pwc-archive/papers-with-abstracts huggingface dataset:
PyCaMa: Python for cash management
Date: 2017-02-18 00:00:00
Tasks: Management

Selecting the best policy to keep the balance between what a company holds in
cash and what is placed in alternative investments is by no means
straightforward. We here introduce PyCaMa, a Python module for multiobjective
cash management based on linear programming that allows to derive optimal
policies for cash management with multiple bank accounts in terms of both cost
and risk of policies.
Uncertain Volatility Models with Stochastic Bounds
Date: 2017-02-16 00:00:00

In this paper, we propose the uncertain volatility models with stochastic
bounds. Like the regular uncertain volatility models, we know only that the
true model lies in a family of progressively measurable and bounded processes,
but instead of using two deterministic bounds, the uncertain volatility
fluctuates between two stochastic bounds generated by its inherent stochastic
volatility process. This brings better accuracy and is consistent with the
observed volatility path such as for the VIX as a proxy for instance. We apply
the regular perturbation analysis upon the worst case scenario price, and
derive the first order approximation in the regime of slowly varying stochastic
bounds. The original problem which involves solving a fully nonlinear PDE in
dimension two for the worst case scenario price, is reduced to solving a
nonlinear PDE in dimension one and a linear PDE with source, which gives a
tremendous computational advantage. Numerical experiments show that this
approximation procedure performs very well, even in the regime of moderately
slow varying stochastic bounds.
Luandri: a Clean Lua Interface to the Indri Search Engine
Authors: Mitra Bhaskar, Diaz Fernando, Craswell Nick
Date: 2017-02-16 00:00:00
Tasks: Information Retrieval, Retrieval, Text Matching

In recent years, the information retrieval (IR) community has witnessed the
first successful applications of deep neural network models to short-text
matching and ad-hoc retrieval. It is exciting to see the research on deep
neural networks and IR converge on these tasks of shared interest. However, the
two communities have less in common when it comes to the choice of programming
languages. Indri, an indexing framework popularly used by the IR community, is
written in C++, while Torch, a popular machine learning library for deep
learning, is written in the light-weight scripting language Lua. To bridge this
gap, we introduce Luandri (pronounced "laundry"), a simple interface for
exposing the search capabilities of Indri to Torch models implemented in Lua.
Modeling the prevalence of Schistosoma mansoni infection in an endemic population
Date: 2017-02-16 00:00:00

We investigate theoretically if treatment alone can reduce the
schistosomiasis's prevalence in an infected population, in a long-lasting
sustainable way. We use a non-linear system of ordinary differential equations
(a SI system combined with a logistic population growth) which describes the
time evolution of the non-infected and infected populations, in terms of the
recovering, infection, and demographic rates.
  Our model leads to the conclusion that the only way to eliminate this endemic
disease is to implement public health policies aimed at both treatment and
environment.
  We apply our model to the endemic area of Virgem das Gra\c{c}as, in Brazil,
where the prevalence of Schistosiamisis in 2001 was greater than 50\%. The
epidemiological data are extracted from a longitudinal study carried on the
region between 2001 and 2010 and the demographic parameter from official
Brazilian population data. When these estimated parameters are entered, our
model gives a limit prevalence for Virgem das Gra\c{c}as of 11\%, which is
still significantly high even though treatment in accordance to the government
regulations is systematically performed. This estimative reinforces once again,
that in order to eliminate Schistomiasis, public health policies aimed at
treatment, sanitation, including snail control, and health education programs
are mandatory.
Detecting and modelling delayed density-dependence in abundance time series of a small mammal (Didelphis aurita)
Date: 2017-02-16 00:00:00
Tasks: Time Series, Time Series Analysis

We study the population size time series of a Neotropical small mammal with
the intent of detecting and modelling population regulation processes generated
by density-dependent factors and their possible delayed effects. The
application of analysis tools based on principles of statistical generality are
nowadays a common practice for describing these phenomena, but, in general,
they are more capable of generating clear diagnosis rather than granting
valuable modelling. For this reason, in our approach, we detect the principal
temporal structures on the bases of different correlation measures, and from
these results we build an ad-hoc minimalist autoregressive model that
incorporates the main drivers of the dynamics. Surprisingly our model is
capable of reproducing very well the time patterns of the empirical series and,
for the first time, clearly outlines the importance of the time of attaining
sexual maturity as a central temporal scale for the dynamics of this species.
In fact, an important advantage of this analysis scheme is that all the model
parameters are directly biologically interpretable and potentially measurable,
allowing a consistency check between model outputs and independent
measurements.
A Biased Look at Phase Locking: Brief Critical Review and Proposed Remedy
Date: 2017-02-17 00:00:00

A number of popular measures of dependence between pairs of band-limited
signals rely on analytic phase. A common misconception is that the dependence
revealed by these measures must be specific to the spectral range of the
filtered input signals. Implicitly or explicitly, obtaining analytic phase
involves normalizing the signal by its own envelope, which is a nonlinear
operation that introduces broad spectral leakage. We review how this generates
bias and complicates the interpretation of commonly used measures of phase
locking. A specific example of this effect may create spurious phase locking as
a consequence of nonzero circular mean in the phase of input signals, which can
be viewed as spectral leakage to 0 Hz. Corrections for this problem which
recenter or uniformize the distribution of phase may fail when the amplitudes
of the compared signals are correlated. To address the more general problem of
spectral bias, a novel measure of phase locking is proposed, the
amplitude-weighted phase locking value (awPLV). This measure is closely related
to coherence, but it removes ambiguities of interpretation that detract from
the latter.
A reaction-diffusion model for the progression of Parkinson's disease
Date: 2017-02-17 00:00:00

The temporal and spatial development of Parkinson's disease has been
characterised as the progressive formation of {\alpha}-synuclein aggregations
through susceptible neuronal pathways. This article describes a new model for
this progression mechanism in which Parkinsonian damage moves over time through
the nervous system by the combined effect of the reaction kinetics of
pathogenesis and molecular diffusion. In the reaction-diffusion model, the
change from a healthy state to the disease state advances through the nervous
system as a wave front of Parkinsonian damage, marking its path by
accumulations of damaged {\alpha}-synuclein and neurotoxic levels of oxidative
species. Progression according to this model follows the most vulnerable routes
through the nervous system as described by Braak's staging theory and predicts
that damage will advance at differing speeds depending upon the level and
number of risk factors, in a manner that gives new insights into the variations
with which Parkinson's disease develops.
Instant determination of the potential biomarker heparan sulfate in human plasma by a mix-and-read fluorescence assay
Date: 2017-02-17 00:00:00

Heparan sulfate (HS) is a linear, polydisperse sulfated polysaccharide
belonging to the glycosaminoglycan family. HS proteoglycans are ubiquitously
found at the cell surface and extracellular matrix in animal species. HS is
involved in the interaction with a wide variety of proteins and the regulation
of many biological activities. In certain pathologic conditions, expression and
shedding of HS proteoglycans is overregulated, or enzymatic degradation of HS
in lysosomes is deficient, both leading to excess circulating free HS chains in
blood plasma. HS has therefore been suggested as a biomarker for various severe
disease states. The structural heterogeneity makes the quantification of
heparan sulfate in complex matrices such as human plasma challenging. HS plasma
levels are usually quantified by either disaccharide analysis or enzyme linked
immunosorbent assay(ELISA). Both methods require time-consuming
multistep-protocols. We describe here the instant detection of heparan sulfate
in spiked plasma samples by the Heparin Red Kit, a commercial mix-and-read
fluorescence microplate assay. The method enables HS quantification in the low
microgram per mL range without sample pretreatment. Heparin Red appears to be
sufficiently sensitive for the detection of highly elevated HS levels as
reported for mucopolysaccharidosis, graft versus host disease after
transplantation, dengue infection or septic shock. This study is a significant
step toward the development of a convenient and fast method for the
quantification of HS in human plasma, with the potential to simplify the
detection and advance the acceptance of HS as a biomarker.
A mathematical model of low grade gliomas treated with temozolomide and its therapeutical implications
Date: 2017-02-17 00:00:00
Tasks: Management

Low grade gliomas (LGGs) are infiltrative and incurable primary brain tumours
with typically slow evolution. These tumours usually occur in young and
otherwise healthy patients, bringing controversies in treatment planning since
aggressive treatment may lead to undesirable side effects. Thus, for management
decisions it would be valuable to obtain early estimates of LGG growth
potential. Here we propose a simple mathematical model of LGG growth and its
response to chemotherapy which allows the growth of LGGs to be described in
real patients. The model predicts, and our clinical data confirms, that the
speed of response to chemotherapy is related to tumour aggressiveness.
Moreover, we provide a formula for the time to radiological progression, which
can be possibly used as a measure of tumour aggressiveness. Finally, we suggest
that the response to a few chemotherapy cycles upon diagnosis might be used to
predict tumour growth and to guide therapeutical actions on the basis of the
findings.
The amazing power of dimensional analysis: Quantifying market impact
Date: 2017-09-27 00:00:00

This note complements the inspiring work on dimensional analysis and market
microstructure by Kyle and Obizhaeva [18]. Following closely these authors, our
main result shows by a similar argument as usually applied in physics the
following remarkable fact. If the market impact of a meta-order only depends on
four well-defined and financially meaningful variables, then -- up to a
constant -- there is only one possible form of this dependence. In particular,
the market impact is proportional to the square-root of the size of the
meta-order.
  This theorem can be regarded as a special case of a more general result of
Kyle and Obizhaeva. These authors consider five variables which might have an
influence on the size of the market impact. In this case one finds a richer
variety of possible functional relations which we precisely characterize. We
also discuss the analogies to classical arguments from physics, such as the
period of a pendulum.
Network Flow Based Post Processing for Sales Diversity
Authors: Antikacioglu Arda, Ravi R
Date: 2017-02-17 00:00:00
Tasks: Collaborative Filtering, Diversity, Recommendation Systems

Collaborative filtering is a broad and powerful framework for building
recommendation systems that has seen widespread adoption. Over the past decade,
the propensity of such systems for favoring popular products and thus creating
echo chambers have been observed. This has given rise to an active area of
research that seeks to diversify recommendations generated by such algorithms.
  We address the problem of increasing diversity in recommendation systems that
are based on collaborative filtering that use past ratings to predicting a
rating quality for potential recommendations. Following our earlier work, we
formulate recommendation system design as a subgraph selection problem from a
candidate super-graph of potential recommendations where both diversity and
rating quality are explicitly optimized: (1) On the modeling side, we define a
new flexible notion of diversity that allows a system designer to prescribe the
number of recommendations each item should receive, and smoothly penalizes
deviations from this distribution. (2) On the algorithmic side, we show that
minimum-cost network flow methods yield fast algorithms in theory and practice
for designing recommendation subgraphs that optimize this notion of diversity.
(3) On the empirical side, we show the effectiveness of our new model and
method to increase diversity while maintaining high rating quality in standard
rating data sets from Netflix and MovieLens.
Pupil size behavior during on line processing of sentences
Date: 2017-02-18 00:00:00
Tasks: Sentence

In the present work we analyzed the pupil size behavior of forty subjects
while they read well defined sentences with different contextual predictability
(i.e., regular sentences and proverbs). In general, pupil size increased when
reading regular sentences, but when readers realized that they were reading
proverbs their pupils strongly increase until finishing proverbs' reading. Our
results suggest that an increased pupil size is not limited to cognitive load
(i.e., relative difficulty in processing) because when participants accurately
recognized words during reading proverbs, theirs pupil size increased too. Our
results show that pupil size dynamics may be a reliable measure to investigate
the cognitive processes involved in sentence processing and memory functioning.
Temporal and Spatial Turnpike-Type Results Under Forward Time-Monotone Performance Criteria
Date: 2017-02-18 00:00:00

We present turnpike-type results for the risk tolerance function in an
incomplete market setting under time-monotone forward performance criteria. We
show that, contrary to the classical case, the temporal and spatial limits do
not coincide. We also show that they depend directly on the left- and right-end
of the support of an underlying measure, which is used to construct the forward
performance criterion. We provide examples with discrete and continuous
measures, and discuss the asymptotic behavior of the risk tolerance for each
case.
Social learning in a simple task allocation game
Date: 2017-02-19 00:00:00

We investigate the effects of social interactions in task al- location using
Evolutionary Game Theory (EGT). We propose a simple task-allocation game and
study how different learning mechanisms can give rise to specialised and non-
specialised colonies under different ecological conditions. By combining
agent-based simulations and adaptive dynamics we show that social learning can
result in colonies of generalists or specialists, depending on ecological
parameters. Agent-based simulations further show that learning dynamics play a
crucial role in task allocation. In particular, introspective individual
learning readily favours the emergence of specialists, while a process
resembling task recruitment favours the emergence of generalists.
Relation between regional uncertainty spillovers in the global banking system
Date: 2017-02-20 00:00:00
Tasks: Relation

We report on time-varying network connectedness within three banking systems:
North America, the EU, and ASEAN. The original method by Diebold and Yilmaz is
improved by using exponentially weighted daily returns and ridge regularization
on vector autoregression (VAR) and forecast error variance decomposition
(FEVD). We compute the total network connectedness for each of the three
banking systems, which quantifies regional uncertainty. Results over rolling
windows of 300 days during the period between 2005 and 2015 reveal changing
uncertainty patterns which are similar across regions, with common peaks
associated with identifiable exogenous events. Lead-lag relationships among
changes of total network connectedness of the three systems, quantified by
transfer entropy, reveal that uncertainties in the three regional systems are
significantly causally related, with the North American system having the
largest influence on EU and ASEAN.
MOLIERE: Automatic Biomedical Hypothesis Generation System
Authors: Sybrandt Justin, Shtutman Michael, Safro Ilya
Date: 2017-05-31 00:00:00

Hypothesis generation is becoming a crucial time-saving technique which
allows biomedical researchers to quickly discover implicit connections between
important concepts. Typically, these systems operate on domain-specific
fractions of public medical data. MOLIERE, in contrast, utilizes information
from over 24.5 million documents. At the heart of our approach lies a
multi-modal and multi-relational network of biomedical objects extracted from
several heterogeneous datasets from the National Center for Biotechnology
Information (NCBI). These objects include but are not limited to scientific
papers, keywords, genes, proteins, diseases, and diagnoses. We model hypotheses
using Latent Dirichlet Allocation applied on abstracts found near shortest
paths discovered within this network, and demonstrate the effectiveness of
MOLIERE by performing hypothesis generation on historical data. Our network,
implementation, and resulting data are all publicly available for the broad
scientific community.
Global stability with selection in integro-differential Lotka-Volterra systems modelling trait-structured populations
Date: 2017-04-14 00:00:00

We analyse the asymptotic behaviour of integro-differential equations
modelling $N$ populations in interaction, all structured by different traits.
Interactions are modelled by non-local terms involving linear combinations of
the total number of individuals in each population. These models have already
been shown to be suitable for the modelling of drug resistance in cancer, and
they generalise the usual Lotka-Volterra ordinary differential equations. Our
aim is to give conditions under which there is persistence of all species.
Through the analysis of a Lyapunov function, our first main result gives a
simple and general condition on the matrix of interactions, together with a
convergence rate. The second main result establishes another type of condition
in the specific case of mutualistic interactions. When either of these
conditions is met, we describe which traits are asymptotically selected.
Are tumor cell lineages solely shaped by mechanical forces?
Authors: Mathieu Leroy-Lerêtre, Giacomo Dimarco, Martine Cazales, Marie-Laure Boizeau, Bernard Ducommun, Valèrie Lobjois, Pierre Degond
Date: 2017-02-20 00:00:00

This paper investigates cells proliferation dynamics in small tumor cell aggregates using an individual based model (IBM). The simulation model is designed to study the morphology of the cell population and of the cell lineages as well as the impact of the orientation of the division plane on this morphology. Our IBM model is based on the hypothesis that cells are incompressible objects that grow in size and divide once a threshold size is reached, and that newly born cell adhere to the existing cell cluster. We performed comparisons between the simulation model and experimental data by using several statistical indicators. The results suggest that the emergence of particular morphologies can be explained by simple mechanical interactions.
Electrocorticographic Dynamics Predict Visually Guided Motor Imagery of Grasp Shaping
Date: 2017-02-21 00:00:00
Tasks: Motor Imagery

Identification of intended movement type and movement phase of hand grasp
shaping are critical features for the control of volitional neuroprosthetics.
We demonstrate that neural dynamics during visually-guided imagined grasp
shaping can encode intended movement. We apply Procrustes analysis and LASSO
regression to achieve 72% accuracy (chance = 25%) in distinguishing between
visually-guided imagined grasp trajectories. Further, we can predict the stage
of grasp shaping in the form of elapsed time from start of trial (R2=0.4). Our
approach contributes to more accurate single-trial decoding of higher-level
movement goals and the phase of grasping movements in individuals not trained
with brain-computer interfaces. We also find that the overall time-varying
trajectory structure of imagined movements tend to be consistent within
individuals, and that transient trajectory deviations within trials return to
the task-dependent trajectory mean. These overall findings may contribute to
the further understanding of the cortical dynamics of human motor imagery.
Player Skill Decomposition in Multiplayer Online Battle Arenas
Authors: Chen Zhengxing, Sun Yizhou, El-nasr Magy Seif, Nguyen Truong-Huy D.
Date: 2017-02-21 00:00:00

Successful analysis of player skills in video games has important impacts on
the process of enhancing player experience without undermining their continuous
skill development. Moreover, player skill analysis becomes more intriguing in
team-based video games because such form of study can help discover useful
factors in effective team formation. In this paper, we consider the problem of
skill decomposition in MOBA (MultiPlayer Online Battle Arena) games, with the
goal to understand what player skill factors are essential for the outcome of a
game match. To understand the construct of MOBA player skills, we utilize
various skill-based predictive models to decompose player skills into
interpretative parts, the impact of which are assessed in statistical terms. We
apply this analysis approach on two widely known MOBAs, namely League of
Legends (LoL) and Defense of the Ancients 2 (DOTA2). The finding is that base
skills of in-game avatars, base skills of players, and players'
champion-specific skills are three prominent skill components influencing LoL's
match outcomes, while those of DOTA2 are mainly impacted by in-game avatars'
base skills but not much by the other two.
A mathematical model for gene evolution after whole genome duplication
Date: 2017-02-21 00:00:00
Tasks: valid

Whole genome duplication (WGD) is one of the most important events in the
molecular evolution of organisms. In fish species, a WGD is considered to have
occurred in the ancestral lineage of teleosts. Recent comprehensive ortholog
comparisons among teleost genomes have provided useful data and insights into
the fate of redundant genes generated by WGD. Based on these data, a
mathematical model is proposed to explain the evolutionary scenario of genes
after WGD. The model is parameterized taking into account an equilibrium
between i) rapid loss of either of the duplicate genes and ii) moderate
functional differentiation of each of duplicate genes, both of which are
followed by slow gene loss under purifying selection. This model predicts that,
in the teleost lineage, a maximum of about 3000 gene pairs may have
differentiated functionally during 90 million years after WGD. Thus, the
present study provides a possibility that the whole impact of WGD can be
quantitatively assessed according to the model parameters, before details of
genomic structural changes or functional differentiation are investigated. If
the equilibrium model is valid not only for teleosts but also for other
lineages that have undergone WGDs, correlations between the assessment indices
and evolutionarily significant events, such as the diversification of species
or the occurrence of novel phenotypes, could be tested and compared among those
lineages.