pwc-archive/papers-with-abstracts huggingface dataset:
A Survey of Channel Modeling for UAV Communications
Date: 2018-01-23 00:00:00
Tasks: Survey

Unmanned aerial vehicles (UAVs) have gained great interest for rapid
deployment in both civil and military applications. UAV communication has its
own distinctive channel characteristics compared with widely used cellular and
satellite systems. Thus, accurate channel characterization is crucial for the
performance optimization and design of efficient UAV communication systems.
However, several challenges exist in UAV channel modeling. For example,
propagation characteristics of UAV channels are still less explored for spatial
and temporal variations in non\textendash stationary channels. Also, airframe
shadowing has not yet been investigated for small size rotary UAVs. This paper
provides an extensive survey on the measurement campaigns launched for UAV
channel modeling using low altitude platforms and discusses various channel
characterization efforts. We also review the contemporary perspective of UAV
channel modeling approaches and outline some future research challenges in this
domain.
Strategic nomadic-colonial switching: Stochastic noise and subsidence-recovery cycles
Date: 2018-01-23 00:00:00

Previously, we developed a population model incorporating the Allee effect
and periodic environmental fluctuations, in which organisms alternate between
nomadic and colonial behaviours. This switching strategy is regulated by
biological clocks and the abundance of environmental resources, and can lead to
population persistence despite both behaviours being individually losing. In
the present study, we consider stochastic noise models in place of the original
periodic ones, thereby allowing a wider range of environmental fluctuations to
be modelled. The theoretical framework is generalized to account for resource
depletion by both nomadic and colonial sub-populations, and an ecologically
realistic population size-dependent switching scheme is proposed. We
demonstrate the robustness of the modified switching scheme to stochastic
noise, and we also present the intriguing possibility of consecutive
subsidence-recovery cycles within the resulting population dynamics. Our
results have relevance in biological and physical systems.
Double-Stage Delay Multiply and Sum Beamforming Algorithm: Application to Linear-Array Photoacoustic Imaging
Date: 2018-01-23 00:00:00

Photoacoustic imaging (PAI) is an emerging medical imaging modality capable
of providing high spatial resolution of Ultrasound (US) imaging and high
contrast of optical imaging. Delay-and-Sum (DAS) is the most common beamforming
algorithm in PAI. However, using DAS beamformer leads to low resolution images
and considerable contribution of off-axis signals. A new paradigm namely
Delay-Multiply-and-Sum (DMAS), which was originally used as a reconstruction
algorithm in confocal microwave imaging, was introduced to overcome the
challenges in DAS. DMAS was used in PAI systems and it was shown that this
algorithm results in resolution improvement and sidelobe degrading. However,
DMAS is still sensitive to high levels of noise, and resolution improvement is
not satisfying. Here, we propose a novel algorithm based on DAS algebra inside
DMAS formula expansion, Double Stage DMAS (DS-DMAS), which improves the image
resolution and levels of sidelobe, and is much less sensitive to high level of
noise compared to DMAS. The performance of DS-DMAS algorithm is evaluated
numerically and experimentally. The resulted images are evaluated qualitatively
and quantitatively using established quality metrics including signal-to-noise
ratio (SNR), full-width-half-maximum (FWHM) and contrast ratio (CR). It is
shown that DS-DMAS outperforms DAS and DMAS at the expense of higher
computational load. DS-DMAS reduces the lateral valley for about 15 dB and
improves the SNR and FWHM better than 13% and 30%, respectively. Moreover, the
levels of sidelobe are reduced for about 10 dB in comparison with those in
DMAS.
Object-based Multipass InSAR via Robust Low Rank Tensor Decomposition
Date: 2018-01-23 00:00:00
Tasks: Object, Retrieval, Tensor Decomposition

The most unique advantage of multipass SAR interferometry (InSAR) is the
retrieval of long term geophysical parameters, e.g. linear deformation rates,
over large areas. Recently, an object-based multipass InSAR framework has been
proposed in [1], as an alternative to the typical single-pixel methods, e.g.
Persistent Scatterer Interferometry (PSI), or pixel-cluster-based methods, e.g.
SqueeSAR. This enables the exploitation of inherent properties of InSAR phase
stacks on an object level. As a followon, this paper investigates the inherent
low rank property of such phase tensors, and proposes a Robust Multipass InSAR
technique via Object-based low rank tensor decomposition (RoMIO). We
demonstrate that the filtered InSAR phase stacks can improve the accuracy of
geophysical parameters estimated via conventional multipass InSAR techniques,
e.g. PSI, by a factor of ten to thirty in typical settings. The proposed method
is particularly effective against outliers, such as pixels with unmodeled
phases. These merits in turn can effectively reduce the number of images
required for a reliable estimation. The promising performance of the proposed
method is demonstrated using high-resolution TerraSAR-X image stacks.
The SARptical Dataset for Joint Analysis of SAR and Optical Image in Dense Urban Area
Date: 2018-01-23 00:00:00

The joint interpretation of very high resolution SAR and optical images in
dense urban area are not trivial due to the distinct imaging geometry of the
two types of images. Especially, the inevitable layover caused by the
side-looking SAR imaging geometry renders this task even more challenging. Only
until recently, the "SARptical" framework [1], [2] proposed a promising
solution to tackle this. SARptical can trace individual SAR scatterers in
corresponding high-resolution optical images, via rigorous 3-D reconstruction
and matching. This paper introduces the SARptical dataset, which is a dataset
of over 10,000 pairs of corresponding SAR, and optical image patches extracted
from TerraSAR-X high-resolution spotlight images and aerial UltraCAM optical
images. This dataset opens new opportunities of multisensory data analysis. One
can analyze the geometry, material, and other properties of the imaged object
in both SAR and optical image domain. More advanced applications such as SAR
and optical image matching via deep learning [3] is now also possible.
Automatic Detection and Positioning of Ground Control Points Using TerraSAR-X Multi-Aspect Acquisitions
Date: 2018-01-23 00:00:00

Geodetic stereo Synthetic Aperture Radar (SAR) is capable of absolute
three-dimensional localization of natural Persistent Scatterer (PS)s which
allows for Ground Control Point (GCP) generation using only SAR data. The
prerequisite for the method to achieve high precision results is the correct
detection of common scatterers in SAR images acquired from different viewing
geometries. In this contribution, we describe three strategies for automatic
detection of identical targets in SAR images of urban areas taken from
different orbit tracks. Moreover, a complete work-flow for automatic generation
of large number of GCPs using SAR data is presented and its applicability is
shown by exploiting TerraSAR-X (TS-X) high resolution spotlight images over the
city of Oulu, Finland and a test site in Berlin, Germany.
A Hardware-Efficient Synchronization in L-DACS1 for Aeronautical Communications
Date: 2018-01-19 00:00:00
Tasks: Management, Retrieval

L-band digital aeronautical communication system type-1 (L-DACS1) is an
emerging standard that aims at enhancing air traffic management by
transitioning the traditional analog aeronautical communication systems to the
superior and highly efficient digital domain. L-DACS1 employs modern and
efficient orthogonal frequency-division multiplexing (OFDM) modulation
technique to achieve more efficient and higher data rate in comparison to the
existing aeronautical communication systems. However, the performance of OFDM
systems is very sensitive to synchronization errors such as symbol timing
offset (STO) and carrier frequency offset (CFO). STO and CFO estimations are
extremely important for maintaining orthogonality among the subcarriers for the
retrieval of information. This paper proposes a novel efficient hardware
synchronizer for L-DACS1 systems that offers robust performance at low power
and low hardware resource usage. Monte Carlo simulations show that the proposed
synchronization algorithm provides accurate STO estimation as well as
fractional CFO estimation. Implementation of the proposed synchronizer on a
widely used field-programmable gate array (FPGA) (Xilinx xc7z020clg484-1)
results in a very low hardware usage which consumed 6.5%, 3.7%, and 6.4% of the
total number of lookup tables, flip-flops, and digital signal processing
blocks, respectively. The dynamic power of the proposed synchronizer is below 1
mW.
Universal and idiosyncratic characteristic lengths in bacterial genomes
Date: 2018-01-23 00:00:00

In condensed matter physics, simplified descriptions are obtained by
coarse-graining the features of a system at a certain characteristic length,
defined as the typical length beyond which some properties are no longer
correlated. From a physics standpoint, in vitro DNA has thus a characteristic
length of 300 base pairs (bp), the Kuhn length of the molecule beyond which
correlations in its orientations are typically lost. From a biology standpoint,
in vivo DNA has a characteristic length of 1000 bp, the typical length of
genes. Since bacteria live in very different physico-chemical conditions and
since their genomes lack translational invariance, whether larger, universal
characteristic lengths exist is a non-trivial question. Here, we examine this
problem by leveraging the large number of fully sequenced genomes available in
public databases. By analyzing GC content correlations and the evolutionary
conservation of gene contexts (synteny) in hundreds of bacterial chromosomes,
we conclude that a fundamental characteristic length around 10-20 kb can be
defined. This characteristic length reflects elementary structures involved in
the coordination of gene expression, which are present all along the genome of
nearly all bacteria. Technically, reaching this conclusion required us to
implement methods that are insensitive to the presence of large idiosyncratic
genomic features, which may co-exist along these fundamental universal
structures.
Gaussian Approximation of a Risk Model with Non-Stationary Hawkes Arrivals of Claims
Date: 2019-08-21 00:00:00

We consider a classical risk process with arrival of claims following a
non-stationary Hawkes process. We study the asymptotic regime when the premium
rate and the baseline intensity of the claims arrival process are large, and
claim size is small. The main goal of the article is to establish a diffusion
approximation by verifying a functional central limit theorem and to compute
the ruin probability in finite-time horizon. Numerical results will also be
given.
A digital microarray using interferometric detection of plasmonic nanorod labels
Date: 2018-01-23 00:00:00
Tasks: Diagnostic, Sensitivity

DNA and protein microarrays are a high-throughput technology that allow the
simultaneous quantification of tens of thousands of different biomolecular
species. The mediocre sensitivity and dynamic range of traditional fluorescence
microarrays compared to other techniques have been the technology's Achilles'
Heel, and prevented their adoption for many biomedical and clinical diagnostic
applications. Previous work to enhance the sensitivity of microarray readout to
the single-molecule ('digital') regime have either required signal amplifying
chemistry or sacrificed throughput, nixing the platform's primary advantages.
Here, we report the development of a digital microarray which extends both the
sensitivity and dynamic range of microarrays by about three orders of
magnitude. This technique uses functionalized gold nanorods as single-molecule
labels and an interferometric scanner which can rapidly enumerate individual
nanorods by imaging them with a 10x objective lens. This approach does not
require any chemical enhancement such as silver deposition, and scans arrays
with a throughput similar to commercial fluorescence devices. By combining
single-nanoparticle enumeration and ensemble measurements of spots when the
particles are very dense, this system achieves a dynamic range of about one
million directly from a single scan.
Bayesian reconstruction of HIV transmission trees from viral sequences and uncertain infection times
Date: 2018-01-23 00:00:00
Tasks: Bayesian Inference

Genetic sequence data of pathogens are increasingly used to investigate
transmission dynamics in both endemic diseases and disease outbreaks; such
research can aid in development of appropriate interventions and in design of
studies to evaluate them. Several methods have been proposed to infer
transmission chains from sequence data; however, existing methods do not
generally reliably reconstruct transmission trees because genetic sequence data
or inferred phylogenetic trees from such data are insufficient for accurate
inference regarding transmission chains. In this paper, we demonstrate the lack
of a one-to-one relationship between phylogenies and transmission trees, and
also show that information regarding infection times together with genetic
sequences permit accurate reconstruction of transmission trees. We propose a
Bayesian inference method for this purpose and demonstrate that precision of
inference regarding these transmission trees depends on precision of the
estimated times of infection. We also illustrate the use of these methods to
study features of epidemic dynamics, such as the relationship between
characteristics of nodes and average number of outbound edges or inbound
edges-- signifying possible transmission events from and to nodes. We study the
performance of the proposed method in simulation experiments and demonstrate
its superiority in comparison to an alternative method. We apply them to a
transmission cluster in San Diego and investigate the impact of biological,
behavioral, and demographic factors.
Protecting Pegged Currency Markets from Speculative Investors
Date: 2018-01-23 00:00:00
Tasks: Position

We consider a stochastic game between a trader and a central bank in a target zone market with a lower currency peg. This currency peg is maintained by the central bank through the generation of permanent price impact, thereby aggregating an ever increasing risky position in foreign reserves. We describe this situation mathematically by means of two coupled singular control problems, where the common singularity arises from a local time along a random curve. Our first result identifies a certain local time as that central bank strategy for which this risk position is minimized. We then consider the worst-case situation the central bank may face by identifying that strategy of the strategic investor that maximizes the expected inventory of the central bank under a cost criterion, thus establishing a Stackelberg equilibrium in our model.
Generalised Lyapunov Functions and Functionally Generated Trading Strategies
Date: 2018-01-24 00:00:00

This paper investigates the dependence of functional portfolio generation,
introduced by Fernholz (1999), on an extra finite variation process. The
framework of Karatzas and Ruf (2017) is used to formulate conditions on trading
strategies to be strong arbitrage relative to the market over sufficiently
large time horizons. A mollification argument and Komlos theorem yield a
general class of potential arbitrage strategies. These theoretical results are
complemented by several empirical examples using data from the S&P 500 stocks.
Zero-Delay Gaussian Joint Source-Channel Coding for the Interference Channel
Date: 2018-01-24 00:00:00
Tasks: Quantization

This paper studies zero-delay joint source channel coding (JSCC) for
transmission of correlated Gaussian sources over a Gaussian interference
channel (GIC). We propose to adopt delay-free hybrid digital and analog (HDA)
scheme, which is, transmitting the superposition of scaled source and its
quantized version after applying scalar quantization to the source at each
transmitter. At the corresponding receiver, two kinds of estimators are
presented. It is shown that both the schemes, when optimized, beat the uncoded
transmission if the channel signal-to-noise ratio (CSNR) is higher than a
threshold value for different correlation coefficients and interference values.
Snapshot light-field laryngoscope
Date: 2018-01-24 00:00:00

The convergence of recent advances in optical fabrication and digital
processing yields a new generation of imaging technology: light-field cameras,
which bridge the realms of applied mathematics, optics, and high-performance
computing. Herein for the first time, we introduce the paradigm of light-field
imaging into laryngoscopy. The resultant probe can image the three-dimensional
(3D) shape of vocal folds within a single camera exposure. Furthermore, to
improve the spatial resolution, we developed an image fusion algorithm,
providing a simple solution to a long-standing problem in light-field imaging.
Navigation of brain networks
Date: 2018-01-24 00:00:00
Tasks: Functional Connectivity

Understanding the mechanisms of neural communication in large-scale brain
networks remains a major goal in neuroscience. We investigated whether
navigation is a parsimonious routing model for connectomics. Navigating a
network involves progressing to the next node that is closest in distance to a
desired destination. We developed a measure to quantify navigation efficiency
and found that connectomes in a range of mammalian species (human, mouse and
macaque) can be successfully navigated with near-optimal efficiency (>80% of
optimal efficiency for typical connection densities). Rewiring network topology
or repositioning network nodes resulted in 45%-60% reductions in navigation
performance. Specifically, we found that brain networks cannot be progressively
rewired (randomized or clusterized) to result in topologies with significantly
improved navigation performance. Navigation was also found to: i) promote a
resource-efficient distribution of the information traffic load, potentially
relieving communication bottlenecks; and, ii) explain significant variation in
functional connectivity. Unlike prevalently studied communication strategies in
connectomics, navigation does not mandate biologically unrealistic assumptions
about global knowledge of network topology. We conclude that the wiring and
spatial embedding of brain networks is conducive to effective decentralized
communication. Graph-theoretic studies of the connectome should consider
measures of network efficiency and centrality that are consistent with
decentralized models of neural communication.
Spurious seasonality detection: a non-parametric test proposal
Date: 2018-01-24 00:00:00
Tasks: Time Series, Time Series Analysis

This paper offers a general and comprehensive definition of the
day-of-the-week effect. Using symbolic dynamics, we develop a unique test based
on ordinal patterns in order to detect it. This test uncovers the fact that the
so-called "day-of-the-week" effect is partly an artifact of the hidden
correlation structure of the data. We present simulations based on artificial
time series as well. Whereas time series generated with long memory are prone
to exhibit daily seasonality, pure white noise signals exhibit no pattern
preference. Since ours is a non parametric test, it requires no assumptions
about the distribution of returns so that it could be a practical alternative
to conventional econometric tests. We made also an exhaustive application of
the here proposed technique to 83 stock indices around the world. Finally, the
paper highlights the relevance of symbolic analysis in economic time series
studies.
Stock returns forecast: an examination by means of Artificial Neural Networks
Date: 2018-01-24 00:00:00

The validity of the Efficient Market Hypothesis has been under severe
scrutiny since several decades. However, the evidence against it is not
conclusive. Artificial Neural Networks provide a model-free means to analize
the prediction power of past returns on current returns. This chapter analizes
the predictability in the intraday Brazilian stock market using a
backpropagation Artificial Neural Network. We selected 20 stocks from Bovespa
index, according to different market capitalization, as a proxy for stock size.
We find that predictability is related to capitalization. In particular, larger
stocks are less predictable than smaller ones.
A Distributed Processing Architecture for Modular and Scalable Massive MIMO Base Stations
Date: 2018-01-24 00:00:00

In this work, a scalable and modular architecture for massive MIMO base
stations with distributed processing is proposed. New antennas can readily be
added by adding a new node as each node handles all the additional involved
processing. The architecture supports conjugate beamforming, zero-forcing, and
MMSE, where for the two latter cases a central matrix inversion is required.
The impact of the time required for this matrix inversion is carefully analyzed
along with a generic frame format. As part of the contribution, careful
computational, memory, and communication analyses are presented. It is shown
that all computations can be mapped to a single computational structure and
that a processing node consisting of a single such processing element can
handle a broad range of bandwidths and number of terminals.
Understanding news story chains using information retrieval and network clustering techniques
Authors: Nicholls Tom, Bright Jonathan
Date: 2018-01-24 00:00:00
Tasks: Articles, Clustering, Information Retrieval, Retrieval, valid

Content analysis of news stories (whether manual or automatic) is a
cornerstone of the communication studies field. However, much research is
conducted at the level of individual news articles, despite the fact that news
events (especially significant ones) are frequently presented as "stories" by
news outlets: chains of connected articles covering the same event from
different angles. These stories are theoretically highly important in terms of
increasing public recall of news items and enhancing the agenda-setting power
of the press. Yet thus far, the field has lacked an efficient method for
detecting groups of articles which form stories in a way that enables their
analysis.
  In this work, we present a novel, automated method for identifying linked
news stories from within a corpus of articles. This method makes use of
techniques drawn from the field of information retrieval to identify textual
closeness of pairs of articles, and then clustering techniques taken from the
field of network analysis to group these articles into stories. We demonstrate
the application of the method to a corpus of 61,864 articles, and show how it
can efficiently identify valid story clusters within the corpus. We use the
results to make observations about the prevalence and dynamics of stories
within the UK news media, showing that more than 50% of news production takes
place within stories.
Mean-potential law in evolutionary games
Date: 2018-01-24 00:00:00

The Letter presents a novel way to connect random walks, stochastic
differential equations, and evolutionary game theory. We introduce a new
concept of potential function for discrete-space stochastic systems. It is
based on a correspondence between one-dimensional stochastic differential
equations and random walks, which may be exact not only in the continuous limit
but also in finite-state spaces. Our method is useful for computation of
fixation probabilities in discrete stochastic dynamical systems with two
absorbing states. We apply it to evolutionary games, formulating two simple and
intuitive criteria for evolutionary stability of pure Nash equilibria in finite
populations. In particular, we show that the $1/3$ law of evolutionary games,
introduced by Nowak et al [Nature, 2004], follows from a more general
mean-potential law.