pwc-archive/papers-with-abstracts huggingface dataset:
On Correlated Defaults and Incomplete Information
Date: 2016-01-27 00:00:00

In this paper, we study a continuous time structural asset value model for
two correlated firms using a two-dimensional Brownian motion. We consider the
situation of incomplete information, where the information set available to the
market participants includes the default time of each firm and the periodic
asset value reports. In this situation, the default time of each firm becomes a
totally inaccessible stopping time to the market participants. The original
structural model is first transformed to a reduced-form model. Then the
conditional distribution of the default time together with the asset value of
each name are derived. We prove the existence of the intensity processes of
default times and also give the explicit form of the intensity processes.
Numerical studies on the intensities of the two correlated names are conducted
for some special cases. We also indicate the possible future research extension
into three names case by considering a special correlation structure.
Mathematical modeling to elucidate brain tumor abrogation by immunotherapy with T11 target structure
Date: 2015-05-15 00:00:00

T11 Target structure (T11TS), a membrane glycoprotein isolated from sheep
erythrocytes, reverses the immune suppressed state of brain tumor induced
animals by boosting the functional status of the immune cells. This study aims
at aiding in the design of more efficacious brain tumor therapies with T11
target structure. We propose a mathematical model for brain tumor (glioma) and
the immune system interactions, which aims in designing efficacious brain tumor
therapy. The model encompasses considerations of the interactive dynamics of
macrophages, cytotoxic T lymphocytes, glioma cells, TGF-$\beta$, IFN-$\gamma$
and the T11TS. The system undergoes sensitivity analysis, that determines which
state variables are sensitive to the given parameters and the parameters are
estimated from the published data. Computer simulations were used for model
verification and validation, which highlight the importance of T11 target
structure in brain tumor therapy.
Analysis of heat kernel highlights the strongly modular and heat-preserving structure of proteins
Date: 2015-03-16 00:00:00

In this paper, we study the structure and dynamical properties of protein
contact networks with respect to other biological networks, together with
simulated archetypal models acting as probes. We consider both classical
topological descriptors, such as the modularity and statistics of the shortest
paths, and different interpretations in terms of diffusion provided by the
discrete heat kernel, which is elaborated from the normalized graph Laplacians.
A principal component analysis shows high discrimination among the network
types, either by considering the topological and heat kernel based vector
characterizations. Furthermore, a canonical correlation analysis demonstrates
the strong agreement among those two characterizations, providing thus an
important justification in terms of interpretability for the heat kernel.
Finally, and most importantly, the focused analysis of the heat kernel provides
a way to yield insights on the fact that proteins have to satisfy specific
structural design constraints that the other considered networks do not need to
obey. Notably, the heat trace decay of an ensemble of varying-size proteins
denotes subdiffusion, a peculiar property of proteins.
The Shifting Sands of Creative Thinking: Connections to Dual Process Theory
Date: 2019-07-15 00:00:00

Dual process models of cognition suggest there are two kinds of thought:
rapid, automatic Type 1 processes, and effortful, controlled Type 2 processes.
Models of creative thinking also distinguish between two sets of processes:
those involved in the generation of ideas, and those involved with their
refinement, evaluation and/or selection. Here we review dual process models in
both these literatures and delineate the similarities and differences. Both
generative and evaluative creative processing modes involve elements that have
been attributed to each of the dual processes of cognition. We explore the
notion that creative thinking may rest upon the nature of a shifting process
between generative and evaluative modes of thought. We suggest that through a
synthesis application of the evidence bases on dual process models of cognition
and from neuroimaging, together with developing chronometric approaches to
explore the shifting process, could assist the development of interventions to
facilitate creativity.
Why Blind-Variation Selective-Retention is an Inappropriate Explanatory Framework for Creativity
Date: 2019-07-15 00:00:00

Simonton is attempting to salvage the Blind Variation Selective Retention
theory of creativity (often referred to as the Darwinian theory of creativity)
by dissociating it from Darwinism. This is a necessary move for complex reasons
outlined in detail elsewhere. However, whether or not one calls BVSR a
Darwinian theory, it is still a variation-and-selection theory.
Variation-and-selection was put forward to solve a certain kind of paradox,
that of how biological change accumulates (that is, over generations, species
become more adapted to their environment) despite being discarded at the end of
each generation (that is, parents don't transmit to offspring knowledge or
bodily changes acquired during their lifetimes, e.g., you don't inherit your
mother's ear piercings). This paradox does not exist with respect to creative
thought. There is no discarding of acquired change when ideas are transmitted
amongst individuals; we share with others modified versions of the ideas we
were exposed to on a regular basis.
Accurate Liability Estimation Improves Power in Ascertained Case Control Studies
Date: 2016-02-21 00:00:00

Linear mixed models (LMMs) have emerged as the method of choice for
confounded genome-wide association studies. However, the performance of LMMs in
non-randomly ascertained case-control studies deteriorates with increasing
sample size. We propose a framework called LEAP (Liability Estimator As a
Phenotype, https://github.com/omerwe/LEAP) that tests for association with
estimated latent values corresponding to severity of phenotype, and demonstrate
that this can lead to a substantial power increase.
Custom v. Standardized Risk Models
Date: 2015-05-15 00:00:00
Tasks: Quant Trading

We discuss when and why custom multi-factor risk models are warranted and
give source code for computing some risk factors. Pension/mutual funds do not
require customization but standardization. However, using standardized risk
models in quant trading with much shorter holding horizons is suboptimal: 1)
longer horizon risk factors (value, growth, etc.) increase noise trades and
trading costs; 2) arbitrary risk factors can neutralize alpha; 3)
"standardized" industries are artificial and insufficiently granular; 4)
normalization of style risk factors is lost for the trading universe; 5)
diversifying risk models lowers P&L correlations, reduces turnover and market
impact, and increases capacity. We discuss various aspects of custom risk model
building.
General markers of conscious visual perception and their timing
Date: 2015-08-26 00:00:00
Tasks: Specificity

The goal of the present investigation was to identify reliable markers of
conscious visual perception and to characterize their onset latency and its
variability. To that end many visual stimuli from different categories were
presented at near-threshold contrast and contrastive analyses were carried out
on 100 balanced subsets of the data. N200 and P300 were the two reliable
markers of conscious perception common to all perceived stimuli and absent for
all nonperceived stimuli. The estimated mean onset latency for both markers was
shortly after 200 ms. However, the onset latency of both of these markers of
conscious perception showed considerable variability depending on which subsets
of the data were considered. Some of this variability could be attributed to
noise, but it was first and foremost the amplitude fluctuation in the condition
without conscious perception that explained the variability in onset latencies
of the markers of conscious perception. The present results help to understand
why different studies have observed different onset times for the neural
correlates of conscious perception. Moreover, the consciousness markers
explored here have more generality as stimulus specificity was reduced.
Circumstances in which parsimony but not compatibility will be provably misleading
Date: 2015-01-20 00:00:00

Phylogenetic methods typically rely on an appropriate model of how data
evolved in order to infer an accurate phylogenetic tree. For molecular data,
standard statistical methods have provided an effective strategy for extracting
phylogenetic information from aligned sequence data when each site (character)
is subject to a common process. However, for other types of data (e.g.
morphological data), characters can be too ambiguous, homoplastic or saturated
to develop models that are effective at capturing the underlying process of
change. To address this, we examine the properties of a classic but neglected
method for inferring splits in an underlying tree, namely, maximum
compatibility. By adopting a simple and extreme model in which each character
either fits perfectly on some tree, or is entirely random (but it is not known
which class any character belongs to) we are able to derive exact and explicit
formulae regarding the performance of maximum compatibility. We show that this
method is able to identify a set of non-trivial homoplasy-free characters, when
the number $n$ of taxa is large, even when the number of random characters is
large. By contrast, we show that a method that makes more uniform use of all
the data --- maximum parsimony --- can provably estimate trees in which {\em
none} of the original homoplasy-free characters support splits.
Evolutionary food web model based on body masses gives realistic networks with permanent species turnover
Date: 2015-04-17 00:00:00

The networks of predator-prey interactions in ecological systems are
remarkably complex, but nevertheless surprisingly stable in terms of long term
persistence of the system as a whole. In order to understand the mechanism
driving the complexity and stability of such food webs, we developed an
eco-evolutionary model in which new species emerge as modifications of existing
ones and dynamic ecological interactions determine which species are viable.
The food-web structure thereby emerges from the dynamical interplay between
speciation and trophic interactions. The proposed model is less abstract than
earlier evolutionary food web models in the sense that all three evolving
traits have a clear biological meaning, namely the average body mass of the
individuals, the preferred prey body mass, and the width of their potential
prey body mass spectrum. We observed networks with a wide range of sizes and
structures and high similarity to natural food webs. The model networks exhibit
a continuous species turnover, but massive extinction waves that affect more
than $50 \%$ of the network are not observed.
Beyond the Power Law: Uncovering Stylized Facts in Interbank Networks
Date: 2015-01-28 00:00:00

We use daily data on bilateral interbank exposures and monthly bank balance
sheets to study network characteristics of the Russian interbank market over
Aug 1998 - Oct 2004. Specifically, we examine the distributions of (un)directed
(un)weighted degree, nodal attributes (bank assets, capital and
capital-to-assets ratio) and edge weights (loan size and counterparty
exposure). We search for the theoretical distribution that fits the data best
and report the "best" fit parameters. We observe that all studied distributions
are heavy tailed. The fat tail typically contains 20% of the data and can be
mostly described well by a truncated power law. Also the power law, stretched
exponential and log-normal provide reasonably good fits to the tails of the
data. In most cases, however, separating the bulk and tail parts of the data is
hard, so we proceed to study the full range of the events. We find that the
stretched exponential and the log-normal distributions fit the full range of
the data best. These conclusions are robust to 1) whether we aggregate the data
over a week, month, quarter or year; 2) whether we look at the "growth" versus
"maturity" phases of interbank market development; and 3) with minor
exceptions, whether we look at the "normal" versus "crisis" operation periods.
In line with prior research, we find that the network topology changes greatly
as the interbank market moves from a "normal" to a "crisis" operation period.
Probing the Mind Behind the (Literal and Figurative) Lightbulb
Date: 2015-02-25 00:00:00

After doing away with the evolutionary scaffold for BVSR, what remains is a
notion of "blindness" that does not distinguish BVSR from other theories of
creativity, and an assumption that creativity can be understood by treating
ideas as discrete, countable entities, as opposed to different external
manifestations of a singular gradually solidifying internal conception.
Uprooted from Darwinian theory, BVSR lacks a scientific framework that can be
called upon to generate hypotheses and test them. In lieu of such a framework,
hypotheses appear to be generated on the basis of previous data--they are not
theory-driven. The paper does not explain how the hypothesis that creativity is
enhanced by engagement in a "network of enterprises" is derived from BVSR; this
hypothesis is more compatible with competing conceptions of creativity. The
notion that creativity involves backtracking conflates evidence for
backtracking with respect to the external output with evidence for backtracking
of the conception of the invention. The first does not imply the second; a
creator can set aside a creative output but cannot go back to the conception of
the task he/she had prior to generating that output. The notion that creativity
entails superfluity (i.e., many ideas have "zero usefulness") is misguided;
usefulness is context-dependent, moreover, the usefulness of an idea may reside
in its being a critical stepping-stone to a subsequent idea.
Can Sol's Explanation for the Evolution of Animal Innovation Account for Human Innovation?
Date: 2019-07-15 00:00:00

Sol argues that innovation propensity is not a specialized adaptation
resulting from targeted selection but an instance of exaptation because
selection cannot act on situations that are only encountered once. In
exaptation, a trait that originally evolved to solve one problem is co-opted to
solve a new problem; thus the trait or traits in question must be necessary and
sufficient to solve the new problem. Sol claims that traits such as persistence
and neophilia, are necessary and sufficient for animal innovation, which is a
matter of trial and error. We suggest that this explanation does not extend to
human innovation, which involves strategy, logic, intuition, and insight, and
requires traits that evolved, not as a byproduct of some other function, but
for the purpose of coming up with adaptive responses to environmental
variability itself. We point to an agent based model that indicates the
feasibility of two such proposed traits: (1) chaining, the ability to construct
complex thoughts from simple ones, and (2) contextual focus, the ability to
shift between convergent and divergent modes of thought. We agree that there is
a sense in which innovation is exaptation--it occurs when an existing object or
behaviour is adapted to new needs or tastes--and refer to a mathematical model
of biological and cultural exaltation. We conclude that much is gained by
comparing and contrasting animal and human innovation.
Integrating alignment-based and alignment-free sequence similarity measures for biological sequence classification
Date: 2015-01-20 00:00:00

Alignment-based sequence similarity searches, while accurate for some type of
sequences, can produce incorrect results when used on more divergent but
functionally related sequences that have undergone the sequence rearrangements
observed in many bacterial and viral genomes. Here, we propose a classification
model that exploits the complementary nature of alignment-based and
alignment-free similarity measures with the aim to improve the accuracy with
which DNA and protein sequences are characterized. Our model classifies
sequences using a combined sequence similarity score calculated by adaptively
weighting the contribution of different sequence similarity measures. Weights
are determined independently for each sequence in the test set and reflect the
discriminatory ability of individual similarity measures in the training set.
Since the similarity between some sequences is determined more accurately with
one type of measure rather than another, our classifier allows different sets
of weights to be associated with different sequences. Using five different
similarity measures we show that our model significantly improves the
classification accuracy over the current composition and alignment based
models, when predicting the taxonomic lineage for both short viral sequence
fragments and complete viral sequences. We also show that our model can be used
effectively for the classification of reads from a real metagenome dataset as
well as protein sequences.
Computational Screening of Tip and Stalk Cell Behavior Proposes a Role for Apelin Signaling in Sprout Progression
Date: 2016-07-19 00:00:00
Tasks: Prediction

Angiogenesis involves the formation of new blood vessels by sprouting or
splitting of existing blood vessels. During sprouting, a highly motile type of
endothelial cell, called the tip cell, migrates from the blood vessels followed
by stalk cells, an endothelial cell type that forms the body of the sprout. To
get more insight into how tip cells contribute to angiogenesis, we extended an
existing computational model of vascular network formation based on the
cellular Potts model with tip and stalk differentiation, without making a
priori assumptions about the differences between tip cells and stalk cells. To
predict potential differences, we looked for parameter values that make tip
cells (a) move to the sprout tip, and (b) change the morphology of the
angiogenic networks. The screening predicted that if tip cells respond less
effectively to an endothelial chemoattractant than stalk cells, they move to
the tips of the sprouts, which impacts the morphology of the networks. A
comparison of this model prediction with genes expressed differentially in tip
and stalk cells revealed that the endothelial chemoattractant Apelin and its
receptor APJ may match the model prediction. To test the model prediction we
inhibited Apelin signaling in our model and in an \emph{in vitro} model of
angiogenic sprouting, and found that in both cases inhibition of Apelin or of
its receptor APJ reduces sprouting. Based on the prediction of the
computational model, we propose that the differential expression of Apelin and
APJ yields a "self-generated" gradient mechanisms that accelerates the
extension of the sprout.
On the spike train variability characterized by variance-to-mean power relationship
Date: 2015-05-21 00:00:00

We propose a statistical method for modeling the non-Poisson variability of
spike trains observed in a wide range of brain regions. Central to our approach
is the assumption that the variance and the mean of interspike intervals are
related by a power function characterized by two parameters: the scale factor
and exponent. It is shown that this single assumption allows the variability of
spike trains to have an arbitrary scale and various dependencies on the firing
rate in the spike count statistics, as well as in the interval statistics,
depending on the two parameters of the power function. We also propose a
statistical model for spike trains that exhibits the variance-to-mean power
relationship, and based on this a maximum likelihood method is developed for
inferring the parameters from rate-modulated spike trains. The proposed method
is illustrated on simulated and experimental spike trains.
Population pressure and global markets drive a decade of forest cover change in Africa's Albertine Rift
Date: 2015-11-26 00:00:00

The Albertine Rift region faces rapid human population growth, while being a
biodiversity hotspot. Using satellite-derived continuous forest cover change
data, we examined national socioeconomic, demographic, and agricultural
production data, and local demographic and geographic variables to assess
multilevel forces driving significant local forest cover loss and gain outside
protected areas during the first decade of this century. Because the processes
that drive forest cover loss and gain are expected to be different, we
constructed models of change in each direction. Although forest cover change
varied by country, national level population change was the strongest driver of
forest loss rate for all countries, with a population doubling predicted to
cause 2.06 percent annual cover loss, while doubling tea production was
predicted to cause 1.90 percent. The rate of forest cover gain was associated
positively with increased production of the local staple crop cassava, but
negatively with local population density and meat production, suggesting
production drivers at multiple levels mitigate reforestation. We found a small,
but significant, decrease of forest cover loss rate with increasing distance
from protected areas, supporting studies suggesting higher rates of landscape
change near protected areas. While local population density mitigated the rate
of forest cover gain, cover loss also correlated to lower local population
density, an apparent paradox, but consistent with findings that larger scale
forces outweigh local drivers of deforestation. This implicates demographic and
market forces at national and international scales as critical drivers of
change, calling into question the necessary scale of forest protection policy
in this biodiversity hotspot.
Risk Premia: Asymmetric Tail Risks and Excess Returns
Date: 2015-10-30 00:00:00

We present extensive evidence that ``risk premium'' is strongly correlated
with tail-risk skewness but very little with volatility. We introduce a new,
intuitive definition of skewness and elicit an approximately linear relation
between the Sharpe ratio of various risk premium strategies (Equity,
Fama-French, FX Carry, Short Vol, Bonds, Credit) and their negative skewness.
We find a clear exception to this rule: trend following has both positive
skewness and positive excess returns. This is also true, albeit less markedly,
of the Fama-French ``Value'' factor and of the ``Low Volatility'' strategy.
This suggests that some strategies are not risk premia but genuine market
anomalies. Based on our results, we propose an objective criterion to assess
the quality of a risk-premium portfolio.