time to a central node for analysis and 3D mapping of the target gas. The results highlights the accomplishment of the first flight mission of a solar powered UAV equipped with a CO2 sensing system integrated with a WSN. The system provides an effective 3D monitoring and can be used in a wide range of environmental applications such as agriculture, bushfires, mining studies, zoology and botanical studies using a ubiquitous low cost technology.

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.

Identity-by-Descent Mapping to Detect Rare Variants Conferring Susceptibility to Multiple Sclerosis

Genome-wide association studies (GWAS) have identified around 60 common variants associated with multiple sclerosis (MS), but these loci only explain a fraction of the heritability of MS. Some missing heritability may be caused by rare variants that have been suggested to play an important role in the aetiology of complex diseases such as MS. However current genetic and statistical methods for detecting rare variants are expensive and time consuming. 'Population-based linkage analysis' (PBLA) or so called identity-by-descent (IBD) mapping is a novel way to detect rare variants in extant GWAS datasets. We employed BEAGLE fastIBD to search for rare MS variants utilising IBD mapping in a large GWAS dataset of 3,543 cases and 5,898 controls. We identified a genome-wide significant linkage signal on chromosome 19 (LOD = 4.65; p = 1.9×10-6). Network analysis of cases and controls sharing haplotypes on chromosome 19 further strengthened the association as there are more large networks of cases sharing haplotypes than controls. This linkage region includes a cluster of zinc finger genes of unknown function. Analysis of genome wide transcriptome data suggests that genes in this zinc finger cluster may be involved in very early developmental regulation of the CNS. Our study also indicates that BEAGLE fastIBD allowed identification of rare variants in large unrelated population with moderate computational intensity. Even with the development of whole-genome sequencing, IBD mapping still may be a promising way to narrow down the region of interest for sequencing priority. © 2013 Lin et al.

Using relationship mapping to understand sustainable housing stakeholders’ actions

The production of sustainable housing requires the cooperation of a variety of participants with different goals, needs, levels of commitment and cultures. To achieve mainstream net zero energy housing objectives, there is arguably a need for a non-linear network of collaboration between all the stakeholders. In order to create and improve such collaborative networks between stakeholders, we first need to map stakeholders’ relationships, processes, and practices. This paper discusses compares and contrasts maps of the sustainable housing production life-cycle in Australia, developed from different perspectives. The paper highlights the strengths and weaknesses of each visualization, clarifying where gaps in connectivity exist within existing industry networks. Understanding these gaps will help researchers and practitioners identify how to improve the collaboration between participants in the housing industry. This in turn may improve decision making across all stakeholder groups, leading to mainstream implementation of sustainability into the housing industry.

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.