Expansion algorithm for the density matrix

A purification algorithm for expanding the single-particle density matrix in terms of the Hamiltonian operator is proposed. The scheme works with a predefined occupation and requires less than half the number of matrix-matrix multiplications compared to existing methods at low (90%) occupancy. The expansion can be used with a fixed chemical potential in which case it is an asymmetric generalization of and a substantial improvement over grand canonical McWeeny purification. It is shown that the computational complexity, measured as number of matrix multiplications, essentially is independent of system size even for metallic materials with a vanishing band gap.Comment: 5 pages, 4 figures, to appear in Phys. Rev.

Curvature correction to the mobility of fluid membrane inclusions

For the first time, using rigorous low-Reynolds-number hydrodynamic theory on curved surfaces via a Stokeslet-type approach, we provide a general and concise expression for the leading-order curvature correction to the canonical, planar, Saffman-Delbrück value of the diffusion constant for a small inclusion embedded in an arbitrarily (albeit weakly) curved fluid membrane. In order to demonstrate the efficacy and utility of this wholly general result, we apply our theory to the specific case of calculating the diffusion coefficient of a locally curvature inducing membrane inclusion. By including both the effects of inclusion and membrane elasticity, as well as their respective thermal shape fluctuations, excellent agreement is found with recently published experimental data on the surface tension dependent mobility of membrane bound inclusions

The International Pulsar Timing Array: First data release

International audienceThe highly stable spin of neutron stars can be exploited for a variety of (astro)physical investigations. In particular, arrays of pulsars with rotational periods of the order of milliseconds can be used to detect correlated signals such as those caused by gravitational waves. Three such 'pulsar timing arrays' (PTAs) have been set up around the world over the past decades and collectively form the 'International' PTA (IPTA). In this paper, we describe the first joint analysis of the data from the three regional PTAs, i.e. of the first IPTA data set. We describe the available PTA data, the approach presently followed for its combination and suggest improvements for future PTA research. Particular attention is paid to subtle details (such as underestimation of measurement uncertainty and long-period noise) that have often been ignored but which become important in this unprecedentedly large and inhomogeneous data set. We identify and describe in detail several factors that complicate IPTA research and provide recommendations for future pulsar timing efforts. The first IPTA data release presented here (and available on-line) is used to demonstrate the IPTA's potential of improving upon gravitational-wave limit

Linear rheology as a potential monitoring tool for sputum in patients with Chronic Obstructive Pulmonary Disease (COPD)

Sputum samples from Chronic Obstructive Pulmonary Disease (COPD) patients were investigated using rheology, simple mathematical modelling and Scanning Electron Microscopy (SEM). The samples were all collected from patients within two days of their admission to Prince Philip Hospital due to an exacerbation of their COPD. Oscillatory and creep rheological techniques were used to measure changes in viscoelastic properties at different frequencies over time, and COPD sputum was observed to behave as a viscoelastic solid at all frequencies studied. Comparing the rheology of exacerbated COPD sputum with healthy sputum (not diagnosed with a respiratory disease) revealed significant differences in response to oscillatory shear and creep-recovery experiments, which highlights the potential clinical benefits of better understanding sputum viscoelasticity. A common power law model G(t)=G0(tτ0)−m was successfully fitted to experimental rheology data over the range of frequencies studied. A comparison was made between clinical data and the power law index m obtained from rheology, which suggests that an important possible future application of this work is as a potential biomarker for COPD severity

Highly sensitive covalently functionalised integrated silicon nanowire biosensor devices for detection of cancer risk biomarker

In this article we present ultra-sensitive, silicon nanowire (SiNW)-based biosensor devices for the detection of disease biomarkers. An electrochemically induced functionalisation method has been employed to graft antibodies targeted against the prostate cancer risk biomarker 8-hydroxydeoxyguanosine (8-OHdG) to SiNW surfaces. The antibody-functionalised SiNW sensor has been used to detect binding of the 8-OHdG biomarker to the SiNW surface within seconds of exposure. Detection of 8-OHdG concentrations as low as 1 ng/ml (3.5 nM) has been demonstrated. The active device has been bonded to a disposable printed circuit which can be inserted into an electronic readout system as part of an integrated Point of Care (POC) diagnostic. The speed, sensitivity and ease of detection of biomarkers using SiNW sensors render them ideal for eventual POC diagnostics

Systemizing Virtual Learning and Technologies by Managing Organizational Competency and Talents

The article presents promising components and practices of virtual learning and technologies and discusses how systemization can be made through managing organizational competency and talents. The main goal is to suggest how technologies should be incorporated within an organization to improve the effectiveness of employees’ learning, performance, and development. For technology implementation and adoption, we also introduce models for examining organizational maturity levels and integrating technologies.We argue that virtual learning and technologies are fundamentally pressing HRD roles to change from experts of learning and development to work solution partners leading and supporting the creation of a smart organization.Yeshttps://us.sagepub.com/en-us/nam/manuscript-submission-guideline

Searching for continuous Gravitational Waves in the second data release of the International Pulsar Timing Array

The International Pulsar Timing Array 2nd data release is the combination of data sets from worldwide collaborations. In this study, we search for continuous waves: gravitational wave signals produced by individual supermassive black hole binaries in the local universe. We consider binaries on circular orbits and neglect the evolution of orbital frequency over the observational span. We find no evidence for such signals and set sky averaged 95 per cent upper limits on their amplitude h95. The most sensitive frequency is 10 nHz with h95 = 9.1 × 10-15. We achieved the best upper limit to date at low and high frequencies of the PTA band thanks to improved effective cadence of observations. In our analysis, we have taken into account the recently discovered common red noise process, which has an impact at low frequencies. We also find that the peculiar noise features present in some pulsars data must be taken into account to reduce the false alarm. We show that using custom noise models is essential in searching for continuous gravitational wave signals and setting the upper limit

Generative Adversarial Networks for Scintillation Signal Simulation in EXO-200

Generative Adversarial Networks trained on samples of simulated or actual events have been proposed as a way of generating large simulated datasets at a reduced computational cost. In this work, a novel approach to perform the simulation of photodetector signals from the time projection chamber of the EXO-200 experiment is demonstrated. The method is based on a Wasserstein Generative Adversarial Network - a deep learning technique allowing for implicit non-parametric estimation of the population distribution for a given set of objects. Our network is trained on real calibration data using raw scintillation waveforms as input. We find that it is able to produce high-quality simulated waveforms an order of magnitude faster than the traditional simulation approach and, importantly, generalize from the training sample and discern salient high-level features of the data. In particular, the network correctly deduces position dependency of scintillation light response in the detector and correctly recognizes dead photodetector channels. The network output is then integrated into the EXO-200 analysis framework to show that the standard EXO-200 reconstruction routine processes the simulated waveforms to produce energy distributions comparable to that of real waveforms. Finally, the remaining discrepancies and potential ways to improve the approach further are highlighted.Comment: 20 pages, 10 figure