pwc-archive/papers-with-abstracts huggingface dataset:
Approximations of Algorithmic and Structural Complexity Validate Cognitive-behavioural Experimental Results
Authors: Hector Zenil, James A. R. Marshall, Jesper Tegnér
Date: 2015-09-21 00:00:00

Being able to objectively characterise the intrinsic complexity of behavioural patterns resulting from human or animal decisions is fundamental for deconvolving cognition and designing autonomous artificial intelligence systems. Yet complexity is difficult in practice, particularly when strings are short. By numerically approximating algorithmic (Kolmogorov) complexity (K), we establish an objective tool to characterise behavioural complexity. Next, we approximate structural (Bennett's Logical Depth) complexity (LD) to assess the amount of computation required for generating a behavioural string. We apply our toolbox to three landmark studies of animal behaviour of increasing sophistication and degree of environmental influence, including studies of foraging communication by ants, flight patterns of fruit flies, and tactical deception and competition (e.g., predator-prey) strategies. We find that ants harness the environmental condition in their internal decision process, modulating their behavioural complexity accordingly. Our analysis of flight (fruit flies) invalidated the common hypothesis that animals navigating in an environment devoid of stimuli adopt a random strategy. Fruit flies exposed to a featureless environment deviated the most from Levy flight, suggesting an algorithmic bias in their attempt to devise a useful (navigation) strategy. Similarly, a logical depth analysis of rats revealed that the structural complexity of the rat always ends up matching the structural complexity of the competitor, with the rats' behaviour simulating algorithmic randomness. Finally, we discuss how experiments on how humans perceive randomness suggest the existence of an algorithmic bias in our reasoning and decision processes, in line with our analysis of the animal experiments.
Variance-reduced simulation of stochastic agent-based models for tumor growth
Date: 2015-10-14 00:00:00

We investigate a hybrid PDE/Monte Carlo technique for the variance reduced
simulation of an agent-based multiscale model for tumor growth. The variance
reduction is achieved by combining a simulation of the stochastic agent-based
model on the microscopic scale with a deterministic solution of a simplified
(coarse) partial differential equation (PDE) on the macroscopic scale as a
control variable. We show that this technique is able to significantly reduce
the variance with only the (limited) additional computational cost associated
with the deterministic solution of the coarse PDE. We illustrate the
performance with numerical experiments in different regimes, both in the
avascular and vascular stage of tumor growth.
Florigens and antiflorigens: a molecular genetic understanding
Date: 2015-09-21 00:00:00

Florigens, the leaf-derived signals that initiate flowering, have been
described as mysterious, elusive and the Holy Grail of plant biology.
On the no-arbitrage market and continuity in the Hurst parameter
Date: 2015-10-13 00:00:00

We consider a market with fractional Brownian motion with stochastic
integrals generated by the Riemann sums. We found that this market is arbitrage
free if admissible strategies that are using observations with an arbitrarily
small delay. Moreover, we found that this approach eliminates the discontinuity
of the stochastic integrals with respect to the Hurst parameter H at H=1/2.
Les indicateus avanc\'es de l'inflation en RDCongo
Date: 2015-09-22 00:00:00

This study aims to identify the leading of inflation indicators of monetary
policy in DRC. The results reveal that the most relevant inflation indicators
usually come from the monetary origin than the real sector. Variance
decomposition analyzes place in the foreground the rate of exchange, the money
supply and the public consumption like the most relevant indicators. In order
to achieve its goal of price stability and to support a strong economic growth,
the intermediate objective of the Central Bank baseded on the controle of the
money supply seems to be less relevant. Relates to a high level of the
dollarization, the central bank will be able to adopt either the strategy of
nominal anchoring of the rate of exchange, this calls the return of the fixed
exchange regime or to adopt a strategy of inflation targeting is to restore the
credibility of the monetary policy
Hairpins Participating in Folding of Human Telomeric Sequence Quadruplexes Studied by Standard and T-REMD Simulations
Date: 2015-09-22 00:00:00

DNA G-hairpins are potential key structures participating in folding of human
telomeric guanine quadruplexes (GQ). We examined their properties by standard
MD simulations starting from the folded state and long T-REMD starting from the
unfolded state, accumulating ~130 {\mu}s of atomistic simulations. Antiparallel
G-hairpins should spontaneously form in all stages of the folding to support
lateral and diagonal loops, with sub-{\mu}s scale rearrangements between them.
We found no clear predisposition for direct folding into specific GQ topologies
with specific syn/anti patterns. Our key prediction stemming from the T-REMD is
that an ideal unfolded ensemble of the full GQ sequence populates all 4096
syn/anti combinations of its four G-stretches. The simulations can propose
idealized folding pathways but we explain that such few-state pathways may be
misleading. In the context of the available experimental data, the simulations
strongly suggest that the GQ folding could be best understood by the kinetic
partitioning mechanism with a set of deep competing minima on the folding
landscape, with only a small fraction of molecules directly folding to the
native fold. The landscape should further include nonspecific collapse
processes where the molecules move via diffusion and consecutive random rare
transitions, which could, e.g., structure the propeller loops.
Diverse Yet Efficient Retrieval using Hash Functions
Authors: Rao Vidyadhar, Jain Prateek, Jawahar C. V
Date: 2015-10-15 00:00:00
Tasks: Diversity, Retrieval

Typical retrieval systems have three requirements: a) Accurate retrieval
i.e., the method should have high precision, b) Diverse retrieval, i.e., the
obtained set of points should be diverse, c) Retrieval time should be small.
However, most of the existing methods address only one or two of the above
mentioned requirements. In this work, we present a method based on randomized
locality sensitive hashing which tries to address all of the above requirements
simultaneously. While earlier hashing approaches considered approximate
retrieval to be acceptable only for the sake of efficiency, we argue that one
can further exploit approximate retrieval to provide impressive trade-offs
between accuracy and diversity. We extend our method to the problem of
multi-label prediction, where the goal is to output a diverse and accurate set
of labels for a given document in real-time. Moreover, we introduce a new
notion to simultaneously evaluate a method's performance for both the precision
and diversity measures. Finally, we present empirical results on several
different retrieval tasks and show that our method retrieves diverse and
accurate images/labels while ensuring $100x$-speed-up over the existing diverse
retrieval approaches.
Mental Wormholes and the Inner Workings of Creative Thought
Date: 2019-07-15 00:00:00

Most creative outputs are readily classified as belonging to a particular
domain such as art, music, or technology. But does that mean the creative
thinking that goes into creative projects respects these conventional domain
boundaries? This question provides the departure point for this chapter. It is
an important question because the answer has implications for how the creative
process works, as well as for computational models of creativity, and thus it
provides the departure point for this chapter. Next we look at how the creator
sifts out from its vast contents those items to incorporate into a creative
project, and converts these raw materials into a form that "gels" with the
project. We then explore the hypothesis that the creative mind forges "mental
wormholes" that connect concepts and percepts, often from seemingly disparate
domains. Finally, we take a brief look at the strangely non-compositional
interactions that take place in these mental wormholes, and techniques being
advanced to model and understand these interactions.
On the impact of Masking and Blocking Hypotheses for measuring efficacy of new tuberculosis vaccines
Date: 2015-09-22 00:00:00
Tasks: Blocking

Over the past 60 years, the Mycobacterium bovis bacille Calmette-Gu\'erin
(BCG) has been used worldwide to prevent tuberculosis (TB). However, BCG has
shown a very variable efficacy in different trials, showing a wide range of
protection in adults against pulmonary TB. Previous studies indicate that this
failure is related to pre-existing immune response to antigens that are common
to environmental sources of mycobacterial antigens and Mycobacterium
tuberculosis. Specifically, two different mechanisms have been hypothesized:
the masking, (previous sensitization confers some level of protection against
TB), and the blocking (previous immune response prevent vaccine taking of a new
TB vaccine), effects. In this work we introduce a series of models to
discriminate between masking and blocking mechanisms and address their relative
likelihood. The application of our models to interpret the results coming from
the BCG-REVAC clinical trials, specifically designed for the study of sources
of efficacy variability yields estimates that are consistent with high levels
of blocking (41% in Manaus -95% C.I. [14%-68%]- and 96% in Salvador -95% C.I.
[52%-100%]-), and no support for masking to play any relevant role in modifying
vaccine efficacy either alone or aside blocking. The quantification of these
effects around a plausible model constitutes a relevant step towards impact
evaluation of novel anti-tuberculosis vaccines, which are susceptible of being
affected by similar effects if applied on individuals previously exposed to
mycobacterial antigens.
Exact propagation without analytical solutions
Date: 2015-09-22 00:00:00
Tasks: Position

We present a simulation algorithm that accurately propagates a molecule pair
using large time steps without the need to invoke the full exact analytical
solutions of the Smoluchowski diffusion equation. Because the proposed method
only uses uniform and Gaussian random numbers, it allows for position updates
that are two to three orders of magnitude faster than those of a corresponding
scheme based on full solutions, while mantaining the same degree of accuracy.
Neither simplifying nor ad hoc assumptions that are foreign to the underlying
Smoluchowski theory are employed, instead, the algorithm faithfully
incorporates the individual elements of the theoretical model. The method is
flexible and applicable in 1, 2 and 3 dimensions, suggesting that it may find
broad usage in various stochastic simulation algorithms. We demonstrate the
algorithm for the case of a non-reactive, irreversible and reversible reacting
molecule pair.
Design and Implementation of Domain based Semantic Hidden Web Crawler
Authors: Manvi, Bhatia Komal Kumar, Dixit Ashutosh
Date: 2015-09-23 00:00:00
Tasks: Form

Web is a wide term which mainly consists of surface web and hidden web. One
can easily access the surface web using traditional web crawlers, but they are
not able to crawl the hidden portion of the web. These traditional crawlers
retrieve contents from web pages, which are linked by hyperlinks ignoring the
information hidden behind form pages, which cannot be extracted using simple
hyperlink structure. Thus, they ignore large amount of data hidden behind
search forms. This paper emphasizes on the extraction of hidden data behind
html search forms. The proposed technique makes use of semantic mapping to fill
the html search form using domain specific database. Using semantics to fill
various fields of a form leads to more accurate and qualitative data
extraction.
On the parameters affecting dual-target-function evaluation of single-particle selection from cryo-electron micrographs
Date: 2015-09-23 00:00:00

In the analysis of frozen hydrated biomolecules by single-particle
cryo-electron microscopy, template-based particle picking by a target function
called fast local correlation (FLC) allows a large number of particle images to
be automatically picked from micrographs. A second, independent target function
based on maximum likelihood (ML) can be used to align the images and verify the
presence of signal in the picked particles. Although the paradigm of this
dual-target-function (DTF) evaluation of single-particle selection has been
practiced in recent years, it remains unclear how the performance of this DTF
approach is affected by the signal-to-noise ratio of the images and by the
choice of references for FLC and ML. Here we examine this problem through a
systematic study of simulated data, followed by experimental substantiation. We
quantitatively pinpoint the critical signal-to-noise ratio (SNR), at which the
DTF approach starts losing its ability to select and verify particles from
cryo-EM micrographs. A Gaussian model is shown to be as effective in picking
particles as a single projection view of the imaged molecule in the tested
cases. For both simulated micrographs and real cryo-EM data of the 173-kDa
glucose isomerase complex, we found that the use of a Gaussian model to
initialize the target functions suppressed the detrimental effect of reference
bias in template-based particle selection. Given a sufficient signal-to-noise
ratio in the images and the appropriate choice of references, the DTF approach
can expedite the automated assembly of single-particle data sets.
Robust Parameter Estimation for Biological Systems: A Study on the Dynamics of Microbial Communities
Date: 2015-09-23 00:00:00
Tasks: parameter estimation

Interest in the study of in-host microbial communities has increased in
recent years due to our improved understanding of the communities' significant
role in host health. As a result, the ability to model these communities using
differential equations, for example, and analyze the results has become
increasingly relevant. The size of the models and limitations in data
collection among many other considerations require that we develop new
parameter estimation methods to address the challenges that arise when using
traditional parameter estimation methods for models of these in-host microbial
communities. In this work, we present the challenges that appear when applying
traditional parameter estimation techniques to differential equation models of
microbial communities, and we provide an original, alternative method to those
techniques. We show the derivation of our method and how our method avoids the
limitations of traditional techniques while including additional benefits. We
also provide simulation studies to demonstrate our method's viability, the
application of our method to a model of intestinal microbial communities to
demonstrate the insights that can be gained from our method, and sample code to
give readers the opportunity to apply our method to their own research.
Wolbachia infection in a sex-structured mosquito population carrying West Nile virus
Date: 2017-01-12 00:00:00

Wolbachia is possibly the most studied reproductive parasite of arthropod
species. It appears to be a promising candidate for biocontrol of some mosquito
borne diseases. We begin by developing a sex-structured model for a Wolbachia
infected mosquito population. Our model incorporates the key effects of
Wolbachia infection including cytoplasmic incompatibility and male killing. We
also allow the possibility of reduced reproductive output, incomplete maternal
transmission, and different mortality rates for uninfected/infected male/female
individuals. We study the existence and local stability of equilibria,
including the biologically relevant and interesting boundary equilibria. For
some biologically relevant parameter regimes there may be multiple coexistence
steady states including, very importantly, a coexistence steady state in which
Wolbachia infected individuals dominate. We also extend the model to
incorporate West Nile virus (WNv) dynamics, using an SEI modelling approach.
Recent evidence suggests that a particular strain of Wolbachia infection
significantly reduces WNv replication in Aedes aegypti. We model this via
increased time spent in the WNv-exposed compartment for Wolbachia infected
female mosquitoes. A basic reproduction number $R_0$ is computed for the WNv
infection. Our results suggest that, if the mosquito population consists mainly
of Wolbachia infected individuals, WNv eradication is likely if WNv replication
in Wolbachia infected individuals is sufficiently reduced.
Prediction of Metabolic Pathways Involvement in Prokaryotic UniProtKB Data by Association Rule Mining
Date: 2016-09-19 00:00:00
Tasks: Protein Annotation

The widening gap between known proteins and their functions has encouraged
the development of methods to automatically infer annotations. Automatic
functional annotation of proteins is expected to meet the conflicting
requirements of maximizing annotation coverage, while minimizing erroneous
functional assignments. This trade-off imposes a great challenge in designing
intelligent systems to tackle the problem of automatic protein annotation. In
this work, we present a system that utilizes rule mining techniques to predict
metabolic pathways in prokaryotes. The resulting knowledge represents
predictive models that assign pathway involvement to UniProtKB entries. We
carried out an evaluation study of our system performance using
cross-validation technique. We found that it achieved very promising results in
pathway identification with an F1-measure of 0.982 and an AUC of 0.987. Our
prediction models were then successfully applied to 6.2 million
UniProtKB/TrEMBL reference proteome entries of prokaryotes. As a result,
663,724 entries were covered, where 436,510 of them lacked any previous pathway
annotations.
Control and controllability of nonlinear dynamical networks: a geometrical approach
Date: 2015-09-23 00:00:00

In spite of the recent interest and advances in linear controllability of
complex networks, controlling nonlinear network dynamics remains to be an
outstanding problem. We develop an experimentally feasible control framework
for nonlinear dynamical networks that exhibit multistability (multiple
coexisting final states or attractors), which are representative of, e.g., gene
regulatory networks (GRNs). The control objective is to apply parameter
perturbation to drive the system from one attractor to another, assuming that
the former is undesired and the latter is desired. To make our framework
practically useful, we consider RESTRICTED parameter perturbation by imposing
the following two constraints: (a) it must be experimentally realizable and (b)
it is applied only temporarily. We introduce the concept of ATTRACTOR NETWORK,
in which the nodes are the distinct attractors of the system, and there is a
directional link from one attractor to another if the system can be driven from
the former to the latter using restricted control perturbation. Introduction of
the attractor network allows us to formulate a controllability framework for
nonlinear dynamical networks: a network is more controllable if the underlying
attractor network is more strongly connected, which can be quantified. We
demonstrate our control framework using examples from various models of
experimental GRNs. A finding is that, due to nonlinearity, noise can
counter-intuitively facilitate control of the network dynamics.
Market Making with Model Uncertainty
Date: 2015-11-10 00:00:00
Tasks: model

Pari-mutuel markets are trading platforms through which the common market
maker simultaneously clears multiple contingent claims markets. This market has
several distinctive properties that began attracting the attention of the
financial industry in the 2000s. For example, the platform aggregates liquidity
from the individual contingent claims market into the common pool while
shielding the market maker from potential financial loss. The contribution of
this paper is two-fold. First, we provide a new economic interpretation of the
market-clearing strategy of a pari-mutuel market that is well known in the
literature. The pari-mutuel auctioneer is shown to be equivalent to the market
maker with extreme ambiguity aversion for the future contingent event. Second,
based on this theoretical understanding, we present a new market-clearing
algorithm called the Knightian Pari-mutuel Mechanism (KPM). The KPM retains
many interesting properties of pari-mutuel markets while explicitly controlling
for the market maker's ambiguity aversion. In addition, the KPM is
computationally efficient in that it is solvable in polynomial time.
Functional effects of schizophrenia-linked genetic variants on intrinsic single-neuron excitability: A modeling study
Date: 2015-09-24 00:00:00

Background: Recent genome-wide association studies (GWAS) have identified a
large number of genetic risk factors for schizophrenia (SCZ) featuring ion
channels and calcium transporters. For some of these risk factors, independent
prior investigations have examined the effects of genetic alterations on the
cellular electrical excitability and calcium homeostasis. In the present
proof-of-concept study, we harnessed these experimental results for modeling of
computational properties on layer V cortical pyramidal cell and identify
possible common alterations in behavior across SCZ-related genes.
  Methods: We applied a biophysically detailed multi-compartmental model to
study the excitability of a layer V pyramidal cell. We reviewed the literature
on functional genomics for variants of genes associated with SCZ, and used
changes in neuron model parameters to represent the effects of these variants.
  Results: We present and apply a framework for examining the effects of subtle
single nucleotide polymorphisms in ion channel and Ca2+ transporter-encoding
genes on neuron excitability. Our analysis indicates that most of the
considered SCZ- related genetic variants affect the spiking behavior and
intracellular calcium dynamics resulting from summation of inputs across the
dendritic tree.
  Conclusions: Our results suggest that alteration in the ability of a single
neuron to integrate the inputs and scale its excitability may constitute a
fundamental mechanistic contributor to mental disease, alongside with the
previously proposed deficits in synaptic communication and network behavior.
TRONCO: an R package for the inference of cancer progression models from heterogeneous genomic data
Date: 2016-02-10 00:00:00

Motivation: We introduce TRONCO (TRanslational ONCOlogy), an open-source R
package that implements the state-of-the-art algorithms for the inference of
cancer progression models from (epi)genomic mutational profiles. TRONCO can be
used to extract population-level models describing the trends of accumulation
of alterations in a cohort of cross-sectional samples, e.g., retrieved from
publicly available databases, and individual-level models that reveal the
clonal evolutionary history in single cancer patients, when multiple samples,
e.g., multiple biopsies or single-cell sequencing data, are available. The
resulting models can provide key hints in uncovering the evolutionary
trajectories of cancer, especially for precision medicine or personalized
therapy.
  Availability: TRONCO is released under the GPL license, it is hosted in the
Software section at http://bimib.disco.unimib.it/ and archived also at
bioconductor.org.
  Contact: tronco@disco.unimib.it