pwc-archive/papers-with-abstracts huggingface dataset:
Data science skills for referees: I Biological X-ray crystallography
Date: 2017-04-28 00:00:00
Tasks: Articles

Since there is now a growing wish by referees to judge the underpinning data
for a submitted article it is timely to provide a summary of the data
evaluation checks required to be done by a referee. As these checks will vary
from field to field this article focuses on the needs of biological X-ray
crystallography articles, which is the predominantly used method leading to
depositions in the PDB. The expected referee checks of data underpinning an
article are described with examples. These checks necessarily include that a
referee checks the PDB validation report for each crystal structure
accompanying the article submission; this check whilst necessary is not
sufficient for a complete evaluation. A referee would be expected to undertake
one cycle of model refinement of the authors biological macromolecule
coordinates against the authors processed diffraction data and look at the
various validation checks of the model and Fo-Fc electron density maps in e.g.
Phenix_refine and in COOT. If the referee deems necessary the diffraction data
images should be reprocessed (e.g. to a different diffraction resolution than
the authors submission). This can be requested to be done by the authors or if
the referee prefers can be undertaken directly by the referee themselves. A
referee wishing to do these data checks may wish to receive a certificate that
they have command of these data science skills. The organisation of such
voluntary certification training can e.g. be via those crystallography
associations duly recognised by the IUCr to issue such certificates.
The right tool for the right question --- beyond the encoding versus decoding dichotomy
Date: 2017-04-28 00:00:00

There are two major questions that neuroimaging studies attempt to answer:
First, how are sensory stimuli represented in the brain (which we term the
stimulus-based setting)? And, second, how does the brain generate cognition
(termed the response-based setting)? There has been a lively debate in the
neuroimaging community whether encoding and decoding models can provide
insights into these questions. In this commentary, we construct two simple and
analytically tractable examples to demonstrate that while an encoding model
analysis helps with the former, neither model is appropriate to satisfactorily
answer the latter question. Consequently, we argue that if we want to
understand how the brain generates cognition, we need to move beyond the
encoding versus decoding dichotomy and instead discuss and develop tools that
are specifically tailored to our endeavour.
Learning Spatiotemporal-Aware Representation for POI Recommendation
Authors: Liu Bei, Qian Tieyun, Liu Bing, Hong Liang, You Zhenni, Li Yuxiang
Date: 2017-04-28 00:00:00
Tasks: Graph Embedding, Knowledge Graph Embedding, Representation Learning

The wide spread of location-based social networks brings about a huge volume
of user check-in data, which facilitates the recommendation of points of
interest (POIs). Recent advances on distributed representation shed light on
learning low dimensional dense vectors to alleviate the data sparsity problem.
Current studies on representation learning for POI recommendation embed both
users and POIs in a common latent space, and users' preference is inferred
based on the distance/similarity between a user and a POI. Such an approach is
not in accordance with the semantics of users and POIs as they are inherently
different objects. In this paper, we present a novel spatiotemporal aware (STA)
representation, which models the spatial and temporal information as \emph{a
relationship connecting users and POIs}. Our model generalizes the recent
advances in knowledge graph embedding. The basic idea is that the embedding of
a $<$time, location$>$ pair corresponds to a translation from embeddings of
users to POIs. Since the POI embedding should be close to the user embedding
plus the relationship vector, the recommendation can be performed by selecting
the top-\emph{k} POIs similar to the translated POI, which are all of the same
type of objects. We conduct extensive experiments on two real-world datasets.
The results demonstrate that our STA model achieves the state-of-the-art
performance in terms of high recommendation accuracy, robustness to data
sparsity and effectiveness in handling cold start problem.
BeWith: A Between-Within Method to Discover Relationships between Cancer Modules via Integrated Analysis of Mutual Exclusivity, Co-occurrence and Functional Interactions
Date: 2017-04-28 00:00:00

The analysis of the mutational landscape of cancer, including mutual
exclusivity and co-occurrence of mutations, has been instrumental in studying
the disease. We hypothesized that exploring the interplay between
co-occurrence, mutual exclusivity, and functional interactions between genes
will further improve our understanding of the disease and help to uncover new
relations between cancer driving genes and pathways. To this end, we designed a
general framework, BeWith, for identifying modules with different combinations
of mutation and interaction patterns. We focused on three different settings of
the BeWith schema: (i) BeME-WithFun in which the relations between modules are
enriched with mutual exclusivity while genes within each module are
functionally related; (ii) BeME-WithCo which combines mutual exclusivity
between modules with co-occurrence within modules; and (iii) BeCo-WithMEFun
which ensures co-occurrence between modules while the within module relations
combine mutual exclusivity and functional interactions. We formulated the
BeWith framework using Integer Linear Programming (ILP), enabling us to find
optimally scoring sets of modules. Our results demonstrate the utility of
BeWith in providing novel information about mutational patterns, driver genes,
and pathways. In particular, BeME-WithFun helped identify functionally coherent
modules that might be relevant for cancer progression. In addition to finding
previously well-known drivers, the identified modules pointed to the importance
of the interaction between NCOR and NCOA3 in breast cancer. Additionally, an
application of the BeME-WithCo setting revealed that gene groups differ with
respect to their vulnerability to different mutagenic processes, and helped us
to uncover pairs of genes with potentially synergetic effects, including a
potential synergy between mutations in TP53 and metastasis related DCC gene.
Fourier-based classification of protein secondary structures
Date: 2017-04-28 00:00:00
Tasks: Classification

The correct prediction of protein secondary structures is one of the key
issues in predicting the correct protein folded shape, which is used for
determining gene function. Existing methods make use of amino acids properties
as indices to classify protein secondary structures, but are faced with a
significant number of misclassifications. The paper presents a technique for
the classification of protein secondary structures based on protein
"signal-plotting" and the use of the Fourier technique for digital signal
processing. New indices are proposed to classify protein secondary structures
by analyzing hydrophobicity profiles. The approach is simple and
straightforward. Results show that the more types of protein secondary
structures can be classified by means of these newly-proposed indices.
Derivation of a novel efficient supervised learning algorithm from cortical-subcortical loops
Date: 2017-04-28 00:00:00
Tasks: Classification

Although brain circuits presumably carry out useful perceptual algorithms,
few instances of derived biological methods have been found to compete
favorably against algorithms that have been engineered for specific
applications. We forward a novel analysis of function of cortico-striatal
loops, which constitute more than 80% of the human brain, thus likely
underlying a broad range of cognitive functions. We describe a family of
operations performed by the derived method, including a nonstandard method for
supervised classification, which may underlie some forms of
cortically-dependent associative learning. The novel supervised classifier is
compared against widely-used algorithms for classification, including support
vector machines (SVM) and k-nearest neighbor methods, achieving corresponding
classification rates --- at a fraction of the time and space costs. This
represents an instance of a biologically-derived algorithm comparing favorably
against widely used machine learning methods on well-studied tasks.
Consciousness is not a physically provable property
Date: 2017-09-26 00:00:00

We present a logical proof that computing machines, and by extension physical
systems, can never be certain if they possess conscious awareness. This implies
that human consciousness is associated with a violation of energy conservation.
We examine the significance that a particular interpretation of quantum
mechanics, known as single mind Q (Barrett 1999), might have for the detection
of such a violation. Finally we apply single mind Q to the problem of free will
as it arises in some celebrated experiments by the neurophysiologist Benjamin
Libet.
Detecting abnormality in heart dynamics from multifractal analysis of ECG signals
Date: 2017-04-29 00:00:00

The characterization of heart dynamics with a view to distinguish abnormal
from normal behavior is an interesting topic in clinical sciences. Here we
present an analysis of the Electro-cardiogram (ECG) signals obtained under
controlled conditions from several healthy and unhealthy subjects using the
framework of multifractal analysis. Our analysis differs from the conventional
nonlinear analysis in that the information contained in the amplitude
variations of the signal is being extracted and quantified. The results thus
obtained reveal that the attractor underlying the dynamics of the heart has
multifractal structure and the resultant multifractal spectra can clearly
separate healthy subjects from unhealthy ones. We use supervised machine
learning approach to build a model that predicts the group label of a new
subject with very high accuracy on the basis of the multifractal parameters. By
comparing the range of scaling indices in the multifractal spectra with that of
beat replicated data from the same ECG, we show how each ECG can be checked for
abnormality for variations within itself.
Emotion Specification from Musical Stimuli: An EEG Study with AFA and DFA
Date: 2017-04-29 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

The present study reports interesting findings in regard to emotional arousal
based activities while listening to two Hindustani classical ragas of contrast
emotion. EEG data was taken on 5 naive listeners while they listened to two
ragas Bahar and Mia ki Malhar which are conventionally known to portray
contrast emotions. The EEG data were analyzed with the help of two robust non
linear tools viz. Adaptive Fractal Analysis (AFA) and Detrended Fluctuation
Analysis (DFA). A comparative study of the Hurst Exponents obtained from the
two methods have been shown which shows that DFA provides more rigorous results
compared to AFA when it comes to the scaling analysis of biosignal data. The
results and implications have been discussed in detail.
On the ability to reconstruct ancestral genomes from Mycobacterium genus
Date: 2017-04-30 00:00:00

Technical signs of progress during the last decades has led to a situation in
which the accumulation of genome sequence data is increasingly fast and cheap.
The huge amount of molecular data available nowadays can help addressing new
and essential questions in Evolution. However, reconstructing evolution of DNA
sequences requires models, algorithms, statistical and computational methods of
ever increasing complexity. Since most dramatic genomic changes are caused by
genome rearrangements (gene duplications, gain/loss events), it becomes crucial
to understand their mechanisms and reconstruct ancestors of the given genomes.
This problem is known to be NP-complete even in the "simplest" case of three
genomes. Heuristic algorithms are usually executed to provide approximations of
the exact solution.
  We state that, even if the ancestral reconstruction problem is NP-hard in
theory, its exact resolution is feasible in various situations, encompassing
organelles and some bacteria. Such accurate reconstruction, which identifies
too some highly homoplasic mutations whose ancestral status is undecidable,
will be initiated in this work-in-progress, to reconstruct ancestral genomes of
two Mycobacterium pathogenetic bacterias. By mixing automatic reconstruction of
obvious situations with human interventions on signaled problematic cases, we
will indicate that it should be possible to achieve a concrete, complete, and
really accurate reconstruction of lineages of the Mycobacterium tuberculosis
complex. Thus, it is possible to investigate how these genomes have evolved
from their last common ancestors.
Risk Minimization, Regret Minimization and Progressive Hedging Algorithms
Date: 2020-06-14 00:00:00
Tasks: Decision Making, Decision Making Under Uncertainty

This paper begins with a study on the dual representations of risk and regret
measures and their impact on modeling multistage decision making under
uncertainty. A relationship between risk envelopes and regret envelopes is
established by using the Lagrangian duality theory. Such a relationship opens a
door to a decomposition scheme, called progressive hedging, for solving
multistage risk minimization and regret minimization problems. In particular,
the classical progressive hedging algorithm is modified in order to handle a
new class of linkage constraints that arises from reformulations and other
applications of risk and regret minimization problems. Numerical results are
provided to show the efficiency of the progressive hedging algorithms.
Portfolio Choice with Small Temporary and Transient Price Impact
Authors: Ibrahim Ekren, Johannes Muhle-Karbe
Date: 2017-05-01 00:00:00

We study portfolio selection in a model with both temporary and transient price impact introduced by Garleanu and Pedersen (2016). In the large-liquidity limit where both frictions are small, we derive explicit formulas for the asymptotically optimal trading rate and the corresponding minimal leading-order performance loss. We find that the losses are governed by the volatility of the frictionless target strategy, like in models with only temporary price impact. In contrast, the corresponding optimal portfolio not only tracks the frictionless optimizer, but also exploits the displacement of the market price from its unaffected level.
Overcoming the ill-posedness through discretization in vector tomography: Reconstruction of irrotational vector fields
Date: 2017-04-27 00:00:00

Vector tomography methods intend to reconstruct and visualize vector fields
in restricted domains by measuring line integrals of projections of these
vector fields. Here, we deal with the reconstruction of irrotational vector
functions from boundary measurements. As the majority of inverse problems,
vector field recovery is an ill posed in the continuous domain and therefore
further assumptions, measurements and constraints should be imposed for the
full vector field estimation. The reconstruction idea in the discrete domain
relies on solving a numerical system of linear equations which derives from the
approximation of the line integrals along lines which trace the bounded domain.
This work presents an extensive description of a vector field recovery, the
fundamental assumptions and the ill conditioning of this inverse problem. More
importantly we show that this inverse problem is regularized via the domain
discretization, i.e. we show that the recovery of an irrotational vector field
within a discrete grid employing a finite set of longitudinal line integrals,
leads to a consistent linear system which has bounded solution errors. We
elaborate on the estimation of the solution's error and we prove that this
relative error is finite and therefore a stable vector field reconstruction is
ensured. Such theoretical aspects are critical for future implementations of
vector tomography in practical applications like the inverse bioelectric field
problem. We validate our theoretical results by performing simulations that
reconstruct smooth irrotational fields based solely on a finite number of
boundary measurements and without the need of any additional or prior
information (e.g. transversal line integrals or source free assumption).
Looking for the right mate in diploid species: how dominance relationships affect population differentiation in sexual trait?
Date: 2018-01-23 00:00:00

Divergence between populations for a given trait can be driven by natural or
sexual selection, interacting with migration behaviour. Mating preference for
different phenotypes can lead to the emergence and persistence of
differentiated populations. Dominance between alleles encoding for divergent
phenotypes can interfere in such processes. Using a diploid model of trait
determining both mating success and migration rate, we explored differentiation
between two connected populations, assuming either co-dominance or strict
dominance between alleles. The model assumes that individuals prefer mating
with partners displaying the same phenotype and therefore tend to move to the
other population when their own phenotype is rare. We show that the emergence
of differentiated populations in this diploid model is limited as compared to
results obtained with the same model assuming haploidy. When assuming
co-dominance, differentiation arises only when migration is limited as compared
to preference. Such differentiation is less dependent on migration when
assuming strict dominance between haplotypes. Dominant alleles frequently
invade populations because their phenotype is more frequently expressed,
resulting in higher mating success and rapid decrease in migration. However,
depending on the initial distribution of alleles, this advantage associated
with dominance (i.e. Haldane's sieve) may lead to fixation of the dominant
allele throughout both populations. Depending on the initial distribution of
heterozygotes, persistence of polymorphisms within populations can also occur
because heterozygotes displaying the predominant phenotype benefit from mating
preferences. Altogether, our results highlight that heterozygotes' behaviour
has a strong impact on population differentiation and stress out the need of
diploid models of differentiation and speciation driven by natural and sexual
selection.
Pricing Variance Swaps on Time-Changed Markov Processes
Date: 2019-11-15 00:00:00

We prove that the variance swap rate (fair strike) equals the price of a
co-terminal European-style contract when the underlying is an exponential
Markov process, time-changed by an arbitrary continuous stochastic clock, which
has arbitrary correlation with the driving Markov process, provided that the
payoff function $G$ of the European contract satisfies an ordinary
integro-differential equation, which depends only on the dynamics of the Markov
process, not on the clock. We present examples of Markov processes where the
function $G$ that prices the variance swap can be computed explicitly. In
general, the solutions $G$ are not contained in the logarithmic family
previously obtained in the special case where the Markov process is a L\'evy
process.
Estimating and interpreting secondary attack risk: Binomial considered harmful
Date: 2020-07-17 00:00:00
Tasks: Attribute, Survival Analysis

The household secondary attack risk (SAR), often called the secondary attack
rate or secondary infection risk, is the probability of infectious contact from
an infectious household member A to a given household member B, where we define
infectious contact to be a contact sufficient to infect B if he or she is
susceptible. Estimation of the SAR is an important part of understanding and
controlling the transmission of infectious diseases. In practice, it is most
often estimated using binomial models such as logistic regression, which
implicitly attribute all secondary infections in a household to the primary
case. In the simplest case, the number of secondary infections in a household
with m susceptibles and a single primary case is modeled as a binomial(m, p)
random variable where p is the SAR. Although it has long been understood that
transmission within households is not binomial, it is thought that multiple
generations of transmission can be safely neglected when p is small. We use
probability generating functions and simulations to show that this is a
mistake. The proportion of susceptible household members infected can be
substantially larger than the SAR even when p is small. As a result, binomial
estimates of the SAR are biased upward and their confidence intervals have poor
coverage probabilities even if adjusted for clustering. Accurate point and
interval estimates of the SAR can be obtained using longitudinal chain binomial
models or pairwise survival analysis, which account for multiple generations of
transmission within households, the ongoing risk of infection from outside the
household, and incomplete follow-up. We illustrate the practical implications
of these results in an analysis of household surveillance data collected by the
Los Angeles County Department of Public Health during the 2009 influenza A
(H1N1) pandemic.
A Time Series Analysis-Based Forecasting Framework for the Indian Healthcare Sector
Date: 2017-04-25 00:00:00
Tasks: Diversity, Time Series, Time Series Analysis

Designing efficient and robust algorithms for accurate prediction of stock
market prices is one of the most exciting challenges in the field of time
series analysis and forecasting. With the exponential rate of development and
evolution of sophisticated algorithms and with the availability of fast
computing platforms, it has now become possible to effectively and efficiently
extract, store, process and analyze high volume of stock market data with
diversity in its contents. Availability of complex algorithms which can execute
very fast on parallel architecture over the cloud has made it possible to
achieve higher accuracy in forecasting results while reducing the time required
for computation. In this paper, we use the time series data of the healthcare
sector of India for the period January 2010 till December 2016. We first
demonstrate a decomposition approach of the time series and then illustrate how
the decomposition results provide us with useful insights into the behavior and
properties exhibited by the time series. Further, based on the structural
analysis of the time series, we propose six different methods of forecasting
for predicting the time series index of the healthcare sector. Extensive
results are provided on the performance of the forecasting methods to
demonstrate their effectiveness.
Stochastic modelling of non-stationary financial assets
Date: 2017-04-30 00:00:00
Tasks: Time Series, Time Series Analysis

We model non-stationary volume-price distributions with a log-normal
distribution and collect the time series of its two parameters. The time series
of the two parameters are shown to be stationary and Markov-like and
consequently can be modelled with Langevin equations, which are derived
directly from their series of values. Having the evolution equations of the
log-normal parameters, we reconstruct the statistics of the first moments of
volume-price distributions which fit well the empirical data. Finally, the
proposed framework is general enough to study other non-stationary stochastic
variables in other research fields, namely biology, medicine and geology.