pwc-archive/papers-with-abstracts huggingface dataset:
Cellular function given parametric variation: excitability in the Hodgkin-Huxley model
Date: 2018-05-19 00:00:00

How is reliable physiological function maintained in cells despite
considerable variability in the values of key parameters of multiple
interacting processes that govern that function? Here we use the classic
Hodgkin-Huxley formulation of the squid giant axon action potential to propose
a possible approach to this problem. Although the full Hodgkin-Huxley model is
very sensitive to fluctuations that independently occur in its many parameters,
the outcome is in fact determined by simple combinations of these parameters
along two physiological dimensions: Structural and Kinetic (denoted $S$ and
$K$). Structural parameters describe the properties of the cell, including its
capacitance and the densities of its ion channels. Kinetic parameters are those
that describe the opening and closing of the voltage-dependent conductances.
The impacts of parametric fluctuations on the dynamics of the system, seemingly
complex in the high dimensional representation of the Hodgkin-Huxley model, are
tractable when examined within the $S-K$ plane. We demonstrate that slow
inactivation, a ubiquitous activity-dependent feature of ionic channels, is a
powerful local homeostatic control mechanism that stabilizes excitability amid
changes in structural and kinetic parameters.
Bitcoin price and its marginal cost of production: support for a fundamental value
Date: 2018-05-19 00:00:00
Tasks: regression

This study back-tests a marginal cost of production model proposed to value
the digital currency bitcoin. Results from both conventional regression and
vector autoregression (VAR) models show that the marginal cost of production
plays an important role in explaining bitcoin prices, challenging recent
allegations that bitcoins are essentially worthless. Even with markets pricing
bitcoin in the thousands of dollars each, the valuation model seems robust. The
data show that a price bubble that began in the Fall of 2017 resolved itself in
early 2018, converging with the marginal cost model. This suggests that while
bubbles may appear in the bitcoin market, prices will tend to this bound and
not collapse to zero.
Predictive Estimation of the Optimal Signal Strength from Unmanned Aerial Vehicle over Internet of Things Using ANN
Date: 2018-05-19 00:00:00

This paper proposes an intelligent technique for maximizing the network
connectivity and provisioning desired quality of service (QoS) of integration
of internet of things (IoT) and unmanned aerial vehicle (UAV). Prediction of
the signal strength and fading channel conditions enable adaptive data
transmission which turn enhances the QoS for the end users/ devices with
reducing the power consumption for data transmissions. UAV is data gathering
robot from the difficult or impossible area for humans to reach. Hence,
Atmospheric dynamics and environment influence the signal strength during
traveling in space among UAV, IoT devices, and humankind. Therefore, Signal
moving from the smart UAV is sensitive to the effects of attenuation,
reflection, diffraction, scattering, and shadowing. We analysis the ability ANN
to predictively estimate the signal strength and channel propagation from the
drone and physical medium parameters, using ANN. Moreover, the results show
that the distortion of the signal can be reduced and enhanced significantly.
Cell population heterogeneity driven by stochastic partition and growth optimality
Date: 2019-06-19 00:00:00

A fundamental question in biology is how cell populations evolve into
different subtypes based on homogeneous processes at the single cell level.
Here we show that population bimodality can emerge even when biological
processes are homogenous at the cell level and the environment is kept
constant. Our model is based on the stochastic partitioning of a cell component
with an optimal copy number. We show that the existence of unimodal or bimodal
distributions depends on the variance of partition errors and the growth rate
tolerance around the optimal copy number. In particular, our theory provides a
consistent explanation for the maintenance of aneuploid states in a population.
The proposed model can also be relevant for other cell components such as
mitochondria and plasmids, whose abundances affect the growth rate and are
subject to stochastic partition at cell division.
Poster: Resource Allocation with Conflict Resolution for Vehicular Sidelink Broadcast Communications
Date: 2018-05-18 00:00:00

In this paper we present a graph-based resource allocation scheme for
sidelink broadcast V2V communications. Harnessing available information on
geographical position of vehicles and spectrum resources utilization, eNodeBs
are capable of allotting the same set of sidelink resources to different
vehicles distributed among several communications clusters. Within a
communications cluster, it is crucial to prevent time-domain allocation
conflicts since vehicles cannot transmit and receive simultaneously, i.e., they
must transmit in orthogonal time resources. In this research, we present a
solution based on a bipartite graph, where vehicles and spectrum resources are
represented by vertices whereas the edges represent the achievable rate in each
resource based on the SINR that each vehicle perceives. The aforementioned time
orthogonality constraint can be approached by aggregating conflicting vertices
into macro-vertices which, in addition, reduces the search complexity. We show
mathematically and through simulations that the proposed approach yields an
optimal solution. In addition, we provide simulations showing that the proposed
method outperforms other competing approaches, specially in scenarios with high
vehicular density.
Neural computation of surface border ownership and relative surface depth from ambiguous contrast inputs
Date: 2018-05-21 00:00:00

The segregation of image parts into foreground and background is an important
aspect of the neural computation of 3D scene perception. To achieve such
segregation, the brain needs information about border ownership; that is, the
belongingness of a contour to a specific surface represented in the image. This
article presents psychophysical data derived from 3D percepts of figure and
ground that were generated by presenting 2D images composed of spatially
disjoint shapes that pointed inward or outward relative to the continuous
boundaries that they induced along their collinear edges. The shapes in some
images had the same contrast (black or white) with respect to the background
gray. Other images included opposite contrasts along each induced continuous
boundary. Psychophysical results show that figure vs ground judgment
probabilities in response to these ambiguous displays are determined by the
orientation of contrasts only, not by their relative contrasts, despite the
fact that many border ownership cells in cortical area V2 respond to a
preferred relative contrast. The FACADE and 3D LAMINART models are used to
explain these data.
Impulsive Noise Immunity of Multidimensional Pulse Position Modulation
Date: 2018-05-21 00:00:00
Tasks: Decoder, Position

We describe block oriented multidimensional pulse position modulation and its
resilience against impulsive noise. The modulation implements the encoder and
part of the decoder of the BBC algorithm. We tested the modulation on circuits
that send and detect a pulse based signal in the presence of impulsive noise.
We measured the packet error rate vs. signal to noise ratio and we compared it
with published error rates for OFDM. We found an error rate of 2 x 10^(-5) at a
signal to noise ratio of 16 dB without forward error correction and a data rate
of 64 kbit /sec.
A reply to 'Ranging Behavior Drives Parasite Richness: A More Parsimonious Hypothesis'
Date: 2018-06-14 00:00:00

This preprint has been reviewed and recommended by Peer Community In Ecology
(https://dx.doi.org/10.24072/pci.ecology.100001). In a recent article,
Bicca-Marques and Calegaro-Marques [2016] discussed the putative assumptions
related to an interpretation we provided regarding an observed positive
relationship between weekly averaged parasite richness of a group of mandrills
(Mandrillus sphinx) and their daily path lengths (DPL), published earlier in
the same journal (Brockmeyer et al., 2015). In our article, we proposed, inter
alia, that "the daily travels of mandrills could be seen as a way to escape
contaminated habitats on a local scale". In their article, Bicca-Marques and
Calegaro-Marques [2016] proposed an alternative mechanism that they considered
to be more parsimonious. In their view, increased DPL also increases exposure
to novel parasites from the environment. In other words, while we proposed that
elevated DPL may be a consequence of elevated parasite richness, they viewed it
as a cause. We are happy to see that our study attracted so much interest that
it evoked a public comment. We are also grateful to Bicca-Marques and
Calegaro-Marques [2016] for pointing out an obvious alternative scenario that
we failed to discuss and for laying out several key factors and assumptions
that should be addressed by future studies examining the links between parasite
risk and group ranging. We use this opportunity to advance this discourse by
responding to some of the criticisms raised in their discussion of our article.
In this reply, we briefly contextualize the main object of criticism. We then
discuss the putative parsimony of the two competing scenarios.
On Distributed Nonlinear Signal Analytics : Bandwidth and Approximation Error Tradeoffs
Date: 2018-05-22 00:00:00

Analytics will be a part of the upcoming smart city and Internet of Things
(IoT). The focus of this work is approximate distributed signal analytics. It
is envisaged that distributed IoT devices will record signals, which may be of
interest to the IoT cloud. Communication of these signals from IoT devices to
the IoT cloud will require (lowpass) approximations. Linear signal
approximations are well known in the literature. It will be outlined that in
many IoT analytics problems, it is desirable that the approximated signals (or
their analytics) should always over-predict the exact signals (or their
analytics). This distributed nonlinear approximation problem has not been
studied before. An algorithm to perform distributed over-predictive signal
analytics in the IoT cloud, based on signal approximations by IoT devices, is
proposed. The fundamental tradeoff between the signal approximation bandwidth
used by IoT devices and the approximation error in signal analytics at the IoT
cloud is quantified for the class of differentiable signals. Simulation results
are also presented.
Globally Optimized Mutual Influence Aware Ranking in E-Commerce Search
Authors: Zhuang Tao, Ou Wenwu, Wang Zhirong
Date: 2018-05-22 00:00:00
Tasks: global-optimization

In web search, mutual influences between documents have been studied from the
perspective of search result diversification. But the methods in web search is
not directly applicable to e-commerce search because of their differences. And
little research has been done on the mutual influences between items in
e-commerce search. We propose a global optimization framework for mutual
influence aware ranking in e-commerce search. Our framework directly optimizes
the Gross Merchandise Volume (GMV) for ranking, and decomposes ranking into two
tasks. The first task is mutual influence aware purchase probability
estimation. We propose a global feature extension method to incorporate mutual
influences into the features of an item. We also use Recurrent Neural Network
(RNN) to capture influences related to ranking orders in purchase probability
estimation. The second task is to find the best ranking order based on the
purchase probability estimations. We treat the second task as a sequence
generation problem and solved it using the beam search algorithm. We performed
online A/B test on a large e-commerce search engine. The results show that our
method brings a 5% increase in GMV for the search engine over a strong
baseline.
Simulation of Large Scale Neural Networks for Evaluation Applications
Date: 2018-05-20 00:00:00
Tasks: Benchmarking

Understanding the complexity of biological neural networks like the human
brain is one of the scientific challenges of our century. The organization of
the brain can be described at different levels, ranging from small neural
networks to entire brain regions. Existing methods for the description of
functionally or effective connectivity are based on the analysis of relations
between the activities of different neural units by detecting correlations or
information flow. This is a crucial step in understanding neural disorders like
Alzheimers disease and their causative factors. To evaluate these estimation
methods, it is necessary to refer to a neural network with known connectivity,
which is typically unknown for natural biological neural networks. Therefore,
network simulations, also in silico, are available. In this work, the in silico
simulation of large scale neural networks is established and the influence of
different topologies on the generated patterns of neuronal signals is
investigated. The goal is to develop standard evaluation methods for
neurocomputational algorithms with a realistic large scale model to enable
benchmarking and comparability of different studies.
CARE-rCortex: a Matlab toolbox for the analysis of CArdio-REspiratory-related activity in the Cortex
Date: 2018-10-12 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Background: Although cardio-respiratory (CR) system is generally controlled
by the autonomic nervous system, interactions between the cortex and these
primary functions are receiving an increasing interest in neurosciences. New
method: In general, the timing of such internally paced events (e.g. heartbeats
or respiratory cycles) may display a large variability. For the analysis of
such CR event-related EEG potentials, a baseline must be correctly associated
to each cycle of detected events. The open-source toolbox CARE-rCortex provides
an easy-to-use interface to detect CR events, define baselines, and analyse in
time-frequency (TF) domain the CR-based EEG potentials. Results: CARE-rCortex
provides some practical tools to detect and validate these CR events. Users can
define baselines time-locked to a phase of respiratory or heart cycle. A
statistical test has also been integrated to highlight significant points of
the TF maps with respect to the baseline. We illustrate the use of CARE-rCortex
with the analysis of two real cardio-respiratory datasets. Comparison with
existing methods: Compared to other open-source toolboxes, CARE-rCortex allows
users to automatically detect CR events, to define and check baselines for each
detected event. Different baseline normalizations can be used in the TF
analysis of EEG epochs. Conclusions: The analysis of CR-related EEG activities
could provide valuable information about cognitive or pathological brain
states. CARE-rCortex runs in Matlab as a plug-in of the EEGLAB software, and it
is publicly available at https://github.com/FannyGrosselin/CARE-rCortex.
Early Cancer Detection in Blood Vessels Using Mobile Nanosensors
Date: 2018-05-22 00:00:00
Tasks: Anomaly Detection

In this paper, we propose using mobile nanosensors (MNSs) for early stage
anomaly detection. For concreteness, we focus on the detection of cancer cells
located in a particular region of a blood vessel. These cancer cells produce
and emit special molecules, so-called biomarkers, which are symptomatic for the
presence of anomaly, into the cardiovascular system. Detection of cancer
biomarkers with conventional blood tests is difficult in the early stages of a
cancer due to the very low concentration of the biomarkers in the samples
taken. However, close to the cancer cells, the concentration of the cancer
biomarkers is high. Hence, detection is possible if a sensor with the ability
to detect these biomarkers is placed in the vicinity of the cancer cells.
Therefore, in this paper, we study the use of MNSs that are injected at a
suitable injection site and can move through the blood vessels of the
cardiovascular system, which potentially contain cancer cells. These MNSs can
be activated by the biomarkers close to the cancer cells, where the biomarker
concentration is sufficiently high. Eventually, the MNSs are collected by a
fusion center (FC) where their activation levels are read and exploited to
declare the presence of anomaly. We analytically derive the biomarker
concentration as well as the probability mass function of the MNSs' activation
levels and validate the obtained results via particle-based simulations. Then,
we derive the optimal decision rule for the FC regarding the presence of
anomaly assuming that the entire network is known at the FC. Finally, for the
FC, we propose a simple sum detector that does not require knowledge of the
network topology. Our simulations reveal that while the optimal detector
achieves a higher performance than the sum detector, both proposed detectors
significantly outperform a benchmark scheme that used fixed nanosensors at the
FC.
Sensitivity of Regular Estimators
Date: 2018-05-22 00:00:00
Tasks: counterfactual, Sensitivity

This paper studies local asymptotic relationship between two scalar
estimates. We define sensitivity of a target estimate to a control estimate to
be the directional derivative of the target functional with respect to the
gradient direction of the control functional. Sensitivity according to the
information metric on the model manifold is the asymptotic covariance of
regular efficient estimators. Sensitivity according to a general policy metric
on the model manifold can be obtained from influence functions of regular
efficient estimators. Policy sensitivity has a local counterfactual
interpretation, where the ceteris paribus change to a counterfactual
distribution is specified by the combination of a control parameter and a
Riemannian metric on the model manifold.
Model Selection in Time Series Analysis: Using Information Criteria as an Alternative to Hypothesis Testing
Date: 2018-05-23 00:00:00
Tasks: Model Selection, Relation, Time Series, Time Series Analysis

The issue of model selection in applied research is of vital importance.
Since the true model in such research is not known, which model should be used
from among various potential ones is an empirical question. There might exist
several competitive models. A typical approach to dealing with this is classic
hypothesis testing using an arbitrarily chosen significance level based on the
underlying assumption that a true null hypothesis exists. In this paper we
investigate how successful this approach is in determining the correct model
for different data generating processes using time series data. An alternative
approach based on more formal model selection techniques using an information
criterion or cross-validation is suggested and evaluated in the time series
environment via Monte Carlo experiments. This paper also explores the
effectiveness of deciding what type of general relation exists between two
variables (e.g. relation in levels or relation in first differences) using
various strategies based on hypothesis testing and on information criteria with
the presence or absence of unit roots.
Concentration of dynamic risk measures in a Brownian filtration
Date: 2018-05-23 00:00:00

Motivated by liquidity risk in mathematical finance, D. Lacker introduced
concentration inequalities for risk measures, i.e. upper bounds on the
\emph{liquidity risk profile} of a financial loss. We derive these inequalities
in the case of time-consistent dynamic risk measures when the filtration is
assumed to carry a Brownian motion. The theory of backward stochastic
differential equations (BSDEs) and their dual formulation plays a crucial role
in our analysis. Natural by-products of concentration of risk measures are a
description of the tail behavior of the financial loss and transport-type
inequalities in terms of the generator of the BSDE, which in the present case
can grow arbitrarily fast.
A method for partitioning the information contained in a protein sequence between its structure and function
Date: 2018-05-23 00:00:00
Tasks: Protein Folding

Proteins employ the information stored in the genetic code and translated
into their sequences to carry out well-defined functions in the cellular
environment. The possibility to encode for such functions is controlled by the
balance between the amount of information supplied by the sequence and that
left after that the protein has folded into its structure. We developed a
computational algorithm to evaluate the amount of information necessary to
specify the protein structure, keeping into account the thermodynamic
properties of protein folding. We thus show that the information remaining in
the protein sequence after encoding for its structure (the 'information gap')
is very close to what needed to encode for its function and interactions. Then,
by predicting the information gap directly from the protein sequence, we show
that it may be possible to use these insights from information theory to
discriminate between ordered and disordered proteins, to identify unknown
functions, and to optimize designed proteins sequences.
Maximum-entropy and representative samples of neuronal activity: a dilemma
Date: 2020-10-19 00:00:00

The present work shows that the maximum-entropy method can be applied to a
sample of neuronal recordings along two different routes: (1) apply to the
sample; or (2) apply to a larger, unsampled neuronal population from which the
sample is drawn, and then marginalize to the sample. These two routes give
inequivalent results. The second route can be further generalized to the case
where the size of the larger population is unknown. Which route should be
chosen? Some arguments are presented in favour of the second. This work also
presents and discusses probability formulae that relate states of knowledge
about a population and its samples, and that may be useful for sampling
problems in neuroscience.
The Role of Multiplex Network Structure in Cooperation through Generalized Reciprocity
Date: 2019-06-13 00:00:00

Recent studies suggest that the emergence of cooperative behavior can be
explained by generalized reciprocity, a behavioral mechanism based on the
principle of "help anyone if helped by someone". In complex systems, the
cooperative dynamics is largely determined by the network structure which
dictates the interactions among neighboring individuals. These interactions
often exhibit multidimensional features, either as relationships of different
types or temporal dynamics, both of which may be modeled as a "multiplex"
network. Against this background, here we advance the research on cooperation
models inspired by generalized reciprocity by considering a multidimensional
networked society. Our results reveal that a multiplex network structure may
enhance the role of generalized reciprocity in promoting cooperation, whereby
some of the network dimensions act as a latent support for the others. As a
result, generalized reciprocity forces the cooperative contributions of the
individuals to concentrate in the dimension which is most favorable for the
existence of cooperation.