pwc-archive/papers-with-abstracts huggingface dataset:
Currency target zone modeling: An interplay between physics and economics
Date: 2015-10-23 00:00:00

We study the performance of the euro/Swiss franc exchange rate in the
extraordinary period from September 6, 2011 and January 15, 2015 when the Swiss
National Bank enforced a minimum exchange rate of 1.20 Swiss francs per euro.
Based on the analogy between Brownian motion in finance and physics, the
first-order effect of such a steric constraint would enter a priori in the form
of a repulsive entropic force associated with the paths crossing the barrier
that are forbidden. Non-parametric empirical estimates of drift and volatility
show that the predicted first-order analogy between economics and physics are
incorrect. The clue is to realise that the random walk nature of financial
prices results from the continuous anticipations of traders about future
opportunities, whose aggregate actions translate into an approximate efficient
market with almost no arbitrage opportunities. With the Swiss National Bank
stated commitment to enforce the barrier, traders's anticipation of this action
leads to a vanishing drift together with a volatility of the exchange rate that
depends on the distance to the barrier. We give direct quantitative empirical
evidence that this effect is well described by Krugman's target zone model
[P.R. Krugman. The Quarterly Journal of Economics, 106(3):669-682, 1991].
Motivated by the insights from this economical model, we revise the initial
economics-physics analogy and show that, within the context of hindered
diffusion, the two systems can be described with the same mathematics after
all. Using a recently proposed extended analogy in terms of a colloidal
Brownian particle embedded in a fluid of molecules associated with the
underlying order book, we derive that, close to the restricting boundary, the
dynamics of both systems is described by a stochastic differential equation
with a very small constant drift and a linear diffusion coefficient.
Analyzing Organizational Routines in Online Knowledge Collaborations: A Case for Sequence Analysis in CSCW
Authors: Keegan Brian C., Lev Shakked, Arazy Ofer
Date: 2015-08-28 00:00:00
Tasks: Articles

Research into socio-technical systems like Wikipedia has overlooked important
structural patterns in the coordination of distributed work. This paper argues
for a conceptual reorientation towards sequences as a fundamental unit of
analysis for understanding work routines in online knowledge collaboration. We
outline a research agenda for researchers in computer-supported cooperative
work (CSCW) to understand the relationships, patterns, antecedents, and
consequences of sequential behavior using methods already developed in fields
like bio-informatics. Using a data set of 37,515 revisions from 16,616 unique
editors to 96 Wikipedia articles as a case study, we analyze the prevalence and
significance of different sequences of editing patterns. We illustrate the
mixed method potential of sequence approaches by interpreting the frequent
patterns as general classes of behavioral motifs. We conclude by discussing the
methodological opportunities for using sequence analysis for expanding existing
approaches to analyzing and theorizing about co-production routines in online
knowledge collaboration.
Heterotic Risk Models
Date: 2016-01-23 00:00:00
Tasks: Position

We give a complete algorithm and source code for constructing what we refer
to as heterotic risk models (for equities), which combine: i) granularity of an
industry classification; ii) diagonality of the principal component factor
covariance matrix for any sub-cluster of stocks; and iii) dramatic reduction of
the factor covariance matrix size in the Russian-doll risk model construction.
This appears to prove a powerful approach for constructing out-of-sample stable
short-lookback risk models. Thus, for intraday mean-reversion alphas based on
overnight returns, Sharpe ratio optimization using our heterotic risk models
sizably improves the performance characteristics compared to weighted
regressions based on principal components or industry classification. We also
give source code for: a) building statistical risk models; and ii) Sharpe ratio
optimization with homogeneous linear constraints and position bounds.
Detecting intraday financial market states using temporal clustering
Date: 2017-02-24 00:00:00
Tasks: Clustering

We propose the application of a high-speed maximum likelihood clustering
algorithm to detect temporal financial market states, using correlation
matrices estimated from intraday market microstructure features. We first
determine the ex-ante intraday temporal cluster configurations to identify
market states, and then study the identified temporal state features to extract
state signature vectors which enable online state detection. The state
signature vectors serve as low-dimensional state descriptors which can be used
in learning algorithms for optimal planning in the high-frequency trading
domain. We present a feasible scheme for real-time intraday state detection
from streaming market data feeds. This study identifies an interesting
hierarchy of system behaviour which motivates the need for time-scale-specific
state space reduction for participating agents.
Demography-based adaptive network model reproduces the spatial organization of human linguistic groups
Date: 2015-08-20 00:00:00

The distribution of human linguistic groups presents a number of interesting
and non-trivial patterns. The distributions of the number of speakers per
language and the area each group covers follow log-normal distributions, while
population and area fulfill an allometric relationship. The topology of
networks of spatial contacts between different linguistic groups has been
recently characterized, showing atypical properties of the degree distribution
and clustering, among others. Human demography, spatial conflicts, and the
construction of networks of contacts between linguistic groups are mutually
dependent processes. Here we introduce an adaptive network model that takes all
of them into account and successfully reproduces, using only four model
parameters, not only those features of linguistic groups already described in
the literature, but also correlations between demographic and topological
properties uncovered in this work. Besides their relevance when modeling and
understanding processes related to human biogeography, our adaptive network
model admits a number of generalizations that broaden its scope and make it
suitable to represent interactions between agents based on population dynamics
and competition for space.
The nonlinear Bernstein-Schr\"odinger equation in Economics
Date: 2015-07-14 00:00:00

In this paper we relate the Equilibrium Assignment Problem (EAP), which is
underlying in several economics models, to a system of nonlinear equations that
we call the "nonlinear Bernstein-Schr\"odinger system", which is well-known in
the linear case, but whose nonlinear extension does not seem to have been
studied. We apply this connection to derive an existence result for the EAP,
and an efficient computational method.
Competition of residents and invaders in a variable environment: Response to enemies and dangerous noise
Date: 2015-08-21 00:00:00

The possible control of competitive invasion by infection of the invader and
multiplicative noise is studied. The basic model is the Lotka-Volterra
competition system with emergent carrying capacities. Several stationary
solutions of the non-infected and infected system are identified as well as
parameter ranges of bistability. The latter are used for the numerical study of
invasion phenomena. The diffusivities, the infection but in particular the
white and colored multiplicative noise are the control parameters. It is shown
that not only competition, possible infection and mobilities are important
drivers of the invasive dynamics but also the noise and especially its color
and the functional response of populations to the emergence of noise.
ATP dependent NS3 helicase interaction with RNA: insights from molecular simulations
Date: 2015-08-21 00:00:00

Non structural protein 3 (NS3) helicase from hepatitis C virus is an enzyme
that unwinds and translocates along nucleic acids with an ATP-dependent
mechanism and has a key role in the replication of the viral RNA. An
inchworm-like mechanism for translocation has been proposed based on crystal
structures and single molecule experiments. We here perform atomistic molecular
dynamics in explicit solvent on the microsecond time scale of the available
experimental structures. We also construct and simulate putative intermediates
for the translocation process, and we perform non-equilibrium targeted
simulations to estimate their relative stability. For each of the simulated
structures we carefully characterize the available conformational space, the
ligand binding pocket, and the RNA binding cleft. The analysis of the hydrogen
bond network and of the non-equilibrium trajectories indicates an ATP-dependent
stabilization of one of the protein conformers. Additionally, enthalpy
calculations suggest that entropic effects might be crucial for the
stabilization of the experimentally observed structures.
Localized modulated wave solutions in diffusive glucose-insulin systems
Date: 2016-04-28 00:00:00

We investigate intercellular insulin dynamics in an array of diffusively
coupled pancreatic islet \b{eta}-cells. The cells are connected via gap
junction coupling, where nearest neighbor interactions are included. Through
the multiple scale expansion in the semi-discrete approximation, we show that
the insulin dynamics can be governed by the complex Ginzburg-Landau equation.
The localized solutions of this equation are reported. The results suggest from
the biophysical point of view that the insulin propagates in pancreatic islet
\b{eta}-cells using both temporal and spatial dimensions in the form of
localized modulated waves.
Super-replication in Fully Incomplete Markets
Date: 2016-09-12 00:00:00

In this work we introduce the notion of fully incomplete markets. We prove
that for these markets the super-replication price coincide with the model free
super-replication price. Namely, the knowledge of the model does not reduce the
super-replication price. We provide two families of fully incomplete models:
stochastic volatility models and rough volatility models. Moreover, we give
several computational examples. Our approach is purely probabilistic.
Measuring Financial Sentiment to Predict Financial Instability: A New Approach based on Text Analysis
Date: 2015-06-14 00:00:00

Following the financial crisis of the late 2000s, policy makers have shown
considerable interest in monitoring financial stability. Several central banks
now publish indices of financial stress, which are essentially based upon
market related data. In this paper, we examine the potential for improving the
indices by deriving information about emotion shifts in the economy. We report
on a new approach, based on the content analysis of very large text databases,
and termed directed algorithmic text analysis. The algorithm identifies, very
rapidly, shifts through time in the relations between two core emotional
groups. The method is robust. The same word-list is used to identify the two
emotion groups across different studies. Membership of the words in the lists
has been validated in psychological experiments. The words consist of everyday
English words with no specific economic meaning. Initial results show promise.
An emotion index capturing shifts between the two emotion groups in texts
potentially referring to the whole US economy improves the one-quarter ahead
consensus forecasts for real GDP growth. More specifically, the same indices
are shown to Granger cause both the Cleveland and St Louis Federal Reserve
Indices of Financial Stress.
Modeling, Simulating, and Parameter Fitting of Biochemical Kinetic Experiments
Date: 2015-08-21 00:00:00
Tasks: parameter estimation

In many chemical and biological applications, systems of differential
equations containing unknown parameters are used to explain empirical
observations and experimental data. The DEs are typically nonlinear and
difficult to analyze, requiring numerical methods to approximate the solutions.
Compounding this difficulty are the unknown parameters in the DE system, which
must be given specific numerical values in order for simulations to be run.
  Estrogen receptor protein dimerization is used as an example to demonstrate
model construction, reduction, simulation, and parameter estimation.
Mathematical, computational, and statistical methods are applied to empirical
data to deduce kinetic parameter estimates and guide decisions regarding future
experiments and modeling. The process demonstrated serves as a pedagogical
example of quantitative methods being used to extract parameter values from
biochemical data models.
Fruit flies and moduli: interactions between biology and mathematics
Date: 2015-08-21 00:00:00

Possibilities for using geometry and topology to analyze statistical problems
in biology raise a host of novel questions in geometry, probability, algebra,
and combinatorics that demonstrate the power of biology to influence the future
of pure mathematics. This expository article is a tour through some biological
explorations and their mathematical ramifications. The article starts with
evolution of novel topological features in wing veins of fruit flies, which are
quantified using the algebraic structure of multiparameter persistent homology.
The statistical issues involved highlight mathematical implications of sampling
from moduli spaces. These lead to geometric probability on stratified spaces,
including the sticky phenomenon for Frechet means and the origin of this
mathematical area in the reconstruction of phylogenetic trees.
Simplified models of pacemaker spiking in raphe and locus coeruleus neurons
Date: 2015-08-22 00:00:00

Many central neurons, and in particular certain brainstem aminergic neurons
exhibit spontaneous and fairly regular spiking with frequencies of order a few
Hz. A large number of ion channel types contribute to such spiking so that
accurate modeling of spike generation leads to the requirement of solving very
large systems of differential equations, ordinary in the first instance. Since
analysis of spiking behavior when many synaptic inputs are active adds further
to the number of components, it is useful to have simplified mathematical
models of spiking in such neurons so that, for example, stochastic features of
inputs and output spike trains can be incorporated. In this article we
investigate two simple two-component models which mimic features of spiking in
serotonergic neurons of the dorsal raphe nucleus and noradrenergic neurons of
the locus coeruleus. The first model is of the Fitzhugh-Nagumo type and the
second is a reduced Hodgkin-Huxley model. For each model solutions are computed
with two representative sets of parameters. Frequency versus input currents are
found and reveal Hodgkin type 2 behavior. For the first model a bifurcation and
phase plane analysis supports these findings. The spike trajectories in the
second model are very similar to those in DRN SE pacemaker activity but there
are more parameters than in the Fitzhugh-Nagumo type model. The article
concludes with a brief review of previous modeling of these types of neurons
and its relevance to studies of serotonergic involvement in spatial working
memory and obsessive-compulsive disorder.
RNA thermodynamic structural entropy
Date: 2015-08-22 00:00:00

Conformational entropy for atomic-level, three dimensional biomolecules is
known experimentally to play an important role in protein-ligand
discrimination, yet reliable computation of entropy remains a difficult
problem. Here we describe the first two accurate and efficient algorithms to
compute the conformational entropy for RNA secondary structures, with respect
to the Turner energy model, where free energy parameters are determined from UV
aborption experiments. An algorithm to compute the derivational entropy for RNA
secondary structures had previously been introduced, using stochastic context
free grammars (SCFGs). However, the numerical value of derivational entropy
depends heavily on the chosen context free grammar and on the training set used
to estimate rule probabilities. Using data from the Rfam database, we determine
that both of our thermodynamic methods, which agree in numerical value, are
substantially faster than the SCFG method. Thermodynamic structural entropy is
much smaller than derivational entropy, and the correlation between
length-normalized thermodynamic entropy and derivational entropy is moderately
weak to poor. In applications, we plot the structural entropy as a function of
temperature for known thermoswitches, determine that the correlation between
hammerhead ribozyme cleavage activity and total free energy is improved by
including an additional free energy term arising from conformational entropy,
and plot the structural entropy of windows of the HIV-1 genome. Our software
RNAentropy can compute structural entropy for any user-specified temperature,
and supports both the Turner'99 and Turner'04 energy parameters. It follows
that RNAentropy is state-of-the-art software to compute RNA secondary structure
conformational entropy. The software is available at
http://bioinformatics.bc.edu/clotelab/RNAentropy.
Optimal percentage of inhibitory synapses in multi-task learning
Date: 2015-08-24 00:00:00
Tasks: Multi-Task Learning

Performing more tasks in parallel is a typical feature of complex brains.
These are characterized by the coexistence of excitatory and inhibitory
synapses, whose percentage in mammals is measured to have a typical value of
20-30\%. Here we investigate parallel learning of more Boolean rules in
neuronal networks. We find that multi-task learning results from the
alternation of learning and forgetting of the individual rules. Interestingly,
a fraction of 30\% inhibitory synapses optimizes the overall performance,
carving a complex backbone supporting information transmission with a minimal
shortest path length. We show that 30\% inhibitory synapses is the percentage
maximizing the learning performance since it guarantees, at the same time, the
network excitability necessary to express the response and the variability
required to confine the employment of resources.
Analysis of Reaction Network Systems Using Tropical Geometry
Date: 2015-08-24 00:00:00

We discuss a novel analysis method for reaction network systems with
polynomial or rational rate functions. This method is based on computing
tropical equilibrations defined by the equality of at least two dominant
monomials of opposite signs in the differential equations of each dynamic
variable. In algebraic geometry, the tropical equilibration problem is
tantamount to finding tropical prevarieties, that are finite intersections of
tropical hypersurfaces. Tropical equilibrations with the same set of dominant
monomials define a branch or equivalence class. Minimal branches are
particularly interesting as they describe the simplest states of the reaction
network. We provide a method to compute the number of minimal branches and to
find representative tropical equilibrations for each branch.
The 'who' and 'what' of #diabetes on Twitter
Authors: Beguerisse-Díaz Mariano, McLennan Amy K., Garduño-Hernández Guillermo, Barahona Mauricio, Ulijaszek Stanley J.
Date: 2017-01-30 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction, Information Retrieval, Retrieval

Social media are being increasingly used for health promotion, yet the
landscape of users, messages and interactions in such fora is poorly
understood. Studies of social media and diabetes have focused mostly on
patients, or public agencies addressing it, but have not looked broadly at all
the participants or the diversity of content they contribute. We study Twitter
conversations about diabetes through the systematic analysis of 2.5 million
tweets collected over 8 months and the interactions between their authors. We
address three questions: (1) what themes arise in these tweets?, (2) who are
the most influential users?, (3) which type of users contribute to which
themes? We answer these questions using a mixed-methods approach, integrating
techniques from anthropology, network science and information retrieval such as
thematic coding, temporal network analysis, and community and topic detection.
Diabetes-related tweets fall within broad thematic groups: health information,
news, social interaction, and commercial. At the same time, humorous messages
and references to popular culture appear consistently, more than any other type
of tweet. We classify authors according to their temporal 'hub' and 'authority'
scores. Whereas the hub landscape is diffuse and fluid over time, top
authorities are highly persistent across time and comprise bloggers, advocacy
groups and NGOs related to diabetes, as well as for-profit entities without
specific diabetes expertise. Top authorities fall into seven interest
communities as derived from their Twitter follower network. Our findings have
implications for public health professionals and policy makers who seek to use
social media as an engagement tool and to inform policy design.
Change Blindness in 3D Virtual Reality
Date: 2015-08-24 00:00:00
Tasks: Change Detection

In the present change blindness study subjects explored stereoscopic three
dimensional (3D) environments through a virtual reality (VR) headset. A novel
method that tracked the subjects' head movements was used for inducing changes
in the scene whenever the changing object was out of the field of view. The
effect of change location (foreground or background in 3D depth) on change
blindness was investigated. Two experiments were conducted, one in the lab (n =
50) and the other online (n = 25). Up to 25% of the changes were undetected and
the mean overall search time was 27 seconds in the lab study. Results indicated
significantly lower change detection success and more change cycles if the
changes occurred in the background, with no differences in overall search
times. The results confirm findings from previous studies and extend them to 3D
environments. The study also demonstrates the feasibility of online VR
experiments.
Hydroassets Portfolio Management for Intraday Electricity Trading from a Discrete Time Stochastic Optimization Perspective
Date: 2017-08-24 00:00:00
Tasks: Management, Stochastic Optimization

Hydro storage system optimization is becoming one of the most challenging
tasks in Energy Finance. While currently the state-of-the-art of the commercial
software in the industry implements mainly linear models, we would like to
introduce risk aversion and a generic utility function. At the same time, we
aim to develop and implement a computational efficient algorithm, which is not
affected by the curse of dimensionality and does not utilize subjective
heuristics to prevent it. For the short term power market we propose a
simultaneous solution for both dispatch and bidding problems.
  Following the Blomvall and Lindberg (2002) interior point model, we set up a
stochastic multiperiod optimization procedure by means of a "bushy" recombining
tree that provides fast computational results. Inequality constraints are
packed into the objective function by the logarithmic barrier approach and the
utility function is approximated by its second order Taylor polynomial. The
optimal solution for the original problem is obtained as a diagonal sequence
where the first diagonal dimension is the parameter controlling the logarithmic
penalty and the second is the parameter for the Newton step in the construction
of the approximated solution. Optimal intraday electricity trading and water
values for hydro assets as shadow prices are computed. The algorithm is
implemented in Mathematica.