pwc-archive/papers-with-abstracts huggingface dataset:
A changepoint approach for the identification of financial extreme regimes
Date: 2019-02-25 00:00:00

Inference over tails is usually performed by fitting an appropriate limiting
distribution over observations that exceed a fixed threshold. However, the
choice of such threshold is critical and can affect the inferential results.
Extreme value mixture models have been defined to estimate the threshold using
the full dataset and to give accurate tail estimates. Such models assume that
the tail behavior is constant for all observations. However, the extreme
behavior of financial returns often changes considerably in time and such
changes occur by sudden shocks of the market. Here we extend the extreme value
mixture model class to formally take into account distributional extreme
changepoints, by allowing for the presence of regime-dependent parameters
modelling the tail of the distribution. This extension formally uses the full
dataset to both estimate the thresholds and the extreme changepoint locations,
giving uncertainty measures for both quantities. Estimation of functions of
interest in extreme value analyses is performed via MCMC algorithms. Our
approach is evaluated through a series of simulations, applied to real data
sets and assessed against competing approaches. Evidence demonstrates that the
inclusion of different extreme regimes outperforms both static and dynamic
competing approaches in financial applications.
Market efficiency, liquidity, and multifractality of Bitcoin: A dynamic study
Date: 2019-02-25 00:00:00
Tasks: Time Series, Time Series Analysis

This letter investigates the dynamic relationship between market efficiency,
liquidity, and multifractality of Bitcoin. We find that before 2013 liquidity
is low and the Hurst exponent is less than 0.5, indicating that the Bitcoin
time series is anti-persistent. After 2013, as liquidity increased, the Hurst
exponent rose to approximately 0.5, improving market efficiency. For several
periods, however, the Hurst exponent was found to be significantly less than
0.5, making the time series anti-persistent during those periods. We also
investigate the multifractal degree of the Bitcoin time series using the
generalized Hurst exponent and find that the multifractal degree is related to
market efficiency in a non-linear manner.
A Categorical Compositional Distributional Modelling for the Language of Life
Date: 2019-08-13 00:00:00
Tasks: Sentence

The Categorical Compositional Distributional (DisCoCat) Model is a powerful
mathematical model for composing the meaning of sentences in natural languages.
Since we can think of biological sequences as the "language of life", it is
attempting to apply the DisCoCat model on the language of life to see if we can
obtain new insights and a better understanding of the latter. In this work, we
took an initial step towards that direction. In particular, we choose to focus
on proteins as the linguistic features of protein are the most prominent as
compared with other macromolecules such as DNA or RNA. Concretely, we treat
each protein as a sentence and its constituent domains as words. The meaning of
a word or the sentence is just its biological function, and the arrangement of
domains in a protein corresponds to the syntax. Putting all those into the
DisCoCat framework, we can "compute" the function of a protein based on the
functions of its domains with the grammar rules that combine them together.
Since the functions of both the protein and its domains are represented in
vector spaces, we provide a novel way to formalize the functional
representation of proteins.
Holocrine Secretion and Kino Flow in Angiosperms: Their Role and Physiological Advantages in Plant Defence Mechanisms
Authors: Paulo Cabrita
Date: 2019-02-20 00:00:00
Tasks: Anatomy

Kinos are plant exudates, rich in polyphenols, produced by several angiosperms in reaction to damage. They flow out of kino veins, schizolysigenous ducts composing an anatomically distinct continuous system of tangentially anastomosing lacunae produced by the vascular cambium, which encircle the plant. Kino is secreted holocrinously into the vein lumen by a cambiform epithelium lined by suberized cells that separate kino veins from the surrounding axial parenchyma. A model describing kino flow in eucalypts is presented to investigate how vein distribution and structure, as well as kino holocrine loading, crystallization, and viscosity affect flow. Considering viscosity, vein anatomy, and a time-dependent holocrine loading of kino, the unsteady Stokes equation was applied. Qualitatively, kino flow is similar to resin flow. There is an increase in flow towards the vein open end, and both pressure and flow depend on vein dimensions, kino properties and holocrine loading. However, kino veins present a much smaller specific resistance to flow compared to resin ducts. Also, unlike resin loading in conifers, holocrine kino loading is not pressure-driven. The pressure and pressure gradient required to drive an equally fast flow are smaller than what is observed on the resin ducts of conifers. These results agree with previous observations on some angiosperms and suggest that holocrinous gum flow may have lower metabolic energy costs; thus presenting physiological advantages and possibly constituting an evolutionary step of angiosperms in using internal secretory systems in plant defence mechanisms compared to resin flow in conifers. Understanding of how these physiological and morphological parameters affect kino flow might be useful for selecting species and developing more sustainable and economically viable methods of tapping gum and gum resin in angiosperms.
PReS: Power Peak Reduction by Real-time Scheduling for Urban Railway Transit
Date: 2019-02-21 00:00:00
Tasks: Scheduling

Railway transportation is one of the most popular options for Urban Massive
Transportation Systems (UMTS) because of many attractive features. A robust
electric power supply is essential to enable normal operation. However, the
power peaks appearing at the start time of the vehicles put heavy pressure on
the power grid. Reduction of the power peak is a key issue in improving urban
railway transit's power efficiency. Researchers have tried to address this
problem by making a delicate timetable, but this method often failed to serve
the purpose because of the punctuality problem. In this work, taking advantage
of real-time estimation of the single train's power consumption, an online
Power peak Reduction by real-time Scheduling (PReS) solution for trains'
departure is proposed. Particularly, a Binary Integer Programming (BIP) model
is introduced that is able to avoid power consumption peak caused by multiple
trains departure simultaneously. The simulation result verified that the
proposed real-time scheduling approach can effectively reduce the occurrences
of power peak without bringing in additional train travel delay.
Theory for the optimal detection of time-varying signals in cellular sensing systems
Date: 2019-02-25 00:00:00

Living cells often need to measure chemical concentrations that vary in time.
To this end, they deploy many resources, e.g. receptors, downstream signaling
molecules, time and energy. Here, we present a theory for the optimal design of
a large class of sensing systems that need to detect time-varying signals, a
receptor driving a push-pull network. The theory is based on the concept of the
dynamic input-output relation, which describes the mapping between the current
ligand concentration and the average receptor occupancy over the past
integration time. This concept is used to develop the idea that the cell
employs its push-pull network to estimate the receptor occupancy and then uses
this estimate to infer the current ligand concentration by inverting the
dynamic input-output relation. The theory reveals that the sensing error can be
decomposed into two terms: the sampling error in the estimate of the receptor
occupancy and the dynamical error that arises because the average ligand
concentration over the past integration time may not reflect the current ligand
concentration. The theory generalizes the design principle of optimal resource
allocation previously identified for static signals, which states that in an
optimally designed sensing system the three fundamental resource classes of
receptors and their integration time, readout molecules, and energy are equally
limiting so that no resource is wasted. However, in contrast to static signals,
receptors and power cannot be traded freely against time to reach a desired
sensing precision: there exists an optimal integration time that maximizes the
sensing precision, which depends on the number of receptors, the receptor
correlation time, and the correlation time and variance of the input signal.
Applying our theory to the chemotaxis system of Escherichia coli indicates that
this bacterium has evolved to optimally sense shallow gradients.
Compressed ghost edge imaging
Date: 2019-02-22 00:00:00
Tasks: compressed sensing, Edge Detection, Object

In this paper, we propose an advanced framework of ghost edge imaging, named
compressed ghost edge imaging (CGEI). In the scheme, a set of structured
speckle patterns with pixel shifting are illuminated on an unknown object, and
the output is collected by a bucket detector without any spatial resolution. By
using compressed sensing algorithm, we obtain the horizontal and vertical edge
information of the unknown object with the bucket detector detection results
and the known structured speckle patterns. The edge is finally constructed by
the two-dimentional edge information. The experimental and numerical
simulations results show that the proposed scheme has a higher quality and
reduces the number of measurements, in comparison with the existed edge
detection schemes based on ghost imaging.
CoHSI V: Identical multiple scale-independent systems within genomes and computer software
Authors: Les Hatton, Gregory Warr
Date: 2019-02-25 00:00:00

A mechanism-free and symbol-agnostic conservation principle, the Conservation of Hartley-Shannon Information (CoHSI) is predicted to constrain the structure of discrete systems regardless of their origin or function. Despite their distinct provenance, genomes and computer software share a simple structural property; they are linear symbol-based discrete systems, and thus they present an opportunity to test in a comparative context the predictions of CoHSI. Here, without any consideration of, or relevance to, their role in specifying function, we identify that 10 representative genomes (from microbes to human) and a large collection of software contain identically structured nested subsystems. In the case of base sequences in genomes, CoHSI predicts that if we split the genome into n-tuples (a 2-tuple is a pair of consecutive bases; a 3-tuple is a trio and so on), without regard for whether or not a region is coding, then each collection of n-tuples will constitute a homogeneous discrete system and will obey a power-law in frequency of occurrence of the n-tuples. We consider 1-, 2-, 3-, 4-, 5-, 6-, 7- and 8-tuples of ten species and demonstrate that the predicted power-law behavior is emphatically present, and furthermore as predicted, is insensitive to the start window for the tuple extraction i.e. the reading frame is irrelevant. We go on to provide a proof of Chargaff's second parity rule and on the basis of this proof, predict higher order tuple parity rules which we then identify in the genome data. CoHSI predicts precisely the same behavior in computer software. This prediction was tested and confirmed using 2-, 3- and 4-tuples of the hexadecimal representation of machine code in multiple computer programs, underlining the fundamental role played by CoHSI in defining the landscape in which discrete symbol-based systems must operate.
Slow Beam Steering and NOMA for Indoor Multi-User Visible Light Communications
Date: 2019-02-22 00:00:00

Visible light communication (VLC) is an emerging technology that enables
broadband data rates using the visible spectrum. In this paper, considering
slow beam steering where VLC beam directions are assumed to be fixed during a
transmission frame, we find the steering angles that simultaneously serve
multiple users within the frame duration and maximize the data rates. This is
achieved by solving a non-convex optimization problem using a grid-based search
and majorization-minimization (MM) procedure. Subsequently, we consider
multiple steerable beams with a larger number of users in the network and
propose an algorithm to cluster users and serve each cluster with a separate
beam. We optimize the transmit power of each beam to maximize the data rates.
Finally, we propose a non-orthogonal multiple access (NOMA) scheme for the beam
steering and user clustering scenario, to further increase the data rates of
the users. The simulation results show that the proposed beam steering method
can efficiently serve a high number of users, and with power optimization, a
data rate gain up to ten times is possible. The simulation results for NOMA
suggests an additional 10 Mbps sum rate gain for each NOMA user pair.
An integrated algorithm for evaluating plug-in electric vehicle impact on the state of power grid assets
Date: 2019-03-11 00:00:00

Plug-in Electric Vehicles (PEV) exert an increasingly disruptive influence on
power delivery systems with penetration surge in the past decade. Therefore,
accurately assessing their impact plays a crucial role in managing grid assets
and maintaining power grids reliability. However, PEV loads are stochastic and
impulsive, which means they are of high power density and vary in a fast and
discrete manner. These load characteristics make conventional assessment
methods unsuitable. This paper proposes an algorithm, which captures the
inter-temporal response of grid assets and allows fast assessment through an
integrated interface. To realize these advantageous features, we establish
analytical models for two generic classes of grid assets (continuous and
discrete operating assets) and recast their cost functions in the statistical
settings of PEV charging. Distinct from simulation-based methods, the proposed
method is analytical, and thus greatly reduce the computation resources and
data required for accurate assessment. The effectiveness of the proposed
algorithm has been demonstrated on a set of power distribution networks in
Columbus metropolitan area, in comparison with the conventional assessment
methods.
Helminth Dynamics: Mean Number of Worms, Reproductive Rates
Authors: Arni S. R. Srinivasa Rao, Roy M. Anderson
Date: 2019-02-25 00:00:00

We derive formulas to compute mean number of worms in a newly Helminth infected population before secondary infections are started (population is closed). We have proved the two types of growth functions arise in this process as measurable functions.
Binscatter Regressions
Authors: Matias D. Cattaneo, Richard K. Crump, Max H. Farrell, Yingjie Feng
Date: 2019-02-25 00:00:00
Tasks: quantile regression, regression, Uncertainty Quantification

We introduce the package Binsreg, which implements the binscatter methods developed by Cattaneo, Crump, Farrell, and Feng (2024b,a). The package includes seven commands: binsreg, binslogit, binsprobit, binsqreg, binstest, binspwc, and binsregselect. The first four commands implement binscatter plotting, point estimation, and uncertainty quantification (confidence intervals and confidence bands) for least squares linear binscatter regression (binsreg) and for nonlinear binscatter regression (binslogit for Logit regression, binsprobit for Probit regression, and binsqreg for quantile regression). The next two commands focus on pointwise and uniform inference: binstest implements hypothesis testing procedures for parametric specifications and for nonparametric shape restrictions of the unknown regression function, while binspwc implements multi-group pairwise statistical comparisons. Finally, the command binsregselect implements data-driven number of bins selectors. The commands offer binned scatter plots, and allow for covariate adjustment, weighting, clustering, and multi-sample analysis, which is useful when studying treatment effect heterogeneity in randomized and observational studies, among many other features.
Moment Closure Stability Analysis of Stochastic Reaction Networks with Oscillatory Dynamics
Date: 2019-02-25 00:00:00

Biochemical reactions with oscillatory behavior play an essential role in
synthetic biology at the microscopic scale. Although a robust stability theory
for deterministic chemical oscillators in the macroscopic limit exists, the
dynamical stability of stochastic oscillators is an object of ongoing research.
The Chemical Master Equation along with kinetic Monte Carlo simulations
constitute the most accurate approach to modeling microscopic systems. However,
because of the challenges of solving the fully probabilistic model, most
studies in stability analysis have focused on the description of externally
disturbed oscillators. Here we apply the Maximum Entropy Principle as closure
criterion for moment equations of oscillatory networks and perform the
stability analysis of the internally disturbed Brusselator network.
Particularly, we discuss the effects of kinetic and size parameters on the
dynamics of this stochastic oscillatory system with intrinsic noise. Our
numerical experiments reveal that changes in kinetic parameters lead to
phenomenological and dynamical Hopf bifurcations, while reduced system sizes in
the oscillatory region can reverse the stochastic Hopf dynamical bifurcations
at the ensemble level. This is a unique feature of the stochastic dynamics of
oscillatory systems, with an unknown parallel in the macroscopic limit.
A circuit for precise random frequency synthesis via a frequency locked loop
Date: 2019-02-25 00:00:00

Frequency synthesis (FS) is a technique vital for all kinds of radio
frequency (RF) communications, such as: mobile phones, Bluetooth, Wi-Fi, radio,
TV and satellite, and in other equipment requiring periodic signals of stable
and programmable frequency. In this work, we present a generalization of the FS
technique to random, non-periodic signals, whose main use is in the new area of
stochastic neuromorphic computing and, information security and
instrumentation. Since conventional FS circuits cannot work with random
signals, we introduce a novel random frequency ratio detector, that works both
with random and periodic signals.
Limited processivity of single motors improves overall transport flux of self-assembled motor-cargo complexes
Authors: Keshav B. Patel, Shengtan Mao, M. Gregory Forest, Samuel K. Lai, Jay M. Newby
Date: 2019-02-26 00:00:00

Single kinesin molecular motors can processively move along a microtubule (MT) a few micrometers on average before dissociating. However, cellular length scales over which transport occurs are several hundred microns and more. Why seemingly unreliable motors are used to transport cellular cargo remains poorly understood. We propose a new theory for how low processivity, the average length of a single bout of directed motion, can enhance cellular transport when motors and cargoes must first diffusively self assemble into complexes. We employ stochastic modeling to determine the effect of processivity on overall cargo transport flux. We show that, under a wide range of physiologically relevant conditions, possessing "infinite" processivity does not maximize flux along MTs. Rather, we find that low processivity i.e., weak binding of motors to MTs, is optimal. These results shed light on the relationship between processivity and transport efficiency and offer a new theory for the physiological benefits of low motor processivity.
Fetus: the radar of maternal stress, a cohort study
Date: 2019-02-26 00:00:00

Objective: We hypothesized that prenatal stress (PS) exerts lasting impact on
fetal heart rate (fHR). We sought to validate the presence of such PS signature
in fHR by measuring coupling between maternal HR (mHR) and fHR. Study design:
Prospective observational cohort study in stressed group (SG) mothers with
controls matched for gestational age during screening at third trimester using
Cohen Perceived Stress Scale (PSS) questionnaire with PSS-10 equal or above 19
classified as SG. Women with PSS-10 less than 19 served as control group (CG).
Setting: Klinikum rechts der Isar of the Technical University of Munich.
Population: Singleton 3rd trimester pregnant women. Methods: Transabdominal
fetal electrocardiograms (fECG) were recorded. We deployed a signal processing
algorithm termed bivariate phase-rectified signal averaging (BPRSA) to quantify
coupling between mHR and fHR resulting in a fetal stress index (FSI). Maternal
hair cortisol was measured at birth. Differences were assumed to be significant
for p value less than 0.05. Main Outcome Measures: Differences for FSI between
both groups. Results: We screened 1500 women enrolling 538 of which 16.5 %
showed a PSS-10 score equal or above 19 at 34+0 weeks. Fifty five women
eventually comprised the SG and n=55 served as CG. Median PSS was 22.0 (IQR
21.0-24.0) in the SG and 9.0 (6.0-12.0) in the CG, respectively. Maternal hair
cortisol was higher in SG than CG at 86.6 (48.0-169.2) versus 53.0 (34.4-105.9)
pg/mg. At 36+5 weeks, FSI was significantly higher in fetuses of stressed
mothers when compared to controls [0.43 (0.18-0.85) versus 0.00 (-0.49-0.18)].
Conclusion: Our findings show a persistent effect of PS affecting fetuses in
the last trimester.
Throughput Optimization for Wireless Powered Interference Channels
Date: 2019-02-26 00:00:00

This paper studies a general multi-user wireless powered interference channel
(IFC) under the harvest-then-transmit protocol, where the communication in
channel coherence time consists of two phases, namely wireless energy transfer
(WET) and wireless information transfer (WIT). In the first phase, all energy
transmitters (ETs) transmit energy signals to information transmitters (ITs)
via collaborative waveform design, while in the second phase, each IT transmits
an information signal to its intended ET using the harvested energy in the
previous phase. The aim is to jointly design the WET-WIT time allocation, the
(deterministic) transmit signal at the first phase, and the transmit power of
ITs in the second phase to optimize the network throughput. The design problems
are non-convex and hence difficult to solve globally. To deal with them, we
propose efficient iterative algorithms based on alternating projections; then,
the majorization-minimization technique is used to tackle the nonconvex
sub-problems in each iteration. We also extend the devised design methodology
by considering imperfect channel state information (CSI) and non-linearity in
energy harvesting circuit. The proposed algorithms are locally convergent and
can provide high-quality suboptimal solutions to the design problems.
Simulation results show the effectiveness of the proposed algorithms under
various setups.
74.38 Tb/s Transmission Over 6300 km Single Mode Fiber with Hybrid EDFA/Raman Amplifiers
Date: 2019-02-26 00:00:00

Transmission of 306x35 GBd, dual polarization, 64-ary geometrically shaped
channels over 90x70 km of SMF was demonstrated, achieving a net throughput of
74.38 Tb/s. A combination of hybrid fiber spans and EDFA/Raman amplifiers
enabled a continuous gain bandwidth of 10.8 THz.
Detection of Gait Asymmetry Using Indoor Doppler Radar
Date: 2019-02-26 00:00:00
Tasks: Privacy Preserving

Doppler radar systems enable unobtrusive and privacy-preserving long-term
monitoring of human motions indoors. In particular, a person's gait can provide
important information about their state of health. Utilizing micro-Doppler
signatures, we show that radar is capable of detecting small differences
between the step motions of the two legs, which results in asymmetric gait.
Image-based and physical features are extracted from the radar return signals
of several individuals, including four persons with different diagnosed gait
disorders. It is shown that gait asymmetry is correctly detected with high
probability, irrespective of the underlying pathology, for at least one motion
direction.