Deep Learning-Aided Subspace-Based DOA Recovery for Sparse Arrays

Sparse arrays enable resolving more direction of arrivals (DoAs) than antenna elements using non-uniform arrays. This is typically achieved by reconstructing the covariance of a virtual large uniform linear array (ULA), which is then processed by subspace DoA estimators. However, these method assume that the signals are non-coherent and the array is calibrated; the latter often challenging to achieve in sparse arrays, where one cannot access the virtual array elements. In this work, we propose Sparse-SubspaceNet, which leverages deep learning to enable subspace-based DoA recovery from sparse miscallibrated arrays with coherent sources. Sparse- SubspaceNet utilizes a dedicated deep network to learn from data how to compute a surrogate virtual array covariance that is divisible into distinguishable subspaces. By doing so, we learn to cope with coherent sources and miscalibrated sparse arrays, while preserving the interpretability and the suitability of model-based subspace DoA estimators.Comment: 4 pages, 4 figure

Implementation and Evaluation of Power Consumption of an Iris Pre-processing Algorithm on Modern FPGA

In this article, the efficiency and applicability of several power reduction techniques applied on a modern 65nm FPGA is described. For image erosion and dilation algorithms, two major solutions were tested and compared with respect to power and energy consumption. Firstly the algorithm was run on a general purpose processor (gpp) NIOS and then hardware architecture of an Intellectual Property (IP) was designed. Furthermore IPs design was improved by applying a number of power optimization techniques. They involved RTL level clock gating, power driven synthesis with fitting and appropriate coding style. Results show that hardware implementation is much more energy efficient than a general purpose processor and power optimization schemes can reduce the overall power consumption on an FPGA

CN and HCN in Dense Interstellar Clouds

We present a theoretical investigation of CN and HCN molecule formation in dense interstellar clouds. We study the gas-phase CN and HCN production efficiencies from the outer photon-dominated regions (PDRs) into the opaque cosmic-ray dominated cores. We calculate the equilibrium densities of CN and HCN, and of the associated species C+, C, and CO, as functions of the far-ultraviolet (FUV) optical depth. We consider isothermal gas at 50 K, with hydrogen particle densities from 10^2 to 10^6 cm^-3. We study clouds that are exposed to FUV fields with intensities 20 to 2*10^5 times the mean interstellar FUV intensity. We assume cosmic-ray H2 ionization rates ranging from 5*10^-17 s^-1, to an enhanced value of 5*10^-16 s^-1. We also examine the sensitivity of the density profiles to the gas-phase sulfur abundance.Comment: Accepted for publication in ApJ, 33 pages, 8 figure

Psychosocial aspects of closed- and open-loop insulin delivery: closing the loop in adults with Type 1 diabetes in the home setting.

AIMS: To explore the psychosocial experiences of closed-loop technology and to compare ratings of closed- and open-loop technology for adults with Type 1 diabetes taking part in a randomized crossover study. METHODS: Adults (aged > 18 years) on insulin pump therapy were recruited to receive a first phase of either real-time continuous glucose monitoring with overnight closed-loop or real-time continuous glucose monitoring alone (open-loop) followed by a second phase of the alternative treatment in random order, at home for 4 weeks, unsupervised. Participants were invited to share their views in semi-structured interviews. The impact of the closed-loop technology, positive and negative aspects of living with the device overnight, along with the hopes and anxieties of the participants, were explored. RESULTS: The participants in the trial were 24 adults with a mean (sd) age of 43 (12) years, of whom 54% were men. The mean (range) interview duration was 26 (12-46) min. Content and thematic analysis showed the following key positive themes: improved blood glucose control (n = 16); reassurance/reduced worry (n = 16); improved overnight control leading to improved daily functioning and diabetes control (n = 16); and improved sleep (n = 8). The key negative themes were: technical difficulties (n = 24); intrusiveness of alarms (n = 13); and size of equipment (n = 7). Of the 24 participant, 20 would recommend the closed-loop technology. CONCLUSIONS: Closed-loop therapy has positive effects when it works in freeing participants from the demands of self-management. The downside was technical difficulties, particularly concerning the pump and 'connectivity', which it is hoped will improve. Future research should continue to explore the acceptability of the closed-loop system as a realistic therapy option, taking account of user concerns as new systems are designed. Failure to do this may reduce the eventual utility of new systems.Diabetes UKThis is the accepted manuscript. The final version is available at http://dx.doi.org/10.1111/dme.12706

Self-Consistent Field study of Polyelectrolyte Brushes

We formulate a self-consistent field theory for polyelectrolyte brushes in the presence of counterions. We numerically solve the self-consistent field equations and study the monomer density profile, the distribution of counterions, and the total charge distribution. We study the scaling relations for the brush height and compare them to the prediction of other theories. We find a weak dependence of the brush height on the grafting density.We fit the counterion distribution outside the brush by the Gouy-Chapman solution for a virtual charged wall. We calculate the amount of counterions outside the brush and find that it saturates as the charge of the polyelectrolytes increases

Detecting K-complexes for sleep stage identification using nonsmooth optimisation

The process of sleep stage identification is a labour-intensive task that involves the specialized interpretation of the polysomnographic signals captured from a patient’s overnight sleep session. Automating this task has proven to be challenging for data mining algorithms because of noise, complexity and the extreme size of data. In this paper we apply nonsmooth optimization to extract key features that lead to better accuracy. We develop a specific procedure for identifying K-complexes, a special type of brain wave crucial for distinguishing sleep stages. The procedure contains two steps. We first extract “easily classified” K-complexes, and then apply nonsmooth optimization methods to extract features from the remaining data and refine the results from the first step. Numerical experiments show that this procedure is efficient for detecting K-complexes. It is also found that most classification methods perform significantly better on the extracted features

Infrared spectroscopy of NGC 1068: Probing the obscured ionizing AGN continuum

The ISO-SWS 2.5-45 um infrared spectroscopic observations of the nucleus of the Seyfert 2 galaxy NGC 1068 (see companion paper) are combined with a compilation of UV to IR narrow emission line data to determine the spectral energy distribution (SED) of the obscured extreme-UV continuum that photoionizes the narrow line emitting gas in the active galactic nucleus. We search a large grid of gas cloud models and SEDs for the combination that best reproduces the observed line fluxes and NLR geometry. Our best fit model reproduces the observed line fluxes to better than a factor of 2 on average and is in general agreement with the observed NLR geometry. It has two gas components that are consistent with a clumpy distribution of dense outflowing gas in the center and a more extended distribution of less dense and more clumpy gas farther out that has no net outflow. The best fit SED has a deep trough at ~4 Ryd, which is consistent with an intrinsic Big Blue Bump that is partially absorbed by ~6x10^19 cm^-2 of neutral hydrogen interior to the NLR.Comment: 15 pp, 4 figures, ApJ accepte

Short-lived star-forming giant clumps in cosmological simulations of z~2 disks

Many observed massive star-forming z\approx2 galaxies are large disks that exhibit irregular morphologies, with \sim1kpc, \sim10^(8-10)Msun clumps. We present the largest sample to date of high-resolution cosmological SPH simulations that zoom-in on the formation of individual M*\sim10^(10.5)Msun galaxies in \sim10^(12)Msun halos at z\approx2. Our code includes strong stellar feedback parameterized as momentum-driven galactic winds. This model reproduces many characteristic features of this observed class of galaxies, such as their clumpy morphologies, smooth and monotonic velocity gradients, high gas fractions (f_g\sim50%) and high specific star-formation rates (\gtrsim1Gyr^(-1)). In accord with recent models, giant clumps (Mclump\sim(5x10^8-10^9)Msun) form in-situ via gravitational instabilities. However, the galactic winds are critical for their subsequent evolution. The giant clumps we obtain are short-lived and are disrupted by wind-driven mass loss. They do not virialise or migrate to the galaxy centers as suggested in recent work neglecting strong winds. By phenomenologically implementing the winds that are observed from high-redshift galaxies and in particular from individual clumps, our simulations reproduce well new observational constraints on clump kinematics and clump ages. In particular, the observation that older clumps appear closer to their galaxy centers is reproduced in our simulations, as a result of inside-out formation of the disks rather than inward clump migration.Comment: 11 pages, 6 figures, 1 table. Accepted for publication in the Astrophysical Journa

Unsupervised home use of an overnight closed-loop system over 3-4 weeks: a pooled analysis of randomized controlled studies in adults and adolescents with type 1 diabetes.

AIMS: To compare overnight closed-loop and sensor-augmented pump therapy in patients with type 1 diabetes by combining data collected during free-living unsupervised randomized crossover home studies. METHODS: A total of 40 participants with type 1 diabetes, of whom 24 were adults [mean ± standard deviation (s.d.) age 43 ± 12 years and glycated haemoglobin (HbA1c) 8.0 ± 0.9%] and 16 were adolescents (mean ± s.d. age 15.6 ± 3.6 years and HbA1c 8.1 ± 0.8%), underwent two periods of sensor-augmented pump therapy in the home setting, in combination with or without an overnight closed-loop insulin delivery system that uses a model predictive control algorithm to direct insulin delivery. The order of the two interventions was random; each period lasted 4 weeks in adults and 3 weeks in adolescents. The primary outcome was time during which sensor glucose readings were in the target range of 3.9-8.0 mmol/l. RESULTS: The proportion of time when sensor glucose was in the target range (3.9-8.0 mmol/l) overnight (between 24:00 and 08:00 hours) was 18.5% greater during closed-loop insulin delivery than during sensor-augmented therapy (p < 0.001). Closed-loop therapy significantly reduced mean overnight glucose levels by 0.9 mmol/l (p < 0.001), with no difference in glycaemic variability, as measured by the standard deviation of sensor glucose. Time spent above the target range was reduced (p = 0.001), as was time spent in hypoglycaemia (<3.9 mmol/l; p = 0.014) during closed-loop therapy. Lower mean overnight glucose levels during closed-loop therapy were brought about by increased overnight insulin delivery (p < 0.001) without changes to the total daily delivery (p = 0.84). CONCLUSION: Overnight closed-loop insulin therapy at home in adults and adolescents with type 1 diabetes is feasible, showing improvements in glucose control and reducing the risk of nocturnal hypoglycaemia.Juvenile Diabetes Research Foundation (#22-2009-802) and Diabetes UK (BDA07/0003549) with additional support for the Artificial Pancreas work by National Institute of Diabetes and Digestive and Kidney Diseases (1R01DK085621), and National Institute for Health Research Cambridge Biomedical Research Centre. Abbott Diabetes Care supplied continuous glucose delivery devices and sensors and modified devices to facilitate real-time connectivity.This if the final version of the article. It was originally published by Wiley in Diabetes, Obesity and Metabolism at http://onlinelibrary.wiley.com/doi/10.1111/dom.12427/abstrac

Indonesians Human Leukocyte Antigen (HLA) Distributions and Correlations with Global Diseases

In Human, Major Histocompatibility Complex known as Human Leukocyte Antigen (HLA). The HLA grouped into three subclasses regions: the class I region, the class II region, and the class III region. There are thousands of polymorphic HLAs, many of them are proven to have correlations with diseases. Indonesia consists of diverse ethnicity people and populations. It carries a unique genetic diversity between one and another geographical positions. This paper aims to extract Indonesians HLA allele data, mapping the data, and correlating them with global diseases. From the study, it is found that global diseases, like Crohn’s disease, rheumatoid arthritis, Graves’ disease, gelatin allergy, T1D, HIV, systemic lupus erythematosus, juvenile chronic arthritis, and Mycobacterial disease (tuberculosis and leprosy) suspected associated with the Indonesian HLA profiles