pwc-archive/papers-with-abstracts huggingface dataset:
The Excess Returns of "Quality" Stocks: A Behavioral Anomaly
Date: 2016-01-18 00:00:00

This note investigates the causes of the quality anomaly, which is one of the
strongest and most scalable anomalies in equity markets. We explore two
potential explanations. The "risk view", whereby investing in high quality
firms is somehow riskier, so that the higher returns of a quality portfolio are
a compensation for risk exposure. This view is consistent with the Efficient
Market Hypothesis. The other view is the "behavioral view", which states that
some investors persistently underestimate the true value of high quality firms.
We find no evidence in favor of the "risk view": The returns from investing in
quality firms are abnormally high on a risk-adjusted basis, and are not prone
to crashes. We provide novel evidence in favor of the "behavioral view": In
their forecasts of future prices, and while being overall overoptimistic,
analysts systematically underestimate the future return of high quality firms,
compared to low quality firms.
Crunching Mortality and Life Insurance Portfolios with extended CreditRisk+
Date: 2016-11-25 00:00:00

Using an extended version of the credit risk model CreditRisk+, we develop a
flexible framework with numerous applications amongst which we find stochastic
mortality modelling, forecasting of death causes as well as profit and loss
modelling of life insurance and annuity portfolios which can be used in
(partial) internal models under Solvency II. Yet, there exists a fast and
numerically stable algorithm to derive loss distributions exactly, even for
large portfolios. We provide various estimation procedures based on publicly
available data. Compared to the Lee-Carter model, we have a more flexible
framework, get tighter bounds and can directly extract several sources of
uncertainty. Straight-forward model validation techniques are available.
Metabolic Flux Analysis in Isotope Labeling Experiments using the Adjoint Approach
Date: 2016-01-18 00:00:00

Comprehension of metabolic pathways is considerably enhanced by metabolic
flux analysis (MFA-ILE) in isotope labeling experiments. The balance equations
are given by hundreds of algebraic (stationary MFA) or ordinary differential
equations (nonstationary MFA), and reducing the number of operations is
therefore a crucial part of reducing the computation cost. The main bottleneck
for deterministic algorithms is the computation of derivatives, particularly
for nonstationary MFA. In this article we explain how the overall
identification process may be speeded up by using the adjoint approach to
compute the gradient of the residual sum of squares. The proposed approach
shows significant improvements in terms of complexity and computation time when
it is compared with the usual (direct) approach. Numerical results are obtained
for the central metabolic pathways of Escherichia coli and are validated
against reference software in the stationary case. The methods and algorithms
described in this paper are included in the sysmetab software package
distributed under an Open Source license at
http://forge.scilab.org/index.php/p/sysmetab/.
Dynamic Information Retrieval: Theoretical Framework and Application
Authors: Sloan Marc, Wang Jun
Date: 2016-01-18 00:00:00
Tasks: Information Retrieval, Retrieval

Theoretical frameworks like the Probability Ranking Principle and its more
recent Interactive Information Retrieval variant have guided the development of
ranking and retrieval algorithms for decades, yet they are not capable of
helping us model problems in Dynamic Information Retrieval which exhibit the
following three properties; an observable user signal, retrieval over multiple
stages and an overall search intent. In this paper a new theoretical framework
for retrieval in these scenarios is proposed. We derive a general dynamic
utility function for optimizing over these types of tasks, that takes into
account the utility of each stage and the probability of observing user
feedback. We apply our framework to experiments over TREC data in the dynamic
multi page search scenario as a practical demonstration of its effectiveness
and to frame the discussion of its use, its limitations and to compare it
against the existing frameworks.
A Term-Based Methodology for Query Reformulation Understanding
Authors: Sloan Marc, Yang Hui, Wang Jun
Date: 2016-01-19 00:00:00
Tasks: Session Search

Key to any research involving session search is the understanding of how a
user's queries evolve throughout the session. When a user creates a query
reformulation, he or she is consciously retaining terms from their original
query, removing others and adding new terms. By measuring the similarity
between queries we can make inferences on the user's information need and how
successful their new query is likely to be. By identifying the origins of added
terms we can infer the user's motivations and gain an understanding of their
interactions.
  In this paper we present a novel term-based methodology for understanding and
interpreting query reformulation actions. We use TREC Session Track data to
demonstrate how our technique is able to learn from query logs and we make use
of click data to test user interaction behavior when reformulating queries. We
identify and evaluate a range of term-based query reformulation strategies and
show that our methods provide valuable insight into understanding query
reformulation in session search.
Unified Growth Theory Contradicted by the Absence of Takeoffs in the Gross Domestic Product
Date: 2016-03-25 00:00:00

Data describing historical economic growth are analysed. They demonstrate
convincingly that the takeoffs from stagnation to growth, claimed in the
Unified Growth Theory, never happened. This theory is again contradicted by
data, which were used, but never properly analysed, during its formulation. The
absence of the claimed takeoffs demonstrates also that the postulate of the
differential takeoffs is contradicted by data.
A Theoretical Analysis of Two-Stage Recommendation for Cold-Start Collaborative Filtering
Authors: Zhao Xiaoxue, Wang Jun
Date: 2016-01-18 00:00:00
Tasks: Collaborative Filtering

In this paper, we present a theoretical framework for tackling the cold-start
collaborative filtering problem, where unknown targets (items or users) keep
coming to the system, and there is a limited number of resources (users or
items) that can be allocated and related to them. The solution requires a
trade-off between exploitation and exploration as with the limited
recommendation opportunities, we need to, on one hand, allocate the most
relevant resources right away, but, on the other hand, it is also necessary to
allocate resources that are useful for learning the target's properties in
order to recommend more relevant ones in the future. In this paper, we study a
simple two-stage recommendation combining a sequential and a batch solution
together. We first model the problem with the partially observable Markov
decision process (POMDP) and provide an exact solution. Then, through an
in-depth analysis over the POMDP value iteration solution, we identify that an
exact solution can be abstracted as selecting resources that are not only
highly relevant to the target according to the initial-stage information, but
also highly correlated, either positively or negatively, with other potential
resources for the next stage. With this finding, we propose an approximate
solution to ease the intractability of the exact solution. Our initial results
on synthetic data and the Movie Lens 100K dataset confirm the performance gains
of our theoretical development and analysis.
General Equilibrium and Recession Phenomenon
Date: 2016-01-19 00:00:00

The theorems we proved describe the structure of economic equilibrium in the
exchange economy model. We have studied the structure of property vectors under
given structure of demand vectors at which given price vector is equilibrium
one. On this ground, we describe the general structure of the equilibrium state
and give characteristic of equilibrium state describing economic recession. The
theory developed is applied to explain the state of the economy in some
European countries.
Highly connected neurons spike less frequently in balanced networks
Date: 2016-01-19 00:00:00

Many biological neuronal networks exhibit highly variable spiking activity.
Balanced networks offer a parsimonious model of this variability. In balanced
networks, strong excitatory synaptic inputs are canceled by strong inhibitory
inputs on average and spiking activity is driven by transient breaks in this
balance. Most previous studies of balanced networks assume a homogeneous or
distance-dependent connectivity structure, but connectivity in biological
cortical networks is more intricate. We use a heterogeneous mean-field theory
of balanced networks to show that heterogeneous in-degrees can break balance,
but balance can be restored by heterogeneous out-degrees that are correlated
with in-degrees. In all examples considered, we find that highly connected
neurons spike less frequently, consistent with recent experimental
observations.
Nonbinary tree-based phylogenetic networks
Date: 2016-09-30 00:00:00

Rooted phylogenetic networks are used to describe evolutionary histories that
contain non-treelike evolutionary events such as hybridization and horizontal
gene transfer. In some cases, such histories can be described by a phylogenetic
base-tree with additional linking arcs, which can for example represent gene
transfer events. Such phylogenetic networks are called tree-based. Here, we
consider two possible generalizations of this concept to nonbinary networks,
which we call tree-based and strictly-tree-based nonbinary phylogenetic
networks. We give simple graph-theoretic characterizations of tree-based and
strictly-tree-based nonbinary phylogenetic networks. Moreover, we show for each
of these two classes that it can be decided in polynomial time whether a given
network is contained in the class. Our approach also provides a new view on
tree-based binary phylogenetic networks. Finally, we discuss two examples of
nonbinary phylogenetic networks in biology and show how our results can be
applied to them.
A Simple Measure of Economic Complexity
Authors: Sabiou Inoua
Date: 2016-01-19 00:00:00
Tasks: Diversity

Contrary to conventional economic growth theory, which reduces a country's output to one aggregate variable (GDP), product diversity is central to economic development, as recent 'economic complexity' research suggests. A country's product diversity reflects its diversity of knowhow or 'capabilities'. Researchers proposed the Economic Complexity Index (ECI) and the country Fitness index to estimate a country's number of capabilities from international export data; these measures predict economic growth better than conventional variables such as human capital. This paper offers a simpler measure of a country's knowhow, Log Product Diversity (or LPD, the logarithm of a country's number of products), which can be derived from a one-parameter combinatorial model of production in which a set of knowhows combine with some probability to turn raw materials into a product. ECI and log-fitness can be interpreted theoretically (using the combinatorial model) and empirically as potentially noisy estimates of LPD; moreover, controlling for natural resources, the simple measure better explains the cross-country differences in GDP and in GDP per capita.
Temporal meta-states are associated with differential patterns of dynamic connectivity, network topology and attention
Date: 2016-08-15 00:00:00

Little is currently known about the coordination of neural activity over
longitudinal time-scales and how these changes relate to behavior. To
investigate this issue, we used resting-state fMRI data from a single
individual to identify the presence of two distinct temporal states that
fluctuated over the course of 18 months. We then demonstrated that these
temporal states were associated with distinct neural dynamics within individual
scanning sessions. In addition, the temporal states were also related to
significant alterations in global efficiency, as well as differences in
self-reported attention. These patterns were replicated in a separate
longitudinal dataset, providing further supportive evidence for the presence of
fluctuations in functional network topology over time. Together, our results
underscore the importance of longitudinal phenotyping in cognitive
neuroscience.
Econo- and socio- physics based remarks on the economical growth of the World
Date: 2016-01-17 00:00:00

It has been shown that the long term evolution of the Gross Product of the
World after World War II can be well portrayed by the exponential function with
the crossover at the year 1973, cinsiding with the Oil Crisis onset. For the
the Standard and Poor 500 index the single exponential behavior extends down to
at least the mid of the nineteen century. It is notable that the detailed
short-term insight focused on the last quarter of century revealed the
emergence of the power like dependence. However, such dependences can be
introduced only when taking into account the behavior at reference-baselines
years. The possible relationship to the growth/death evolution of
microorganisms is also discussed. The report proposes the new discussion of the
past and nowadays time of the global economy. It recalls econonophysics and
sociophysics as disciplines within which the effective parameterization of
trends is possible. Finally, possible future trends are discussed.
Validating non-invasive EEG source imaging using optimal electrode configurations on a representative rat head model
Date: 2016-01-25 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Sensitivity

The curtain of technical limitations impeding rat multichannel non-invasive
electroencephalography (EEG) has risen. Given the importance of this
preclinical model, development and validation of EEG source imaging (ESI) is
essential. We investigate the validity of well-known human ESI methodologies in
rats which individual tissue geometries have been approximated by those
extracted from an MRI template, leading also to imprecision in electrode
localizations. With the half and fifth sensitivity volumes we determine both
the theoretical minimum electrode separation for non-redundant scalp EEG
measurements and the electrode sensitivity resolution, which vary over the
scalp because of the head geometry. According to our results, electrodes should
be at least ~3-3.5 mm apart for an optimal configuration. The sensitivity
resolution is generally worse for electrodes at the boundaries of the scalp
measured region, though, by analogy with human montages, concentrates the
sensitivity enough to localize sources. Cram\'er-Rao lower bounds of source
localization errors indicate it is theoretically possible to achieve ESI
accuracy at the level of anatomical structures, such as the stimulus-specific
somatosensory areas, using the template. More validation for this approximation
is provided through the comparison between the template and the individual lead
field matrices, for several rats. Finally, using well-accepted inverse methods,
we demonstrate that somatosensory ESI is not only expected but also allows
exploring unknown phenomena related to global sensory integration. Inheriting
the advantages and pitfalls of human ESI, rat ESI will boost the understanding
of brain pathophysiological mechanisms and the evaluation of ESI methodologies,
new pharmacological treatments and ESI-based biomarkers.
Understanding the functional impact of copy number alterations in breast cancer using a network modeling approach
Date: 2016-01-19 00:00:00

Copy number alterations (CNAs) are thought to account for 85% of the
variation in gene expression observed among breast tumours. The expression of
cis-associated genes is impacted by CNAs occurring at proximal loci of these
genes, whereas the expression of trans-associated genes is impacted by CNAs
occurring at distal loci. While a majority of these CNA-driven genes
responsible for breast tumourigenesis are cis-associated, trans-associated
genes are thought to further abet the development of cancer and influence
disease outcomes in patients. Here we present a network-based approach that
integrates copy-number and expression profiles to identify putative cis- and
trans-associated genes in breast cancer pathogenesis. We validate these cis-
and trans-associated genes by employing them to subtype a large cohort of
breast tumours obtained from the METABRIC consortium, and demonstrate that
these genes accurately reconstruct the ten subtypes of breast cancer. We
observe that individual breast cancer subtypes are driven by distinct sets of
cis- and trans-associated genes. Among the cis-associated genes, we recover
several known drivers of breast cancer (e.g. CCND1, ERRB2, MDM2 and ZNF703) and
some novel putative drivers (e.g. BRF2 and SF3B3). siRNA-mediated knockdown of
BRF2 across a panel of breast cancer cell lines showed significant reduction
specifically in cell proliferation in HER2+ lines, thereby indicating that BRF2
could be a context-dependent oncogene and potentially targetable in these
lines. Among the trans-associated genes, we identify modules of immune-response
(CD2, CD19, CD38 and CD79B), mitotic/cell-cycle kinases (e.g. AURKB, MELK, PLK1
and TTK), and DNA-damage response genes (e.g. RFC4 and FEN1).
TROM: A Testing-based Method for Finding Transcriptomic Similarity of Biological Samples
Date: 2016-08-30 00:00:00

Comparative transcriptomics has gained increasing popularity in genomic
research thanks to the development of high-throughput technologies including
microarray and next-generation RNA sequencing that have generated numerous
transcriptomic data. An important question is to understand the conservation
and differentiation of biological processes in different species. We propose a
testing-based method TROM (Transcriptome Overlap Measure) for comparing
transcriptomes within or between different species, and provide a different
perspective to interpret transcriptomic similarity in contrast to traditional
correlation analyses. Specifically, the TROM method focuses on identifying
associated genes that capture molecular characteristics of biological samples,
and subsequently comparing the biological samples by testing the overlap of
their associated genes. We use simulation and real data studies to demonstrate
that TROM is more powerful in identifying similar transcriptomes and more
robust to stochastic gene expression noise than Pearson and Spearman
correlations. We apply TROM to compare the developmental stages of six
Drosophila species, C. elegans, S. purpuratus, D. rerio and mouse liver, and
find interesting correspondence patterns that imply conserved gene expression
programs in the development of these species. The TROM method is available as
an R package on CRAN (http://cran.r-project.org/) with manuals and source codes
available at http://www.stat.ucla.edu/ jingyi.li/software-and-data/trom.html.
Portfolio Optimisation Under Flexible Dynamic Dependence Modelling
Date: 2016-01-20 00:00:00

Signals coming from multivariate higher order conditional moments as well as
the information contained in exogenous covariates, can be effectively exploited
by rational investors to allocate their wealth among different risky investment
opportunities. This paper proposes a new flexible dynamic copula model being
able to explain and forecast the time-varying shape of large dimensional asset
returns distributions. Moreover, we let the univariate marginal distributions
to be driven by an updating mechanism based on the scaled score of the
conditional distribution. This framework allows us to introduce time-variation
in up to the fourth moment of the conditional distribution. The time-varying
dependence pattern is subsequently modelled as function of a latent Markov
Switching process, allowing also for the inclusion of exogenous covariates in
the dynamic updating equation. We empirically assess that the proposed model
substantially improves the optimal portfolio allocation of rational investors
maximising their expected utility.
Dangerous connections: on binding site models of infectious disease dynamics
Date: 2016-01-27 00:00:00

We formulate models for the spread of infection on networks that are amenable
to analysis in the large population limit. We distinguish three different
levels: (1) binding sites, (2) individuals, and (3) the population. In the
tradition of Physiologically Structured Population Models, the formulation
starts on the individual level. Influences from the `outside world' on an
individual are captured by environmental variables. These environmental
variables are population level quantities. A key characteristic of the network
models is that individuals can be decomposed into a number of conditionally
independent components: each individual has a fixed number of `binding sites'
for partners. The Markov chain dynamics of binding sites are described by only
a few equations. In particular, individual-level probabilities are obtained
from binding-site-level probabilities by combinatorics while population-level
quantities are obtained by averaging over individuals in the population. Thus
we are able to characterize population-level epidemiological quantities, such
as $R_0$, $r$, the final size, and the endemic equilibrium, in terms of the
corresponding variables.
Tracing Digital Footprints to Academic Articles: An Investigation of PeerJ Publication Referral Data
Authors: Wang Xianwen, Xu Shenmeng, Fang Zhichao
Date: 2016-01-20 00:00:00
Tasks: Articles

In this study, we propose a novel way to explore the patterns of people's
visits to academic articles. About 3.4 million links to referral source of
visitors of 1432 papers published in the journal of PeerJ are collected and
analyzed. We find that at least 57% visits are from external referral sources,
among which General Search Engine, Social Network, and News & Blog are the top
three categories of referrals. Academic Resource, including academic search
engines and academic publishers' sites, is the fourth largest category of
referral sources. In addition, our results show that Google contributes
significantly the most in directing people to scholarly articles. This
encompasses the usage of Google the search engine, Google Scholar the academic
search engine, and diverse specific country domains of them. Focusing on
similar disciplines to PeerJ's publication scope, NCBI is the academic search
engine on which people are the most frequently directed to PeerJ. Correlation
analysis and regression analysis indicates that papers with more mentions are
expected to have more visitors, and Facebook, Twitter and Reddit are the most
commonly used social networking tools that refer people to PeerJ.
On "A General Framework for Pricing Asian Options Under Markov Processes"
Date: 2016-01-20 00:00:00

Cai, Song and Kou (2015) [Cai, N., Y. Song, S. Kou (2015) A general framework
for pricing Asian options under Markov processes. Oper. Res. 63(3): 540-554]
made a breakthrough by proposing a general framework for pricing both
discretely and continuously monitored Asian options under one-dimensional
Markov processes. In this note, under the setting of continuous-time Markov
chain (CTMC), we explicitly carry out the inverse Z-transform and the inverse
Laplace transform respectively for the discretely and the continuously
monitored cases. The resulting explicit single Laplace transforms improve their
Theorem 2, p.543, and numerical studies demonstrate the gain in efficiency.