Prosecutors’ perceptions of the utility of video-evidence for adult complainants of sexual assault

Playing an adult sexual complainant’s video-recorded police interview as the basis for his or her evidence-in-chief is a reform Australia could adopt to help improve criminal justice responses to these cases. This article presents a qualitative evaluation of prosecutor’s support for this reform and their views about what conditions would determine its utility. Focus groups were held with 13 prosecutors from across New Zealand (which already has this reform) and Australia. Collectively, prosecutors supported the availability of video-evidence for adult complainants. They perceived the utility of this reform depends on the following conditions: (1) the quality of the police interview; (2) how credibly the complainant presents on video; (3) contextual factors that influence the complainant’s ability to give live evidence; and (4) the degree of stakeholder support. These findings suggest that Australia should extend video-evidence to adult complainants of sexual assault guided by careful planning aroundthese four areas.

Expected shannon entropy and shannon differentiation between subpopulations for neutral genes under the finite island model

Shannon entropy H and related measures are increasingly used in molecular ecology and population genetics because (1) unlike measures based on heterozygosity or allele number, these measures weigh alleles in proportion to their population fraction, thus capturing a previously-ignored aspect of allele frequency distributions that may be important in many applications; (2) these measures connect directly to the rich predictive mathematics of information theory; (3) Shannon entropy is completely additive and has an explicitly hierarchical nature; and (4) Shannon entropy-based differentiation measures obey strong monotonicity properties that heterozygosity-based measures lack. We derive simple new expressions for the expected values of the Shannon entropy of the equilibrium allele distribution at a neutral locus in a single isolated population under two models of mutation: the infinite allele model and the stepwise mutation model. Surprisingly, this complex stochastic system for each model has an entropy expressable as a simple combination of well-known mathematical functions. Moreover, entropy- and heterozygosity-based measures for each model are linked by simple relationships that are shown by simulations to be approximately valid even far from equilibrium. We also identify a bridge between the two models of mutation. We apply our approach to subdivided populations which follow the finite island model, obtaining the Shannon entropy of the equilibrium allele distributions of the subpopulations and of the total population. We also derive the expected mutual information and normalized mutual information ("Shannon differentiation") between subpopulations at equilibrium, and identify the model parameters that determine them. We apply our measures to data from the common starling (Sturnus vulgaris) in Australia. Our measures provide a test for neutrality that is robust to violations of equilibrium assumptions, as verified on real world data from starlings.

Utility of biological sensor tags in animal conservation

Electronic tags (both biotelemetry and biologging platforms) have informed conservation and resource management policy and practice by providing vital information on the spatial ecology of animals and their environments. However, the extent of the contribution of biological sensors (within electronic tags) that measure an animal's state (e.g., heart rate, body temperature, and details of locomotion and energetics) is less clear. A literature review revealed that, despite a growing number of commercially available state sensor tags and enormous application potential for such devices in animal biology, there are relatively few examples of their application to conservation. Existing applications fell under 4 main themes: quantifying disturbance (e.g., ecotourism, vehicular and aircraft traffic), examining the effects of environmental change (e.g., climate change), understanding the consequences of habitat use and selection, and estimating energy expenditure. We also identified several other ways in which sensor tags could benefit conservation, such as determining the potential efficacy of management interventions. With increasing sensor diversity of commercially available platforms, less invasive attachment techniques, smaller device sizes, and more researchers embracing such technology, we suggest that biological sensor tags be considered a part of the necessary toolbox for conservation. This approach can measure (in real time) the state of free-ranging animals and thus provide managers with objective, timely, relevant, and accurate data to inform policy and decision making.

Evaluating and predicting risk to a large reptile (Varanus varius) from feral cat baiting protocols

Control of introduced predators to mitigate biodiversity impacts is a pressing conservation challenge. Across Australia feral cats (Felis catus) are a major threat to terrestrial biodiversity. Currently feral cat control is hindered by the limited utility of existing predator baiting methods. Further proposed control methods include use of the novel poison para-aminopropiophenone (PAPP) which may present a hazard to some native animal populations. Here we used experimental and predictive approaches to evaluate feral cat bait take by a large native Australian predatory reptile the Lace monitor (Varanus varius). These lizards would be expected to readily detect, ingest and consume a lethal dose (depending on toxin) from surface-laid baits intended for feral cat control if a precautionary approach was not adopted when baiting. We modelled V. varius bait take using experimental and predictive biophysical modelling approaches to evaluate temporal effects of climate variables on V. varius activity and hence potential for bait removal. Finally we conducted a pre-PAPP baiting site occupancy assessment of V. varius within Wilson Promontory National Park (WPNP) to provide a basis for monitoring any longer term population effects of cat baiting. V. varius removed 7 % of deployed baits from 73 % of bait stations across another study area in Far Eastern Victoria. Daily bait removal was positively correlated with maximum temperature and solar radiation. Biophysical modelling for Far Eastern Victoria predicted that maximum temperatures <19.5 °C prevented V. varius activity and hence opportunity for bait removal. V. varius in WPNP was undetectable suggesting aerial baiting posed limited hazard to this species at this location. Depending how climate influences annual activity patterns and the specific poison, surface-laid baits could pose a significant mortality risk to V. varius. However, use of biophysical models to predict periods of V. varius inactivity may provide a novel means to reduce non-target bait take by this predator.

Differentially private random forest with high utility

Privacy-preserving data mining has become an active focus of the research community in the domains where data are sensitive and personal in nature. For example, highly sensitive digital repositories of medical or financial records offer enormous values for risk prediction and decision making. However, prediction models derived from such repositories should maintain strict privacy of individuals. We propose a novel random forest algorithm under the framework of differential privacy. Unlike previous works that strictly follow differential privacy and keep the complete data distribution approximately invariant to change in one data instance, we only keep the necessary statistics (e.g. variance of the estimate) invariant. This relaxation results in significantly higher utility. To realize our approach, we propose a novel differentially private decision tree induction algorithm and use them to create an ensemble of decision trees. We also propose feasible adversary models to infer about the attribute and class label of unknown data in presence of the knowledge of all other data. Under these adversary models, we derive bounds on the maximum number of trees that are allowed in the ensemble while maintaining privacy. We focus on binary classification problem and demonstrate our approach on four real-world datasets. Compared to the existing privacy preserving approaches we achieve significantly higher utility.

Correlated differential privacy: hiding information in non-IID data set

Privacy preserving on data mining and data release has attracted an increasing research interest over a number of decades. Differential privacy is one influential privacy notion that offers a rigorous and provable privacy guarantee for data mining and data release. Existing studies on differential privacy assume that in a data set, records are sampled independently. However, in real-world applications, records in a data set are rarely independent. The relationships among records are referred to as correlated information and the data set is defined as correlated data set. A differential privacy technique performed on a correlated data set will disclose more information than expected, and this is a serious privacy violation. Although recent research was concerned with this new privacy violation, it still calls for a solid solution for the correlated data set. Moreover, how to decrease the large amount of noise incurred via differential privacy in correlated data set is yet to be explored. To fill the gap, this paper proposes an effective correlated differential privacy solution by defining the correlated sensitivity and designing a correlated data releasing mechanism. With consideration of the correlated levels between records, the proposed correlated sensitivity can significantly decrease the noise compared with traditional global sensitivity. The correlated data releasing mechanism correlated iteration mechanism is designed based on an iterative method to answer a large number of queries. Compared with the traditional method, the proposed correlated differential privacy solution enhances the privacy guarantee for a correlated data set with less accuracy cost. Experimental results show that the proposed solution outperforms traditional differential privacy in terms of mean square error on large group of queries. This also suggests the correlated differential privacy can successfully retain the utility while preserving the privacy.

Inter-hospital 'patient expect' calls of clinical handovers for expected patients transferred from rural to metropolitan hospitals: a retrospective clinical audit

Patients requiring inter-hospital air transport across large geographical spaces are at significant risk of adverse outcomes. The aims of this study were to examine the characteristics of clinical handover conducted by telephone and subsequently transcribed in medical records during the inter-hospital transfer of rural patients, and to identify any deficits of this telephone clinical handover. A retrospective audit was conducted of transcribed telephone handovers ('patient expect' calls) occurring with inter-hospital transfers from two rural hospitals to a metropolitan tertiary hospital of all rural patients (n = 127) between January and June 2012. Patient transport between various sites occurred through the Royal Flying Doctor Service. For these hospitals, patient expect calls constituted the only handover record for clinicians during the time of patient transport. Information on patient identification stickers relating to patients' age or gender did not always correspond with details collected during patient expect calls. The name of a clinician at the receiving hospital authorising the transfer was provided in 14 calls (11.1%). It was difficult to determine who made and received calls, and who accepted responsibility for patients at the receiving site. Deterioration in a patient's condition was made in three calls. Actions to be taken after patients' arrival were included in 24 (19%) calls. Planning was restricted to identifying who to contact to review instructions. Inconsistent and overuse of abbreviations was likely to have affected the ability to accurately read back patient information. Crucial information was missing from calls, which may have contributed to delayed and inappropriate delivery of care.

Privacy aware K-means clustering with high utility

Privacy-preserving data mining aims to keep data safe, yet useful. But algorithms providing strong guarantees often end up with low utility. We propose a novel privacy preserving framework that thwarts an adversary from inferring an unknown data point by ensuring that the estimation error is almost invariant to the inclusion/exclusion of the data point. By focusing directly on the estimation error of the data point, our framework is able to significantly lower the perturbation required. We use this framework to propose a new privacy aware K-means clustering algorithm. Using both synthetic and real datasets, we demonstrate that the utility of this algorithm is almost equal to that of the unperturbed K-means, and at strict privacy levels, almost twice as good as compared to the differential privacy counterpart.

Effective Data Management Enables Intelligent Utility Management

Instrumentation and automation plays a vital role to managing the water industry. These systems generate vast amounts of data that must be effectively managed in order to enable intelligent decision making. Time series data management software, commonly known as data historians are used for collecting and managing real-time (time series) information. More advanced software solutions provide a data infrastructure or utility wide Operations Data Management System (ODMS) that stores, manages, calculates, displays, shares, and integrates data from multiple disparate automation and business systems that are used daily in water utilities. These ODMS solutions are proven and have the ability to manage data from smart water meters to the collaboration of data across third party corporations. This paper focuses on practical, utility successes in the water industry where utility managers are leveraging instantaneous access to data from proven, commercial off-the-shelf ODMS solutions to enable better real-time decision making. Successes include saving $650,000 / year in water loss control, safeguarding water quality, saving millions of dollars in energy management and asset management. Immediate opportunities exist to integrate the research being done in academia with these ODMS solutions in the field and to leverage these successes to utilities around the world.

Um modelo de competição baseada em tempo nos mercados financeiros

O objetivo deste trabalho é modelar o comportamento estratégico dos indivíduos diante de um choque estocástico que desloca o preço de determinado ativo financeiro do seu equilíbrio inicial. Investiga-se o caminho do preço de mercado em direção ao novo equilíbrio, conduzido pelas sucessivas negociações dos agentes em busca de oportunidades de obter lucros imediatos. Os operadores, que por suposição possuem funções de utilidade avessas ao risco, devem escolher a quantidade ótima transacionada e quanto devem aguardar para executar as suas ordens, tendo em vista a diminuição da volatilidade do preço do ativo à medida que as transações se sucedem após o choque. Procura-se demonstrar que os operadores que aceitam incorrer em riscos mais elevados negociam com maior frequência e em volumes e velocidades maiores, usufruindo lucros esperados mais altos que os demais.

Tests of conditional asset pricing models in the brazilian stock market

In this paper, we test a version of the conditional CAPM with respect to a local market portfolio, proxied by the Brazilian stock index during the period 1976-1992. We also test a conditional APT modeI by using the difference between the 3-day rate (Cdb) and the overnight rate as a second factor in addition to the market portfolio in order to capture the large inflation risk present during this period. The conditional CAPM and APT models are estimated by the Generalized Method of Moments (GMM) and tested on a set of size portfolios created from individual securities exchanged on the Brazilian markets. The inclusion of this second factor proves to be important for the appropriate pricing of the portfolios.

Nonparametric specification tests for conditional duration models

This paper deals with the testing of autoregressive conditional duration (ACD) models by gauging the distance between the parametric density and hazard rate functions implied by the duration process and their non-parametric estimates. We derive the asymptotic justification using the functional delta method for fixed and gamma kernels, and then investigate the finite-sample properties through Monte Carlo simulations. Although our tests display some size distortion, bootstrapping suffices to correct the size without compromising their excellent power. We show the practical usefulness of such testing procedures for the estimation of intraday volatility patterns.