Phase Transitions in the One-Dimensional Pair-Hopping Model: a Renormalization Group Study

The phase diagram of a one-dimensional tight-binding model with a pair-hopping term (amplitude V) has been the subject of some controvery. Using two-loop renormalization group equations and the density matrix renormalization group with lengths L<=60, we argue that no spin-gap transition occurs at half-filling for positive V, contrary to recent claims. However, we point out that away from half-filling, a *phase-separation* transition occurs at finite V. This transition and the spin-gap transition occuring at half-filling and *negative* V are analyzed numerically.Comment: 7 pages RevTeX, 6 uuencoded figures which can be (and by default are) directly included. Received by Phys. Rev. B 20 April 199

gem-Dibromocyclopropanes and enzymatically derived cis-1,2-dihydrocatechols as building blocks in alkaloid synthesis

The application of the title building blocks, the 6,6-dibromobicyclo[3.1.0]hexanes and the cis-1,2-dihydrocatechols, to the total synthesis of crinine and lycorinine alkaloids is described.We thank the Australian Research Council and the Institute of Advanced Studies for generous financial support

Dynamical Correlation Functions using the Density Matrix Renormalization Group

The density matrix renormalization group (DMRG) method allows for very precise calculations of ground state properties in low-dimensional strongly correlated systems. We investigate two methods to expand the DMRG to calculations of dynamical properties. In the Lanczos vector method the DMRG basis is optimized to represent Lanczos vectors, which are then used to calculate the spectra. This method is fast and relatively easy to implement, but the accuracy at higher frequencies is limited. Alternatively, one can optimize the basis to represent a correction vector for a particular frequency. The correction vectors can be used to calculate the dynamical correlation functions at these frequencies with high accuracy. By separately calculating correction vectors at different frequencies, the dynamical correlation functions can be interpolated and pieced together from these results. For systems with open boundaries we discuss how to construct operators for specific wavevectors using filter functions.Comment: minor revision, 10 pages, 15 figure

Missing data in randomized controlled trials testing palliative interventions pose a significant risk of bias and loss of power: a systematic review and meta-analyses

Objectives To assess the risk posed by missing data (MD) to the power and validity of trials evaluating palliative interventions. Study Design and Setting A systematic review of MD in published randomized controlled trials (RCTs) of palliative interventions in participants with life-limiting illnesses was conducted, and random-effects meta-analyses and metaregression were performed. CENTRAL, MEDLINE, and EMBASE (2009-2014) were searched with no language restrictions. Results One hundred and eight RCTs representing 15,560 patients were included. The weighted estimate for MD at the primary endpoint was 23.1% (95% confidence interval [CI] 19.3, 27.4). Larger MD proportions were associated with increasing numbers of questions/tests requested (odds ratio [OR] , 1.19; 95% CI 1.05, 1.35) and with longer study duration (OR, 1.09; 95% CI 1.02, 1.17). Meta-analysis found evidence of differential rates of MD between trial arms, which varied in direction (OR, 1.04; 95% CI 0.90, 1.20; I 2 35.9, P = 0.001). Despite randomization, MD in the intervention arms (vs. control) were more likely to be attributed to disease progression unrelated to the intervention (OR, 1.31; 95% CI 1.02, 1.69). This was not the case for MD due to death (OR, 0.92; 95% CI 0.78, 1.08). Conclusion The overall proportion and differential rates and reasons for MD reduce the power and potentially introduce bias to palliative care trials

Spectral Function for the S=1 Heisenberg Antiferromagetic Chain

We study the spectral function, $S(k,\omega)$ for the spin-1, one dimensional antiferromagnetic chain using a time-dependent density matrix renormalizaton group (DMRG) numerical method. We develop methods for extrapolating the time dependent correlation functions to larger times in order to enhance the frequency resolution. The resulting spectral functions are impressively precise and accurate. Our results confirm many qualitative expectations from non-linear $\sigma$ model methods and test them quantitatively. The critical wave-vector at which the single particle excitation emerges from the 2-particle continuum is estimated to be $0.23\pi-0.24\pi$.Comment: 12 pages, 19 fig

Analyses of Sensitivity to the Missing-at-Random Assumption Using Multiple Imputation With Delta Adjustment: Application to a Tuberculosis/HIV Prevalence Survey With Incomplete HIV-Status Data.

Multiple imputation with delta adjustment provides a flexible and transparent means to impute univariate missing data under general missing-not-at-random mechanisms. This facilitates the conduct of analyses assessing sensitivity to the missing-at-random (MAR) assumption. We review the delta-adjustment procedure and demonstrate how it can be used to assess sensitivity to departures from MAR, both when estimating the prevalence of a partially observed outcome and when performing parametric causal mediation analyses with a partially observed mediator. We illustrate the approach using data from 34,446 respondents to a tuberculosis and human immunodeficiency virus (HIV) prevalence survey that was conducted as part of the Zambia-South Africa TB and AIDS Reduction Study (2006-2010). In this study, information on partially observed HIV serological values was supplemented by additional information on self-reported HIV status. We present results from 2 types of sensitivity analysis: The first assumed that the degree of departure from MAR was the same for all individuals with missing HIV serological values; the second assumed that the degree of departure from MAR varied according to an individual's self-reported HIV status. Our analyses demonstrate that multiple imputation offers a principled approach by which to incorporate auxiliary information on self-reported HIV status into analyses based on partially observed HIV serological values