pwc-archive/papers-with-abstracts huggingface dataset:
AligNet: Alignment of Protein-Protein Interaction Networks
Date: 2019-02-19 00:00:00

One of the most difficult problems difficult problem in systems biology is to
discover protein-protein interactions as well as their associated functions.
The analysis and alignment of protein-protein interaction networks (PPIN),
which are the standard model to describe protein-protein interactions, has
become a key ingredient to obtain functional orthologs as well as evolutionary
conserved pathways and protein complexes. Several methods have been proposed to
solve the PPIN alignment problem, aimed to match conserved subnetworks or
functionally related proteins. However, the right balance between considering
network topology and biological information is one of the most difficult and
key points in any PPIN alignment algorithm which, unfortunately, remains
unsolved. Therefore, in this work, we propose AligNet, a new method and
software tool for the pairwise global alignment of PPIN that produces
biologically meaningful alignments and more efficient computations than
state-of-the-art methods and tools, by achieving a good balance between
structural matching and protein function conservation as well as reasonable
running times.
The Essential Role of Thermodynamics in metabolic network modeling: physical insights and computational challenges
Date: 2019-02-19 00:00:00

Quantitative studies of cell metabolism are often based on large chemical
reaction network models. A steady state approach is suited to analyze phenomena
on the timescale of cell growth and circumvents the problem of incomplete
experimental knowledge on kinetic laws and parameters, but it shall be
supported by a correct implementation of thermodynamic constraints. In this
article we review the latter aspect highlighting its computational challenges
and physical insights. The simple introduction of Gibbs inequalities avoids the
presence of unfeasible loops allowing for correct timescale analysis but leads
to possibly non-convex feasible flux spaces, whose exploration needs efficient
algorithms. We shorty review on the implementation of thermodynamics through
variational principles in constraints based models of metabolic networks.
Identifying Fake News from Twitter Sharing Data: A Large-Scale Study
Authors: Agrawal Rakshit, de Alfaro Luca, Ballarin Gabriele, Moret Stefano, Di Pierro Massimo, Tacchini Eugenio, Della Vedova Marco L.
Date: 2019-02-10 00:00:00

Social networks offer a ready channel for fake and misleading news to spread
and exert influence. This paper examines the performance of different
reputation algorithms when applied to a large and statistically significant
portion of the news that are spread via Twitter. Our main result is that simple
crowdsourcing-based algorithms are able to identify a large portion of fake or
misleading news, while incurring only very low false positive rates for
mainstream websites. We believe that these algorithms can be used as the basis
of practical, large-scale systems for indicating to consumers which news sites
deserve careful scrutiny and skepticism.
Real-time PCG Anomaly Detection by Adaptive 1D Convolutional Neural Networks
Date: 2019-02-19 00:00:00
Tasks: Anomaly Detection, Diagnostic

The heart sound signals (Phonocardiogram - PCG) enable the earliest
monitoring to detect a potential cardiovascular pathology and have recently
become a crucial tool as a diagnostic test in outpatient monitoring to assess
heart hemodynamic status. The need for an automated and accurate anomaly
detection method for PCG has thus become imminent. To determine the
state-of-the-art PCG classification algorithm, 48 international teams competed
in the PhysioNet (CinC) Challenge at 2016 over the largest benchmark dataset
with 3126 records with the classification outputs, normal (N), abnormal (A) and
unsure - too noisy (U). In this study, our aim is to push this frontier
further; however, we focus deliberately on the anomaly detection problem while
assuming a reasonably high Signal-to-Noise Ratio (SNR) on the records. By using
1D Convolutional Neural Networks trained with a novel data purification
approach, we aim to achieve the highest detection performance and a real-time
processing ability with significantly lower delay and computational complexity.
The experimental results over the high-quality subset of the same benchmark
dataset shows that the proposed approach achieves both objectives. Furthermore,
our findings reveal the fact that further improvements indeed require a
personalized (patient-specific) approach to avoid major drawbacks of a global
PCG classification approach.
Recombination and mutational robustness in neutral fitness landscapes
Date: 2019-10-21 00:00:00

Mutational robustness quantifies the effect of random mutations on fitness.
When mutational robustness is high, most mutations do not change fitness or
have only a minor effect on it. From the point of view of fitness landscapes,
robust genotypes form neutral networks of almost equal fitness. Using
deterministic population models it has been shown that selection favors
genotypes inside such networks, which results in increased mutational
robustness. Here we demonstrate that this effect is massively enhanced by
recombination. Our results are based on a detailed analysis of mesa-shaped
fitness landscapes, where we derive precise expressions for the dependence of
the robustness on the landscape parameters for recombining and non-recombining
populations. In addition, we carry out numerical simulations on different types
of random holey landscapes as well as on an empirical fitness landscape. We
show that the mutational robustness of a genotype generally correlates with its
recombination weight, a new measure that quantifies the likelihood for the
genotype to arise from recombination. We argue that the favorable effect of
recombination on mutational robustness is a highly universal feature that may
have played an important role in the emergence and maintenance of mechanisms of
genetic exchange.
Self-learning photonic signal processor with an optical neural network chip
Date: 2019-02-18 00:00:00
Tasks: Self-Learning

Photonic signal processing is essential in the optical communication and
optical computing. Numerous photonic signal processors have been proposed, but
most of them exhibit limited reconfigurability and automaticity. A feature of
fully automatic implementation and intelligent response is highly desirable for
the multipurpose photonic signal processors. Here, we report and experimentally
demonstrate a fully self-learning and reconfigurable photonic signal processor
based on an optical neural network chip. The proposed photonic signal processor
is capable of performing various functions including multichannel optical
switching, optical multiple-input-multiple-output descrambler and tunable
optical filter. All the functions are achieved by complete self-learning. Our
demonstration suggests great potential for chip-scale fully programmable
optical signal processing with artificial intelligence.
Neural Video Compression using Spatio-Temporal Priors
Date: 2019-02-21 00:00:00
Tasks: MS-SSIM, Optical Flow Estimation, SSIM, Video Compression

The pursuit of higher compression efficiency continuously drives the advances
of video coding technologies. Fundamentally, we wish to find better
"predictions" or "priors" that are reconstructed previously to remove the
signal dependency efficiently and to accurately model the signal distribution
for entropy coding. In this work, we propose a neural video compression
framework, leveraging the spatial and temporal priors, independently and
jointly to exploit the correlations in intra texture, optical flow based
temporal motion and residuals. Spatial priors are generated using downscaled
low-resolution features, while temporal priors (from previous reference frames
and residuals) are captured using a convolutional neural network based
long-short term memory (ConvLSTM) structure in a temporal recurrent fashion.
All of these parts are connected and trained jointly towards the optimal
rate-distortion performance. Compared with the High-Efficiency Video Coding
(HEVC) Main Profile (MP), our method has demonstrated averaged 38%
Bjontegaard-Delta Rate (BD-Rate) improvement using standard common test
sequences, where the distortion is multi-scale structural similarity (MS-SSIM).
In Vivo Wireless Sensors for Gut Microbiome Redox Monitoring
Date: 2019-07-28 00:00:00
Tasks: Translation

A perturbed gut microbiome has recently been linked with multiple disease
processes, yet researchers currently lack tools that can provide in vivo,
quantitative, and real-time insight into these processes and associated
host-microbe interactions. We propose an in vivo wireless implant for
monitoring gastrointestinal tract redox states using oxidation-reduction
potentials (ORP). The implant is powered and conveniently interrogated via
ultrasonic waves. We engineer the sensor electronics, electrodes, and
encapsulation materials for robustness in vivo, and integrate them into an
implant that endures autoclave sterilization and measures ORP for 12 days
implanted in the cecum of a live rat. The presented implant platform paves the
way for long-term experimental testing of biological hypotheses, offering new
opportunities for understanding gut redox pathophysiology mechanisms, and
facilitating translation to disease diagnosis and treatment applications.
Eliciting ambiguity with mixing bets
Authors: Patrick Schmidt
Date: 2019-02-20 00:00:00
Tasks: Decision Making

Preferences for mixing can reveal ambiguity perception and attitude on a single event. The validity of the approach is discussed for multiple preference classes including maxmin, maxmax, variational, and smooth second-order preferences. An experimental implementation suggests that participants perceive almost as much ambiguity for the stock index and actions of other participants as for the Ellsberg urn, indicating the importance of ambiguity in real-world decision-making.
Divestment may burst the carbon bubble if investors' beliefs tip to anticipating strong future climate policy
Date: 2019-02-20 00:00:00

To achieve the ambitious aims of the Paris climate agreement, the majority of
fossil-fuel reserves needs to remain underground. As current national
government commitments to mitigate greenhouse gas emissions are insufficient by
far, actors such as institutional and private investors and the social movement
on divestment from fossil fuels could play an important role in putting
pressure on national governments on the road to decarbonization. Using a
stochastic agent-based model of co-evolving financial market and investors'
beliefs about future climate policy on an adaptive social network, here we find
that the dynamics of divestment from fossil fuels shows potential for social
tipping away from a fossil-fuel based economy. Our results further suggest that
socially responsible investors have leverage: a small share of 10--20\,\% of
such moral investors is sufficient to initiate the burst of the carbon bubble,
consistent with the Pareto Principle. These findings demonstrate that
divestment has potential for contributing to decarbonization alongside other
social movements and policy instruments, particularly given the credible
imminence of strong international climate policy. Our analysis also indicates
the possible existence of a carbon bubble with potentially destabilizing
effects to the economy.
Robustness of a Biomolecular Oscillator to Pulse Perturbations
Authors: Soumyadip Banerjee, Shaunak Sen
Date: 2019-02-20 00:00:00

Biomolecular oscillators can function robustly in the presence of environmental perturbations, which can either be static or dynamic. While the effect of different circuit parameters and mechanisms on the robustness to steady perturbations has been investigated, the scenario for dynamic perturbations is relatively unclear. To address this we use a benchmark three protein oscillator design - the repressilator - and investigate its robustness to pulse perturbations, computationally as well as using analytical tools of Floquet theory. We find that the metric provided by direct computations of the time it takes for the oscillator to settle after a pulse perturbation is applied, correlates well with the metric provided by Floquet theory. We investigate the parametric dependence of the Floquet metric, finding that the parameters that increase the effective delay enhance robustness to pulse perturbation. We find that the structural changes such as increasing the number of proteins in a ring oscillator as well as adding positive feedback, both of which increase effective delay, facilitates such robustness. These results highlight such design principles, especially the role of delay, for designing an oscillator that is robust to pulse perturbation.
Cell pelotons: a model of early evolutionary cell sorting, with application to slime mold D. discoideum
Date: 2019-02-20 00:00:00

A theoretical model is presented for early evolutionary cell sorting within
cellular aggregates. The model involves an energy-saving mechanism and
principles of collective self-organization analogous to those observed in
bicycle pelotons (groups of cyclists). The theoretical framework is applied to
slime-mold slugs (Dictyostelium discoideum) and incorporated into a computer
simulation which demonstrates principally the sorting of cells between the
anterior and posterior slug regions. The simulation relies on an existing
simulation of bicycle peloton dynamics which is modified to incorporate a
limited range of cell metabolic capacities among heterogeneous cells, along
with a tunable energy-expenditure parameter, referred to as an 'output-level'
or 'starvation-level' to reflect diminishing energetic supply, proto-cellular
dynamics are modelled for three output phases: 'active', 'suffering', and
'dying or dead.' Adjusting the starvation parameter causes cell differentiation
and sorting into sub-groups within the cellular aggregate. Tuning of the
starvation parameter demonstrates how weak or expired cells shuffle backward
within the cellular aggregate.
Dual-polarisation Non-linear Frequency-division Multiplexed Transmission with b-Modulation
Date: 2019-02-20 00:00:00

There has been much interest in the non-linear frequency-division
multiplexing (NFDM) transmission scheme in the optical fibre communication
system. Up to date, most of the demonstrated NFDM schemes have employed only
single polarisation for data transmission. Employing both polarisations can
potentially double the data rate of NFDM systems. We investigate in simulation
a dual-polarisation NFDM transmission with data modulation on the
$b$-coefficient. First, a transformation that facilitates the dual-polarisation
$b$-modulation was built upon an existing transformation. Second, the $q_c$-
and $b$-modulation for dual-polarisation were compared in terms of Q-factor,
spectral efficiency (SE), and correlation of sub-carriers. The correlation is
quantified via information theoretic metrics, joint and individual entropy. The
polarisation-multiplexed $b$-modulation system shows 1 dB Q-factor improvement
over $q_c$-modulation system due to a weaker correlation of sub-carriers and
less effective noise. Finally, the $b$-modulation system was optimised for high
data rate, achieving a record net data rate of 400 Gbps (SE of 7.2 bit/s/Hz)
over $12\times 80$ km of standard single-mode fibre (SSMF) with erbium-doped
fibre amplifiers (EDFAs). Based on the above simulation results, we further
point out the drawbacks of our current system and quantify the error introduced
by the transceiver algorithms and non-integrability of the channel.
Quantitative analysis of timing in animal vocal sequences
Date: 2019-02-20 00:00:00

Timing features such as the silence gaps between vocal units -- inter-call
intervals (ICIs) -- often correlate with biological information such as context
or genetic information. Such correlates between the ICIs and biological
information have been reported for a diversity of animals. Yet, few
quantitative approaches for investigating timing exist to date. Here, we
propose a novel approach for quantitatively comparing timing in animal
vocalisations in terms of the typical ICIs. As features, we use the
distribution of silence gaps parametrised with a kernel density estimate (KDE)
and compare the distributions with the symmetric Kullback-Leibler divergence
(sKL-divergence). We use this technique to compare timing in vocalisations of
two frog species, a group of zebra finches and calls from parrots of the same
species. As a main finding, we demonstrate that in our dataset, closely related
species have more similar distributions than species genetically more distant,
with sKL-divergences across-species larger than within-species distances.
Compared with more standard methods such as Fourier analysis, the proposed
method is more robust to different durations present in the data samples,
flexibly applicable to different species and easy to interpret. Investigating
timing in animal vocalisations may thus contribute to taxonomy, support
conservation efforts by helping monitoring animals in the wild and may shed
light onto the origins of timing structures in animal vocal communication.
Massive MIMO is a Reality -- What is Next? Five Promising Research Directions for Antenna Arrays
Date: 2019-06-12 00:00:00

Massive MIMO (multiple-input multiple-output) is no longer a "wild" or
"promising" concept for future cellular networks - in 2018 it became a reality.
Base stations (BSs) with 64 fully digital transceiver chains were commercially
deployed in several countries, the key ingredients of Massive MIMO have made it
into the 5G standard, the signal processing methods required to achieve
unprecedented spectral efficiency have been developed, and the limitation due
to pilot contamination has been resolved. Even the development of fully digital
Massive MIMO arrays for mmWave frequencies - once viewed prohibitively
complicated and costly - is well underway. In a few years, Massive MIMO with
fully digital transceivers will be a mainstream feature at both sub-6 GHz and
mmWave frequencies. In this paper, we explain how the first chapter of the
Massive MIMO research saga has come to an end, while the story has just begun.
The coming wide-scale deployment of BSs with massive antenna arrays opens the
door to a brand new world where spatial processing capabilities are
omnipresent. In addition to mobile broadband services, the antennas can be used
for other communication applications, such as low-power machine-type or
ultra-reliable communications, as well as non-communication applications such
as radar, sensing and positioning. We outline five new Massive MIMO related
research directions: Extremely large aperture arrays, Holographic Massive MIMO,
Six-dimensional positioning, Large-scale MIMO radar, and Intelligent Massive
MIMO.
Robust Ranking of Happiness Outcomes: A Median Regression Perspective
Authors: Le-Yu Chen, Ekaterina Oparina, Nattavudh Powdthavee, Sorawoot Srisuma
Date: 2019-02-20 00:00:00
Tasks: regression

Ordered probit and logit models have been frequently used to estimate the mean ranking of happiness outcomes (and other ordinal data) across groups. However, it has been recently highlighted that such ranking may not be identified in most happiness applications. We suggest researchers focus on median comparison instead of the mean. This is because the median rank can be identified even if the mean rank is not. Furthermore, median ranks in probit and logit models can be readily estimated using standard statistical softwares. The median ranking, as well as ranking for other quantiles, can also be estimated semiparametrically and we provide a new constrained mixed integer optimization procedure for implementation. We apply it to estimate a happiness equation using General Social Survey data of the US.
Biophysics at the coffee shop: lessons learned working with George Oster
Authors: Oleg Igoshin, Jing Chen, Jianhua Xing, Jian Liu, Timothy C. Elston, Michael Grabe, Kenneth S. Kim, Jasmine Nirody, Padmini Rangamani, Sean Sun, Hongyun Wang, Charles Wolgemuth
Date: 2019-02-20 00:00:00
Tasks: Descriptive, Physical Intuition

Over the past 50 years, the use of mathematical models, derived from physical reasoning, to describe molecular and cellular systems has evolved from an art of the few to a cornerstone of biological inquiry. George Oster stood out as a pioneer of this paradigm shift from descriptive to quantitative biology not only through his numerous research accomplishments, but also through the many students and postdocs he mentored over his long career. Those of us fortunate enough to have worked with George agree that his sharp intellect, physical intuition and passion for scientific inquiry not only inspired us as scientists but also greatly influenced the way we conduct research. We would like to share a few important lessons we learned from George in honor of his memory and with the hope that they may inspire future generations of scientists.
Stress-induced hyperalgesia instead of analgesia in patients with chronic musculoskeletal pain
Authors: Martin Löffler, Pia Schneider, Sigrid Schuh-Hofer, Sandra Kamping, Katrin Usai, Rolf-Detlef Treede, Frauke Nees, Herta Flor
Date: 2019-02-20 00:00:00

Many individuals with chronic musculoskeletal pain (CMP) show impairments in their pain-modulatory capacity. Although stress plays an important role in chronic pain, it is not known if stress-induced analgesia (SIA) is affected in patients with CMP. We investigated SIA in 22 patients with CMP and 18 pain-free participants. Pain thresholds, pain tolerance and suprathreshold pain ratings were examined before and after a cognitive stressor that typically induces pain reduction (SIA). Whereas the controls displayed a significant increase in pain threshold in response to the stressor, the patients with CMP showed no analgesia. In addition, increased pain intensity ratings after the stressor indicated hyperalgesia (SIH) in the patients with CMP compared to controls. An exploratory analysis showed no significant association of SIA or SIH with spatial pain extent. We did not observe significant changes in pain tolerance or pain unpleasantness ratings after the stressor in patients with CMP or controls. Our data suggest that altered stress-induced pain modulation is an important mechanism involved in CMP. Future studies need to clarify the psychobiological mechanisms of these stress-induced alterations in pain processing and determine the role of contributing factors such as early childhood trauma, catastrophizing, comorbidity with mental disorders and genetic predisposition.
First assessment of the plant phenology index (PPI) for estimating gross primary productivity in African semi-arid ecosystems
Date: 2019-02-21 00:00:00
Tasks: Earth Observation

The importance of semi-arid ecosystems in the global carbon cycle as sinks
for CO2 emissions has recently been highlighted. Africa is a carbon sink and
nearly half its area comprises arid and semi-arid ecosystems. However, there
are uncertainties regarding CO2 fluxes for semi-arid ecosystems in Africa,
particularly savannas and dry tropical woodlands. In order to improve on
existing remote-sensing based methods for estimating carbon uptake across
semi-arid Africa we applied and tested the recently developed plant phenology
index (PPI). We developed a PPI-based model estimating gross primary
productivity (GPP) that accounts for canopy water stress, and compared it
against three other Earth observation-based GPP models: the temperature and
greenness model, the greenness and radiation model and a light use efficiency
model. The models were evaluated against in situ data from four semi-arid sites
in Africa with varying tree canopy cover (3 to 65 percent). Evaluation results
from the four GPP models showed reasonable agreement with in situ GPP measured
from eddy covariance flux towers (EC GPP) based on coefficient of variation,
root-mean-square error, and Bayesian information criterion. The PPI-based GPP
model was able to capture the magnitude of EC GPP better than the other tested
models. The results of this study show that a PPI-based GPP model is a
promising tool for the estimation of GPP in the semi-arid ecosystems of Africa.