Finite-temperature time-dependent variation with multiple Davydov states

The Dirac-Frenkel time-dependent variational approach with Davydov Ans\"atze is a sophisticated, yet efficient technique to obtain an acuurate solution to many-body Schr\"odinger equations for energy and charge transfer dy- namics in molecular aggregates and light-harvesting complexes. We extend this variational approach to finite temperatures dynamics of the spin-boson model by adopting a Monte Carlo importance sampling method. In or- der to demonstrate the applicability of this approach, we compare real-time quantum dynamics of the spin-boson model calculated with that from numerically exact iterative quasiadiabatic propagator path integral (QUAPI) technique. The comparison shows that our variational approach with the single Davydov Ans\"atze is in excellent agreement with the QUAPI method at high temperatures, while the two differ at low temperatures. Accuracy in dynamics calculations employing a multitude of Davydov trial states is found to improve substantially over the single Davydov Ansatz, especially at low temperatures. At a moderate computational cost, our variational approach with the multiple Davydov Ansatz is shown to provide accurate spin-boson dynamics over a wide range of temperatures and bath spectral densities.Comment: 8 pages, 3 figure

Is attending a mental process?

The nature of attention has been the topic of a lively research programme in psychology for over a century. But there is widespread agreement that none of the theories on offer manage to fully capture the nature of attention. Recently, philosophers have become interested in the debate again after a prolonged period of neglect. This paper contributes to the project of explaining the nature of attention. It starts off by critically examining Christopher Mole’s prominent “adverbial” account of attention, which traces the failure of extant psychological theories to their assumption that attending is a kind of process. It then defends an alternative, process-based view of the metaphysics of attention, on which attention is understood as an activity and not, as psychologists seem to implicitly assume, an accomplishment. The entrenched distinction between accomplishments and activities is shown to shed new light on the metaphysics of attention. It also provides a novel diagnosis of the empirical state of play

Cosmological constraints on the generalized holographic dark energy

We use the Markov ChainMonte Carlo method to investigate global constraints on the generalized holographic (GH) dark energy with flat and non-flat universe from the current observed data: the Union2 dataset of type supernovae Ia (SNIa), high-redshift Gamma-Ray Bursts (GRBs), the observational Hubble data (OHD), the cluster X-ray gas mass fraction, the baryon acoustic oscillation (BAO), and the cosmic microwave background (CMB) data. The most stringent constraints on the GH model parameter are obtained. In addition, it is found that the equation of state for this generalized holographic dark energy can cross over the phantom boundary wde =-1.Comment: 14 pages, 5 figures. arXiv admin note: significant text overlap with arXiv:1105.186

WHAM Observations of H-alpha Emission from High Velocity Clouds in the M, A, and C Complexes

The first observations of the recently completed Wisconsin H-Alpha Mapper (WHAM) facility include a study of emission lines from high velocity clouds in the M, A, and C complexes, with most of the observations on the M I cloud. We present results including clear detections of H-alpha emission from all three complexes with intensities ranging from 0.06 R to 0.20 R. In every observed direction where there is significant high velocity H I gas seen in the 21 cm line we have found associated ionized hydrogen emitting the H-alpha line. The velocities of the H-alpha and 21 cm emission are well correlated in every case except one, but the intensities are not correlated. There is some evidence that the ionized gas producing the H-alpha emission envelopes the 21 cm emitting neutral gas but the H-alpha "halo", if present, is not large. If the H-alpha emission arises from the photoionization of the H I clouds, then the implied Lyman continuum flux F_{LC} at the location of the clouds ranges from 1.3 to 4.2 x 10^5 photons cm^{-2} s^{-1}. If, on the other hand, the ionization is due to a shock arising from the collision of the high-velocity gas with an ambient medium in the halo, then the density of the pre-shocked gas can be constrained. We have also detected the [S II] 6716 angstrom line from the M I cloud and have evidence that the [S II] to H-alpha ratio varies with location on the cloud.Comment: 32 pages, 18 figures, to appear in ApJ (Sept. 10, 1998

On low temperature kinetic theory; spin diffusion, Bose Einstein condensates, anyons

The paper considers some typical problems for kinetic models evolving through pair-collisions at temperatures not far from absolute zero, which illustrate specific quantum behaviours. Based on these examples, a number of differences between quantum and classical Boltzmann theory is then discussed in more general terms.Comment: 25 pages, minor updates of previous versio

Learning Optimal Deep Projection of 18^{18}F-FDG PET Imaging for Early Differential Diagnosis of Parkinsonian Syndromes

Several diseases of parkinsonian syndromes present similar symptoms at early stage and no objective widely used diagnostic methods have been approved until now. Positron emission tomography (PET) with $^{18}$F-FDG was shown to be able to assess early neuronal dysfunction of synucleinopathies and tauopathies. Tensor factorization (TF) based approaches have been applied to identify characteristic metabolic patterns for differential diagnosis. However, these conventional dimension-reduction strategies assume linear or multi-linear relationships inside data, and are therefore insufficient to distinguish nonlinear metabolic differences between various parkinsonian syndromes. In this paper, we propose a Deep Projection Neural Network (DPNN) to identify characteristic metabolic pattern for early differential diagnosis of parkinsonian syndromes. We draw our inspiration from the existing TF methods. The network consists of a (i) compression part: which uses a deep network to learn optimal 2D projections of 3D scans, and a (ii) classification part: which maps the 2D projections to labels. The compression part can be pre-trained using surplus unlabelled datasets. Also, as the classification part operates on these 2D projections, it can be trained end-to-end effectively with limited labelled data, in contrast to 3D approaches. We show that DPNN is more effective in comparison to existing state-of-the-art and plausible baselines.Comment: 8 pages, 3 figures, conference, MICCAI DLMIA, 201