pwc-archive/papers-with-abstracts huggingface dataset:
Chebyshev Reduced Basis Function applied to Option Valuation
Date: 2017-06-22 00:00:00

We present a numerical method for the frequent pricing of financial
derivatives that depends on a large number of variables. The method is based on
the construction of a polynomial basis to interpolate the value function of the
problem by means of a hierarchical orthogonalization process that allows to
reduce the number of degrees of freedom needed to have an accurate
representation of the value function. In the paper we consider, as an example,
a GARCH model that depends on eight parameters and show that a very large
number of contracts for different maturities and asset and parameters values
can be valued in a small computational time with the proposed procedure. In
particular the method is applied to the problem of model calibration. The
method is easily generalizable to be used with other models or problems.
Group-wise 3D registration based templates to study the evolution of ant worker neuroanatomy
Date: 2017-08-21 00:00:00

The evolutionary success of ants and other social insects is considered to be
intrinsically linked to division of labor and emergent collective intelligence.
The role of the brains of individual ants in generating these processes,
however, is poorly understood. One genus of ant of special interest is
Pheidole, which includes more than a thousand species, most of which are
dimorphic, i.e. their colonies contain two subcastes of workers: minors and
majors. Using confocal imaging and manual annotations, it has been demonstrated
that minor and major workers of different ages of three species of Pheidole
have distinct patterns of brain size and subregion scaling. However, these
studies require laborious effort to quantify brain region volumes and are
subject to potential bias. To address these issues, we propose a group-wise 3D
registration approach to build for the first time bias-free brain atlases of
intra- and inter-subcaste individuals and automatize the segmentation of new
individuals.
Control of Dynamics in Brain Networks
Date: 2018-05-14 00:00:00

The ability to effectively control brain dynamics holds great promise for the
enhancement of cognitive function in humans, and the betterment of their
quality of life. Yet, successfully controlling dynamics in neural systems is
challenging, in part due to the immense complexity of the brain and the large
set of interactions that can drive any single change. While we have gained some
understanding of the control of single neurons, the control of large-scale
neural systems -- networks of multiply interacting components -- remains poorly
understood. Efforts to address this gap include the construction of tools for
the control of brain networks, mostly adapted from control and dynamical
systems theory. Informed by current opportunities for practical intervention,
these theoretical contributions provide models that draw from a wide array of
mathematical approaches. We present intriguing recent developments for
effective strategies of control in dynamic brain networks, and we also describe
potential mechanisms that underlie such processes. We review efforts in the
control of general neurophysiological processes with implications for brain
development and cognitive function, as well as the control of altered
neurophysiological processes in medical contexts such as anesthesia
administration, seizure suppression, and deep-brain stimulation for Parkinson's
disease. We conclude with a forward-looking discussion regarding how emerging
results from network control -- especially approaches that deal with nonlinear
dynamics or more realistic trajectories for control transitions -- could be
used to directly address pressing questions in neuroscience.
Metabolite patterns reveal regulatory responses to genetic perturbations
Date: 2017-01-10 00:00:00

Genetic and environmental perturbation experiments have been used to study
microbes in a bid to gain insight into transcriptional regulation, adaptive
evolution, and other cellular dynamics. These studies have potential in
enabling rational strain design. Unfortunately, experimentally determined
intracellular flux distribution are often inconsistent or incomparable due to
different experimental conditions and methodologies. Computational strain
design relies on constraint-based reconstruction and analysis (COBRA)
techniques to predict the effect of gene knockouts such as flux balance
analysis (FBA), regulatory on/off minimization(ROOM), minimization of metabolic
adjustment (MOMA), relative optimality in metabolic networks (RELATCH). Most of
these knock-out prediction methods are based on conserving inherent flux
patterns (between wild type and mutant) that are thought to be representative
of the cellular regulatory structure. However, it has been recently
demonstrated that these methods show poor agreement with experimental data. To
improve the fidelity of knockout predictions and subsequent computational
strain design, we developed REMEP, a metabolite-centric method. We demonstrate
the improved performance of REMEP by comparing the different methods on
experimental knockout data of E. coli, and S. cerevisiae grown in batch
cultures. REMEP retains most of the features of earlier algorithms but is much
more accurate in capturing cellular responses to genetic perturbations. A
primary reason for this is that REMEP relies on the assumption that cellular
regulatory structure leaves a signature on metabolite patterns and not just
flux patterns. REMEP will also prove useful in uncovering novel insights into
cellular regulation and control.
Asset correlation estimation for inhomogeneous exposure pools
Date: 2019-09-11 00:00:00
Tasks: Time Series, Time Series Analysis

A possible data source for the estimation of asset correlations is default
time series. This study investigates the systematic error that is made if the
exposure pool underlying a default time series is assumed to be homogeneous
when in reality it is not. We find that the asset correlation will always be
underestimated if homogeneity with respect to the probability of default (PD)
is wrongly assumed, and the error is the larger the more spread out the PD is
within the exposure pool. If the exposure pool is inhomogeneous with respect to
the asset correlation itself then the error may be going in both directions,
but for most PD- and asset correlation ranges relevant in practice the asset
correlation is systematically underestimated. Both effects stack up and the
error tends to become even larger if in addition a negative correlation between
asset correlation and PD is assumed, which is plausible in many circumstances
and consistent with the Basel RWA formula. It is argued that the generic
inhomogeneity effect described is one of the reasons why asset correlations
measured from default data tend to be lower than asset correlations derived
from asset value data.
Differentially Private Neighborhood-based Recommender Systems
Authors: Wang Jun, Tang Qiang
Date: 2017-03-10 00:00:00
Tasks: Recommendation Systems

Privacy issues of recommender systems have become a hot topic for the society
as such systems are appearing in every corner of our life. In contrast to the
fact that many secure multi-party computation protocols have been proposed to
prevent information leakage in the process of recommendation computation, very
little has been done to restrict the information leakage from the
recommendation results. In this paper, we apply the differential privacy
concept to neighborhood-based recommendation methods (NBMs) under a
probabilistic framework. We first present a solution, by directly calibrating
Laplace noise into the training process, to differential-privately find the
maximum a posteriori parameters similarity. Then we connect differential
privacy to NBMs by exploiting a recent observation that sampling from the
scaled posterior distribution of a Bayesian model results in provably
differentially private systems. Our experiments show that both solutions allow
promising accuracy with a modest privacy budget, and the second solution yields
better accuracy if the sampling asymptotically converges. We also compare our
solutions to the recent differentially private matrix factorization (MF)
recommender systems, and show that our solutions achieve better accuracy when
the privacy budget is reasonably small. This is an interesting result because
MF systems often offer better accuracy when differential privacy is not
applied.
Stability for gains from large investors' strategies in M1/J1 topologies
Date: 2018-03-26 00:00:00

We prove continuity of a controlled SDE solution in Skorokhod's $M_1$ and
$J_1$ topologies and also uniformly, in probability, as a non-linear functional
of the control strategy. The functional comes from a finance problem to model
price impact of a large investor in an illiquid market. We show that
$M_1$-continuity is the key to ensure that proceeds and wealth processes from
(self-financing) c\`{a}dl\`{a}g trading strategies are determined as the
continuous extensions for those from continuous strategies. We demonstrate by
examples how continuity properties are useful to solve different stochastic
control problems on optimal liquidation and to identify asymptotically
realizable proceeds.
Structural propagation in a production network with restoring substitution elasticities
Date: 2018-04-13 00:00:00
Tasks: Clustering

We model an economy-wide production network by cascading binary compounding
functions, based on the sequential processing nature of the production
activities. As we observe a hierarchy among the intermediate processes spanning
the empirical input--output transactions, we utilize a stylized sequence of
processes for modeling the intra-sectoral production activities. Under the
productivity growth that we measure jointly with the state-restoring elasticity
parameters for each sectoral activity, the network of production completely
replicates the records of multi-sectoral general equilibrium prices and shares
for all factor inputs observed in two temporally distant states. Thereupon, we
study propagation of a small exogenous productivity shock onto the structure of
production networks by way of hierarchical clustering.
Sedimentation of Reversibly Interacting Macromolecules with Changes in Fluorescence Quantum Yield
Date: 2017-02-21 00:00:00

Sedimentation velocity analytical ultracentrifugation with fluorescence
detection has emerged as a powerful method for the study of interacting systems
of macromolecules. It combines picomolar sensitivity with high hydrodynamic
resolution, and can be carried out with photoswitchable fluorophores for
multi-component discrimination, to determine the stoichiometry, affinity, and
shape of macromolecular complexes with dissociation equilibrium constants from
picomolar to micromolar. A popular approach for data interpretation is the
determination of the binding affinity by isotherms of weight-average
sedimentation coefficients, sw. A prevailing dogma in sedimentation analysis is
that the weight-average sedimentation coefficient from the transport method
corresponds to the signal- and population-weighted average of all species. We
show that this does not always hold true for systems that exhibit significant
signal changes with complex formation - properties that may be readily
encountered in practice, e.g., from a change in fluorescence quantum yield.
Coupled transport in the reaction boundary of rapidly reversible systems can
make significant contributions to the observed migration in a way that cannot
be accounted for in the standard population-based average. Effective particle
theory provides a simple physical picture for the reaction-coupled migration
process. On this basis we develop a more general binding model that converges
to the well-known form of sw with constant signals, but can account
simultaneously for hydrodynamic co-transport in the presence of changes in
fluorescence quantum yield. We believe this will be useful when studying
interacting systems exhibiting fluorescence quenching, enhancement or Forster
resonance energy transfer with transport methods.
Information theory, predictability, and the emergence of complex life
Date: 2017-10-17 00:00:00

Despite the obvious advantage of simple life forms capable of fast
replication, different levels of cognitive complexity have been achieved by
living systems in terms of their potential to cope with environmental
uncertainty. Against the inevitable cost associated to detecting environmental
cues and responding to them in adaptive ways, we conjecture that the potential
for predicting the environment can overcome the expenses associated to
maintaining costly, complex structures. We present a minimal formal model
grounded in information theory and selection, in which successive generations
of agents are mapped into transmitters and receivers of a coded message. Our
agents are guessing machines and their capacity to deal with environments of
different complexity defines the conditions to sustain more complex agents.
Dynamical compensation in biological systems as a particular case of structural non-identifiability
Date: 2017-01-10 00:00:00

Dynamical compensation (DC) has been recently defined as the ability of a
biological system to keep its output dynamics unchanged in the face of varying
parameters. This concept is purported to describe a design principle that
provides robustness to physiological circuits. Here we note the similitude
between DC and Structural Identifiability (SI), and we argue that the former
can be explained in terms of (lack of) the latter. We propose to exploit this
fact by using currently existing tools for SI analysis to perform DC analysis.
We demonstrate the feasibility of this approach with four physiological
circuits, for which we confirm the correspondence between DC and lack of SI. We
also warn that care should we taken when using an unidentifiable model to
extract biological insight, since lack of SI can be the result of an
inappropriate choice of model structure and therefore not necessarily a sign of
biological robustness.
Loose social organisation of AB strain zebrafish groups in a two-patch environment
Date: 2017-01-10 00:00:00

We explore the collective behaviours of 7 group sizes: 1, 2, 3, 5, 7, 10 and
20 AB zebrafish (Danio rerio) in a constraint environment composed of two
identical squared rooms connected by a corridor. This simple set-up is similar
to a natural patchy environment. We track the positions and the identities of
the fish and compute the metrics at the group and at the individual levels.
First, we show that the size of the population affects the behaviour of each
individual in a group, the cohesion of the groups, the preferential
interactions and the transition dynamics between the two rooms. Second, during
collective departures, we show that the rankings of exit correspond to the
topological organisations of the fish prior to their collective departure with
no leadership. This spatial organisation emerge in the group a few seconds
before a collective departure. These results provide new evidences on the
spatial organisation of the groups and the effect of the population size on
individual and collective behaviours in a patchy environment.
Issues in data expansion in understanding criticality in biological systems
Date: 2017-07-17 00:00:00

At the point of a second order phase transition also termed as a critical
point, systems display long range order and their macroscopic behaviors are
independent of the microscopic details making up the system. Due to these
properties, it has long been speculated that biological systems that show
similar behavior despite having very different microscopics, may be operating
near a critical point. Recent methods in neuroscience are making it possible to
explore whether criticality exists in neural networks. Despite being large in
size, many data sets are still only a minute sample of the neural system and
methods towards expanding these data sets have to be considered in order to
study the existence of criticality. In this work we develop an analytical
method of expanding a dataset to the large N limit so that statements about the
critical nature of the data set could be made. We also show using a particular
dataset analyzed computationally in literature that expanding data sets keeping
the moments of the original data set need not lead to unique values of the
critical temperature when the large N limit is considered analytically, despite
the mirage of them appearing to do so when analyzed computationally. This
suggests that not all available data sets from experiments are amenable for
understanding the critically of the underlying system.
On Low Overlap Among Search Results of Academic Search Engines
Authors: Mitra Anasua, Awekar Amit
Date: 2017-01-10 00:00:00
Tasks: Articles

Number of published scholarly articles is growing exponentially. To tackle
this information overload, researchers are increasingly depending on niche
academic search engines. Recent works have shown that two major general web
search engines: Google and Bing, have high level of agreement in their top
search results. In contrast, we show that various academic search engines have
low degree of agreement among themselves. We performed experiments using 2500
queries over four academic search engines. We observe that overlap in search
result sets of any pair of academic search engines is significantly low and in
most of the cases the search result sets are mutually exclusive. We also
discuss implications of this low overlap.
Economic information from Smart Meter: Nexus Between Demand Profile and Electricity Retail Price Between Demand Profile and Electricity Retail Price
Date: 2016-09-16 00:00:00
Tasks: Clustering

In this paper, we demonstrate that a consumer's marginal system impact is
only determined by their demand profile rather than their demand level. Demand
profile clustering is identical to cluster consumers according to their
marginal impacts on system costs. A profile-based uniform-rate price is
economically efficient as real-time pricing. We develop a criteria system to
evaluate the economic efficiency of an implemented retail price scheme in a
distribution system by comparing profile clustering and daily-average
clustering. Our criteria system can examine the extent of a retail price
scheme's inefficiency even without information about the distribution system's
daily cost structure. We analyze data from a real distribution system in China.
In this system, targeting each consumer's high-impact days is more efficient
than target high-impact consumers.
The Influence of Collaboration in Procurement Relationships
Date: 2016-10-10 00:00:00
Tasks: Management, Survey

Supply Chain Management often requires independent organizations to work
together to achieve shared objectives. This collaboration is necessary when
coordinated actions benefit the group more than the uncoordinated efforts of
individual firms. Despite the commonly reported benefits that can be gained in
close relationships, recent research has indicated that collaboration attempts
between purchasing firms and their suppliers have not been as widespread as
anticipated. Using a survey of procurement professionals, this research
investigates how the purchasing function utilizes collaboration in its supply
chain relationships. Structural equation modeling is used to identify how
information sharing, decision synchronization, incentive alignment,
collaborative communication, and trust impact collaboration, as well as how
collaboration impacts performance. Results from 86 survey responses indicate
that firms are still not fully utilizing collaborative relationships.
Sur la d\'ecomposabilit\'e empirique des indicateurs de pauvret\'e
Date: 2016-12-26 00:00:00

We study the empirical decomposition of poverty indicators. This property is
very important and convenient in the context of the fight against poverty.
Indeed, it makes it possible to put in place sectoral poverty reduction
policies on the basis of a relevant stratification laid down at the outset. The
simultaneous impacts of these policies, measured as reduction gains over the
population as a whole, is then obtained by aggregating those obtained at each
stratum by a relatively simple formula. It turns out that indicators as
important as those of Sen and Shorrocks do not verify this property contrary to
the elements of the class of Foster - Greer and Thorbecke. Given the data from
the 1996 Senegalese Survey of Households (ESAM), we show that the lack of
decomposability of these indicators on the income variable for several types of
population stratification is practically zero , of the order of one to two per
thousand. This makes it possible to use the decomposition of the Sen and
Shorrocks indicators without any untoward consequences. An explanatory model of
these results is presented for future research.
Mathematical models describing the effects of different tax evasion behaviors
Date: 2016-12-19 00:00:00

Microscopic models describing a whole of economic interactions in a closed
society are considered. The presence of a tax system combined with a
redistribution process is taken into account, as well as the occurrence of tax
evasion. In particular, the existence is postulated, in relation to the level
of evasion, of different individual taxpayer behaviors. The effects of the
mentioned different behaviors on shape and features of the emerging income
distribution profile are investigated qualitatively and quantitatively.
Numerical solutions show that the Gini inequality index of the total population
increases when the evasion level is higher, but does not depend significantly
on the evasion spread. For fixed spread, the relative difference between the
average incomes of the worst evaders and honest taxpayers increases
approximately as a quadratic function of the evasion level.
Phase-type Approximation of the Gerber-Shiu Function
Date: 2017-01-10 00:00:00
Tasks: Vocal Bursts Type Prediction

The Gerber-Shiu function provides a way of measuring the risk of an insurance
company. It is given by the expected value of a function that depends on the
ruin time, the deficit at ruin, and the surplus prior to ruin. Its computation
requires the evaluation of the overshoot/undershoot distributions of the
surplus process at ruin. In this paper, we use the recent developments of the
fluctuation theory and approximate it in a closed form by fitting the
underlying process by phase-type Levy processes. A sequence of numerical
results are given.
Modeling stochastic skew of FX options using SLV models with stochastic spot/vol correlation and correlated jumps
Date: 2017-01-18 00:00:00
Tasks: All

It is known that the implied volatility skew of FX options demonstrates a
stochastic behavior which is called stochastic skew. In this paper we create
stochastic skew by assuming the spot/instantaneous variance correlation to be
stochastic. Accordingly, we consider a class of SLV models with stochastic
correlation where all drivers - the spot, instantaneous variance and their
correlation are modeled by Levy processes. We assume all diffusion components
to be fully correlated as well as all jump components. A new fully implicit
splitting finite-difference scheme is proposed for solving forward PIDE which
is used when calibrating the model to market prices of the FX options with
different strikes and maturities. The scheme is unconditionally stable, of
second order of approximation in time and space, and achieves a linear
complexity in each spatial direction. The results of simulation obtained by
using this model demonstrate capacity of the presented approach in modeling
stochastic skew.