pwc-archive/papers-with-abstracts huggingface dataset:
Extracting Geography from Trade Data
Date: 2016-07-23 00:00:00
Tasks: Dimensionality Reduction

Understanding international trade is a fundamental problem in economics --
one standard approach is via what is commonly called the "gravity equation",
which predicts the total amount of trade $F_ij$ between two countries $i$ and
$j$ as $$ F_{ij} = G \frac{M_i M_j}{D_{ij}},$$ where $G$ is a constant, $M_i,
M_j$ denote the "economic mass" (often simply the gross domestic product) and
$D_{ij}$ the "distance" between countries $i$ and $j$, where "distance" is a
complex notion that includes geographical, historical, linguistic and
sociological components. We take the \textit{inverse} route and ask ourselves
to which extent it is possible to reconstruct meaningful information about
countries simply from knowing the bilateral trade volumes $F_{ij}$: indeed, we
show that a remarkable amount of geopolitical information can be extracted. The
main tool is a spectral decomposition of the Graph Laplacian as a tool to
perform nonlinear dimensionality reduction. This may have further applications
in economic analysis and provides a data-based approach to "trade distance".
Learning to Allocate Limited Time to Decisions with Different Expected Outcomes
Date: 2016-07-18 00:00:00

The goal of this article is to investigate how human participants allocate
their limited time to decisions with different properties. We report the
results of two behavioral experiments. In each trial of the experiments, the
participant must accumulate noisy information to make a decision. The
participants received positive and negative rewards for their correct and
incorrect decisions, respectively. The stimulus was designed such that
decisions based on more accumulated information were more accurate but took
longer. Therefore, the total outcome that a participant could achieve during
the limited experiments' time depended on her "decision threshold", the amount
of information she needed to make a decision. In the first experiment, two
types of trials were intermixed randomly: hard and easy. Crucially, the hard
trials were associated with smaller positive and negative rewards than the easy
trials. A cue presented at the beginning of each trial would indicate the type
of the upcoming trial. The optimal strategy was to adopt a small decision
threshold for hard trials. The results showed that several of the participants
did not learn this simple strategy. We then investigated how the participants
adjusted their decision threshold based on the feedback they received in each
trial. To this end, we developed and compared 10 computational models for
adjusting the decision threshold. The models differ in their assumptions on the
shape of the decision thresholds and the way the feedback is used to adjust the
decision thresholds. The results of Bayesian model comparison showed that a
model with time-varying thresholds whose parameters are updated by a
reinforcement learning algorithm is the most likely model.
Structured Models of Cell Migration Incorporating Molecular Binding Processes
Date: 2016-07-18 00:00:00

The dynamic interplay between collective cell movement and the various
molecules involved in the accompanying cell signalling mechanisms plays a
crucial role in many biological processes including normal tissue development
and pathological scenarios such as wound healing and cancer. Information about
the various structures embedded within these processes allows a detailed
exploration of the binding of molecular species to cell-surface receptors
within the evolving cell population. In this paper we establish a general
spatio-temporal-structural framework that enables the description of molecular
binding to cell membranes coupled with the cell population dynamics. We first
provide a general theoretical description for this approach and then illustrate
it with two examples arising from cancer invasion.
A Local Counter-Regulatory Motif Modulates the Global Phase of Hormonal Oscillations
Authors: Dong-Ho Park, Taegeun Song, Danh-Tai Hoang, Jin Xu, Junghyo Jo
Date: 2016-07-19 00:00:00

Counter-regulatory elements maintain dynamic equilibrium ubiquitously in living systems. The most prominent example, which is critical to mammalian survival, is that of pancreatic {\alpha} and {\beta} cells producing glucagon and insulin for glucose homeostasis. These cells are not found in a single gland but are dispersed in multiple micro-organs known as the islets of Langerhans. Within an islet, these two reciprocal cell types interact with each other and with an additional cell type: the {\delta} cell. By testing all possible motifs governing the interactions of these three cell types, we found that a unique set of positive/negative intra-islet interactions between different islet cell types functions not only to reduce the superficially wasteful zero-sum action of glucagon and insulin but also to enhance/suppress the synchronization of hormone secretions between islets under high/normal glucose conditions. This anti-symmetric interaction motif confers effective controllability for network (de)synchronization.
What causes the increase in aggregation as a parasite moves up a food chain?
Date: 2016-07-19 00:00:00

General laws in ecological parasitology are scarce. Here we evaluate data
published by over 100 authors to determine whether the number of hosts in a
life cycle is associated with the degree of aggregation of fish parasites at
different stages. Parasite species were grouped taxonomically to produce 20 or
more data points per group as far as possible. Most parasites that remained at
one trophic level were less aggregated than those that had passed up a food
chain. We use a stochastic model to show that high parasite overdispersion in
predators can be solely the result of the accumulation of parasites in their
prey. The model is further developed to show that a change in the predators
feeding behaviour with age may further increase parasite aggregation.
Smoothing the payoff for efficient computation of Basket option prices
Date: 2017-02-24 00:00:00
Tasks: Numerical Integration

We consider the problem of pricing basket options in a multivariate Black
Scholes or Variance Gamma model. From a numerical point of view, pricing such
options corresponds to moderate and high dimensional numerical integration
problems with non-smooth integrands. Due to this lack of regularity, higher
order numerical integration techniques may not be directly available, requiring
the use of methods like Monte Carlo specifically designed to work for
non-regular problems. We propose to use the inherent smoothing property of the
density of the underlying in the above models to mollify the payoff function by
means of an exact conditional expectation. The resulting conditional
expectation is unbiased and yields a smooth integrand, which is amenable to the
efficient use of adaptive sparse grid cubature. Numerical examples indicate
that the high-order method may perform orders of magnitude faster compared to
Monte Carlo or Quasi Monte Carlo in dimensions up to 35.
Identification of market trends with string and D2-brane maps
Date: 2016-07-18 00:00:00
Tasks: Time Series, Time Series Analysis, Time Series Forecasting

The multi dimensional string objects are introduced as a new alternative for
an application of string models for time series forecasting in trading on
financial markets. The objects are represented by open string with 2-endpoints
and D2-brane, which are continuous enhancement of 1-endpoint open string model.
We show how new object properties can change the statistics of the predictors,
which makes them the candidates for modeling a wide range of time series
systems. String angular momentum is proposed as another tool to analyze the
stability of currency rates except the historical volatility. To show the
reliability of our approach with application of string models for time series
forecasting we present the results of real demo simulations for four currency
exchange pairs.
A Comparison of Nineteen Various Electricity Consumption Forecasting Approaches and Practicing to Five Different Households in Turkey
Date: 2016-07-18 00:00:00
Tasks: Time Series Analysis

The accuracy of the household electricity consumption forecast is vital in
taking better cost effective and energy efficient decisions. In order to design
accurate, proper and efficient forecasting model, characteristics of the series
have to been analyzed. The source of time series data comes from Online
Enerjisa System, the system of electrical energy provider in capital of Turkey,
which consumers can reach their latest two year period electricity
consumptions; in our study the period was May 2014 to May 2016. Various
techniques had been applied in order to analyze the data; classical
decomposition models; standard typed and also with the centering moving average
method, regression equations, exponential smoothing models and ARIMA models. In
our study, nine teen different approaches; all of these have at least
diversified aspects of methodology, had been compared and the best model for
forecasting were decided by considering the smallest values of MAPE, MAD and
MSD. As a first step we took the time period May 2014 to May 2016 and found
predicted value for June 2016 with the best forecasting model. After finding
the best forecasting model and fitted value for June 2016, than validating
process had been taken place; we made comparisons to see how well the real
value of June 2016 and forecasted value for that specific period matched.
Afterwards we made electrical consumption forecast for the following 3 months;
June-September 2016 for each of five households individually.
A Local-Global LDA Model for Discovering Geographical Topics from Social Media
Authors: Qiang Siwei, Wang Yongkun, Jin Yaohui
Date: 2016-07-20 00:00:00

Micro-blogging services can track users' geo-locations when users check-in
their places or use geo-tagging which implicitly reveals locations. This "geo
tracking" can help to find topics triggered by some events in certain regions.
However, discovering such topics is very challenging because of the large
amount of noisy messages (e.g. daily conversations). This paper proposes a
method to model geographical topics, which can filter out irrelevant words by
different weights in the local and global contexts. Our method is based on the
Latent Dirichlet Allocation (LDA) model but each word is generated from either
a local or a global topic distribution by its generation probabilities. We
evaluated our model with data collected from Weibo, which is currently the most
popular micro-blogging service for Chinese. The evaluation results demonstrate
that our method outperforms other baseline methods in several metrics such as
model perplexity, two kinds of entropies and KL-divergence of discovered
topics.
Characterization and reduction of variability in selection based on effect-size using association measures in cohort study of heterogeneous diseases
Date: 2017-05-27 00:00:00

Cohort studies employ pairwise measures of association to quantify
dependencies among conditions and exposures. To reliably use these measures to
draw conclusions about the underlying association strengths requires that the
measures be robust and unbiased. These considerations assume greater
significance when applied to disease networks, where associations among
heterogeneous pairs of diseases are ranked. Using disease diagnoses data from a
large cohort of 5.5 million individuals, we develop a comprehensive methodology
to characterize the bias of standard association measures like relative risk
and $\phi$ correlation. To overcome these biases, we devise a novel measure
based on a stochastic model for disease development. The new measure is
demonstrated to have the least overall bias and hence would be most suitable
for application to heterogeneous disease cohorts.
Statistical inference for the doubly stochastic self-exciting process
Date: 2017-06-27 00:00:00

We introduce and show the existence of a Hawkes self-exciting point process
with exponentially-decreasing kernel and where parameters are time-varying. The
quantity of interest is defined as the integrated parameter
$T^{-1}\int_0^T\theta_t^*dt$, where $\theta_t^*$ is the time-varying parameter,
and we consider the high-frequency asymptotics. To estimate it na\"ively, we
chop the data into several blocks, compute the maximum likelihood estimator
(MLE) on each block, and take the average of the local estimates. The
asymptotic bias explodes asymptotically, thus we provide a non-na\"ive
estimator which is constructed as the na\"ive one when applying a first-order
bias reduction to the local MLE. We show the associated central limit theorem.
Monte Carlo simulations show the importance of the bias correction and that the
method performs well in finite sample, whereas the empirical study discusses
the implementation in practice and documents the stochastic behavior of the
parameters.
Species survival and scaling laws in hostile and disordered environments
Date: 2016-10-11 00:00:00

In this work we study the likelihood of survival of single-species in the
context of hostile and disordered environments. Population dynamics in this
environment, as modeled by the Fisher equation, is characterized by negative
average growth rate, except in some random spatially distributed patches that
may support life. In particular, we are interested in the phase diagram of the
survival probability and in the critical size problem, i.e., the minimum patch
size required for surviving in the long time dynamics. We propose a measure for
the critical patch size as being proportional to the participation ratio (PR)
of the eigenvector corresponding to the largest eigenvalue of the linearized
Fisher dynamics. We obtain the (extinction-survival) phase diagram and the
probability distribution function (PDF) of the critical patch sizes for two
topologies, namely, the one-dimensional system and the fractal Peano basin. We
show that both topologies share the same qualitative features, but the fractal
topology requires higher spatial fluctuations to guarantee species survival. We
perform a finite-size scaling and we obtain the associated scaling exponents.
In addition, we show that the PDF of the critical patch sizes has an universal
shape for the 1D case in terms of the model parameters (diffusion, growth rate,
etc.). In contrast, the diffusion coefficient has a drastic effect on the PDF
of the critical patch sizes of the fractal Peano basin, and it does not obey
the same scaling law of the 1D case.
Effects of Sea Level Rise on Economy of the United States
Date: 2016-07-21 00:00:00

We report the first ex post study of the economic impact of sea level rise.
We apply two econometric approaches to estimate the past effects of sea level
rise on the economy of the USA, viz. Barro type growth regressions adjusted for
spatial patterns and a matching estimator. Unit of analysis is 3063 counties of
the USA. We fit growth regressions for 13 time periods and we estimated
numerous varieties and robustness tests for both growth regressions and
matching estimator. Although there is some evidence that sea level rise has a
positive effect on economic growth, in most specifications the estimated
effects are insignificant. We therefore conclude that there is no stable,
significant effect of sea level rise on economic growth. This finding
contradicts previous ex ante studies.
Do branch lengths help to locate a tree in a phylogenetic network?
Date: 2016-07-21 00:00:00

Phylogenetic networks are increasingly used in evolutionary biology to
represent the history of species that have undergone reticulate events such as
horizontal gene transfer, hybrid speciation and recombination. One of the most
fundamental questions that arise in this context is whether the evolution of a
gene with one copy in all species can be explained by a given network. In
mathematical terms, this is often translated in the following way: is a given
phylogenetic tree contained in a given phylogenetic network? Recently this tree
containment problem has been widely investigated from a computational
perspective, but most studies have only focused on the topology of the phylo-
genies, ignoring a piece of information that, in the case of phylogenetic
trees, is routinely inferred by evolutionary analyses: branch lengths. These
measure the amount of change (e.g., nucleotide substitutions) that has occurred
along each branch of the phylogeny. Here, we study a number of versions of the
tree containment problem that explicitly account for branch lengths. We show
that, although length information has the potential to locate more precisely a
tree within a network, the problem is computationally hard in its most general
form. On a positive note, for a number of special cases of biological
relevance, we provide algorithms that solve this problem efficiently. This
includes the case of networks of limited complexity, for which it is possible
to recover, among the trees contained by the network with the same topology as
the input tree, the closest one in terms of branch lengths.
Bayesian uncertainty analysis for complex systems biology models: emulation, global parameter searches and evaluation of gene functions
Date: 2018-01-12 00:00:00

Background: Many mathematical models have now been employed across every area
of systems biology. These models increasingly involve large numbers of unknown
parameters, have complex structure which can result in substantial evaluation
time relative to the needs of the analysis, and need to be compared to observed
data. The correct analysis of such models usually requires a global parameter
search, over a high dimensional parameter space, that incorporates and respects
the most important sources of uncertainty. This can be an extremely difficult
task, but it is essential for any meaningful inference or prediction to be made
about any biological system. It hence represents a fundamental challenge for
the whole of systems biology.
  Results: Bayesian statistical methodology for the uncertainty analysis of
complex models is introduced, which is designed to address the high dimensional
global parameter search problem. Bayesian emulators that mimic the systems
biology model but which are extremely fast to evaluate are embedded within an
iterative history match: an efficient method to search high dimensional spaces
within a more formal statistical setting, while incorporating major sources of
uncertainty. The approach is demonstrated via application to two models of
hormonal crosstalk in Arabidopsis root development, which have 32 rate
parameters, for which we identify the sets of rate parameter values that lead
to acceptable matches to observed trend data. The biological consequences of
the resulting comparison, including the evaluation of gene functions, are
described.
Systemic Risk and Stochastic Games with Delay
Date: 2016-07-21 00:00:00

We propose a model of inter-bank lending and borrowing which takes into
account clearing debt obligations. The evolution of log-monetary reserves of
$N$ banks is described by coupled diffusions driven by controls with delay in
their drifts. Banks are minimizing their finite-horizon objective functions
which take into account a quadratic cost for lending or borrowing and a linear
incentive to borrow if the reserve is low or lend if the reserve is high
relative to the average capitalization of the system. As such, our problem is
an $N$-player linear-quadratic stochastic differential game with delay. An
open-loop Nash equilibrium is obtained using a system of fully coupled forward
and advanced backward stochastic differential equations. We then describe how
the delay affects liquidity and systemic risk characterized by a large number
of defaults. We also derive a close-loop Nash equilibrium using an HJB
approach.
HyperLogLog Hyper Extended: Sketches for Concave Sublinear Frequency Statistics
Authors: Cohen Edith
Date: 2017-02-24 00:00:00

One of the most common statistics computed over data elements is the number
of distinct keys. A thread of research pioneered by Flajolet and Martin three
decades ago culminated in the design of optimal approximate counting sketches,
which have size that is double logarithmic in the number of distinct keys and
provide estimates with a small relative error. Moreover, the sketches are
composable, and thus suitable for streamed, parallel, or distributed
computation.
  We consider here all statistics of the frequency distribution of keys, where
a contribution of a key to the aggregate is concave and grows (sub)linearly
with its frequency. These fundamental aggregations are very common in text,
graphs, and logs analysis and include logarithms, low frequency moments, and
capping statistics.
  We design composable sketches of double-logarithmic size for all concave
sublinear statistics. Our design combines theoretical optimality and practical
simplicity. In a nutshell, we specify tailored mapping functions of data
elements to output elements so that our target statistics on the data elements
is approximated by the (max-) distinct statistics of the output elements, which
can be approximated using off-the-shelf sketches. Our key insight is relating
these target statistics to the {\em complement Laplace} transform of the input
frequencies.
Leaky Integrate-and-Fire Neuron under Poisson Stimulation
Date: 2016-07-22 00:00:00

We consider a single Leaky integrate-and-fire neuron stimulated with Poisson
process. We develop a method, which allows one to obtain the first passage time
probability density function without any additional approximations.
Perspective: Melanoma diagnosis and monitoring: Sunrise for melanoma therapy but early detection remains in the shade
Date: 2016-07-25 00:00:00
Tasks: Melanoma Diagnosis

Melanoma is one of the most dangerous forms of cancer. The five-year survival
rate is 98% if it is detected early. However, this rate plummets to 63% for
regional disease and 17% when tumors have metastasized, that is, spread to
distant sites. Furthermore, the incidence of melanoma has been rising by about
3% per year, whereas the incidence of cancers that are more common is
decreasing. A handful of targeted therapies have recently become available that
have finally shown real promise for treatment, but for reasons that remain
unclear only a fraction of patients respond long term. These drugs often
increase survival by only a few months in metastatic patient groups before
relapse occurs. More effective treatment may be possible if a diagnosis can be
made when the tumor burden is still low. Here, an overview of the current
state-of-the-art is provided along with an argument for newer technologies
towards early point-of-care diagnosis of melanoma.