pwc-archive/papers-with-abstracts huggingface dataset:
Permutation, Multiscale and Modified Multiscale Entropies a Natural Complexity for Low-High Infection Level Intracellular Viral Reaction Kinetics
Date: 2017-03-28 00:00:00

Viral infectious diseases, such as HIV virus growth, cause an important
health concern. Study of intracellular viral processes can provide us to
develop drug and understanding the drug dose to decrease the HIV virus in
during growth. Kinetics Monte Carlo simulation has been done for solving Master
equation about dynamics of intracellular viral reaction kinetics. Scaling
relationship between equilibrium time and initial population of template has
been found as power low, , where N , are the number of initial population of
template species , equilibrium time, a = 163.1 , b = -0.1429 respectively.
Stochastic dynamics shows that increasing initial population of template
decreases the time of equilibrium. Entropy generation has been considered in
low, intermediate and high infection level of intracellular viral kinetics
reaction in during dynamical process. Permutation, multi scaling and modified
multiscaling entropies have been calculated for three kinds of species in
intracellular reaction dynamics, genome, structural protein, and template. Our
result shows that presence of noise in dynamical process of intracellular
reaction will change order of permutation entropy for the mentioned of three
species. In addition to multiscaling entropy is computed for mentioned model
and it has the following order: template > structural protein> genome.
Dependency of permutation entropy result to permutation order becomes small in
high infection level in intracellular viral kinetics dynamics. At short time
scale in intracellular reaction dynamics, convergency of permutation entropy
occurs with medium permutation order value. In the big time scale of
intracellular dynamics, permutation entropy scale with permutation order n as a
scaling relation .
Minimal Immunogenic Epitopes Have Nine Amino Acids
Date: 2016-07-22 00:00:00
Tasks: Descriptive

To be cost-effective, biomedical proteins must be optimized with regard to
many factors. Road maps are customary for large-scale projects, and here
descriptive methods based on bioinformatic fractal thermodynamic scales are
tested against an important example, HPV vaccine. Older scales from before 2000
are found to yield inconclusive results, but modern bioinformatic scales are
amazingly accurate, with a high level of internal consistency, and little
ambiguity.
Multicellular self-organization of P. aeruginosa due to interactions with secreted trails
Date: 2016-09-19 00:00:00

Guided movement in response to slowly diffusing polymeric trails provides a
unique mechanism for self-organization of some microorganisms. To elucidate how
this signaling route leads to microcolony formation, we experimentally probe
the trajectory and orientation of Pseudomonas aeruginosa that propel themselves
on a surface using type IV pili motility appendages, which preferentially
attach to deposited exopolysaccharides. We construct a stochastic model by
analyzing single-bacterium trajectories, and show that the resulting
theoretical prediction for the many-body behavior of the bacteria is in
quantitative agreement with our experimental characterization of how cells
explore the surface via a power law strategy.
Searching for the Internet of Things on the Web: Where It Is and What It Looks Like
Authors: Shemshadi Ali, Sheng Quan Z., Zhang Wei Emma, Sun Aixin, Qin Yongrui, Yao Lina
Date: 2016-07-23 00:00:00

The Internet of Things (IoT), in general, is a compelling paradigm that aims
to connect everyday objects to the Internet. Nowadays, IoT is considered as one
of the main technologies which contribute towards reshaping our daily lives in
the next decade. IoT unlocks many exciting new opportunities in a variety of
applications in research and industry domains. However, many have complained
about the absence of the real-world IoT data. Unsurprisingly, a common question
that arises regularly nowadays is "Does the IoT already exist?". So far, little
has been known about the real-world situation on IoT, its attributes, the
presentation of data and user interests. To answer this question, in this work,
we conduct an in-depth analytical investigation on real IoT data. More
specifically, we identify IoT data sources over the Web and develop a crawler
engine to collect large-scale real-world IoT data for the first time. We make
the results of our work available to the public in order to assist the
community in the future research. In particular, we collect the data of nearly
two million Internet connected objects and study trends in IoT using a
real-world query set from an IoT search engine. Based on the collected data and
our analysis, we identify the typical characteristics of IoT data. The most
intriguing finding of our study is that IoT data is mainly disseminated using
Web Mapping while the emerging IoT solutions such as the Web of Things, are
currently not well adopted. On top of our findings, we further discuss future
challenges and open research problems in the IoT area.
Neurophysiological Investigation of Context Modulation based on Musical Stimulus
Date: 2016-07-23 00:00:00

There are numerous studies which suggest that perhaps music is truly the
language of emotions. Music seems to have an almost willful, evasive quality,
defying simple explanation, and indeed requires deeper neurophysiological
investigations to gain a better understanding. The current study makes an
attempt in that direction to explore the effect of context on music perception.
To investigate the same, we measured Galvanic Skin Responses (GSR) and
self-reported emotion on 18 participants while listening to different Ragas
(musical stimulus) composed of different Rasa's (emotional expression) in the
different context (Neutral, Pleasant, and Unpleasant). The IAPS pictures were
used to induce the emotional context in participants. Our results from this
study suggest that the context can modulate emotional response in music
perception but only for a shorter time scale. Interestingly, here we
demonstrate by combining GSR and self-reports that this effect gradually
vanishes over time and shows emotional adaptation irrespective of context. The
overall findings suggest that specific context effects of music perception are
transitory in nature and gets saturated on a longer time scale.
Model-Independent Price Bounds for Catastrophic Mortality Bonds
Authors: Raj Kumari Bahl, Sotirios Sabanis
Date: 2016-07-24 00:00:00
Tasks: model

In this paper, we are concerned with the valuation of Catastrophic Mortality Bonds and, in particular, we examine the case of the Swiss Re Mortality Bond 2003 as a primary example of this class of assets. This bond was the first Catastrophic Mortality Bond to be launched in the market and encapsulates the behaviour of a well-defined mortality index to generate payoffs for bondholders. Pricing these type of bonds is a challenging task and no closed form solution exists in the literature. In our approach, we express the payoff of such a bond in terms of the payoff of an Asian put option and present a new approach to derive model-independent bounds exploiting comonotonic theory as illustrated in \cite{prime1}, \cite{2} and \cite{Simon} for the pricing of Asian options. We carry out Monte Carlo simulations to estimate the bond price and illustrate the quality of the bounds.
Emergence of Sparsely Synchronized Rhythms and Their Responses to External Stimuli in An Inhomogeneous Small-World Complex Neuronal Network
Date: 2016-08-16 00:00:00

We consider an inhomogeneous small-world network (SWN) composed of inhibitory
short-range (SR) and long-range (LR) interneurons. By varying the fraction of
LR interneurons $p_{long}$, we investigate the effect of network architecture
on emergence of sparsely synchronized rhythms, and make comparison with that in
the Watts-Strogatz SWN. Although SR and LR interneurons have the same average
in- and out-degrees, their betweenness centralities (characterizing the
potentiality in controlling communication between other interneurons) are
distinctly different. Hence, in view of the betweenness, SWNs we consider are
inhomogeneous, unlike the "canonical" Watts-Strogatz SWN with nearly same
betweenness centralities. As $p_{long}$ is increased, the average path length
becomes shorter, and the load of communication traffic is less concentrated on
LR interneurons, which leads to better efficiency of global communication
between interneurons. Eventually, when passing a critical value
$p_{long}^{(c)}$ $(\simeq 0.16)$, sparsely synchronized rhythms are found to
emerge. We also consider two cases of external time-periodic stimuli applied to
sub-groups of LR and SR interneurons, respectively. Dynamical responses (such
as synchronization suppression and enhancement) to these two cases of stimuli
are studied and discussed in relation to the betweenness centralities of
stimulated interneurons, representing the effectiveness for transfer of
stimulation effect in the whole network.
Modeling of Ribosome Dynamics on a ds-mRNA under an External Load
Date: 2016-07-25 00:00:00
Tasks: Translation

Protein molecules in cells are synthesized by macromolecular machines called
ribosomes. According to recent experimental data, we reduce the complexity of
the ribosome and propose a model to express its activity in six main states.
Using our model, we study the translation rate in different biological relevant
situations in the presence of external force, and translation through the RNA
double stranded region in the absence or presence of the external force. In the
present study, we give a quantitative theory for translation rate and show that
the ribosome behaves more like a Brownian Ratchet motor. Our findings could
shed some light on understanding behaviors of the ribosome in biological
conditions.
A model for the integration of conflicting exogenous and endogenous signals by dendritic cells
Date: 2016-07-25 00:00:00

Cells of the immune system are confronted with opposing pro- and
anti-inflammatory signals. Dendritic cells (DC) integrate these cues to make
informed decisions whether to initiate an immune response. Confronted with
exogenous microbial stimuli, DC endogenously produce both anti- (IL-10) and
pro-inflammatory (TNF$\alpha$) cues whose joint integration controls the cell's
final decision. We combine experimental measurements with theoretical modeling
to quantitatively describe the integration mode of these opposing signals. We
propose a two step integration model that modulates the effect of the two types
of signals: an initial bottleneck integrates both signals (IL-10 and
TNF$\alpha$), the output of which is later modulated by the anti-inflammatory
signal. We show that the anti-inflammatory IL-10 signaling is long ranged, as
opposed to the short-ranged pro-inflammatory TNF$\alpha$ signaling. The model
suggests that the population averaging and modulation of the pro-inflammatory
response by the anti-inflammatory signal is a safety guard against excessive
immune responses.
Semi-phenomenological classification models of the genetic code(s) using q-deformed numbers
Date: 2016-07-25 00:00:00

The mathematical concept of q-deformations, in particular the one of
qnumbers, is used to study the genetic code(s). After considering two kinds of
q-numbers, for comparison, a phenomenological classification scheme of the
genetic code together with its numerous minor variants is, first, established.
Next, numbers describing the presence of additional amino acids, such as
Selenocysteine or/and Pyrrolysine, are also produced. Finally, a minimal number
of amino acids, which could fit the small number of them which are thought to
have been involved, at the origin of life on Earth, is found. All together,
these results constitute our final semi-phenomenological model.
An Empirical Biomarker-based Calculator for Autosomal Recessive Polycystic Kidney Disease - The Nieto-Narayan Formula
Date: 2016-07-26 00:00:00
Tasks: Management

Autosomal polycystic kidney disease (ARPKD) is associated with progressive
enlargement of the kidneys fuelled by the formation and expansion of
fluid-filled cysts. The disease is congenital and children that do not succumb
to it during the neonatal period will, by age 10 years, more often than not,
require nephrectomy+renal replacement therapy for management of both pain and
renal insufficiency. Since increasing cystic index (CI; percent of kidney
occupied by cysts) drives both renal expansion and organ dysfunction,
management of these patients, including decisions such as elective nephrectomy
and prioritization on the transplant waitlist, could clearly benefit from
serial determination of CI. So also, clinical trials in ARPKD evaluating the
efficacy of novel drug candidates could benefit from serial determination of
CI. Although ultrasound is currently the imaging modality of choice for
diagnosis of ARPKD, its utilization for assessing disease progression is highly
limited. Magnetic resonance imaging or computed tomography, although more
reliable for determination of CI, are expensive, time-consuming and somewhat
impractical in the pediatric population. Using a well-established mammalian
model of ARPKD, we undertook a big data-like analysis of minimally- or
non-invasive serum and urine biomarkers of renal injury/dysfunction to derive a
family of equations for estimating CI. We then applied a signal averaging
protocol to distill these equations to a single empirical formula for
calculation of CI. Such a formula will eventually find use in identifying and
monitoring patients at high risk for progressing to end-stage renal disease and
aid in the conduct of clinical trials.
The fallacy of evidence based policy
Date: 2015-05-25 00:00:00
Tasks: Descriptive

The use of science for policy is at the core of a perfect storm generated by
the insurgence of several concurrent crises: of science, of trust, of
sustainability. The modern positivistic model of science for policy, known as
evidence based policy, is based on dramatic simplifications and compressions of
available perceptions of the state of affairs and possible explanations
(hypocognition). This model can result in flawed prescriptions. The flaws
become more evident when dealing with complex issues characterized by
concomitant uncertainties in the normative, descriptive and ethical domains. In
this situation evidence-based policy may concur to the fragility of the social
system. Science plays an important role in reducing the feeling of
vulnerability of humans by projecting a promise of protection against
uncertainties. In many applications quantitative science is used to remove
uncertainty by transforming it into probability, so that mathematical modelling
can play the ritual role of haruspices. This epistemic governance arrangement
is today in crisis. The primacy of science to adjudicate political issues must
pass through an assessment of the level of maturity and effectiveness of the
various disciplines deployed. The solution implies abandoning dreams of
prediction, control and optimization obtained by relying on a limited set of
simplified narratives to define the problem and moving instead to an open
exploration of a broader set of plausible and relevant stories. Evidence based
policy has to be replaced by robust policy, where robustness is tested with
respect to feasibility (compatibility with processes outside human control);
viability (compatibility with processes under human control, in relation to
both the economic and technical dimensions), and desirability domain
(compatibility with a plurality of normative considerations relevant to a
plurality of actors).
Generalization of the Ewens sampling formula to arbitrary fitness landscapes
Date: 2016-07-25 00:00:00

In considering evolution of transcribed regions, regulatory modules, and
other genomic loci of interest, we are often faced with a situation in which
the number of allelic states greatly exceeds the population size. In this
limit, the population eventually adopts a steady state characterized by
mutation-selection-drift balance. Although new alleles continue to be explored
through mutation, the statistics of the population, and in particular the
probabilities of seeing specific allelic configurations in samples taken from a
population, do not change with time. In the absence of selection, probabilities
of allelic configurations are given by the Ewens sampling formula, widely used
in population genetics to detect deviations from neutrality. Here we develop an
extension of this formula to arbitrary, possibly epistatic, fitness landscapes.
Although our approach is general, we focus on the class of landscapes in which
alleles are grouped into two, three, or several fitness states. This class of
landscapes yields sampling probabilities that are computationally more
tractable, and can form a basis for the inference of selection signatures from
sequence data. We demonstrate that, for a sizeable range of mutation rates and
selection coefficients, the steady-state allelic diversity is not neutral.
Therefore, it may be used to infer selection coefficients, as well as other key
evolutionary parameters, using high-throughput sequencing of evolving
populations to collect data on locus polymorphisms. We also find that our
theory remains sufficiently accurate even if the assumptions such as the
infinite allele limit and the "full connectivity" assumption in which each
allele can mutate into any other allele are relaxed. Thus, our framework
establishes a theoretical foundation for inferring selection signatures from
samples of sequences produced by evolution on epistatic fitness landscapes.
Genomic data analysis in tree spaces
Date: 2016-07-25 00:00:00
Tasks: Dimensionality Reduction

Recently, an elegant approach in phylogenetics was introduced by
Billera-Holmes-Vogtmann that allows a systematic comparison of different
evolutionary histories using the metric geometry of tree spaces. In many
problem settings one encounters heavily populated phylogenetic trees, where the
large number of leaves encumbers visualization and analysis in the relevant
evolutionary moduli spaces. To address this issue, we introduce tree
dimensionality reduction, a structured approach to reducing large phylogenetic
trees to a distribution of smaller trees. We prove a stability theorem ensuring
that small perturbations of the large trees are taken to small perturbations of
the resulting distributions.
  We then present a series of four biologically motivated applications to the
analysis of genomic data, spanning cancer and infectious disease. The first
quantifies how chemotherapy can disrupt the evolution of common leukemias. The
second examines a link between geometric information and the histologic grade
in relapsed gliomas, where longer relapse branches were specific to high grade
glioma. The third concerns genetic stability of xenograft models of cancer,
where heterogeneity at the single cell level increased with later mouse
passages. The last studies genetic diversity in seasonal influenza A virus. We
apply tree dimensionality reduction to 24 years of longitudinally collected
H3N2 hemagglutinin sequences, generating distributions of smaller trees
spanning between three and five seasons. A negative correlation is observed
between the influenza vaccine effectiveness during a season and the variance of
the distributions produced using preceding seasons' sequence data. We also show
how tree distributions relate to antigenic clusters and choice of influenza
vaccine. Our formalism exposes links between viral genomic data and clinical
observables such as vaccine selection and efficacy.
Verso folio: Diversified Ranking for Large Graphs with Context-Aware Considerations
Authors: Tsatsanifos George
Date: 2016-07-25 00:00:00

This work is pertaining to the diversified ranking of web-resources and
interconnected documents that rely on a network-like structure, e.g. web-pages.
A practical example of this would be a query for the k most relevant web-pages
that are also in the same time as dissimilar with each other as possible.
Relevance and dissimilarity are quantified using an aggregation of network
distance and context similarity. For example, for a specific configuration of
the problem, we might be interested in web-pages that are similar with the
query in terms of their textual description but distant from each other in
terms of the web-graph, e.g. many clicks away. In retrospect, a dearth of work
can be found in the literature addressing this problem taking the network
structure formed by the document links into consideration.
  In this work, we propose a hill-climbing approach that is seeded with a
document collection which is generated using greedy heuristics to diversify
initially. More importantly, we tackle the problem in the context of web-pages
where there is an underlying network structure connecting the available
documents and resources. This is a significant difference to the majority of
works that tackle the problem in terms of either content definitions, or the
graph structure of the data, but never addressing both aspects simultaneously.
To the best of our knowledge, this is the very first effort that can be found
to combine both aspects of this important problem in an elegant fashion by also
allowing a great degree of flexibility on how to configure the trade-offs of
(i) document relevance over result-items' dissimilarity, and (ii) network
distance over content relevance or dissimilarity. Last but not least, we
present an extensive evaluation of our methods that demonstrate the
effectiveness and efficiency thereof.
The Rank Effect for Commodities
Date: 2016-07-26 00:00:00

We uncover a large and significant low-minus-high rank effect for commodities
across two centuries. There is nothing anomalous about this anomaly, nor is it
clear how it can be arbitraged away. Using nonparametric econometric methods,
we demonstrate that such a rank effect is a necessary consequence of a
stationary relative asset price distribution. We confirm this prediction using
daily commodity futures prices and show that a portfolio consisting of
lower-ranked, lower-priced commodities yields 23% higher annual returns than a
portfolio consisting of higher-ranked, higher-priced commodities. These excess
returns have a Sharpe ratio nearly twice as high as the U.S. stock market yet
are uncorrelated with market risk. In contrast to the extensive literature on
asset pricing factors and anomalies, our results are structural and rely on
minimal and realistic assumptions for the long-run behavior of relative asset
prices.
Single Stage Prediction with Embedded Topic Modeling of Online Reviews for Mobile App Management
Authors: Mankad Shawn, Hu Shengli, Gopal Anandasivam
Date: 2018-02-19 00:00:00
Tasks: Benchmarking, Management, software testing

Mobile apps are one of the building blocks of the mobile digital economy. A
differentiating feature of mobile apps to traditional enterprise software is
online reviews, which are available on app marketplaces and represent a
valuable source of consumer feedback on the app. We create a supervised topic
modeling approach for app developers to use mobile reviews as useful sources of
quality and customer feedback, thereby complementing traditional software
testing. The approach is based on a constrained matrix factorization that
leverages the relationship between term frequency and a given response variable
in addition to co-occurrences between terms to recover topics that are both
predictive of consumer sentiment and useful for understanding the underlying
textual themes. The factorization is combined with ordinal regression to
provide guidance from online reviews on a single app's performance as well as
systematically compare different apps over time for benchmarking of features
and consumer sentiment. We apply our approach using a dataset of over 100,000
mobile reviews over several years for three of the most popular online travel
agent apps from the iTunes and Google Play marketplaces.