pwc-archive/papers-with-abstracts huggingface dataset:
Detecting causality in Plant electrical signal by a hybrid causal analysis approach
Date: 2017-03-22 00:00:00

At present, multi-electrode array (MEA) approach and optical recording allow
us to acquire plant electrical activity with higher spatio-temporal resolution.
To understand the dynamic information flow of the electrical signaling system
and estimate the effective connectivity, we proposed a solution to combine the
two casualty analysis approaches, i.e. Granger causality and transfer entropy,
which they complement each other to measure dynamics effective connectivity of
the complex system. Our findings in three qualitatively different levels of
plant bioelectrical activities revealed direction of information flow and
dynamic complex causal connectives by using the two causal analysis approaches,
especially indicated that the direction of information flow is not only along
the longitudinal section but also spreading in transection.
Dynamical mechanism of atrial fibrillation: a topological approach
Date: 2017-09-13 00:00:00

While spiral wave breakup has been implicated in the emergence of atrial
fibrillation, its role in maintaining this complex type of cardiac arrhythmia
is less clear. We used the Karma model of cardiac excitation to investigate the
dynamical mechanisms that sustain atrial fibrillation once it has been
established. The results of our numerical study show that spatiotemporally
chaotic dynamics in this regime can be described as a dynamical equilibrium
between topologically distinct types of transitions that increase or decrease
the number of wavelets, in general agreement with the multiple wavelets
hypothesis. Surprisingly, we found that the process of continuous excitation
waves breaking up into discontinuous pieces plays no role whatsoever in
maintaining spatiotemporal complexity. Instead this complexity is maintained as
a dynamical balance between wave coalescence -- a unique, previously
unidentified, topological process that increases the number of wavelets -- and
wave collapse -- a different topological process that decreases their number.
Detecting Resting-state Neural Connectivity Using Dynamic Network Analysis on Multiband fMRI Data
Date: 2017-03-30 00:00:00

This paper describes an approach of using dynamic Structural Equation
Modeling (SEM) analysis to estimate the connectivity networks from
resting-state fMRI data measured by a multiband EPI sequence. Two structural
equation models were estimated at each voxel with respect to the sensory-motor
network and default-mode network. The resulting connectivity maps indicate that
supplementary motor area has significant connections to left/right primary
motor areas, and medial prefrontal cortex link significantly with posterior
cingulate cortex and inferior parietal lobules. The results imply that high
temporal resolution images obtained with multiband fMRI data can provide
dynamic and directional information on the neural connectivity.
Quadratic approximation of slow factor of volatility in a Multi-factor Stochastic volatility Model
Date: 2017-03-31 00:00:00
Tasks: ARC

In the present work, we propose a new multifactor stochastic volatility model
in which slow factor of volatility is approximated by a parabolic arc. We
retain ourselves to the perturbation technique to obtain approximate expression
for European option prices. We introduce the notion of modified Black-Scholes
price. We obtain a simplified expression for European option price which is
perturbed around the modified Black-Scholes price and have also obtained the
expression of modified price in terms of Black-Scholes price.
Amoeboid-mesenchymal migration plasticity promotes invasion only in complex heterogeneous microenvironments
Date: 2017-03-31 00:00:00

During tissue invasion individual tumor cells exhibit two interconvertible
migration modes, namely mesenchymal and amoeboid migration. The cellular
microenvironment triggers the switch between both modes, thereby allowing
adaptation to dynamic conditions. It is, however, unclear if this
amoeboid-mesenchymal migration plasticity contributes to a more effective tumor
invasion. We address this question with a mathematical model, where the
amoeboid-mesenchymal migration plasticity is regulated in response to local
extracellular matrix resistance. Our numerical analysis reveals that
extracellular matrix structure and presence of a chemotactic gradient are key
determinants of the model behavior. Only in complex microenvironments, if the
extracellular matrix is highly heterogeneous and a chemotactic gradient directs
migration, the amoeboid-mesenchymal migration plasticity allows a more
widespread invasion compared to the non-switching amoeboid and mesenchymal
modes. Importantly, these specific conditions are characteristic for in vivo
tumor invasion. Thus, our study suggests that \emph{in vitro} systems aiming at
unraveling the underlying molecular mechanisms of tumor invasion should take
into account the complexity of the microenvironment by considering the combined
effects of structural heterogeneities and chemical gradients on cell migration.
Evidence of evolutionary selection for co-translational folding
Date: 2017-10-10 00:00:00
Tasks: Protein Folding, Translation

Recent experiments and simulations have demonstrated that proteins can fold
on the ribosome. However, the extent and generality of fitness effects
resulting from co-translational folding remain open questions. Here we report a
genome-wide analysis that uncovers evidence of evolutionary selection for
co-translational folding. We describe a robust statistical approach to identify
loci within genes that are both significantly enriched in slowly translated
codons and evolutionarily conserved. Surprisingly, we find that domain
boundaries can explain only a small fraction of these conserved loci. Instead,
we propose that regions enriched in slowly translated codons are associated
with co-translational folding intermediates, which may be smaller than a single
domain. We show that the intermediates predicted by a native-centric model of
co-translational folding account for the majority of these loci across more
than 500 E. coli proteins. By making a direct connection to protein folding,
this analysis provides strong evidence that many synonymous substitutions have
been selected to optimize translation rates at specific locations within genes.
More generally, our results indicate that kinetics, and not just
thermodynamics, can significantly alter the efficiency of self-assembly in a
biological context.
Analysis and prediction of protein folding energy changes upon mutation by element specific persistent homology
Date: 2017-03-31 00:00:00
Tasks: Protein Folding

Motivation: Site directed mutagenesis is widely used to understand the
structure and function of biomolecules. Computational prediction of protein
mutation impacts offers a fast, economical and potentially accurate alternative
to laboratory mutagenesis. Most existing methods rely on geometric
descriptions, this work introduces a topology based approach to provide an
entirely new representation of protein mutation impacts that could not be
obtained from conventional techniques. Results: Topology based mutation
predictor (T-MP) is introduced to dramatically reduce the geometric complexity
and number of degrees of freedom of proteins, while element specific persistent
homology is proposed to retain essential biological information. The present
approach is found to outperform other existing methods in globular protein
mutation impact predictions. A Pearson correlation coefficient of 0.82 with an
RMSE of 0.92 kcal/mol is obtained on a test set of 350 mutation samples. For
the prediction of membrane protein stability changes upon mutation, the
proposed topological approach has a 84% higher Pearson correlation coefficient
than the current state-of-the-art empirical methods, achieving a Pearson
correlation of 0.57 and an RMSE of 1.09 kcal/mol in a 5-fold cross validation
on a set of 223 membrane protein mutation samples.
TopologyNet: Topology based deep convolutional neural networks for biomolecular property predictions
Date: 2017-03-31 00:00:00
Tasks: speech-recognition, Speech Recognition

Although deep learning approaches have had tremendous success in image, video
and audio processing, computer vision, and speech recognition, their
applications to three-dimensional (3D) biomolecular structural data sets have
been hindered by the entangled geometric complexity and biological complexity.
We introduce topology, i.e., element specific persistent homology (ESPH), to
untangle geometric complexity and biological complexity. ESPH represents 3D
complex geometry by one-dimensional (1D) topological invariants and retains
crucial biological information via a multichannel image representation. It is
able to reveal hidden structure-function relationships in biomolecules. We
further integrate ESPH and convolutional neural networks to construct a
multichannel topological neural network (TopologyNet) for the predictions of
protein-ligand binding affinities and protein stability changes upon mutation.
To overcome the limitations to deep learning arising from small and noisy
training sets, we present a multitask topological convolutional neural network
(MT-TCNN). We demonstrate that the present TopologyNet architectures outperform
other state-of-the-art methods in the predictions of protein-ligand binding
affinities, globular protein mutation impacts, and membrane protein mutation
impacts.
AutoSVD++: An Efficient Hybrid Collaborative Filtering Model via Contractive Auto-encoders
Authors: Zhang Shuai, Yao Lina, Xu Xiwei
Date: 2017-06-13 00:00:00
Tasks: Collaborative Filtering, Computational Efficiency, Feature Engineering

Collaborative filtering (CF) has been successfully used to provide users with
personalized products and services. However, dealing with the increasing
sparseness of user-item matrix still remains a challenge. To tackle such issue,
hybrid CF such as combining with content based filtering and leveraging side
information of users and items has been extensively studied to enhance
performance. However, most of these approaches depend on hand-crafted feature
engineering, which are usually noise-prone and biased by different feature
extraction and selection schemes. In this paper, we propose a new hybrid model
by generalizing contractive auto-encoder paradigm into matrix factorization
framework with good scalability and computational efficiency, which jointly
model content information as representations of effectiveness and compactness,
and leverage implicit user feedback to make accurate recommendations. Extensive
experiments conducted over three large scale real datasets indicate the
proposed approach outperforms the compared methods for item recommendation.
Identifying melancholic depression biomarker using whole-brain functional connectivity
Date: 2017-05-14 00:00:00
Tasks: Functional Connectivity

By focusing on melancholic features with biological homogeneity, this study
aimed to identify a small number of critical functional connections (FCs) that
were specific only to the melancholic type of MDD. On the resting-state fMRI
data, classifiers were developed to differentiate MDD patients from healthy
controls (HCs). The classification accuracy was improved from 50 % (93 MDD and
93 HCs) to 70% (66 melancholic MDD and 66 HCs), when we specifically focused on
the melancholic MDD with moderate or severer level of depressive symptoms. It
showed 65% accuracy for the independent validation cohort. The biomarker score
distribution showed improvements with escitalopram treatments, and also showed
significant correlations with depression symptom scores. This classifier was
specific to melancholic MDD, and it did not generalize in other mental
disorders including autism spectrum disorder (ASD, 54% accuracy) and
schizophrenia spectrum disorder (SSD, 45% accuracy). Among the identified 12
FCs from 9,316 FCs between whole brain anatomical node pairs, the left DLPFC /
IFG region, which has most commonly been targeted for depression treatments,
and its functional connections between Precuneus / PCC, and between right DLPFC
/ SMA areas had the highest contributions. Given the heterogeneity of the MDD,
focusing on the melancholic features is the key to achieve high classification
accuracy. The identified FCs specifically predicted the melancholic MDD and
associated with subjective depressive symptoms. These results suggested key FCs
of melancholic depression, and open doors to novel treatments targeting these
regions in the future.
A Mathematical Framework for Consciousness in Neural Networks
Authors: T. R. Lima
Date: 2017-04-04 00:00:00
Tasks: Philosophy

This paper presents a novel mathematical framework for bridging the explanatory gap (Levine, 1983) between consciousness and its physical correlates. Specifically, we propose that qualia correspond to singularities in the mathematical representations of neural network topology. Crucially, we do not claim that qualia are singularities or that singularities "explain" why qualia feel as they do. Instead, we propose that singularities serve as principled, coordinate-invariant markers of points where attempts at purely quantitative description of a system's dynamics reach an in-principle limit. By integrating these formal markers of irreducibility into models of the physical correlates of consciousness, we establish a framework that recognizes qualia as phenomena inherently beyond reduction to complexity, computation, or information. This approach draws on insights from philosophy of mind, mathematics, cognitive neuroscience, and artificial intelligence (AI). It does not solve the hard problem of consciousness (Chalmers, 1995), but it advances the discourse by integrating the irreducible nature of qualia into a rigorous, physicalist framework. While primarily theoretical, these insights also open avenues for future AI and artificial consciousness (AC) research, suggesting that recognizing and harnessing irreducible topological features may be an important unlock in moving beyond incremental, scale-based improvements and toward artificial general intelligence (AGI) and AC.
Ranking with social cues: Integrating online review scores and popularity information
Authors: Analytis Pantelis P., Delfino Alexia, Kämmer Juliane, Moussaïd Mehdi, Joachims Thorsten
Date: 2017-06-23 00:00:00
Tasks: Diversity

Online marketplaces, search engines, and databases employ aggregated social
information to rank their content for users. Two ranking heuristics commonly
implemented to order the available options are the average review score and
item popularity-that is, the number of users who have experienced an item.
These rules, although easy to implement, only partly reflect actual user
preferences, as people may assign values to both average scores and popularity
and trade off between the two. How do people integrate these two pieces of
social information when making choices? We present two experiments in which we
asked participants to choose 200 times among options drawn directly from two
widely used online venues: Amazon and IMDb. The only information presented to
participants was the average score and the number of reviews, which served as a
proxy for popularity. We found that most people are willing to settle for items
with somewhat lower average scores if they are more popular. Yet, our study
uncovered substantial diversity of preferences among participants, which
indicates a sizable potential for personalizing ranking schemes that rely on
social information.
From homogeneous to heterogeneous network alignment via colored graphlets
Date: 2018-03-28 00:00:00

Network alignment (NA) compares networks with the goal of finding a node
mapping that uncovers highly similar (conserved) network regions. Existing NA
methods are homogeneous, i.e., they can deal only with networks containing
nodes and edges of one type. Due to increasing amounts of heterogeneous network
data with nodes or edges of different types, we extend three recent
state-of-the-art homogeneous NA methods, WAVE, MAGNA++, and SANA, to allow for
heterogeneous NA for the first time. We introduce several algorithmic
novelties. Namely, these existing methods compute homogeneous graphlet-based
node similarities and then find high-scoring alignments with respect to these
similarities, while simultaneously maximizing the amount of conserved edges.
Instead, we extend homogeneous graphlets to their heterogeneous counterparts,
which we then use to develop a new measure of heterogeneous node similarity.
Also, we extend $S^3$, a state-of-the-art measure of edge conservation for
homogeneous NA, to its heterogeneous counterpart. Then, we find high-scoring
alignments with respect to our heterogeneous node similarity and edge
conservation measures. In evaluations on synthetic and real-world biological
networks, our proposed heterogeneous NA methods lead to higher-quality
alignments and better robustness to noise in the data than their homogeneous
counterparts. The software and data from this work is available upon request.
Resting-state functional connectivity-based biomarkers and functional MRI-based neurofeedback for psychiatric disorders: a challenge for developing theranostic biomarkers
Date: 2017-08-18 00:00:00
Tasks: Functional Connectivity

Psychiatric research has been hampered by an explanatory gap between
psychiatric symptoms and their neural underpinnings, which has resulted in poor
treatment outcomes. This situation has prompted us to shift from symptom-based
diagnosis to data-driven diagnosis, aiming to redefine psychiatric disorders as
disorders of neural circuitry. Promising candidates for data-driven diagnosis
include resting-state functional connectivity MRI (rs-fcMRI)-based biomarkers.
Although biomarkers have been developed with the aim of diagnosing patients and
predicting the efficacy of therapy, the focus has shifted to the identification
of biomarkers that represent therapeutic targets, which would allow for more
personalized treatment approaches. This type of biomarker (i.e., theranostic
biomarker) is expected to elucidate the disease mechanism of psychiatric
conditions and to offer an individualized neural circuit-based therapeutic
target based on the neural cause of a condition. To this end, researchers have
developed rs-fcMRI-based biomarkers and investigated a causal relationship
between potential biomarkers and disease-specific behavior using functional MRI
(fMRI)-based neurofeedback on functional connectivity. In this review, we
introduce recent approach for creating a theranostic biomarker, which consists
mainly of two parts: (i) developing an rs-fcMRI-based biomarker that can
predict diagnosis and/or symptoms with high accuracy, and (ii) the introduction
of a proof-of-concept study investigating the relationship between normalizing
the biomarker and symptom changes using fMRI-based neurofeedback. In parallel
with the introduction of recent studies, we review rs-fcMRI-based biomarker and
fMRI-based neurofeedback, focusing on the technological improvements and
limitations associated with clinical use.
Multivariate Geometric Expectiles
Date: 2018-01-17 00:00:00

A generalization of expectiles for d-dimensional multivariate distribution
functions is introduced. The resulting geometric expectiles are unique
solutions to a convex risk minimization problem and are given by d-dimensional
vectors. They are well behaved under common data transformations and the
corresponding sample version is shown to be a consistent estimator. We
exemplify their usage as risk measures in a number of multivariate settings,
highlighting the influence of varying margins and dependence structures.
Spatial statistics for gaze patterns in scene viewing: Effects of repeated viewing
Date: 2018-11-13 00:00:00

Scene viewing is used to study attentional selection in complex but still
controlled environments. One of the main observations on eye movements during
scene viewing is the inhomogeneous distribution of fixation locations: While
some parts of an image are fixated by almost all observers and are inspected
repeatedly by the same observer, other image parts remain unfixated by
observers even after long exploration intervals. Here, we apply spatial point
process methods to investigate the relationship between pairs of fixations.
More precisely, we use the pair correlation function (PCF), a powerful
statistical tool, to evaluate dependencies between fixation locations along
individual scanpaths. We demonstrate that aggregation of fixation locations
within four degrees is stronger than expected from chance. Furthermore, the PCF
reveals stronger aggregation of fixations when the same image is presented a
second time. We use simulations of a dynamical model to show that a narrower
spatial attentional span may explain differences in pair correlations between
the first and the second inspection of the same image.
Topological metrizations of trees, and new quartet methods of tree inference
Date: 2019-05-13 00:00:00

Topological phylogenetic trees can be assigned edge weights in several
natural ways, highlighting different aspects of the tree. Here the rooted
triple and quartet metrizations are introduced, and applied to formulate novel
fast methods of inferring large trees from rooted triple and quartet data.
These methods can be applied in new statistically consistent procedures for
inference of a species tree from gene trees under the multispecies coalescent
model.
On a pricing problem for a multi-asset option with general transaction costs
Date: 2018-09-27 00:00:00

We consider a Black-Scholes type equation arising on a pricing model for a
multi-asset option with general transaction costs. The pioneering work of
Leland is thus extended in two different ways: on the one hand, the problem is
multi-dimensional since it involves different underlying assets; on the other
hand, the transaction costs are not assumed to be constant (i.e. a fixed
proportion of the traded quantity). In this work, we generalize Leland's
condition and prove the existence of a viscosity solution for the corresponding
fully nonlinear initial value problem using Perron method. Moreover, we develop
a numerical ADI scheme to find an approximated solution. We apply this method
on a specific multi-asset derivative and we obtain the option price under
different pricing scenarios.
Prediction with Dimension Reduction of Multiple Molecular Data Sources for Patient Survival
Date: 2017-07-17 00:00:00
Tasks: Dimensionality Reduction

Predictive modeling from high-dimensional genomic data is often preceded by a
dimension reduction step, such as principal components analysis (PCA). However,
the application of PCA is not straightforward for multi-source data, wherein
multiple sources of 'omics data measure different but related biological
components. In this article we utilize recent advances in the dimension
reduction of multi-source data for predictive modeling. In particular, we apply
exploratory results from Joint and Individual Variation Explained (JIVE), an
extension of PCA for multi-source data, for prediction of differing response
types. We conduct illustrative simulations to illustrate the practical
advantages and interpretability of our approach. As an application example we
consider predicting survival for Glioblastoma Multiforme (GBM) patients from
three data sources measuring mRNA expression, miRNA expression, and DNA
methylation. We also introduce a method to estimate JIVE scores for new samples
that were not used in the initial dimension reduction, and study its
theoretical properties; this method is implemented in the R package R.JIVE on
CRAN, in the function 'jive.predict'.