IS-impact utility and intuitiveness : a rigorous approach to relevance

The study proposes to test the ‘IS-Impact’ index as Analytic Theory (AT). To (a) methodically evaluate the ‘relevance’ qualities of IS-Impact; namely, Utility & Intuitiveness. In so doing, to (b) document an exemplar of ‘a rigorous approach to relevance’, while (c) treating the overarching study as a higher-order case study having AT as the unit-of-analysis, and assessing adequacy of the 6 AT qualities, both for IS-Impact and for similar taxonomies. Also to (d) look beyond IS-Impact to other forms of Design Science, considering the generality of the AT qualities; and (e) further validating IS-Impact in new system organisation contexts taking account of contemporary understandings of construct theorisation, operationalization and validation.

Exponentiated gradient algorithms for conditional random fields and max-margin Markov Networks

Log-linear and maximum-margin models are two commonly-used methods in supervised machine learning, and are frequently used in structured prediction problems. Efficient learning of parameters in these models is therefore an important problem, and becomes a key factor when learning from very large data sets. This paper describes exponentiated gradient (EG) algorithms for training such models, where EG updates are applied to the convex dual of either the log-linear or max-margin objective function; the dual in both the log-linear and max-margin cases corresponds to minimizing a convex function with simplex constraints. We study both batch and online variants of the algorithm, and provide rates of convergence for both cases. In the max-margin case, O(1/ε) EG updates are required to reach a given accuracy ε in the dual; in contrast, for log-linear models only O(log(1/ε)) updates are required. For both the max-margin and log-linear cases, our bounds suggest that the online EG algorithm requires a factor of n less computation to reach a desired accuracy than the batch EG algorithm, where n is the number of training examples. Our experiments confirm that the online algorithms are much faster than the batch algorithms in practice. We describe how the EG updates factor in a convenient way for structured prediction problems, allowing the algorithms to be efficiently applied to problems such as sequence learning or natural language parsing. We perform extensive evaluation of the algorithms, comparing them to L-BFGS and stochastic gradient descent for log-linear models, and to SVM-Struct for max-margin models. The algorithms are applied to a multi-class problem as well as to a more complex large-scale parsing task. In all these settings, the EG algorithms presented here outperform the other methods.

Utility of routine data sources for feedback on the quality of cancer care: an assessment based on clinical practice guidelines

Background Not all cancer patients receive state-of-the-art care and providing regular feedback to clinicians might reduce this problem. The purpose of this study was to assess the utility of various data sources in providing feedback on the quality of cancer care. Methods Published clinical practice guidelines were used to obtain a list of processes-of-care of interest to clinicians. These were assigned to one of four data categories according to their availability and the marginal cost of using them for feedback. Results Only 8 (3%) of 243 processes-of-care could be measured using population-based registry or administrative inpatient data (lowest cost). A further 119 (49%) could be measured using a core clinical registry, which contains information on important prognostic factors (e.g., clinical stage, physiological reserve, hormone-receptor status). Another 88 (36%) required an expanded clinical registry or medical record review; mainly because they concerned long-term management of disease progression (recurrences and metastases) and 28 (11.5%) required patient interview or audio-taping of consultations because they involved information sharing between clinician and patient. Conclusion The advantages of population-based cancer registries and administrative inpatient data are wide coverage and low cost. The disadvantage is that they currently contain information on only a few processes-of-care. In most jurisdictions, clinical cancer registries, which can be used to report on many more processes-of-care, do not cover smaller hospitals. If we are to provide feedback about all patients, not just those in larger academic hospitals with the most developed data systems, then we need to develop sustainable population-based data systems that capture information on prognostic factors at the time of initial diagnosis and information on management of disease progression.

Mean and variance of estimates of the bispectrum of a harmonic random process-an analysis including leakage effects

Analytical expressions are derived for the mean and variance, of estimates of the bispectrum of a real-time series assuming a cosinusoidal model. The effects of spectral leakage, inherent in discrete Fourier transform operation when the modes present in the signal have a nonintegral number of wavelengths in the record, are included in the analysis. A single phase-coupled triad of modes can cause the bispectrum to have a nonzero mean value over the entire region of computation owing to leakage. The variance of bispectral estimates in the presence of leakage has contributions from individual modes and from triads of phase-coupled modes. Time-domain windowing reduces the leakage. The theoretical expressions for the mean and variance of bispectral estimates are derived in terms of a function dependent on an arbitrary symmetric time-domain window applied to the record. the number of data, and the statistics of the phase coupling among triads of modes. The theoretical results are verified by numerical simulations for simple test cases and applied to laboratory data to examine phase coupling in a hypothesis testing framework

Compilation of somatic mutations of the CDKN2 gene in human cancers : non-random distribution of base substitutions

The CDKN2 gene, encoding the cyclin-dependent kinase inhibitor p16, is a tumour suppressor gene that maps to chromosome band 9p21-p22. The most common mechanism of inactivation of this gene in human cancers is through homozygous deletion; however, in a smaller proportion of tumours and tumour cell lines intragenic mutations occur. In this study we have compiled a database of over 120 published point mutations in the CDKN2 gene from a wide variety of tumour types. A further 50 deletions, insertions, and splice mutations in CDKN2 have also been compiled. Furthermore, we have standardised the numbering of all mutations according to the full-length 156 amino acid form of p16. From this study we are able to define several hot spots, some of which occur at conserved residues within the ankyrin domains of p16. While many of the hotspots are shared by a number of cancers, the relative importance of each position varies, possibly reflecting the role of different carcinogens in the development of certain tumours. As reported previously, the mutational spectrum of CDKN2 in melanomas differs from that of internal malignancies and supports the involvement of UV in melanoma tumorigenesis. Notably, 52% of all substitutions in melanoma-derived samples occurred at just six nucleotide positions. Nonsense mutations comprise a comparatively high proportion of mutations present in the CDKN2 gene, and possible explanations for this are discussed.

The role of idea novelty and relatedness in nascent ventures

The study of venture idea characteristics and the contextual fit between venture ideas and individuals are key research goals in entrepreneurship (Davidsson, 2004). However, to date there has been limited scholarly attention given to these phenomena. Accordingly, this study aims to help fill the gap by investigating the importance of novelty and relatedness of venture ideas in entrepreneurial firms. On the premise that new venture creation is a process and that research should be focused on the early stages of the venturing process, this study primarily focuses its attention on examining how venture idea novelty and relatedness affect the performance in the venture creation process. Different types and degrees of novelty are considered here. Relatedness is shown to be based on individuals’ prior knowledge and resource endowment. Performance in the venture creation process is evaluated according to four possible outcomes: making progress, getting operational, being terminated and achieving positive cash flow. A theoretical model is developed demonstrating the relationship between these variables along with the investment of time and money. Several hypotheses are developed to be tested. Among them, it is hypothesised that novelty hinders short term performance in the venture creation process. On the other hand knowledge and resource relatedness are hypothesised to promote performance. An experimental study was required in order to understand how different types and degrees of novelty and relatedness of venture ideas affect the attractiveness of venture ideas in the eyes of experienced entrepreneurs. Thus, the empirical work in this thesis was based on two separate studies. In the first one, a conjoint analysis experiment was conducted on 32 experienced entrepreneurs in order to ascertain attitudinal preferences regarding venture idea attractiveness based on novelty, relatedness and potential financial gains. This helped to estimate utility values for different levels of different attributes of venture ideas and their relative importance in the attractiveness. The second study was a longitudinal investigation of how venture idea novelty and relatedness affect the performance in the venture creation process. The data for this study is from the Comprehensive Australian Study for Entrepreneurial Emergence (CAUSEE) project that has been established in order to explore the new venture creation process in Australia. CAUSEE collects data from a representative sample of over 30,000 households in Australia using random digit dialling (RDD) telephone interviews. From these cases, data was collected at two points in time during a 12 month period from 493 firms, who are currently involved in the start-up process. Hypotheses were tested and inferences were derived through descriptive statistics, confirmatory factor analysis and structural equation modelling. Results of study 1 indicate that venture idea characteristics have a role in the attractiveness and entrepreneurs prefer to introduce a moderate degree of novelty across all types of venture ideas concerned. Knowledge relatedness is demonstrated to be a more significant factor in attractiveness than resource relatedness. Results of study 2 show that the novelty hinders nascent venture performance. On the other hand, resource relatedness has a positive impact on performance unlike knowledge relatedness which has none. The results of these studies have important implications for potential entrepreneurs, investors, researchers, consultants etc. by developing a better understanding in the venture creation process and its success factors in terms of both theory and practice.

Assessment of function and clinical utility of alcohol and other drug web sites : an observational, qualitative study

Background The increasing popularity and use of the internet makes it an attractive option for providing health information and treatment, including alcohol/other drug use. There is limited research examining how people identify and access information about alcohol or other drug (AOD) use online, or how they assess the usefulness of the information presented. This study examined the strategies that individuals used to identify and navigate a range of AOD websites, along with the attitudes concerning presentation and content. Methods Members of the general community in Brisbane and Roma (Queensland, Australia) were invited to participate in a 30-minute search of the internet for sites related to AOD use, followed by a focus group discussion. Fifty one subjects participated in the study across nine focus groups. Results Participants spent a maximum of 6.5 minutes on any one website, and less if the user was under 25 years of age. Time spent was as little as 2 minutes if the website was not the first accessed. Participants recommended that AOD-related websites should have an engaging home or index page, which quickly and accurately portrayed the site’s objectives, and provided clear site navigation options. Website content should clearly match the title and description of the site that is used by internet search engines. Participants supported the development of a portal for AOD websites, suggesting that it would greatly facilitate access and navigation. Treatment programs delivered online were initially viewed with caution. This appeared to be due to limited understanding of what constituted online treatment, including its potential efficacy. Conclusions A range of recommendations arise from this study regarding the design and development of websites, particularly those related to AOD use. These include prudent use of text and information on any one webpage, the use of graphics and colours, and clear, uncluttered navigation options. Implications for future website development are discussed.

Clinical utility of mental state screening as a predictor of intellectual outcomes 6 months after diagnosis of type 1 diabetes

Background Screening tests of basic cognitive status or ‘mental state’ have been shown to predict mortality and functional outcomes in adults. This study examined the relationship between mental state and outcomes in children with type 1 diabetes. Objective We aimed to determine whether mental state at diagnosis predicts longer term cognitive function of children with a new diagnosis of type 1 diabetes. Methods Mental state of 87 patients presenting with newly diagnosed type 1 diabetes was assessed using the School-Years Screening Test for the Evaluation of Mental Status. Cognitive abilities were assessed 1 wk and 6 months postdiagnosis using standardized tests of attention, memory, and intelligence. Results Thirty-seven children (42.5%) had reduced mental state at diagnosis. Children with impaired mental state had poorer attention and memory in the week following diagnosis, and, after controlling for possible confounding factors, significantly lower IQ at 6 months compared to those with unimpaired mental state (p < 0.05). Conclusions Cognition is impaired acutely in a significant number of children presenting with newly diagnosed type 1 diabetes. Mental state screening is an effective method of identifying children at risk of ongoing cognitive difficulties in the days and months following diagnosis. Clinicians may consider mental state screening for all newly diagnosed diabetic children to identify those at risk of cognitive sequelae.

Sequential fusion of decisions from adaptive and random samples for controlled verification of errors

Fusion techniques have received considerable attention for achieving performance improvement with biometrics. While a multi-sample fusion architecture reduces false rejects, it also increases false accepts. This impact on performance also depends on the nature of subsequent attempts, i.e., random or adaptive. Expressions for error rates are presented and experimentally evaluated in this work by considering the multi-sample fusion architecture for text-dependent speaker verification using HMM based digit dependent speaker models. Analysis incorporating correlation modeling demonstrates that the use of adaptive samples improves overall fusion performance compared to randomly repeated samples. For a text dependent speaker verification system using digit strings, sequential decision fusion of seven instances with three random samples is shown to reduce the overall error of the verification system by 26% which can be further reduced by 6% for adaptive samples. This analysis novel in its treatment of random and adaptive multiple presentations within a sequential fused decision architecture, is also applicable to other biometric modalities such as finger prints and handwriting samples.

Modeling random effect and excess zeros in road traffic accident prediction

Poisson distribution has often been used for count like accident data. Negative Binomial (NB) distribution has been adopted in the count data to take care of the over-dispersion problem. However, Poisson and NB distributions are incapable of taking into account some unobserved heterogeneities due to spatial and temporal effects of accident data. To overcome this problem, Random Effect models have been developed. Again another challenge with existing traffic accident prediction models is the distribution of excess zero accident observations in some accident data. Although Zero-Inflated Poisson (ZIP) model is capable of handling the dual-state system in accident data with excess zero observations, it does not accommodate the within-location correlation and between-location correlation heterogeneities which are the basic motivations for the need of the Random Effect models. This paper proposes an effective way of fitting ZIP model with location specific random effects and for model calibration and assessment the Bayesian analysis is recommended.