pwc-archive/papers-with-abstracts huggingface dataset:
BIBI: Bayesian Inference of Breed Composition
Date: 2017-09-26 00:00:00
Tasks: Bayesian Inference, regression

The aim of this paper was to develop statistical models to estimate
individual breed composition based on the previously proposed idea of
regressing discrete random variables corresponding to counts of reference
alleles of biallelic molecular markers located across the genome on the allele
frequencies of each marker in the pure (base) breeds. Some of the existing
regression-based methods do not guarantee that estimators of breed composition
will lie in the appropriate parameter space and none of them account for
uncertainty about allele frequencies in the pure breeds, that is, uncertainty
about the design matrix. In order to overcome these limitations, we proposed
two Bayesian generalized linear models. For each individual, both models assume
that the counts of the reference allele at each marker locus follow independent
Binomial distributions, use the logit link, and pose a Dirichlet prior over the
vector of regression coefficients (which corresponds to breed composition).
This prior guarantees that point estimators of breed composition like the
posterior mean pertain to the appropriate space. The difference between these
models is that model termed BIBI does not account for uncertainty about the
design matrix, while model termed BIBI2 accounts for such an uncertainty by
assigning independent Beta priors to the entries of this matrix. We implemented
these models in a multibreed Angus-Brahman population. Posterior means were
used as point estimators of breed composition. In addition, the ordinary least
squares estimator proposed by Kuehn et al. (2011) (OLSK) was also computed.
BIBI and BIBI2 estimated breed composition more accurately than OLSK, and BIBI2
had an 8.3% improvement in accuracy as compared to BIBI.
The case of 'Less is more': Modelling risk-preference with Expected Downside Risk
Date: 2017-04-18 00:00:00

This paper discusses an alternative explanation for the empirical findings
contradicting the positive relationship between risk (variance) and reward
(expected return). We show that these contradicting results might be due to the
false definition of risk-perception, which we correct by introducing Expected
Downside Risk (EDR). The EDR parameter, similar to the Expected Shortfall or
Conditional Value-at-Risk, measures the tail risk, however, fits and better
explains the utility perception of investors. Our results indicate that when
using the EDR as risk measure, both the positive and negative relationship
between expected return and risk can be derived under standard conditions (e.g.
expected utility theory and positive risk-aversion). Therefore, no alternative
psychological explanation or additional boundary condition on utility theory is
required to explain the phenomenon. Furthermore, we show empirically that it is
a more precise linear predictor of expected return than volatility, both for
individual assets and portfolios.
NetREX: Network Rewiring using EXpression - Towards Context Specific Regulatory Networks
Date: 2017-04-24 00:00:00

Understanding gene regulation is a fundamental step towards understanding of
how cells function and respond to environmental cues and perturbations. An
important step in this direction is to infer the transcription factor-gene
regulatory network (GRN). However gene regulatory networks are typically
constructed disregarding the fact that regulatory programs are conditioned on
tissue type, developmental stage, sex, and other factors. Collecting multitude
of features required for a reliable construction of GRNs such as physical
features and functional features for every context of interest is costly.
Therefore we need methods that is able to use the knowledge of a
context-agnostic network for construction of a context specific regulatory
network.
  To address this challenge we developed a computational approach that uses
context specific expression data and a GRN constructed in a different but
related context to construct a context specific GRN. Our method, NetREX, is
inspired by network component analysis that estimates TF activities and their
influences on target genes given predetermined topology of a TF-gene network.
To predict a network under a different condition, NetREX removes the
restriction that the topology of the TF-gene network is fixed and allows for
adding and removing edges to that network. To solve the corresponding
optimization problem, we provide a general mathematical framework allowing use
of the recently proposed PALM technique and develop a convergent algorithm.
  We tested our NetREX on simulated data and subsequently applied it to gene
expression data in adult females from Drosophila deletion (DrosDel) panel. The
networks predicted by NetREX showed higher biological consistency than
alternative approaches. In addition, we used the list of recently identified
targets of the Doublesex (DSX) transcription factor to demonstrate the
predictive power of our method.
Quantifying instabilities in Financial Markets
Date: 2017-04-18 00:00:00

Financial global crisis has devastating impacts to economies since early XX
century and continues to impose increasing collateral damages for governments,
enterprises, and society in general. Up to now, all efforts to obtain efficient
methods to predict these events have been disappointing. However, the quest for
a robust estimator of the degree of the market efficiency, or even, a crisis
predictor, is still one of the most studied subjects in the field. We present
here an original contribution that combines Information Theory with graph
concepts, to study the return rate series of 32 global trade markets.
Specifically, we propose a very simple quantifier that shows to be highly
correlated with global financial instability periods, being also a good
estimator of the market crisis risk and market resilience. We show that this
estimator displays striking results when applied to countries that played
central roles during the last major global market crisis. The simplicity and
effectiveness of our quantifier allow us to anticipate its use in a wide range
of disciplines.
Deduplication in a massive clinical note dataset
Authors: Shenoy Sanjeev, Kuo Tsung-Ting, Gabriel Rodney, McAuley Julian, Hsu Chun-Nan
Date: 2017-04-19 00:00:00
Tasks: Clustering

Duplication, whether exact or partial, is a common issue in many datasets. In
clinical notes data, duplication (and near duplication) can arise for many
reasons, such as the pervasive use of templates, copy-pasting, or notes being
generated by automated procedures. A key challenge in removing such near
duplicates is the size of such datasets; our own dataset consists of more than
10 million notes. To detect and correct such duplicates requires algorithms
that both accurate and highly scalable. We describe a solution based on
Minhashing with Locality Sensitive Hashing. In this paper, we present the
theory behind this method and present a database-inspired approach to make the
method scalable. We also present a clustering technique using disjoint sets to
produce dense clusters, which speeds up our algorithm.
Global Stability for a HIV/AIDS Model
Date: 2017-04-19 00:00:00
Tasks: model

We investigate global stability properties of a HIV/AIDS population model
with constant recruitment rate, mass action incidence, and variable population
size. Existence and uniqueness results for disease-free and endemic equilibrium
points are proved. Global stability of the equilibria is obtained through
Lyapunov's direct method and LaSalle's invariance principle.
Multi-scale detection of hierarchical community architecture in structural and functional brain networks
Date: 2017-04-19 00:00:00
Tasks: Community Detection, Functional Connectivity

Community detection algorithms have been widely used to study the
organization of complex systems like the brain. A principal appeal of these
techniques is their ability to identify a partition of brain regions (or nodes)
into communities, where nodes within a community are densely interconnected. In
their simplest application, community detection algorithms are agnostic to the
presence of community hierarchies, but a common characteristic of many neural
systems is a nested hierarchy. To address this limitation, we exercise a
multi-scale extension of a community detection technique known as modularity
maximization, and we apply the tool to both synthetic graphs and graphs derived
from human structural and functional imaging data. Our multi-scale community
detection algorithm links a graph to copies of itself across neighboring
topological scales, thereby becoming sensitive to conserved community
organization across neighboring levels of the hierarchy. We demonstrate that
this method allows for a better characterization of topological inhomogeneities
of the graph's hierarchy by providing a local (node) measure of community
stability and inter-scale reliability across topological scales. We compare the
brain's structural and functional network architectures and demonstrate that
structural graphs display a wider range of topological scales than functional
graphs. Finally, we build a multimodal multiplex graph that combines structural
and functional connectivity in a single model, and we identify the topological
scales where resting state functional connectivity and underlying structural
connectivity show similar versus unique hierarchical community architecture.
Together, our results showcase the advantages of the multi-scale community
detection algorithm in studying hierarchical community structure in brain
graphs, and they illustrate its utility in modeling multimodal neuroimaging
data.
The Magic Barrier Revisited: Accessing Natural Limitations of Recommender Assessment
Authors: Jasberg Kevin, Sizov Sergej
Date: 2017-04-19 00:00:00
Tasks: Decision Making, Recommendation Systems

Recommender systems nowadays have many applications and are of great economic
benefit. Hence, it is imperative for success-oriented companies to compare
different of such systems and select the better one for their purposes. To this
end, various metrics of predictive accuracy are commonly used, such as the Root
Mean Square Error (RMSE), or precision and recall. All these metrics more or
less measure how well a recommender system can predict human behaviour.
  Unfortunately, human behaviour is always associated with some degree of
uncertainty, making the evaluation difficult, since it is not clear whether a
deviation is system-induced or just originates from the natural variability of
human decision making. At this point, some authors speculated that we may be
reaching some Magic Barrier where this variability prevents us from getting
much more accurate.
  In this article, we will extend the existing theory of the Magic Barrier into
a new probabilistic but a yet pragmatic model. In particular, we will use
methods from metrology and physics to develop easy-to-handle quantities for
computation to describe the Magic Barrier for different accuracy metrics and
provide suggestions for common application. This discussion is substantiated by
comprehensive experiments with real users and large-scale simulations on a
high-performance cluster.
Starvation Dynamics of a Greedy Forager
Date: 2017-06-20 00:00:00

We investigate the dynamics of a greedy forager that moves by random walking
in an environment where each site initially contains one unit of food. Upon
encountering a food-containing site, the forager eats all the food there and
can subsequently hop an additional $\mathcal{S}$ steps without food before
starving to death. Upon encountering an empty site, the forager goes hungry and
comes one time unit closer to starvation. We investigate the new feature of
forager greed; if the forager has a choice between hopping to an empty site or
to a food-containing site in its nearest neighborhood, it hops preferentially
towards food. If the neighboring sites all contain food or are all empty, the
forager hops equiprobably to one of these neighbors. Paradoxically, the
lifetime of the forager can depend non-monotonically on greed, and the sense of
the non-monotonicity is opposite in one and two dimensions. Even more
unexpectedly, the forager lifetime in one dimension is substantially enhanced
when the greed is negative; here the forager tends to avoid food in its local
neighborhood. We also determine the average amount of food consumed at the
instant when the forager starves. We present analytic, heuristic, and numerical
results to elucidate these intriguing phenomena.
Rigidity strengthening is a vital mechanism for protein-ligand binding
Date: 2017-03-31 00:00:00
Tasks: Drug Design, Protein Design

Protein-ligand binding is essential to almost all life processes. The
understanding of protein-ligand interactions is fundamentally important to
rational drug design and protein design. Based on large scale data sets, we
show that protein rigidity strengthening or flexibility reduction is a pivoting
mechanism in protein-ligand binding. Our approach based solely on rigidity is
able to unveil a surprisingly long range contribution of four residue layers to
protein-ligand binding, which has a ramification for drug and protein design.
Additionally, the present work reveals that among various pairwise
interactions, the short range ones within the distance of the van der Waals
diameter are most important. It is found that the present approach outperforms
all the other state-of-the-art scoring functions for protein-ligand binding
affinity predictions of two benchmark data sets
GlnK facilitates the dynamic regulation of bacterial nitrogen assimilation
Date: 2017-04-19 00:00:00

Ammonium assimilation in E. coli is regulated by two paralogous proteins
(GlnB and GlnK), which orchestrate interactions with regulators of gene
expression, transport proteins and metabolic pathways. Yet how they conjointly
modulate the activity of glutamine synthetase (GS), the key enzyme for nitrogen
assimilation, is poorly understood. We combine experiments and theory to study
the dynamic roles of GlnB and GlnK during nitrogen starvation and upshift. We
measure time-resolved in vivo concentrations of metabolites, total and
post-translationally modified proteins, and develop a concise biochemical model
of GlnB and GlnK that incorporates competition for active and allosteric sites,
as well as functional sequestration of GlnK. The model predicts the responses
of GS, GlnB and GlnK under time-varying external ammonium level in the wild
type and two genetic knock-outs. Our results show that GlnK is tightly
regulated under nitrogen-rich conditions, yet it is expressed during ammonium
run-out and starvation. This suggests a role for GlnK as a buffer of nitrogen
shock after starvation, and provides a further functional link between nitrogen
and carbon metabolisms.
Crystallization via tubing microfluidics permits both in situ and ex situ X-ray diffraction
Date: 2017-03-22 00:00:00

We used a microfluidic platform to address the problems of obtaining
diffraction quality crystals and crystal handling during transfer to the X-ray
diffractometer. We optimize crystallization conditions of a pharmaceutical
protein and collect X-ray data both in situ and ex situ.
Simple wealth distribution model causing inequality-induced crisis without external shocks
Date: 2017-04-21 00:00:00

We address the issue of the dynamics of wealth accumulation and economic
crisis triggered by extreme inequality, attempting to stick to most possibly
intrinsic assumptions. Our general framework is that of pure or modified
multiplicative processes, basically geometric Brownian motions. In contrast
with the usual approach of injecting into such stochastic agent models either
specific, idiosyncratic internal nonlinear interaction patterns, or macroscopic
disruptive features, we propose a dynamic inequality model where the attainment
of a sizable fraction of the total wealth by very few agents induces a crisis
regime with strong intermittency, the explicit coupling between the richest and
the rest being a mere normalization mechanism, hence with minimal extrinsic
assumptions. The model thus harnesses the recognized lack of ergodicity of
geometric Brownian motions. It also provides a statistical intuition to the
consequences of Thomas Piketty's recent "$r>g$" (return rate $>$ growth rate)
paradigmatic analysis of very-long-term wealth trends. We suggest that the
"water-divide" of wealth flow may define effective classes, making an objective
entry point to calibrate the model. Consistently, we check that a tax mechanism
associated to a few percent relative bias on elementary daily transactions is
able to slow or stop the build-up of large wealth. When extreme fluctuations
are tamed down to a stationary regime with sizable but steadier inequalities,
it should still offer opportunities to study the dynamics of crisis and the
inner effective classes induced through external or internal factors.
Taming Asynchrony for Attractor Detection in Large Boolean Networks (Technical Report)
Date: 2017-06-13 00:00:00

Boolean networks is a well-established formalism for modelling biological
systems. A vital challenge for analysing a Boolean network is to identify all
the attractors. This becomes more challenging for large asynchronous Boolean
networks, due to the asynchronous updating scheme. Existing methods are
prohibited due to the well-known state-space explosion problem in large Boolean
networks. In this paper, we tackle this challenge by proposing a SCC-based
decomposition method. We prove the correctness of our proposed method and
demonstrate its efficiency with two real-life biological networks.
On mean-variance hedging under partial observations and terminal wealth constraints
Date: 2017-04-21 00:00:00

In the paper, a mean-square minimization problem under terminal wealth
constraint with partial observations is studied. The problem is naturally
connected to the mean-variance hedging problem under incomplete information. A
new approach to solving this problem is proposed. The paper provides a solution
when the underlying pricing process is a square-integrable semimartingale. The
proposed method for the study is based on the martingale representation. In
special cases, the Clark-Ocone representation can be used to obtain explicit
solutions. The results and the method are illustrated and supported by example
with two correlated geometric Brownian motions.
Interactions of Solitary Pulses of E. coli in a One-Dimensional Nutrient Gradient
Date: 2017-04-21 00:00:00

We study an anomalous behavior observed in interacting E. coli populations.
When two populations of E. coli are placed on opposite ends of a long channel
with a supply of nutrient between them, they will travel as pulses toward one
another up the nutrient gradient. We present experimental evidence that,
counterintuitively, the two pulses will in some cases change direction and be-
gin moving away from each other and the nutrient back toward the end of the
channel from which they originated. Simulations of the Keller-Segel chemotaxis
model reproduce the experimental results. To gain better insight to the
phenomenon, we introduce a heuristic approximation to the spatial profile of
each population in the Keller-Segel model to derive a system of ordinary
differential equations approximating the temporal dynamics of its center of
mass and width. This approximate model simplifies analysis of the global
dynamics of the bacterial system and allows us to efficiently explore the
qualitative behavior changes across variations of parameters, and thereby
provides experimentally testable hypotheses about the mechanisms behind the
turnaround behavior.
Pairs Trading under Drift Uncertainty and Risk Penalization
Date: 2018-09-23 00:00:00
Tasks: Portfolio Optimization, Position

In this work, we study a dynamic portfolio optimization problem related to
pairs trading, which is an investment strategy that matches a long position in
one security with a short position in another security with similar
characteristics. The relationship between pairs, called a spread, is modeled by
a Gaussian mean-reverting process whose drift rate is modulated by an
unobservable continuous-time, finite-state Markov chain. Using the classical
stochastic filtering theory, we reduce this problem with partial information to
the one with full information and solve it for the logarithmic utility
function, where the terminal wealth is penalized by the riskiness of the
portfolio according to the realized volatility of the wealth process. We
characterize optimal dollar-neutral strategies as well as optimal value
functions under full and partial information and show that the certainty
equivalence principle holds for the optimal portfolio strategy. Finally, we
provide a numerical analysis for a toy example with a two-state Markov chain.
Distant Supervision for Topic Classification of Tweets in Curated Streams
Authors: Mohammed Salman, Ghelani Nimesh, Lin Jimmy
Date: 2017-04-22 00:00:00
Tasks: Classification, General Classification, Topic Classification

We tackle the challenge of topic classification of tweets in the context of
analyzing a large collection of curated streams by news outlets and other
organizations to deliver relevant content to users. Our approach is novel in
applying distant supervision based on semi-automatically identifying curated
streams that are topically focused (for example, on politics, entertainment, or
sports). These streams provide a source of labeled data to train topic
classifiers that can then be applied to categorize tweets from more
topically-diffuse streams. Experiments on both noisy labels and human
ground-truth judgments demonstrate that our approach yields good topic
classifiers essentially "for free", and that topic classifiers trained in this
manner are able to dynamically adjust for topic drift as news on Twitter
evolves.
The effect of heterogeneity on financial contagion due to overlapping portfolios
Date: 2017-04-22 00:00:00

We consider a model of financial contagion in a bipartite network of assets
and banks recently introduced in the literature, and we study the effect of
power law distributions of degree and balance-sheet size on the stability of
the system. Relative to the benchmark case of banks with homogeneous degrees
and balance-sheet sizes, we find that if banks have a power-law degree
distribution the system becomes less robust with respect to the initial failure
of a random bank, and that targeted shocks to the most specialised banks (i.e.
banks with low degrees) or biggest banks increases the probability of observing
a cascade of defaults. In contrast, we find that a power-law degree
distribution for assets increases stability with respect to random shocks, but
not with respect to targeted shocks. We also study how allocations of capital
buffers between banks affects the system's stability, and we find that
assigning capital to banks in relation to their level of diversification
reduces the probability of observing cascades of defaults relative to size
based allocations. Finally, we propose a non-capital based policy that improves
the resilience of the system by introducing disassortative mixing between banks
and assets.