ACE research vignette 037 : Bill Gates’ mug shot : Is there a secret link between rule-breaking, crime, and entrepreneurship?

This series of research vignettes is aimed at sharing current and interesting research findings from international entrepreneurship researchers. In this vignette, Dr. Martin Obschonka, considers the relationship between entrepreneurship and rule-breaking.

Gone Girl and the mediatization of crime

Gone Girl (David Fincher, 2014) will not be remembered for its representation of journalists, although both lead characters are, as the narrative opens in 2012, magazine writers made redundant in the wake of the 2008 global financial crisis. To this extent they personify the “death of journalism” narrative of recent years in the United States, but we never see them in a newsroom or doing journalistic work. The marriage of Nick and Amy Dunne (Ben Affleck and Rosamund Pike) is cast as a victim of, among other things, the downturn in the US economy which accompanied the credit crunch. But this is not the subject of Gone Girl, so much as a context for the marital dysfunctionality at the heart of its plot...

When death is not a crime : challenges for police and policing

The over-representation of vulnerable populations within the criminal justice system, and the role of police in perpetuating this, has long been a topic of discussion in criminology. What is less discussed is the way in which non -criminal investigations by police, in areas like a death investigation, may perpetuate similar types of engagement with vulnerable populations. In Australia, as elsewhere, it is the police who are responsible for investigating both suspicious and violent deaths like homicide as well as non - suspicious, violent deaths like accidents and suicides. Police are also the agents tasked with investigating deaths which are neither violent nor suspicious but occur outside hospitals and other care facilities. This paper reports on how the police describe - or are described by others - their role in a non - suspicious death investigation, and the challenges that such investigations raise for police and policing.

Binding properties of peptidic affinity ligands for plasmid DNA capture and detection

Peptides constructed from α-helical subunits of the Lac repressor protein (LacI) were designed then tailored to achieve particular binding kinetics and dissociation constants for plasmid DNA purification and detection. Surface plasmon resonance was employed for quantification and characterization of the binding of double stranded Escherichia coli plasmid DNA (pUC19) via the lac operon (lacO) to "biomimics" of the DNA binding domain of LacI. Equilibrium dissociation constants (K D), association (k a), and dissociation rates (k d) for the interaction between a suite of peptide sequences and pUC19 were determined. K D values measured for the binding of pUC19 to the 47mer, 27mer, 16mer, and 14mer peptides were 8.8 ± 1.3 × 10 -10 M, 7.2 ± 0.6 × 10 -10 M, 4.5 ± 0.5 × 10 -8 M, and 6.2 ± 0.9 × 10 -6 M, respectively. These findings show that affinity peptides, composed of subunits from a naturally occurring operon-repressor interaction, can be designed to achieve binding characteristics suitable for affinity chromatography and biosensor devices.

Corner detection in images under different noise levels

Corner detection has shown its great importance in many computer vision tasks. However, in real-world applications, noise in the image strongly affects the performance of corner detectors. Few corner detectors have been designed to be robust to heavy noise by now, partly because the noise could be reduced by a denoising procedure. In this paper, we present a corner detector that could find discriminative corners in images contaminated by noise of different levels, without any denoising procedure. Candidate corners (i.e., features) are firstly detected by a modified SUSAN approach, and then false corners in noise are rejected based on their local characteristics. Features in flat regions are removed based on their intensity centroid, and features on edge structures are removed using the Harris response. The detector is self-adaptive to noise since the image signal-to-noise ratio (SNR) is automatically estimated to choose an appropriate threshold for refining features. Experimental results show that our detector has better performance at locating discriminative corners in images with strong noise than other widely used corner or keypoint detectors.

Assessing the impact of crime on residential property markets

The aim of this research is to determine if a range of crimes in a suburb have an impact on the residential property sectors in that particular suburb. With the increasing media coverage of crime in specific locations, this knowledge of crime in Brisbane Australia is more available to potential residential property buyers This research is based on the analysis of the crime statistics for 30 suburbs in Brisbane across a range of major crime activities and compares the level of crime to property median prices, sales volume and in a range of suburbs the volume of sale and lease listings. The results of the research show a significant variation in the response of buyers in residential property markets based on the type of crime and the socio-economic status of the suburb. In a range of suburbs, value factors other than crime are the major drivers of the market. The study provides an insight into consumer behaviour in a major city and the response of residential property buyers to an increasing level and awareness of crime statistics in the suburbs they are considering to buy. Implications of this research are that with a greater level of awareness of factors that could be a disadvantage to some potential buyers are not always reflected across a full residential property market. Valuers, property financiers and the public need to be aware of the type of crime and locations that have a direct impact on property prices and saleability These results expand on the current knowledge of value drivers in major residential property markets.

‘Only a pawn in their game’ : crime, risk and politics in the case of Robert Fardon

In 2003 Robert Fardon was the first prisoner to be detained under the Dangerous Prisoners (Sexual Offenders) Act 2003 (Qld), the first of the new generation preventive detention laws enacted in Australia and directed at keeping sex offenders in prison or under supervision beyond the expiry of their sentences where a court decides, on the basis of psychiatric assessments, that unconditional release would create an unacceptable risk to the community. A careful examination of Fardon’s case shows the extent to which the administration of the regime was from the outset governed by politics and political calculation rather than the logic of risk management and community protection. In 2003 Robert Fardon was the first person detained under the Dangerous Prisoners (Sexual Offenders) Act 2003 (Qld) (hereafter DPSOA), a newly enacted Queensland law aimed at the preventive detention of sex offenders. It was the first of a new generation of such laws introduced in Australia, now also in force in NSW, Western Australia and Victoria. The laws have been widely criticized by lawyers, academics and others (Keyzer and McSherry 2009; Edgely 2007). In this article I want to focus on the details of how the Queensland law was administered in Fardon’s case, he being perhaps the most well-known prisoner detained under such laws and certainly the longest held. It will show, I hope, that seemingly abstract rule of law principles invoked by other critics are not simply abstract: they afford a crucial practical safeguard against the corruption of criminal justice in which the ends both of community protection and of justice give way to opportunistic exploitation of ‘the mythic resonance of crime and punishment for electoral purposes’ (Scheingold 1998: 888).

Staged crime scenes : literature and types

Many things can hamper an investigation. For example, the crime may be a truly random occurrence without links between the victim and the offender, evidence may not be acknowledged or properly collected, and the crime type itself may influence solvability. In other cases still, offenders actively seek to hamper the police investigation in an effort to avoid being caught and going to prison. In fact, the literature on homicide notes that it is not uncommon in many cases of this type for the offender to engage in precautionary acts (Turvey, 2007)...

On the use of optical flow for scene change detection and description

We propose the use of optical flow information as a method for detecting and describing changes in the environment, from the perspective of a mobile camera. We analyze the characteristics of the optical flow signal and demonstrate how robust flow vectors can be generated and used for the detection of depth discontinuities and appearance changes at key locations. To successfully achieve this task, a full discussion on camera positioning, distortion compensation, noise filtering, and parameter estimation is presented. We then extract statistical attributes from the flow signal to describe the location of the scene changes. We also employ clustering and dominant shape of vectors to increase the descriptiveness. Once a database of nodes (where a node is a detected scene change) and their corresponding flow features is created, matching can be performed whenever nodes are encountered, such that topological localization can be achieved. We retrieve the most likely node according to the Mahalanobis and Chi-square distances between the current frame and the database. The results illustrate the applicability of the technique for detecting and describing scene changes in diverse lighting conditions, considering indoor and outdoor environments and different robot platforms.

Automatic UAV forced landing site detection using machine learning

The commercialization of aerial image processing is highly dependent on the platforms such as UAVs (Unmanned Aerial Vehicles). However, the lack of an automated UAV forced landing site detection system has been identified as one of the main impediments to allow UAV flight over populated areas in civilian airspace. This article proposes a UAV forced landing site detection system that is based on machine learning approaches including the Gaussian Mixture Model and the Support Vector Machine. A range of learning parameters are analysed including the number of Guassian mixtures, support vector kernels including linear, radial basis function Kernel (RBF) and polynormial kernel (poly), and the order of RBF kernel and polynormial kernel. Moreover, a modified footprint operator is employed during feature extraction to better describe the geometric characteristics of the local area surrounding a pixel. The performance of the presented system is compared to a baseline UAV forced landing site detection system which uses edge features and an Artificial Neural Network (ANN) region type classifier. Experiments conducted on aerial image datasets captured over typical urban environments reveal improved landing site detection can be achieved with an SVM classifier with an RBF kernel using a combination of colour and texture features. Compared to the baseline system, the proposed system provides significant improvement in term of the chance to detect a safe landing area, and the performance is more stable than the baseline in the presence of changes to the UAV altitude.

Green-fluorescent protein facilitates rapid in vivo detection of genetically transformed plant cells

Early detection of plant transformation events is necessary for the rapid establishment and optimization of plant transformation protocols. We have assessed modified versions of the green fluorescent protein (GFP) from Aequorea victoria as early reporters of plant transformation using a dissecting fluorescence microscope with appropriate filters. Gfp-expressing cells from four different plant species (sugarcane, maize, lettuce, and tobacco) were readily distinguished, following either Agrobacterium-mediated or particle bombardment-mediated transformation. The identification of gfp-expressing sugarcane cells allowed for the elimination of a high proportion of non-expressing explants and also enabled visual selection of dividing transgenic cells, an early step in the generation of transgenic organisms. The recovery of transgenic cell clusters was streamlined by the ability to visualize gfp-expressing tissues in vitro.

Application of anomaly technique in wind turbine bearing fault detection

Bearing faults are the most common cause of wind turbine failures. Unavailability and maintenance cost of wind turbines are becoming critically important, with their fast growing in electric networks. Early fault detection can reduce outage time and costs. This paper proposes Anomaly Detection (AD) machine learning algorithms for fault diagnosis of wind turbine bearings. The application of this method on a real data set was conducted and is presented in this paper. For validation and comparison purposes, a set of baseline results are produced using the popular one-class SVM methods to examine the ability of the proposed technique in detecting incipient faults.

Pan-Plasmodium band sensitivity for Plasmodium falciparum detection in combination malaria rapid diagnostic tests and implications for clinical management

Background: Malaria rapid diagnostic tests (RDTs) are appropriate for case management, but persistent antigenaemia is a concern for HRP2-detecting RDTs in endemic areas. It has been suggested that pan-pLDH test bands on combination RDTs could be used to distinguish persistent antigenaemia from active Plasmodium falciparum infection, however this assumes all active infections produce positive results on both bands of RDTs, an assertion that has not been demonstrated. Methods: In this study, data generated during the WHO-FIND product testing programme for malaria RDTs was reviewed to investigate the reactivity of individual test bands against P. falciparum in 18 combination RDTs. Each product was tested against multiple wild-type P. falciparum only samples. Antigen levels were measured by quantitative ELISA for HRP2, pLDH and aldolase. Results: When tested against P. falciparum samples at 200 parasites/μL, 92% of RDTs were positive; 57% of these on both the P. falciparum and pan bands, while 43% were positive on the P. falciparum band only. There was a relationship between antigen concentration and band positivity; ≥4 ng/mL of HRP2 produced positive results in more than 95% of P. falciparum bands, while ≥45 ng/mL of pLDH was required for at least 90% of pan bands to be positive. Conclusions: In active P. falciparum infections it is common for combination RDTs to return a positive HRP2 band combined with a negative pan-pLDH band, and when both bands are positive, often the pan band is faint. Thus active infections could be missed if the presence of a HRP2 band in the absence of a pan band is interpreted as being caused solely by persistent antigenaemia.

Improving robot vision models for object detection through interaction

We propose a method for learning specific object representations that can be applied (and reused) in visual detection and identification tasks. A machine learning technique called Cartesian Genetic Programming (CGP) is used to create these models based on a series of images. Our research investigates how manipulation actions might allow for the development of better visual models and therefore better robot vision. This paper describes how visual object representations can be learned and improved by performing object manipulation actions, such as, poke, push and pick-up with a humanoid robot. The improvement can be measured and allows for the robot to select and perform the `right' action, i.e. the action with the best possible improvement of the detector.