pwc-archive/papers-with-abstracts huggingface dataset:
A Fast Recommendation Algorithm for Social Tagging Systems : A Delicious Case
Authors: Zhao Yao-Dong, Cai Shi-Min, Tang Ming, Shang Ming-Sheng
Date: 2015-12-28 00:00:00
Tasks: Clustering, Collaborative Filtering, CPU, Recommendation Systems

The tripartite graph is one of the commonest topological structures in social
tagging systems such as Delicious, which has three types of nodes (i.e., users,
URLs and tags). Traditional recommender systems developed based on
collaborative filtering for the social tagging systems bring very high demands
on CPU time cost. In this paper, to overcome this drawback, we propose a novel
approach that extracts non-overlapping user clusters and corresponding
overlapping item clusters simultaneously through coarse clustering to
accelerate the user-based collaborative filtering and develop a fast
recommendation algorithm for the social tagging systems. The experimental
results show that the proposed approach is able to dramatically reduce the
processing time cost greater than $90\%$ and relatively enhance the accuracy in
comparison with the ordinary user-based collaborative filtering algorithm.
Role of spatial averaging in multicellular gradient sensing
Date: 2015-12-28 00:00:00

Gradient sensing underlies important biological processes including
morphogenesis, polarization, and cell migration. The precision of gradient
sensing increases with the length of a detector (a cell or group of cells) in
the gradient direction, since a longer detector spans a larger range of
concentration values. Intuition from analyses of concentration sensing suggests
that precision should also increase with detector length in the direction
transverse to the gradient, since then spatial averaging should reduce the
noise. However, here we show that, unlike for concentration sensing, the
precision of gradient sensing decreases with transverse length for the simplest
gradient sensing model, local excitation--global inhibition (LEGI). The reason
is that gradient sensing ultimately relies on a subtraction of measured
concentration values. While spatial averaging indeed reduces the noise in these
measurements, which increases precision, it also reduces the covariance between
the measurements, which results in the net decrease in precision. We
demonstrate how a recently introduced gradient sensing mechanism, regional
excitation--global inhibition (REGI), overcomes this effect and recovers the
benefit of transverse averaging. Using a REGI-based model, we compute the
optimal two- and three-dimensional detector shapes, and argue that they are
consistent with the shapes of naturally occurring gradient-sensing cell
populations.
Detecting local processing unit in drosophila brain by using network theory
Date: 2015-12-28 00:00:00
Tasks: Community Detection

Community detection method in network theory was applied to the neuron
network constructed from the image overlapping between neuron pairs to detect
the Local Processing Unit (LPU) automatically in Drosophila brain. 26
communities consistent with the known LPUs, and 13 subdivisions were found.
Besides, 45 tracts were detected and could be discriminated from the LPUs by
analyzing the distribution of participation coefficient P. Furthermore, layer
structures in fan-shaped body (FB) were observed which coincided with the
images shot by the optical devices, and a total of 13 communities were proven
closely related to FB. The method proposed in this work was proven effective to
identify the LPU structure in Drosophila brain irrespectively of any subjective
aspect, and could be applied to the relevant areas extensively.
Inferring Volatility in the Heston Model and its Relatives -- an Information Theoretical Approach
Date: 2015-12-28 00:00:00

Stochastic volatility models describe asset prices $S_t$ as driven by an
unobserved process capturing the random dynamics of volatility $\sigma_t$.
Here, we quantify how much information about $\sigma_t$ can be inferred from
asset prices $S_t$ in terms of Shannon's mutual information $I(S_t :
\sigma_t)$. This motivates a careful numerical and analytical study of
information theoretic properties of the Heston model. In addition, we study a
general class of discrete time models motivated from a machine learning
perspective. In all cases, we find a large uncertainty in volatility estimates
for quite fundamental information theoretic reasons.
Early Recognition of Emerging Flu Strain Clusters
Date: 2015-12-28 00:00:00

Minimizing time delays in manufacturing vaccines appropriate to rapidly
mutating viruses is the key step for improving vaccine effectiveness. The
vaccine for the H3N2 flu type has failed for the last two years (~ 15%
effective). Here we summarize the state of the predictive art and report the
most current results for H3N2 flu vaccine design. Using a 2006 model of
dimensional reduction of viral mutational complexity, we show that this model
can reduce vaccine time delays by a year or more in some cases.
Dynamic Range in the C.elegans Brain Network
Date: 2015-12-28 00:00:00
Tasks: Community Detection

We study external, electrical perturbations and their responses in the brain
dynamic network of the \textit{Caenorhabditis elegans} soil worm, given by the
connectome of its large somatic nervous system. Our analysis is inspired by a
realistic experiment where one stimulates externally specific parts of the
brain and studies the persistent neural activity triggered in other cortical
regions. In this work, we perturb groups of neurons that form communities,
identified by the walktrap community detection method, by trains of
stereotypical electrical Poissonian impulses and study the propagation of
neural activity to other communities by measuring the corresponding dynamic
ranges and Steven law exponents. We show that when one perturbs specific
communities, keeping the rest unperturbed, the external stimulations are able
to propagate to some of them but not to all. There are also perturbations that
do not trigger any response. We found that this depends on the initially
perturbed community. Finally, we relate our findings for the former cases with
low neural synchronization, self-criticality and large information flow
capacity, and interpret them as the ability of the brain network to respond to
external perturbations when it works at criticality and its information flow
capacity becomes maximal.
Spatial pattern and power function deviation in a cellular automata model of an ant population
Date: 2015-12-29 00:00:00

A spatially explicit model previously developed to apply to a population of
arboreal nesting ants in southern Mexico is explored in detail, including
notions of criticality and robust criticality and expected power law
adjustment.
The Role of Time in Making Risky Decisions and the Function of Choice
Date: 2015-12-27 00:00:00

The prospects of Kahneman and Tversky, Mega Million and Powerball lotteries,
St. Petersburg paradox, premature profits and growing losses criticized by
Livermore are reviewed under an angle of view comparing mathematical
expectations with awards received. Original prospects have been formulated as a
one time opportunity. An award value depends on the number of times the game is
played. The random sample mean is discussed as a universal award. The role of
time in making a risky decision is important as long as the frequency of games
and playing time affect their number. A function of choice mapping properties
of two-point random variables to fractions of respondents choosing them is
proposed.
Evolutionary and topological properties of gene modules and driver mutations in a leukemia gene regulatory network
Date: 2015-12-29 00:00:00

The diverse, specialized genes in today's lifeforms evolved from a common
core of ancient, elementary genes. However, these genes did not evolve
individually: gene expression is controlled by a complex network of
interactions, and alterations in one gene may drive reciprocal changes in its
proteins' binding partners. We show that the topology of a leukemia gene
regulatory network is strongly coupled with evolutionary properties.
Slowly-evolving ("cold"), old genes tend to interact with each other, as do
rapidly-evolving ("hot"), young genes, causing genes to evolve in clusters. We
argue that gene duplication placed old, cold genes at the center of the
network, and young, hot genes on the periphery, and demonstrate this with
single-node centrality measures and two new measures of efficiency. Integrating
centrality measures with evolutionary information, we define a
medically-relevant "cancer network core," strongly enriched for common cancer
mutations ($p=2\times 10^{-14}$). This could aid in identifying driver
mutations and therapeutic targets.
On Optimal Pricing Model for Multiple Dealers in a Competitive Market
Date: 2015-12-30 00:00:00

In this paper, the optimal pricing strategy in Avellande-Stoikov's for a
monopolistic dealer is extended to a general situation where multiple dealers
are present in a competitive market. The dealers' trading intensities, their
optimal bid and ask prices and therefore their spreads are derived when the
dealers are informed the severity of the competition. The effects of various
parameters on the bid-ask quotes and profits of the dealers in a competitive
market are also discussed. This study gives some insights on the average
spread, profit of the dealers in a competitive trading environment.
Selection maintaining protein stability at equilibrium
Date: 2015-12-30 00:00:00

The common understanding of protein evolution has been that neutral or
slightly deleterious mutations are fixed by random drift, and evolutionary rate
is determined primarily by the proportion of neutral mutations. However, recent
studies have revealed that highly expressed genes evolve slowly because of
fitness costs due to misfolded proteins. Here we study selection maintaining
protein stability.
  Protein fitness is taken to be $s = \kappa \exp(\beta\Delta G) (1 -
\exp(\beta\Delta\Delta G))$, where $s$ and $\Delta\Delta G$ are selective
advantage and stability change of a mutant protein, $\Delta G$ is the folding
free energy of the wild-type protein, and $\kappa$ represents protein abundance
and indispensability. The distribution of $\Delta\Delta G$ is approximated to
be a bi-Gaussian function, which represents structurally slightly- or
highly-constrained sites. Also, the mean of the distribution is negatively
proportional to $\Delta G$.
  The evolution of this gene has an equilibrium ($\Delta G_e$) of protein
stability, the range of which is consistent with experimental values. The
probability distribution of $K_a/K_s$, the ratio of nonsynonymous to synonymous
substitution rate per site, over fixed mutants in the vicinity of the
equilibrium shows that nearly neutral selection is predominant only in
low-abundant, non-essential proteins of $\Delta G_e > -2.5$ kcal/mol. In the
other proteins, positive selection on stabilizing mutations is significant to
maintain protein stability at equilibrium as well as random drift on slightly
negative mutations, although the average $\langle K_a/K_s \rangle$ is less than
1. Slow evolutionary rates can be caused by high protein
abundance/indispensability, which produces positive shifts of $\Delta\Delta G$
through decreasing $\Delta G_e$, and by strong structural constraints, which
directly make $\Delta\Delta G$ more positive.
Quasispecies dynamics on a network of interacting genotypes and idiotypes: Applications to autoimmunity and immunodeficiency
Date: 2015-12-30 00:00:00

In spite of their many facets, the phenomena of autoimmunity and
immunodeficiency seem to be related to each other through the subtle links
connecting retroviral mutation and action to immune response and adaptation. In
a previous work, we introduced a network model of how a set of interrelated
genotypes (called a quasispecies, in the stationary state) and a set of
interrelated idiotypes (an idiotypic network) interact. That model, which does
not cover the case of a retroviral quasispecies, was instrumental for the study
of quasispecies survival when confronting the immune system and led to the
conclusion that, unlike what happens when a quasispecies is left to evolve by
itself, letting genotypes mutate too infrequently leads to the destruction of
the quasispecies. Here we extend that genotype-idiotype interaction model by
the addition of a further parameter ($\nu$) to account for the action of
retroviruses (i.e., the destruction of idiotypes by genotypes). We give
simulation results within a suitable parameter niche, highlighting the issues
of quasispecies survival and of the onset of autoimmunity through the
appearance of the so-called pathogenic idiotypes. Our main findings refer to
how $\nu$ and $\lambda$, a parameter describing the rate at which idiotypes get
stimulated, relate to each other. While for $\nu>\lambda$ the quasispecies
survives at the expense of weakening the immune system significantly or even
destroying it, for $\nu<\lambda$ the fittest genotypes of the quasispecies
become mimicked inside the immune system as pathogenic idiotypes. The latter is
in agreement with the current understanding of the HIV quasispecies.
Expanded Distance-based Phylogenetic Analyses of Fossil Homo Skull Shape Evolution
Date: 2015-12-30 00:00:00

Analyses of a set of 47 fossil and 4 modern skulls using phylogenetic
geometric morphometric methods corroborate and refine earlier results. These
include evidence that the African Iwo Eleru skull, only about 12,000 years old,
indeed represents a new species of near human. In contrast, the earliest known
anatomically modern human skull, Qafzeh 9, the skull of Eve from
Israel/Palestine, is validated as fully modern in form. Analyses clearly show
evidence of archaic introgression into Gravettian, pre_Gravettian, Qafzeh, and
Upper Cave (China) populations of near modern humans, and in about that order
of increasing archaic content. The enigmatic Saldahna (Elandsfontein) skull
emerges as a probable first representative of that lineage, which exclusive of
Neanderthals that, eventually lead to modern humans. There is also evidence
that the poorly dated Kabwe (Broken Hill) skull represents a much earlier
distinct lineage. The clarity of the results bode well for quantitative
statistical phylogenetic methods making significant inroads in the stalemates
of paleoanthropology.
Modelling the Role of Nitric Oxide in Cerebral Autoregulation
Date: 2015-12-30 00:00:00

Malfunction of the system which regulates the bloodflow in the brain is a
major cause of stroke and dementia, costing many lives and many billions of
pounds each year in the UK alone. This regulatory system, known as cerebral
autoregulation, has been the subject of much experimental and mathematical
investigation yet our understanding of it is still quite limited. One area in
which our understanding is particularly lacking is that of the role of nitric
oxide, understood to be a potent vasodilator. The interactions of nitric oxide
with the better understood myogenic response remain un-modelled and poorly
understood. In this thesis we present a novel model of the arteriolar control
mechanism, comprising a mixture of well-established and new models of
individual processes, brought together for the first time. We show that this
model is capable of reproducing experimentally observed behaviour very closely
and go on to investigate its stability in the context of the vasculature of the
whole brain. In conclusion we find that nitric oxide, although it plays a
central role in determining equilibrium vessel radius, is unimportant to the
dynamics of the system and its responses to variation in arterial blood
pressure. We also find that the stability of the system is very sensitive to
the dynamics of Ca$^{2+}$ within the muscle cell, and that self-sustaining
Ca$^{2+}$ waves are not necessary to cause whole-vessel radius oscillations
consistent with vasomotion.
Asymmetric disease dynamics in multihost interconnected networks
Date: 2016-06-17 00:00:00

Epidemic spread in single-host systems strongly depends on the population's
contact network. However, little is known regarding the spread of epidemics
across networks representing populations of multiple hosts. We explored
cross-species transmission in a multilayer network where layers represent
populations of two distinct hosts, and disease can spread across intralayer
(within-host) and interlayer (between-host) edges. We developed an analytic
framework for the SIR epidemic model to examine the effect of (i) source of
infection and (ii) between-host asymmetry in infection probabilities, on
disease risk. We measured risk as outbreak probability and outbreak size in a
focal host, represented by one network layer. Numeric simulations were used to
validate the analytic formulations. We found that outbreak probability is
determined by a complex interaction between source of infection and
between-host infection probabilities, whereas outbreak size is mainly affected
by the non-focal host to focal host infection probability alone. Hence,
inter-specific asymmetry in infection probabilities shapes disease dynamics in
multihost networks. These results expand current theory of monolayer networks,
where outbreak size and probability are considered equal, highlighting the
importance of considering multiple measures of disease risk. Our study advances
understanding of multihost systems and non-biological systems with asymmetric
flow rates.
A Model for Direction Sensing in Dictyostelium Discoideum: Ras Activity and Symmetry Breaking Driven by a Gbetagamma- Mediated, Galpha2-Ric8 -- Dependent Signal Transduction Network
Date: 2015-12-31 00:00:00

Many eukaryotic cells, including Dictyostelium discoideum (Dicty),
neutrophils and other cells of the immune system, can detect and reliably
orient themselves in chemoattractant gradients. In Dicty, signal detection and
transduction involves a G-protein-coupled receptor (GPCR) through which
extracellular cAMP signals are transduced into Ras activation via an
intermediate heterotrimeric G-protein (G2). Ras activation is the first
polarized response to cAMP gradients in Dicty. Recent work has revealed mutiple
new characteristics of Ras activation in Dicty, thereby providing new insights
into direction sensing mechanisms and pointing to the need for new models of
chemotaxis. Here we propose a novel reaction-diffusion model of Ras activation
based on three major components: one involving the GPCR, one centered on G2,
and one involving the monomeric G protein Ras. In contrast to existing local
excitation, global inhibition (LEGI) models of direction sensing, in which a
fast-responding but slowly-diffusing activator and a slow-acting rapidly
diffusing inhibitor set up an internal gradient of activity, our model is based
on equal diffusion coefficients for all cytosolic species, and the unbalanced
local sequestration of some species leads to gradient sensing and
amplification. We show that Ric8-modulated G2 cycling between the cytosol and
membrane can account for many of the observed responses in Dicty. including
imperfect adaptation, multiple phases of Ras activity in a cAMP gradient,
rectified directional sensing, and cellular memory.
On the Fractal Geometry of the Balance Sheet and the Fractal Index of Insolvency Risk
Date: 2015-12-31 00:00:00

This paper reviews the economic and theoretical foundations of insolvency
risk measurement and capital adequacy rules. The proposed new measure of
insolvency risk is constructed by disentangling assets, debt and equity at the
micro-prudential firm level. This new risk index is the Firm Insolvency Risk
Index (FIRI) which is symmetrical, proportional and scale invariant. We
demonstrate that the balance sheet can be shown to evolve with a fractal
pattern. As such we construct a fractal index that can measure the risk of
assets. This index can differentiate between the similarity and dissimilarity
in asset risk, and it will also possess the properties of being self-similar
and invariant to firm characteristics that make up its asset composition hence
invariant to all types of risk derived from assets. Self-similarity and scale
invariance across the cross section allows direct comparison of degrees of risk
in assets. This is by comparing the risk dissimilarity of assets. Being
naturally bounded to its highest upper bound, (0,2], the fractal index is able
to serve like a risk thermometer. We assign geometric probabilities of
insolvency P (equity is equal or less than 0 conditional on debt being greater
than 0).
De novo visual proteomics in single cells through pattern mining
Date: 2016-02-11 00:00:00
Tasks: Electron Tomography

Cryo-electron tomography enables 3D visualization of cells in a near native
state at molecular resolution. The produced cellular tomograms contain detailed
information about all macromolecular complexes, their structures, their
abundances and their specific spatial locations in the cell. However,
extracting this information is very challenging and current methods usually
rely on templates of known structure. Here, we formulate a template-free visual
proteomics analysis as a de novo pattern mining problem and propose a new
framework called "Multi Pattern Pursuit" for supporting proteome-scale de novo
discovery of macromolecular complexes in cellular tomograms without using
templates of known structures. Our tests on simulated and experimental
tomograms show that our method is a promising tool for template-free visual
proteomics analysis.
Brighter than the sun: Powerscape visualizations illustrate power needs in neuroscience and psychology
Date: 2015-12-31 00:00:00
Tasks: Form

Participant needs to achieve a given power are frequently underestimated.
This is particularly problematic when effect sizes are small, such as is common
in neuroscience and psychology. We provide tools to make these demands
immediately obvious in the form of a powerscape visualization.
Wild herbivores in forests: four case studies
Date: 2015-12-12 00:00:00

A three population system with a top predator population, i.e. the
herbivores, and two prey populations, grass and trees, is considered to model
the interaction of herbivores with natural resources. We apply the model for
four natural mountain parks in Northern Italy, three located in the Eastern
Alps, two of which in the Dolomites and one in the Julian Alps, and one in the
Maritime Alps, Northwest Italy. The simulations, based on actual data gathered
from contacts with rangers and parks administrators, field samplings and
published material, provide useful information on the behavior of the
vegetation-wild herbivores interactions and the possible medium-long term
evolution of these ecosystems. At the same time they show that these ecosystems
are in a very delicate situation, for which the animal populations could become
extinguished in case of adverse environmental conditions. The determination of
the so called sensitivity surfaces support our findings and indicate some
possible preventive measures to the park admistrators.