pwc-archive/papers-with-abstracts huggingface dataset:
A hierarchical neural stimulation model for pain relief by variation of coil design parameters
Date: 2016-09-13 00:00:00

Neural stimulation represents a powerful technique for neural disorder
treatment. This paper deals with optimization of coil design parameters to be
used during stimulation for modulation of neuronal firing to achieve pain
relief. Pain mechanism is briefly introduced and a hierarchical stimulation
model from coil stimulation to neuronal firing is proposed. Electromagnetic
field distribution for circular, figure of 8 and Magnetic resonance coupling
figure of 8 coils are analyzed with respect to the variation of stimulation
parameters such as distance between coils, stimulation frequency, number of
turns and radius of coils. MRC figure of 8 coils were responsible for inducing
the maximum Electric field for same amount of driving current in coils.
Variation of membrane potential, ion channel conductance and neuronal firing
frequency in a pyramidal neuronal model due to magnetic and acoustic
stimulation are studied. The frequency of neuronal firing for cortical neurons
is higher during pain state, compared to no pain state. Lowest neuronal firing
frequency 18 Hz was found for MRC figure of 8 coils, compared to 30 Hz for
circular coils. Therefore, MRC figure of 8 coils are most effective for
modulation of neuronal firing, thereby achieving pain relief in comparison to
other coils considered in this study
Efficiency of a Stochastic Search with Punctual and Costly Restarts
Date: 2016-09-13 00:00:00

The mean completion time of a stochastic process may be rendered finite and
minimised by a judiciously chosen restart protocol, which may either be
stochastic or deterministic. Here we study analytically an arbitrary stochastic
search subject to an arbitrary restart protocol, each characterised by a
distribution of waiting times. By a direct enumeration of paths we construct
the joint distribution of completion time and restart number, in a form
amenable to analytical evaluation or quadrature; thereby we optimise the search
over both time and potentially costly restart events. Analysing the effect of a
punctual, i.e. almost deterministic, restart, we demonstrate that the optimal
completion time always increases proportionately with the variance of the
restart distribution; the constant of proportionality depends only on the
search process. We go on to establish simple bounds on the optimal restart
time. Our results are relevant to the analysis and rational design of efficient
and optimal restart protocols.
Visualizing septins in early Drosophila embryos
Date: 2016-09-13 00:00:00

Functional studies in Drosophila have been key for establishing a role for
the septin family of proteins in animal cell division and thus extending for
the first time observations from the budding yeast to animal cells. Visualizing
the distribution of specific septins in different Drosophila tissues and, in
particular, in the Drosophila embryo, together with biochemical and mutant
phenotype data, has contributed important advances to our understanding of
animal septin biology, suggesting roles in processes other than in cytokinesis.
Septin localization using immunofluorescence assays has been possible due to
the generation of antibodies against different Drosophila septins. The recent
availability of lines expressing fluorescent protein fusions of specific
septins further promises to facilitate studies on septin dynamics. Here, we
provide protocols for preparing early Drosophila embryos to visualize septins
using immunofluorescence assays and live fluorescence microscopy. The genetic
tractability of the Drosophila embryo together with its amenability to
high-resolution fluorescence microscopy promises to provide novel insights into
animal septin structure and function.
Purification of recombinant human and Drosophila septin hexamers for TIRF assays of actin-septin filament assembly
Date: 2016-09-13 00:00:00

Septins are guanine nucleotide-binding proteins that are conserved from fungi
to humans. Septins assemble into heterooligomeric complexes and higher-order
structures with key roles in various cellular functions including cell
migration and division. The mechanisms by which septins assemble and interact
with other cytoskeletal elements like actin remain elusive. A powerful approach
to address this question is by cell-free reconstitution of pu- rified
cytoskeletal proteins combined with fluorescence microscopy. Here, we describe
procedures for the purification of recombinant Drosophila and human septin
hexamers from Escherichia coli and reconstitution of actin-septin coassembly.
These procedures can be used to compare assembly of Drosophila and human
septins and their coassembly with the actin cytoskeleton by total internal
reflection fluorescence microscopy.
Modeling the lowest-cost splitting of a herd of cows by optimizing a cost function
Date: 2017-07-19 00:00:00

Animals live in groups to defend against predation and to obtain food.
However, for some animals --- especially ones that spend long periods of time
feeding --- there are costs if a group chooses to move on before their
nutritional needs are satisfied. If the conflict between feeding and keeping up
with a group becomes too large, it may be advantageous to some animals to split
into subgroups of animals with similar nutritional needs. We model the costs
and benefits of splitting by a herd of cows using a cost function (CF) that
quantifies individual variation in hunger, desire to lie down, and predation
risk. We model the costs associated with hunger and lying desire as the
standard deviations of individuals within a group, and we model predation risk
as an inverse exponential function of group size. We minimize the cost function
over all plausible groups that can arise from a given herd and study the
dynamics of group splitting. We explore our model using two examples: (1) we
consider group switching and group fission in a herd of relatively homogeneous
cows; and (2) we examine a herd with an equal number of adult males (larger
animals) and adult females (smaller animals).
The qualitative analysis of the impact of media delay on the control of infectious disease
Date: 2016-09-12 00:00:00

In this paper, we consider the impact of time delay by media on the control
of the disease. We set up a class of SISM epidemic model with the time delay
and the cumulative density of awareness caused by media. The sufficient
condition of global asymptotic stability of disease-free equilibrium is
approved. We get the global stability of the epidemic equilibrium and the
existence conditions of Hopf bifurcation. Numerical simulations are presented
to illustrate the analytical results. Finally, we analyze the influence of
parameters on the control of infectious disease by combining the data of H1N1.
By shortening the time of media lag, increasing transmission rate of media and
the implementation rate of the media project, the spread of disease will be
controlled effectively.
The Dynamic Behavior (Effects) of Impulsive Toxicant Input on a Single-Species Population in a Small Polluted Environment
Date: 2016-09-12 00:00:00

In this paper, we study a single-species population model with pulse toxicant
input to a small polluted environment. The intrinsic rate of population is
affected by environment and toxin in organisms. The toxin in organisms is
influenced by toxin in environment and the food chain. A new mathematical model
is derived. By the Pulse Compare Theorem, we find the surviving threshold of
the population and obtain the sufficient conditions of persistence and
extinction of the population.
Sets of FCS experiments to quantify free diffusion coefficients in reaction-diffusion systems. The case of Ca2+ and its dyes
Date: 2016-09-13 00:00:00

Many cell signaling pathways involve the diffusion of messengers that
bind/unbind to intracellular components. Quantifying their net transport rate
under different conditions, then requires having separate estimates of their
free diffusion coefficient and binding/unbinding rates. In this paper, we show
how performing sets of Fluorescence Correlation Spectroscopy (FCS) experiments
under different conditions, it is possible to quantify free diffusion
coefficients and on and off rates of reaction-diffusion systems. We develop the
theory and present a practical implementation for the case of the universal
second messenger, calcium (Ca$^{2+}$) and single-wavelength dyes that increase
their fluorescence upon Ca$^{2+}$ binding. We validate the approach with
experiments performed in aqueous solutions containing Ca$^{2+}$ and Fluo4
dextran (both in its High and Low Affinity versions). Performing FCS
experiments with tetramethylrhodamine-dextran in Xenopus laevis oocytes, we
infer the corresponding free diffusion coefficients in the cytosol of these
cells. Our approach can be extended to other physiologically relevant
reaction-diffusion systems to quantify biophysical parameters that determine
the dynamics of various variables of interest.
On Human Consciousness
Date: 2016-09-11 00:00:00
Tasks: Decision Making

We consider the implications of the mathematical analysis of
neurone-to-neurone dynamical complex networks. We show how the dynamical
behaviour of small scale strongly connected networks lead naturally to
non-binary information processing and thus multiple hypothesis decision making,
even at the very lowest level of the brain's architecture. In turn we build on
these ideas to address the hard problem of consciousness. We discuss how a
proposed "dual hierarchy model", made up form of both external perceived,
physical, elements of increasing complexity, and internal mental elements
(experiences), may support a leaning and evolving consciousness. We discuss the
idea that a human brain ought to be able to re-conjure subjective mental
feelings at will and thus these cannot depend on internal nose (chatter) or
internal instability-driven activity. An immediate consequence of this model,
grounded in dynamical systems and non-binary information processing, is that
finite human brains must always be learning or forgetteing and that any
possible subjective internal feeling that may be idealised with a countable
infinity of facets, can never be learned by zombies or automata: though it can
be experienced more and more fully by an evolving brain (yet never in totality,
not even in a lifetime).
Closed-form solutions for worst-case law invariant risk measures with application to robust portfolio optimization
Date: 2016-09-13 00:00:00
Tasks: Form, Portfolio Optimization

Worst-case risk measures refer to the calculation of the largest value for
risk measures when only partial information of the underlying distribution is
available. For the popular risk measures such as Value-at-Risk (VaR) and
Conditional Value-at-Risk (CVaR), it is now known that their worst-case
counterparts can be evaluated in closed form when only the first two moments
are known for the underlying distribution. These results are remarkable since
they not only simplify the use of worst-case risk measures but also provide
great insight into the connection between the worst-case risk measures and
existing risk measures. We show in this paper that somewhat surprisingly
similar closed-form solutions also exist for the general class of law invariant
coherent risk measures, which consists of spectral risk measures as special
cases that are arguably the most important extensions of CVaR. We shed light on
the one-to-one correspondence between a worst-case law invariant risk measure
and a worst-case CVaR (and a worst-case VaR), which enables one to carry over
the development of worst-case VaR in the context of portfolio optimization to
the worst-case law invariant risk measures immediately.
Role of Mobility and Health Disparities on the Transmission Dynamics of Tuberculosis
Date: 2016-09-14 00:00:00

The transmission dynamics of Tuberculosis (TB) involve complex
epidemiological and socio-economical interactions between individuals living in
highly distinct regional conditions. The level of exogenous reinfection and
first time infection rates within high-incidence settings may influence the
impact of control programs on TB prevalence. This study aims at enhancing the
understanding of TB dynamics via the study of scenarios, within {\it
simplified}, two patch, risk-defined environments, in the presence of short
term mobility and variations in reinfection and infection rates. The modeling
framework captures the role of individuals' `daily' dynamics within and between
places of residency, work or business via the proportion of time spent in
residence and as visitors to TB-risk environments (patches). As a result, the
{\it effective population size} of Patch $i$ (home of $i$-residents) at time
$t$ must account for visitors and residents of Patch $i$, at time $t$. The
impact that {\it effective population size} and the distribution of {\it
individuals' residence times} in different patches have on TB transmission and
control are studied using selected scenarios where risk is defined by the
estimated or perceive first time infection and/or exogenous re-infection rates.
Our results suggest that, under certain conditions, allowing infected
individuals to move from high to low TB prevalence areas (for example via the
sharing of treatment and isolation facilities) may lead to a reduction in the
total TB prevalence in the overall, here two-patch, population.
Non-random network connectivity comes in pairs
Date: 2016-12-13 00:00:00

Overrepresentation of bidirectional connections in local cortical networks
has been repeatedly reported and is in the focus of the ongoing discussion of
non-random connectivity. Here we show in a brief mathematical analysis that in
a network in which connection probabilities are symmetric in pairs, $P_{ij} =
P_{ji}$, the occurrence of bidirectional connections and non-random structures
are inherently linked; an overabundance of reciprocally connected pairs emerges
necessarily when the network structure deviates from a random network in any
form.
Document Filtering for Long-tail Entities
Authors: Reinanda Ridho, Meij Edgar, de Rijke Maarten
Date: 2016-09-14 00:00:00
Tasks: Informativeness, Knowledge Base Construction

Filtering relevant documents with respect to entities is an essential task in
the context of knowledge base construction and maintenance. It entails
processing a time-ordered stream of documents that might be relevant to an
entity in order to select only those that contain vital information.
State-of-the-art approaches to document filtering for popular entities are
entity-dependent: they rely on and are also trained on the specifics of
differentiating features for each specific entity. Moreover, these approaches
tend to use so-called extrinsic information such as Wikipedia page views and
related entities which is typically only available only for popular head
entities. Entity-dependent approaches based on such signals are therefore
ill-suited as filtering methods for long-tail entities. In this paper we
propose a document filtering method for long-tail entities that is
entity-independent and thus also generalizes to unseen or rarely seen entities.
It is based on intrinsic features, i.e., features that are derived from the
documents in which the entities are mentioned. We propose a set of features
that capture informativeness, entity-saliency, and timeliness. In particular,
we introduce features based on entity aspect similarities, relation patterns,
and temporal expressions and combine these with standard features for document
filtering. Experiments following the TREC KBA 2014 setup on a publicly
available dataset show that our model is able to improve the filtering
performance for long-tail entities over several baselines. Results of applying
the model to unseen entities are promising, indicating that the model is able
to learn the general characteristics of a vital document. The overall
performance across all entities---i.e., not just long-tail entities---improves
upon the state-of-the-art without depending on any entity-specific training
data.
Generalized flexibility-rigidity index
Date: 2016-09-14 00:00:00

Flexibility-rigidity index (FRI) has been developed as a robust, accurate and
efficient method for macromolecular thermal fluctuation analysis and B-factor
prediction. The performance of FRI depends on its formulations of rigidity
index and flexibility index. In this work, we introduce alternative rigidity
and flexibility formulations. The structure of the classic Gaussian surface is
utilized to construct a new type of rigidity index, which leads to a new class
of rigidity densities with the classic Gaussian surface as a special case.
Additionally, we introduce a new type of flexibility index based on the domain
indicator property of normalized rigidity density. These generalized FRI (gFRI)
methods have been extensively validated by the B-factor predictions of 364
proteins. Significantly outperforming the classic Gaussian network model (GNM),
gFRI is a new generation of methodologies for accurate, robust and efficient
analysis of protein flexibility and fluctuation. Finally, gFRI based molecular
surface generation and flexibility visualization are demonstrated.
An epidemiological model of viral infections in a Varroa-infested bee colony: the case of a bee-dependent mite population size
Date: 2016-09-14 00:00:00
Tasks: Epidemiology

In recent years the spread of the ectoparasitic mite Varroa destructor has
become the most serious threat to worldwide apiculture. In the model presented
here we extend the bee population dynamics with mite viral epidemiology
examined in an earlier paper by allowing a bee-dependent mite population size.
The results of the analysis match field observations well and give a clear
explanation of how Varroa affects the epidemiology of certain naturally
occurring bee viruses, causing considerable damages to colonies. The model
allows only four possible stable equilibria, using known field parameters. The
first one contains only the thriving healthy bees. Here the disease is
eradicated and also the mites are wiped out. Alternatively, we find the
equilibrium still with no mite population, but with endemic disease among the
thriving bee population. Thirdly, infected bees coexist with the mites in the
Varroa invasion scenario; in this situation the disease invades the hive,
driving the healthy bees to extinction and therefore affecting all the bees.
Coexistence is also possible, with both populations of bees and mites thriving
and with the disease endemically affecting both species. The analysis is in
line with field observations in natural honey bee colonies. Namely, these
diseases are endemic and if the mite population is present, necessarily the
viral infection occurs. Further, in agreement with the fact that the presence
of Varroa increments the viral transmission, the whole bee population may
become infected when the disease vector is present in the beehive. Also, a low
horizontal transmission rate of the virus among the honey bees will help in
protecting the bee colonies from Varroa infestation and viral epidemics.
Community detection in weighted brain connectivity networks beyond the resolution limit
Date: 2016-09-14 00:00:00
Tasks: Community Detection, Functional Connectivity

Graph theory provides a powerful framework to investigate brain functional
connectivity networks and their modular organization. However, most graph-based
methods suffer from a fundamental resolution limit that may have affected
previous studies and prevented detection of modules, or communities, that are
smaller than a specific scale. Surprise, a resolution-limit-free function
rooted in discrete probability theory, has been recently introduced and applied
to brain networks, revealing a wide size-distribution of functional modules, in
contrast with many previous reports. However, the use of Surprise is limited to
binary networks, while brain networks are intrinsically weighted, reflecting a
continuous distribution of connectivity strengths between different brain
regions. Here, we propose Asymptotical Surprise, a continuous version of
Surprise, for the study of weighted brain connectivity networks, and validate
this approach in synthetic networks endowed with a ground-truth modular
structure. We compare Asymptotical Surprise with leading community detection
methods currently in use and show its superior sensitivity in the detection of
small modules even in the presence of noise and intersubject variability such
as those observed in fMRI data. Finally, we apply our novel approach to
functional connectivity networks from resting state fMRI experimenta, and
demonstrate a heterogeneous modular organization, with a wide distribution of
clusters spanning multiple scales.
The Origin of Motif Families in Food Webs
Date: 2016-09-14 00:00:00

Food webs have been found to exhibit remarkable motif profiles, patterns in
the relative prevalences of all possible three-species sub-graphs, and this has
been related to ecosystem properties such as stability and robustness.
Analysing 46 food webs of various kinds, we find that most food webs fall into
one of two distinct motif families. The separation between the families is well
predicted by a global measure of hierarchical order in directed networks -
trophic coherence. We find that trophic coherence is also a good predictor for
the extent of omnivory, defined as the tendency of species to feed on multiple
trophic levels. We compare our results to a network assembly model that admits
tunable trophic coherence via a single free parameter. The model is able to
generate food webs in either of the two families by varying this parameter, and
correctly classifies almost all the food webs in our database. This establishes
a link between global order and local preying patterns in food webs.
Inhibitory loop robustly induces anticipated synchronization in neuronal microcircuits
Date: 2016-09-14 00:00:00
Tasks: Diversity

We investigate the synchronization properties between two excitatory coupled
neurons in the presence of an inhibitory loop mediated by an interneuron.
Dynamical inhibition together with noise independently applied to each neuron
provide phase diversity in the dynamics of the neuronal motif. We show that the
interplay between the coupling strengths and external noise controls the phase
relations between the neurons in a counter-intuitive way. For a master-slave
configuration (unidirectional coupling) we find that the slave can anticipate
the master, on average, if the slave is subject to the inhibitory feedback. In
this non-usual regime, called anticipated synchronization (AS), the phase of
the post-synaptic neuron is advanced with respect to that of the pre-synaptic
neuron. We also show that the AS regime survives even in the presence of
unbalanced bidirectional excitatory coupling. Moreover, for the symmetric
mutually coupled situation, the neuron that is subject to the inhibitory loop
leads in phase.
Stochastic evolution in populations of ideas
Date: 2016-09-14 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning (RL)

It is known that learning of players who interact in a repeated game can be
interpreted as an evolutionary process in a population of ideas. These
analogies have so far mostly been established in deterministic models, and
memory loss in learning has been seen to act similarly to mutation in
evolution. We here propose a representation of reinforcement learning as a
stochastic process in finite "populations of ideas". The resulting birth-death
dynamics has absorbing states and allows for the extinction or fixation of
ideas, marking a key difference to mutation-selection processes in finite
populations. We characterize the outcome of evolution in populations of ideas
for several classes of symmetric and asymmetric games.