pwc-archive/papers-with-abstracts huggingface dataset:
The design of a proto-animal brain based upon spike timing
Date: 2016-12-16 00:00:00

A basal animal model is described as an organism similar to a Limpet that is
attached to the sea floor living in a reproductive community. Its brain model
uses logic cells (gates) to create a high frequency spike generator. Addition
logic cells create a timing framework based upon Pulse Width Modulation (PWM),
and create multi-cell, spike driven muscle actuators and bi-directional shift
registers that serve as memories. Together, these logic cells generate a pulse
train that forms a recurrent fractal in the form of coherent square waves that
consist of an equal number of set pulses and reset pulses. These pulses drive
the actuators that pump water and food through its shell. Some of these
individuals lose their permanent attachment to the sea floor, and evolve the
ability to move around using the feeding motions. This creates the hazard of
getting stuck against an object or moving away from its breeding community.
They evolve a sensor system and logic cells that produce a restoring motion for
each avoidance motion. This keeps the animal in the specific region of an
object that it encounters (hefting), and maintains the zero sum (coherence) of
the fractal, and connects the organism cyberneticly to objects that are sensed
in its environment. The logic units evolve to store permanent memories, and add
plus and minus pulse trains together into a single pulse train that allow the
animal to follow a moving object (imprinting). Additional logic units evolve
that allow the animal to heft using contacts with multiple objects that allow
the animal to return to its point of origin (reproductive migration) using
permanent memories that are created when a pulse is not reset to zero.So,
information is stored in multiple logic cells instead of a connection to one
logic cell, and is based upon spike timing rather than static (synaptic)
connections between cells.
Introducing Gaussian covariance graph models in genome-wide prediction
Date: 2017-04-12 00:00:00
Tasks: Prediction

Several statistical models used in genome-wide prediction assume independence
of marker allele substitution effects, but it is known that these effects might
be correlated. In statistics, graphical models have been identified as a useful
tool for covariance estimation in high dimensional problems and it is an area
that has recently experienced a great expansion. In Gaussian covariance graph
models (GCovGM), the joint distribution of a set of random variables is assumed
to be Gaussian and the pattern of zeros of the covariance matrix is encoded in
terms of an undirected graph G. In this study, methods adapting the theory of
GCovGM to genome-wide prediction were developed (Bayes GCov, Bayes GCov-KR and
Bayes GCov-H). In simulated and real datasets, improvements in correlation
between phenotypes and predicted breeding values and accuracies of predicted
breeding values were found. Our models account for correlation of marker
effects and permit to accommodate general structures as opposed to models
proposed in previous studies which consider spatial correlation only. In
addition, they allow incorporation of biological information in the prediction
process through its use when constructing graph G, and their extension to the
multiallelic loci case is straightforward.
Using the Context of User Feedback in Recommender Systems
Authors: Peska Ladislav  Charles University in Prague, Faculty of Mathematics
  and Physics
Date: 2016-12-15 00:00:00
Tasks: Recommendation Systems

Our work is generally focused on recommending for small or medium-sized
e-commerce portals, where explicit feedback is absent and thus the usage of
implicit feedback is necessary. Nonetheless, for some implicit feedback
features, the presentation context may be of high importance. In this paper, we
present a model of relevant contextual features affecting user feedback,
propose methods leveraging those features, publish a dataset of real e-commerce
users containing multiple user feedback indicators as well as its context and
finally present results of purchase prediction and recommendation experiments.
Off-line experiments with real users of a Czech travel agency website
corroborated the importance of leveraging presentation context in both purchase
prediction and recommendation tasks.
Predictability Hidden by Anomalous Observations
Date: 2016-12-15 00:00:00
Tasks: regression

Testing procedures for predictive regressions with lagged autoregressive
variables imply a suboptimal inference in presence of small violations of ideal
assumptions. We propose a novel testing framework resistant to such violations,
which is consistent with nearly integrated regressors and applicable to
multi-predictor settings, when the data may only approximately follow a
predictive regression model. The Monte Carlo evidence demonstrates large
improvements of our approach, while the empirical analysis produces a strong
robust evidence of market return predictability hidden by anomalous
observations, both in- and out-of-sample, using predictive variables such as
the dividend yield or the volatility risk premium.
Estimation of neuronal interaction graph from spike train data
Date: 2017-10-11 00:00:00

One of the main current issues in Neurobiology concerns the understanding of
interrelated spiking activity among multineuronal ensembles and differences
between stimulus-driven and spontaneous activity in neurophysiological
experiments. Multi electrode array recordings that are now commonly used
monitor neuronal activity in the form of spike trains from many well identified
neurons. A basic question when analyzing such data is the identification of the
directed graph describing "synaptic coupling" between neurons. In this article
we deal with this matter working with a high quality multielectrode array
recording dataset (Pouzat et al., 2015) from the first olfactory relay of the
locust, $Schistocerca$ $americana$. From a mathematical point of view this
paper presents two novelties. First we propose a procedure allowing to deal
with the small sample sizes met in actual datasets. Moreover we address the
sensitive case of partially observed networks. Our starting point is the
procedure introduced in Duarte et al. (2016). We evaluate the performance of
both original and improved procedures through simulation studies, which are
also used for parameter tuning and for exploring the effect of recording only a
small subset of the neurons of a network.
Predictive coding in area V4: dynamic shape discrimination under partial occlusion
Date: 2016-12-16 00:00:00

The primate visual system has an exquisite ability to discriminate partially
occluded shapes. Recent electrophysiological recordings suggest that response
dynamics in intermediate visual cortical area V4, shaped by feedback from
prefrontal cortex (PFC), may play a key role. To probe the algorithms that may
underlie these findings, we build and test a model of V4 and PFC interactions
based on a hierarchical predictive coding framework. We propose that
probabilistic inference occurs in two steps. Initially, V4 responses are driven
solely by bottom-up sensory input and are thus strongly influenced by the level
of occlusion. After a delay, V4 responses combine both feedforward input and
feedback signals from the PFC; the latter reflect predictions made by PFC about
the visual stimulus underlying V4 activity. We find that this model captures
key features of V4 and PFC dynamics observed in experiments. Specifically, PFC
responses are strongest for occluded stimuli and delayed responses in V4 are
less sensitive to occlusion, supporting our hypothesis that the feedback
signals from PFC underlie robust discrimination of occluded shapes. Thus, our
study proposes that area V4 and PFC participate in hierarchical inference, with
feedback signals encoding top-down predictions about occluded shapes.
Analyzing Web Archives Through Topic and Event Focused Sub-collections
Authors: Gossen Gerhard, Demidova Elena, Risse Thomas
Date: 2016-12-16 00:00:00

Web archives capture the history of the Web and are therefore an important
source to study how societal developments have been reflected on the Web.
However, the large size of Web archives and their temporal nature pose many
challenges to researchers interested in working with these collections. In this
work, we describe the challenges of working with Web archives and propose the
research methodology of extracting and studying sub-collections of the archive
focused on specific topics and events. We discuss the opportunities and
challenges of this approach and suggest a framework for creating
sub-collections.
Growth over time-correlated disorder: a spectral approach to Mean-field
Date: 2016-12-16 00:00:00

We generalize a model of growth over a disordered environment, to a large
class of It\=o processes. In particular, we study how the microscopic
properties of the noise influence the macroscopic growth rate. The present
model can account for growth processes in large dimensions, and provides a bed
to understand better the trade-off between exploration and exploitation. An
additional mapping to the Schr\"ordinger equation readily provides a set of
disorders for which this model can be solved exactly. This mean-field approach
exhibits interesting features, such as a freezing transition and an optimal
point of growth, that can be studied in details, and gives yet another
explanation for the occurrence of the $\textit{Zipf law}$ in complex,
well-connected systems.
Extreme prices in electricity balancing markets from an approach of statistical physics
Date: 2016-12-16 00:00:00

An increase in energy production from renewable energy sources is viewed as a
crucial achievement in most industrialized countries.
  The higher variability of power production via renewables leads to a rise in
ancillary service costs over the power system, in particular costs within the
electricity balancing markets, mainly due to an increased number of extreme
price spikes. This study focuses on forecasting the behavior of price and
volumes of the Italian balancing market in the presence of an increased share
of renewable energy sources. Starting from configurations of load and power
production, which guarantee a stable performance, we implement fluctuations in
the load and in renewables; in particular we artificially increase the
contribution of renewables as compared to conventional power sources to cover
the total load. We then forecast the amount of provided energy in the balancing
market and its fluctuations, which are induced by production and consumption.
Within an approach of agent based modeling we estimate the resulting energy
prices and costs. While their average values turn out to be only slightly
affected by an increased contribution from renewables, the probability for
extreme price events is shown to increase along with undesired peaks in the
costs.
Sojourn times and fixation dynamics in multi-player games with fluctuating environments
Date: 2016-12-16 00:00:00

We study evolutionary multi-player games in finite populations, subject to
fluctuating environments. The population undergoes a birth-death process with
absorbing states, and the environment follows a Markovian process, resulting in
a fluctuating payoff matrix for the evolutionary game. Our focus is on the
fixation or extinction of a single mutant in a population of wildtypes. We show
that the nonlinear nature of fitnesses in multi-player games gives rise to an
intricate interplay of selection, genetic drift and environmental fluctuations.
This generates effects not seen in simpler two-player games. To analyse
trajectories towards fixation we analytically calculate sojourn times for
general birth-death processes in populations of two types of individuals and in
fluctuating environments.
Gr\"obner Bases of Neural Ideals
Date: 2018-04-20 00:00:00
Tasks: Form

The brain processes information about the environment via neural codes. The
neural ideal was introduced recently as an algebraic object that can be used to
better understand the combinatorial structure of neural codes. Every neural
ideal has a particular generating set, called the canonical form, that directly
encodes a minimal description of the receptive field structure intrinsic to the
neural code. On the other hand, for a given monomial order, any polynomial
ideal is also generated by its unique (reduced) Gr\"obner basis with respect to
that monomial order. How are these two types of generating sets -- canonical
forms and Gr\"obner bases -- related? Our main result states that if the
canonical form of a neural ideal is a Gr\"obner basis, then it is the universal
Gr\"obner basis (that is, the union of all reduced Gr\"obner bases).
Furthermore, we prove that this situation -- when the canonical form is a
Gr\"obner basis -- occurs precisely when the universal Gr\"obner basis contains
only pseudo-monomials (certain generalizations of monomials). Our results
motivate two questions: (1)~When is the canonical form a Gr\"obner basis?
(2)~When the universal Gr\"obner basis of a neural ideal is {\em not} a
canonical form, what can the non-pseudo-monomial elements in the basis tell us
about the receptive fields of the code? We give partial answers to both
questions. Along the way, we develop a representation of pseudo-monomials as
hypercubes in a Boolean lattice.
Quantifying the Heat Dissipation from a Molecular Motor's Transport Properties in Nonequilibrium Steady States
Date: 2016-12-28 00:00:00

Theoretical analysis, which maps single molecule time trajectories of a
molecular motor onto unicyclic Markov processes, allows us to evaluate the heat
dissipated from the motor and to elucidate its dependence on the mean velocity
and diffusivity. Unlike passive Brownian particles in equilibrium, the velocity
and diffusion constant of molecular motors are closely inter-related to each
other. In particular, our study makes it clear that the increase of diffusivity
with the heat production is a natural outcome of active particles, which is
reminiscent of the recent experimental premise that the diffusion of an
exothermic enzyme is enhanced by the heat released from its own catalytic
turnover. Compared with freely diffusing exothermic enzymes, kinesin-1 whose
dynamics is confined on one-dimensional tracks is highly efficient in
transforming conformational fluctuations into a locally directed motion, thus
displaying a significantly higher enhancement in diffusivity with its turnover
rate. Putting molecular motors and freely diffusing enzymes on an equal
footing, our study offers thermodynamic basis to understand the heat enhanced
self-diffusion of exothermic enzymes.
Resolving the paradox of evolvability with learning theory: How evolution learns to improve evolvability on rugged fitness landscapes
Date: 2016-12-18 00:00:00
Tasks: Learning Theory

It has been hypothesized that one of the main reasons evolution has been able
to produce such impressive adaptations is because it has improved its own
ability to evolve -- "the evolution of evolvability". Rupert Riedl, for
example, an early pioneer of evolutionary developmental biology, suggested that
the evolution of complex adaptations is facilitated by a developmental
organization that is itself shaped by past selection to facilitate evolutionary
innovation. However, selection for characteristics that enable future
innovation seems paradoxical: natural selection cannot favor structures for
benefits they have not yet produced, and favoring characteristics for benefits
that have already been produced does not constitute future innovation. Here we
resolve this paradox by exploiting a formal equivalence between the evolution
of evolvability and learning systems. We use the conditions that enable simple
learning systems to generalize, i.e., to use past experience to improve
performance on previously unseen, future test cases, to demonstrate conditions
where natural selection can systematically favor developmental organizations
that benefit future evolvability. Using numerical simulations of evolution on
highly epistatic fitness landscapes, we illustrate how the structure of evolved
gene regulation networks can result in increased evolvability capable of
avoiding local fitness peaks and discovering higher fitness phenotypes. Our
findings support Riedl's intuition: Developmental organizations that "mimic"
the organization of constraints on phenotypes can be favored by short-term
selection and also facilitate future innovation. Importantly, the conditions
that enable the evolution of such surprising evolvability follow from the same
non-mysterious conditions that permit generalization in learning systems.
A representation-theoretic approach to the calculation of evolutionary distance in bacteria
Date: 2016-12-19 00:00:00

In the context of bacteria and models of their evolution under genome
rearrangement, we explore a novel application of group representation theory to
the inference of evolutionary history. Our contribution is to show, in a very
general maximum likelihood setting, how to use elementary matrix algebra to
sidestep intractable combinatorial computations and convert the problem into
one of eigenvalue estimation amenable to standard numerical approximation
techniques.
Long-term sustained malaria control leads to inbreeding and fragmentation of Plasmodium vivax populations
Date: 2017-06-23 00:00:00
Tasks: Diversity

Plasmodium vivax populations are more resistant to malaria control strategies
than Plasmodium falciparum, maintaining high genetic diversity and gene flow
even at low transmission. To quantify the impact of declining transmission on
P. vivax populations, we investigated population genetic structure over time
during intensified control efforts and over a wide range of transmission
intensities and spatial scales in the Southwest Pacific. Analysis of 887 P.
vivax microsatellite haplotypes (Papua New Guinea, PNG = 443, Solomon Islands =
420, Vanuatu =24) revealed substantial population structure among countries and
modestly declining diversity as transmission decreases over space and time. In
the Solomon Islands, which has had sustained control efforts for 20 years,
significant population structure was observed on different spatial scales down
to the sub-village level. Up to 37% of alleles were partitioned between
populations and significant multilocus linkage disequilibrium was observed
indicating substantial inbreeding. High levels of haplotype relatedness around
households and within a range of 300m are consistent with a focal and clustered
infections suggesting that restricted local transmission occurs within the
range of vector movement and that subsequent focal inbreeding may be a key
factor contributing to the observed population structure. We conclude that
unique transmission strategies, including relapse allows P. vivax populations
to withstand pressure from control efforts for longer than P. falciparum.
However sustained control efforts do eventually impact parasite population
structure and with further control pressure, populations may eventually
fragment into clustered foci that could be targeted for elimination.
Optimal Investment under Information Driven Contagious Distress
Date: 2016-12-19 00:00:00

We introduce a dynamic optimization framework to analyze optimal portfolio
allocations within an information driven contagious distress model. The
investor allocates his wealth across several stocks whose growth rates and
distress intensities are driven by a hidden Markov chain, and also influenced
by the distress state of the economy. We show that the optimal investment
strategies depend on the gradient of value functions, recursively linked to
each other via the distress states. We establish uniform bounds for the
solutions to a sequence of approximation problems, show their convergence to
the unique Sobolev solution of the recursive system of Hamilton-Jacobi-Bellman
partial differential equations (HJB PDEs), and prove a verification theorem. We
provide a numerical study to illustrate the sensitivity of the strategies to
contagious distress, stock volatilities and risk aversion.
Data-Driven Relevance Judgments for Ranking Evaluation
Authors: Moniz Nuno, Torgo Luís, Vinagre João
Date: 2016-12-19 00:00:00
Tasks: Information Retrieval, Retrieval

Ranking evaluation metrics are a fundamental element of design and
improvement efforts in information retrieval. We observe that most popular
metrics disregard information portrayed in the scores used to derive rankings,
when available. This may pose a numerical scaling problem, causing an under- or
over-estimation of the evaluation depending on the degree of divergence between
the scores of ranked items. The purpose of this work is to propose a principled
way of quantifying multi-graded relevance judgments of items and enable a more
accurate penalization of ordering errors in rankings. We propose a data-driven
generation of relevance functions based on the degree of the divergence amongst
a set of items' scores and its application in the evaluation metric Normalized
Discounted Cumulative Gain (nDCG). We use synthetic data to demonstrate the
interest of our proposal and a combination of data on news items from Google
News and their respective popularity in Twitter to show its performance in
comparison to the standard nDCG. Results show that our proposal is capable of
providing a more fine-grained evaluation of rankings when compared to the
standard nDCG, and that the latter frequently under- or over-estimates its
evaluation scores in light of the divergence of items' scores.
The iCrawl Wizard -- Supporting Interactive Focused Crawl Specification
Authors: Gossen Gerhard, Demidova Elena, Risse Thomas
Date: 2016-12-19 00:00:00

Collections of Web documents about specific topics are needed for many areas
of current research. Focused crawling enables the creation of such collections
on demand. Current focused crawlers require the user to manually specify
starting points for the crawl (seed URLs). These are also used to describe the
expected topic of the collection. The choice of seed URLs influences the
quality of the resulting collection and requires a lot of expertise. In this
demonstration we present the iCrawl Wizard, a tool that assists users in
defining focused crawls efficiently and semi-automatically. Our tool uses major
search engines and Social Media APIs as well as information extraction
techniques to find seed URLs and a semantic description of the crawl intent.
Using the iCrawl Wizard even non-expert users can create semantic
specifications for focused crawlers interactively and efficiently.
A Scalable Document-based Architecture for Text Analysis
Authors: Truică Ciprian-Octavian  ERIC, Darmont Jérôme  ERIC, Velcin Julien  ERIC
Date: 2016-12-19 00:00:00
Tasks: LEMMA, Part-Of-Speech Tagging

Analyzing textual data is a very challenging task because of the huge volume
of data generated daily. Fundamental issues in text analysis include the lack
of structure in document datasets, the need for various preprocessing steps
%(e.g., stem or lemma extraction, part-of-speech tagging, named entities
recognition...), and performance and scaling issues. Existing text analysis
architectures partly solve these issues, providing restrictive data schemas,
addressing only one aspect of text preprocessing and focusing on one single
task when dealing with performance optimization. %As a result, no definite
solution is currently available. Thus, we propose in this paper a new generic
text analysis architecture, where document structure is flexible, many
preprocessing techniques are integrated and textual datasets are indexed for
efficient access. We implement our conceptual architecture using both a
relational and a document-oriented database. Our experiments demonstrate the
feasibility of our approach and the superiority of the document-oriented
logical and physical implementation.