pwc-archive/papers-with-abstracts huggingface dataset:
TDMR Detection System with Local Area Influence Probabilistic a Priori Detector
Date: 2019-04-13 00:00:00
Tasks: Decoder

We propose a three-track detection system for two dimensional magnetic
recording (TDMR) in which a local area influence probabilistic (LAIP) detector
works with a trellis-based Bahl-Cocke-Jelinek-Raviv (BCJR) detector to remove
intersymbol interference (ISI) and intertrack interference (ITI) among coded
data bits as well as media noise due to magnetic grain-bit interactions. Two
minimum mean-squared error (MMSE) linear equalizers with different response
targets are employed before the LAIP and BCJR detectors. The LAIP detector
considers local grain-bit interactions and passes coded bit log-likelihood
ratios (LLRs) to the channel decoder, whose output LLRs serve as a priori
information to the BCJR detector, which is followed by a second channel
decoding pass. Simulation results under 1-shot decoding on a
grain-flipping-probability (GFP) media model show that the proposed LAIP/BCJR
detection system achieves density gains of 6.8% for center-track detection and
1.2% for three-track detection compared to a standard BCJR/1D-PDNP. The
proposed system's BCJR detector bit error rates (BERs) are lower than those of
a recently proposed two-track BCJR/2D-PDNP system by factors of (0.55, 0.08)
for tracks 1 and 2 respectively.
Nash Bargaining Over Margin Loans to Kelly Gamblers
Authors: Alex Garivaltis
Date: 2019-04-14 00:00:00

I derive practical formulas for optimal arrangements between sophisticated stock market investors (namely, continuous-time Kelly gamblers or, more generally, CRRA investors) and the brokers who lend them cash for leveraged bets on a high Sharpe asset (i.e. the market portfolio). Rather than, say, the broker posting a monopoly price for margin loans, the gambler agrees to use a greater quantity of margin debt than he otherwise would in exchange for an interest rate that is lower than the broker would otherwise post. The gambler thereby attains a higher asymptotic capital growth rate and the broker enjoys a greater rate of intermediation profit than would obtain under non-cooperation. If the threat point represents a vicious breakdown of negotiations (resulting in zero margin loans), then we get an elegant rule of thumb: $r_L^*=(3/4)r+(1/4)(\nu-\sigma^2/2)$, where $r$ is the broker's cost of funds, $\nu$ is the compound-annual growth rate of the market index, and $\sigma$ is the annual volatility. We show that, regardless of the particular threat point, the gambler will negotiate to size his bets as if he himself could borrow at the broker's call rate.
Brain on the 3D Visual Art through Virtual Reality; Introducing Neuro-Art in a Case Investigation
Date: 2019-04-14 00:00:00

The reciprocal impact of applied neuroscience and cognitive studies on
humanities has been extensive and growing over the past 30 years of research.
Studies on neuroaesthetics have provided novel insights in visual arts, music
as well as abstract and dramatic art. Neuro-Art is an experimental concept in
applied neuroscience where scientists can study the mechanistic pathways
involved for instance in visual art through which creativity and artistic
capacity might receive further empowerment. Based on the existing evidence, at
least 3 large-scale brain networks are involved simultaneously when one is
submitted to a creativity-related task. The question whether the key brain
regions involved in visual art creativity can be identified and receive
neuromodulation to get empowered prompted us to perform the present case
investigation. Virtual reality and functional quantitative
electroencephalography upon 2- vs 3-dimentional painting were employed to study
cortical neurodynamics in a professional painting artist.
A robust DOA estimation method for a linear microphone array under reverberant and noisy environments
Date: 2019-04-14 00:00:00
Tasks: Onset Detection

A robust method for linear array is proposed to address the difficulty of
direction-of-arrival (DOA) estimation in reverberant and noisy environments. A
direct-path dominance test based on the onset detection is utilized to extract
time-frequency bins containing the direct propagation of the speech. The
influence of the transient noise, which severely contaminates the onset test,
is mitigated by a proper transient noise determination scheme. Then for voice
features, a two-stage procedure is designed based on the extracted bins and an
effective dereverberation method, with robust but possibly biased estimation
from middle frequency bins followed by further refinement in higher frequency
bins. The proposed method effectively alleviates the estimation bias caused by
the linear arrangement of microphones, and has stable performance under noisy
and reverberant environments. Experimental evaluation using a 4-element
microphone array demonstrates the efficacy of the proposed method.
Computational distributed fiber-optic sensing
Date: 2019-04-14 00:00:00
Tasks: Image Reconstruction

Ghost imaging allows image reconstruction by correlation measurements between
a light beam that interacts with the object without spatial resolution and a
spatially resolved light beam that never interacts with the object. The two
light beams are copies of each other. Its computational version removes the
requirement of a spatially resolved detector when the light intensity pattern
is pre-known. Here, we exploit the temporal analogue of computational ghost
imaging, and demonstrate a computational distributed fiber-optic sensing
technique. Temporal images containing spatially distributed scattering
information used for sensing purposes are retrieved through correlating the
"integrated" backscattered light and the pre-known binary patterns. The
sampling rate required for our technique is inversely proportional to the total
time duration of a binary sequence, so that it can be significantly reduced
compared to that of the traditional methods. Our experiments demonstrate a 3
orders of magnitude reduction in the sampling rate, offering great
simplification and cost reduction in the distributed fiber-optic sensors.
Are the better cooperators dormant or quiescent?
Date: 2019-04-14 00:00:00

Despite the wealth of empirical and theoretical studies, the origin and
maintenance of cooperation is still an evolutionary riddle. In this context,
ecological life-history traits which affect the efficiency of selection may
play a role, though these are often ignored. We consider here species such as
bacteria, fungi, invertebrates and plants which exhibit resting stages in the
form of a quiescent state or a seedbank. When quiescent, individuals are
inactive and reproduce upon activation, while under seed bank parents produce
offspring remaining dormant for different amount of time. We assume weak
frequency-dependent selection modeled using game-theory and the prisoners
dilemma (cooperation/defect) as payoff matrix. The cooperators and defectors
are allowed to evolve different quiescence or dormancy times. By means of
singular perturbation theory we reduce the model to a one-dimensional equation
resembling the well known replicator equation, where the gain functions are
scaled with lumped parameters reflecting the time scale of the resting state of
the cooperators and defectors. If both time scales are identical cooperation
cannot persist in a homogeneous population. If, however, the time scale of the
cooperator is distinctively different from that of the defector, cooperation
may become a locally asymptotically stable strategy. Interestingly enough, in
the seedbank case the cooperator needs to be faster than the defector, while in
the quiescent case the cooperator has to be slower. We use adaptive dynamics to
identify situations where cooperation may evolve and form a convergent stable
ESS. We conclude by highlighting the relevance fo these results for many
non-model species and the maintenance of cooperation in microbial, invertebrate
or plant populations.
Eight-dimensional Polarization-ring-switching Modulation Formats
Date: 2019-09-21 00:00:00

We propose two 8-dimensional (8D) modulation formats (8D-2048PRS-T1 and
8D-2048PRS-T2) with a spectral efficiency of 5.5 bit/4D-sym, where the 8
dimensions are obtained from two time slots and two polarizations. Both formats
provide a higher tolerance to nonlinearity by selecting symbols with
nonidentical states of polarization (SOPs) in two time slots. The performance
of these novel 8D modulation formats is assessed in terms of the effective
signal-to-noise ratio (SNR) and normalized generalized mutual information.
8D-2048PRS-T1 is more suitable for high SNRs, while 8D-2048PRS-T2 is shown to
be more tolerant to nonlinearities. A sensitivity improvement of at least 0.25
dB is demonstrated by maximizing normalized generalized mutual information
(NGMI). For a long-haul nonlinear optical fiber transmission system, the
benefit of mitigating the nonlinearity is demonstrated and a reach increase of
6.7% (560 km) over time-domain hybrid four-dimensional two-amplitude
eight-phase shift keying (TDH-4D-2A8PSK) is observed.
Eliciting Preferences of Ridehailing Users and Drivers: Evidence from the United States
Date: 2019-04-14 00:00:00

Transportation Network Companies (TNCs) are changing the transportation
ecosystem, but micro-decisions of drivers and users need to be better
understood to assess the system-level impacts of TNCs. In this regard, we
contribute to the literature by estimating a) individuals' preferences of being
a rider, a driver, or a non-user of TNC services; b) preferences of ridehailing
users for ridepooling; c) TNC drivers' choice to switch to vehicles with better
fuel economy, and also d) the drivers' decision to buy, rent or lease new
vehicles with driving for TNCs being a major consideration. Elicitation of
drivers' preferences using a unique sample (N=11,902) of the U.S. population
residing in TNC-served areas is the key feature of this study. The statistical
analysis indicates that ridehailing services are mainly attracting personal
vehicle users as riders, without substantially affecting demand for transit.
Moreover, around 10% of ridehailing users reported postponing the purchase of a
new car due to the availability of TNC services. The model estimation results
indicate that the likelihood of being a TNC user increases with the increase in
age for someone younger than 44 years, but the pattern is reversed post 44
years. This change in direction of the marginal effect of age is insightful as
the previous studies have reported a negative association. We also find that
postgraduate drivers who live in metropolitan regions are more likely to switch
to fuel-efficient vehicles. These findings would inform transportation planners
and TNCs in developing policies to improve the fuel economy of the fleet.
Beam Profiler Network (BPNet) -- A Deep Learning Approach to Mode Demultiplexing of Laguerre-Gaussian Optical Beams
Date: 2019-04-14 00:00:00

The transverse field profile of light is being recognized as a resource for
classical and quantum communications for which reliable methods of sorting or
demultiplexing spatial optical modes are required. Here, we demonstrate,
experimentally, state-of-the-art mode demultiplexing of Laguerre-Gaussian beams
according to both their orbital angular momentum and radial topological numbers
using a flow of two concatenated deep neural networks. The first network serves
as a transfer function from experimentally-generated to ideal
numerically-generated data, while using a unique "Histogram Weighted Loss"
function that solves the problem of images with limited significant
information. The second network acts as a spatial-modes classifier. Our method
uses only the intensity profile of modes or their superposition, making the
phase information redundant.
Uneven rock-paper-scissors models: patterns and coexistence
Date: 2019-05-14 00:00:00

We study a class of the stochastic May-Leonard models, with three species
dominating each other in a cyclic nonhierarchical way, according to the
rock-paper-scissors game. We introduce an unevenness in the system, by
considering that one of the species is weaker because of a lower selection
probability. The simulation results show that the pattern formation is
drastically affected by the presence of the weaker species, with no spiral
waves arising immediately from random initial conditions. Instead,
single-species spatial domains cyclically dominate the entire territory until a
region occupied by the weaker species is sufficiently narrow to be crossed by
individuals without being selected. This leads to the appearance of spatial
patterns responsible for the species coexistence. We verify that the asymmetry
in the selection probabilities leads to different spatial autocorrelation
function and average relative species abundances. Finally, we investigate the
coexistence probability and show that the surviving species depends on the
level of unevenness of the model and the mobility of individuals.
Cramer-Rao Bound for Estimation After Model Selection and its Application to Sparse Vector Estimation
Authors: Elad Meir, Tirza Routtenberg
Date: 2019-04-15 00:00:00
Tasks: Model Selection, parameter estimation

In many practical parameter estimation problems, such as coefficient estimation of polynomial regression, the true model is unknown and thus, a model selection step is performed prior to estimation. The data-based model selection step affects the subsequent estimation. In particular, the oracle Cram\'er-Rao bound (CRB), which is based on knowledge of the true model, is inappropriate for post-model-selection performance analysis and system design outside the asymptotic region. In this paper, we investigate post-model-selection parameter estimation of a vector with an unknown support set, where this support set represents the model. We analyze the estimation performance of coherent estimators that force unselected parameters to zero. We use the mean-squared-selected-error (MSSE) criterion and introduce the concept of selective unbiasedness in the sense of Lehmann unbiasedness. We derive a non-Bayesian Cram\'er-Raotype bound on the MSSE and on the mean-squared-error (MSE) of any coherent estimator with a specific selective-bias function in the Lehmann sense. We implement the selective CRB for the special case of sparse vector estimation with an unknown support set. Finally, we demonstrate in simulations that the proposed selective CRB is an informative lower bound on the performance of the maximum selected likelihood estimator for a general linear model with the generalized information criterion and for sparse vector estimation with one step thresholding. It is shown that for these cases the selective CRB outperforms the oracle CRB and Sando-Mitra-Stoica CRB (SMS-CRB) [1].
Estimation of Cross-Sectional Dependence in Large Panels
Date: 2019-04-15 00:00:00
Tasks: Dimensionality Reduction, Time Series, Time Series Analysis

Accurate estimation for extent of cross{sectional dependence in large panel
data analysis is paramount to further statistical analysis on the data under
study. Grouping more data with weak relations (cross{sectional dependence)
together often results in less efficient dimension reduction and worse
forecasting. This paper describes cross-sectional dependence among a large
number of objects (time series) via a factor model and parameterizes its extent
in terms of strength of factor loadings. A new joint estimation method,
benefiting from unique feature of dimension reduction for high dimensional time
series, is proposed for the parameter representing the extent and some other
parameters involved in the estimation procedure. Moreover, a joint asymptotic
distribution for a pair of estimators is established. Simulations illustrate
the effectiveness of the proposed estimation method in the finite sample
performance. Applications in cross-country macro-variables and stock returns
from S&P 500 are studied.
A tale of two species: How humans shape dog behaviour in urban habitats
Date: 2019-04-12 00:00:00

Species inhabiting urban environments experience enormous anthropogenic
stress. Behavioural plasticity and flexibility of temperament are crucial to
successful urban-adaptation. Urban free-ranging dogs experience variable human
impact, from positive to negative and represent an ideal system to evaluate the
effects of human-induced stress on behavioural plasticity. We tested 600 adult
dogs from 60 sites across India, categorised as high - HF, low - LF, and
intermediate - IF human flux zones, to understand their sociability towards an
unfamiliar human. Dogs in the HF and IF zones were bolder and as compared to
their shy counterparts in LF zones. The IF zone dogs were the most sociable.
This is the first-ever study aimed to understand how the experiences of
interactions with humans in its immediate environment might shape the responses
of an animal to humans. This is very relevant in the context of human-animal
conflict induced by rapid urbanization and habitat loss across the world.
Low-cost spectrogram based counterfeit medicine detection
Date: 2019-04-10 00:00:00
Tasks: regression

Contaminated substances such as counterfeit medication and food contami-nated
with pesticide residue is a pandemic of utmost urgency. Spectroscopy and
chromatography methods are often used but are expensive and complex and as such
a need exists for a device that can be easily operated in developing
commu-nities. We present a hacked visible spectrometer based contaminated
substance detector using machine learning. The Support Vector Machine (SVM),
Logistic Regression, linear Regression and Convolutional Neural Network (CNN)
models have been implemented and are trained on the acquired spectrum data. Our
results show that a lowcost method of identifying contaminated substances is
achievable with very high accuracy.
Identification of Parameters for Large-scale Models in Systems Biology
Date: 2019-04-15 00:00:00
Tasks: Sensitivity

Inverse problem for the identification of the parameters for large-scale
systems of nonlinear ordinary differential equations (ODEs) arising in systems
biology is analyzed. In a recent paper in \textit{Mathematical Biosciences,
305(2018), 133-145}, the authors implemented the numerical method suggested by
one of the authors in \textit{J. Optim. Theory Appl., 85, 3(1995), 509-526} for
identification of parameters in moderate scale models of systems biology. This
method combines Pontryagin optimization or Bellman's quasilinearization with
sensitivity analysis and Tikhonov regularization. We suggest modification of
the method by embedding a method of staggered corrector for sensitivity
analysis and by enhancing multi-objective optimization which enables
application of the method to large-scale models with practically
non-identifiable parameters based on multiple data sets, possibly with partial
and noisy measurements. We apply the modified method to a benchmark model of a
three-step pathway modeled by 8 nonlinear ODEs with 36 unknown parameters and
two control input parameters. The numerical results demonstrate geometric
convergence with a minimum of five data sets and with minimum measurements per
data set. Software package \textit{qlopt} is developed and posted in GitHub.
MATLAB package AMIGO2 is used to demonstrate advantage of \textit{qlopt} over
most popular methods/software such as \textit{lsqnonlin}, \textit{fmincon} and
\textit{nl2sol}.
From multi-dimensional black scholes to Hamilton jacobi
Date: 2019-04-16 00:00:00

The first widely used financial model is linked to dynamical Hamilton jacobi
model
Transmit MIMO Radar Beampattern Design Via Optimization on the Complex Circle Manifol
Date: 2019-04-15 00:00:00

The ability of Multiple-Input Multiple-Output (MIMO) radar systems to adapt
waveforms across antennas allows flexibility in the transmit beampattern
design. In cognitive radar, a popular cost function is to minimize the
deviation against an idealized beampattern (which is arrived at with knowledge
of the environment). The optimization of the transmit beampattern becomes
particularly challenging in the presence of practical constraints on the
transmit waveform. One of the hardest of such constraints is the non-convex
constant modulus constraint, which has been the subject of much recent work. In
a departure from most existing approaches, we develop a solution that involves
direct optimization over the non-convex complex circle manifold. That is, we
derive a new projection, descent, and retraction (PDR) update strategy that
allows for monotonic cost function improvement while maintaining feasibility
over the complex circle manifold (constant modulus set). For quadratic cost
functions (as is the case with beampattern deviation), we provide analytical
guarantees of monotonic cost function improvement along with proof of
convergence to a local minima. We evaluate the proposed PDR algorithm against
other candidate MIMO beampattern design methods and show that PDR can
outperform competing wideband beampattern design methods while being
computationally less expensive. Finally, orthogonality across antennas is
incorporated in the PDR framework by adding a penalty term to the beampattern
cost function. Enabled by orthogonal waveforms, robustness to target direction
mismatch is also demonstrated.
A spline interpolation based data reconstruction technique for estimation of strain time constant in ultrasound poroelastography
Date: 2019-04-16 00:00:00

Ultrasound poroelastography is a cost-effective non-invasive imaging
technique, which is able to reconstruct several mechanical parameters of cancer
and normal tissue such as Young's modulus, Poisson's ratio, interstitial
permeability and vascular permeability. To estimate the permeabilities,
estimation of the strain time constant (TC) is required, which is a challenging
task because of non-linearity of the exponential strain curve and noise present
in the experimental data. Moreover, noise in many strain frames becomes very
high because of motion artifacts from the sonographer, animal/patient and/or
the environment. Therefore, using these frames in computation of strain TC can
lead to inaccurate estimates of the mechanical parameters. In this letter, we
introduce a cubic spline based interpolation method, which uses only the good
frames (frame of high SNR) to reconstruct the information of the bad frames
(frames of low SNR) and estimate the strain TC. We prove with finite element
simulation that the proposed reconstruction method can improve the estimation
accuracy of the strain TC by 46% in comparison to the estimates from noisy
data, and 37% in comparison to the estimates from Kalman filtered data at an
SNR of 30dB. Based on the high accuracy of the proposed method in estimating
strain TC from poroelastography data, the proposed method can be preferred
technique by the clinicians and researchers interested in non-invasive imaging
of tissue mechanical parameters.