pwc-archive/papers-with-abstracts huggingface dataset:
Reconstruction of Order Flows using Aggregated Data
Date: 2016-04-10 00:00:00

In this work we investigate tick-by-tick data provided by the TRTH database
for several stocks on three different exchanges (Paris - Euronext, London and
Frankfurt - Deutsche B\"orse) and on a 5-year span. We use a simple algorithm
that helps the synchronization of the trades and quotes data sources, providing
enhancements to the basic procedure that, depending on the time period and the
exchange, are shown to be significant. We show that the analysis of the
performance of this algorithm turns out to be a a forensic tool assessing the
quality of the aggregated database: we are able to track through the data some
significant technical changes that occurred on the studied exchanges. We also
illustrate the fact that the choices made when reconstructing order flows have
consequences on the quantitative models that are calibrated afterwards on such
data. Our study also provides elements on the trade signature, and we are able
to give a more refined look at the standard Lee-Ready procedure, giving new
elements on the way optimal lags should be chosen when using this method. The
findings are in line with both financial reasoning and the analysis of an
illustrative Poisson model of the order flow.
Robust Individual Circadian Parameter Estimation for Biosignal-based Personalisation of Cancer Chronotherapy
Date: 2016-04-11 00:00:00
Tasks: parameter estimation

In cancer treatment, chemotherapy is administered according a constant
schedule. The chronotherapy approach, considering chronobiological drug
delivery, adapts the chemotherapy profile to the circadian rhythms of the human
organism. This reduces toxicity effects and at the same time enhances
efficiency of chemotherapy. To personalize cancer treatment, chemotherapy
profiles have to be further adapted to individual patients. Therefore, we
present a new model to represent cycle phenomena in circadian rhythms. The
model enables a more precise modelling of the underlying circadian rhythms. In
comparison with the standard model, our model delivers better results in all
defined quality indices. The new model can be used to adapt the chemotherapy
profile efficiently to individual patients. The adaption to individual patients
contributes to the aim of personalizing cancer therapy.
Noise Processing by MicroRNA-Mediated Circuits: the Incoherent Feed-Forward Loop, Revisited
Date: 2016-04-11 00:00:00

The intrinsic stochasticity of gene expression is usually mitigated in higher
eukaryotes by post-transcriptional regulation channels that stabilise the
output layer, most notably protein levels. The discovery of small non-coding
RNAs (miRNAs) in specific motifs of the genetic regulatory network has led to
identifying noise buffering as the possible key function they exert in
regulation. Recent in vitro} and in silico studies have corroborated this
hypothesis. It is however also known that miRNA-mediated noise reduction is
hampered by transcriptional bursting in simple topologies. Here, using
stochastic simulations validated by analytical calculations based on van
Kampen's expansion, we revisit the noise-buffering capacity of the
miRNA-mediated Incoherent Feed Forward Loop (IFFL), a small module that is
widespread in the gene regulatory networks of higher eukaryotes, in order to
account for the effects of intermittency in the transcriptional activity of the
modulator gene. We show that bursting considerably alters the circuit's ability
to control static protein noise. By comparing with other regulatory
architectures, we find that direct transcriptional regulation significantly
outperforms the IFFL in a broad range of kinetic parameters. This suggests
that, under pulsatile inputs, static noise reduction may be less important than
dynamical aspects of noise and information processing in characterising the
performance of regulatory elements.
EEG in the classroom: Synchronised neural recordings during video presentation
Date: 2016-12-27 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

We performed simultaneous recordings of electroencephalography (EEG) from
multiple students in a classroom, and measured the inter-subject correlation
(ISC) of activity evoked by a common video stimulus. The neural reliability, as
quantified by ISC, has been linked to engagement and attentional modulation in
earlier studies that used high-grade equipment in laboratory settings. Here we
reproduce many of the results from these studies using portable low-cost
equipment, focusing on the robustness of using ISC for subjects experiencing
naturalistic stimuli. The present data shows that stimulus-evoked neural
responses, known to be modulated by attention, can be tracked in for groups of
students with synchronized EEG acquisition. This is a step towards real-time
inference of engagement in the classroom.
Chromosome-wide simulations uncover folding pathway and 3D organization of interphase chromosomes
Date: 2016-04-11 00:00:00

Three-dimensional interphase organization of metazoan genomes has been linked
to cellular identity. However, the principles governing 3D interphase genome
architecture and its faithful transmission through disruptive events of
cell-cycle, like mitosis, are not fully understood. By using Brownian dynamics
simulations of Drosophila chromosome 3R up to time-scales of minutes, we show
that chromatin binding profile of Polycomb-repressive-complex-1 robustly
predicts a sub-set of topologically associated domains (TADs), and inclusion of
other factors recapitulates the profile of all TADs, as observed
experimentally. Our simulations show that chromosome 3R attains interphase
organization from mitotic state by a two-step process in which formation of
local TADs is followed by long-range interactions. Our model also explains
statistical features and tracks the assembly kinetics of polycomb subnuclear
clusters. In conclusion, our approach can be used to predict structural and
kinetic features of 3D chromosome folding and its associated proteins in
biological relevant genomic and time scales.
Efficient Index Maintenance Under Dynamic Genome Modification
Date: 2016-04-11 00:00:00

Efficient text indexing data structures have enabled large-scale genomic
sequence analysis and are used to help solve problems ranging from assembly to
read mapping. However, these data structures typically assume that the
underlying reference text is static and will not change over the course of the
queries being made. Some progress has been made in exploring how certain text
indices, like the suffix array, may be updated, rather than rebuilt from
scratch, when the underlying reference changes. Yet, these update operations
can be complex in practice, difficult to implement, and give fairly pessimistic
worst-case bounds. We present a novel data structure, SkipPatch, for
maintaining a k-mer-based index over a dynamically changing genome. SkipPatch
pairs a hash-based k-mer index with an indexable skip list that is used to
efficiently maintain the set of edits that have been applied to the original
genome. SkipPatch is practically fast, significantly outperforming the dynamic
extended suffix array in terms of update and query speed.
Graph-based Collaborative Ranking
Authors: Shams Bita, Haratizadeh Saman
Date: 2017-01-31 00:00:00
Tasks: Collaborative Filtering, Collaborative Ranking

Data sparsity, that is a common problem in neighbor-based collaborative
filtering domain, usually complicates the process of item recommendation. This
problem is more serious in collaborative ranking domain, in which calculating
the users similarities and recommending items are based on ranking data. Some
graph-based approaches have been proposed to address the data sparsity problem,
but they suffer from two flaws. First, they fail to correctly model the users
priorities, and second, they cannot be used when the only available data is a
set of ranking instead of rating values. In this paper, we propose a novel
graph-based approach, called GRank, that is designed for collaborative ranking
domain. GRank can correctly model users priorities in a new tripartite graph
structure, and analyze it to directly infer a recommendation list. The
experimental results show a significant improvement in recommendation quality
compared to the state of the art graph-based recommendation algorithms and
other collaborative ranking techniques.
From sample to knowledge: Towards an integrated approach for neuroscience discovery
Date: 2017-01-23 00:00:00

Imaging methods used in modern neuroscience experiments are quickly producing
large amounts of data capable of providing increasing amounts of knowledge
about neuroanatomy and function. A great deal of information in these datasets
is relatively unexplored and untapped. One of the bottlenecks in knowledge
extraction is that often there is no feedback loop between the knowledge
produced (e.g., graph, density estimate, or other statistic) and the earlier
stages of the pipeline (e.g., acquisition). We thus advocate for the
development of sample-to-knowledge discovery pipelines that one can use to
optimize acquisition and processing steps with a particular end goal (i.e.,
piece of knowledge) in mind. We therefore propose that optimization takes place
not just within each processing stage but also between adjacent (and
non-adjacent) steps of the pipeline. Furthermore, we explore the existing
categories of knowledge representation and models to motivate the types of
experiments and analysis needed to achieve the ultimate goal. To illustrate
this approach, we provide an experimental paradigm to answer questions about
large-scale synaptic distributions through a multimodal approach combining
X-ray microtomography and electron microscopy.
The subjective discount factor and the coefficient of relative risk aversion under time-additive isoelastic expected utility model
Date: 2016-06-29 00:00:00

By analysing the restrictions that ensure the existence of capital market
equilibrium, we show that the coefficient of relative risk aversion and the
subjective discount factor cannot be high simultaneously as they are supposed
to be to make the standard asset pricing consistent with financial stylised
facts.
The free energy landscape of GABA binding to a pentameric ligand-gated ion channel and its disruption by mutations
Date: 2016-05-27 00:00:00

Pentameric ligand-gated ion channels (pLGICs) of the Cys-loop superfamily are
important neuroreceptors that mediate fast synaptic transmission. They are
activated by the binding of a neurotransmitter, but the details of this process
are still not fully understood. As a prototypical pLGIC, here we choose the
insect resistance to dieldrin (RDL) receptor, involved in the resistance to
insecticides, and investigate the binding of the neurotransmitter GABA to its
extracellular domain at the atomistic level. We achieve this by means of
$\mu$-sec funnel-metadynamics simulations, which efficiently enhance the
sampling of bound and unbound states by using a funnel-shaped restraining
potential to limit the exploration in the solvent. We reveal the sequence of
events in the binding process, from the capture of GABA from the solvent to its
pinning between the charged residues Arg111 and Glu204 in the binding pocket.
We characterize the associated free energy landscapes in the wild-type RDL
receptor and in two mutant forms, where the key residues Arg111 and Glu204 are
mutated to Ala. Experimentally these mutations produce non-functional channels,
which is reflected in the reduced ligand binding affinities, due to the loss of
essential interactions. We also analyze the dynamical behaviour of the crucial
loop C, whose opening allows the access of GABA to the binding site, while its
closure locks the ligand into the protein. The RDL receptor shares structural
and functional features with other pLGICs, hence our work outlines a valuable
protocol to study the binding of ligands to pLGICs beyond conventional docking
and molecular dynamics techniques.
Protein sequestration versus Hill-type repression in circadian clock models
Date: 2016-04-12 00:00:00
Tasks: Rhythm, Vocal Bursts Type Prediction

Circadian (~24hr) clocks are self-sustained endogenous oscillators with which
organisms keep track of daily and seasonal time. Circadian clocks frequently
rely on interlocked transcriptional- translational feedback loops to generate
rhythms that are robust against intrinsic and extrinsic perturbations. To
investigate the dynamics and mechanisms of the intracellular feedback loops in
circadian clocks, a number of mathematical models have been developed. The
majority of the models use Hill functions to describe transcriptional
repression in a way that is similar to the Goodwin model. Recently, a new class
of models with protein sequestration-based repression has been introduced.
Here, we discuss how this new class of models differs dramatically from those
based on Hill-type repression in several fundamental aspects: conditions for
rhythm generation, robust network designs and the periods of coupled
oscillators. Consistently, these fundamental properties of circadian clocks
also differ among Neurospora, Drosophila, and mammals depending on their key
transcriptional repression mechanisms (Hill-type repression or protein
sequestration). Based on both theoretical and experimental studies, this review
highlights the importance of careful modeling of transcriptional repression
mechanisms in molecular circadian clocks.
Deterministic evolution of an asexual population under the action of beneficial and deleterious mutations on additive fitness landscapes
Date: 2016-08-31 00:00:00

We study a continuous time model for the frequency distribution of an
infinitely large asexual population in which both beneficial and deleterious
mutations occur and the fitness is additive. When beneficial mutations are
ignored, the exact solution for the frequency distribution is known to be a
Poisson distribution. Here we include beneficial mutations and obtain exact
expressions for the frequency distribution at all times using an eigenfunction
expansion method. We find that the stationary distribution is non-Poissonian
and related to the Bessel function of the first kind. We also provide suitable
approximations for the stationary distribution and the time to relax to the
steady state. Our exact results, especially at mutation-selection equilibrium,
can be useful in developing semi-deterministic approaches to understand
stochastic evolution.
Chiron: A Robust Recommendation System with Graph Regularizer
Authors: Fadaee Saber Shokat, Ghaemi Mohammad Sajjad, Sundaram Ravi, Soufiani Hossein Azari
Date: 2016-11-15 00:00:00
Tasks: Clustering, Collaborative Filtering, Dimensionality Reduction, Recommendation Systems

Recommendation systems have been widely used by commercial service providers
for giving suggestions to users. Collaborative filtering (CF) systems, one of
the most popular recommendation systems, utilize the history of behaviors of
the aggregate user-base to provide individual recommendations and are effective
when almost all users faithfully express their opinions. However, they are
vulnerable to malicious users biasing their inputs in order to change the
overall ratings of a specific group of items. CF systems largely fall into two
categories - neighborhood-based and (matrix) factorization-based - and the
presence of adversarial input can influence recommendations in both categories,
leading to instabilities in estimation and prediction. Although the robustness
of different collaborative filtering algorithms has been extensively studied,
designing an efficient system that is immune to manipulation remains a
significant challenge. In this work we propose a novel "hybrid" recommendation
system with an adaptive graph-based user/item similarity-regularization -
"Chiron". Chiron ties the performance benefits of dimensionality reduction
(through factorization) with the advantage of neighborhood clustering (through
regularization). We demonstrate, using extensive comparative experiments, that
Chiron is resistant to manipulation by large and lethal attacks.
A Possible Link Between Pyriproxyfen and Microcephaly
Date: 2016-04-13 00:00:00

The Zika virus is the primary suspect in the large increase in microcephaly
cases in 2015-6 in Brazil, however its role is unconfirmed despite individual
cases of viral infections found in neural tissue. Here we consider the
alternative that the insecticide pyriproxyfen, used in Brazilian drinking water
for mosquito control, may actually be the cause. Pyriproxifen is an analog of
juvenile hormone, which corresponds in mammals to regulatory molecules
including retinoic acid, a vitamin A metabolite, with which it has
cross-reactivity and whose application during development causes microcephaly.
Methoprene, another juvenile hormone analog approved as an insecticide has
metabolites that bind to the retinoid X receptor, and causes developmental
disorders in mammals. Isotretinoin is another example of a retinoid causing
microcephaly in human babies via activation of the retinoid X receptor.
Moreover, tests of pyriproxyfen by the manufacturer, Sumitomo, widely quoted as
giving no evidence for developmental toxicity, actually found some evidence for
such an effect, including low brain mass and arhinencephaly--incomplete
formation of the anterior cerebral hemispheres--in rat pups. Finally, the
pyriproxyfen use in Brazil is unprecedented--it has never before been applied
to a water supply on such a scale. Claims that it is not being used in Recife,
the epicenter of microcephaly cases, do not distinguish the metropolitan area
of Recife, where it is widely used, and the municipality, where it is not.
Given this combination of information we strongly recommend that the use of
pyriproxyfen in Brazil be suspended pending further investigation.
Evidence of Self-Organization in Time Series of Capital Markets
Date: 2017-03-27 00:00:00
Tasks: Time Series, Time Series Analysis

A methodology is developed to identify, as units of study, each decrease in
the value of a stock from a given maximum price level. A critical level in the
amount of price declines is found to separate a segment operating under a
random walk from a segment operating under a power law. This level is
interpreted as a point of phase transition into a self-organized system.
Evidence of self-organization was found in all the stock market indices studied
but in none of the control synthetic random series. Findings partially explain
the fractal structure characteristic of financial time series and suggest that
price fluctuations adopt two different operating regimes. We propose to
identify downward movements larger than the critical level apparently subject
to the power law, as self-organized states, and price decreases smaller than
the critical level, as a random walk with the Markov property.
Balancing Between Over-Weighting and Under-Weighting in Supervised Term Weighting
Authors: Wu Haibing, Gu Xiaodong
Date: 2016-04-14 00:00:00
Tasks: Sentiment Analysis, Sentiment Classification, Text Categorization

Supervised term weighting could improve the performance of text
categorization. A way proven to be effective is to give more weight to terms
with more imbalanced distributions across categories. This paper shows that
supervised term weighting should not just assign large weights to imbalanced
terms, but should also control the trade-off between over-weighting and
under-weighting. Over-weighting, a new concept proposed in this paper, is
caused by the improper handling of singular terms and too large ratios between
term weights. To prevent over-weighting, we present three regularization
techniques: add-one smoothing, sublinear scaling and bias term. Add-one
smoothing is used to handle singular terms. Sublinear scaling and bias term
shrink the ratios between term weights. However, if sublinear functions scale
down term weights too much, or the bias term is too large, under-weighting
would occur and harm the performance. It is therefore critical to balance
between over-weighting and under-weighting. Inspired by this insight, we also
propose a new supervised term weighting scheme, regularized entropy (re). Our
re employs entropy to measure term distribution, and introduces the bias term
to control over-weighting and under-weighting. Empirical evaluations on topical
and sentiment classification datasets indicate that sublinear scaling and bias
term greatly influence the performance of supervised term weighting, and our re
enjoys the best results in comparison with existing schemes.
Prevention and control of Zika fever as a mosquito-borne and sexually transmitted disease
Date: 2016-04-14 00:00:00

The ongoing Zika virus (ZIKV) epidemic poses a major global public health
emergency. It is known that ZIKV is spread by \textit{Aedes} mosquitoes, recent
studies show that ZIKV can also be transmitted via sexual contact and cases of
sexually transmitted ZIKV have been confirmed in the U.S., France, and Italy.
How sexual transmission affects the spread and control of ZIKV infection is not
well-understood. We presented a mathematical model to investigate the impact of
mosquito-borne and sexual transmission on spread and control of ZIKV and used
the model to fit the ZIKV data in Brazil, Colombia, and El Salvador. Based on
the estimated parameter values, we calculated the median and confidence
interval of the basic reproduction number R0=2.055 (95% CI: 0.523-6.300), in
which the distribution of the percentage of contribution by sexual transmission
is 3.044 (95% CI: 0.123-45.73). Our study indicates that R0 is most sensitive
to the biting rate and mortality rate of mosquitoes while sexual transmission
increases the risk of infection and epidemic size and prolongs the outbreak. In
order to prevent and control the transmission of ZIKV, it must be treated as
not only a mosquito-borne disease but also a sexually transmitted disease.
Toward an Evolutionary-Predictive Foundation for Creativity
Date: 2016-04-14 00:00:00

Dietrich and Haider (2014) justify their integrative framework for creativity
founded on evolutionary theory and prediction research on the grounds that
"theories and approaches guiding empirical research on creativity have not been
supported by the neuroimaging evidence". Although this justification is
controversial, the general direction holds promise. This commentary clarifies
points of disagreement and unresolved issues, and addresses mis-applications of
evolutionary theory that lead the authors to adopt a Darwinian (versus
Lamarckian) approach. To say that creativity is Darwinian is not to say that it
consists of variation plus selection---in the everyday sense of the term---as
the authors imply; it is to say that evolution is occurring because selection
is affecting the distribution of randomly generated heritable variation across
generations. In creative thought the distribution of variants is not key, i.e.,
one is not inclined toward idea A because 60% of one's candidate ideas are
variants of A while only 40% are variants of B; one is inclined toward
whichever seems best. The authors concede that creative variation is partly
directed; however, the greater the extent to which variants are generated
non-randomly, the greater the extent to which the distribution of variants can
reflect not selection but the initial generation bias. Since each thought in a
creative process can alter the selective criteria against which the next is
evaluated, there is no demarcation into generations as assumed in a Darwinian
model. We address the authors' claim that reduced variability and individuality
are more characteristic of Lamarckism than Darwinian evolution, and note that a
Lamarckian approach to creativity has addressed the challenge of modeling the
emergent features associated with insight.