A Deep Learning Framework for Optimization of MISO Downlink Beamforming

Beamforming is an effective means to improve the quality of the received signals in multiuser multiple-input-singleoutput (MISO) systems. Traditionally, finding the optimal beamforming solution relies on iterative algorithms, which introduces high computational delay and is thus not suitable for realtime implementation. In this paper, we propose a deep learning framework for the optimization of downlink beamforming. In particular, the solution is obtained based on convolutional neural networks and exploitation of expert knowledge, such as the uplink-downlink duality and the known structure of optimal solutions. Using this framework, we construct three beamforming neural networks (BNNs) for three typical optimization problems, i.e., the signal-to-interference-plus-noise ratio (SINR) balancing problem, the power minimization problem, and the sum rate maximization problem. For the former two problems the BNNs adopt the supervised learning approach, while for the sum rate maximization problem a hybrid method of supervised and unsupervised learning is employed. Simulation results show that the BNNs can achieve near-optimal solutions to the SINR balancing and power minimization problems, and a performance close to that of the weighted minimum mean squared error algorithm for the sum rate maximization problem, while in all cases enjoy significantly reduced computational complexity. In summary, this work paves the way for fast realization of optimal beamforming in multiuser MISO systems

Time-dependent appearance of myofibroblasts in granulation tissue of human skin wounds

Human skin wounds (66) inflicted between 20 h and 7 months prior to biopsy were studied. In order to identify the type of cellular differentiation of the fibroblastic cells in the granulation tissue, alpha-smooth muscle actin and desmin were immunohistochemically localized. The value of any presumed time-dependent appearance and/or disappearance of positively stained cells was tested for the estimation of wound age. In skin specimens with a wound age less than 5 days (n =15) no typical granulation tissue had developed and no alpha-actin-positive myofibroblasts could be detected. The first appearance of positively reacting myofibroblasts was noted in a 5-day-old wound. In 57% of the lesions with a wound age between 5 and 31 days (25 out of 44 cases) typical granulation tissue formation was present and myofibroblasts with positive reaction for alpha-smooth muscle actin could be identified. Numerous positively reacting cells could generally be found in wounds aged between 16 and 31 days, but also in wounds less than 16 days old. In 29% of the cases with a wound age of more than 31 days (2 out of 7 cases) alpha-sma-positive myofibroblasts also occured. Fibroblastic cells positive for desmin could not be seen at all in our series. Our results demonstrate the appearance of alpha-sma-positive myofibroblasts with the initial formation of typical granulation tissue in human skin lesions as early as approximately 5 days after wounding. In contrast to recent experimental results these cells remained detectable in wounds aged more than 2 months in some cases. The immunohistochemical detection of actin-positive cells, therefore, demonstrates whether an unknown skin wound is aged approximately 5 days or more. Even though a time-dependent decrease of myofibroblasts in human granulation tissue after 31 days in human wounds seems probable, the extended presence (up to about 2 months) of these cells allows no further exact age determination of older wounds

Adaptive Seeding for Gaussian Mixture Models

We present new initialization methods for the expectation-maximization algorithm for multivariate Gaussian mixture models. Our methods are adaptions of the well-known $K$-means++ initialization and the Gonzalez algorithm. Thereby we aim to close the gap between simple random, e.g. uniform, and complex methods, that crucially depend on the right choice of hyperparameters. Our extensive experiments indicate the usefulness of our methods compared to common techniques and methods, which e.g. apply the original $K$-means++ and Gonzalez directly, with respect to artificial as well as real-world data sets.Comment: This is a preprint of a paper that has been accepted for publication in the Proceedings of the 20th Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2016. The final publication is available at link.springer.com (http://link.springer.com/chapter/10.1007/978-3-319-31750-2 24

Emergent excitations in a geometrically frustrated magnet

Frustrated systems are ubiquitous and interesting because their behavior is difficult to predict. Magnetism offers extreme examples in the form of spin lattices where all interactions between spins cannot be simultaneously satisfied. Such geometrical frustration leads to macroscopic degeneracies, and offers the possibility of qualitatively new states of matter whose nature has yet to be fully understood. Here we have discovered how novel composite spin degrees of freedom can emerge from frustrated interactions in the cubic spinel ZnCr2O4. Upon cooling, groups of six spins self-organize into weakly interacting antiferromagnetic loops whose directors, defined as the unique direction along which the spins are aligned parallel or antiparallel, govern all low temperature dynamics. The experimental evidence comes from a measurement of the magnetic form factor by inelastic neutron scattering. While the data bears no resemblance to the atomic form factor for chromium, they are perfectly consistent with the form factor for hexagonal spin loop directors. The hexagon directors are to a first approximation decoupled from each other and hence their reorientations embody the long-sought local zero energy modes for the pyrochlore lattice.Comment: 10 pages, 4 figures upon reques

Efficiency of an intervention package for arterial hypertension comprising telemanagement in a Cameroonian rural setting: The TELEMED-CAM study

Introduction: Sub-Saharan Africa has a disproportionate burden of disease and an extreme shortage of health workforce. Therefore, adequate care for emerging chronic diseases can be very challenging. We implemented and evaluated the effectiveness of an intervention package comprising telecare as a mean for improving the outcomes of care for hypertension in Rural Sub-Saharan Africa. Methods: The study involved a telemedicine center based at the Yaounde General Hospital (5 cardiologists) in the Capital city of Cameroon, and 30 remote rural health centers within the vicinity of Yaoundé (20 centers (103 patients) in the usual care group, and 10 centers (165 patients) in the intervention groups). The total duration of the intervention was 24 weeks. Results: Participants in the intervention group had higher baseline systolic (SBP) and diastolic (DBP) blood pressure, and included fewer individuals with diabetes than those in the usual care group (all p<0.01). Otherwise, the baseline profile was mostly similar between the two groups. During follow-up, more participants in the intervention groups achieved optimal BP control, driven primarily by greater improvement of BP control among High risk participants (hypertension stage III) in the intervention group. Conclusion: An intervention package comprising tele-support to general practitioners and nurses is effective in improving the management and outcome of care for hypertension in rural underserved populations. This can potentially help in addressing the shortage of trained health workforce for chronic disease management in some settings. However context-specific approaches and cost-effectiveness data are needed to improve the application of telemedicine for chronic disease management in resource-limited settings.Key words: Hypertension, control, telemedicine, Cameroon, sub-saharan Afric

How Atomic Steps Modify Diffusion and Inter-adsorbate Forces: Empirical Evidence from Hopping Dynamics in Na/Cu(115).

We followed the collective atomic-scale motion of Na atoms on a vicinal Cu(115) surface within a time scale of pico- to nanoseconds using helium spin echo spectroscopy. The well-defined stepped structure of Cu(115) allows us to study the effect that atomic steps have on the adsorption properties, the rate for motion parallel and perpendicular to the step edge, and the interaction between the Na atoms. With the support of a molecular dynamics simulation we show that the Na atoms perform strongly anisotropic 1D hopping motion parallel to the step edges. Furthermore, we observe that the spatial and temporal correlations between the Na atoms that lead to collective motion are also anisotropic, suggesting the steps efficiently screen the lateral interaction between Na atoms residing on different terraces.This work was supported by the German-Israeli Foundation for Scientific Research and Development, the Israeli Science Foundation (Grant No. 2011185), the German Science Foundation (DFG) through contract MO 960/18-1, the Cluster of Excellence RESOLV (EXC 1069), and the European Research Council under the European Union’s seventh framework program (FP/2007-2013)/ERC Grant 307267.This is the author accepted manuscript. The final version is available from ACS via http://dx.doi.org/10.1021/acs.jpclett.5b0193

Bifidobacterium longum 1714 as a translational psychobiotic: modulation of stress, electrophysiology and neurocognition in healthy volunteers

The emerging concept of psychobiotics—live microorganisms with a potential mental health benefit—represents a novel approach for the management of stress-related conditions. The majority of studies have focused on animal models. Recent preclinical studies have identified the B. longum 1714 strain as a putative psychobiotic with an impact on stress-related behaviors, physiology and cognitive performance. Whether such preclinical effects could be translated to healthy human volunteers remains unknown. We tested whether psychobiotic consumption could affect the stress response, cognition and brain activity patterns. In a within-participants design, healthy volunteers (N=22) completed cognitive assessments, resting electroencephalography and were exposed to a socially evaluated cold pressor test at baseline, post-placebo and post-psychobiotic. Increases in cortisol output and subjective anxiety in response to the socially evaluated cold pressor test were attenuated. Furthermore, daily reported stress was reduced by psychobiotic consumption. We also observed subtle improvements in hippocampus-dependent visuospatial memory performance, as well as enhanced frontal midline electroencephalographic mobility following psychobiotic consumption. These subtle but clear benefits are in line with the predicted impact from preclinical screening platforms. Our results indicate that consumption of B. longum 1714 is associated with reduced stress and improved memory. Further studies are warranted to evaluate the benefits of this putative psychobiotic in relevant stress-related conditions and to unravel the mechanisms underlying such effects

A projective Dirac operator on CP^2 within fuzzy geometry

We propose an ansatz for the commutative canonical spin_c Dirac operator on CP^2 in a global geometric approach using the right invariant (left action-) induced vector fields from SU(3). This ansatz is suitable for noncommutative generalisation within the framework of fuzzy geometry. Along the way we identify the physical spinors and construct the canonical spin_c bundle in this formulation. The chirality operator is also given in two equivalent forms. Finally, using representation theory we obtain the eigenspinors and calculate the full spectrum. We use an argument from the fuzzy complex projective space CP^2_F based on the fuzzy analogue of the unprojected spin_c bundle to show that our commutative projected spin_c bundle has the correct SU(3)-representation content.Comment: reduced to 27 pages, minor corrections, minor improvements, typos correcte

Machine-learning of atomic-scale properties based on physical principles

We briefly summarize the kernel regression approach, as used recently in materials modelling, to fitting functions, particularly potential energy surfaces, and highlight how the linear algebra framework can be used to both predict and train from linear functionals of the potential energy, such as the total energy and atomic forces. We then give a detailed account of the Smooth Overlap of Atomic Positions (SOAP) representation and kernel, showing how it arises from an abstract representation of smooth atomic densities, and how it is related to several popular density-based representations of atomic structure. We also discuss recent generalisations that allow fine control of correlations between different atomic species, prediction and fitting of tensorial properties, and also how to construct structural kernels---applicable to comparing entire molecules or periodic systems---that go beyond an additive combination of local environments