Pass the scissors : consumers close in on the fashion industry

The fashion ecosystem is at boiling point as consumers turn up the heat in all areas of the fashion value, trend and supply chain. While traditionally fashion has been a monologue from designer brand to consumer, new technology and the virtual world has given consumers a voice to engage brands in a conversation to express evolving needs, ideas and feedback. Product customisation is no longer innovative. Successful brands are including customers in the design process and holding conversations ‘with’ them to improve product, manufacturing, sales, distribution, marketing and sustainable business practices. Co-creation and crowd sourcing are integral to any successful business model and designers and manufacturers are supplying the technology or tools for these creative, active, participatory ‘prosumers’. With this collaboration however, there arises a worrying trend for fashion professionals. The ‘design it yourself’, ‘indiepreneur’ who with the combination of technology, the internet, excess manufacturing capacity, crowd funding and the idea of sharing the creative integrity of a product (‘copyleft’ not copyright) is challenging the notion that the fashion supply chain is complex. The passive ‘consumer’ no longer exists. Fashion designers now share the stage with ‘amateur’ creators who are disrupting every activity they touch, while being motivated by profit as well as a quest for originality and innovation. This paper examines the effects this ‘consumer’ engagement is having on traditional fashion models and the fashion supply chain. Crowd sourcing, crowd funding, co-creating, design it yourself, global sourcing, the virtual supply chain, social media, online shopping, group buying, consumer to consumer marketing and retail, and branding the ‘individual’ are indicative of the new consumer-driven fashion models. Consumers now drive the fashion industry - from setting trends, through to creating, producing, selling and marketing product. They can turn up the heat at any time _ and any point _ in the fashion supply chain. They are raising the temperature at each and every stage of the chain, decreasing or eliminating the processes involved: decreasing the risk of fashion obsolescence, quantities for manufacture, complexity of distribution and the consumption of product; eliminating certain stages altogether and limiting the brand as custodians of marketing. Some brands are discovering a new ‘enemy’ – the very people they are trying to sell to. Keywords: fashion supply chain, virtual world, consumer, ‘prosumers’, co-creation, fashion designers

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 ion channel dynamics through reflected stochastic differential equations

Ion channels are membrane proteins that open and close at random and play a vital role in the electrical dynamics of excitable cells. The stochastic nature of the conformational changes these proteins undergo can be significant, however current stochastic modeling methodologies limit the ability to study such systems. Discrete-state Markov chain models are seen as the "gold standard," but are computationally intensive, restricting investigation of stochastic effects to the single-cell level. Continuous stochastic methods that use stochastic differential equations (SDEs) to model the system are more efficient but can lead to simulations that have no biological meaning. In this paper we show that modeling the behavior of ion channel dynamics by a reflected SDE ensures biologically realistic simulations, and we argue that this model follows from the continuous approximation of the discrete-state Markov chain model. Open channel and action potential statistics from simulations of ion channel dynamics using the reflected SDE are compared with those of a discrete-state Markov chain method. Results show that the reflected SDE simulations are in good agreement with the discrete-state approach. The reflected SDE model therefore provides a computationally efficient method to simulate ion channel dynamics while preserving the distributional properties of the discrete-state Markov chain model and also ensuring biologically realistic solutions. This framework could easily be extended to other biochemical reaction networks. © 2012 American Physical Society.

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.

A Kallikrein 15 (KLK15) single nucleotide polymorphism located close to a novel exon shows evidence of association with poor ovarian cancer survival

KLK15 over-expression is reported to be a significant predictor of reduced progression-free survival and overall survival in ovarian cancer. Our aim was to analyse the KLK15 gene for putative functional single nucleotide polymorphisms (SNPs) and assess the association of these and KLK15 HapMap tag SNPs with ovarian cancer survival. Results In silico analysis was performed to identify KLK15 regulatory elements and to classify potentially functional SNPs in these regions. After SNP validation and identification by DNA sequencing of ovarian cancer cell lines and aggressive ovarian cancer patients, 9 SNPs were shortlisted and genotyped using the Sequenom iPLEX Mass Array platform in a cohort of Australian ovarian cancer patients (N = 319). In the Australian dataset we observed significantly worse survival for the KLK15 rs266851 SNP in a dominant model (Hazard Ratio (HR) 1.42, 95% CI 1.02-1.96). This association was observed in the same direction in two independent datasets, with a combined HR for the three studies of 1.16 (1.00-1.34). This SNP lies 15bp downstream of a novel exon and is predicted to be involved in mRNA splicing. The mutant allele is also predicted to abrogate an HSF-2 binding site. Conclusions We provide evidence of association for the SNP rs266851 with ovarian cancer survival. Our results provide the impetus for downstream functional assays and additional independent validation studies to assess the role of KLK15 regulatory SNPs and KLK15 isoforms with alternative intracellular functional roles in ovarian cancer survival.

Document clustering evaluation : Divergence from a random baseline

Divergence from a random baseline is a technique for the evaluation of document clustering. It ensures cluster quality measures are performing work that prevents ineffective clusterings from giving high scores to clusterings that provide no useful result. These concepts are defined and analysed using intrinsic and extrinsic approaches to the evaluation of document cluster quality. This includes the classical clusters to categories approach and a novel approach that uses ad hoc information retrieval. The divergence from a random baseline approach is able to differentiate ineffective clusterings encountered in the INEX XML Mining track. It also appears to perform a normalisation similar to the Normalised Mutual Information (NMI) measure but it can be applied to any measure of cluster quality. When it is applied to the intrinsic measure of distortion as measured by RMSE, subtraction from a random baseline provides a clear optimum that is not apparent otherwise. This approach can be applied to any clustering evaluation. This paper describes its use in the context of document clustering evaluation.

Researching doctoral pedagogy close up : design and action in two doctoral programs

With growing international interest in diversifying sites for pedagogical work within the doctorate, doctoral programs of different kinds are being developed in different disciplinary, institutional and national settings. However, little is known about how the pedagogical work of these programs is designed and enacted, and with what effects. In this paper, we present two cases of doctoral pedagogical work being undertaken within different disciplinary and institutional settings to describe how learning opportunities were designed and to theorise what it means to be engaged in doing doctoral pedagogy. Starting from the position that working from a design model supports systematic and rigorous documentation and development of pedagogy, we employ the twin concepts of design and action, drawing broadly on rhetorical and ethnomethodological understandings of pedagogy as social action. Of particular interest within the concept of design itself is the concept of enactment, the translation of designs into the practices of doctoral work. Together, the two cases become a resource for ‘slowing down’ and making visible the practices of doctoral pedagogy that often go unrecognised because they appear so ordinary and everyday. This call for examining close-up existing doctoral education practices and relationships is attending to the ‘next challenge for doctoral education’ (Green, 2009).

Management of delirium in medicine : experience of a close observation unit

Objective(s): A new model of care for the management of patients with delirium was developed and evaluated. Method: A 4-bedded Close Observation Unit (COU) was introduced. The model comprised an education strategy for assistants in nursing (AIN), environmental adaptations and AIN to patient ratio of 1:4. Outcomes in all patients with delirium before and after introduction of the new model of care were compared. Results: 105 patients were admitted to COU, of whom 100 (95%) were diagnosed with delirium. In-hospital mortality improved after introduction of the unit (15% versus 5%; p=0.002) without significant change in length of stay, discharge destination or falls frequency. Conclusion: A dedicated unit for delirium management within medicine achieved a reduction in mortality.

Comparison of PCR, nested PCR, and random amplified polymorphic DNA PCR for detection and typing of Ureaplasma urealyticum in specimens from pregnant women

A PCR assay, using three primer pairs, was developed for the detection of Ureaplasma urealyticum, parvo biovar, mba types 1, 3, and 6, in cultured clinical specimens. The primer pairs were designed by using the polymorphic base positions within a 310- to 311-bp fragment of the 5* end and upstream control region of the mba gene. The specificity of the assay was confirmed with reference serovars 1, 3, 6, and 14 and by the amplified-fragment sizes (81 bp for mba 1, 262 bp for mba 3, and 193 bp for mba 6). A more sensitive nested PCR was also developed. This involved a first-step PCR, using the primers UMS-125 and UMA226, followed by the nested mba-type PCR described above. This nested PCR enabled the detection and typing of small numbers of U. urealyticum cells, including mixtures, directly in original clinical specimens. By using random amplified polymorphic DNA (RAPD) PCR with seven arbitrary primers, we were also able to differentiate the two biovars of U. urealyticum and to identify 13 RAPD-PCR subtypes. By applying these subtyping techniques to clinical samples collected from pregnant women, we established that (i) U. urealyticum is often a persistent colonizer of the lower genital tract from early midtrimester until the third trimester of pregnancy, (ii) mba type 6 was isolated significantly more often (P 5 0.048) from women who delivered preterm than from women who delivered at term, (iii) no particular ureaplasma subtype(s) was associated with placental infections and/or adverse pregnancy outcomes, and (iv) the ureaplasma subtypes most frequently isolated from women were the same subtypes most often isolated from infected placentas.

On the probability density function of the product of Rayleigh distributed random variables

In this paper we investigate the distribution of the product of Rayleigh distributed random variables. Considering the Mellin-Barnes inversion formula and using the saddle point approach we obtain an upper bound for the product distribution. The accuracy of this tail-approximation increases as the number of random variables in the product increase.

Social signal processing for pain monitoring using a hidden conditional random field

utomatic pain monitoring has the potential to greatly improve patient diagnosis and outcomes by providing a continuous objective measure. One of the most promising methods is to do this via automatically detecting facial expressions. However, current approaches have failed due to their inability to: 1) integrate the rigid and non-rigid head motion into a single feature representation, and 2) incorporate the salient temporal patterns into the classification stage. In this paper, we tackle the first problem by developing a “histogram of facial action units” representation using Active Appearance Model (AAM) face features, and then utilize a Hidden Conditional Random Field (HCRF) to overcome the second issue. We show that both of these methods improve the performance on the task of pain detection in sequence level compared to current state-of-the-art-methods on the UNBC-McMaster Shoulder Pain Archive.