pwc-archive/papers-with-abstracts huggingface dataset:
Perennial life-histories and demographic advantages may play contradictory roles in the evolution of plant mating systems
Date: 2016-04-21 00:00:00

When predicting the fate and consequences of recurring deleterious mutations
in self-fertilising populations most models developed make the assumption that
populations have discrete non-overlapping generations. This makes them
biologically irrelevant when considering perennial species with over-lapping
generations and where mating occurs independently of the age group. Previous
models studying the effect of perennial life-histories on the genetic
properties of populations in the presence of self-fertilisation have done so
considering age-dependent selection and have found that, contrary to empirical
observations, perennial populations should exhibit lower levels of inbreeding
depression. Here we propose a simple deterministic model in continuous time
with selection at different fitness traits and feedback between population
fitness and size. We find that a perennial life-history can result in high
levels of inbreeding depression in spite of inbreeding, due to higher
frequencies of heterozygous individuals at the adult stage. We also propose
that there may be demographic advantages for self-fertilisation that are
independent of reproductive success.
Entangling credit and funding shocks in interbank markets
Date: 2016-04-22 00:00:00

Credit and liquidity risks represent main channels of financial contagion for
interbank lending markets. On one hand, banks face potential losses whenever
their counterparties are under distress and thus unable to fulfill their
obligations. On the other hand, solvency constraints may force banks to recover
lost fundings by selling their illiquid assets, resulting in effective losses
in the presence of fire sales - that is, when funding shortcomings are
widespread over the market. Because of the complex structure of the network of
interbank exposures, these losses reverberate among banks and eventually get
amplified, with potentially catastrophic consequences for the whole financial
system. Building on Debt Rank [Battiston et al., 2012], in this work we define
a systemic risk metric that estimates the potential amplification of losses in
interbank markets accounting for both credit and liquidity contagion channels:
the Debt-Solvency Rank. We implement this framework on a dataset of 183
European banks that were publicly traded between 2004 and 2013, showing indeed
that liquidity spillovers substantially increase systemic risk, and thus cannot
be neglected in stress-test scenarios. We also provide additional evidence that
the interbank market was extremely fragile up to the 2008 financial crisis,
becoming slightly more robust only afterwards.
Excluded volume effects in on- and off-lattice reaction-diffusion models
Date: 2016-04-22 00:00:00

Mathematical models are important tools to study the excluded volume effects
on reaction-diffusion systems, which are known to play an important role inside
living cells. Detailed microscopic simulations with off-lattice Brownian
dynamics become computationally expensive in crowded environments. In this
paper we therefore investigate to which extent on-lattice approximations, so
called Cellular Automata models, can be used to simulate reactions and
diffusion in the presence of crowding molecules. We show that the diffusion is
most severely slowed down in the off-lattice model, since randomly distributed
obstacles effectively exclude more volume than those ordered on an artificial
grid. Crowded reaction rates can be both increased and decreased by the grid
structure and it proves important to model the molecules with realistic sizes
when excluded volume is taken into account. The grid artifacts increase with
increasing crowder density and we conclude that the computationally more
efficient on-lattice simulations are accurate approximations only for low
crowder densities.
PyPanda: a Python Package for Gene Regulatory Network Reconstruction
Date: 2016-07-12 00:00:00

PANDA (Passing Attributes between Networks for Data Assimilation) is a gene
regulatory network inference method that uses message-passing to integrate
multiple sources of 'omics data. PANDA was originally coded in C++. In this
application note we describe PyPanda, the Python version of PANDA. PyPanda runs
considerably faster than the C++ version and includes additional features for
network analysis. Availability: The open source PyPanda Python package is
freely available at https://github.com/davidvi/pypanda. Contact: d.g.p.van
ijzendoorn@lumc.nl
On the Optimal Dividend Problem for Insurance Risk Models with Surplus-Dependent Premiums
Date: 2016-04-23 00:00:00

This paper concerns an optimal dividend distribution problem for an insurance
company with surplus-dependent premium. In the absence of dividend payments,
such a risk process is a particular case of so-called piecewise deterministic
Markov processes. The control mechanism chooses the size of dividend payments.
The objective consists in maximazing the sum of the expected cumulative
discounted dividend payments received until the time of ruin and a penalty
payment at the time of ruin, which is an increasing function of the size of the
shortfall at ruin. A complete solution is presented to the corresponding
stochastic control problem. We identify the associated Hamilton-Jacobi-Bellman
equation and find necessary and sufficient conditions for optimality of a
single dividend-band strategy, in terms of particular Gerber-Shiu functions. A
number of concrete examples are analyzed.
Concurrent Credit Portfolio Losses
Date: 2017-01-22 00:00:00

We consider the problem of concurrent portfolio losses in two non-overlapping
credit portfolios. In order to explore the full statistical dependence
structure of such portfolio losses, we estimate their empirical pairwise
copulas. Instead of a Gaussian dependence, we typically find a strong asymmetry
in the copulas. Concurrent large portfolio losses are much more likely than
small ones. Studying the dependences of these losses as a function of portfolio
size, we moreover reveal that not only large portfolios of thousands of
contracts, but also medium-sized and small ones with only a few dozens of
contracts exhibit notable portfolio loss correlations. Anticipated
idiosyncratic effects turn out to be negligible. These are troublesome insights
not only for investors in structured fixed-income products, but particularly
for the stability of the financial sector.
Modelling the Structure of a Protein Domain (N-terminal of XPB) Linked with Protein Synthesis, DNA Damage Repair, Rare Diseases, Cancer Therapeutics, and Tuberculosis
Date: 2016-04-23 00:00:00
Tasks: NER

In this work, we develop first near-complete 3D models for NTD-hXPB - the
N-terminal protein domain of the human transcription factor XPB. The results
are very significant as NTD-hXPB plays a critical role in the synthesis of
proteins (specifically transcription) and DNA damage repair (specifically
nucleotide excision repair). NTD-hXPB is directly implicated in rare diseases
XP-B, XP-CS, and TTD2, whose symptoms include neurodegenerative disorders,
premature aging, and decreased fertility. NTDhXPB is also linked to anti-cancer
therapies. As a bi-product we derived 3D models of NTD-mXPB - homologue of
NTD-hXPB in mycobacterium tuberculosis aka MTB (causative agent of most cases
of tuberculosis, which surpassed HIV as #1 infectious disease killer in 2014).
These could be potential target for TB therapeutics. Our ab-initio modelling
protocol takes advantage of recent powerful advances (prediction of contact
residues) in protein structure modeling. Our protocol also includes human in
the loop, inspired by the prevailing theory that computational abilities of
human minds can be powerfully harnessed in engineering/problem-solving. Using
the developed models in this paper, we are able to propose significant insights
into (a) the role of NTD-hXPB in the process of transcription and DNA damage
repair (specifically NER), (b) the critical interactions of NTD-hXPB with
another co-labourer protein p52, (c) diseases associated with NTD-hXPB, and (d)
alterations in functionalities between NTD-hXPB (in human) and NTD-mXPB (in TB
pathogen).
Entropy and credit risk in highly correlated markets
Date: 2016-04-24 00:00:00

We compare two models of corporate default by calculating the
Jeffreys-Kullback-Leibler divergence between their predicted default
probabilities when asset correlations are either high or low. Our main results
show that the divergence between the two models increases in highly correlated,
volatile, and large markets, but that it is closer to zero in small markets,
when asset correlations are low and firms are highly leveraged. These findings
suggest that during periods of financial instability the single-and
multi-factor models of corporate default will generate increasingly
inconsistent predictions.
Analyzing User Preference for Social Image Recommendation
Authors: Liu Xianming, Tsai Min-Hsuan, Huang Thomas
Date: 2016-04-24 00:00:00
Tasks: Recommendation Systems

With the incredibly growing amount of multimedia data shared on the social
media platforms, recommender systems have become an important necessity to ease
users' burden on the information overload. In such a scenario, extensive amount
of heterogeneous information such as tags, image content, in addition to the
user-to-item preferences, is extremely valuable for making effective
recommendations. In this paper, we explore a novel hybrid algorithm termed {\em
STM}, for image recommendation. STM jointly considers the problem of image
content analysis with the users' preferences on the basis of sparse
representation. STM is able to tackle the challenges of highly sparse user
feedbacks and cold-start problmes in the social network scenario. In addition,
our model is based on the classical probabilistic matrix factorization and can
be easily extended to incorporate other useful information such as the social
relationships. We evaluate our approach with a newly collected 0.3 million
social image data set from Flickr. The experimental results demonstrate that
sparse topic modeling of the image content leads to more effective
recommendations, , with a significant performance gain over the
state-of-the-art alternatives.
Finding a Mate With Eusocial Skills
Date: 2016-04-25 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

Sexual reproductive behavior has a necessary social coordination component as
willing and capable partners must both be in the right place at the right time.
It has recently been demonstrated that many social organizations that support
sexual reproduction can evolve in the absence of social coordination between
agents (e.g. herding, assortative mating, and natal philopatry). In this paper
we explore these results by including social transfer mechanisms to our agents
and contrasting their reproductive behavior with a control group without social
transfer mechanisms. We conclude that similar behaviors emerge in our social
learning agents as those that emerged in the non-social learning agents. Social
learners were more inclined towards natal philopatry. Social learners also
evolved a culture of eusociality including reproductive division of labor.
Revisiting chaos in stimulus-driven spiking networks: signal encoding and discrimination
Date: 2016-04-26 00:00:00
Tasks: Decoder

Highly connected recurrent neural networks often produce chaotic dynamics,
meaning their precise activity is sensitive to small perturbations. What are
the consequences for how such networks encode streams of temporal stimuli? On
the one hand, chaos is a strong source of randomness, suggesting that small
changes in stimuli will be obscured by intrinsically generated variability. On
the other hand, recent work shows that the type of chaos that occurs in spiking
networks can have a surprisingly low-dimensional structure, suggesting that
there may be "room" for fine stimulus features to be precisely resolved. Here
we show that strongly chaotic networks produce patterned spikes that reliably
encode time-dependent stimuli: using a decoder sensitive to spike times on
timescales of 10's of ms, one can easily distinguish responses to very similar
inputs. Moreover, recurrence serves to distribute signals throughout chaotic
networks so that small groups of cells can encode substantial information about
signals arriving elsewhere. A conclusion is that the presence of strong chaos
in recurrent networks does not prohibit precise stimulus encoding.
Feedback-based Approach to Introduce Freshness in Recommendations
Authors: Malladi Hari Krishna, Thunuguntla Saikiran
Date: 2016-04-26 00:00:00
Tasks: Diversity, Recommendation Systems

Recommender systems usually face the problem of serving the same
recommendations across multiple sessions regardless of whether the user is
interested in them or not, thereby reducing their effectiveness. To add
freshness to the recommended products, we introduce a feedback loop where the
set of recommended products in the current session depend on the user's
interaction with the previously recommended sets. We also describe ways of
addressing freshness when there is little or even no direct user interaction.
We define a metric to quantify freshness by reducing the problem to measuring
temporal diversity.
Linear models for the impact of order flow on prices II. The Mixture Transition Distribution model
Date: 2016-04-26 00:00:00

Modeling the impact of the order flow on asset prices is of primary
importance to understand the behavior of financial markets. Part I of this
paper reported the remarkable improvements in the description of the price
dynamics which can be obtained when one incorporates the impact of past returns
on the future order flow. However, impact models presented in Part I consider
the order flow as an exogenous process, only characterized by its two-point
correlations. This assumption seriously limits the forecasting ability of the
model. Here we attempt to model directly the stream of discrete events with a
so-called Mixture Transition Distribution (MTD) framework, introduced
originally by Raftery (1985). We distinguish between price-changing and non
price-changing events and combine them with the order sign in order to reduce
the order flow dynamics to the dynamics of a four-state discrete random
variable. The MTD represents a parsimonious approximation of a full high-order
Markov chain. The new approach captures with adequate realism the conditional
correlation functions between signed events for both small and large tick
stocks and signature plots. From a methodological viewpoint, we discuss a novel
and flexible way to calibrate a large class of MTD models with a very large
number of parameters. In spite of this large number of parameters, an
out-of-sample analysis confirms that the model does not overfit the data.
Critical fluctuations of the electrical activity of the heart: Shortcomings of models of excitability and interpretation
Date: 2016-04-22 00:00:00

We report unexpected evidence of critical fluctuations of the electric
potential of the heart during atrial fibrillation in humans. Scale invariance
and long range correlations are found, which we show cannot be accounted for
solely with the property of excitability, since disorder emerges by the
formation of chaotic patterns in excitable media. To shed light on the data, we
discuss the hypothesis that, in fact, fibrillation appears through a phase
transition, which we compare on phenomenological grounds to a quenched-in
disorder magnetic transition. We infer that, during propagation of pulses,
random pinning might occur due to random modulation of the gap junction
channels.
Convection-Enhanced Biopatterning with Recirculation of Hydrodynamically Confined Nanoliter Volumes of Reagents
Date: 2016-04-26 00:00:00

We present a new methodology for efficient and high-quality patterning of
biological reagents for surface-based biological assays. The method relies on
hydrodynamically confined nanoliter volumes of reagents to interact with the
substrate at the micrometer-length scale. We study the interplay between
diffusion, advection, and surface chemistry and present the design of a
noncontact scanning microfluidic device to efficiently present reagents on
surfaces. By leveraging convective flows, recirculation, and mixing of a
processing liquid, this device overcomes limitations of existing biopatterning
approaches, such as passive diffusion of analytes, uncontrolled wetting, and
drying artifacts. We demonstrate the deposition of analytes, showing a 2- to
5-fold increase in deposition rate together with a 10-fold reduction in analyte
consumption while ensuring less than 6% variation in pattern homogeneity on a
standard biological substrate. In addition, we demonstrate the recirculation of
a processing liquid using a microfluidic probe (MFP) in the context of a
surface assay for (i) probing 12 independent areas with a single microliter of
processing liquid and (ii) processing a 2 mm2 surface to create 170 antibody
spots of 50 x 100 {\mu}m2 area using 1.6 {\mu}L of liquid. We observe high
pattern quality, conservative usage of reagents, micrometer precision of
localization and convection-enhanced fast deposition. Such a device and method
may facilitate quantitative biological assays and spur the development of the
next generation of protein microarrays.
Is the public sector of your country a diffusion borrower? Empirical evidence from Brazil
Date: 2016-04-26 00:00:00
Tasks: Management

We propose a diffusion process to describe the global dynamic evolution of
credit operations at a national level given observed operations at a
subnational level in a sovereign country. Empirical analysis with a unique
dataset from Brazilian federate constituents supports the conclusions. Despite
the heterogeneity observed in credit operations at a subnational level, the
aggregated dynamics at a national level were accurately described with the
proposed model. Results may guide management of public finances, particularly
debt manager authorities in charge of reaching surplus targets.
Large-Scale Query-by-Image Video Retrieval Using Bloom Filters
Authors: Araujo Andre, Chaves Jason, Lakshman Haricharan, Angst Roland, Girod Bernd
Date: 2016-07-12 00:00:00
Tasks: Retrieval, Video Retrieval

We consider the problem of using image queries to retrieve videos from a
database. Our focus is on large-scale applications, where it is infeasible to
index each database video frame independently. Our main contribution is a
framework based on Bloom filters, which can be used to index long video
segments, enabling efficient image-to-video comparisons. Using this framework,
we investigate several retrieval architectures, by considering different types
of aggregation and different functions to encode visual information -- these
play a crucial role in achieving high performance. Extensive experiments show
that the proposed technique improves mean average precision by 24% on a public
dataset, while being 4X faster, compared to the previous state-of-the-art.
Estimating psychopathological networks: be careful what you wish for
Date: 2017-09-11 00:00:00

Network models, in which psychopathological disorders are conceptualized as a
complex interplay of psychological and biological components, have become
increasingly popular in the recent psychopathological literature. These network
models often contain significant numbers of unknown parameters, yet the sample
sizes available in psychological research are limited. As such, general
assumptions about the true network are introduced to reduce the number of free
parameters. Incorporating these assumptions, however, means that the resulting
network will lead to reflect the particular structure assumed by the estimation
method---a crucial and often ignored aspect of psychopathological networks. For
example, observing a sparse structure and simultaneously assuming a sparse
structure does not imply that the true model is, in fact, sparse. To illustrate
this point, we discuss recent literature and show the effect of the assumption
of sparsity in three simulation studies.
Convex Hedging in Incomplete Markets
Date: 2016-04-27 00:00:00

In incomplete financial markets not every contingent claim can be replicated
by a self-financing strategy. The risk of the resulting shortfall can be
measured by convex risk measures, recently introduced by F\"ollmer, Schied
(2002). The dynamic optimization problem of finding a self-financing strategy
that minimizes the convex risk of the shortfall can be split into a static
optimization problem and a representation problem. It follows that the optimal
strategy consists in superhedging the modified claim $\widetilde{\varphi}H$,
where $H$ is the payoff of the claim and $\widetilde{\varphi}$ is the solution
of the static optimization problem, the optimal randomized test.
  In this paper, we will deduce necessary and sufficient optimality conditions
for the static problem using convex duality methods. The solution of the static
optimization problem turns out to be a randomized test with a typical
$0$-$1$-structure.
I. Dissociation free energies in drug-receptor systems via non equilibrium alchemical simulations: theoretical framework
Date: 2016-04-27 00:00:00

In this contribution I critically revise the alchemical reversible approach
in the context of the statistical mechanics theory of non covalent bonding in
drug receptor systems. I show that most of the pitfalls and entanglements for
the binding free energies evaluation in computer simulations are rooted in the
equilibrium assumption that is implicit in the reversible method. These
critical issues can be resolved by using a non-equilibrium variant of the
alchemical method in molecular dynamics simulations, relying on the production
of many independent trajectories with a continuous dynamical evolution of an
externally driven alchemical coordinate, completing the decoupling of the
ligand in a matter of few tens of picoseconds rather than nanoseconds. The
absolute binding free energy can be recovered from the annihilation work
distributions by applying an unbiased unidirectional free energy estimate, on
the assumption that any observed work distribution is given by a mixture of
normal distributions, whose components are identical in either direction of the
non-equilibrium process, with weights regulated by the Crooks theorem. I
finally show that the inherent reliability and accuracy of the unidirectional
estimate of the decoupling free energies, based on the production of few
hundreds of non-equilibrium independent sub-nanoseconds unrestrained alchemical
annihilation processes, is a direct consequence of the funnel-like shape of the
free energy surface in molecular recognition. An application of the technique
on a real drug-receptor system is presented in the companion paper.