pwc-archive/papers-with-abstracts huggingface dataset:
A simplified static frequency converter model for electromechanical transient stability studies of 16$\frac{2}{3}$ Hz railways
Date: 2019-01-30 00:00:00

With increased share of Static Frequency Converters (SFCs) in 16$\frac{2}{3}$
Hz railway grids concerns about stability have increased. Stability studies for
such low-frequency railway grids are few, and models that describe SFC dynamics
are especially few. This paper presents an open SFC model for electromechanical
stability studies in the phasor domain, suited for 16$\frac{2}{3}$ Hz
synchronous railway grids. The developed and proposed SFC model is implemented
in MatLab Simulink, together with grid and loads. Numerical studies are made,
in which the proposed SFC model is validated against both measured RMS-phasor
amplitude of voltage and current at the railway grid side of an SFC. The SFC
model developed is able to reproduce the measured RMS voltage and current with
an acceptable accuracy.
How to Increase Global Wealth Inequality for Fun and Profit
Date: 2018-11-12 00:00:00

We point out a simple equities trading strategy that allows a sufficiently
large, market-neutral, quantitative hedge fund to achieve outsized returns
while simultaneously contributing significantly to increasing global wealth
inequality. Overnight and intraday return distributions in major equity indices
in the United States, Canada, France, Germany, and Japan suggest a few such
firms have been implementing this strategy successfully for more than
twenty-five years.
A migrating epithelial monolayer flows like a Maxwell viscoelastic liquid
Authors: S. Tlili, M. Durande, C. Gay, B. Ladoux, F. Graner, H. Delanoë-Ayari
Date: 2018-11-12 00:00:00

We perform a bidimensional Stokes experiment in an active cellular material: an autonomously migrating monolayer of Madin-Darby Canine Kidney (MDCK) epithelial cells flows around a circular obstacle within a long and narrow channel, involving an interplay between cell shape changes and neighbour rearrangements. Based on image analysis of tissue flow and coarse-grained cell anisotropy, we determine the tissue strain rate, cell deformation and rearrangement rate fields, which are spatially heterogeneous. We find that the cell deformation and rearrangement rate fields correlate strongly, which is compatible with a Maxwell viscoelastic liquid behaviour (and not with a Kelvin-Voigt viscoelastic solid behaviour). The value of the associated relaxation time is measured as $\tau = 70 \pm 15$~min, is observed to be independent of obstacle size and division rate, and is increased by inhibiting myosin activity. In this experiment, the monolayer behaves as a flowing material with a Weissenberg number close to one which shows that both elastic and viscous effects can have comparable contributions in the process of collective cell migration.
Electrophysiological indicators of gesture perception
Date: 2018-11-13 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Gesture Generation, regression

Background: While there has been abundant research concerning neurological
responses to gesture generation, the time course of gesture processing is not
well understood. Specifically, it is not clear if or how particular
characteristics within the kinematic execution of gestures capture attention
and aid in the classification of gestures with communicative intent. If indeed
key features of gestures with perceptual saliency exist, such features could
help form the basis of a compact representation of the gestures in memory.
Methods: This study used a set of available gesture videos as stimuli. The
timing for salient features of performed gestures was determined by isolating
inflection points in the hands' motion trajectories. Participants passively
viewed the gesture videos while continuous EEG data was collected. We focused
on mu oscillations (10 Hz) and used linear regression to test for associations
between the timing of mu oscillations and inflection points in motion
trajectories. Results: Peaks in the EEG signals at central and occipital
electrodes were used to isolate the salient events within each gesture. EEG
power oscillations were detected 343 and 400ms on average after inflection
points at occipital and central electrodes, respectively. A regression model
showed that inflection points in the motion trajectories strongly predicted
subsequent mu oscillations (R^2=0.961, p<.01). Conclusion: The results suggest
that coordinated activity in the visual and motor cortices are highly
correlated with key motion components within gesture trajectories. These points
may be associated with neural signatures used to encode gestures in memory for
later identification and even recognition.
SMERC: Social media event response clustering using textual and temporal information
Authors: Mathews Peter, Gray Caitlin, Mitchell Lewis, Nguyen Giang T., Bean Nigel G.
Date: 2018-11-13 00:00:00
Tasks: Clustering, Event Detection

Tweet clustering for event detection is a powerful modern method to automate
the real-time detection of events. In this work we present a new tweet
clustering approach, using a probabilistic approach to incorporate temporal
information. By analysing the distribution of time gaps between tweets we show
that the gaps between pairs of related tweets exhibit exponential decay,
whereas the gaps between unrelated tweets are approximately uniform. Guided by
this insight, we use probabilistic arguments to estimate the likelihood that a
pair of tweets are related, and build an improved clustering method. Our method
Social Media Event Response Clustering (SMERC) creates clusters of tweets based
on their tendency to be related to a single event. We evaluate our method at
three levels: through traditional event prediction from tweet clustering, by
measuring the improvement in quality of clusters created, and also comparing
the clustering precision and recall with other methods. By applying SMERC to
tweets collected during a number of sporting events, we demonstrate that
incorporating temporal information leads to state of the art clustering
performance.
Suppression of the Spectral Cross Talk of Optogenetic Switching by Stimulated Depletion Quenching. Theoretical Analysis
Date: 2018-11-13 00:00:00

Optogenetics is a rapidly growing field of biotechnology, potentially
allowing a deeper understanding and control of complex biological networks. The
major challenge is the multiplexed control of several optogenetic components in
the presence of significant spectral cross talk. We propose and demonstrate
through simulations a new control approach of Stimulated Depletion Quenching.
This approach is applied to the phytochrome Cph8 bidirectional optogenetic
switch, and the results show significant improvement of its dynamic range.
M Equilibrium: A theory of beliefs and choices in games
Authors: Jacob K. Goeree, Philippos Louis
Date: 2018-11-13 00:00:00

We introduce a set-valued solution concept, M equilibrium, to capture empirical regularities from over half a century of game-theory experiments. We show M equilibrium serves as a meta theory for various models that hitherto were considered unrelated. M equilibrium is empirically robust and, despite being set-valued, falsifiable. We report results from a series of experiments comparing M equilibrium to leading behavioral-game-theory models and demonstrate its virtues in predicting observed choices and stated beliefs. Data from experimental games with a unique pure-strategy Nash equilibrium and multiple M equilibria exhibit coordination problems that could not be anticipated through the lens of existing models.
User Demand Based Precoding for G.fast DSL Systems
Date: 2018-11-13 00:00:00

It can be observed that the achievable rate region of a G.fast DSL system is
no longer rectangular, as it is the case for vectored VDSL systems, due to
stronger crosstalk couplings at high frequencies. Therefore, alternative
operating points that are not optimal in a sum-rate sense may be utilized to
adapt the system performance to the users' actual demands. To this end, we
propose a new precoding scheme based on defining a subset of prioritized users,
where we optimize the sum-rate of the prioritized users under a minimum rate
guarantee for the remaining users. We present a solution based on Lagrangian
duality theory and propose a well-performing one-step heuristic solution. By
means of simulations, we show that significant rate gains for the prioritized
users can be obtained by the proposed precoding scheme.
The need for an integrative thinking to fight against emerging infectious diseases
Authors: C. Burdet, J. Guegan, X. Duval, M. Le Tyrant, H. Bergeron, C. Manuguerra, J. Raude, C. Leport, P. Zylberman
Date: 2018-11-13 00:00:00

We present here the proceedings of the 5th seminar on emerging infectious diseases (EIDs), held in Paris on March 22nd, 2016, with seven priority proposals that can be outlined as follows:$\bullet$Encourage research on the prediction, screening and early detection of new risks of infection$\bullet$Develop research and surveillance concerning transmission of pathogens between animals and humans, with their reinforcement in particular in intertropical areas (`hot-spots') thanks to public support$\bullet$Pursue aid development and support in these areas of prevention and training for local health personnel, and to foster risk awareness in the population$\bullet$Ensure adapted patient care in order to promote adherence to treatment and to epidemic propagation reduction measures$\bullet$Develop greater sensitization and training among politicians and healthcare providers, in order to better prepare them to respond to new types of crises$\bullet$Modify the logic of governance, drawing from all available modes of communication and incorporating new information-sharing tools$\bullet$Develop economic research on the fight against EIDs, taking into account specific driving factors in order to create a balance between preventive and treatment approaches.
Unsupervised organization of cervical cells using high resolution digital holographic microscopy
Date: 2018-11-13 00:00:00
Tasks: Image Clustering, Vocal Bursts Intensity Prediction

We report results on unsupervised organization of cervical cells using
microscopy of Pap-smear samples in brightfield (3-channel colour) as well as
high resolution quantitative phase imaging modalities. A number of
morphological parameters are measured for each of the 1450 cell nuclei (from
$10$ woman subjects) imaged in this study. The Principal Component Analysis
(PCA) methodology applied to this data shows that the cell image clustering
performance improves significantly when brightfield as well as phase
information is utilized for PCA as compared to when brightfield-only
information is used. The results point to the feasibility of an image-based
tool that will be able to mark suspicious cells for further examination by the
pathologist. More importantly our results suggest that the information in
quantitative phase images of cells that is typically not used in clinical
practice is valuable for automated cell classification applications in general.
Analysis of structure and dynamics in three-neuron motifs
Date: 2018-11-13 00:00:00

In neural networks with identical neurons, the matrix of connection weights
completely describes the network structure and thereby determines how it is
processing information. However, due to the non-linearity of these systems, it
is not clear if similar microscopic connection structures also imply similar
functional properties, or if a network is impacted more by macroscopic
structural quantities, such as the ratio of excitatory and inhibitory
connections (balance), or the ratio of non-zero connections (density). To
clarify these questions, we focus on motifs of three binary neurons with
discrete ternary connection strengths, an important class of network building
blocks that can be analyzed exhaustively. We develop new, permutation-invariant
metrics to quantify the structural and functional distance between two given
network motifs. We then use multidimensional scaling to identify and visualize
clusters of motifs with similar structural and functional properties. Our
comprehensive analysis reveals that the function of a neural network is only
weakly correlated with its microscopic structure, but depends strongly on the
balance of the connections.
Quantifying the performance of high-throughput directed evolution protocols
Date: 2018-11-13 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Most protocols for the high-throughput directed evolution of enzymes rely on
random encapsulation to link phenotype and genotype. In order to optimize these
approaches, or compare one to another, one needs a measure of their performance
at extracting the best variants. We introduce here a new metric named the
Selection Quality Index (SQI), which can be computed from a simple mock
experiment with a known initial fraction of active variants. As opposed to
previous approaches, our index integrates the random co-encapsulation of
entities in compartments and comes with a straightforward experimental
interpretation. We further show how this new metric can be used to extract
general trends of protocol efficiency, or reveal hidden mechanisms such as a
counterintuitive form of beneficial poisoning in the Compartmentalized
Self-Replication protocol.
The strength of protein-protein interactions controls the information capacity and dynamical response of signaling networks
Date: 2018-11-23 00:00:00

Eukaryotic cells transmit information by signaling through complex networks
of interacting proteins. Here we develop a theoretical and computational
framework that relates the biophysics of protein-protein interactions (PPIs)
within a signaling network to its information processing properties. To do so,
we generalize statistical physics-inspired models for protein binding to
account for interactions that depend on post-translational state (e.g.
phosphorylation). By combining these models with information-theoretic methods,
we find that PPIs are a key determinant of information transmission within a
signaling network, with weak interactions giving rise to "noise" that
diminishes information transmission. While noise can be mitigated by increasing
interaction strength, the accompanying increase in transmission comes at the
expense of a slower dynamical response. This suggests that the biophysics of
signaling protein interactions give rise to a fundamental "speed-information"
trade-off. Surprisingly, we find that cross-talk between pathways in complex
signaling networks do not significantly alter information capacity--an
observation that may partially explain the promiscuity and ubiquity of weak
PPIs in heavily interconnected networks. We conclude by showing how our
framework can be used to design synthetic biochemical networks that maximize
information transmission, a procedure we dub "InfoMax" design.
First passage time distribution for spiking neuron with delayed excitatory feedback
Date: 2019-08-19 00:00:00

A class of spiking neuronal models with threshold 2 is considered. It is
defined by a set of conditions typical for basic threshold-type models, such as
the leaky integrate-and-fire (LIF) or the binding neuron model and also for
some artificial neurons. A neuron is stimulated with a Poisson stream of
excitatory impulses. Each output impulse is conveyed through the feedback line
to the neuron input after finite delay $\Delta$. This impulse is identical to
those delivered from the input stream. We have obtained a general relation
allowing calculating exactly the probability density function (PDF) $p(t)$ for
distribution of the first passage time of crossing the threshold, which is the
distribution of output interspike intervals (ISI) values for this neuron. The
calculation is based on known PDF $p^0(t)$ for that same neuron without
feedback, intensity of the input stream $\lambda$ and properties of the
feedback line. Also, we derive exact relation for calculating the moments of
$p(t)$ based on known moments of $p^0(t)$.
  The obtained general expression for $p(t)$ is checked numerically using Monte
Carlo simulation for the case of LIF model. The course of $p(t)$ has a
$\delta$-function type peculiarity. This fact contributes to the discussion
about the possibility to model neuronal activity with Poisson process,
supporting the "no" answer.
Health Care Expenditures, Financial Stability, and Participation in the Supplemental Nutrition Assistance Program (SNAP)
Date: 2018-11-13 00:00:00
Tasks: Nutrition

This paper examines the association between household healthcare expenses and
participation in the Supplemental Nutrition Assistance Program (SNAP) when
moderated by factors associated with financial stability of households. Using a
large longitudinal panel encompassing eight years, this study finds that an
inter-temporal increase in out-of-pocket medical expenses increased the
likelihood of household SNAP participation in the current period. Financially
stable households with precautionary financial assets to cover at least 6
months worth of household expenses were significantly less likely to
participate in SNAP. The low income households who recently experienced an
increase in out of pocket medical expenses but had adequate precautionary
savings were less likely than similar households who did not have precautionary
savings to participate in SNAP. Implications for economists, policy makers, and
household finance professionals are discussed.
Linking de novo assembly results with long DNA reads by dnaasm-link application
Date: 2018-11-13 00:00:00

Currently, third-generation sequencing techniques, which allow to obtain much
longer DNA reads compared to the next-generation sequencing technologies, are
becoming more and more popular. There are many possibilities to combine data
from next-generation and third-generation sequencing.
  Herein, we present a new application called dnaasm-link for linking contigs,
a result of \textit{de novo} assembly of second-generation sequencing data,
with long DNA reads. Our tool includes an integrated module to fill gaps with a
suitable fragment of appropriate long DNA read, which improves the consistency
of the resulting DNA sequences. This feature is very important, in particular
for complex DNA regions, as presented in the paper. Finally, our implementation
outperforms other state-of-the-art tools in terms of speed and memory
requirements, which may enable the usage of the presented application for
organisms with a large genome, which is not possible in~existing applications.
  The presented application has many advantages as (i) significant memory
optimization and reduction of computation time (ii) filling the gaps through
the appropriate fragment of a specified long DNA read (iii) reducing number of
spanned and unspanned gaps in the existing genome drafts.
  The application is freely available to all users under GNU Library or Lesser
General Public License version 3.0 (LGPLv3). The demo application, docker image
and source code are available at http://dnaasm.sourceforge.net.
Wavelet Based Dictionaries for Piecewise Dimensionality Reduction of ECG Signals
Date: 2019-06-10 00:00:00
Tasks: Dimensionality Reduction

Dimensionality reduction of ECG signals is considered within the framework of
sparse representation. The approach constructs the signal model by selecting
elementary components from a redundant dictionary via a greedy strategy. The
proposed wavelet dictionaries are built from the multiresolution scheme, but
translating the prototypes within a shorter step than that corresponding to the
wavelet basis. The reduced representation of the signal is shown to be suitable
for compression at low level distortion. In that regard, compression results
are superior to previously reported benchmarks on the MIT-BIH Arrhythmia data
set.
Cross-Sectional Variation of Intraday Liquidity, Cross-Impact, and their Effect on Portfolio Execution
Date: 2018-11-13 00:00:00

The composition of natural liquidity has been changing over time. An analysis
of intraday volumes for the S&P500 constituent stocks illustrates that (i)
volume surprises, i.e., deviations from their respective forecasts, are
correlated across stocks, and (ii) this correlation increases during the last
few hours of the trading session. These observations could be attributed, in
part, to the prevalence of portfolio trading activity that is implicit in the
growth of ETF, passive and systematic investment strategies; and, to the
increased trading intensity of such strategies towards the end of the trading
session, e.g., due to execution of mutual fund inflows/outflows that are
benchmarked to the closing price on each day. In this paper, we investigate the
consequences of such portfolio liquidity on price impact and portfolio
execution. We derive a linear cross-asset market impact from a stylized model
that explicitly captures the fact that a certain fraction of natural liquidity
providers only trade portfolios of stocks whenever they choose to execute. We
find that due to cross-impact and its intraday variation, it is optimal for a
risk-neutral, cost minimizing liquidator to execute a portfolio of orders in a
coupled manner, as opposed to a separable VWAP-like execution that is often
assumed. The optimal schedule couples the execution of the various orders so as
to be able to take advantage of increased portfolio liquidity towards the end
of the day. A worst case analysis shows that the potential cost reduction from
this optimized execution schedule over the separable approach can be as high as
6% for plausible model parameters. Finally, we discuss how to estimate
cross-sectional price impact if one had a dataset of realized portfolio
transaction records that exploits the low-rank structure of its coefficient
matrix suggested by our analysis.
Identification and estimation of multinomial choice models with latent special covariates
Authors: Nail Kashaev
Date: 2018-11-13 00:00:00

Identification of multinomial choice models is often established by using special covariates that have full support. This paper shows how these identification results can be extended to a large class of multinomial choice models when all covariates are bounded. I also provide a new $\sqrt{n}$-consistent asymptotically normal estimator of the finite-dimensional parameters of the model.
Estimation of High-Dimensional Seemingly Unrelated Regression Models
Date: 2018-11-13 00:00:00
Tasks: regression, Vocal Bursts Intensity Prediction

In this paper, we investigate seemingly unrelated regression (SUR) models
that allow the number of equations (N) to be large, and to be comparable to the
number of the observations in each equation (T). It is well known in the
literature that the conventional SUR estimator, for example, the generalized
least squares (GLS) estimator of Zellner (1962) does not perform well. As the
main contribution of the paper, we propose a new feasible GLS estimator called
the feasible graphical lasso (FGLasso) estimator. For a feasible implementation
of the GLS estimator, we use the graphical lasso estimation of the precision
matrix (the inverse of the covariance matrix of the equation system errors)
assuming that the underlying unknown precision matrix is sparse. We derive
asymptotic theories of the new estimator and investigate its finite sample
properties via Monte-Carlo simulations.