Dynamical generalization of a solvable family of two-electron model atoms with general interparticle repulsion

Holas, Howard and March [Phys. Lett. A {\bf 310}, 451 (2003)] have obtained analytic solutions for ground-state properties of a whole family of two-electron spin-compensated harmonically confined model atoms whose different members are characterized by a specific interparticle potential energy u($r_{12}$). Here, we make a start on the dynamic generalization of the harmonic external potential, the motivation being the serious criticism levelled recently against the foundations of time-dependent density-functional theory (e.g. [J. Schirmer and A. Dreuw, Phys. Rev. A {\bf 75}, 022513 (2007)]). In this context, we derive a simplified expression for the time-dependent electron density for arbitrary interparticle interaction, which is fully determined by an one-dimensional non-interacting Hamiltonian. Moreover, a closed solution for the momentum space density in the Moshinsky model is obtained.Comment: 5 pages, submitted to J. Phys.

A classical Over Barrier Model to compute charge exchange between ions and one-optical-electron atoms

In this paper we study theoretically the process of electron capture between one-optical-electron atoms (e.g. hydrogenlike or alkali atoms) and ions at low-to-medium impact velocities (v/v_e <= 1) working on a modification of an already developed classical Over Barrier Model (OBM) [V. Ostrovsky, J. Phys. B: At. Mol. Opt. Phys. {\bf 28} 3901 (1995)], which allows to give a semianalytical formula for the cross sections. The model is discussed and then applied to a number of test cases including experimental data as well as data coming from other sophisticated numerical simulations. It is found that the accuracy of the model, with the suggested corrections and applied to quite different situations, is rather high.Comment: 12 pages REVTEX, 5 EPSF figures, submitted to Phys Rev

Atomistic simulations of self-trapped exciton formation in silicon nanostructures: The transition from quantum dots to nanowires

Using an approximate time-dependent density functional theory method, we calculate the absorption and luminescence spectra for hydrogen passivated silicon nanoscale structures with large aspect ratio. The effect of electron confinement in axial and radial directions is systematically investigated. Excited state relaxation leads to significant Stokes shifts for short nanorods with lengths less than 2 nm, but has little effect on the luminescence intensity. The formation of self-trapped excitons is likewise observed for short nanostructures only; longer wires exhibit fully delocalized excitons with neglible geometrical distortion at the excited state minimum.Comment: 10 pages, 4 figure

Wireless Stimulation of Antennal Muscles in Freely Flying Hawkmoths Leads to Flight Path Changes

Insect antennae are sensory organs involved in a variety of behaviors, sensing many different stimulus modalities. As mechanosensors, they are crucial for flight control in the hawkmoth Manduca sexta. One of their roles is to mediate compensatory reflexes of the abdomen in response to rotations of the body in the pitch axis. Abdominal motions, in turn, are a component of the steering mechanism for flying insects. Using a radio controlled, programmable, miniature stimulator, we show that ultra-low-current electrical stimulation of antennal muscles in freely-flying hawkmoths leads to repeatable, transient changes in the animals' pitch angle, as well as less predictable changes in flight speed and flight altitude. We postulate that by deflecting the antennae we indirectly stimulate mechanoreceptors at the base, which drive compensatory reflexes leading to changes in pitch attitude.United States. Defense Advanced Research Projects Agenc

Multiclass prediction of different dementia syndromes based on multi-centric volumetric MRI imaging

IntroductionDementia syndromes can be difficult to diagnose. We aimed at building a classifier for multiple dementia syndromes using magnetic resonance imaging (MRI).MethodsAtlas-based volumetry was performed on T1-weighted MRI data of 426 patients and 51 controls from the multi-centric German Research Consortium of Frontotemporal Lobar Degeneration including patients with behavioral variant frontotemporal dementia, Alzheimer’s disease, the three subtypes of primary progressive aphasia, i.e., semantic, logopenic and nonfluent-agrammatic variant, and the atypical parkinsonian syndromes progressive supranuclear palsy and corticobasal syndrome. Support vector machine classification was used to classify each patient group against controls (binary classification) and all seven diagnostic groups against each other in a multi-syndrome classifier (multiclass classification).ResultsThe binary classification models reached high prediction accuracies between 71 and 95% with a chance level of 50%. Feature importance reflected disease-specific atrophy patterns. The multi-syndrome model reached accuracies of more than three times higher than chance level but was far from 100%. Multi-syndrome model performance was not homogenous across dementia syndromes, with better performance in syndromes characterized by regionally specific atrophy patterns. Whereas diseases generally could be classified vs controls more correctly with increasing severity and duration, differentiation between diseases was optimal in disease-specific windows of severity and duration.DiscussionResults suggest that automated methods applied to MR imaging data can support physicians in diagnosis of dementia syndromes. It is particularly relevant for orphan diseases beside frequent syndromes such as Alzheimer’s disease

Extended Classical Over-Barrier Model for Collisions of Highly Charged Ions with Conducting and Insulating Surfaces

We have extended the classical over-barrier model to simulate the neutralization dynamics of highly charged ions interacting under grazing incidence with conducting and insulating surfaces. Our calculations are based on simple model rates for resonant and Auger transitions. We include effects caused by the dielectric response of the target and, for insulators, localized surface charges. Characteristic deviations regarding the charge transfer processes from conducting and insulating targets to the ion are discussed. We find good agreement with previously published experimental data for the image energy gain of a variety of highly charged ions impinging on Au, Al, LiF and KI crystals.Comment: 32 pages http://pikp28.uni-muenster.de/~ducree

Comparative analysis of machine learning algorithms for multi-syndrome classification of neurodegenerative syndromes

Importance: The entry of artificial intelligence into medicine is pending. Several methods have been used for the predictions of structured neuroimaging data, yet nobody compared them in this context. Objective: Multi-class prediction is key for building computational aid systems for differential diagnosis. We compared support vector machine, random forest, gradient boosting, and deep feed-forward neural networks for the classification of different neurodegenerative syndromes based on structural magnetic resonance imaging. Design, setting, and participants: Atlas-based volumetry was performed on multi-centric T1-weighted MRI data from 940 subjects, i.e., 124 healthy controls and 816 patients with ten different neurodegenerative diseases, leading to a multi-diagnostic multi-class classification task with eleven different classes. Interventions: N.A. Main outcomes and measures: Cohen's kappa, accuracy, and F1-score to assess model performance. Results: Overall, the neural network produced both the best performance measures and the most robust results. The smaller classes however were better classified by either the ensemble learning methods or the support vector machine, while performance measures for small classes were comparatively low, as expected. Diseases with regionally specific and pronounced atrophy patterns were generally better classified than diseases with widespread and rather weak atrophy. Conclusions and relevance: Our study furthermore underlines the necessity of larger data sets but also calls for a careful consideration of different machine learning methods that can handle the type of data and the classification task best

Observations and modeling of areal surface albedo and surface types in the Arctic

An accurate representation of the annual evolution of surface albedo of the Arctic Ocean, especially during the melting period, is crucial to obtain reliable climate model predictions in the Arctic. Therefore, the output of the surface albedo scheme of a coupled regional climate model (HIRHAM–NAOSIM) was evaluated against airborne and ground-based measurements. The observations were conducted during five aircraft campaigns in the European Arctic at different times of the year between 2017 and 2022; one of them was part of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in 2020. We applied two approaches for the evaluation: (a) relying on measured input parameters of surface type fraction and surface skin temperature (offline) and (b) using HIRHAM–NAOSIM simulations independently of observational data (online). From the offline method we found a seasonally dependent bias between measured and modeled surface albedo. In spring, the cloud effect on surface broadband albedo was overestimated by the surface albedo parametrization (mean albedo bias of 0.06), while the surface albedo scheme for cloudless cases reproduced the measured surface albedo distributions for all seasons. The online evaluation revealed an overestimation of the modeled surface albedo resulting from an overestimation of the modeled cloud cover. Furthermore, it was shown that the surface type parametrization contributes significantly to the bias in albedo, especially in summer (after the drainage of melt ponds) and autumn (onset of refreezing). The lack of an adequate model representation of the surface scattering layer, which usually forms on bare ice in summer, contributed to the underestimation of surface albedo during that period. The difference between modeled and measured net irradiances for selected flights during the five airborne campaigns was derived to estimate the impact of the model bias for the solar radiative energy budget at the surface. We revealed a negative bias between modeled and measured net irradiances (median: −6.4 W m−2) for optically thin clouds, while the median value of only 0.1 W m−2 was determined for optically thicker clouds.</p