pwc-archive/papers-with-abstracts huggingface dataset:
Superparamagnetism of tryptophan and walk memory of proteins
Date: 2015-08-27 00:00:00

Superparamagnetism of tryptophan implying the presence of magnetic domain is
reported. The observation helps us to conceive assembly of proteins as a
physical lattice gas with multidimensional Ising character, each lattice points
assuming discrete spin states. When magnetic field is applied the equilibrium
is lost and the population density of one spin state increases (unidirectional
alignment), resulting in net magnetization. Spatial coherence between the
identical spin states further imparts a ferromagnetic memory. This effect is
observed using direct nanoscale video imaging. Out of the three proteins
ferritin serum albumin and fibrinogen, fibrinogen showed an attenuated
response, the protein being essentially one dimensional. Eventually, Ising
lattice is capable of showing ferromagnetic memory only when it has a higher
dimensional character. The study highlights possible presence of long range
spatial coherence at physiological condition and a plausible microscopic origin
of the same.
Communication shapes sensory response in multicellular networks
Date: 2016-03-17 00:00:00

Collective sensing by interacting cells is observed in a variety of
biological systems, and yet a quantitative understanding of how sensory
information is collectively encoded is lacking. Here we investigate the
ATP-induced calcium dynamics of monolayers of fibroblast cells that communicate
via gap junctions. Combining experiments and stochastic modeling, we find that
increasing the ATP stimulus increases the propensity for calcium oscillations
despite large cell-to-cell variability. The model further predicts that the
oscillation propensity increases not only with the stimulus, but also with the
cell density due to increased communication. Experiments confirm this
prediction, showing that cell density modulates the collective sensory
response. We further implicate cell-cell communication by coculturing the
fibroblasts with cancer cells, which we show act as "defects" in the
communication network, thereby reducing the oscillation propensity. These
results suggest that multicellular networks sit at a point in parameter space
where cell-cell communication has a significant effect on the sensory response,
allowing cells to simultaneously respond to a sensory input and to the presence
of neighbors.
Vaccine escape in 2013-4 and the hydropathic evolution of glycoproteins of A/H3N2 viruses
Date: 2015-08-27 00:00:00

More virulent strains of influenza virus subtypes H1N1 appeared widely in
2007 and H3N2 in 2011, and especially 2013-4, when the effectiveness of the
H3N2 vaccine decreased nearly to zero. The amino acid differences of
neuraminidase from prior less virulent strains appear to be small (<1%) when
tabulated through sequence alignments and counting site identities and
similarities. Here we show how analyzing fractal hydropathic forces responsible
for neuraminidase globular compaction and modularity quantifies the mutational
origins of increased virulence. It also predicts vaccine escape and specifies
optimized targets for the 2015 H3N2 vaccine different from the WHO target.
Unlike some earlier methods based on measuring hemagglutinin antigenic drift
and ferret sera, which take several years, cover only a few candidate strains,
and are ambiguous, the new methods are timely and can be completed, using NCBI
and GISAID amino acid sequences only, in a few days.
Time-dependent scaling patterns in high frequency financial data
Date: 2015-12-16 00:00:00
Tasks: Time Series, Time Series Analysis, Vocal Bursts Intensity Prediction

We measure the influence of different time-scales on the dynamics of
financial market data. This is obtained by decomposing financial time series
into simple oscillations associated with distinct time-scales. We propose two
new time-varying measures: 1) an amplitude scaling exponent and 2) an
entropy-like measure. We apply these measures to intraday, 30-second sampled
prices of various stock indices. Our results reveal intraday trends where
different time-horizons contribute with variable relative amplitudes over the
course of the trading day. Our findings indicate that the time series we
analysed have a non-stationary multifractal nature with predominantly
persistent behaviour at the middle of the trading session and anti-persistent
behaviour at the open and close. We demonstrate that these deviations are
statistically significant and robust.
Early warning of large volatilities based on recurrence interval analysis in Chinese stock markets
Date: 2015-08-29 00:00:00
Tasks: Decision Making, Management

Being able to forcast extreme volatility is a central issue in financial risk
management. We present a large volatility predicting method based on the
distribution of recurrence intervals between volatilities exceeding a certain
threshold $Q$ for a fixed expected recurrence time $\tau_Q$. We find that the
recurrence intervals are well approximated by the $q$-exponential distribution
for all stocks and all $\tau_Q$ values. Thus a analytical formula for
determining the hazard probability $W(\Delta t |t)$ that a volatility above $Q$
will occur within a short interval $\Delta t$ if the last volatility exceeding
$Q$ happened $t$ periods ago can be directly derived from the $q$-exponential
distribution, which is found to be in good agreement with the empirical hazard
probability from real stock data. Using these results, we adopt a
decision-making algorithm for triggering the alarm of the occurrence of the
next volatility above $Q$ based on the hazard probability. Using a "receiver
operator characteristic" (ROC) analysis, we find that this predicting method
efficiently forecasts the occurrance of large volatility events in real stock
data. Our analysis may help us better understand reoccurring large volatilities
and more accurately quantify financial risks in stock markets.
Combining exome and gene expression datasets in one graphical model of disease to empower the discovery of disease mechanisms
Date: 2015-08-30 00:00:00
Tasks: Data Integration

Identifying genes associated with complex human diseases is one of the main
challenges of human genetics and computational medicine. To answer this
question, millions of genetic variants get screened to identify a few of
importance. To increase the power of identifying genes associated with diseases
and to account for other potential sources of protein function aberrations, we
propose a novel factor-graph based model, where much of the biological
knowledge is incorporated through factors and priors. Our extensive simulations
show that our method has superior sensitivity and precision compared to
variant-aggregating and differential expression methods. Our integrative
approach was able to identify important genes in breast cancer, identifying
genes that had coding aberrations in some patients and regulatory abnormalities
in others, emphasizing the importance of data integration to explain the
disease in a larger number of patients.
Forecasting Exchange Rates Using Time Series Analysis: The sample of the currency of Kazakhstan
Date: 2015-08-30 00:00:00
Tasks: Time Series, Time Series Analysis

This paper models yearly exchange rates between USD/KZT, EUR/KZT and SGD/KZT,
and compares the actual data with developed forecasts using time series
analysis over the period from 2006 to 2014. The official yearly data of
National Bank of the Republic of Kazakhstan is used for present study. The main
goal of this paper is to apply the ARIMA model for forecasting of yearly
exchange rates of USD/KZT, EUR/KZT and SGD/KZT. The accuracy of the forecast is
compared with Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE)
and Root Mean Squared Error (RMSE).
Noise expands the response range of the Bacillus subtilis competence circuit
Date: 2015-08-30 00:00:00
Tasks: Decision Making

Gene regulatory circuits must contend with intrinsic noise that arises due to
finite numbers of proteins. While some circuits act to reduce this noise,
others appear to exploit it. A striking example is the competence circuit in
Bacillus subtilis, which exhibits much larger noise in the duration of its
competence events than a synthetically constructed analog that performs the
same function. Here, using stochastic modeling and fluorescence microscopy, we
show that this larger noise allows cells to exit terminal phenotypic states,
which expands the range of stress levels to which cells are responsive and
leads to phenotypic heterogeneity at the population level. This is an important
example of how noise confers a functional benefit in a genetic decision-making
circuit.
Approximating the Sum of Correlated Lognormals: An Implementation
Date: 2015-08-30 00:00:00

Lognormal random variables appear naturally in many engineering disciplines,
including wireless communications, reliability theory, and finance. So, too,
does the sum of (correlated) lognormal random variables. Unfortunately, no
closed form probability distribution exists for such a sum, and it requires
approximation. Some approximation methods date back over 80 years and most take
one of two approaches, either: 1) an approximate probability distribution is
derived mathematically, or 2) the sum is approximated by a single lognormal
random variable. In this research, we take the latter approach and review a
fairly recent approximation procedure proposed by Mehta, Wu, Molisch, and Zhang
(2007), then implement it using C++. The result is applied to a discrete time
model commonly encountered within the field of financial economics.
An in-host model of HIV incorporating latent infection and viral mutation
Date: 2015-08-30 00:00:00

We construct a seven-component model of the in-host dynamics of the Human
Immunodeficiency Virus Type-1 (i.e, HIV) that accounts for latent infection and
the propensity of viral mutation. A dynamical analysis is conducted and a
theorem is presented which characterizes the long time behavior of the model.
Finally, we study the effects of an antiretroviral drug and treatment
implications.
Body size affects the strength of social interactions and spatial organisation of a schooling fish (Pseudomugil signifer)
Date: 2017-04-19 00:00:00

While a rich variety of self-propelled particle models propose to explain the
collective motion of fish and other animals, rigorous statistical comparison
between models and data remains a challenge. Plausible models should be
flexible enough to capture changes in the collective behaviour of animal groups
at their different developmental stages and group sizes. Here we analyse the
statistical properties of schooling fish (Pseudomugil signifer) through a
combination of experiments and simulations. We make novel use of a Boltzmann
inversion method, usually applied in molecular dynamics, to identify the
effective potential of the mean force of fish interactions. Specifically, we
show that larger fish have a larger repulsion zone, but stronger attraction,
resulting in greater alignment in their collective motion. We model the
collective dynamics of schools using a self-propelled particle model, modified
to include varying particle speed and a local repulsion rule. We demonstrate
that the statistical properties of the fish schools are reproduced by our
model, thereby capturing a number of features of the behaviour and development
of schooling fish.
Dynamical features of the MAPK cascade
Date: 2015-08-31 00:00:00

The MAP kinase cascade is an important signal transduction system in
molecular biology for which a lot of mathematical modelling has been done. This
paper surveys what has been proved mathematically about the qualitative
properties of solutions of the ordinary differential equations arising as
models for this biological system. It focusses, in particular, on the issues of
multistability and the existence of sustained oscillations. It also gives a
concise introduction to the mathematical techniques used in this context,
bifurcation theory and geometric singular perturbation theory, as they relate
to these specific examples. In addition further directions are presented in
which the applications of these techniques could be extended in the future.
Fundamental Properties of the Evolution of Mutational Robustness
Date: 2015-08-31 00:00:00

Evolution on neutral networks of genotypes has been found in models to
concentrate on genotypes with high mutational robustness, to a degree
determined by the topology of the network. Here analysis is generalized beyond
neutral networks to arbitrary selection and parent-offspring transmission. In
this larger realm, geometric features determine mutational robustness: the
alignment of fitness with the orthogonalized eigenvectors of the mutation
matrix weighted by their eigenvalues. "House of cards" mutation is found to
preclude the evolution of mutational robustness. Genetic load is shown to
increase with increasing mutation in arbitrary single and multiple locus
fitness landscapes. The rate of decrease in population fitness can never grow
as mutation rates get higher, showing that "error catastrophes" for genotype
frequencies never cause precipitous losses of population fitness. The
"inclusive inheritance" approach taken here naturally extends these results to
a new concept of dispersal robustness.
A reduced-form model for level-1 limit order books
Date: 2016-11-14 00:00:00
Tasks: Form

One popular approach to model the limit order books dynamics of the best bid
and ask at level-1 is to use the reduced-form diffusion approximations. It is
well known that the biggest contributing factor to the price movement is the
imbalance of the best bid and ask. We investigate the data of the level-1 limit
order books of a basket of stocks and study the numerical evidence of drift,
correlation, volatility and their dependence on the imbalance. Based on the
numerical discoveries, we develop a nonparametric discrete model for the
dynamics of the best bid and ask, which can be approximated by a reduced-form
model with analytical tractability that can fit the empirical data of
correlation, volatilities and probability of price movement simultaneously.
Topological schemas of cognitive maps and spatial learning in the hippocampus
Date: 2016-03-20 00:00:00
Tasks: Hippocampus

Spatial navigation in mammals is based on building a mental representation of
their environment---a cognitive map. However, both the nature of this cognitive
map and its underpinning in neural structures and activity remains vague. A key
difficulty is that these maps are collective, emergent phenomena that cannot be
reduced to a simple combination of inputs provided by individual neurons. In
this paper we suggest computational frameworks for integrating the spiking
signals of individual cells into a spatial map, which we call schemas. We
provide examples of four schemas defined by different types of topological
relations that may be neurophysiologically encoded in the brain and demonstrate
that each schema provides its own large-scale characteristics of the
environment---the schema integrals. Moreover, we find that, in all cases, these
integrals are learned at a rate which is faster than the rate of complete
training of neural networks. Thus, the proposed schema framework differentiates
between the cognitive aspect of spatial learning and the physiological aspect
at the neural network level.
Assessing systemic risk due to fire sales spillover through maximum entropy network reconstruction
Date: 2018-07-30 00:00:00

Assessing systemic risk in financial markets is of great importance but it
often requires data that are unavailable or available at a very low frequency.
For this reason, systemic risk assessment with partial information is
potentially very useful for regulators and other stakeholders. In this paper we
consider systemic risk due to fire sales spillover and portfolio rebalancing by
using the risk metrics defined by Greenwood et al. (2015). By using the Maximum
Entropy principle we propose a method to assess aggregated and single bank's
systemicness and vulnerability and to statistically test for a change in these
variables when only the information on the size of each bank and the
capitalization of the investment assets are available. We prove the
effectiveness of our method on 2001-2013 quarterly data of US banks for which
portfolio composition is available.
The dynamics of simple gene-network motifs subject to extrinsic fluctuations
Date: 2016-01-30 00:00:00

Cellular processes do not follow deterministic rules; even in identical
environments genetically identical cells can make random choices leading to
different phenotypes. This randomness originates from fluctuations present in
the biomolecular interaction networks. Most previous work has been focused on
the intrinsic noise (IN) of these networks. Yet, especially for
high-copy-number biomolecules, extrinsic or environmental noise (EN) has been
experimentally shown to dominate the variation. Here, we develop an analytical
formalism that allows for calculation of the effect of EN on gene-expression
motifs. We introduce a method for modeling bounded EN as an auxiliary species
in the master equation. The method is fully generic and is not limited to
systems with small EN magnitudes. We focus our study on motifs that can be
viewed as the building blocks of genetic switches: a nonregulated gene, a
self-inhibiting gene, and a self-promoting gene. The role of the EN properties
(magnitude, correlation time, and distribution) on the statistics of interest
are systematically investigated, and the effect of fluctuations in different
reaction rates is compared. Due to its analytical nature, our formalism can be
used to quantify the effect of EN on the dynamics of biochemical networks and
can also be used to improve the interpretation of data from single-cell
gene-expression experiments.
Cointegrating Jumps: an Application to Energy Facilities
Date: 2016-07-28 00:00:00
Tasks: Form

Based on the concept of self-decomposable random variables we discuss the
application of a model for a pair of dependent Poisson processes to energy
facilities. Due to the resulting structure of the jump events we can see the
self-decomposability as a form of cointegration among jumps. In the context of
energy facilities, the application of our approach to model power or gas
dynamics and to evaluate transportation assets seen as spread options is
straightforward. We study the applicability of our methodology first assuming a
Merton market model with two underlying assets; in a second step we consider
price dynamics driven by an exponential mean-reverting Geometric
Ornstein-Uhlenbeck plus compound Poisson that are commonly used in the energy
field. In this specific case we propose a price spot dynamics for each
underlying that has the advantage of being treatable to find non-arbitrage
conditions. In particular we can find close-form formulas for vanilla options
so that the price and the Greeks of spread options can be calculated in close
form using the Margrabe formula (if the strike is zero) or some other well
known approximation.
Correction to Black-Scholes formula due to fractional stochastic volatility
Date: 2017-03-19 00:00:00

Empirical studies show that the volatility may exhibit correlations that
decay as a fractional power of the time offset. The paper presents a rigorous
analysis for the case when the stationary stochastic volatility model is
constructed in terms of a fractional Ornstein Uhlenbeck process to have such
correlations. It is shown how the associated implied volatility has a term
structure that is a function of maturity to a fractional power.
Control principles of metabolic networks
Date: 2015-08-17 00:00:00

Deciphering the control principles of metabolism and its interaction with
other cellular functions is central to biomedicine and biotechnology. Yet,
understanding the efficient control of metabolic fluxes remains elusive for
large-scale metabolic networks. Existing methods either require specifying a
cellular objective or are limited to small networks due to computational
complexity. Here we develop an efficient computational framework for flux
control by introducing a complete set of flux coupling relations. We analyze 23
metabolic networks from all kingdoms of life, and identify the driver reactions
facilitating their control on a large scale. We find that most unicellular
organisms require less extensive control than multicellular organisms. The
identified driver reactions are under strong transcriptional regulation in
Escherichia coli. In human cancer cells driver reactions play pivotal roles in
tumor development, representing potential therapeutic targets. The proposed
framework helps us unravel the regulatory principles of complex diseases and
design novel engineering strategies at the interface of gene regulation,
signaling, and metabolism.
The Poker-Litigation Game
Date: 2015-06-20 00:00:00
Tasks: Game of Poker

Is litigation a serious search for truth or simply a game of skill or luck?
Although the process of litigation has been modeled as a Prisoner's Dilemma, as
a War of Attrition, as a Game of Chicken and even as a simple coin toss, no one
has formally modeled litigation as a game of poker. This paper is the first to
do so. We present a simple "poker-litigation game" and find the optimal
strategy for playing this game.
Dynamic Model of the Price Dispersion of Homogeneous Goods
Date: 2015-05-27 00:00:00

Presented is an analytic microeconomic model of the temporal price dispersion
of homogeneous goods in polypoly markets. This new approach is based on the
idea that the price dispersion has its origin in the dynamics of the purchase
process. The price dispersion is determined by the chance that demanded and
supplied product units meet in a given price interval. It can be characterized
by a fat-tailed Laplace distribution for short and by a lognormal distribution
for long time horizons. Taking random temporal variations of demanded and
supplied units into account both the mean price and also the standard deviation
of the price dispersion are governed by a lognormal distribution. A comparison
with empirical investigations confirms the model statements.
Tax Bond Creation Using a Structural Model and its Extensions
Date: 2015-05-14 00:00:00

This article describes and explores taxes and debt in finance. Here a
situation is thought about, where tax payments would qualify to be considered
as debt. Using this principle we can infer that it is possible to create and
price a type of bond (Tax Normalization Guarantee) for companies, which would
allow them to enter in temporary tax breaks to allow them to free capital.
Finally it is explored a way to structure these bonds in financial products and
valuate them.
On the emergence of scale-free production networks
Date: 2016-09-19 00:00:00

We propose a simple dynamical model of the formation of production networks
among monopolistically competitive firms. The model subsumes the standard
general equilibrium approach \`a la Arrow-Debreu but displays a wide set of
potential dynamic behaviors. It robustly reproduces key stylized facts of
firms' demographics. Our main result is that competition between intermediate
good producers generically leads to the emergence of scale-free production
networks.
Interdisciplinary Business Games on Sustainable Development: Theoretical Foundations and Prospects of Implementation
Date: 2015-05-28 00:00:00

The article defines the place of business games among all games in general
based on the classification by F.G. Junger; it provides critical analysis of
existing business games types; it also formulates requirements and lays
theoretical foundations and elements of the methodology and organization of
interdisciplinary business games (IBG) on sustainable development as a special
type of business games. In addition, it examines the prospects of IBG
implementation in higher education for sustainable development, using
information technology and computer resources.