pwc-archive/papers-with-abstracts huggingface dataset:
PubTree: A Hierarchical Search Tool for the MEDLINE Database
Authors: Rowe William, Dobson Paul D., Constantinides Bede, Platt Mark
Date: 2017-02-26 00:00:00
Tasks: Articles, Retrieval

Keeping track of the ever-increasing body of scientific literature is an
escalating challenge. We present PubTree a hierarchical search tool that
efficiently searches the PubMed/MEDLINE dataset based upon a decision tree
constructed using >26 million abstracts. The tool is implemented as a webpage,
where users are asked a series of eighteen questions to locate pertinent
articles. The implementation of this hierarchical search tool highlights issues
endemic with document retrieval. However, the construction of this tree
indicates that with future developments hierarchical search could become an
effective tool (or adjunct) in the mining of biological literature.
Intra-protein binding peptide fragments have specific and intrinsic sequence patterns
Date: 2017-02-26 00:00:00
Tasks: Protein Folding

The key finding in the DNA double helix model is the specific pairing or
binding between nucleotides A-T and C-G, and the pairing rules are the molecule
basis of genetic code. Unfortunately, no such rules have been discovered for
proteins. Here we show that similar rules and intrinsic sequence patterns
between intra-protein binding peptide fragments do exist, and they can be
extracted using a deep learning algorithm. Multi-millions of binding and
non-binding peptide fragments from currently available protein X-ray structures
are classified with an accuracy of up to 93%. This discovery has the potential
in helping solve protein folding and protein-protein interaction problems, two
open and fundamental problems in molecular biology.
Modeling mechanical interactions in growing populations of rod-shaped bacteria
Date: 2017-02-26 00:00:00

Advances in synthetic biology allow us to engineer bacterial collectives with
pre-specified characteristics. However, the behavior of these collectives is
difficult to understand, as cellular growth and division as well as
extra-cellular fluid flow lead to complex, changing arrangements of cells
within the population. To rationally engineer and control the behavior of cell
collectives we need theoretical and computational tools to understand their
emergent spatiotemporal dynamics. Here, we present an agent-based model that
allows growing cells to detect and respond to mechanical interactions.
Crucially, our model couples the dynamics of cell growth to the cell's
environment: Mechanical constraints can affect cellular growth rate and a cell
may alter its behavior in response to these constraints. This coupling links
the mechanical forces that influence cell growth and emergent behaviors in cell
assemblies. We illustrate our approach by showing how mechanical interactions
can impact the dynamics of bacterial collectives growing in microfluidic traps.
Mutual Information based labelling and comparing clusters
Authors: Koopman Rob, Wang Shenghui
Date: 2017-02-27 00:00:00
Tasks: Articles, Clustering

After a clustering solution is generated automatically, labelling these
clusters becomes important to help understanding the results. In this paper, we
propose to use a Mutual Information based method to label clusters of journal
articles. Topical terms which have the highest Normalised Mutual Information
(NMI) with a certain cluster are selected to be the labels of the cluster.
Discussion of the labelling technique with a domain expert was used as a check
that the labels are discriminating not only lexical-wise but also semantically.
Based on a common set of topical terms, we also propose to generate lexical
fingerprints as a representation of individual clusters. Eventually, we
visualise and compare these fingerprints of different clusters from either one
clustering solution or different ones.
Economic inequality and mobility for stochastic models with multiplicative noise
Date: 2017-02-23 00:00:00

In this article, we discuss a dynamical stochastic model that represents the
time evolution of income distribution of a population, where the dynamics
develop from an interplay of multiple economic exchanges in the presence of
multiplicative noise. The model remit stretches beyond the conventional
framework of a Langevin-type kinetic equation in that our model dynamics is
self-consistently constrained by dynamical conservation laws emerging from
population and wealth conservation. This model is numerically solved and
analyzed to interpret the inequality of income as a function of relevant
dynamical parameters like the {\it mobility} $M$ and the {\it total income}
$\mu$. In our model, inequality is quantified by the {\it Gini index} $G$. In
particular, correlations between any two of the mobility index $M$ and/or the
total income $\mu$ with the Gini index $G$ are investigated and compared with
the analogous correlations resulting from an equivalent additive noise model.
Our findings highlight the importance of a multiplicative noise based economic
modeling structure in the analysis of inequality. The model also depicts the
nature of correlation between mobility and total income of a population from
the perspective of inequality measure.
The role of the observer in goal-directed behavior
Date: 2017-02-27 00:00:00

In goal-directed behavior, a large number of possible initial states end up
in the pursued goal. The accompanying information loss implies that
goal-oriented behavior is in one-to-one correspondence with an open subsystem
whose entropy decreases in time. Yet ultimately, the laws of physics are
reversible, so systems capable of yielding goal-directed behavior must transfer
the information about initial conditions to other degrees of freedom outside
the boundaries of the agent. To operate steadily, they must consume ordered
degrees of freedom provided as input, and be dispensed of disordered outputs
that act as wastes from the point of view of the aimed objective. Here I argue
that a physical system may or may not display goal-directed behavior depending
on what exactly is defined as the agent. The borders of the agent must be
carefully tailored so as to entail the appropriate information balance sheet.
In this game, observers play the role of tailors: They design agents by setting
the limits of the system of interest. Brain-guided subjects perform this
creative observation task naturally, implying that the observation of
goal-oriented behavior is a goal-oriented behavior in itself. Minds evolved to
cut out pieces of reality and endow them with intentionality, because ascribing
intentionality is an efficient way of modeling the world, and making
predictions. One most remarkable agent of whom we have indisputable evidence of
its goal-pursuing attitude is the self. Notably, this agent is simultaneously
the subject and the object of observation.
Inferring processes of cultural transmission: the critical role of rare variants in distinguishing neutrality from novelty biases
Date: 2017-04-25 00:00:00

Neutral evolution assumes that there are no selective forces distinguishing
different variants in a population. Despite this striking assumption, many
recent studies have sought to assess whether neutrality can provide a good
description of different episodes of cultural change. One approach has been to
test whether neutral predictions are consistent with observed progeny
distributions, recording the number of variants that have produced a given
number of new instances within a specified time interval: a classic example is
the distribution of baby names. Using an overlapping generations model we show
that these distributions consist of two phases: a power law phase with a
constant exponent of -3/2, followed by an exponential cut-off for variants with
very large numbers of progeny. Maximum likelihood estimations of the model
parameters provide a direct way to establish whether observed empirical
patterns are consistent with neutral evolution. We apply our approach to a
complete data set of baby names from Australia. Crucially we show that analyses
based on only the most popular variants, as is often the case in studies of
cultural evolution, can provide misleading evidence for underlying transmission
hypotheses. While neutrality provides a plausible description of progeny
distributions of abundant variants, rare variants deviate from neutrality.
Further, we develop a simulation framework that allows for the detection of
alternative cultural transmission processes. We show that anti-novelty bias is
able to replicate the complete progeny distribution of the Australian data set.
Reinterpreting Maximum Entropy in Ecology: a null hypothesis constrained by ecological mechanism
Date: 2017-04-28 00:00:00

Simplified mechanistic models in ecology have been criticized for the fact
that a good fit to data does not imply the mechanism is true: pattern does not
equal process. In parallel, the maximum entropy principle (MaxEnt) has been
applied in ecology to make predictions constrained by just a handful of state
variables, like total abundance or species richness. But an outstanding
question remains: what principle tells us which state variables to constrain?
Here we attempt to solve both problems simultaneously, by translating a given
set of mechanisms into the state variables to be used in MaxEnt, and then using
this MaxEnt theory as a null model against which to compare mechanistic
predictions. In particular, we identify the sufficient statistics needed to
parametrize a given mechanistic model from data and use them as MaxEnt
constraints. Our approach isolates exactly what mechanism is telling us over
and above the state variables alone.
Estimating the reproductive number, total outbreak size, and reporting rates for Zika epidemics in South and Central America
Date: 2017-02-27 00:00:00
Tasks: Uncertainty Quantification

As South and Central American countries prepare for increased birth defects
from Zika virus outbreaks and plan for mitigation strategies to minimize
ongoing and future outbreaks, understanding important characteristics of Zika
outbreaks and how they vary across regions is a challenging and important
problem. We developed a mathematical model for the 2015 Zika virus outbreak
dynamics in Colombia, El Salvador, and Suriname. We fit the model to publicly
available data provided by the Pan American Health Organization, using
Approximate Bayesian Computation to estimate parameter distributions and
provide uncertainty quantification. An important model input is the at-risk
susceptible population, which can vary with a number of factors including
climate, elevation, population density, and socio-economic status. We informed
this initial condition using the highest historically reported dengue incidence
modified by the probable dengue reporting rates in the chosen countries. The
model indicated that a country-level analysis was not appropriate for Colombia.
We then estimated the basic reproduction number, or the expected number of new
human infections arising from a single infected human, to range between 4 and 6
for El Salvador and Suriname with a median of 4.3 and 5.3, respectively. We
estimated the reporting rate to be around 16% in El Salvador and 18% in
Suriname with estimated total outbreak sizes of 73,395 and 21,647 people,
respectively. The uncertainty in parameter estimates highlights a need for
research and data collection that will better constrain parameter ranges.
Spontaneous Activity in the Visual Cortex is Organized by Visual Streams
Date: 2017-02-28 00:00:00

Large-scale functional networks have been extensively studied using resting
state functional magnetic resonance imaging. However, the pattern,
organization, and function of fine-scale network activity remain largely
unknown. Here we characterized the spontaneously emerging visual cortical
activity by applying independent component analysis to resting state fMRI
signals exclusively within the visual cortex. In this sub-system scale, we
observed about 50 spatially independent components that were reproducible
within and across subjects, and analyzed their spatial patterns and temporal
relationships to reveal the intrinsic parcellation and organization of the
visual cortex. We found that the visual cortical parcels were aligned with the
steepest gradient of cortical myelination, and organized into functional
modules segregated along the dorsal/ventral pathways and foveal/peripheral
early visual areas. In contrast, cortical retinotopy, folding, and
cytoarchitecture impose limited constraints to the organization of resting
state activity. From these findings, we conclude that spontaneous activity
patterns in the visual cortex are primarily organized by visual streams, likely
reflecting feedback network interactions.
Graph-Facilitated Resonant Mode Counting in Stochastic Interaction Networks
Date: 2017-02-28 00:00:00

Oscillations in a stochastic dynamical system, whose deterministic
counterpart has a stable steady state, are a widely reported phenomenon.
Traditional methods of finding parameter regimes for stochastically-driven
resonances are, however, cumbersome for any but the smallest networks. In this
letter we show by example of the Brusselator how to use real root counting
algorithms and graph theoretic tools to efficiently determine the number of
resonant modes and parameter ranges for stochastic oscillations. We argue that
stochastic resonance is a network property by showing that resonant modes only
depend on the squared Jacobian matrix $J^2$ , unlike deterministic oscillations
which are determined by $J$. By using graph theoretic tools, analysis of
stochastic behaviour for larger networks is simplified and chemical reaction
networks with multiple resonant modes can be identified easily.
Interbank Credit and the Money Manufacturing Process. A Systemic Perspective on Financial Stability
Date: 2017-02-28 00:00:00

Interbank lending and borrowing occur when financial institutions seek to
settle and refinance their mutual positions over time and circumstances. This
interactive process involves money creation at the aggregate level.
Coordination mismatch on interbank credit may trigger systemic crises. This
happened when, since summer 2007, interbank credit coordination did not longer
work smoothly across financial institutions, eventually requiring exceptional
monetary policies by central banks, and guarantee and bailout interventions by
governments. Our article develops an interacting heterogeneous agents-based
model of interbank credit coordination under minimal institutions. First, we
explore the link between interbank credit coordination and the money generation
process. Contrary to received understanding, interbank credit has the capacity
to make the monetary system unbound. Second, we develop simulation analysis on
imperfect interbank credit coordination, studying impact of interbank dynamics
on financial stability and resilience at individual and aggregate levels.
Systemically destabilizing forces prove to be related to the working of the
banking system over time, especially interbank coordination conditions and
circumstances.
Robust and Consistent Estimation of Generators in Credit Risk
Date: 2017-10-15 00:00:00

Bond rating Transition Probability Matrices (TPMs) are built over a one-year
time-frame and for many practical purposes, like the assessment of risk in
portfolios or the computation of banking Capital Requirements (e.g. the new
IFRS 9 regulation), one needs to compute the TPM and probabilities of default
over a smaller time interval. In the context of continuous time Markov chains
(CTMC) several deterministic and statistical algorithms have been proposed to
estimate the generator matrix. We focus on the Expectation-Maximization (EM)
algorithm by Bladt and Sorensen (2005) for a CTMC with an absorbing state for
such estimation.
  This work's contribution is threefold. Firstly, we provide directly
computable closed-form expressions for quantities appearing in the EM algorithm
and associated information matrix, allowing to easily approximate confidence
intervals. Previously, these quantities had to be estimated numerically and
considerable computational speedups have been gained. Secondly, we prove
convergence to a single set of parameters under very weak conditions (for the
TPM problem). Finally, we provide a numerical benchmark of our results against
other known algorithms, in particular, on several problems related to credit
risk. The EM algorithm we propose, padded with the new formulas (and error
criteria), outperforms other known algorithms in several metrics, in
particular, with much less overestimation of probabilities of default in higher
ratings than other statistical algorithms.
Optimal Investment and Pricing in the Presence of Defaults
Date: 2017-02-28 00:00:00

We consider the optimal investment problem when the traded asset may default,
causing a jump in its price. For an investor with constant absolute risk
aversion, we compute indifference prices for defaultable bonds, as well as a
price for dynamic protection against default. For the latter problem, our work
complements Sircar & Zariphopoulou (2007), where it is implicitly assumed the
investor is protected against default. We consider a factor model where the
asset's instantaneous return, variance, correlation and default intensity are
driven by a time-homogenous diffusion X taking values in an arbitrary region E.
We identify the certainty equivalent with a semi-linear degenerate elliptic
partial differential equation with quadratic growth in both function and
gradient. Under a minimal integrability assumption on the market price of risk,
we show the certainty equivalent is a classical solution. In particular, our
results cover when X is a one-dimensional affine diffusion and when returns,
variances and default intensities are also affine. Numerical examples highlight
the relationship between the factor process and both the indifference price and
default insurance. Lastly, we show the insurance protection price is not the
default intensity under the dual optimal measure.
Stochastic and Information-thermodynamic Structures of Population Dynamics in Fluctuating Environment
Date: 2017-05-27 00:00:00
Tasks: Decision Making, Relation

Adaptation in a fluctuating environment is a process of fueling environmental
information to gain fitness. Living systems have gradually developed strategies
for adaptation from random and passive diversification of the phenotype to more
proactive decision making, in which environmental information is sensed and
exploited more actively and effectively. Understanding the fundamental relation
between fitness and information is therefore crucial to clarify the limits and
universal properties of adaptation. In this work, we elucidate the underlying
stochastic and information-thermodynamic structure in this process, by deriving
causal fluctuation relations (FRs) of fitness and information. Combined with a
duality between phenotypic and environmental dynamics, the FRs reveal the limit
of fitness gain, the relation of time reversibility with the achievability of
the limit, and the possibility and condition for gaining excess fitness due to
environmental fluctuation. The loss of fitness due to causal constraints and
the limited capacity of real organisms is shown to be the difference between
time-forward and time-backward path probabilities of phenotypic and
environmental dynamics. Furthermore, the FRs generalize the concept of
evolutionary stable state (ESS) for fluctuating environment by giving the
probability that the optimal strategy on average can be invaded by a suboptimal
one owing to rare environmental fluctuation. These results clarify the
information thermodynamic structures in adaptation and evolution.
Binary Funding Impacts in Derivative Valuation
Authors: Junbeom Lee, Chao Zhou
Date: 2017-03-01 00:00:00

We discuss the binary nature of funding impact in derivative valuation. Under some conditions, funding is either a cost or a benefit, i.e., one of the lending/borrowing rates does not play a role in pricing derivatives. When derivatives are priced, considering different lending/borrowing rates leads to semi-linear BSDEs and PDEs, and thus it is necessary to solve the equations numerically. However, once it can be guaranteed that only one of the rates affects pricing, linear equations can be recovered and analytical formulae can be derived. Moreover, as a byproduct, our results explain how debt value adjustment (DVA) and funding benefits are dissimilar. It is often believed that considering both DVA and funding benefits results in a double-counting issue but it will be shown that the two components are affected by different mathematical structures of derivative transactions. We find that funding benefit is related to the decreasing property of the payoff function, but this relationship decreases as the funding choices of underlying assets are transferred to repo markets.
How does geographical distance translate into genetic distance?
Date: 2018-09-26 00:00:00
Tasks: Diversity

Geographic structure can affect patterns of genetic differentiation and
speciation rates. In this article, we investigate the dynamics of genetic
distances in a geographically structured metapopulation. We model the
metapopulation as a weighted directed graph, with d vertices corresponding to d
subpopulations that evolve according to an individual based model. The dynamics
of the genetic distances is then controlled by two types of transitions
-mutation and migration events. We show that, under a rare mutation - rare
migration regime, intra subpopulation diversity can be neglected and our model
can be approximated by a population based model. We show that under a large
population - large number of loci limit, the genetic distance between two
subpopulations converges to a deterministic quantity that can asymptotically be
expressed in terms of the hitting time between two random walks in the
metapopulation graph. Our result shows that the genetic distance between two
subpopulations does not only depend on the direct migration rates between them
but on the whole metapopulation structure.
Combating the Cold Start User Problem in Model Based Collaborative Filtering
Authors: Biswas Sampoorna, Lakshmanan Laks V. S., Ray Senjuti Basu
Date: 2017-02-18 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

For tackling the well known cold-start user problem in model-based
recommender systems, one approach is to recommend a few items to a cold-start
user and use the feedback to learn a profile. The learned profile can then be
used to make good recommendations to the cold user. In the absence of a good
initial profile, the recommendations are like random probes, but if not chosen
judiciously, both bad recommendations and too many recommendations may turn off
a user. We formalize the cold-start user problem by asking what are the $b$
best items we should recommend to a cold-start user, in order to learn her
profile most accurately, where $b$, a given budget, is typically a small
number. We formalize the problem as an optimization problem and present
multiple non-trivial results, including NP-hardness as well as hardness of
approximation. We furthermore show that the objective function, i.e., the least
square error of the learned profile w.r.t. the true user profile, is neither
submodular nor supermodular, suggesting efficient approximations are unlikely
to exist. Finally, we discuss several scalable heuristic approaches for
identifying the $b$ best items to recommend to the user and experimentally
evaluate their performance on 4 real datasets. Our experiments show that our
proposed accelerated algorithms significantly outperform the prior art in
runnning time, while achieving similar error in the learned user profile as
well as in the rating predictions.