Popular dimension reduction and visualisation algorithms rely on the assumption that input dissimilarities are typically Euclidean, for instance Metric Multidimensional Scaling, t-distributed Stochastic Neighbour Embedding and the Gaussian Process Latent Variable Model. It is well known that this assumption does not hold for most datasets and often high-dimensional data sits upon a manifold of unknown global geometry. We present a method for improving the manifold charting process, coupled with Elastic MDS, such that we no longer assume that the manifold is Euclidean, or of any particular structure. We draw on the benefits of different dissimilarity measures allowing for the relative responsibilities, under a linear combination, to drive the visualisation process.

to assist in the early warning of deterioration in hospitalised children we studied the feasibility of collecting continuous wireless physiological data using Lifetouch (ecG-derived heart and respiratory rate) and WristOx2 (pulse-oximetry and derived pulse rate) sensors. We compared our bedside paediatric early warning (peW) score and a machine learning automated approach: a Real-time Adaptive Predictive Indicator of Deterioration (RAPID) to identify children experiencing significant clinical deterioration. 982 patients contributed 7,073,486 min during 1,263 monitoring sessions. The proportion of intended monitoring time was 93% for Lifetouch and 55% for WristOx2. Valid clinical data was 63% of intended monitoring time for Lifetouch and 50% WristOx2. 29 patients experienced 36 clinically significant deteriorations. The RAPID Index detected significant deterioration more frequently (77% to 97%) and earlier than the PEW score ≥ 9/26. High sensitivity and negative predictive value for the RAPID Index was associated with low specificity and low positive predictive value. We conclude that it is feasible to collect clinically valid physiological data wirelessly for 50% of intended monitoring time. The RAPID Index identified more deterioration, before the PEW score, but has a low specificity. By using the RAPID Index with a PEW system some life-threatening events may be averted. Paediatric Early Warning (PEW) systems have reduced the late or undetected clinical deterioration experienced by some hospitalised children 1-3. Following the introduction of the bedside PEW score in Birmingham Children's Hospital, a multidisciplinary team identified missed opportunities and recommended more continuous monitoring for visualisation of trends and a smart alarm for earlier detection of deterioration. Continuous and intermittent monitoring has led to early identification of patient deterioration, increased rapid response activations and improvements in completeness of vital signs documentation 4,5. Standard monitors require sensors to be hard-wired to patients and require additional software to extract data for advanced analytics 6. Wireless monitoring offers a potentially more comfortable and economical solution although accuracy, continuity, patient tolerability and power management are challenging 7,8. A number of sensors and monitoring systems have been developed and shown to be feasible but few have been sufficiently developed and tested in large clinical trials 7-15. The advantage of more continuous monitoring for ward patients is that trends and Big Data analytics can be used to improve detection of deterioration 16-18. The intent is not that smart alarms replace clinical surveillance but that they are used in addition to current systems to support and augment decision making 19-21. Neither continuous wireless monitoring nor integrated smart alarms have been reported in the context of paediatric wards. This study tests the feasibility of collecting sufficiently useful continuous wireless monitoring and the feasibility of a re...

Poverty, the quintessential denominator of a developing nation, has been traditionally defined against an arbitrary poverty line; individuals (or countries) below this line are deemed poor and those above it, not so! This has two pitfalls. First, absolute reliance on a single poverty line, based on basic food consumption, and not on total consumption distribution, is only a partial poverty index at best. Second, a single expense descriptor is an exogenous quantity that does not evolve from income-expenditure statistics. Using extensive income-expenditure statistics from India, here we show how a self-consistent endogenous poverty line can be derived from an agent-based stochastic model of market exchange, combining all expenditure modes (basic food, other food and non-food), whose parameters are probabilistically estimated using advanced Machine Learning tools. Our mathematical study establishes a consumption based poverty measure that combines labor, commodity, and asset market outcomes, delivering an excellent tool for economic policy formulation.

Abstract-Creating human-informative signal processing systems for the underwater acoustic environment that do not generate operator cognitive saturation and overload is a major challenge. To alleviate cognitive operator overload, we present a visual analytics methodology in which multiple beam-formed sonar returns are mapped to an optimized 2-D visual representation, which preserves the relevant data structure. This representation alerts the operator as to which beams are likely to contain anomalous information by modeling a latent distribution of information for each beam. Sonar operators therefore focus their attention only on the surprising events. In addition to the principled visualization of high-dimensional uncertain data, the system quantifies anomalous information using a Fisher Information measure. Central to this process is the novel use of both signal and noise observation modeling to characterize the sensor information. A demonstration of detecting exceptionally low signal-to-noise ratio targets embedded in real-world 33-beam passive sonar data is presented.