Distributed maximum likelihood for simultaneous self-localization and tracking in sensor networks

We show that the sensor self-localization problem can be cast as a static parameter estimation problem for Hidden Markov Models and we implement fully decentralized versions of the Recursive Maximum Likelihood and on-line Expectation-Maximization algorithms to localize the sensor network simultaneously with target tracking. For linear Gaussian models, our algorithms can be implemented exactly using a distributed version of the Kalman filter and a novel message passing algorithm. The latter allows each node to compute the local derivatives of the likelihood or the sufficient statistics needed for Expectation-Maximization. In the non-linear case, a solution based on local linearization in the spirit of the Extended Kalman Filter is proposed. In numerical examples we demonstrate that the developed algorithms are able to learn the localization parameters. © 2012 IEEE.

Insulin analog preparations and their use in children and adolescents with type 1 diabetes mellitus.

Standard or 'traditional' human insulin preparations such as regular soluble insulin and neutral protamine Hagedorn (NPH) insulin have shortcomings in terms of their pharmacokinetic and pharmacodynamic properties that limit their clinical efficacy. Structurally modified insulin molecules or insulin 'analogs' have been developed with the aim of delivering insulin replacement therapy in a more physiological manner. In the last 10 years, five insulin analog preparations have become commercially available for clinical use in patients with type 1 diabetes mellitus: three 'rapid' or fast-acting analogs (insulin lispro, aspart, and glulisine) and two long-acting analogs (insulin glargine and detemir). This review highlights the specific pharmacokinetic properties of these new insulin analog preparations and focuses on their potential clinical advantages and disadvantages when used in children and adolescents with type 1 diabetes mellitus. The fast-acting analogs specifically facilitate more flexible insulin injection timing with regard to meals and activities, whereas the long-acting analogs have a more predictable profile of action and lack a peak effect. To date, clinical trials in children and adolescents have been few in number, but the evidence available from these and from other studies carried out in adults with type 1 diabetes suggest that they offer significant benefits in terms of reduced frequency of nocturnal hypoglycemia, better postprandial blood glucose control, and improved quality of life when compared with traditional insulins. In addition, insulin detemir therapy is unique in that patients may benefit from reduced risk of excessive weight, particularly during adolescence. Evidence for sustained long-term improvements in glycosylated hemoglobin, on the other hand, is modest. Furthermore, alterations to insulin/insulin-like growth factor I receptor binding characteristics have also raised theoretical concerns that insulin analogs may have an increased mitogenic potential and risk of tumor development, although evidence from both in vitro and in vivo animal studies do not support this assertion. Long-term surveillance has been recommended and further carefully designed prospective studies are needed to evaluate the overall benefits and clinical efficacy of insulin analog therapy in children and adolescents with type 1 diabetes.

Distributed maximum likelihood for self-localization in sensor networks

We show that the sensor localization problem can be cast as a static parameter estimation problem for Hidden Markov Models and we develop fully decentralized versions of the Recursive Maximum Likelihood and the Expectation-Maximization algorithms to localize the network. For linear Gaussian models, our algorithms can be implemented exactly using a distributed version of the Kalman filter and a message passing algorithm to propagate the derivatives of the likelihood. In the non-linear case, a solution based on local linearization in the spirit of the Extended Kalman Filter is proposed. In numerical examples we show that the developed algorithms are able to learn the localization parameters well.

Neural activity associated with the passive prediction of ambiguity and risk for aversive events.

In economic decision making, outcomes are described in terms of risk (uncertain outcomes with certain probabilities) and ambiguity (uncertain outcomes with uncertain probabilities). Humans are more averse to ambiguity than to risk, with a distinct neural system suggested as mediating this effect. However, there has been no clear disambiguation of activity related to decisions themselves from perceptual processing of ambiguity. In a functional magnetic resonance imaging (fMRI) experiment, we contrasted ambiguity, defined as a lack of information about outcome probabilities, to risk, where outcome probabilities are known, or ignorance, where outcomes are completely unknown and unknowable. We modified previously learned pavlovian CS+ stimuli such that they became an ambiguous cue and contrasted evoked brain activity both with an unmodified predictive CS+ (risky cue), and a cue that conveyed no information about outcome probabilities (ignorance cue). Compared with risk, ambiguous cues elicited activity in posterior inferior frontal gyrus and posterior parietal cortex during outcome anticipation. Furthermore, a similar set of regions was activated when ambiguous cues were compared with ignorance cues. Thus, regions previously shown to be engaged by decisions about ambiguous rewarding outcomes are also engaged by ambiguous outcome prediction in the context of aversive outcomes. Moreover, activation in these regions was seen even when no actual decision is made. Our findings suggest that these regions subserve a general function of contextual analysis when search for hidden information during outcome anticipation is both necessary and meaningful.

Student-t processes as alternatives to Gaussian processes

We investigate the Student-t process as an alternative to the Gaussian process as a non-parametric prior over functions. We derive closed form expressions for the marginal likelihood and predictive distribution of a Student-t process, by integrating away an inverse Wishart process prior over the co-variance kernel of a Gaussian process model. We show surprising equivalences between different hierarchical Gaussian process models leading to Student-t processes, and derive a new sampling scheme for the inverse Wishart process, which helps elucidate these equivalences. Overall, we show that a Student-t process can retain the attractive properties of a Gaussian process - a nonparamet-ric representation, analytic marginal and predictive distributions, and easy model selection through covariance kernels - but has enhanced flexibility, and predictive covariances that, unlike a Gaussian process, explicitly depend on the values of training observations. We verify empirically that a Student-t process is especially useful in situations where there are changes in covariance structure, or in applications such as Bayesian optimization, where accurate predictive covariances are critical for good performance. These advantages come at no additional computational cost over Gaussian processes.

Methods of patterning magnetic fields using bulk superconductors

High Temperature superconductors are able to carry very high current densities, and thereby sustain very high magnetic fields. There are many projects which use the first property and these have concentrated on power generation, transmission and utilization, however there are relatively few which are currently exploiting the ability to sustain high magnetic fields. There are two main reasons for this: high field wound magnets can and have been made from both BSCCO and YBCO but currently their cost is much higher than the alternative provided by low Tc materials such as Nb3Sn and NbTi. An alternative form of the material is the bulk form which can be magnetized to high fields and using flux pumping this can be done in situ. This paper explores some of the applications of bulk superconductors and describes methods of producing field patterns using the highly uniform magnetic fields required for MRI and accelerator magnets as the frame of reference. The patterns are not limited to uniform fields and it is entirely possible to produce a field varying sinusoidally in space such as would be required for a motor or a generator. The scheme described in this paper describes a dipole magnet such as is found in an accelerator magnet. The tunnel is 30 × 50 × 1000 mm and we achieve a uniformity of better than 200 ppm over the 1000 mm length and better than 1 ppm over the central 500 mm region. The paper presents results for both the overall uniformity and the integrated uniformity which is 302 ppm over the 1000 mm length. © 2010 IEEE.

Structured discriminative models for noise robust continuous speech recognition

Recently there has been interest in structured discriminative models for speech recognition. In these models sentence posteriors are directly modelled, given a set of features extracted from the observation sequence, and hypothesised word sequence. In previous work these discriminative models have been combined with features derived from generative models for noise-robust speech recognition for continuous digits. This paper extends this work to medium to large vocabulary tasks. The form of the score-space extracted using the generative models, and parameter tying of the discriminative model, are both discussed. Update formulae for both conditional maximum likelihood and minimum Bayes' risk training are described. Experimental results are presented on small and medium to large vocabulary noise-corrupted speech recognition tasks: AURORA 2 and 4. © 2011 IEEE.

Feasibility study of carbon nanotube microneedles for rapid transdermal drug delivery

We introduce a new approach for fabricating hollow microneedles using vertically-aligned carbon nanotubes (VA-CNTs) for rapid transdermal drug delivery. Here, we discuss the fabrication of the microneedles emphasizing the overall simplicity and flexibility of the method to allow for potential industrial application. By capitalizing on the nanoporosity of the CNT bundles, uncured polymer can be wicked into the needles ultimately creating a high strength composite of aligned nanotubes and polymer. Flow through the microneedles as well as in vitro penetration of the microneedles into swine skin is demonstrated. Furthermore, we present a trade study comparing the difficulty and complexity of the fabrication process of our CNT-polymer microneedles with other standard microneedle fabrication approaches. Copyright © Materials Research Society 2013.