pwc-archive/papers-with-abstracts huggingface dataset:
Systematic evaluation of the population-level effects of alternative treatment strategies on the basic reproduction number
Date: 2018-07-12 00:00:00
Tasks: valid

An approach to estimate the influence of the treatment-type controls on the
basic reproduction number, R 0 , is proposed and elaborated. The presented
approach allows one to estimate the effect of a given treatment strategy or to
compare a number of different treatment strategies on the basic reproduction
number. All our results are valid for sufficiently small values of the control.
However, in many cases it is possible to extend this analysis to larger values
of the control as was illustrated by examples.
The sound of an axon's growth
Authors: Frederic Folz, Lukas Wettmann, Giovanna Morigi, Karsten Kruse
Date: 2018-07-12 00:00:00

Axons are linear processes of nerve cells that can range from a few tens of micrometers up to meters in length. In addition to external cues, the length of an axon is also regulated by unknown internal mechanisms. Molecular motors have been suggested to generate oscillations with an axon length-dependent frequency that could be used to measure an axon's extension. Here, we present a mechanism that depends on the spectral decomposition of the oscillatory signal to determine the axon length.
Optimal Physical Preprocessing for Example-Based Super-Resolution
Date: 2018-07-12 00:00:00
Tasks: Deep Learning, Image Reconstruction, Super-Resolution

In example-based super-resolution, the function relating low-resolution
images to their high-resolution counterparts is learned from a given dataset.
This data-driven approach to solving the inverse problem of increasing image
resolution has been implemented with deep learning algorithms. In this work, we
explore modifying the imaging hardware in order to collect more informative
low-resolution images for better ultimate high-resolution image reconstruction.
We show that this "physical preprocessing" allows for improved image
reconstruction with deep learning in Fourier ptychographic microscopy.
  Fourier ptychographic microscopy is a technique allowing for both high
resolution and high field-of-view at the cost of temporal resolution. In
Fourier ptychographic microscopy, variable illumination patterns are used to
collect multiple low-resolution images. These low-resolution images are then
computationally combined to create an image with resolution exceeding that of
any single image from the microscope. We use deep learning to jointly optimize
the illumination pattern with the post-processing reconstruction algorithm for
a given sample type, allowing for single-shot imaging with both high resolution
and high field-of-view. We demonstrate, with simulated data, that the joint
optimization yields improved image reconstruction as compared with sole
optimization of the post-processing reconstruction algorithm.
Leveraging Catalog Knowledge Graphs for Query Attribute Identification in E-Commerce Sites
Authors: Ranganath Suhas
Date: 2018-07-13 00:00:00
Tasks: Attribute, Knowledge Graphs

Millions of people use online e-commerce platforms to search and buy
products. Identifying attributes in a query is a critical component in
connecting users to relevant items. However, in many cases, the queries have
multiple attributes, and some of them will be in conflict with each other. For
example, the query "maroon 5 dvds" has two candidate attributes, the color
"maroon" or the band "maroon 5", where only one of the attributes can be
present. In this paper, we address the problem of resolving conflicting
attributes in e-commerce queries. A challenge in this problem is that knowledge
bases like Wikipedia that are used to understand web queries are not focused on
the e-commerce domain. E-commerce search engines, however, have access to the
catalog which contains detailed information about the items and its attributes.
We propose a framework that constructs knowledge graphs from catalog to resolve
conflicting attributes in e-commerce queries. Our experiments on real-world
queries on e-commerce platforms demonstrate that resolving conflicting
attributes by leveraging catalog information significantly improves attribute
identification, and also gives out more relevant search results.
Emergence of correlations between securities at short time scales
Date: 2018-07-13 00:00:00
Tasks: Management

The correlation matrix is the key element in optimal portfolio allocation and
risk management. In particular, the eigenvectors of the correlation matrix
corresponding to large eigenvalues can be used to identify the market mode,
sectors and style factors. We investigate how these eigenvalues depend on the
time scale of securities returns in the U.S. market. For this purpose,
one-minute returns of the largest 533 U.S. stocks are aggregated at different
time scales and used to estimate the correlation matrix and its spectral
properties. We propose a simple lead-lag factor model to capture and reproduce
the observed time-scale dependence of eigenvalues. We reveal the emergence of
several dominant eigenvalues as the time scale increases. This important
finding evidences that the underlying economic and financial mechanisms
determining the correlation structure of securities depend as well on time
scales.
Ten Simple Rules When Considering Retirement
Authors: Philip E. Bourne
Date: 2018-07-13 00:00:00
Tasks: Articles

This is an article submitted to the Ten Simple Rules series of professional development articles published by PLOS Computational Biology.
On Universal Sensor Registration
Date: 2018-07-13 00:00:00

We present a simple approach for sensor registration in target tracking
applications. The proposed method uses targets of opportunity and, without
making assumptions on their dynamical models, allows simultaneous calibration
of multiple three- and two-dimensional sensors. Whereas for two-sensor
scenarios only relative registration is possible, in practical cases with three
or more sensors unambiguous absolute calibration may be achieved. The derived
algorithms are straightforward to implement and do not require tuning of
parameters. The performance of the algorithms is tested in a numerical study.
Local learning rules to attenuate forgetting in neural networks
Date: 2018-07-13 00:00:00

Hebbian synaptic plasticity inevitably leads to interference and forgetting
when different, overlapping memory patterns are sequentially stored in the same
network. Recent work on artificial neural networks shows that an
information-geometric approach can be used to protect important weights to slow
down forgetting. This strategy however is biologically implausible as it
requires knowledge of the history of previously learned patterns. In this work,
we show that a purely local weight consolidation mechanism, based on estimating
energy landscape curvatures from locally available statistics, prevents pattern
interference. Exploring a local calculation of energy curvature in the
sparse-coding limit, we demonstrate that curvature-aware learning rules reduce
forgetting in the Hopfield network. We further show that this method connects
information-geometric global learning rules based on the Fisher information to
local spike-dependent rules accessible to biological neural networks. We
conjecture that, if combined with other learning procedures, it could provide a
building-block for content-aware learning strategies that use only quantities
computable in biological neural networks to attenuate pattern interference and
catastrophic forgetting. Additionally, this work clarifies how global
information-geometric structure in a learning problem can be exposed in local
model statistics, building a deeper theoretical connection between the
statistics of single units in a network, and the global structure of the
collective learning space.
Performance of Angle of Arrival Detection Using MUSIC Algorithm in Inter-Satellite Link
Date: 2018-07-13 00:00:00

An attitude of satellite is not always static, sometimes it moves randomly
and the antenna pointing of satellite is harder to achieve line of sight
communication to other satellite when it is outage by tumbling effect. In order
to determine an appropriate direction of satellite antenna in inter-satellite
link, this paper analyze estimation performance of the direction of arrival
(DoA) using MUSIC algorithm from connected satellite signal source. It differs
from optical measurement, magnetic field measurement, inertial measurement, and
global positioning system (GPS) attitude determination. The proposed method is
characterized by taking signal source from connected satellites, after that the
main satellite processed the information to obtain connected satellites antenna
direction. The simulation runs only on the direction of azimuth. The simulation
result shows that MUSIC algorithm processing time is faster than satellite
movement time in orbit on altitude of 830 km with the period of 101 minutes.
With the use of a 50 elements array antenna in spacing of 0.5 wavelength, the
total of 20 angle of arrival (AoA) can be detected in 0.98 seconds of
processing time when using MUSIC algorithm.
Augmented Generator Sub-transient Model Using Dynamic Phasor Measurements
Date: 2018-07-13 00:00:00

In this article, we present a new model for a synchronous generator based on
phasor measurement units (PMUs) data. The proposed sub-transient model allows
to estimate the dynamic state variables as well as to calibrate model
parameters. The motivation for this new model is to use more efficiently the
PMU measurements which are becoming widely available in power grids. The
concept of phasor derivative is applied, which not only includes the signal
phase derivative but also its amplitude derivative. Applying known non-linear
estimation techniques, we study the merits of this new model. In particular, we
test robustness by considering a generator with different mechanical power
controls.
Nutrient removal from agricultural run-off in demonstrative full scale tubular photobioreactors for microalgae growth
Date: 2018-07-12 00:00:00

The objective of this paper is to present the design, construction and
operation of 3 full scale semi-closed, horizontal tubular photobioreactors
(PBR) used to remove nutrients of a mixture of agricultural run-off (90%) and
treated domestic wastewater (10%). The microalgal biomass produced in the PBRs
was harvested in a static lamella settling tank. Each PBR treated in average
2.3 m3/d. PBRs were submitted to strong seasonal changes regarding solar
radiation and temperature, which had a direct impact in the activity of
microalgae and the efficiency of the system. Higher mixed liquor pH values were
registered in summer (daily average> 10). Most of the influent and effluent
nitrogen content was inorganic (average of 9.0 mg N/L and 3.17 mg N/L,
respectively), and in the form of nitrate (62% and 50%, respectively). Average
nitrogen removal efficiency was 65%, with values of around 90% in summer, 80%
in autumn, 50 % in winter and 60% in spring. Most of the influent and effluent
phosphorus content was in the form of ortophosphate. Influent average was 0.62
mg P/L, but with great variations and in a considerable number of samples not
detected. Removal efficiency (when influent values were detected) was very high
during all the study, usually greater than 95%, and there were not clear
seasonal trends for efficiency as observed for TIN. Volumetric biomass
production greatly changed between seasons with much lower values in winter (7
g VSS/m3d) than in summer (43 g VSS/m3d). Biomass separation efficiency of the
settler was very good in either terms of turbidity and total suspended solids,
being most of the time lower than 5 UNT and 15 mg/L, respectively. Overall this
study demonstrated the reliable and good effectiveness of microalgae based
technologies such as the PBR to remove nutrients at a full scale size.
Half-Duplex and Full-Duplex AF and DF Relaying with Energy-Harvesting in Log-Normal Fading
Date: 2018-07-13 00:00:00

Energy-harvesting (EH) and wireless power transfer in cooperative relaying
networks have recently attracted a considerable amount of research attention.
Most of the existing work on this topic however focuses on Rayleigh fading
channels, which represent outdoor environments. In contrast, this paper is
dedicated to analyze the performance of dual-hop relaying systems with EH over
indoor channels characterized by log-normal fading. Both half-duplex (HD) and
full-duplex (FD) relaying mechanisms are studied in this work with
decode-and-forward (DF) and amplify-and-forward (AF) relaying protocols. In
addition, three EH schemes are investigated, namely, time switching relaying,
power splitting relaying and ideal relaying receiver which serves as a lower
bound. The system performance is evaluated in terms of the ergodic outage
probability for which we derive accurate analytical expressions. Monte Carlo
simulations are provided throughout to validate the accuracy of our analysis.
Results reveal that, in both HD and FD scenarios, AF relaying performs only
slightly worse than DF relaying which can make the former a more efficient
solution when the processing energy cost at the DF relay is taken into account.
It is also shown that FD relaying systems can generally outperform HD relaying
schemes as long as the loop-back interference in FD is relatively small.
Furthermore, increasing the variance of the log-normal channel has shown to
deteriorate the performance in all the relaying and EH protocols considered.
Rebalancing Frequency Considerations for Kelly-Optimal Stock Portfolios in a Control-Theoretic Framework
Date: 2018-08-21 00:00:00

In this paper, motivated by the celebrated work of Kelly, we consider the
problem of portfolio weight selection to maximize expected logarithmic growth.
Going beyond existing literature, our focal point here is the rebalancing
frequency which we include as an additional parameter in our analysis. The
problem is first set in a control-theoretic framework, and then, the main
question we address is as follows: In the absence of transaction costs, does
high-frequency trading always lead to the best performance? Related to this is
our prior work on betting, also in the Kelly context, which examines the impact
of making a wager and letting it ride. Our results on betting frequency can be
interpreted in the context of weight selection for a two-asset portfolio
consisting of one risky asset and one riskless asset. With regard to the
question above, our prior results indicate that it is often the case that there
are no performance benefits associated with high-frequency trading. In the
present paper, we generalize the analysis to portfolios with multiple risky
assets. We show that if there is an asset satisfying a new condition which we
call dominance, then an optimal portfolio consists of this asset alone; i.e.,
the trader has "all eggs in one basket" and performance becomes a constant
function of rebalancing frequency. Said another way, the problem of rebalancing
is rendered moot. The paper also includes simulations which address practical
considerations associated with real stock prices and the dominant asset
condition.
Improving Photoplethysmographic Measurements under Motion Artifacts using Artificial Neural Network for Personal Healthcare
Date: 2018-07-14 00:00:00

Photoplethysmographic (PPG) measurements are susceptible to motion artifacts
(MA) due to movement of the peripheral body parts. In this paper, we present a
new approach to identify the MA corrupted PPG beats and then rectify the beat
morphology using artificial neural network (ANN). Initially, beat quality
assessment was done to identify the clean PPG beats by a pre-trained feedback
ANN to generate a reference beat template for each person. The PPG data was
decomposed using principal component analysis (PCA) and reconstructed using
fixed energy retention. A weight coefficient was assigned for each PPG samples
in such a way that when they are multiplied , the modified beat morphology
matches the reference template. A particle swarm optimization (PSO) based
technique was utilized to select the best weight weight vector coefficients to
tune another feedback ANN, fed with a set of significant features generated by
an auto encoder from PCA reconstructed data. For real time implementation, this
pre-trained ANN was operated in feed-forward mode to directly generate the
weight vectors for any subsequent measurements of PPG. The method was validated
with PPG data collected from 55 human subjects. An average RMSE of 0.28 and SNR
improvement of 14.54 dB was obtained, with an average improvement of 36% and
47% measurement accuracy on crest time and systolic to diastolic peak height
ratio respectively. With IEEE Signal Processing Cup 2015 Challenge database,
Pearson's correlation coefficient between PPG estimated and ECG derived heart
rate was 0.990. The proposed method can be useful for personal health
monitoring applications.
Characterizing Cryptocurrency market with Levy's stable distributions
Date: 2019-11-25 00:00:00

The recent emergence of cryptocurrencies such as Bitcoin and Ethereum has
posed possible alternatives to global payments as well as financial assets
around the globe, making investors and financial regulators aware of the
importance of modeling them correctly. The Levy's stable distribution is one of
the attractive distributions that well describes the fat tails and scaling
phenomena in economic systems. In this paper, we show that the behaviors of
price fluctuations in emerging cryptocurrency markets can be characterized by a
non-Gaussian Levy's stable distribution with $\alpha \simeq 1.4$ under certain
conditions on time intervals ranging roughly from 30 minutes to 4 hours. Our
arguments are developed under quantitative valuation defined as a distance
function using the Parseval's relation in addition to the theoretical
background of the General Central Limit Theorem (GCLT). We also discuss the fit
with the Levy's stable distribution compared to the fit with other
distributions by employing the method based on likelihood ratios. Our approach
can be extended for further analysis of statistical properties and contribute
to developing proper applications for financial modeling.
Adaptive Hierarchical Sensing for the Efficient Sampling of Sparse and Compressible Signals
Date: 2018-07-14 00:00:00
Tasks: compressed sensing

We present the novel adaptive hierarchical sensing algorithm K-AHS, which
samples sparse or compressible signals with a measurement complexity equal to
that of Compressed Sensing (CS). In contrast to CS, K-AHS is adaptive as
sensing vectors are selected while sampling, depending on previous
measurements. Prior to sampling, the user chooses a transform domain in which
the signal of interest is sparse. The corresponding transform determines the
collection of sensing vectors. K-AHS gradually refines initial coarse
measurements to significant signal coefficients in the sparse transform domain
based on a sensing tree which provides a natural hierarchy of sensing vectors.
K-AHS directly provides significant signal coefficients in the sparse transform
domain and does not require a reconstruction stage based on inverse
optimization. Therefore, the K-AHS sensing vectors must not satisfy any
incoherence or restricted isometry property. A mathematical analysis proves the
sampling complexity of K-AHS as well as a general and sufficient condition for
sampling the optimal k-term approximation, which is applied to particular
signal models. The analytical findings are supported by simulations with
synthetic signals and real world images. On standard benchmark images, K-AHS
achieves lower reconstruction errors than CS.
On the optimal choice of strike conventions in exchange option pricing
Date: 2018-07-14 00:00:00

An important but rarely-addressed option pricing question is how to choose
appropriate strikes for implied volatility inputs when pricing more exotic
multi-asset derivatives. By means of Malliavin Calculus we construct an optimal
log-linear strikevconvention for exchange options under stochastic volatility
models. This novel approach allows us to minimize the difference between the
corresponding Margrabe computed price and the true option price. We show that
this optimal convention does not depend on the specific stochastic volatility
model chosen. Numerical examples are given which provide strong support to the
new methodology.
Non-contact Vital Signs Monitoring through Visible Light Sensing
Date: 2019-12-14 00:00:00

This paper presents a non-contact vital signs (respiration and heartbeat)
monitoring system that utilizes visible light sensing (VLS) technology. We have
for the first time demonstrated the ability to wirelessly (non-contact) sense
vital signs using only reflected incoherent light signals from a human subject.
The VLS-based system is implemented by using simple visible light source,
photodetector and data acquisition/processing unit, and is used with the
developed signal processing algorithms to turn slight variations in reflected
light power into accurate measurements of respiration and heart rate. To assess
the accuracy of our method, the results were compared with reliable
measurements using a contact-based monitoring device (ground truth). More than
94% of accuracy was observed in test results including both breathing and
heartbeat rates in different scenarios as compared to the state-of-the-art
baseline methods such as contact-based vitals monitoring devices. These
competitive results have demonstrated that VLS-based vitals monitoring
innovation is indeed a viable, powerful, attractive, low-cost and safe method.
This study represents a substantive departure from the traditional ways of
doing non-contact vitals monitoring methods (e.g., radio-frequency-based radars
and imaging-based cameras) and is poised to make big contributions to this
area. Since vital signs monitoring is a ubiquitous element of medicine, this
work would also impact the entire health care community, from patients in their
homes, to doctor's offices, to large medical institutions and industries. This
technology has potential to address numerous conditions and situations in which
vital signs are critical indicators such as sleep apnea and
human-computer-interaction applications.
ViLDAR - Visible Light Sensing Based Speed Estimation using Vehicle's Headlamps
Date: 2018-07-14 00:00:00
Tasks: Autonomous Vehicles, Management

The introduction of light emitting diodes (LED) in automotive exterior
lighting systems provides opportunities to develop viable alternatives to
conventional communication and sensing technologies. Most of the advanced
driver-assist and autonomous vehicle technologies are based on Radio Detection
and Ranging (RADAR) or Light Detection and Ranging (LiDAR) systems that use
radio frequency or laser signals, respectively. While reliable and real-time
information on vehicle speeds is critical for traffic operations management and
autonomous vehicles safety, RADAR or LiDAR systems have some deficiencies
especially in curved road scenarios where the incidence angle is rapidly
varying. In this paper, we propose a novel speed estimation system so-called
the Visible Light Detection and Ranging (ViLDAR) that builds upon sensing
visible light variation of the vehicle's headlamp. We determine the accuracy of
the proposed speed estimator in straight and curved road scenarios. We further
present how the algorithm design parameters and the channel noise level affect
the speed estimation accuracy. For wide incidence angles, the simulation
results show that the ViLDAR outperforms RADAR/LiDAR systems in both straight
and curved road scenarios. A provisional patent (US#62/541,913) has been
obtained for this work.