Coherent interaction of laser pulses in a resonant optically dense extended medium under the regime of strong field-matter coupling

Nonstationary pump-probe interaction between short laser pulses propagating in a resonant optically dense coherent medium is considered. A special attention is paid to the case, where the density of two-level particles is high enough that a considerable part of the energy of relatively weak external laser-fields can be coherently absorbed and reemitted by the medium. Thus, the field of medium reaction plays a key role in the interaction processes, which leads to the collective behavior of an atomic ensemble in the strongly coupled light-matter system. Such behavior results in the fast excitation interchanges between the field and a medium in the form of the optical ringing, which is analogous to polariton beating in the solid-state optics. This collective oscillating response, which can be treated as successive beats between light wave-packets of different group velocities, is shown to significantly affect propagation and amplification of the probe field under its nonlinear interaction with a nearly copropagating pump pulse. Depending on the probe-pump time delay, the probe transmission spectra show the appearance of either specific doublet or coherent dip. The widths of these features are determined by the density-dependent field-matter coupling coefficient and increase during the propagation. Besides that, the widths of the coherent features, which appear close to the resonance in the broadband probe-spectrum, exceed the absorption-line width, since, under the strong-coupling regime, the frequency of the optical ringing exceeds the rate of incoherent relaxation. Contrary to the stationary strong-field effects, the density- and coordinate-dependent transmission spectra of the probe manifest the importance of the collective oscillations and cannot be obtained in the framework of the single-atom model.Comment: 10 pages, 8 figures, to be published in Phys. Rev.

Advanced Radiation Panel design for applications in National Security and Food Safety

We describe a new concept for a basic radiation detection panel based on conventional scintillator technology and commercially available solid-state photo-detectors. The panels are simple in construction, robust, very efficient and cost-effective and are easily scalable in size, from tens of cm$^2$ to tens of m$^2$. We describe two possible applications: flagging radioactive food coontamination and detection of illicit radio nucleides, such as those potentially used in a terrorist attack with a dirty bomb.Comment: 10 pages, 11 figure

Bargmann-Michel-Telegdi equation and one-particle relativistic approach

A reexamination of the semiclassical approach of the relativistic electron indicates a possible variation of its helicity for electric and magnetic static fields applied along its global motion due to zitterbewegung effects, proportional to the anomalous part of the magnetic moment.Comment: 10 pages, LATEX2E, uses amsb

Exponential Random Graph Modeling for Complex Brain Networks

Exponential random graph models (ERGMs), also known as p* models, have been utilized extensively in the social science literature to study complex networks and how their global structure depends on underlying structural components. However, the literature on their use in biological networks (especially brain networks) has remained sparse. Descriptive models based on a specific feature of the graph (clustering coefficient, degree distribution, etc.) have dominated connectivity research in neuroscience. Corresponding generative models have been developed to reproduce one of these features. However, the complexity inherent in whole-brain network data necessitates the development and use of tools that allow the systematic exploration of several features simultaneously and how they interact to form the global network architecture. ERGMs provide a statistically principled approach to the assessment of how a set of interacting local brain network features gives rise to the global structure. We illustrate the utility of ERGMs for modeling, analyzing, and simulating complex whole-brain networks with network data from normal subjects. We also provide a foundation for the selection of important local features through the implementation and assessment of three selection approaches: a traditional p-value based backward selection approach, an information criterion approach (AIC), and a graphical goodness of fit (GOF) approach. The graphical GOF approach serves as the best method given the scientific interest in being able to capture and reproduce the structure of fitted brain networks

Whole-brain functional hypoconnectivity as an endophenotype of autism in adolescents.

Endophenotypes are heritable and quantifiable markers that may assist in the identification of the complex genetic underpinnings of psychiatric conditions. Here we examined global hypoconnectivity as an endophenotype of autism spectrum conditions (ASCs). We studied well-matched groups of adolescent males with autism, genetically-related siblings of individuals with autism, and typically-developing control participants. We parcellated the brain into 258 regions and used complex-network analysis to detect a robust hypoconnectivity endophenotype in our participant group. We observed that whole-brain functional connectivity was highest in controls, intermediate in siblings, and lowest in ASC, in task and rest conditions. We identified additional, local endophenotype effects in specific networks including the visual processing and default mode networks. Our analyses are the first to show that whole-brain functional hypoconnectivity is an endophenotype of autism in adolescence, and may thus underlie the heritable similarities seen in adolescents with ASC and their relatives.The authors wish to thank the participants and their families for their participation and the autism support organisations who assisted with recruitment. We thank colleagues at the Brain Mapping Unit for methodological discussions and thank Meng-Chuan Lai, Amber Ruigrok and Richard Bethlehem for the same. Data collection was funded by a Clinical Scientist Fellowship from the UK Medical Research Council (MRC) (G0701919) to MDS. LRC was supported by the Gates Cambridge Scholarship Trust. The study was conducted in associated with the National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care (CLAHRC) for Cambridgeshire, and Peterborough National Health Service (NHS) Foundation Trust. The present analysis was funded by a NARSAD Young Investigator award (to MR) and by the Isaac Newton Trust (to MR); RJFY is additionally supported by a Rubicon Fellowship from the Netherlands Organisation for Scientific Research. The Brain Mapping Unit (MR, RLM, RJFY, JS and ETB) is part of the Behavioural & Clinical Neuroscience Institute, which is funded by the MRC and the Wellcome Trust. High performance computing facilities were supported by the NIHR Cambridge Biomedical Research Centre.This is the final version of the article. It first appeared from Elsevier via http://dx.doi.org/10.1016/j.nicl.2015.07.01