pwc-archive/papers-with-abstracts huggingface dataset:
Quantification of sulfated polysaccharides in urine by the Heparin Red mix-and-read fluorescence assay
Date: 2018-11-22 00:00:00

Quantification of sulfated polysaccharides in urine samples is relevant to
pharmacokinetic studies in drug development projects and to the non-invasive
diagnosis and therapy monitoring of mucopolysaccharidoses. The Heparin Red Kit
is a particularly simple and user friendly fluorescence assay for the
quantification of sulfated polysaccharides and has recently emerged as a novel
tool for the monitoring of their blood levels during pharmacokinetic studies in
clinical trials. The standard protocol for the blood plasma matrix is, however,
not suited for urine samples due to matrix interference. The present study
identifies inorganic sulfate as the interfering component in urine. The sulfate
level of urine is typically 1-2 orders of magnitude higher compared with the
blood plasma level. Addition of either hydrochloric acid or magnesium chloride
counteracts sulfate interference but still enables sensitive detection of
sulfated polysaccharides such as heparin, heparan sulfate and dermatan sulfate
at low microgramm per milliliter levels. This study extends the application
range of the Heparin Red Kit by a simple modification of the assay protocol to
the direct quantification of various sulfated polysaccharides in human urine.
Anticipation: an effective evolutionary strategy for a sub-optimal population in a cyclic environment
Date: 2018-11-22 00:00:00

We built a two-state model of an asexually reproducing organism in a periodic
environment endowed with the capability to anticipate an upcoming environmental
change and undergo pre-emptive switching. By virtue of these anticipatory
transitions, the organism oscillates between its two states that is a time
$\theta$ out of sync with the environmental oscillation. We show that an
anticipation-capable organism increases its long-term fitness over an organism
that oscillates in-sync with the environment, provided $\theta$ does not exceed
a threshold. We also show that the long-term fitness is maximized for an
optimal anticipation time that decreases approximately as $1/n$, $n$ being the
number of cell divisions in time $T$. Furthermore, we demonstrate that optimal
"anticipators" outperforms "bet-hedgers" in the range of parameters considered.
For a sub-optimal ensemble of anticipators, anticipation performs better to
bet-hedging only when the variance in anticipation is small compared to the
mean and the rate of pre-emptive transition is high. Taken together, our work
suggests that anticipation increases overall fitness of an organism in a
periodic environment and it is a viable alternative to bet-hedging provided the
error in anticipation is small.
Pilot-Aided Joint-Channel Carrier-Phase Estimation in Space-Division Multiplexed Multicore Fiber Transmission
Date: 2019-10-13 00:00:00

The performance of pilot-aided joint-channel carrier-phase estimation (CPE)
in space-division multiplexed multicore fiber (MCF) transmission with
correlated phase noise is studied. To that end, a system model describing
uncoded MCF transmission where the phase noise comprises a common laser phase
noise, in addition to core- and polarization-specific phase drifts, is
introduced. It is then shown that the system model can be regarded as a special
case of a multidimensional random-walk phase-noise model. A pilot-aided CPE
algorithm developed for this model is used to evaluate two strategies, namely
joint-channel and per-channel CPE. To quantify the performance differences
between the two strategies, their respective phase-noise tolerances are
assessed through Monte Carlo simulations of uncoded transmission for different
modulation formats, pilot overheads, laser linewidths, numbers of spatial
channels, and degrees of phase-noise correlation across the channels. For 20
GBd transmission with 200 kHz combined laser linewidth and 1% pilot overhead,
joint-channel CPE yields up to 3.4 dB improvement in power efficiency or 25.5%
increased information rate. Moreover, through MCF transmission experiments, the
system model is validated and the strategies are compared in terms of
bit-error-rate performance versus transmission distance for uncoded
transmission of different modulation formats. Up to 21% increase in
transmission reach is observed for 1% pilot overhead through the use of
joint-channel CPE.
The Statistical Dictionary-based String Matching Problem
Authors: Suri M., Rini S.
Date: 2018-11-22 00:00:00
Tasks: Position, Retrieval

In the Dictionary-based String Matching (DSM) problem, a retrieval system has
access to a source sequence and stores the position of a certain number of
strings in a posting table. When a user inquires the position of a string, the
retrieval system, instead of searching in the source sequence directly, relies
on the the posting table to answer the query more efficiently. In this paper,
the Statistical DSM problem is a proposed as a statistical and
information-theoretic formulation of the classic DSM problem in which both the
source and the query have a statistical description while the strings stored in
the posting sequence are described as a code. Through this formulation, we are
able to define the efficiency of the retrieval system as the average cost in
answering a users' query in the limit of sufficiently long source sequence.
This formulation is used to study the retrieval performance for the case in
which (i) all the strings of a given length, referred to as k-grams , and (ii)
prefix-free codes.
Recommending Users: Whom to Follow on Federated Social Networks
Authors: Trienes Jan, Cano Andrés Torres, Hiemstra Djoerd
Date: 2018-11-22 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

To foster an active and engaged community, social networks employ
recommendation algorithms that filter large amounts of contents and provide a
user with personalized views of the network. Popular social networks such as
Facebook and Twitter generate follow recommendations by listing profiles a user
may be interested to connect with. Federated social networks aim to resolve
issues associated with the popular social networks - such as large-scale
user-surveillance and the miss-use of user data to manipulate elections - by
decentralizing authority and promoting privacy. Due to their recent emergence,
recommender systems do not exist for federated social networks, yet. To make
these networks more attractive and promote community building, we investigate
how recommendation algorithms can be applied to decentralized social networks.
We present an offline and online evaluation of two recommendation strategies: a
collaborative filtering recommender based on BM25 and a topology-based
recommender using personalized PageRank. Our experiments on a large unbiased
sample of the federated social network Mastodon shows that collaborative
filtering approaches outperform a topology-based approach, whereas both
approaches significantly outperform a random recommender. A subsequent live
user experiment on Mastodon using balanced interleaving shows that the
collaborative filtering recommender performs on par with the topology-based
recommender.
Fault Detection Using Color Blending and Color Transformations
Date: 2018-11-22 00:00:00
Tasks: Fault Detection, Seismic Interpretation

In the field of seismic interpretation, univariate databased maps are
commonly used by interpreters, especially for fault detection. In these maps,
contrast between target regions and the background is one of the main factors
that affect the accuracy of the interpretation. Since univariate data-based
maps are not capable of providing a high contrast representation, to overcome
this issue, we turn these univariate data-based maps into multivariate
data-based representations using color blending. We blend neighboring time
sections, frames that are viewed in the time direction of migrated seismic
volumes, as if they corresponded to the red, green, and blue channels of a
color image. Furthermore, we apply color transformations to extract more
reliable structural information. Experimental results show that the proposed
method improves the accuracy of fault detection by limiting the average
distance between detected fault lines and the ground truth into one pixel.
Bayesian Alternatives to the Black-Litterman Model
Date: 2018-12-25 00:00:00
Tasks: model

The Black-Litterman model combines investors' personal views with historical
data and gives optimal portfolio weights. In this paper we will introduce the
original Black-Litterman model (section 1), we will modify the model such that
it fits in a Bayesian framework by considering the investors' personal views to
be a direct prior on the means of the returns and by adding a typical Inverse
Wishart prior on the covariance matrix of the returns (section 2). Lastly, we
will use Leonard and Hsu's (1992) idea of adding a prior on the logarithm of
the covariance matrix (section 3). Sensitivity simulations for the level of
confidence that the investor has in their own personal views were performed and
performance of the models was assessed on a test data set consisting of returns
over the month of January 2018.
Estimation of Ornstein-Uhlenbeck Process Using Ultra-High-Frequency Data with Application to Intraday Pairs Trading Strategy
Authors: Vladimír Holý, Petra Tomanová
Date: 2018-11-22 00:00:00
Tasks: parameter estimation

When stock prices are observed at high frequencies, more information can be utilized in estimation of parameters of the price process. However, high-frequency data are contaminated by the market microstructure noise which causes significant bias in parameter estimation when not taken into account. We propose an estimator of the Ornstein-Uhlenbeck process based on the maximum likelihood which is robust to the noise and utilizes irregularly spaced data. We also show that the Ornstein-Uhlenbeck process contaminated by the independent Gaussian white noise and observed at discrete equidistant times follows an ARMA(1,1) process. To illustrate benefits of the proposed noise-robust approach, we introduce a novel intraday pairs trading strategy based on the mean-variance optimization. In an empirical study of 7 Big Oil companies, we show that the use of the proposed estimator of the Ornstein-Uhlenbeck process leads to an increase in profitability of the pairs trading strategy.
How to find MH370?
Authors: Martin Kristensen
Date: 2018-11-22 00:00:00

The disappearance of flight MH370 is possibly the greatest mystery in aviation history. A large zone in the Southern Indian Ocean was searched unsuccessfully leaving an open case and an unacceptable situation for the family members. We discuss the scientific difficulties with locating the plane through satellite data and develop an improved analysis using least square curve fitting of analytical non-Euclidean route equations providing robust topology-optimization with perturbation theory handling satellite movement. We find four independent solutions with the final part of the flight following a great circle. Two are located in stable minima for the error-function, and two unstable ones agree poorly with most data. One stable solution coincides with the Inmarsat-result, but fails to explain additional data. Our best solution leads to an entirely different location agreeing with other data from debris, acoustics, an eyewitness report, the received microwave power, several contrails, two seismic detectors at Christmas Island, and with a mushroom impact cloud providing the final proof where to find the rest of the airplane and the black boxes.
Balance of Mechanical Forces Drives Endothelial Gap Formation and May Facilitate Cancer and Immune-Cell Extravasation
Date: 2018-11-22 00:00:00

The formation of gaps in the endothelium is a crucial process underlying both
cancer and immune cell extravasation, contributing to the functioning of the
immune system during infection, the unfavorable development of chronic
inflammation and tumor metastasis. Here, we present a stochastic-mechanical
multiscale model of an endothelial cell monolayer and show that the dynamic
nature of the endothelium leads to spontaneous gap formation, even without
intervention from the transmigrating cells. These gaps preferentially appear at
the vertices between three endothelial cells, as opposed to the border between
two cells. We quantify the frequency and lifetime of these gaps, and validate
our predictions experimentally. Interestingly, we find experimentally that
cancer cells also preferentially extravasate at vertices, even when they first
arrest on borders. This suggests that extravasating cells, rather than
initially signaling to the endothelium, might exploit the autonomously forming
gaps in the endothelium to initiate transmigration.
An Ensemble Framework For Day-Ahead Forecast of PV Output Power in Smart Grids
Date: 2018-11-23 00:00:00
Tasks: Management

The uncertainty associated with solar photo-voltaic (PV) power output is a
big challenge to design, manage and implement effective demand response and
management strategies. Therefore, an accurate PV power output forecast is an
utmost importance to allow seamless integration and a higher level of
penetration. In this research, a neural network ensemble (NNE) scheme is
proposed, which is based on particle swarm optimization (PSO) trained
feedforward neural network (FNN). Five different FFN structures with varying
network complexities are used to achieve the diverse and accurate forecast
results. These results are combined using trim aggregation after removing the
upper and lower forecast error extremes. Correlated variables namely wavelet
transformed historical power output of PV, solar irradiance, wind speed,
temperature and humidity are applied as inputs to the multivariate NNE.
Clearness index is used to classify days into clear, cloudy and partial cloudy
days. Test case studies are designed to predict the solar output for these days
selected from all seasons. The performance of the proposed framework is
analyzed by applying training data set of different resolution, length and
quality from seven solar PV sites of the University of Queensland, Australia.
The forecast results demonstrate that the proposed framework improves the
forecast accuracy significantly in comparison with individual and benchmark
models.
Multivariate Ensemble Forecast Framework for Demand Prediction of Anomalous Days
Date: 2018-11-23 00:00:00
Tasks: Load Forecasting, Management

An accurate load forecast is always important for the power industry and
energy players as it enables stakeholders to make critical decisions. In
addition, its importance is further increased with growing uncertainties in the
generation sector due to the high penetration of renewable energy and the
introduction of demand side management strategies. An incremental improvement
in grid-level demand forecast of anomalous days can potentially save millions
of dollars. However, due to an increasing penetration of renewable energy
resources and their dependency on several meteorological and exogenous
variables, accurate load forecasting of anomalous days has now become very
challenging. To improve the prediction accuracy of the load forecasting, an
ensemble forecast framework (ENFF) is proposed with a systematic combination of
three multiple predictors, namely Elman neural network (ELM), feedforward
neural network (FNN) and radial basis function (RBF) neural network. These
predictors are trained using global particle swarm optimization (GPSO) to
improve their prediction capability in the ENFF. The outputs of individual
predictors are combined using a trim aggregation technique by removing
forecasting anomalies. Real recorded data of New England ISO grid is used for
training and testing of the ENFF for anomalous days. The forecast results of
the proposed ENFF indicate a significant improvement in prediction accuracy in
comparison to autoregressive integrated moving average (ARIMA) and
back-propagation neural networks (BPNN) based benchmark models.
Prediction of Cytochrome P450-Mediated Metabolism Using a Combination of QSAR Derived Reactivity and Induced Fit Docking
Date: 2018-11-23 00:00:00
Tasks: CPU, Molecular Docking

Prediction of metabolism in cytochrome P450s remains to be a crucial yet
challenging topic in discovering and designing drugs, agrochemicals and
nutritional supplements. The problem is challenging because the rate of P450
metabolism depends upon both the intrinsic chemical reactivity of the site and
the protein-ligand geometry that is energetically accessible in the active site
of a given P450 isozyme. We have addressed this problem using a two-level
screening system. The first level implements an empirical QSAR-based scoring
function employing the local chemical motifs to characterize the intrinsic
reactivity. The second level uses molecular docking and molecular mechanics to
account for the geometrical effects, including induced-fit effects in the
protein which can be very important in P450 interactions with ligands. This
approach has achieved high accuracy for both the P450 3A4 and 2D6 isoforms. In
identifying at least one metabolic site in the top two ranked positions, the
prediction rate can reach as high as 92.7% for the test set of isoform 3A4. For
the 2D6 isoform, 100% accuracy is achieved on this basic evaluation metric,
and, because this active site is considerably smaller and more selective than
3A4, very high precision is attained for full prediction of all metabolic
sites. The method also requires considerably less CPU time than our previous
efforts, which involved a large number of expensive simulations for each ligand
to be evaluated. After screening using the empirical score function, only a few
best candidates are left for each ligand, making the number of necessary
estimations in the second level very small, which significantly reduces the
computation time.
New dynamics of energy use and CO2 emissions in China
Date: 2018-11-23 00:00:00

Global achievement of climate change mitigation will heavy reply on how much
of CO2 emission has and will be released by China. After rapid growth of
emissions during last decades, China CO2 emissions declined since 2014 that
driven by decreased coal consumption, suggesting a possible peak of China coal
consumption and CO2 emissions. Here, by combining a updated methodology and
underlying data from different sources, we reported the soaring 5.5% (range:
+2.5% to +8.5% for one sigma) increase of China CO2 emissions in 2018 compared
to 2017, suggesting China CO2 is not yet to peak and leaving a big uncertain to
whether China emission will continue to rise in the future. Although our best
estimate of total emission (9.9Gt CO2 in 2018) is lower than international
agencies in the same year, the results show robust on a record-high energy
consumption and total CO2 emission in 2018. During 2014-2016, China energy
intensity (energy consumption per unit of GDP) and total CO2 emissions has
decreased driven by energy and economic structure optimization. However, the
decrease in emissions is now offset by stimulates of heavy industry production
under economic downturn that driving coal consumption (+5% in 2018), as well as
the surging of natural gas consumption (+18% in 2018) due to the government led
coal-to-gas energy transition to reduce local air pollutions. Timing policy and
actions are urgent needed to address on these new drivers to turn down the
total emission growth trend.
Monopulse beam synthesis using a sparse single-layer of weights
Date: 2018-11-23 00:00:00

A conventional monopulse radar system uses three beams; sum beam, elevation
difference beam and azimuth difference beam, which require different layers of
weights to synthesize each beam independently. Since the multi-layer structure
increases hardware complexity, many simplified structures based on a single
layer of weights have been suggested. In this work, we introduce a new
technique for finding disjoint and fully covering sets of weight vectors, each
of which constitutes a sparse subarray, forming a single beam. Our algorithm
decomposes the original non-convex optimization problem for finding disjoint
weight vectors into a sequence of convex problems. We demonstrate the
convergence of the algorithm and show that the interleaved array structure is
able to meet difficult beam constraints.
Critical slowing down and attractive manifold: a mechanism for dynamic robustness in yeast cell-cycle process
Date: 2018-11-23 00:00:00

The biological processes that execute complex multiple functions, such as
cell cycle, must ensure the order of sequential events and keep the dynamic
robustness against various fluctuations. Here, we examine the dynamic mechanism
and the fundamental structure to achieve these properties in the cell-cycle
process of budding yeast Saccharomyces cerevisiae. We show that the budding
yeast cell-cycle process behaves like an excitable system containing three
well-coupled saddle-node bifurcations to execute DNA replication and mitosis
events. The yeast cell-cycle regulatory network can be separated into G1/S
phase module, early M module and late M phase module, where the positive
feedbacks in each module and the interactions among the modules play important
role. If the cell-cycle process operates near the critical points of the
saddle-node bifurcations, there is a critical slowing down or ghost effect.
This can provide the cell-cycle process with a sufficient duration for each
event and an attractive manifold for the state checking of the completion of
DNA replication and mitosis; moreover, the fluctuation in the early
module/event is forbidden to transmit to the latter module/event. Our results
suggest both a fundamental structure of cell-cycle regulatory network and a
hint for the evolution of eukaryotic cell-cycle processes, from the dynamic
checking mechanism to the molecule checkpoint pathway.
Idiosyncrasies and challenges of data driven learning in electronic trading
Date: 2018-11-30 00:00:00
Tasks: BIG-bench Machine Learning

We outline the idiosyncrasies of neural information processing and machine
learning in quantitative finance. We also present some of the approaches we
take towards solving the fundamental challenges we face.
Fixation probabilities for the Moran process with three or more strategies: general and coupling results
Date: 2018-11-23 00:00:00
Tasks: Math

We study fixation probabilities for the Moran stochastic process for the
evolution of a population with three or more types of individuals and
frequency-dependent fitnesses. Contrarily to the case of populations with two
types of individuals, in which fixation probabilities may be calculated by an
exact formula, here we must solve a large system of linear equations. We first
show that this system always has a unique solution. Other results are upper and
lower bounds for the fixation probabilities obtained by coupling the Moran
process with three strategies with birth-death processes with only two
strategies. We also apply our bounds to the problem of evolution of cooperation
in a population with three types of individuals already studied in a
deterministic setting by N\'u\~nez Rodr\'iguez and Neves (J. Math. Biol. (2016)
73:1665--1690). We argue that cooperators will be fixated in the population
with probability arbitrarily close to 1 for a large region of initial
conditions and large enough population sizes.