pwc-archive/papers-with-abstracts huggingface dataset:
Pink Work: Same-Sex Marriage, Employment and Discrimination
Date: 2018-07-17 00:00:00

This paper analyzes how the legalization of same-sex marriage in the U.S.
affected gay and lesbian couples in the labor market. Results from a
difference-in-difference model show that both partners in same-sex couples were
more likely to be employed, to have a full-time contract, and to work longer
hours in states that legalized same-sex marriage. In line with a theoretical
search model of discrimination, suggestive empirical evidence supports the
hypothesis that marriage equality led to an improvement in employment outcomes
among gays and lesbians and lower occupational segregation thanks to a decrease
in discrimination towards sexual minorities.
Pattern Synthesis via Complex-Coefficient Weight Vector Orthogonal Decomposition
Authors: Xue Shi
Date: 2018-07-18 00:00:00

This paper presents a new array response control scheme named complex-coefficient weight vector orthogonal decomposition ($ \textrm{C}^2\textrm{-WORD} $) and its application to pattern synthesis. The proposed $ \textrm{C}^2\textrm{-WORD} $ algorithm is a modified version of the existing WORD approach. We extend WORD by allowing a complex-valued combining coefficient in $ \textrm{C}^2\textrm{-WORD} $, and find the optimal combining coefficient by maximizing white noise gain (WNG). Our algorithm offers a closed-from expression to precisely control the array response level of a given point starting from an arbitrarily-specified weight vector. In addition, it results less pattern variations on the uncontrolled angles. Elaborate analysis shows that the proposed $ \textrm{C}^2\textrm{-WORD} $ scheme performs at least as good as the state-of-the-art $\textrm{A}^\textrm{2}\textrm{RC} $ or WORD approach. By applying $ \textrm{C}^2\textrm{-WORD} $ successively, we present a flexible and effective approach to pattern synthesis. Numerical examples are provided to demonstrate the flexibility and effectiveness of $ \textrm{C}^2\textrm{-WORD} $ in array response control as well as pattern synthesis.
Pattern Synthesis via Complex-Coefficient Weight Vector Orthogonal Decomposition--Part II: Robust Sidelobe Synthesis
Date: 2018-07-18 00:00:00

In this paper, the complex-coefficient weight vector orthogonal decomposition
($ \textrm{C}^2\textrm{-WORD} $) algorithm proposed in Part I of this two paper
series is extended to robust sidelobe control and synthesis with steering
vector mismatch. Assuming that the steering vector uncertainty is norm-bounded,
we obtain the worst-case upper and lower boundaries of array response. Then, we
devise a robust $ \textrm{C}^2\textrm{-WORD} $ algorithm to control the
response of a sidelobe point by precisely adjusting its upper-boundary response
level as desired. To enhance the practicality of the proposed robust $
\textrm{C}^2\textrm{-WORD} $ algorithm, we also present detailed analyses on
how to determine the upper norm boundary of steering vector uncertainty under
various mismatch circumstances. By applying the robust $
\textrm{C}^2\textrm{-WORD} $ algorithm iteratively, a robust sidelobe synthesis
approach is developed. In this approach, the upper-boundary response is
adjusted in a point-by-point manner by successively updating the weight vector.
Contrary to the existing approaches, the devised robust $
\textrm{C}^2\textrm{-WORD} $ algorithm has an analytical expression and can
work starting from an arbitrarily-specified weight vector. Simulation results
are presented to validate the effectiveness and good performance of the robust
$ \textrm{C}^2\textrm{-WORD} $ algorithm.
Timed Discrete-Event Systems are Synchronous Product Structures
Authors: Liyong Lin
Date: 2018-07-18 00:00:00
Tasks: Scheduling

Timed discrete-event systems (TDES), which is a modelling formalism proposed by Brandin and Wonham, can be used for modelling scheduling and production planning problems. This paper aims to show that TDES are essentially synchronous product structures. The proof is constructive in the sense that a generalized synchronous product rule is provided to generate a TDES from the activity automaton and the timer automata (that is, the syntactic description of the TDES) after some model transformation. We then also explain how the generalized synchronous product operation can be reduced into the standard synchronous product operation and how to reduce the number of (refined) events introduced in the model transformation. Thus, any software that can compute synchronous products can be used to compute a TDES from its activity automaton and its timer automata, after the model transformation.
Comparing Respiratory Monitoring Performance of Commercial Wireless Devices
Date: 2018-07-18 00:00:00
Tasks: Position

This paper addresses the performance of systems which use commercial wireless
devices to make bistatic RF channel measurements for non-contact respiration
sensing. Published research has typically presented results from short
controlled experiments on one system. In this paper, we deploy an extensive
real-world comparative human subject study. We observe twenty patients during
their overnight sleep (a total of 160 hours), during which contact sensors
record ground-truth breathing data, patient position is recorded, and four
different RF breathing monitoring systems simultaneously record measurements.
We evaluate published methods and algorithms. We find that WiFi channel state
information measurements provide the most robust respiratory rate estimates of
the four RF systems tested. However, all four RF systems have periods during
which RF-based breathing estimates are not reliable.
Effect of Sensor Error on the Assessment of Seismic Building Damage
Date: 2018-07-18 00:00:00

Natural disasters affect structural health of buildings, thus directly
impacting public safety. Continuous structural monitoring can be achieved by
deploying an internet of things (IoT) network of distributed sensors in
buildings to capture floor movement. These sensors can be used to compute the
displacements of each floor, which can then be employed to assess building
damage after a seismic event. The peak relative floor displacement is computed,
which is directly related to damage level according to government standards.
With this information, the building inventory can be classified into immediate
occupancy (IO), life safety (LS) or collapse prevention (CP) categories. In
this work, we propose a zero velocity update (ZUPT) technique to minimize
displacement estimation error. Theoretical derivation and experimental
validation are presented. In addition, we investigate modeling sensor error and
interstory drift ratio (IDR) distribution. Moreover, we discuss the impact of
sensor error on the achieved building classification accuracy.
A unifying approach to constrained and unconstrained optimal reinsurance
Date: 2018-07-18 00:00:00

In this paper, we study two classes of optimal reinsurance models from
perspectives of both insurers and reinsurers by minimizing their convex
combination where the risk is measured by a distortion risk measure and the
premium is given by a distortion premium principle. Firstly, we show that how
optimal reinsurance models for the unconstrained optimization problem and
constrained optimization problems can be formulated in a unified way. Secondly,
we propose a geometric approach to solve optimal reinsurance problems directly.
This paper considers a class of increasing convex ceded loss functions and
derives the explicit solutions of the optimal reinsurance which can be in forms
of quota-share, stop-loss, change-loss, the combination of quota-share and
change-loss or the combination of change-loss and change-loss with different
retentions. Finally, we consider two specific cases: Value at Risk (VaR) and
Tail Value at Risk (TVaR).
Greedy Approximate Projection for Magnetic Resonance Fingerprinting with Partial Volumes
Date: 2018-11-28 00:00:00
Tasks: Magnetic Resonance Fingerprinting

In quantitative Magnetic Resonance Imaging, traditional methods suffer from
the so-called Partial Volume Effect (PVE) due to spatial resolution
limitations. As a consequence of PVE, the parameters of the voxels containing
more than one tissue are not correctly estimated. Magnetic Resonance
Fingerprinting (MRF) is not an exception. The existing methods addressing PVE
are neither scalable nor accurate. We propose to formulate the recovery of
multiple tissues per voxel as a nonconvex constrained least-squares
minimisation problem. To solve this problem, we develop a memory efficient,
greedy approximate projected gradient descent algorithm, dubbed GAP-MRF. Our
method adaptively finds the regions of interest on the manifold of fingerprints
defined by the MRF sequence. We generalise our method to compensate for phase
errors appearing in the model, using an alternating minimisation approach. We
show, through simulations on synthetic data with PVE, that our algorithm
outperforms state-of-the-art methods. Our approach is validated on the EUROSPIN
phantom and on in vivo datasets.
Robust Image Identification for Double-Compressed JPEG Images
Authors: Iida Kenta, Kiya Hitoshi
Date: 2018-06-23 00:00:00

It is known that JPEG images uploaded to social networks (SNs) are mostly
re-compressed by the social network providers. Because of such a situation, a
new image identification scheme for double-compressed JPEG images is proposed
in this paper. The aim is to detect single-compressed images that have the same
original image as that of a double-compressed one. In the proposed scheme, the
signs of only DC coefficients in DCT coefficients and one threshold value are
used for the identification. The use of them allows us to robustly avoid errors
caused by double-compression, which are not considered in conventional schemes.
The proposed scheme has applications not only to find uploaded images
corresponding to double-compressed ones, but also to detect some image
integrity. The simulation results demonstrate that the proposed one outperforms
conventional ones including state-of-art image hashing one in terms of the
querying performance.
GOPA: Geometrical Optics Positioning Algorithm Using Spatial Color Coded LEDs (Extended Version)
Date: 2018-06-24 00:00:00
Tasks: Indoor Localization

In this paper, we propose an accurate visible light indoor localization
system for a smartphone using the existing commercial light-emitting diode
(LED) luminaries. The proposed technique, called geometrical optics positioning
algorithm (GOPA), uses spatial color code landmarks alongside angle of arrival
(AOA)-based geometrical algorithm on smartphones to locate the device, and
reserves LED's time-frequency domain modulation to increase the throughput of
the visible light network broadcast from the same luminaries infrastructure.
GOPA algorithm is developed with practical considerations such as flexible hand
gesture and handshake, and it enables both positioning robustness and on-device
processing. By introducing the idea of virtual plane, field of view (FOV) issue
of AOA-based positioning systems is addressed in GOPA. The embedded
accelerometer and front-facing camera of the smartphone are used at the
receiver side to measure the smartphone inclination and acquire the image.
Experimental results show robust two-dimensional ($2$-D) and three-dimensional
($3$-D) positioning. The experimental mean positioning error for $2$-D
positioning is $0.54$ cm, in case one ignoring the tilt. The experimental mean
positioning errors for $3$-D positioning are respectively $1.24$ cm, $1.85$ cm,
and $6.02$ cm for ideal non-tilted and non-oriented, non-tilted but orientated,
and both tilted and orientated scenarios.
Cross Validation Based Model Selection via Generalized Method of Moments
Date: 2018-07-18 00:00:00
Tasks: Model Selection

Structural estimation is an important methodology in empirical economics, and
a large class of structural models are estimated through the generalized method
of moments (GMM). Traditionally, selection of structural models has been
performed based on model fit upon estimation, which take the entire observed
samples. In this paper, we propose a model selection procedure based on
cross-validation (CV), which utilizes sample-splitting technique to avoid
issues such as over-fitting. While CV is widely used in machine learning
communities, we are the first to prove its consistency in model selection in
GMM framework. Its empirical property is compared to existing methods by
simulations of IV regressions and oligopoly market model. In addition, we
propose the way to apply our method to Mathematical Programming of Equilibrium
Constraint (MPEC) approach. Finally, we perform our method to online-retail
sales data to compare dynamic market model to static model.
Spectrum accessing optimization in congestion times in radio cognitive networks based on chaotic neural networks
Date: 2018-07-18 00:00:00

Based on the theory of the Federal Communications Commission, the spectrum
available on cognitive radio networks is limit and the non-optimal use of the
spectrum necessitates the need for a telecommunications model, so that this
pattern can exploit the existing spectral positions. In this spectrum
subscription scenario, when the primary users are not present, it is also
possible to assign this telecommunication to tenants who are unauthorized or
secondary. The challenge of using this scenario is to allocate time-frequency
resources to them and how to access nodes in one channel without any
interactions between primary and secondary users and the throughput will
increase. The main idea of this research is using chaotic recurrent neural
network for improving access to spectrum in congestion times and the main
purposes are reduce interference and increase throughput in cognitive radio
networks. In this method, in addition to the throughput, the amount of unwanted
blockage of packets, the reduction of the cost of operations for secondary
users, the hardware requirements for secondary users and the coefficient of
justice are considered which in fact, it is a new channel assignment process
with respect to the environment response, the updates the probability that the
channels are empty in subsequent periods, and increases the permeability by
reducing interference with chaotic recurrent neural network.
Disentangling and quantifying market participant volatility contributions
Date: 2018-07-18 00:00:00
Tasks: Point Processes

Thanks to the access to labeled orders on the Cac40 index future provided by
Euronext, we are able to quantify market participants contributions to the
volatility in the diffusive limit. To achieve this result we leverage the
branching properties of Hawkes point processes. We find that fast
intermediaries (e.g., market maker type agents) have a smaller footprint on the
volatility than slower, directional agents. The branching structure of Hawkes
processes allows us to examine also the degree of endogeneity of each agent
behavior. We find that high-frequency traders are more endogenously driven than
other types of agents.
A New Index of Human Capital to Predict Economic Growth
Date: 2018-07-18 00:00:00

The accumulation of knowledge required to produce economic value is a process
that often relates to nations economic growth. Such a relationship, however, is
misleading when the proxy of such accumulation is the average years of
education. In this paper, we show that the predictive power of this proxy
started to dwindle in 1990 when nations schooling began to homogenized. We
propose a metric of human capital that is less sensitive than average years of
education and remains as a significant predictor of economic growth when tested
with both cross-section data and panel data. We argue that future research on
economic growth will discard educational variables based on quantity as
predictor given the thresholds that these variables are reaching.
Molecular Imprinting: The missing piece in the puzzle of abiogenesis?
Date: 2018-07-18 00:00:00

In a neglected 2005 paper, Nobel Laureate Paul Lauterbur proposed that
molecular imprinting in amorphous materials -- a phenomenon with an extensive
experimental literature -- played a key role in abiogenesis. The present paper
builds on Lauterbur's idea to propose imprint-mediated templating (IMT), a
mechanism for prebiotic peptide replication that could potentially avoid a
range of difficulties arising in classic gene-first and metabolism-first models
of abiogenesis. Unlike models that propose prebiotic RNA synthesis, activation,
and polymerization based on unknown chemistries, peptide/IMT models are
compatible with demonstrably realistic prebiotic chemistries: synthesis of
dilute mixtures of racemic amino acids from atmospheric gases, and
polymerization of unactivated amino acids on hot, intermittently-wetted
surfaces. Starting from a peptide/IMT-based genetics, plausible processes could
support the elaboration of genetic and metabolic complexity in an early-Earth
environment, both explaining the emergence of homochirality and providing a
potential bridge to nucleic acid metabolism. Peptide/IMT models suggest
directions for both theoretical and experimental inquiry.
Datamining a medieval medical text reveals patterns in ingredient choice that reflect biological activity against the causative agents of specified infections
Date: 2018-07-18 00:00:00

The pharmacopeia used by physicians and lay people in medieval Europe has
largely been dismissed as placebo or superstition. While we now recognise that
some of the materia medica used by medieval physicians could have had useful
biological properties, research in this area is limited by the labour-intensive
process of searching and interpreting historical medical texts. Here, we
demonstrate the potential power of turning medieval medical texts into
contextualised electronic databases amenable to exploration by algorithm. We
use established methodologies from network science to reveal statistically
significant patterns in ingredient selection and usage in a key text, the
fifteenth-century Lylye of Medicynes, focusing on remedies to treat symptoms of
microbial infection. We discuss the potential that these patterns reflect
rational medical decisions. In providing a worked example of data-driven
textual analysis, we demonstrate the potential of this approach to encourage
interdisciplinary collaboration and to shine a new light on the
ethnopharmacology of historical medical texts.
Tracking Sparse mmWave Channel: Performance Analysis under Intra-Cluster Angular Spread
Date: 2018-07-19 00:00:00

Millimeter-wave (mmWave) systems require a large number of antennas at both
base station and user equipment for a desirable link budget. Due to time
varying channel under user mobility, up-to-date channel state information (CSI)
is critical to obtain the required beamforming gain.. The mmWave sparse
multipath channel is commonly exploited in designing tracking algorithms but
practical angular spread is often overlooked. In this work, we study the
performance bound of tracking accuracy in sparse mmWave channel that includes
intra-cluster angular spreads. Power gain from angle-steering-based beamforming
using tracked CSI is then analyzed. The theoretical study provides a design
guideline beam-width in angle-steering under different intra-cluster angular
spreads. We verify the results with two common tracking algorithms including
sector beam tracking and maximum likelihood channel tracking.
Towards Accelerated Greedy Sampling and Reconstruction of Bandlimited Graph Signals
Authors: Abolfazl Hashemi, Rasoul Shafipour, Haris Vikalo, Gonzalo Mateos
Date: 2018-07-19 00:00:00

We study the problem of sampling and reconstructing spectrally sparse graph signals where the objective is to select a subset of nodes of prespecified cardinality that ensures interpolation of the original signal with the lowest possible reconstruction error. This task is of critical importance in Graph signal processing (GSP) and while existing methods generally provide satisfactory performance, they typically entail a prohibitive computational cost when it comes to the study of large-scale problems. Thus, there is a need for accelerated and efficient methods tailored for high-dimensional and large-scale sampling and reconstruction tasks. To this end, we first consider a non-Bayesian scenario and propose an efficient iterative node sampling procedure that in the noiseless case enables exact recovery of the original signal from the set of selected nodes. In the case of noisy measurements, a bound on the reconstruction error of the proposed algorithm is established. Then, we consider the Bayesian scenario where we formulate the sampling task as the problem of maximizing a monotone weak submodular function, and propose a randomized-greedy algorithm to find a sub-optimal subset of informative nodes. We derive worst-case performance guarantees on the mean-square error achieved by the randomized-greedy algorithm for general non-stationary graph signals.
UAV-Based in-band Integrated Access and Backhaul for 5G Communications
Date: 2018-07-19 00:00:00

We introduce the concept of using unmanned aerial vehicles (UAVs) as drone
base stations for in-band Integrated Access and Backhaul (IB-IAB) scenarios for
5G networks. We first present a system model for forward link transmissions in
an IB-IAB multi-tier drone cellular network. We then investigate the key
challenges of this scenario and propose a framework that utilizes the flying
capabilities of the UAVs as the main degree of freedom to find the optimal
precoder design for the backhaul links, user-base station association, UAV 3D
hovering locations, and power allocations. We discuss how the proposed
algorithm can be utilized to optimize the network performance in both large and
small scales. Finally, we use an exhaustive search-based solution to
demonstrate the performance gains that can be achieved from the presented
algorithm in terms of the received signal to interference plus noise ratio
(SINR) and overall network sum-rate.
Simple robust genomic prediction and outlier detection for a multi-environmental field trial
Date: 2018-07-19 00:00:00
Tasks: Outlier Detection, Prediction

The aim of plant breeding trials is often to identify germplasms that are
well adapt to target environments. These germplasms are identified through
genomic prediction from the analysis of multi-environmental field trial (MET)
using linear mixed models. The occurrence of outliers in MET are common and
known to adversely impact accuracy of genomic prediction yet the detection of
outliers, and subsequently its treatment, are often neglected. A number of
reasons stand for this - complex data such as MET give rise to distinct levels
of residuals and thus offers additional challenges of an outlier detection
method and many linear mixed model software are ill-equipped for robust
prediction. We present outlier detection methods using a holistic approach that
borrows the strength across trials. We furthermore evaluate a simple robust
genomic prediction that is applicable to any linear mixed model software. These
are demonstrated using simulation based on two real bread wheat yield METs with
a partially replicated design and an alpha lattice design.