Semi-parametric analysis of multi-rater data

Datasets that are subjectively labeled by a number of experts are becoming more common in tasks such as biological text annotation where class definitions are necessarily somewhat subjective. Standard classification and regression models are not suited to multiple labels and typically a pre-processing step (normally assigning the majority class) is performed. We propose Bayesian models for classification and ordinal regression that naturally incorporate multiple expert opinions in defining predictive distributions. The models make use of Gaussian process priors, resulting in great flexibility and particular suitability to text based problems where the number of covariates can be far greater than the number of data instances. We show that using all labels rather than just the majority improves performance on a recent biological dataset

Fluid mechanical model of the acoustic impedance of small orifices

A fluid mechanical model of the acoustic behavior of small orifices is presented which predicts orifice resistance and reactance as a function of incident sound pressure level, frequency, and orifice geometry. Agreement between predicted and measured values is excellent. The model shows the following: (1) The acoustic flow in immediate neighborhood of the orifice can be modeled as a locally spherical flow. Within this near field, the flow is, to a first approximation, unsteady and incompressible. (2) At very low sound pressure levels, the orifice viscous resistance is directly related to the effect of boundary-layer displacement along the walls containing the orifice, and the orifice reactance is directly related to the inertia of the oscillating flow in the neighborhood of the orifice. (3) For large values of the incident acoustic pressure, the impedance is dominated by nonlinear jet-like effects. (4) For low values of the pressure, the resistance and reactance are roughly equal

Multivariable Repetitive-predictive Controllers using Frequency Decomposition

Repetitive control is a methodology for the tracking of a periodic reference signal. This paper develops a new approach to repetitive control systems design using receding horizon control with frequency decomposition of the reference signal. Moreover, design and implementation issues for this form of repetitive predictive control are investigated from the perspectives of controller complexity and the effects of measurement noise. The analysis is supported by a simulation study on a multi-input multi-output robot arm where the model has been constructed from measured frequency response data, and experimental results from application to an industrial AC motor

Optimising superoscillatory spots for far-field super-resolution imaging

Optical superoscillatory imaging, allowing unlabelled far-field super-resolution, has in recent years become reality. Instruments have been built and their super-resolution imaging capabilities demonstrated. The question is no longer whether this can be done, but how well: what resolution is practically achievable? Numerous works have optimised various particular features of superoscillatory spots, but in order to probe the limits of superoscillatory imaging we need to simultaneously optimise all the important spot features: those that define the resolution of the system. We simultaneously optimise spot size and its intensity relative to the sidebands for various fields of view, giving a set of best compromises for use in different imaging scenarios. Our technique uses the circular prolate spheroidal wave functions as a basis set on the field of view, and the optimal combination of these, representing the optimal spot, is found using a multi-objective genetic algorithm. We then introduce a less computationally demanding approach suitable for real-time use in the laboratory which, crucially, allows independent control of spot size and field of view. Imaging simulations demonstrate the resolution achievable with these spots. We show a three-order-of-magnitude improvement in the efficiency of focusing to achieve the same resolution as previously reported results, or a 26 % increase in resolution for the same efficiency of focusing

Forestland type identification and analysis in Western Massachussetts: A linkage of a LANDSAT forest inventory to an optimization study

Digital land cover files derived from computer processing of LANDSAT and soil productivity data were linked and used by linear programming model to determine production of forested areas under different management strategies. Results of model include maps and data graphics for four-county region in Western Massachusetts

Heterogeneous node responses to multi-type epidemics on networks

Having knowledge of the contact network over which an infection is spreading opens the possibility of making individualized predictions for the likelihood of different nodes to become infected. When multiple infective strains attempt to spread simultaneously we may further ask which strain, or strains, are most likely to infect a particular node. In this article we investigate the heterogeneity in likely outcomes for different nodes in two models of multi-type epidemic spreading processes. For models allowing co-infection we derive message-passing equations whose solution captures how the likelihood of a given node receiving a particular infection depends on both the position of the node in the network and the interaction between the infection types. For models of competing epidemics in which co-infection is impossible, a more complicated analysis leads to the simpler result that node vulnerability factorizes into a contribution from the network topology and a contribution from the infection parameters

The effects of donepezil in Alzheimer's disease - Results from a multinational trial

Donepezil has been shown to be well tolerated and to improve cognition and global function in patients with mild to moderately severe Alzheimer's disease (AD). The current trial was undertaken to investigate further the efficacy and safety of donepezil, in a multinational setting, in patients with mild to moderately severe AD. This 30-week, placebo-controlled, parallel-group study consisted of a 24-week, double-blind treatment phase followed by a 6-week, single-blind, placebo washout. Eight hundred and eighteen patients with mild to moderately severe AD were randomly allocated to treatment with single, daily doses of 5 or 10 mg donepezil, or placebo. The two primary efficacy measures were: a cognitive performance test, the Alzheimer's Disease Assessment Scale-cognitive subscale (ADAS-cog) and a global evaluation, the Clinician's Interview-Based Impression of Change with caregiver input (CIBIC plus). Secondary outcome measures included the Sum of the Boxes of the Clinical Dementia Rating Scale (CDR-SB), a modified Interview for Deterioration in Daily living activities in Dementia (IDDD) and a patient-rated quality of life assessment. Statistically significant improvements in cognitive and global function were observed, as evaluated by ADAS-cog and CIBIC plus, respectively, in both the 5 and 10 mg/day donepezil groups, compared with placebo. Treatment-associated changes were also observed in functional skills, as shown by improved scores on the CDR-SB and the complex-tasks component of the IDDD. A dose-response effect was evident, with the 10 mg/day donepezil group demonstrating greater benefits in all outcome measures than the 5 mg/day group. Donepezil was well tolerated by this patient population and did not produce any clinically significant laboratory test abnormalities. The results of this study confirm that donepezil is effective and well tolerated in treating the symptoms of mild to moderately severe AD

Human-agent collectives

We live in a world where a host of computer systems, distributed throughout our physical and information environments, are increasingly implicated in our everyday actions. Computer technologies impact all aspects of our lives and our relationship with the digital has fundamentally altered as computers have moved out of the workplace and away from the desktop. Networked computers, tablets, phones and personal devices are now commonplace, as are an increasingly diverse set of digital devices built into the world around us. Data and information is generated at unprecedented speeds and volumes from an increasingly diverse range of sources. It is then combined in unforeseen ways, limited only by human imagination. People’s activities and collaborations are becoming ever more dependent upon and intertwined with this ubiquitous information substrate. As these trends continue apace, it is becoming apparent that many endeavours involve the symbiotic interleaving of humans and computers. Moreover, the emergence of these close-knit partnerships is inducing profound change. Rather than issuing instructions to passive machines that wait until they are asked before doing anything, we will work in tandem with highly inter-connected computational components that act autonomously and intelligently (aka agents). As a consequence, greater attention needs to be given to the balance of control between people and machines. In many situations, humans will be in charge and agents will predominantly act in a supporting role. In other cases, however, the agents will be in control and humans will play the supporting role. We term this emerging class of systems human-agent collectives (HACs) to reflect the close partnership and the flexible social interactions between the humans and the computers. As well as exhibiting increased autonomy, such systems will be inherently open and social. This means the participants will need to continually and flexibly establish and manage a range of social relationships. Thus, depending on the task at hand, different constellations of people, resources, and information will need to come together, operate in a coordinated fashion, and then disband. The openness and presence of many distinct stakeholders means participation will be motivated by a broad range of incentives rather than diktat. This article outlines the key research challenges involved in developing a comprehensive understanding of HACs. To illuminate this agenda, a nascent application in the domain of disaster response is presented

Parameter inference in mechanistic models of cellular regulation and signalling pathways using gradient matching

A challenging problem in systems biology is parameter inference in mechanistic models of signalling pathways. In the present article, we investigate an approach based on gradient matching and nonparametric Bayesian modelling with Gaussian processes. We evaluate the method on two biological systems, related to the regulation of PIF4/5 in Arabidopsis thaliana, and the JAK/STAT signal transduction pathway