How robust are the surface temperature fingerprints of the Atlantic Overturning Meridional Circulation on monthly time‐scales?

It has been suggested that changes in the Atlantic Meridional Overturning Circulation (AMOC) can drive sea surface temperature (SST) on monthly timescales [Duchez et al., 2016]. However, with only 11 years of continuous observations, the validity of this result over longer, or different, time‐periods is uncertain. In this study, we use a 120‐yr long control simulation from a high‐resolution climate model to test the robustness of the AMOC fingerprints. The model reproduces the observed AMOC seasonal cycle and its variability, and the observed 5‐month lagged AMOC‐SST fingerprints derived from 11‐years of data. However, the AMOC‐SST fingerprints are very sensitive to the particular time‐period considered. In particular, both the Florida current and the upper mid ocean transport produce highly inconsistent fingerprints when using time‐periods shorter than 30 years. Therefore, several decades of RAPID observations will be necessary to determine the real impact of the AMOC on SSTs at monthly time‐scales

Hierarchical Multiscale Recurrent Neural Networks for Detecting Suicide Notes

Recent statistics in suicide prevention show that people are increasingly posting their last words online and with the unprecedented availability of textual data from social media platforms researchers have the opportunity to analyse such data. Furthermore, psychological studies have shown that our state of mind can manifest itself in the linguistic features we use to communicate. In this paper, we investigate whether it is possible to automatically identify suicide notes from other types of social media blogs in two document-level classification tasks. The first task aims to identify suicide notes from depressed and blog posts in a balanced dataset, whilst the second experiment looks at how well suicide notes can be classified when there is a vast amount of neutral text data, which makes the task more applicable to real-world scenarios. Furthermore we perform a linguistic analysis using LIWC (Linguistic Inquiry and Word Count). We present a learning model for modelling long sequences in two experiment series. We achieve an f1-score of 88.26% over the baselines of 0.60 in experiment 1 and 96.1% over the baseline in experiment 2. Finally, we show through visualisations which features the learning model identifies, these include emotions such as love and personal pronouns

Efficient inference and identifiability analysis for differential equation models with random parameters

Heterogeneity is a dominant factor in the behaviour of many biological processes. Despite this, it is common for mathematical and statistical analyses to ignore biological heterogeneity as a source of variability in experimental data. Therefore, methods for exploring the identifiability of models that explicitly incorporate heterogeneity through variability in model parameters are relatively underdeveloped. We develop a new likelihood-based framework, based on moment matching, for inference and identifiability analysis of differential equation models that capture biological heterogeneity through parameters that vary according to probability distributions. As our novel method is based on an approximate likelihood function, it is highly flexible; we demonstrate identifiability analysis using both a frequentist approach based on profile likelihood, and a Bayesian approach based on Markov-chain Monte Carlo. Through three case studies, we demonstrate our method by providing a didactic guide to inference and identifiability analysis of hyperparameters that relate to the statistical moments of model parameters from independent observed data. Our approach has a computational cost comparable to analysis of models that neglect heterogeneity, a significant improvement over many existing alternatives. We demonstrate how analysis of random parameter models can aid better understanding of the sources of heterogeneity from biological data.Comment: Minor changes to text. Additional results in supplementary material. Additional statistics regarding results given in main and supplementary materia

"Comets" orbiting a black hole

We use a long (300 ksec), continuous Suzaku X-ray observation of the active nucleus in NGC1365 to investigate the structure of the circumnuclear BLR clouds through their occultation of the X-ray source. The variations of the absorbing column density and of the covering factor indicate that the clouds surrounding the black hole are far from having a spherical geometry (as sometimes assumed), instead they have a strongly elongated and cometary shape, with a dense head (n=10^11 cm^-3) and an expanding, dissolving tail. We infer that the cometary tails must be longer than a few times 10^13 cm and their opening angle must be smaller than a few degrees. We suggest that the cometary shape may be a common feature of BLR clouds in general, but which has been difficult to recognize observationally so far. The cometary shape may originate from shocks and hydrodynamical instabilities generated by the supersonic motion of the BLR clouds into the intracloud medium. As a consequence of the mass loss into their tail, we infer that the BLR clouds probably have a lifetime of only a few months, implying that they must be continuously replenished. We also find a large, puzzling discrepancy (two orders of magnitude) between the mass of the BLR inferred from the properties of the absorbing clouds and the mass of the BLR inferred from photoionization models; we discuss the possible solutions to this discrepancy.Comment: Accepted for publication in A&A. 11 pages, 9 figure

Using Epigenetic Networks for the Analysis of Movement Associated with Levodopa Therapy for Parkinson's Disease

© 2016 The Author(s) Levodopa is a drug that is commonly used to treat movement disorders associated with Parkinson's disease. Its dosage requires careful monitoring, since the required amount changes over time, and excess dosage can lead to muscle spasms known as levodopa-induced dyskinesia. In this work, we investigate the potential for using epiNet, a novel artificial gene regulatory network, as a classifier for monitoring accelerometry time series data collected from patients undergoing levodopa therapy. We also consider how dynamical analysis of epiNet classifiers and their transitions between different states can highlight clinically useful information which is not available through more conventional data mining techniques. The results show that epiNet is capable of discriminating between different movement patterns which are indicative of either insufficient or excessive levodopa

Axion-induced oscillations of cooperative electric field in a cosmic magneto-active plasma

We consider one cosmological application of an axionic extension of the Maxwell-Vlasov theory, which describes axionically induced oscillatory regime in the state of global magnetic field evolving in the anisotropic expanding (early) universe. We show that the cooperative electric field in the relativistic plasma, being coupled to the pseudoscalar (axion) and global magnetic fields, plays the role of a regulator in this three-level system; in particular, the cooperative (Vlasov) electric field converts the regime of anomalous growth of the pseudoscalar field, caused by the axion-photon coupling at the inflationary epoch of the universe expansion, into an oscillatory regime with finite density of relic axions. We analyze solutions to the dispersion equations for the axionically induced cooperative oscillations of the electric field in the relativistic plasma.Comment: 7 pages, misprints correcte

Short Circuits in Thermally Ionized Plasmas: A Mechanism for Intermittent Heating of Protoplanetary Disks

Many astrophysical systems of interest, including protoplanetary accretion disks, are made of turbu- lent magnetized gas with near solar metallicity. Thermal ionization of alkali metals in such gas exceeds non-thermal ionization when temperatures climb above roughly 1000 K. As a result, the conductiv- ity, proportional to the ionization fraction, gains a strong, positive dependence on temperature. In this paper, we demonstrate that this relation between the temperature and the conductivity triggers an exponential instability that acts similarly to an electrical short, where the increased conductivity concentrates the current and locally increases the Ohmic heating. This contrasts with the resistiv- ity increase expected in an ideal magnetic reconnection region. The instability acts to focus narrow current sheets into even narrower sheets with far higher currents and temparatures. We lay out the basic principles of this behavior in this paper using protoplanetary disks as our example host system, motivated by observations of chondritic meteorites and their ancestors, dust grains in protoplanetary disks, that reveal the existence of strong, frequent heating events that this instability could explain.Comment: 9 pages, 6 figures, 1 table Accepted, Ap

Patterns of place:an integrated approach for the design and evaluation of real and virtual environments

This chapter describes an approach to the development of virtual representations of real places. The work was funded under the European Union’s €20 m Future and Emerging Technologies theme of the 5th Framework Programme, “Presence”. The aim of the project, called BENOGO, was to develop a novel technology based on real-time image-based rendering (IBR) for representing places in virtual environments. The specific focus of the work presented here concerned how to capture the essential features of real places, and how to represent that knowledge, so that the team developing the IBR-based virtual environments could produce an environment that was as realistic as possible. This involved the development and evaluation of a number of virtual environments and the evolution of two complementary techniques; the Place Probe and Patterns of place

Constraining Torus Models for AGNs Using X-Ray Observations

In Unification Models, Active Galactic Nuclei (AGN) are believed to be surrounded by an axisymmetric structure of dust and gas, which greatly influences their observed properties according to the direction from which they are observed. The main aim of this work is to constrain the properties of this obscuring material using X-Ray observations. The distribution of column densities observed by Chandra in the Chandra Deep Field South is used to determine geometrical constraints for already proposed torus models. It is found that the best torus model is given by a classical `donut shape' with an exponential angular dependency of the density profile. The opening angle is strongly constrained by the observed column densities. Other proposed torus models are clearly rejected by the X-Ray observations.Comment: 10 pages, 4 figures, submitted to A&