pwc-archive/papers-with-abstracts huggingface dataset:
Developing a new biophysical tool to combine magneto-optical tweezers with super-resolution fluorescence microscopy
Date: 2015-06-23 00:00:00
Tasks: Super-Resolution

We present a novel experimental setup in which magnetic and optical tweezers
are combined for torque and force transduction onto single filamentous
molecules in a transverse configuration to allow simultaneous mechanical
measurement and manipulation. Previously we have developed a super-resolution
imaging module which in conjunction with advanced imaging techniques such as
Blinking assisted Localisation Microscopy (BaLM) achieves localisation
precision of single fluorescent dye molecules bound to DNA of ~30 nm along the
contour of the molecule; our work here describes developments in producing a
system which combines tweezing and super-resolution fluorescence imaging. The
instrument also features an acousto-optic deflector that temporally divides the
laser beam to form multiple traps for high throughput statistics collection.
Our motivation for developing the new tool is to enable direct observation of
detailed molecular topological transformation and protein binding event
localisation in a stretching/twisting mechanical assay that previously could
hitherto only be deduced indirectly from the end-to-end length variation of
DNA. Our approach is simple and robust enough for reproduction in the lab
without the requirement of precise hardware engineering, yet is capable of
unveiling the elastic and dynamic properties of filamentous molecules that have
been hidden using traditional tools.
Neural Control of Redundant (Abundant) Systems as Algorithms Stabilizing Subspaces
Date: 2015-06-23 00:00:00

We address the problem of stability of motor actions implemented by the
central nervous system based on simple algorithms potentially reflecting
physical (including physiological) processes within the body. A number of
conceptually simple algorithms that solve motor tasks with a high probability
of success may be based on feedback schemes that ensure stability of subspaces
of neural variables associated with accomplishing those tasks.
  The task is formulated in terms of linear constrains imposed either on the
human body mechanical variables or on neural variables; we discuss three
reference frames relevant to these processes. We discuss underlying basic
principles of such algorithms, their architecture, and efficiency, and compare
the outcomes of implementation of such algorithms with the results of
experiments performed on the human hand.
Computational neuroanatomy: mapping cell-type densities in the mouse brain, simulations from the Allen Brain Atlas
Date: 2015-06-23 00:00:00
Tasks: Specificity

The Allen Brain Atlas (ABA) of the adult mouse consists of digitized
expression profiles of thousands of genes in the mouse brain, co-registered to
a common three-dimensional template (the Allen Reference Atlas). This
brain-wide, genome-wide data set has triggered a renaissance in neuroanatomy.
Its voxelized version (with cubic voxels of side 200 microns) can be analyzed
on a desktop computer using MATLAB. On the other hand, brain cells exhibit a
great phenotypic diversity (in terms of size, shape and electrophysiological
activity), which has inspired the names of some well-studied cell types, such
as granule cells and medium spiny neurons. However, no exhaustive taxonomy of
brain cells is available. A genetic classification of brain cells is under way,
and some cell types have been characterized by their transcriptome profiles.
However, given a cell type characterized by its transcriptome, it is not clear
where else in the brain similar cells can be found. The ABA can been used to
solve this region-specificity problem in a data-driven way: rewriting the
brain-wide expression profiles of all genes in the atlas as a sum of
cell-type-specific transcriptome profiles is equivalent to solving a quadratic
optimization problem at each voxel in the brain. However, the estimated
brain-wide densities of 64 cell types published recently were based on one
series of co-registered coronal in situ hybridization (ISH) images per gene,
whereas the online ABA contains several image series per gene, including
sagittal ones. In the presented work, we simulate the variability of cell-type
densities in a Monte Carlo way by repeatedly drawing a random image series for
each gene and solving optimization problems. This yields error bars on the
region-specificity of cell types.
Bounds on transient instability for complex ecosystems
Date: 2016-06-24 00:00:00

Stability is a desirable property of complex ecosystems. If a community of
interacting species is at a stable equilibrium point then it is able to
withstand small perturbations to component species' abundances without
suffering adverse effects. In ecology, the Jacobian matrix evaluated at an
equilibrium point is known as the community matrix, which describes the
population dynamics of interacting species. A system's asymptotic short- and
long-term behaviour can be determined from eigenvalues derived from the
community matrix. Here we use results from the theory of pseudospectra to
describe intermediate, transient dynamics. We first recover the established
result that the transition from stable to unstable dynamics includes a region
of `transient instability', where the effect of a small perturbation to
species' abundances---to the population vector---is amplified before ultimately
decaying. Then we show that the shift from stability to transient instability
can be affected by uncertainty in, or small changes to, entries in the
community matrix, and determine lower and upper bounds to the maximum amplitude
of perturbations to the population vector. Of five different types of community
matrix, we find that amplification is least severe when predator-prey
interactions dominate. This analysis is relevant to other systems whose
dynamics can be expressed in terms of the Jacobian matrix. Our results will
lead to improved understanding of how multiple perturbations to a complex
system may irrecoverably break stability.
Nonparametric and arbitrage-free construction of call surfaces using l1-recovery
Date: 2016-08-15 00:00:00
Tasks: compressed sensing

This paper is devoted to the application of an $l_1$ -minimisation technique
to construct an arbitrage-free call-option surface. We propose a
nononparametric approach to obtaining model-free call option surfaces that are
perfectly consistent with market quotes and free of static arbitrage. The
approach is inspired from the compressed-sensing framework that is used in
signal processing to deal with under-sampled signals. We address the problem of
fitting the call-option surface to sparse option data. To illustrate the
methodology, we proceed to the construction of the whole call-price surface of
the S\&P500 options, taking into account the arbitrage possibilities in the
time direction. The resulting object is a surface free of both butterfly and
calendar-spread arbitrage that matches the original market points. We then move
on to an FX application, namely the HKD/USD call-option surface.
The effects of MAPK activity on cell-cell adhesion during wound healing
Date: 2015-06-23 00:00:00

The mechanisms underlying collective migration, or the coordinated movement
of a population of cells, are not well understood despite its ubiquitous
nature. As a means to investigate collective migration, we consider a wound
healing scenario in which a population of cells fills in the empty space left
from a scratch wound. Here we present a simplified mathematical model that uses
reaction-diffusion equations to model collective migration during wound healing
with an emphasis on cell movement and its response to both cell signaling and
cell-cell adhesion. We use the model to investigate the effect of the MAPK
signaling cascade on cell-cell adhesion during wound healing after EGF
treatment. Our results suggest that activation of the MAPK signaling cascade
stimulates collective migration through increases in the pulling strength of
leader cells. We further use the model to suggest that treating a cell
population with EGF converts the time to wound closure (as function of wound
area) from parabolic to linear.
Market shape formation, statistical equilibrium and neutral evolution theory
Date: 2015-06-23 00:00:00

Mathematical methods of population genetics and framework of exchangeability
provide a Markov chain model for analysis and interpretation of stochastic
behaviour of equity markets, explaining, in particular, market shape formation,
statistical equilibrium and temporal stability of market weights.
The effect of disorder in the contact probability of elongated conformations of biopolymers
Date: 2015-06-24 00:00:00

Biopolymers are characterized by heterogeneous interactions, and usually
perform their biological tasks forming contacts within domains of limited size.
Combining polymer theory with a replica approach, we study the scaling
properties of the probability of contact formation in random heteropolymers as
a function of their linear distance. It is found that close or above the
theta--point, it is possible to define a contact probability which is typical
(i.e. "self-averaging") for different realizations of the heterogeneous
interactions, and which displays an exponential cut--off, dependent on
temperature and on the interaction range. In many cases this cut--off is
comparable with the typical sizes of domains in biopolymers. While it is well
known that disorder causes interesting effects at low temperature, the behavior
elucidated in the present study is an example of a non--trivial effect at high
temperature.
d'Alembert's direct and inertial forces acting on populations: the Price equation and the fundamental theorem of natural selection
Date: 2015-10-20 00:00:00

I develop a framework for interpreting the forces that act on any population
described by frequencies. The conservation of total frequency, or total
probability, shapes the characteristics of force. I begin with Fisher's
fundamental theorem of natural selection. That theorem partitions the total
evolutionary change of a population into two components. The first component is
the partial change caused by the direct force of natural selection, holding
constant all aspects of the environment. The second component is the partial
change caused by the changing environment. I demonstrate that Fisher's
partition of total change into the direct force of selection and the forces
from the changing environmental frame of reference is identical to d'Alembert's
principle of mechanics, which separates the work done by the direct forces from
the work done by the inertial forces associated with the changing frame of
reference. In d'Alembert's principle, there exist inertial forces from a change
in the frame of reference that exactly balance the direct forces. I show that
the conservation of total probability strongly shapes the form of the balance
between the direct and inertial forces. I then use the strong results for
conserved probability to obtain general results for the change in any system
quantity, such as biological fitness or energy. Those general results derive
from simple coordinate changes between frequencies and system quantities.
Ultimately, d'Alembert's separation of direct and inertial forces provides deep
conceptual insight into the interpretation of forces and the unification of
disparate fields of study.
Commentary: The nature of cancer research
Date: 2015-06-24 00:00:00

Cancer research reflects an implicit conflict. On the one hand, there is an
overwhelming desire to control the disease. We all wish that. On the other
hand, we would like to understand why cancer follows so many clearly defined
yet puzzling patterns. Why is there such regularity in the rates of
progression? Why do different tissues vary so much? There should, of course, be
no conflict between control and understanding. But the history of cancer
research seems to say that those different goals remain oddly estranged. Peto's
1977 article locates the seeds of this conflict most clearly. He describes what
is still the most powerful theoretical perspective for analyzing the causes of
cancer. He presents many key unsolved puzzles within that context. He also says
why most cancer researchers are not interested in these fundamental issues. The
subsequent decades of research grew around this rift, blindly, in the way that
research disciplines often grow. Let us revisit Peto, almost 40 years ago. We
can learn much about the current nature of cancer research.
On Game-Theoretic Risk Management (Part One) -- Towards a Theory of Games with Payoffs that are Probability-Distributions
Date: 2020-04-24 00:00:00
Tasks: Management

Optimal behavior in (competitive) situation is traditionally determined with
the help of utility functions that measure the payoff of different actions.
Given an ordering on the space of revenues (payoffs), the classical axiomatic
approach of von Neumann and Morgenstern establishes the existence of suitable
utility functions, and yields to game-theory as the most prominent
materialization of a theory to determine optimal behavior. Although this
appears to be a most natural approach to risk management too, applications in
critical infrastructures often violate the implicit assumption of actions
leading to deterministic consequences. In that sense, the gameplay in a
critical infrastructure risk control competition is intrinsically random in the
sense of actions having uncertain consequences. Mathematically, this takes us
to utility functions that are probability-distribution-valued, in which case we
loose the canonic (in fact every possible) ordering on the space of payoffs,
and the original techniques of von Neumann and Morgenstern no longer apply.
  This work introduces a new kind of game in which uncertainty applies to the
payoff functions rather than the player's actions (a setting that has been
widely studied in the literature, yielding to celebrated notions like the
trembling hands equilibrium or the purification theorem). In detail, we show
how to fix the non-existence of a (canonic) ordering on the space of
probability distributions by only mildly restricting the full set to a subset
that can be totally ordered. Our vehicle to define the ordering and establish
basic game-theory is non-standard analysis and hyperreal numbers.
Asymmetric evolutionary games
Date: 2015-06-24 00:00:00

Evolutionary game theory is a powerful framework for studying evolution in
populations of interacting individuals. A common assumption in evolutionary
game theory is that interactions are symmetric, which means that the players
are distinguished by only their strategies. In nature, however, the microscopic
interactions between players are nearly always asymmetric due to environmental
effects, differing baseline characteristics, and other possible sources of
heterogeneity. To model these phenomena, we introduce into evolutionary game
theory two broad classes of asymmetric interactions: ecological and genotypic.
Ecological asymmetry results from variation in the environments of the players,
while genotypic asymmetry is a consequence of the players having differing
baseline genotypes. We develop a theory of these forms of asymmetry for games
in structured populations and use the classical social dilemmas, the Prisoner's
Dilemma and the Snowdrift Game, for illustrations. Interestingly, asymmetric
games reveal essential differences between models of genetic evolution based on
reproduction and models of cultural evolution based on imitation that are not
apparent in symmetric games.
Too dynamic to fail. Empirical support for an autocatalytic model of Minsky's financial instability hypothesis
Date: 2015-07-13 00:00:00

Solomon and Golo [1] have recently proposed an autocatalytic
(self-reinforcing) feedback model which couples a macroscopic system parameter
(the interest rate), a microscopic parameter that measures the distribution of
the states of the individual agents (the number of firms in financial
difficulty) and a peer-to-peer network effect (contagion across supply chain
financing). In this model, each financial agent is characterized by its
resilience to the interest rate. Above a certain rate the interest due on the
firm's financial costs exceeds its earnings and the firm becomes susceptible to
failure (ponzi). For the interest rate levels under a certain threshold level,
the firm loans are smaller then its earnings and the firm becomes 'hedge.' In
this paper, we fit the historical data (2002-2009) on interest rate data into
our model, in order to predict the number of the ponzi firms. We compare the
prediction with the data taken from a large panel of Italian firms over a
period of 9 years. We then use trade credit linkages to discuss the connection
between the ponzi density and the network percolation.
  We find that the 'top-down'-'bottom-up' positive feedback loop accounts for
most of the Minsky crisis accelerator dynamics. The peer-to-peer ponzi
companies contagion becomes significant only in the last stage of the crisis
when the ponzi density is above a critical value. Moreover the ponzi contagion
is limited only to the companies that were not dynamic enough to substitute
their distressed clients with new ones. In this respect the data support a view
in which the success of the economy depends on substituting the static
'supply-network' picture with an interacting dynamic agents one.
Spatial structures in a simple model of population dynamics for parasite-host interactions
Date: 2015-06-25 00:00:00

Spatial patterning can be crucially important for understanding the behavior
of interacting populations. Here we investigate a simple model of parasite and
host populations in which parasites are random walkers that must come into
contact with a host in order to reproduce. We focus on the spatial arrangement
of parasites around a single host, and we derive using analytics and numerical
simulations the necessary conditions placed on the parasite fecundity and
lifetime for the populations long-term survival. We also show that the parasite
population can be pushed to extinction by a large drift velocity, but,
counterintuitively, a small drift velocity generally increases the parasite
population.
Detecting single-trial EEG evoked potential using a wavelet domain linear mixed model: application to error potentials classification
Date: 2015-06-25 00:00:00
Tasks: Binary Classification, Dimensionality Reduction, EEG, Electroencephalogram (EEG), parameter estimation

Objective. The main goal of this work is to develop a model for multi-sensor
signals such as MEG or EEG signals, that accounts for the inter-trial
variability, suitable for corresponding binary classification problems. An
important constraint is that the model be simple enough to handle small size
and unbalanced datasets, as often encountered in BCI type experiments.
Approach. The method involves linear mixed effects statistical model, wavelet
transform and spatial filtering, and aims at the characterization of localized
discriminant features in multi-sensor signals. After discrete wavelet transform
and spatial filtering, a projection onto the relevant wavelet and spatial
channels subspaces is used for dimension reduction. The projected signals are
then decomposed as the sum of a signal of interest (i.e. discriminant) and
background noise, using a very simple Gaussian linear mixed model. Main
results. Thanks to the simplicity of the model, the corresponding parameter
estimation problem is simplified. Robust estimates of class-covariance matrices
are obtained from small sample sizes and an effective Bayes plug-in classifier
is derived. The approach is applied to the detection of error potentials in
multichannel EEG data, in a very unbalanced situation (detection of rare
events). Classification results prove the relevance of the proposed approach in
such a context. Significance. The combination of linear mixed model, wavelet
transform and spatial filtering for EEG classification is, to the best of our
knowledge, an original approach, which is proven to be effective. This paper
improves on earlier results on similar problems, and the three main ingredients
all play an important role.
A Bayesian Model of the Litigation Game
Date: 2015-06-20 00:00:00
Tasks: model

Over a century ago, Oliver Wendell Holmes invited scholars to look at the law
through the lens of probability theory: "The prophecies of what the courts will
do in fact, and nothing more pretentious, are what I mean by the law." Yet few
legal scholars have taken up this intriguing invitation. As such, in place of
previous approaches to the study of law, this paper presents a non-normative,
mathematical approach to law and the legal process. Specifically, we present a
formal Bayesian model of civil and criminal litigation, or what we refer to as
the litigation game; that is, instead of focusing on the rules of civil or
criminal procedure or substantive legal doctrine, we ask and attempt to answer
a mathematical question: what is the posterior probability that a defendant in
a civil or criminal trial will be found liable, given that the defendant has,
in fact, committed a wrongful act?
Reduced functional connectivity within the primary motor cortex of patients with brachial plexus injury
Date: 2015-06-25 00:00:00
Tasks: Functional Connectivity

This study aims at the effects of traumatic brachial plexus lesion with root
avulsions (BPA) upon the organization of the primary motor cortex (M1). Nine
right-handed patients with a right BPA in whom an intercostal to
musculocutaneous (ICN-MC) nerve transfer was performed had post-operative
resting state fRMI scanning. The analysis of empirical functional correlations
between neighboring voxels revealed faster decay as a function of distance in
the M1 region corresponding to the arm in BPA patients as compared to the
control group. No differences between the two groups were found in the face
area. We also investigated whether such larger decay in patients could be
attributed to a gray matter diminution in M1. Structural imaging analysis
showed no difference in gray matter density between groups. Our findings
suggest that the faster decay in neighboring functional correlations without
any gray matter diminution in BPA patients could be related to a reduced
activity in intrinsic horizontal connections in M1 responsible by upper limb
motor synergies.
Detecting adaptive evolution in phylogenetic comparative analysis using the Ornstein-Uhlenbeck model
Date: 2015-06-25 00:00:00
Tasks: Model Selection

Phylogenetic comparative analysis is an approach to inferring evolutionary
process from a combination of phylogenetic and phenotypic data. The last few
years have seen increasingly sophisticated models employed in the evaluation of
more and more detailed evolutionary hypotheses, including adaptive hypotheses
with multiple selective optima and hypotheses with rate variation within and
across lineages. The statistical performance of these sophisticated models has
received relatively little systematic attention, however. We conducted an
extensive simulation study to quantify the statistical properties of a class of
models toward the simpler end of the spectrum that model phenotypic evolution
using Ornstein-Uhlenbeck processes. We focused on identifying where, how, and
why these methods break down so that users can apply them with greater
understanding of their strengths and weaknesses. Our analysis identifies three
key determinants of performance: a discriminability ratio, a signal-to-noise
ratio, and the number of taxa sampled. Interestingly, we find that
model-selection power can be high even in regions that were previously thought
to be difficult, such as when tree size is small. On the other hand, we find
that model parameters are in many circumstances difficult to estimate
accurately, indicating a relative paucity of information in the data relative
to these parameters. Nevertheless, we note that accurate model selection is
often possible when parameters are only weakly identified. Our results have
implications for more sophisticated methods inasmuch as the latter are
generalizations of the case we study.