pwc-archive/papers-with-abstracts huggingface dataset:
A noise-induced mechanism for biological homochirality of early life self-replicators
Date: 2015-08-17 00:00:00

The observed single-handedness of biological amino acids and sugars has long
been attributed to autocatalysis. However, the stability of homochiral states
in deterministic autocatalytic systems relies on cross inhibition of the two
chiral states, an unlikely scenario for early life self-replicators. Here, we
present a theory for a stochastic individual-level model of autocatalysis due
to early life self-replicators. Without chiral inhibition, the racemic state is
the global attractor of the deterministic dynamics, but intrinsic
multiplicative noise stabilizes the homochiral states, in both well-mixed and
spatially-extended systems. We conclude that autocatalysis is a viable
mechanism for homochirality, without imposing additional nonlinearities such as
chiral inhibition.
Dissipation in noisy chemical networks: The role of deficiency
Date: 2015-11-11 00:00:00

We study the effect of intrinsic noise on the thermodynamic balance of
complex chemical networks subtending cellular metabolism and gene regulation. A
topological network property called deficiency, known to determine the
possibility of complex behavior such as multistability and oscillations, is
shown to also characterize the entropic balance. In particular, only when
deficiency is zero does the average stochastic dissipation rate equal that of
the corresponding deterministic model, where correlations are disregarded. In
fact, dissipation can be reduced by the effect of noise, as occurs in a toy
model of metabolism that we employ to illustrate our findings. This phenomenon
highlights that there is a close interplay between deficiency and the
activation of new dissipative pathways at low molecule numbers.
The Long-Term Swap Rate and a General Analysis of Long-Term Interest Rates
Authors: Francesca Biagini, Alessandro Gnoatto, Maximilian Härtel
Date: 2015-07-01 00:00:00

We introduce here for the first time the long-term swap rate, characterised as the fair rate of an overnight indexed swap with infinitely many exchanges. Furthermore we analyse the relationship between the long-term swap rate, the long-term yield, see Biagini et al. [2018], Biagini and H\"artel [2014], and El Karoui et al. [1997], and the long-term simple rate, considered in Brody and Hughston [2016] as long-term discounting rate. We finally investigate the existence of these long-term rates in two term structure methodologies, the Flesaker-Hughston model and the linear-rational model. A numerical example illustrates how our results can be used to estimate the non-optional component of a CoCo bond.
Expected Shortfall is jointly elicitable with Value at Risk - Implications for backtesting
Date: 2015-07-12 00:00:00

In this note, we comment on the relevance of elicitability for backtesting
risk measure estimates. In particular, we propose the use of Diebold-Mariano
tests, and show how they can be implemented for Expected Shortfall (ES), based
on the recent result of Fissler and Ziegel (2015) that ES is jointly elicitable
with Value at Risk.
Particle-based simulation of ellipse-shaped particle aggregation as a model for vascular network formation
Date: 2015-08-14 00:00:00

Computational modelling is helpful for elucidating the cellular mechanisms
driving biological morphogenesis. Previous simulation studies of blood vessel
growth based on the Cellular Potts model (CPM) proposed that elongated,
adhesive or mutually attractive endothelial cells suffice for the formation of
blood vessel sprouts and vascular networks. Because each mathematical
representation of a model introduces potential artifacts, it is important that
model results are reproduced using alternative modelling paradigms. Here, we
present a lattice-free, particle-based simulation of the cell elongation model
of vasculogenesis. The new, particle-based simulations confirm the results
obtained from the previous Cellular Potts simulations. Furthermore, our current
findings suggest that the emergence of order is possible with the application
of a high enough attractive force or, alternatively, a longer attraction
radius. The methodology will be applicable to a range of problems in
morphogenesis and noisy particle aggregation in which cell shape is a key
determining factor.
Dynamic similarity promotes interpersonal coordination in joint-action
Date: 2015-12-22 00:00:00

Human movement has been studied for decades and dynamic laws of motion that
are common to all humans have been derived. Yet, every individual moves
differently from everyone else (faster/slower, harder/smoother etc). We propose
here an index of such variability, namely an individual motor signature (IMS)
able to capture the subtle differences in the way each of us moves. We show
that the IMS of a person is time-invariant and that it significantly differs
from those of other individuals. This allows us to quantify the dynamic
similarity, a measure of rapport between dynamics of different individuals'
movements, and demonstrate that it facilitates coordination during interaction.
We use our measure to confirm a key prediction of the theory of similarity that
coordination between two individuals performing a joint-action task is higher
if their motions share similar dynamic features. Furthermore, we use a virtual
avatar driven by an interactive cognitive architecture based on feedback
control theory to explore the effects of different kinematic features of the
avatar motion on the coordination with human players.
Analysis of Professional Trajectories using Disconnected Self-Organizing Maps
Date: 2015-06-30 00:00:00
Tasks: Clustering

In this paper we address an important economic question. Is there, as
mainstream economic theory asserts it, an homogeneous labor market with
mechanisms which govern supply and demand for work, producing an equilibrium
with its remarkable properties? Using the Panel Study of Income Dynamics (PSID)
collected on the period 1984-2003, we study the situations of American workers
with respect to employment. The data include all heads of household (men or
women) as well as the partners who are on the labor market, working or not.
They are extracted from the complete survey and we compute a few relevant
features which characterize the worker's situations. To perform this analysis,
we suggest using a Self-Organizing Map (SOM, Kohonen algorithm) with specific
structure based on planar graphs, with disconnected components (called D-SOM),
especially interesting for clustering. We compare the results to those obtained
with a classical SOM grid and a star-shaped map (called SOS). Each component of
D-SOM takes the form of a string and corresponds to an organized cluster. From
this clustering, we study the trajectories of the individuals among the classes
by using the transition probability matrices for each period and the
corresponding stationary distributions. As a matter of fact, we find clear
evidence of heterogeneous parts, each one with high homo-geneity, representing
situations well identified in terms of activity and wage levels and in degree
of stability in the workplace. These results and their interpretation in
economic terms contribute to the debate about flexibility which is commonly
seen as a way to obtain a better level of equilibrium on the labor market.
Inequality and risk aversion in economies open to altruistic attitudes
Date: 2016-05-11 00:00:00

This paper attempts to find a relationship between agents' risk aversion and
inequality of incomes. Specifically, a model is proposed for the evolution in
time of surplus/deficit distribution, and the long-time distributions are
characterized almost completely. They turn out to be weak Pareto laws with
exponent linked to the relative risk aversion index which, in turn, is supposed
to be the same for every agent. On the one hand, the aforesaid link is
expressed by an affine transformation. On the other hand, the level of the
relative risk aversion index results from a frequency distribution of
observable quantities stemming from how agents interact in an economic sense.
Combination of these facts is conducive to the specification of qualitative and
quantitative characteristics of actions fit for the control of income
concentration.
MiStImm: a simulation tool to compare classical nonsef-centered immune models with a novel self-centered model
Date: 2018-03-31 00:00:00

Our main purpose is to compare classical nonself-centered, two-signal
theoretical models of the adaptive immune system with a novel, self-centered,
one-signal model developed by our research group. Our model hypothesizes that
the immune system of a fetus is capable learning the limited set of self
antigens but unable to prepare itself for the unlimited variety of nonself
antigens. We have built a computational model that simulates the development of
the adaptive immune system. For simplicity, we concentrated on humoral immunity
and its major components: T cells, B cells, antibodies, interleukins,
non-immune self cells, and foreign antigens. Our model is a microscopic one,
similar to the interacting particle models of statistical physics and
agent-based models in immunology. Furthermore, our model is stochastic: events
are considered random and modeled by a continuous time, finite state Markov
process, that is, they are controlled by finitely many independent exponential
clocks.
  We investigate under what conditions can an immune memory be created that
results in a more effective immune response to a repeated infection. The
simulations show that our self-centered model is realistic. Moreover, in case
of a primary adaptive immune reaction, it can destroy infections more
efficiently than a classical nonself-centered model.
  Predictions of our theoretical model were clinically supported by
autoimmune-related adverse events in high-dose immune checkpoint inhibitor
immunotherapy trials and also by safe and successful low-dose immune checkpoint
inhibitor combination treatment of heavily pretreated stage IV cancer patients
who had exhausted all conventional treatments.
  The MiStImm simulation tool and source codes are available at the address
https://github.com/kerepesi/MiStImm.
Filtering Patent Maps for Visualization of Diversification Paths of Inventors and Organizations
Authors: Yan Bowen, Luo Jianxi
Date: 2016-06-25 00:00:00
Tasks: Patent classification

In the information science literature, recent studies have used patent
databases and patent classification information to construct network maps of
patent technology classes. In such a patent technology map, almost all pairs of
technology classes are connected, whereas most of the connections between them
are extremely weak. This observation suggests the possibility of filtering the
patent network map by removing weak links. However, removing links may reduce
the explanatory power of the network on inventor or organization
diversification. The network links may explain the patent portfolio
diversification paths of inventors and inventing organizations. We measure the
diversification explanatory power of the patent network map, and present a
method to objectively choose an optimal trade-off between explanatory power and
removing weak links. We show that this method can remove a degree of
arbitrariness compared with previous filtering methods based on arbitrary
thresholds, and also identify previous filtering methods that created filters
outside the optimal trade-off. The filtered map aims to aid in network
visualization analyses of the technological diversification of inventors,
organizations and other innovation agents, and potential foresight analysis.
Such applications to a prolific inventor (Leonard Forbes) and company (Google)
are demonstrated.
Modeling epidemics on adaptively evolving networks: a data-mining perspective
Date: 2015-06-25 00:00:00

The exploration of epidemic dynamics on dynamically evolving ("adaptive")
networks poses nontrivial challenges to the modeler, such as the determination
of a small number of informative statistics of the detailed network state (that
is, a few "good observables") that usefully summarize the overall (macroscopic,
systems level) behavior. Trying to obtain reduced, small size, accurate models
in terms of these few statistical observables - that is, coarse-graining the
full network epidemic model to a small but useful macroscopic one - is even
more daunting. Here we describe a data-based approach to solving the first
challenge: the detection of a few informative collective observables of the
detailed epidemic dynamics. This will be accomplished through Diffusion Maps, a
recently developed data-mining technique. We illustrate the approach through
simulations of a simple mathematical model of epidemics on a network: a model
known to exhibit complex temporal dynamics. We will discuss potential
extensions of the approach, as well as possible shortcomings.
The Effects of Leverage Requirements and Fire Sales on Financial Contagion via Asset Liquidation Strategies in Financial Networks
Date: 2016-10-13 00:00:00

This paper provides a framework for modeling the financial system with
multiple illiquid assets when liquidation of illiquid assets is caused by
failure to meet a leverage requirement. This extends the network model of
Cifuentes, Shin & Ferrucci (2005) which incorporates a single asset with fire
sales and capital adequacy ratio. This also extends the network model of
Feinstein (2015) which incorporates multiple illiquid assets with fire sales
and no leverage ratios. We prove existence of equilibrium clearing payments and
liquidation prices for a known liquidation strategy when leverage requirements
are required. We also prove sufficient conditions for the existence of an
equilibrium liquidation strategy with corresponding clearing payments and
liquidation prices. Finally we calibrate network models to asset and liability
data for 50 banks in the United States from 2007-2014 in order to draw
conclusions on systemic risk as a function of leverage requirements.
Locating a Tree in a Reticulation-Visible Network in Cubic Time
Date: 2015-11-11 00:00:00

In this work, we answer an open problem in the study of phylogenetic
networks. Phylogenetic trees are rooted binary trees in which all edges are
directed away from the root, whereas phylogenetic networks are rooted acyclic
digraphs. For the purpose of evolutionary model validation, biologists often
want to know whether or not a phylogenetic tree is contained in a phylogenetic
network. The tree containment problem is NP-complete even for very restricted
classes of networks such as tree-sibling phylogenetic networks. We prove that
this problem is solvable in cubic time for stable phylogenetic networks. A
linear time algorithm is also presented for the cluster containment problem.
Quantum Gates and Quantum Circuits of Stock Portfolio
Date: 2015-07-10 00:00:00
Tasks: Time Series, Time Series Analysis

In quantum computation, series of quantum gates have to be arranged in a
predefined sequence that led to a quantum circuit in order to solve a
particular problem. What if the sequence of quantum gates is known but both the
problem to be solved and the outcome of the so defined quantum circuit remain
in the shadow? This is the situation of the stock market. The price time series
of a portfolio of stocks are organized in braids that effectively simulate
quantum gates in the hypothesis of Ising anyons quantum computational model.
Following the prescriptions of Ising anyons model, 1-qubit quantum gates are
constructed for portfolio composed of four stocks. Adding two additional stocks
at the initial portfolio result in 2-qubits quantum gates and circuits.
Hadamard gate, Pauli gates or controlled-Z gate are some of the elementary
quantum gates that are identified in the stock market structure. Addition of
other pairs of stocks, that eventually represent a market index, like Dow Jones
industrial Average, it results in a sequence of n-qubits quantum gates that
form a quantum code. Deciphering this mysterious quantum code of the stock
market is an issue for future investigations.
High Frequency Haplotypes are Expected Events, not Historical Figures
Date: 2015-09-17 00:00:00
Tasks: Attribute, Vocal Bursts Intensity Prediction

Cultural transmission of reproductive success states that successful men have
more children and pass this raised fecundity to their offspring. Balaresque and
colleagues found high frequency haplotypes in a Central Asian Y chromosome
dataset, which they attribute to cultural transmission of reproductive success
by prominent historical men, including Genghis Khan. Using coalescent
simulation, we show that these high frequency haplotypes are consistent with a
neutral model, where they commonly appear simply by chance. Hence, explanations
invoking cultural transmission of reproductive success are statistically
unnecessary.
Model-independent bounds for Asian options: a dynamic programming approach
Date: 2016-07-20 00:00:00

We consider the problem of finding model-independent bounds on the price of
an Asian option, when the call prices at the maturity date of the option are
known. Our methods differ from most approaches to model-independent pricing in
that we consider the problem as a dynamic programming problem, where the
controlled process is the conditional distribution of the asset at the maturity
date. By formulating the problem in this manner, we are able to determine the
model-independent price through a PDE formulation. Notably, this approach does
not require specific constraints on the payoff function (e.g. convexity), and
would appear to generalise to many related problems.
Loops and autonomy promote evolvability of ecosystem networks
Date: 2015-07-10 00:00:00

The structure of ecological networks, in particular food webs, determines
their ability to evolve further, i.e. evolvability. The knowledge about how
food web evolvability is determined by the structures of diverse ecological
networks can guide human interventions purposefully to either promote or limit
evolvability of ecosystems. However, the focus of prior food web studies was on
stability and robustness; little is known regarding the impact of ecological
network structures on their evolvability. To correlate ecosystem structure and
evolvability, we adopt the NK model originally from evolutionary biology to
generate and assess the ruggedness of fitness landscapes of a wide spectrum of
model food webs with gradual variation in the amount of feeding loops and link
density. The variation in network structures is controlled by linkage rewiring.
Our results show that more feeding loops and lower trophic link density, i.e.
higher autonomy of species, of food webs increase the potential for the
ecosystem to generate heritable variations with improved fitness. Our findings
allow the prediction of the evolvability of actual food webs according to their
network structures, and provide guidance to enhancing or controlling the
evolvability of specific ecosystems.
Switching to non-affine stochastic volatility: A closed-form expansion for the Inverse Gamma model
Date: 2016-03-20 00:00:00
Tasks: Form

This paper introduces the Inverse Gamma (IGa) stochastic volatility model
with time-dependent parameters, defined by the volatility dynamics
$dV_{t}=\kappa_{t}\left(\theta_{t}-V_{t}\right)dt+\lambda_{t}V_{t}dB_{t}$. This
non-affine model is much more realistic than classical affine models like the
Heston stochastic volatility model, even though both are as parsimonious (only
four stochastic parameters). Indeed, it provides more realistic volatility
distribution and volatility paths, which translate in practice into more robust
calibration and better hedging accuracy, explaining its popularity among
practitioners. In order to price vanilla options with IGa volatility, we
propose a closed-form volatility-of-volatility expansion. Specifically, the
price of a European put option with IGa volatility is approximated by a
Black-Scholes price plus a weighted combination of Black-Scholes greeks, where
the weights depend only on the four time-dependent parameters of the model.
This closed-form pricing method allows for very fast pricing and calibration to
market data. The overall quality of the approximation is very good, as shown by
several calibration tests on real-world market data where expansion prices are
compared favorably with Monte Carlo simulation results. This paper shows that
the IGa model is as simple, more realistic, easier to implement and faster to
calibrate than classical transform-based affine models. We therefore hope that
the present work will foster further research on non-affine models like the
Inverse Gamma stochastic volatility model, all the more so as this robust model
is of great interest to the industry.
Optimal Vaccination Strategies and Rational Behaviour in Seasonal Epidemics
Date: 2016-03-18 00:00:00

We consider a SIR model with temporary immunity and time dependent
transmission rate. We assume time dependent vaccination which confers the same
immunity as natural infection. We study two types of vaccination strategies: i)
optimal vaccination, in the sense that it minimizes the effort of vaccination
in the set of vaccination strategies for which, for any sufficiently small
perturbation of the disease free state, the number of infectious individuals is
monotonically decreasing; ii) Nash-equilibria strategies where all individuals
simultaneously minimize the joint risk of vaccination versus the risk of the
disease. The former case corresponds to an optimal solution for mandatory
vaccinations, while the second correspond to the equilibrium to be expected if
vaccination is fully voluntary. We are able to show the existence of both an
optimal and Nash strategies in a general setting. In general, these strategies
will not be functions but Radon measures. For specific forms of the
transmission rate, we provide explicit formulas for the optimal and the Nash
vaccination strategies.
Genoogle: an indexed and parallelized search engine for similar DNA sequences
Authors: Albrecht Felipe
Date: 2015-07-10 00:00:00

The search for similar genetic sequences is one of the main bioinformatics
tasks. The genetic sequences data banks are growing exponentially and the
searching techniques that use linear time are not capable to do the search in
the required time anymore. Another problem is that the clock speed of the
modern processors are not growing as it did before, instead, the processing
capacity is growing with the addiction of more processing cores and the
techniques which does not use parallel computing does not have benefits from
these extra cores. This work aims to use data indexing techniques to reduce the
searching process computation cost united with the parallelization of the
searching techniques to use the computational capacity of the multi core
processors. To verify the viability of using these two techniques
simultaneously, a software which uses parallelization techniques with inverted
indexes was developed.
  Experiments were executed to analyze the performance gain when parallelism is
utilized, the search time gain, and also the quality of the results when it
compared with others searching tools. The results of these experiments were
promising, the parallelism gain overcame the expected speedup, the searching
time was 20 times faster than the parallelized NCBI BLAST, and the searching
results showed a good quality when compared with this tool.
  The software source code is available at
https://github.com/felipealbrecht/Genoogle .