pwc-archive/papers-with-abstracts huggingface dataset:
Filament turnover is essential for continuous long range contractile flow in a model actomyosin cortex
Date: 2016-12-22 00:00:00
Tasks: Friction

In this paper, we develop and analyze a minimal model for a 2D network of
cross-linked actin filaments and myosin motors, representing the cortical
cytoskeleton of eukaryotic cells. We implement coarse-grained representations
of force production by myosin motors and stress dissipation through an
effective cross-link friction and filament turnover. We use this model to
characterize how the sustained production of active stress, and the steady
dissipation of elastic stress, depend individually on motor activity, effective
cross-link friction and filament turnover. Then we combine these results to
gain insights into how microscopic network parameters control steady state flow
produced by asymmetric distributions of motor activity. Our results provide a
framework for understanding how local modulation of microscopic interactions
within contractile networks control macroscopic quantities like active stress
and effective viscosity to control cortical deformation and flow at cellular
scales.
Rating evaluation of sports development efficiency using statistical analysis: evidence from Russian football
Date: 2016-12-22 00:00:00

Increasing investments into various dimensions of sports draw a significant
amount of attention to the way these resources are being managed and which
organizations achieve development goals with higher efficiency. This paper
reviews the methodology of designing an efficiency rating model for assessing
sports entities, focusing on the experience of Russian football. The Russian
Regional Efficiency of Football Development model aims to evaluate the regional
federations of the Football Union of Russian via 5 dimensions. The scoring
method of the model is based on the three-sigma rule of distribution. Support
factors in the form of population density and climate were also included, since
Russian regions significantly differentiate in these aspects. The findings of
this paper showcased that not a single region was able to achieve a maximum 5-
star rating, while regions set to host the 2018 FIFA World Cup did not score
better compared to others. In conclusion the authors provide various
suggestions on further developing and implementing rating models within global
sports organizations.
Development of UMLS Based Health Care Web Services for Android Platform
Authors: Soomro Nareena, Soomro Safeeulah, Alansari Zainab, Abbasi Suhni, Belgaum Mohammad Riyaz, Khakwani Abdul Baqi
Date: 2016-12-22 00:00:00

In this fast developing world of information, the amount of medical knowledge
is rising at an exponential level. The UMLS (Unified Medical Language Systems),
is rich knowledge base consisting files and software that provides many health
and biomedical vocabularies and standards. A Web service is a web solution to
facilitate machine-to-machine interaction over a network. Few UMLS web services
are currently available for portable devices, but most of them lack in
efficiency and performance. It is proposed to develop Android-based web
services for healthcare systems underlying rich knowledge source of UMLS. The
experimental evaluation was made to analyse the efficiency and performance
effect with and without using the designed prototype. The understand-ability
and interaction with the prototype were greater than those who used the
alternate sources to obtain the answers to their questions. The overall
performance indicates that the system is convenient and easy to use. The result
of the evaluation clearly proved that designed system retrieves all the
pertinent information better than syntactic searches.
Cross-impact and no-dynamic-arbitrage
Date: 2017-08-25 00:00:00

We extend the "No-dynamic-arbitrage and market impact"-framework of Jim
Gatheral [Quantitative Finance, 10(7): 749-759 (2010)] to the multi-dimensional
case where trading in one asset has a cross-impact on the price of other
assets. From the condition of absence of dynamical arbitrage we derive
theoretical limits for the size and form of cross-impact that can be directly
verified on data. For bounded decay kernels we find that cross-impact must be
an odd and linear function of trading intensity and cross-impact from asset $i$
to asset $j$ must be equal to the one from $j$ to $i$. To test these
constraints we estimate cross-impact among sovereign bonds traded on the
electronic platform MOT. While we find significant violations of the above
symmetry condition of cross-impact, we show that these are not arbitrageable
with simple strategies because of the presence of the bid-ask spread.
Functional Hypergraph Uncovers Novel Covariant Structures over Neurodevelopment
Date: 2016-12-23 00:00:00

Brain development during adolescence is marked by substantial changes in
brain structure and function, leading to a stable network topology in
adulthood. However, most prior work has examined the data through the lens of
brain areas connected to one another in large-scale functional networks. Here,
we apply a recently-developed hypergraph approach that treats network
connections (edges) rather than brain regions as the unit of interest, allowing
us to describe functional network topology from a fundamentally different
perspective. Capitalizing on a sample of 780 youth imaged as part of the
Philadelphia Neurodevelopmental Cohort, this hypergraph representation of
resting-state functional MRI data reveals three distinct classes of
sub-networks (hyperedges): clusters, bridges, and stars, which represent
spatially distributed, bipartite, and focal architectures, respectively.
Cluster hyperedges show a strong resemblance to the functional modules of the
brain including somatomotor, visual, default mode, and salience systems. In
contrast, star hyperedges represent highly localized subnetworks centered on a
small set of regions, and are distributed across the entire cortex. Finally,
bridge hyperedges link clusters and stars in a core-periphery organization.
Notably, developmental changes within hyperedges are ordered in a similar
core-periphery fashion, with the greatest developmental effects occurring in
networked hyperedges within the functional core. Taken together, these results
emphasize that the network organization of human brain emerges across multiple
scales and evolves substantially through the adolescent period.
How fast does the clock of Finance run? - A time-definition enforcing scale invariance and quantifying overnights
Date: 2017-08-11 00:00:00

A symmetry-guided definition of time may enhance and simplify the analysis of
historical series with recurrent patterns and seasonalities. By enforcing
simple-scaling and stationarity of the distributions of returns, we identify a
successful protocol of time definition in Finance. The essential structure of
the stochastic process underlying the series can thus be analyzed within a most
parsimonious symmetry scheme in which multiscaling is reduced in the quest of a
time scale additive and independent of moment-order in the distribution of
returns. At the same time, duration of periods in which markets remain inactive
are properly quantified by the novel clock, and the corresponding (e.g.,
overnight) returns are consistently taken into account for financial
applications.
Speciation: Goldschmidt's Heresy, Once Supported by Gould and Dawkins, is Again Reinstated
Date: 2016-12-22 00:00:00

The view that the initiation of branching into two sympatric species may not
require natural selection emerged in Victorian times (Fleeming Jenkin, George
Romanes, William Bateson). In the 1980s paleontologist Steven Jay Gould gave a
theoretical underpinning of this non-genic "chromosomal" view, thus reinstating
Richard Goldschmidt's "heresy" of the 1930s. From modelling studies with
computer-generated "biomorphs," zoologist Richard Dawkins also affirmed
Goldschmidt, proclaiming the "evolution of evolvability." However, in the
1990s, while Gould and Dawkins were recanting, bioinformatic, biochemical and
cytological studies were providing a deeper underpinning. In 2001 this came
under attack from leaders in the field who favored Dawkins' genic emphasis.
Now, with growing evidence from multiple sources, we can reinstate again
Goldschmidt's view and clarify its nineteenth century roots.
Criticality in Cell Differentiation
Date: 2016-12-23 00:00:00

Cell differentiation is an important process in living organisms.
Differentiation is mostly based on binary decisions with the progenitor cells
choosing between two specific lineages. The differentiation dynamics have both
deterministic and stochastic components. Several theoretical studies suggest
that cell differentiation is a bifurcation phenomenon, well-known in dynamical
systems theory. The bifurcation point has the character of a critical point
with the system dynamics exhibiting specific features in its vicinity. These
include the critical slowing down, rising variance and lag-1 autocorrelation
function, strong correlations between the fluctuations of key variables and
non-Gaussianity in the distribution of fluctuations. Recent experimental
studies provide considerable support to the idea of criticality in cell
differentiation and in other biological processes like the development of the
fruit fly embryo. In this Review, an elementary introduction is given to the
concept of criticality in cell differentiation. The correspondence between the
signatures of criticality and experimental observations on blood cell
differentiation in mice is further highlighted.
Tradeoffs between microbial growth phases lead to frequency-dependent and non-transitive selection
Date: 2018-01-15 00:00:00

Mutations in a microbial population can increase the frequency of a genotype
not only by increasing its exponential growth rate, but also by decreasing its
lag time or adjusting the yield (resource efficiency). The contribution of
multiple life-history traits to selection is a critical question for
evolutionary biology as we seek to predict the evolutionary fates of mutations.
Here we use a model of microbial growth to show there are two distinct
components of selection corresponding to the growth and lag phases, while the
yield modulates their relative importance. The model predicts rich population
dynamics when there are tradeoffs between phases: multiple strains can coexist
or exhibit bistability due to frequency-dependent selection, and strains can
engage in rock-paper-scissors interactions due to non-transitive selection. We
characterize the environmental conditions and patterns of traits necessary to
realize these phenomena, which we show to be readily accessible to experiments.
Our results provide a theoretical framework for analyzing high-throughput
measurements of microbial growth traits, especially interpreting the pleiotropy
and correlations between traits across mutants. This work also highlights the
need for more comprehensive measurements of selection in simple microbial
systems, where the concept of an ordinary fitness landscape breaks down.
Economic Accelerator with Memory: Discrete Time Approach
Date: 2017-07-22 00:00:00

Accelerators with power-law memory are proposed in the framework of the
discrete time approach. To describe discrete accelerators we use the capital
stock adjustment principle, which has been suggested by Matthews.The suggested
discrete accelerators with memory describe the economic processes with the
power-law memory and the periodic sharp splashes (kicks). In continuous time
approach the memory is described by fractional-order differential equations. In
discrete time approach the accelerators with memory are described by discrete
maps with memory, which are derived from the fractional-order differential
equation without approximations. In order to derive these maps we use the
equivalence of fractional-order differential equations and the Volterra
integral equations.
Embedding of biological distribution networks with differing environmental constraints
Date: 2016-12-23 00:00:00

Distribution networks -- from vasculature to urban transportation systems --
are prevalent in both the natural and consumer worlds. These systems are
intrinsically physical in composition and are embedded into real space,
properties that lead to constraints on their topological organization. In this
study, we compare and contrast two types of biological distribution networks:
mycelial fungi and the vasculature system on the surface of rodent brains. Both
systems are alike in that they must route resources efficiently, but they are
also inherently distinct in terms of their growth mechanisms, and in that fungi
are not attached to a larger organism and must often function in unregulated
and varied environments. We begin by uncovering a common organizational
principle -- Rentian scaling -- that manifests as hierarchical network layout
in both physical and topological space. Simulated models of distribution
networks optimized for transport in the presence of fluctuations are also shown
to exhibit this feature in their embedding, with similar scaling exponents.
However, we also find clear differences in how the fungi and vasculature
balance tradeoffs in material cost, efficiency, and robustness. While the
vasculature appear well optimized for low cost, but relatively high efficiency,
the fungi tend to form more expensive but in turn more robust networks. These
differences may be driven by the distinct functions that each system must
perform, and the different habitats in which they reside. As a whole, this work
demonstrates that distribution networks contain a set of common, emergent
design features, as well as tailored optimizations.
Emerging Frontiers of Neuroengineering: A Network Science of Brain Connectivity
Date: 2016-12-23 00:00:00

Neuroengineering is faced with unique challenges in repairing or replacing
complex neural systems that are composed of many interacting parts. These
interactions form intricate patterns over large spatiotemporal scales, and
produce emergent behaviors that are difficult to predict from individual
elements. Network science provides a particularly appropriate framework in
which to study and intervene in such systems, by treating neural elements
(cells, volumes) as nodes in a graph and neural interactions (synapses, white
matter tracts) as edges in that graph. Here, we review the emerging discipline
of network neuroscience, which uses and develops tools from graph theory to
better understand and manipulate neural systems, from micro- to macroscales. We
present examples of how human brain imaging data is being modeled with network
analysis and underscore potential pitfalls. We then highlight current
computational and theoretical frontiers, and emphasize their utility in
informing diagnosis and monitoring, brain-machine interfaces, and brain
stimulation. A flexible and rapidly evolving enterprise, network neuroscience
provides a set of powerful approaches and fundamental insights critical to the
neuroengineer's toolkit.
Cell reprogramming modelled as transitions in a hierarchy of cell cycles
Date: 2017-09-22 00:00:00

We construct a model of cell reprogramming (the conversion of fully
differentiated cells to a state of pluripotency, known as induced pluripotent
stem cells, or iPSCs) which builds on key elements of cell biology viz. cell
cycles and cell lineages. Although reprogramming has been demonstrated
experimentally, much of the underlying processes governing cell fate decisions
remain unknown. This work aims to bridge this gap by modelling cell types as a
set of hierarchically related dynamical attractors representing cell cycles.
Stages of the cell cycle are characterised by the configuration of gene
expression levels, and reprogramming corresponds to triggering transitions
between such configurations. Two mechanisms were found for reprogramming in a
two level hierarchy: cycle specific perturbations and a noise-induced
switching. The former corresponds to a \emph{directed} perturbation that
induces a transition into a cycle state of a different cell type in the potency
hierarchy (mainly a stem cell) whilst the latter is a priori undirected and
could be induced, e.g., by a (stochastic) change in the cellular environment.
These reprogramming protocols were found to be effective in large regimes of
the parameter space and make specific predictions concerning reprogramming
dynamics which are broadly in line with experimental findings.
Optimal decoding of information from a genetic network
Date: 2016-12-23 00:00:00
Tasks: Decoder

Gene expression levels carry information about signals that have functional
significance for the organism. Using the gap gene network in the fruit fly
embryo as an example, we show how this information can be decoded, building a
dictionary that translates expression levels into a map of implied positions.
The optimal decoder makes use of graded variations in absolute expression
level, resulting in positional estimates that are precise to ~1% of the
embryo's length. We test this optimal decoder by analyzing gap gene expression
in embryos lacking some of the primary maternal inputs to the network. The
resulting maps are distorted, and these distortions predict, with no free
parameters, the positions of expression stripes for the pair-rule genes in the
mutant embryos.
The prevalence of chaotic dynamics in games with many players
Date: 2016-12-23 00:00:00

We study adaptive learning in a typical p-player game. The payoffs of the
games are randomly generated and then held fixed. The strategies of the players
evolve through time as the players learn. The trajectories in the strategy
space display a range of qualitatively different behaviors, with attractors
that include unique fixed points, multiple fixed points, limit cycles and
chaos. In the limit where the game is complicated, in the sense that the
players can take many possible actions, we use a generating-functional approach
to establish the parameter range in which learning dynamics converge to a
stable fixed point. The size of this region goes to zero as the number of
players goes to infinity, suggesting that complex non-equilibrium behavior,
exemplified by chaos, may be the norm for complicated games with many players.
Multiscale persistent functions for biomolecular structure characterization
Date: 2016-12-26 00:00:00

In this paper, we introduce multiscale persistent functions for biomolecular
structure characterization. The essential idea is to combine our multiscale
rigidity functions with persistent homology analysis, so as to construct a
series of multiscale persistent functions, particularly multiscale persistent
entropies, for structure characterization. To clarify the fundamental idea of
our method, the multiscale persistent entropy model is discussed in great
detail. Mathematically, unlike the previous persistent entropy or topological
entropy, a special resolution parameter is incorporated into our model. Various
scales can be achieved by tuning its value. Physically, our multiscale
persistent entropy can be used in conformation entropy evaluation. More
specifically, it is found that our method incorporates in it a natural
classification scheme. This is achieved through a density filtration of a
multiscale rigidity function built from bond and/or dihedral angle
distributions. To further validate our model, a systematical comparison with
the traditional entropy evaluation model is done. It is found that our model is
able to preserve the intrinsic topological features of biomolecular data much
better than traditional approaches, particularly for resolutions in the mediate
range. Moreover, our method can be successfully used in protein classification.
For a test database with around nine hundred proteins, a clear separation
between all-alpha and all-beta proteins can be achieved, using only the
dihedral and pseudo-bond angle information. Finally, a special protein
structure index (PSI) is proposed, for the first time, to describe the
"regularity" of protein structures. Essentially, PSI can be used to describe
the "regularity" information in any systems.
Markovian simulation for ancestors trees
Date: 2018-07-17 00:00:00

We present a computational model to reconstruct trees of ancestors for
animals with sexual reproduction. Through a recursive algorithm combined with a
random number generator, it is possible to reproduce the number of ancestors
for each generation and use it to constraint the maximum number of the
following generation. This new model allows to consider the reproductive
preferences of particular species and combine several trees to simulate the
behavior of a population. It is also possible to obtain a description
analytically, considering the simulation as a theoretical stochastic process.
Such process can be generalized in order to use an algorithm associated with it
to simulate other similar processes of stochastic nature. The simulation is
based in the theoretical model previously presented before.
Audio-based Distributional Semantic Models for Music Auto-tagging and Similarity Measurement
Authors: Karamanolakis Giannis, Iosif Elias, Zlatintsi Athanasia, Pikrakis Aggelos, Potamianos Alexandros
Date: 2016-12-26 00:00:00
Tasks: Music Auto-Tagging, TAG

The recent development of Audio-based Distributional Semantic Models (ADSMs)
enables the computation of audio and lexical vector representations in a joint
acoustic-semantic space. In this work, these joint representations are applied
to the problem of automatic tag generation. The predicted tags together with
their corresponding acoustic representation are exploited for the construction
of acoustic-semantic clip embeddings. The proposed algorithms are evaluated on
the task of similarity measurement between music clips. Acoustic-semantic
models are shown to outperform the state-of-the-art for this task and produce
high quality tags for audio/music clips.
Analysis of claims that the brain extracellular impedance is high and non-resistive
Date: 2017-03-26 00:00:00

Numerous measurements in the brain of the impedance between two extracellular
electrodes have shown that it is approximately resistive in the range of
biological interest, $<10\,$kHz, and has a value close to that expected from
the conductivity of physiological saline and the extracellular volume fraction
in brain tissue. Recent work from the group of Claude B\'edard and Alain
Destexhe has claimed that the impedance of the extracellular space is some
three orders of magnitude greater than these values and also displays a
$1/\sqrt{f}$ frequency dependence (above a low-frequency corner frequency).
Their measurements were performed between an intracellular electrode and an
extracellular electrode. It is argued that they incorrectly extracted the
extracellular impedance because of an inaccurate representation of the large,
confounding impedance of the neuronal membrane. In conclusion, no compelling
evidence has been provided to undermine the well established and physically
plausible consensus that the brain extracellular impedance is low and
approximately resistive