A new model for evolution in a spatial continuum

We investigate a new model for populations evolving in a spatial continuum. This model can be thought of as a spatial version of the Lambda-Fleming-Viot process. It explicitly incorporates both small scale reproduction events and large scale extinction-recolonisation events. The lineages ancestral to a sample from a population evolving according to this model can be described in terms of a spatial version of the Lambda-coalescent. Using a technique of Evans(1997), we prove existence and uniqueness in law for the model. We then investigate the asymptotic behaviour of the genealogy of a finite number of individuals sampled uniformly at random (or more generally `far enough apart') from a two-dimensional torus of side L as L tends to infinity. Under appropriate conditions (and on a suitable timescale), we can obtain as limiting genealogical processes a Kingman coalescent, a more general Lambda-coalescent or a system of coalescing Brownian motions (with a non-local coalescence mechanism).Comment: 63 pages, version accepted to Electron. J. Proba

Coalescent simulation in continuous space:Algorithms for large neighbourhood size

Many species have an essentially continuous distribution in space, in which there are no natural divisions between randomly mating subpopulations. Yet, the standard approach to modelling these populations is to impose an arbitrary grid of demes, adjusting deme sizes and migration rates in an attempt to capture the important features of the population. Such indirect methods are required because of the failure of the classical models of isolation by distance, which have been shown to have major technical flaws. A recently introduced model of extinction and recolonisation in two dimensions solves these technical problems, and provides a rigorous technical foundation for the study of populations evolving in a spatial continuum. The coalescent process for this model is simply stated, but direct simulation is very inefficient for large neighbourhood sizes. We present efficient and exact algorithms to simulate this coalescent process for arbitrary sample sizes and numbers of loci, and analyse these algorithms in detail

A wearable brain-computer interface controlled robot

Brain-computer interface (BCI) controlled systems hold great potential for clinical applications especially in assisting neurologically disabled patients to improve their communication processes [1]. Wearable electroencephalogram devices (EEG) are non-intrusive, meaning they do not require insertion of electrodes into the patient’s brain, and are available “off the shelf” with consumer-grade devices such as the MindWave [2]. While such EEG devices do not possess the same high resolution EEG capabilities of medical grade devices, their affordability does make the technology accessible to new applications, such as robotics control and mood deduction [3], and their wearable nature negates the need for invasive surgery. Campbell et al. [4] investigated the potential for wearable consumer grade EEG in creating a BCI. Their aim was to implement a BCI for simple mobile phone operation, which found that a simple task, winking, could be deduced from raw data with a relatively high accuracy, and with processing being performed on a smartphone device [4]. In doing this several limitations were highlighted with EEG devices, including a poor signal-to-noise ratio, which requires further processing to deduce useful information from raw data. Millan et al. achieved relatively sophisticated control of a mobile robot in a simulated environment with a non-intrusive BCI interface [5]. Combining machine learning with subject-device training, they were able to achieve “almost as efficient as manual control”. The primary objective of our project was to build a prototype hardware system to establish the proof of concept of controlling a robotic system by using a wearable EEG device. A very low-cost Arduino [6] based integrated electronics platform was used to implement the BCI controlled robot. The Arduino platform possesses several advantages, such as their affordability, and the large amount of open source hardware and software modules available. Combining MindWave [2] as the off-the-shelf EEG device with the Arduino platform enabled successful processing of wearer’s attention and mediation levels to be used as commands to control the robot. The levels of attention and mediation were calculated within the Mindwave device and transmitted to Arduino through Bluetooth as serial asynchronous data packets. Successful processing of these packets within Arduino eventually translated raw BCI data into useful commands. At the end, the project could demonstrate a priority based robust BCI control protocol, with further integration of sensor signals to the system

Background Independent Algebraic Structures in Closed String Field Theory

We construct a Batalin-Vilkovisky (BV) algebra on moduli spaces of Riemann surfaces. This algebra is background independent in that it makes no reference to a state space of a conformal field theory. Conformal theories define a homomorphism of this algebra to the BV algebra of string functionals. The construction begins with a graded-commutative free associative algebra \C built from the vector space whose elements are orientable subspaces of moduli spaces of punctured Riemann surfaces. The typical element here is a surface with several connected components. The operation $\Delta$ of sewing two punctures with a full twist is shown to be an odd, second order derivation that squares to zero. It follows that (\C, \Delta) is a Batalin-Vilkovisky algebra. We introduce the odd operator $\delta = \partial + \hbar\Delta$, where $\partial$ is the boundary operator. It is seen that $\delta^2=0$, and that consistent closed string vertices define a cohomology class of $\delta$. This cohomology class is used to construct a Lie algebra on a quotient space of \C. This Lie algebra gives a manifestly background independent description of a subalgebra of the closed string gauge algebra.Comment: phyzzx.tex, MIT-CTP-234

Plastic-crystalline solid-state electrolytes: Ionic conductivity and orientational dynamics in nitrile mixtures

Many plastic crystals, molecular solids with long-range, center-of-mass crystalline order but dynamic disorder of the molecular orientations, are known to exhibit exceptionally high ionic conductivity. This makes them promising candidates for applications as solid-state electrolytes, e.g., in batteries. Interestingly, it was found that the mixing of two different plastic-crystalline materials can considerably enhance the ionic dc conductivity, an important benchmark quantity for electrochemical applications. An example is the admixture of different nitriles to succinonitrile, the latter being one of the most prominent plastic-crystalline ionic conductors. However, until now only few such mixtures were studied. In the present work, we investigate succinonitrile mixed with malononitrile, adiponitrile, and pimelonitrile, to which 1 mol% of Li ions were added. Using differential scanning calorimetry and dielectric spectroscopy, we examine the phase behavior and the dipolar and ionic dynamics of these systems. We especially address the mixing-induced enhancement of the ionic conductivity and the coupling of the translational ionic mobility to the molecular reorientational dynamics, probably arising via a "revolving-door" mechanism.Comment: 9 pages, 7 figures; revised version as accepted for publication in J. Chem. Phy