Symmetry properties of vibrational modes in graphene nanoribbons

We present symmetry properties of the lattice vibrations of graphene nanoribbons with pure armchair (AGNR) and zigzag edges (ZGNR). In non-symmorphic nanoribbons the phonon modes at the edge of the Brillouin zone are twofold degenerate, whereas the phonon modes in symmorphic nanoribbons are non-degenerate. We identified the Raman-active and infrared-active modes. We predict 3N and 3(N+1) Raman-active modes for N-ZGNRs and N-AGNRs, respectively (N is the number of dimers per unit cell). These modes can be used for the experimental characterization of graphene nanoribbons. Calculations based on density functional theory suggest that the frequency splitting of the LO and TO in AGNRs (corresponding to the E2g mode in graphene) exhibits characteristic width and family dependence. Further, all graphene nanoribbons have a Raman-active breathing-like mode, the frequency of which is inversely proportional to the nanoribbon width and thus might be used for experimental determination of the width of graphene nanoribbons.Comment: 10 pages, 5 figure

Inducing vortices in a Bose-Einstein condensate using holographically produced light beams

In this paper we demonstrate a technique that can create out-of-equilibrium vortex configurations with almost arbitrary charge and geometry in a Bose-Einstein condensate. We coherently transfer orbital angular momentum from a holographically generated light beam to a Rubidium 87 condensate using a two-photon stimulated Raman process. Using matter wave interferometry, we verify the phase pattern imprinted onto the atomic wave function for a single vortex and a vortex-antivortex pair. In addition to their phase winding, the vortices created with this technique have an associated hyperfine spin texture.Comment: 4 pages, 5 figure

On the Use of Local Assessments for Monitoring Centrally Reviewed Endpoints with Missing Data in Clinical Trials*

Due to ethical and logistical concerns it is common for data monitoring committees to periodically monitor accruing clinical trial data to assess the safety, and possibly efficacy, of a new experimental treatment. When formalized, monitoring is typically implemented using group sequential methods. In some cases regulatory agencies have required that primary trial analyses should be based solely on the judgment of an independent review committee (IRC). The IRC assessments can produce difficulties for trial monitoring given the time lag typically associated with receiving assessments from the IRC. This results in a missing data problem wherein a surrogate measure of response may provide useful information for interim decisions and future monitoring strategies. In this paper, we present statistical tools that are helpful for monitoring a group sequential clinical trial with missing IRC data. We illustrate the proposed methodology in the case of binary endpoints under various missingness mechanisms including missing completely at random assessments and when missingness depends on the IRC’s measurement

Evaluating a Group Sequential Design in the Setting of Nonproportional Hazards

Group sequential methods have been widely described and implemented in a clinical trial setting where parametric and semiparametric models are deemed suitable. In these situations, the evaluation of the operating characteristics of a group sequential stopping rule remains relatively straightforward. However, in the presence of nonproportional hazards survival data nonparametric methods are often used, and the evaluation of stopping rules is no longer a trivial task. Specifically, nonparametric test statistics do not necessarily correspond to a parameter of clinical interest, thus making it difficult to characterize alternatives at which operating characteristics are to be computed. We describe an approach for constructing alternatives under nonproportional hazards using pre-existing pilot data, allowing one to evaluate various operating characteristics of candidate group sequential stopping rules. The method is illustrated via a case study in which testing is based upon a weighted logrank statistic

Choosing the Right Approach at the Right Time: A Comparative Analysis of Casual Effect Estimation using Confounder Adjustment and Instrumental Variables

In observational studies, unobserved confounding is a major barrier in isolating the average causal effect (ACE). In these scenarios, two main approaches are often used: confounder adjustment for causality (CAC) and instrumental variable analysis for causation (IVAC). Nevertheless, both are subject to untestable assumptions and, therefore, it may be unclear which assumption violation scenarios one method is superior in terms of mitigating inconsistency for the ACE. Although general guidelines exist, direct theoretical comparisons of the trade-offs between CAC and the IVAC assumptions are limited. Using ordinary least squares (OLS) for CAC and two-stage least squares (2SLS) for IVAC, we analytically compare the relative inconsistency for the ACE of each approach under a variety of assumption violation scenarios and discuss rules of thumb for practice. Additionally, a sensitivity framework is proposed to guide analysts in determining which approach may result in less inconsistency for estimating the ACE with a given dataset. We demonstrate our findings both through simulation and an application examining whether maternal stress during pregnancy affects a neonate's birthweight. The implications of our findings for causal inference practice are discussed, providing guidance for analysts for judging whether CAC or IVAC may be more appropriate for a given situation

Frequentist Evaluation of Group Sequential Clinical Trial Designs

Group sequential stopping rules are often used as guidelines in the monitoring of clinical trials in order to address the ethical and efficiency issues inherent in human testing of a new treatment or preventive agent for disease. Such stopping rules have been proposed based on a variety of different criteria, both scientific (e.g., estimates of treatment effect) and statistical (e.g., frequentist type I error, Bayesian posterior probabilities, stochastic curtailment). It is easily shown, however, that a stopping rule based on one of those criteria induces a stopping rule on all other criteria. Thus the basis used to initially define a stopping rule is relatively unimportant so long as the operating characteristics of the stopping rule are fully investigated. In this paper we describe how the frequentist operating characteristics of a particular stopping rule might be evaluated in order to ensure that the selected clinical trial design satisfies the constraints imposed by the many different disciplines represented by the clinical trial collaborators

Bayesian Evaluation of Group Sequential Clinical Trial Designs

Clincal trial designs often incorporate a sequential stopping rule to serve as a guide in the early termination of a study. When choosing a particular stopping rule, it is most common to examine frequentist operating characteristics such as type I error, statistical power, and precision of confi- dence intervals (Emerson, et al. [1]). Increasingly, however, clinical trials are designed and analyzed in the Bayesian paradigm. In this paper we describe how the Bayesian operating characteristics of a particular stopping rule might be evaluated and communicated to the scientific community. In particular, we consider a choice of probability models and a family of prior distributions that allows concise presentation of Bayesian properties for a specified sampling plan

Monitoring young associations and open clusters with Kepler in two-wheel mode

We outline a proposal to use the Kepler spacecraft in two-wheel mode to monitor a handful of young associations and open clusters, for a few weeks each. Judging from the experience of similar projects using ground-based telescopes and the CoRoT spacecraft, this program would transform our understanding of early stellar evolution through the study of pulsations, rotation, activity, the detection and characterisation of eclipsing binaries, and the possible detection of transiting exoplanets. Importantly, Kepler's wide field-of-view would enable key spatially extended, nearby regions to be monitored in their entirety for the first time, and the proposed observations would exploit unique synergies with the GAIA ESO spectroscopic survey and, in the longer term, the GAIA mission itself. We also outline possible strategies for optimising the photometric performance of Kepler in two-wheel mode by modelling pixel sensitivity variations and other systematics.Comment: 10 pages, 6 figures, white paper submitted in response to NASA call for community input for alternative science investigations for the Kepler spacecraf

Probing the Superfluid to Mott Insulator Transition at the Single Atom Level

Quantum gases in optical lattices offer an opportunity to experimentally realize and explore condensed matter models in a clean, tunable system. We investigate the Bose-Hubbard model on a microscopic level using single atom-single lattice site imaging; our technique enables space- and time-resolved characterization of the number statistics across the superfluid-Mott insulator quantum phase transition. Site-resolved probing of fluctuations provides us with a sensitive local thermometer, allows us to identify microscopic heterostructures of low entropy Mott domains, and enables us to measure local quantum dynamics, revealing surprisingly fast transition timescales. Our results may serve as a benchmark for theoretical studies of quantum dynamics, and may guide the engineering of low entropy phases in a lattice

Frameworks for Estimating Causal Effects in Observational Settings: Comparing Confounder Adjustment and Instrumental Variables

To estimate causal effects, analysts performing observational studies in health settings utilize several strategies to mitigate bias due to confounding by indication. There are two broad classes of approaches for these purposes: use of confounders and instrumental variables (IVs). Because such approaches are largely characterized by untestable assumptions, analysts must operate under an indefinite paradigm that these methods will work imperfectly. In this tutorial, we formalize a set of general principles and heuristics for estimating causal effects in the two approaches when the assumptions are potentially violated. This crucially requires reframing the process of observational studies as hypothesizing potential scenarios where the estimates from one approach are less inconsistent than the other. While most of our discussion of methodology centers around the linear setting, we touch upon complexities in non-linear settings and flexible procedures such as target minimum loss-based estimation (TMLE) and double machine learning (DML). To demonstrate the application of our principles, we investigate the use of donepezil off-label for mild cognitive impairment (MCI). We compare and contrast results from confounder and IV methods, traditional and flexible, within our analysis and to a similar observational study and clinical trial