At home with the war: The Great War in Victorian private life. The 2014 Augustus Wolskel lecture

This article examines the Great War in Victoria through the lens of private sentiment. It exposes not only the diversity of perspectives and sentiment surrounding the war, but also the stresses endured by Victorians trying to reconcile their commitment to the war with personal and familial needs.Their experience was dominated by a confrontation with powerful currents of anxiety over the war and their loved ones, and increasing tensions within their communities over who was bearing the greater burdens of the war. Investigating private experience of total war at home allows us to see how Victorians made as well as endured the Great War, as their communities struggled to remain cohesive, and individuals struggled to cope with its demands.

War and private sentiment in Australia during 1915

This article investigates the development of a total war mentality in Australia during the First World War. Through a study of private letters and diaries, it observes the much greater level of popular commitment to the war that emerged in the middle of 1915, and an increasing acceptance throughout that year that the expanding war had taken on a life of its own, and that it would not end suddenly or without tremendous sacrifice. By the end of 1915, Australians were showing ever greater levels of dedication to a war offering increasingly less sense of how long it might continue.

Determinants of corporate capital structure: a comparison of public and private firms

 This thesis investigates how capital structure decisions of private and public firms in the UK differ in regards to their ownership structure, information asymmetry (proxied by audit quality) and access to debt capital.

The National Perinatal Depression Initiative: An evaluation of access to general practitioners, psychologists and psychiatrists through the Medicare Benefits Schedule

OBJECTIVE: To evaluate the impact of the National Perinatal Depression Initiative on access to Medicare services for women at risk of perinatal mental illness. METHOD: Retrospective cohort study using difference-in-difference analytical methods to quantify the impact of the National Perinatal Depression Initiative policies on Medicare Benefits Schedule mental health usage by Australian women giving birth between 2006 and 2010. A random sample of women of reproductive age enrolled in Medicare who had not given birth where used as controls. The main outcome measures were the proportions of women giving birth each month who accessed a Medicare Benefits Schedule mental health items during the perinatal period (pregnancy through to the end of the first postnatal year) before and after the introduction of the National Perinatal Depression Initiative. RESULTS: The proportion of women giving birth who accessed at least one mental health item during the perinatal period increased from 88 to 141 per 1000 between 2007 and 2010. The difference-in-difference analysis showed that while there was an overall increase in Medicare Benefits Schedule mental health item access as a result of the National Perinatal Depression Initiative, this did not reach statistical significance. However, the National Perinatal Depression Initiative was found to significantly increase access in subpopulations of women, particularly those aged under 25 and over 34 years living in major cities. CONCLUSION: In the 2 years following its introduction, the National Perinatal Depression Initiative was found to have increased access to Medicare funded mental health services in particular groups of women. However, an overall increase across all groups did not reach statistical significance. Further studies are needed to assess the impact of the National Perinatal Depression Initiative on women during childbearing years, including access to tertiary care, the cost-effectiveness of the initiative, and mental health outcomes. It is recommended that new mental health policy initiatives incorporate a planned strategic approach to evaluation, which includes sufficient follow-up to assess the impact of public health strategies.

Do social capital building strategies influence the financing behavior of chinese private small and medium-sized enterprises?

© 2013 Baylor University. Using data from 65,485 Chinese private small and medium-sized enterprises over the period 2000-2006, we examine the extent to which firms can improve access to debt by adopting strategies aimed at building social capital, namely entertaining and gift giving to others in their social network, and obtaining political affiliation. We find that although entertainment and gift-giving expenditure leads to higher levels of total and short-term debt, it does not enable firms to obtain greater long-term debt. In contrast, we demonstrate that obtaining political affiliation allows firms greater access to long-term debt.

Ownership concentration and product innovation in Chinese private SMEs

Product innovation is extremely important to the growth, success, and ultimate survival of firms. Although its unique features in small and medium-sized enterprises (SMEs) have gained growing attention in the literature, there is limited knowledge as to how ownership concentration moderates the relationship between product innovation and its determinants. Based upon insights from agency and institutional theories, we examine the moderating effects of ownership concentration on the relationship between product innovation and its key determinants in Chinese SMEs, utilizing a large dataset of 43,728 Chinese firms over the period 2005-2006. We focus on examining the differences between single-owner SMEs, where there is dominant control of one family member, and multiple-owner SMEs, where principal-agent conflicts and principal-principal conflicts are more likely to occur. Our findings indicate that single-owned firms tend to convert research and development into product innovation more efficiently than firms with multiple owners, who are typically better at utilizing external sources of knowledge and human capital.

A differentially private algorithm for location data release

The rise of mobile technologies in recent years has led to large volumes of location information, which are valuable resources for knowledge discovery such as travel patterns mining and traffic analysis. However, location dataset has been confronted with serious privacy concerns because adversaries may re-identify a user and his/her sensitivity information from these datasets with only a little background knowledge. Recently, several privacy-preserving techniques have been proposed to address the problem, but most of them lack a strict privacy notion and can hardly resist the number of possible attacks. This paper proposes a private release algorithm to randomize location dataset in a strict privacy notion, differential privacy, with the goal of preserving users’ identities and sensitive information. The algorithm aims to mask the exact locations of each user as well as the frequency that the user visits the locations with a given privacy budget. It includes three privacy-preserving operations: private location clustering shrinks the randomized domain and cluster weight perturbation hides the weights of locations, while private location selection hides the exact locations of a user. Theoretical analysis on privacy and utility confirms an improved trade-off between privacy and utility of released location data. Extensive experiments have been carried out on four real-world datasets, GeoLife, Flickr, Div400 and Instagram. The experimental results further suggest that this private release algorithm can successfully retain the utility of the datasets while preserving users’ privacy.

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.

Royal Australian and New Zealand College of Psychiatrists clinical practice guidelines for mood disorders

© 2015 The Royal Australian and New Zealand College of Psychiatrists. Objectives: To provide guidance for the management of mood disorders, based on scientific evidence supplemented by expert clinical consensus and formulate recommendations to maximise clinical salience and utility. Methods: Articles and information sourced from search engines including PubMed and EMBASE, MEDLINE, PsycINFO and Google Scholar were supplemented by literature known to the mood disorders committee (MDC) (e.g., books, book chapters and government reports) and from published depression and bipolar disorder guidelines. Information was reviewed and discussed by members of the MDC and findings were then formulated into consensus-based recommendations and clinical guidance. The guidelines were subjected to rigorous successive consultation and external review involving: expert and clinical advisors, the public, key stakeholders, professional bodies and specialist groups with interest in mood disorders. Results: The Royal Australian and New Zealand College of Psychiatrists clinical practice guidelines for mood disorders (Mood Disorders CPG) provide up-to-date guidance and advice regarding the management of mood disorders that is informed by evidence and clinical experience. The Mood Disorders CPG is intended for clinical use by psychiatrists, psychologists, physicians and others with an interest in mental health care. Conclusions: The Mood Disorder CPG is the first Clinical Practice Guideline to address both depressive and bipolar disorders. It provides up-to-date recommendations and guidance within an evidence-based framework, supplemented by expert clinical consensus. Mood Disorders Committee: Professor Gin Malhi (Chair), Professor Darryl Bassett, Professor Philip Boyce, Professor Richard Bryant, Professor Paul Fitzgerald, Dr Kristina Fritz, Professor Malcolm Hopwood, Dr Bill Lyndon, Professor Roger Mulder, Professor Greg Murray, Professor Richard Porter and Associate Professor Ajeet Singh. International expert advisors: Professor Carlo Altamura, Dr Francesco Colom, Professor Mark George, Professor Guy Goodwin, Professor Roger McIntyre, Dr Roger Ng, Professor John O'Brien, Professor Harold Sackeim, Professor Jan Scott, Dr Nobuhiro Sugiyama, Professor Eduard Vieta, Professor Lakshmi Yatham. Australian and New Zealand expert advisors: Professor Marie-Paule Austin, Professor Michael Berk, Dr Yulisha Byrow, Professor Helen Christensen, Dr Nick De Felice, A/Professor Seetal Dodd, A/Professor Megan Galbally, Dr Josh Geffen, Professor Philip Hazell, A/Professor David Horgan, A/Professor Felice Jacka, Professor Gordon Johnson, Professor Anthony Jorm, Dr Jon-Paul Khoo, Professor Jayashri Kulkarni, Dr Cameron Lacey, Dr Noeline Latt, Professor Florence Levy, A/Professor Andrew Lewis, Professor Colleen Loo, Dr Thomas Mayze, Dr Linton Meagher, Professor Philip Mitchell, Professor Daniel O'Connor, Dr Nick O'Connor, Dr Tim Outhred, Dr Mark Rowe, Dr Narelle Shadbolt, Dr Martien Snellen, Professor John Tiller, Dr Bill Watkins, Dr Raymond Wu.

Differentially private multi-task learning

Privacy restrictions of sensitive data repositories imply that the data analysis is performed in isolation at each data source. A prime example is the isolated nature of building prognosis models from hospital data and the associated challenge of dealing with small number of samples in risk classes (e.g. suicide) while doing so. Pooling knowledge from other hospitals, through multi-task learning, can alleviate this problem. However, if knowledge is to be shared unrestricted, privacy is breached. Addressing this, we propose a novel multi-task learning method that preserves privacy of data under the strong guarantees of differential privacy. Further, we develop a novel attribute-wise noise addition scheme that significantly lifts the utility of the proposed method. We demonstrate the effectiveness of our method with a synthetic and two real datasets.