A general framework for stochastic traveling waves and patterns, with application to neural field equations

In this paper we present a general framework in which to rigorously study the effect of spatio-temporal noise on traveling waves and stationary patterns. In particular the framework can incorporate versions of the stochastic neural field equation that may exhibit traveling fronts, pulses or stationary patterns. To do this, we first formulate a local SDE that describes the position of the stochastic wave up until a discontinuity time, at which point the position of the wave may jump. We then study the local stability of this stochastic front, obtaining a result that recovers a well-known deterministic result in the small-noise limit. We finish with a study of the long-time behavior of the stochastic wave.Comment: 43 pages, 3 figure

Nanoscale magnetometry through quantum control of nitrogen-vacancy centres in rotationally diffusing nanodiamonds

The confluence of quantum physics and biology is driving a new generation of quantum-based sensing and imaging technology capable of harnessing the power of quantum effects to provide tools to understand the fundamental processes of life. One of the most promising systems in this area is the nitrogen-vacancy centre in diamond - a natural spin qubit which remarkably has all the right attributes for nanoscale sensing in ambient biological conditions. Typically the nitrogen-vacancy qubits are fixed in tightly controlled/isolated experimental conditions. In this work quantum control principles of nitrogen-vacancy magnetometry are developed for a randomly diffusing diamond nanocrystal. We find that the accumulation of geometric phases, due to the rotation of the nanodiamond plays a crucial role in the application of a diffusing nanodiamond as a bio-label and magnetometer. Specifically, we show that a freely diffusing nanodiamond can offer real-time information about local magnetic fields and its own rotational behaviour, beyond continuous optically detected magnetic resonance monitoring, in parallel with operation as a fluorescent biomarker.Comment: 9 pages, with 5 figure

Single atom-scale diamond defect allows large Aharonov-Casher phase

We propose an experiment that would produce and measure a large Aharonov-Casher (A-C) phase in a solid-state system under macroscopic motion. A diamond crystal is mounted on a spinning disk in the presence of a uniform electric field. Internal magnetic states of a single NV defect, replacing interferometer trajectories, are coherently controlled by microwave pulses. The A-C phase shift is manifested as a relative phase, of up to 17 radians, between components of a superposition of magnetic substates, which is two orders of magnitude larger than that measured in any other atom-scale quantum system.Comment: 5 pages, 2 figure

BlinkML: Efficient Maximum Likelihood Estimation with Probabilistic Guarantees

The rising volume of datasets has made training machine learning (ML) models a major computational cost in the enterprise. Given the iterative nature of model and parameter tuning, many analysts use a small sample of their entire data during their initial stage of analysis to make quick decisions (e.g., what features or hyperparameters to use) and use the entire dataset only in later stages (i.e., when they have converged to a specific model). This sampling, however, is performed in an ad-hoc fashion. Most practitioners cannot precisely capture the effect of sampling on the quality of their model, and eventually on their decision-making process during the tuning phase. Moreover, without systematic support for sampling operators, many optimizations and reuse opportunities are lost. In this paper, we introduce BlinkML, a system for fast, quality-guaranteed ML training. BlinkML allows users to make error-computation tradeoffs: instead of training a model on their full data (i.e., full model), BlinkML can quickly train an approximate model with quality guarantees using a sample. The quality guarantees ensure that, with high probability, the approximate model makes the same predictions as the full model. BlinkML currently supports any ML model that relies on maximum likelihood estimation (MLE), which includes Generalized Linear Models (e.g., linear regression, logistic regression, max entropy classifier, Poisson regression) as well as PPCA (Probabilistic Principal Component Analysis). Our experiments show that BlinkML can speed up the training of large-scale ML tasks by 6.26x-629x while guaranteeing the same predictions, with 95% probability, as the full model.Comment: 22 pages, SIGMOD 201

Shannon Information Theory and Molecular Biology

The role and the contribution of Shannon Information Theory to the development of Molecular Biology has been the object of stimulating debates during the last thirty years. This seems to be connected with some semantic charms associated with the use of the word \u201cinformation\u201d in the biological context. Furthermore information itself, if viewed in a broader perspective, is far from being completely defined in a fashion that overcomes the technical level at which the classical Information Theory has been conceived. This review aims at building on the acknowledged contribution of Shannon Information Theory to Molecular Biology, so as to discover if it is only a technical tool to analyze DNA and proteinic sequences, or if it can rise, at least in perspective, to a higher role that exerts an influence on the construction of a suitable model for handling the genetic information in Molecular Biology

Micromechanical Properties of Injection-Molded Starch–Wood Particle Composites

The micromechanical properties of injection molded starch–wood particle composites were investigated as a function of particle content and humidity conditions. The composite materials were characterized by scanning electron microscopy and X-ray diffraction methods. The microhardness of the composites was shown to increase notably with the concentration of the wood particles. In addition,creep behavior under the indenter and temperature dependence were evaluated in terms of the independent contribution of the starch matrix and the wood microparticles to the hardness value. The influence of drying time on the density and weight uptake of the injection-molded composites was highlighted. The results revealed the role of the mechanism of water evaporation, showing that the dependence of water uptake and temperature was greater for the starch–wood composites than for the pure starch sample. Experiments performed during the drying process at 70°C indicated that the wood in the starch composites did not prevent water loss from the samples.Peer reviewe

Variation by Geographic Scale in the Migration-Environment Association: Evidence from Rural South Africa

"Scholarly understanding of human migration's environmental dimensions has greatly advanced in the past several years, motivated in large part by public and policy dialogue around 'climate migrants'. The research presented here advances current demographic scholarship both through its substantive interpretations and conclusions, as well as its methodological approach. We examine temporary rural South African outmigration as related to household-level availability of proximate natural resources. Such 'natural capital' is central to livelihoods in the region, both for sustenance and as materials for market-bound products. The results demonstrate that the association between local environmental resource availability and outmigration is, in general, positive: households with higher levels of proximate natural capital are more likely to engage in temporary migration. In this way, the general findings support the 'environmental surplus' hypothesis that resource security provides a foundation from which households can invest in migration as a livelihood strategy. Such insight stands in contrast to popular dialogue, which tends to view migration as a last resort undertaken only by the most vulnerable households. As another important insight, our findings demonstrate important spatial variation, complicating attempts to generalize migration-environment findings across spatial scales. In our rural South African study site, the positive association between migration and proximate resources is actually highly localized, varying from strongly positive in some villages to strongly negative in others. We explore the socio-demographic factors underlying this 'operational scale sensitivity'. The cross-scale methodologies applied here offer nuance unavailable within more commonly used global regression models, although also introducing complexity that complicates story-telling and inhibits generalizability." (author's abstract