pwc-archive/papers-with-abstracts huggingface dataset:
Evaluation and Ensembling of Methods for Reverse Engineering of Brain Connectivity from Imaging Data
Date: 2016-03-15 00:00:00
Tasks: Benchmarking, Causal Discovery, Experimental Design, Time Series Analysis

Brain science is an evolving research area inviting great enthusiasm with its
potential for providing insights and thereby, preventing, and treating multiple
neuronal disorders affecting millions of patients. Discovery of relationships,
such as brain connectivity, is a major goal in basic, translational, and
clinical science. Algorithms for causal discovery are used in diverse fields
for tackling problems similar to the task of reconstruction of neuronal brain
connectivity. Our aim is to understand the strengths and limitations of these
methods, measure performance and its determinants, and provide insights to
enhance their performance and applicability. We performed extensive empirical
testing and benchmarking of reconstruction performance of several
state-of-the-art algorithms along with several ensemble techniques used to
combine them. Our experiments used a clear and broadly relevant gold standard
based on calcium fluorescence time series recordings of thousands of neurons
sampled from a previously validated realistic, neuronal model. Correlation,
entropy-based measures, Cross-Correlation for short time lags, and Generalized
Transfer Entropy had the best performances with area under ROC curve (AUC) in
the range of 0.7-0.8 even for smaller sample sizes of n = 100 to 1,000 and
converged quickly (at less than n = 1,000). Ensembles of best-performing
methods using random forests and neural networks generated AUC of ~0.9 with n =
10,000. Several important insights regarding parameter choice and sample size
were gained for guiding the experimental design of studies. Our data are also
supportive of the feasibility of reliably reconstructing complex neuronal
connectivity using existing techniques.
Mitigation Procedures to Rank Experts through Information Retrieval Measures
Authors: Vergne Matthieu
Date: 2016-03-16 00:00:00
Tasks: Information Retrieval, Retrieval

In order to find experts, different approaches build rankings of people,
assuming that they are ranked by level of expertise, and use typical
Information Retrieval (IR) measures to evaluate their effectiveness. However,
we figured out that expert rankings (i) tend to be partially ordered, (ii)
incomplete, and (iii) consequently provide more an order rather than absolute
ranks, which is not what usual IR measures exploit. To improve this state of
the art, we propose to revise the formalism used in IR to design proper
measures for comparing expert rankings. In this report, we investigate a first
step by providing mitigation procedures for the three issues, and we analyse IR
measures with the help of these procedures to identify interesting revisions
and remaining limitations. From this analysis, we see that most of the measures
can be exploited for this more generic context because of our mitigation
procedures. Moreover, measures based on precision and recall, usually unable to
consider the order of the ranked items, are of first interest if we represent a
ranking as a set of ordered pairs. Cumulative measures, on the other hand, are
specifically designed for considering the order but suffer from a higher
complexity, motivating the use of precision/recall measures with the right
representation.
Can banks default overnight? Modeling endogenous contagion on O/N interbank market
Date: 2016-03-16 00:00:00

We propose a new model of the liquidity driven banking system focusing on
overnight interbank loans. This significant branch of the interbank market is
commonly neglected in the banking system modeling and systemic risk analysis.
We construct a model where banks are allowed to use both the interbank and the
securities markets to manage their liquidity demand and supply as driven by
prudential requirements in a volatile environment. The network of interbank
loans is dynamic and simulated every day. We show how only the intrasystem cash
fluctuations, without any external shocks, may lead to systemic defaults, what
may be a symptom of the self-organized criticality of the system. We also
analyze the impact of different prudential regulations and market conditions on
the interbank market resilience. We confirm that central bank's asset purchase
programs, limiting the declines in government bond prices, can successfully
stabilize bank's liquidity demand. The model can be used to analyze the
interbank market impact of macroprudential tools.
Optimally controlling the human connectome: the role of network topology
Date: 2016-03-16 00:00:00

To meet ongoing cognitive demands, the human brain must seamlessly transition
from one brain state to another, in the process drawing on different cognitive
systems. How does the brain's network of anatomical connections help facilitate
such transitions? Which features of this network contribute to making one
transition easy and another transition difficult? Here, we address these
questions using network control theory. We calculate the optimal input signals
to drive the brain to and from states dominated by different cognitive systems.
The input signals allow us to assess the contributions made by different brain
regions. We show that such contributions, which we measure as energy, are
correlated with regions' weighted degrees. We also show that the network
communicability, a measure of direct and indirect connectedness between brain
regions, predicts the extent to which brain regions compensate when input to
another region is suppressed. Finally, we identify optimal states in which the
brain should start (and finish) in order to minimize transition energy. We show
that the optimal target states display high activity in hub regions,
implicating the brain's rich club. Furthermore, when rich club organization is
destroyed, the energy cost associated with state transitions increases
significantly, demonstrating that it is the richness of brain regions that
makes them ideal targets.
Evolution of worker policing
Date: 2016-05-10 00:00:00

Workers in insect societies are sometimes observed to kill male eggs of other
workers, a phenomenon known as worker policing. We perform a mathematical
analysis of the evolutionary dynamics of policing. We investigate the selective
forces behind policing for both dominant and recessive mutations for different
numbers of matings of the queen. The traditional, relatedness-based argument
suggests that policing evolves if the queen mates with more than two males, but
does not evolve if the queen mates with a single male. We derive precise
conditions for the invasion and stability of policing alleles. We find that the
relatedness-based argument is not robust with respect to small changes in
colony efficiency caused by policing. We also calculate evolutionarily singular
strategies and determine when they are evolutionarily stable. We use a
population genetics approach that applies to dominant or recessive mutations of
any effect size.
Modeling and Estimation of the Risk When Choosing a Provider
Date: 2016-03-16 00:00:00

The paper provides an algorithm for the risk estimation when a company
selects an outsourcing service provider for innovation product. Calculations
are based on expert surveys conducted among customers and among providers of
outsourcing. The surveys assessed the degree of materiality of species at risk.
Mathematical Modeling of CRISPR-CAS system effects on biofilm formation
Date: 2016-03-16 00:00:00

Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR), linked
with CRISPR associated (CAS) genes, play a profound role in the interactions
between phage and their bacterial hosts. It is now well understood that
CRISPR-CAS systems can confer adaptive immunity against bacteriophage
infections. However, the possibility of failure of CRISPR immunity may lead to
a productive infection by the phage (cell lysis) or lysogeny. Recently,
CRISPR-CAS genes have been implicated in changes to group behaviour, including
biofilm formation, of the bacterium Pseudomonas aeruginosa when lysogenized.
For lysogens with a CRISPR system, another recent experimental study suggests
that bacteriophage re-infection of previously lysogenized bacteria may lead to
cell death. Thus CRISPR immunity can have complex effects on phage-host-lysogen
interactions, particularly in a biofilm. In this contribution, we develop and
analyse a series of models to elucidate and disentangle these interactions.
From a therapeutic standpoint, CRISPR immunity increases biofilm resistance to
phage therapy. Our models predict that lysogens may be able to displace
CRISPR-immune bacteria in a biofilm, and thus suggest strategies to eliminate
phage resistant biofilms.
Market Dynamics vs. Statistics: Limit Order Book Example
Date: 2016-03-30 00:00:00

Commonly used limit order book attributes are empirically considered based on
NASDAQ ITCH data. It is shown that some of them have the properties drastically
different from the ones assumed in many market dynamics study. Because of this
difference we propose to make a transition from "Statistical" type of order
book study (typical for academics) to "Dynamical" type of study (typical for
market practitioners). Based on market data analysis we conclude, that most of
market dynamics information is contained in attributes with spikes (e.g.
executed trades flow $I=dv/dt$), there is no any "stationary case" on the
market and typical market dynamics is a "fast excitation and then slow
relaxation" type of behavior with a wide distribution of excitation frequencies
and relaxation times. A computer code, providing full depth order book
information and recently executed trades is available from authors [1].
Understanding the spatiotemporal pattern of grazing cattle movement
Date: 2016-03-17 00:00:00
Tasks: Decision Making

In this study, we analyse a high-frequency movement dataset for a group of
grazing cattle and investigate their spatiotemporal patterns using a simple
two-state `stop-and-move' mobility model. We find that the dispersal kernel in
the moving state is best described by a mixture exponential distribution,
indicating the hierarchical nature of the movement. On the other hand, the
waiting time appears to be scale-invariant below a certain cut-off and is best
described by a truncated power-law distribution, suggesting heterogenous
dynamics in the non-moving state. We explore possible explanations for the
observed phenomena, covering factors that can play a role in the generation of
mobility patterns, such as the context of grazing environment, the intrinsic
decision-making mechanism or the energy status of different activities. In
particular, we propose a new hypothesis that the underlying movement pattern
can be attributed to the most probable observable energy status under the
maximum entropy configuration. These results are not only valuable for
modelling cattle movement but also provide new insights for understanding the
underlying biological basis of grazing behaviour.
The dual frequency RV-coupling coefficient: a novel measure for quantifying cross-frequency information transactions in the brain
Date: 2016-03-18 00:00:00
Tasks: Time Series, Time Series Analysis

Identifying dynamic transactions between brain regions has become
increasingly important. Measurements within and across brain structures,
demonstrating the occurrence of bursts of beta/gamma oscillations only during
one specific phase of each theta/alpha cycle, have motivated the need to
advance beyond linear and stationary time series models. Here we offer a novel
measure, namely, the "dual frequency RV-coupling coefficient", for assessing
different types of frequency-frequency interactions that subserve information
flow in the brain. This is a measure of coherence between two complex-valued
vectors, consisting of the set of Fourier coefficients for two different
frequency bands, within or across two brain regions. RV-coupling is expressed
in terms of instantaneous and lagged components. Furthermore, by using
normalized Fourier coefficients (unit modulus), phase-type couplings can also
be measured. The dual frequency RV-coupling coefficient is based on previous
work: the second order bispectrum, i.e. the dual-frequency coherence (Thomson
1982; Haykin & Thomson 1998); the RV-coefficient (Escoufier 1973); Gorrostieta
et al (2012); and Pascual-Marqui et al (2011). This paper presents the new
measure, and outlines relevant statistical tests. The novel aspects of the
"dual frequency RV-coupling coefficient" are: (1) it can be applied to two
multivariate time series; (2) the method is not limited to single discrete
frequencies, and in addition, the frequency bands are treated by means of
appropriate multivariate statistical methodology; (3) the method makes use of a
novel generalization of the RV-coefficient for complex-valued multivariate
data; (4) real and imaginary covariance contributions to the RV-coherence are
obtained, allowing the definition of a "lagged-coupling" measure that is
minimally affected by the low spatial resolution of estimated cortical electric
neuronal activity.
Multiple binding sites for transcriptional repressors can produce regular bursting and enhance noise suppression
Date: 2017-05-20 00:00:00

Cells may control fluctuations in protein levels by means of negative
autoregulation, where transcription factors bind DNA sites to repress their own
production. Theoretical studies have assumed a single binding site for the
repressor, while in most species it is found that multiple binding sites are
arranged in clusters. We study a stochastic description of negative
autoregulation with multiple binding sites for the repressor. We find that
increasing the number of binding sites induces regular bursting of gene
products. By tuning the threshold for repression, we show that multiple binding
sites can also suppress fluctuations. Our results highlight possible roles for
the presence of multiple binding sites of negative autoregulators.
R\'enyi entropy, abundance distribution and the equivalence of ensembles
Date: 2016-03-17 00:00:00
Tasks: Diversity, Sociology

Distributions of abundances or frequencies play an important role in many
fields of science, from biology to sociology, as does the R\'enyi entropy,
which measures the diversity of a statistical ensemble. We derive a
mathematical relation between the abundance distribution and the R\'enyi
entropy, by analogy with the equivalence of ensembles in thermodynamics. The
abundance distribution is mapped onto the density of states, and the R\'enyi
entropy to the free energy. The two quantities are related in the thermodynamic
limit by a Legendre transform, by virtue of the equivalence between the
micro-canonical and canonical ensembles. In this limit, we show how the R\'enyi
entropy can be constructed geometrically from rank-frequency plots. This
mapping predicts that non-concave regions of the rank-frequency curve should
result in kinks in the R\'enyi entropy as a function of its order. We
illustrate our results on simple examples, and emphasize the limitations of the
equivalence of ensembles when a thermodynamic limit is not well defined. Our
results help choose reliable diversity measures based on the experimental
accuracy of the abundance distributions in particular frequency ranges.
Allosteric control in icosahedral capsid assembly
Date: 2016-04-27 00:00:00

During the lifecycle of a virus, viral proteins and other components
self-assemble to form a symmetric protein shell called a capsid. This assembly
process is subject to multiple competing constraints, including the need to
form a thermostable shell while avoiding kinetic traps. It has been proposed
that viral assembly satisfies these constraints through allosteric regulation,
including the interconversion of capsid proteins among conformations with
different propensities for assembly. In this article we use computational and
theoretical modeling to explore how such allostery affects the assembly of
icosahedral shells. We simulate assembly under a wide range of protein
concentrations, protein binding affinities, and two different mechanisms of
allosteric control. We find that, above a threshold strength of allosteric
control, assembly becomes robust over a broad range of subunit binding
affinities and concentrations, allowing the formation of highly thermostable
capsids. Our results suggest that allostery can significantly shift the range
of protein binding affinities that lead to successful assembly, and thus should
be accounted for in models that are used to estimate interaction parameters
from experimental data.
The geometric phase of stock trading
Date: 2016-03-17 00:00:00

Geometric phases describe how in a continuous-time dynamical system the
displacement of a variable (called phase variable) can be related to other
variables (shape variables) undergoing a cyclic motion, according to an area
rule. The aim of this paper is to show that geometric phases can exist also for
discrete-time systems, and even when the cycles in shape space have zero area.
A context in which this principle can be applied is stock trading. A zero-area
cycle in shape space represents the type of trading operations normally carried
out by high-frequency traders (entering and exiting a position on a fast
time-scale), while the phase variable represents the cash balance of a trader.
Under the assumption that trading impacts stock prices, even zero-area cyclic
trading operations can induce geometric phases, i.e., profits or losses,
without affecting the stock quote.
Supervised Matrix Factorization for Cross-Modality Hashing
Authors: Liu Hong, Ji Rongrong, Wu Yongjian, Hua Gang
Date: 2016-04-16 00:00:00
Tasks: Quantization, Retrieval, Sentence

Matrix factorization has been recently utilized for the task of multi-modal
hashing for cross-modality visual search, where basis functions are learned to
map data from different modalities to the same Hamming embedding. In this
paper, we propose a novel cross-modality hashing algorithm termed Supervised
Matrix Factorization Hashing (SMFH) which tackles the multi-modal hashing
problem with a collective non-matrix factorization across the different
modalities. In particular, SMFH employs a well-designed binary code learning
algorithm to preserve the similarities among multi-modal original features
through a graph regularization. At the same time, semantic labels, when
available, are incorporated into the learning procedure. We conjecture that all
these would facilitate to preserve the most relevant information during the
binary quantization process, and hence improve the retrieval accuracy. We
demonstrate the superior performance of SMFH on three cross-modality visual
search benchmarks, i.e., the PASCAL-Sentence, Wiki, and NUS-WIDE, with
quantitative comparison to various state-of-the-art methods
Multiscale modeling of diffusion in a crowded environment
Date: 2016-03-12 00:00:00

We present a multiscale approach to model diffusion in a crowded environment
and its effect on the reaction rates. Diffusion in biological systems is often
modeled by a discrete space jump process in order to capture the inherent noise
of biological systems, which becomes important in the low copy number regime.
To model diffusion in the crowded cell environment efficiently, we compute the
jump rates in this mesoscopic model from local first exit times, which account
for the microscopic positions of the crowding molecules, while the diffusing
molecules jump on a coarser Cartesian grid. We then extract a macroscopic
description from the resulting jump rates, where the excluded volume effect is
modeled by a diffusion equation with space dependent diffusion coefficient. The
crowding molecules can be of arbitrary shape and size and numerical experiments
demonstrate that those factors together with the size of the diffusing molecule
play a crucial role on the magnitude of the decrease in diffusive motion. When
correcting the reaction rates for the altered diffusion we can show that
molecular crowding either enhances or inhibits chemical reactions depending on
local fluctuations of the obstacle density.
Unveiling causal activity of complex networks
Date: 2017-08-27 00:00:00

We introduce a novel tool for analyzing complex network dynamics, allowing
for cascades of causally-related events, which we call causal webs (c-webs), to
be separated from other non-causally-related events. This tool shows that
traditionally-conceived avalanches may contain mixtures of spatially-distinct
but temporally-overlapping cascades of events, and dynamical disorder or noise.
In contrast, c-webs separate these components, unveiling previously hidden
features of the network and dynamics. We apply our method to mouse cortical
data with resulting statistics which demonstrate for the first time that
neuronal avalanches are not merely composed of causally-related events.
Local Parametric Estimation in High Frequency Data
Date: 2018-08-21 00:00:00
Tasks: Vocal Bursts Intensity Prediction

In this paper, we give a general time-varying parameter model, where the
multidimensional parameter possibly includes jumps. The quantity of interest is
defined as the integrated value over time of the parameter process $\Theta =
T^{-1} \int_0^T \theta_t^* dt$. We provide a local parametric estimator (LPE)
of $\Theta$ and conditions under which we can show the central limit theorem.
Roughly speaking those conditions correspond to some uniform limit theory in
the parametric version of the problem. The framework is restricted to the
specific convergence rate $n^{1/2}$. Several examples of LPE are studied:
estimation of volatility, powers of volatility, volatility when incorporating
trading information and time-varying MA(1).
Molecular Clock Dating using MrBayes
Date: 2017-11-30 00:00:00
Tasks: Bayesian Inference

This paper provides an overview and a tutorial of molecular clock dating
using MrBayes, which is a software for Bayesian inference of phylogeny. Two
modern approaches, total-evidence dating and node dating, are demonstrated
using a dataset of Hymenoptera with molecular sequences and morphological
characters. The similarity and difference of the two methods are compared and
discussed. Besides, a non-clock analysis is performed on the same dataset to
compare with the molecular clock dating analyses.
Modeling, dynamics and optimal control of Ebola virus spread
Date: 2016-03-18 00:00:00

We present a mathematical analysis of the early detection of Ebola virus. The
propagation of the virus is analysed by using a Susceptible, Infected,
Recovered (SIR) model. In order to provide useful predictions about the
potential transmission of the virus, we analyse and simulate the SIR model with
vital dynamics, by adding demographic effects and an induced death rate. Then,
we compute the equilibria of the model. The numerical simulations confirm the
theoretical analysis. Our study describes the 2015 detection of Ebola virus in
Guinea, the parameters of the model being identified from the World Health
Organization data. Finally, we consider an optimal control problem of the
propagation of the Ebola virus, minimizing the number of infected individuals
while taking into account the cost of vaccination.