575 research outputs found
User Effects in Beam-Space MIMO
The performance and design of the novel single-RF-chain beam-space MIMO
antenna concept is evaluated for the first time in the presence of the user.
First, the variations of different performance parameters are evaluated when
placing a beam-space MIMO antenna in close proximity to the user body in
several typical operating scenarios. In addition to the typical degradation of
conventional antennas in terms of radiation efficiency and impedance matching,
it is observed that the user body corrupts the power balance and the
orthogonality of the beam-space MIMO basis. However, capacity analyses show
that throughput reduction mainly stems from the absorption in user body tissues
rather than from the power imbalance and the correlation of the basis. These
results confirm that the beam-space MIMO concept, so far only demonstrated in
the absence of external perturbation, still performs very well in typical human
body interaction scenarios.Comment: 4 pages, 7 figures, 2 table
Characterizations of Levy Distribution via Sub-Independence of the Random Variables and Truncated Moments
The concept of sub-independence is based on the convolution of the distributions of the random variables. It is much weaker than that of independence, but is shown to be sufficient to yield the conclusions of important theorems and results in probability and statistics. It also provides a measure of dissociation between two random variables which is much stronger than uncorrelatedness. Following Ahsanullah and Nevzorov (2014), we present certain characterizations of Levy distribution based on: (i) the sub-independence of the random variables; (ii) a simple relationship between two truncated moments; (iii) conditional expectation of certain function of the random variable. In case of independence, characterization (i) reduces to that of Ahsanullah and Nevzorov (2014)
Towards the Success Rate of One: Real-time Unconstrained Salient Object Detection
In this work, we propose an efficient and effective approach for
unconstrained salient object detection in images using deep convolutional
neural networks. Instead of generating thousands of candidate bounding boxes
and refining them, our network directly learns to generate the saliency map
containing the exact number of salient objects. During training, we convert the
ground-truth rectangular boxes to Gaussian distributions that better capture
the ROI regarding individual salient objects. During inference, the network
predicts Gaussian distributions centered at salient objects with an appropriate
covariance, from which bounding boxes are easily inferred. Notably, our network
performs saliency map prediction without pixel-level annotations, salient
object detection without object proposals, and salient object subitizing
simultaneously, all in a single pass within a unified framework. Extensive
experiments show that our approach outperforms existing methods on various
datasets by a large margin, and achieves more than 100 fps with VGG16 network
on a single GPU during inference
Recent trends in the frequency and duration of global floods
Frequency and duration of floods are analyzed using the global flood database of the Dartmouth Flood Observatory (DFO) to explore evidence of trends during 1985–2015 at global and latitudinal scales. Three classes of flood duration (i.e., short: 1–7, moderate: 8–20, and long: 21 days and above) are also considered for this analysis. The nonparametric Mann–Kendall trend analysis is used to evaluate three hypotheses addressing potential monotonic trends in the frequency of flood, moments of duration, and frequency of specific flood duration types. We also evaluated if trends could be related to large-scale atmospheric teleconnections using a generalized linear model framework. Results show that flood frequency and the tails of the flood duration (long duration) have increased at both the global and the latitudinal scales. In the tropics, floods have increased 4-fold since the 2000s. This increase is 2.5-fold in the north midlatitudes. However, much of the trend in frequency and duration of the floods can be placed within the long-term climate variability context since the Atlantic Multidecadal Oscillation, North Atlantic Oscillation, and Pacific Decadal Oscillation were the main atmospheric teleconnections explaining this trend. There is no monotonic trend in the frequency of short-duration floods across all the global and latitudinal scales. There is a significant increasing trend in the annual median of flood durations globally and each latitudinal belt, and this trend is not related to these teleconnections. While the DFO data come with a certain level of epistemic uncertainty due to imprecision in the estimation of floods, overall, the analysis provides insights for understanding the frequency and persistence in hydrologic extremes and how they relate to changes in the climate, organization of global and local dynamical systems, and country-scale socioeconomic factors
Collective estimation of multiple bivariate density functions with application to angular-sampling-based protein loop modeling
This article develops a method for simultaneous estimation of density functions for a collection of populations of protein backbone angle pairs using a data-driven, shared basis that is constructed by bivariate spline functions defined on a triangulation of the bivariate domain. The circular nature of angular data is taken into account by imposing appropriate smoothness constraints across boundaries of the triangles. Maximum penalized likelihood is used to fit the model and an alternating blockwise Newton-type algorithm is developed for computation. A simulation study shows that the collective estimation approach is statistically more efficient than estimating the densities individually. The proposed method was used to estimate neighbor-dependent distributions of protein backbone dihedral angles (i.e., Ramachandran distributions). The estimated distributions were applied to protein loop modeling, one of the most challenging open problems in protein structure prediction, by feeding them into an angular-sampling-based loop structure prediction framework. Our estimated distributions compared favorably to the Ramachandran distributions estimated by fitting a hierarchical Dirichlet process model; and in particular, our distributions showed significant improvements on the hard cases where existing methods do not work well
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