pwc-archive/papers-with-abstracts huggingface dataset:
A fractional order recovery SIR model from a stochastic process
Date: 2015-09-30 00:00:00

Over the past several decades there has been a proliferation of
epidemiological models with ordinary derivatives replaced by fractional
derivatives in an an-hoc manner. These models may be mathematically interesting
but their relevance is uncertain. Here we develop an SIR model for an epidemic,
including vital dynamics, from an underlying stochastic process. We show how
fractional differential operators arise naturally in these models whenever the
recovery time from the disease is power law distributed. This can provide a
model for a chronic disease process where individuals who are infected for a
long time are unlikely to recover. The fractional order recovery model is shown
to be consistent with the Kermack-McKendrick age-structured SIR model and it
reduces to the Hethcote-Tudor integral equation SIR model. The derivation from
a stochastic process is extended to discrete time, providing a stable numerical
method for solving the model equations. We have carried out simulations of the
fractional order recovery model showing convergence to equilibrium states. The
number of infecteds in the endemic equilibrium state increases as the
fractional order of the derivative tends to zero.
First-principles molecular structure search with a genetic algorithm
Date: 2015-10-13 00:00:00
Tasks: Computational chemistry

The identification of low-energy conformers for a given molecule is a
fundamental problem in computational chemistry and cheminformatics. We assess
here a conformer search that employs a genetic algorithm for sampling the
low-energy segment of the conformation space of molecules. The algorithm is
designed to work with first-principles methods, facilitated by the
incorporation of local optimization and blacklisting conformers to prevent
repeated evaluations of very similar solutions. The aim of the search is not
only to find the global minimum, but to predict all conformers within an energy
window above the global minimum. The performance of the search strategy is: (i)
evaluated for a reference data set extracted from a database with amino acid
dipeptide conformers obtained by an extensive combined force field and
first-principles search and (ii) compared to the performance of a systematic
search and a random conformer generator for the example of a drug-like ligand
with 43 atoms, 8 rotatable bonds and 1 cis/trans bond.
Approximate hedging problem with transaction costs in stochastic volatility markets
Date: 2015-05-11 00:00:00

This paper studies the problem of option replication in general stochastic
volatility markets with transaction costs, using a new specification for the
volatility adjustment in Leland's algorithm \cite{Leland}. We prove several
limit theorems for the normalized replication error of Leland's strategy, as
well as that of the strategy suggested by L\'epinette. The asymptotic results
obtained not only generalize the existing results, but also enable us to fix
the under-hedging property pointed out by Kabanov and Safarian. We also discuss
possible methods to improve the convergence rate and to reduce the option price
inclusive of transaction costs.
Approximate hedging with proportional transaction costs in stochastic volatility models with jumps
Date: 2019-09-24 00:00:00
Tasks: valid

We study the problem of option replication under constant proportional
transaction costs in models where stochastic volatility and jumps are combined
to capture the market's important features. Assuming some mild condition on the
jump size distribution we show that transaction costs can be approximately
compensated by applying the Leland adjusting volatility principle and the
asymptotic property of the hedging error due to discrete readjustments is
characterized. In particular, the jump risk can be approximately eliminated and
the results established in continuous diffusion models are recovered. The study
also confirms that for the case of constant trading cost rate, the approximate
results established by Kabanov and Safarian (1997)and by Pergamenschikov (2003)
are still valid in jump-diffusion models with deterministic volatility using
the classical Leland parameter in Leland (1986).
Imaging Extracellular Protein Concentration with Nanoplasmonic Sensors
Date: 2015-05-11 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

Extracellular protein concentrations and gradients queue a wide range of
cellular responses, such as cell motility and division. Spatio-temporal
quantification of these concentrations as produced by cells has proven
challenging. As a result, artificial gradients must be introduced to the cell
culture to correlate signal and response. Here we demonstrate a label-free
nanoplasmonic imaging technique that can directly map protein concentrations as
secreted by single cells in real time and which integrates with standard
live-cell microscopes. When used to measure the secretion of antibodies from
hybridoma cells, a broad range of time-dependent concentrations was observed:
from steady-state secretions of 230 pM near the cell surface to large
transients which reached as high as 56 nM over several minutes and then
dissipated. The label-free nature of the technique is minimally invasive and we
anticipate will enable the quantification of deterministic relationships
between secreted protein concentrations and their induced cellular responses.
Near-optimal RNA-Seq quantification
Date: 2015-05-15 00:00:00

We present a novel approach to RNA-Seq quantification that is near optimal in
speed and accuracy. Software implementing the approach, called kallisto, can be
used to analyze 30 million unaligned paired-end RNA-Seq reads in less than 5
minutes on a standard laptop computer while providing results as accurate as
those of the best existing tools. This removes a major computational bottleneck
in RNA-Seq analysis.
Math Search for the Masses: Multimodal Search Interfaces and Appearance-Based Retrieval
Authors: Zanibbi Richard, Orakwue Awelemdy
Date: 2015-05-11 00:00:00
Tasks: Math, Retrieval

We summarize math search engines and search interfaces produced by the
Document and Pattern Recognition Lab in recent years, and in particular the min
math search interface and the Tangent search engine. Source code for both
systems are publicly available. "The Masses" refers to our emphasis on creating
systems for mathematical non-experts, who may be looking to define unfamiliar
notation, or browse documents based on the visual appearance of formulae rather
than their mathematical semantics.
On the sufficiency of pairwise interactions in maximum entropy models of biological networks
Date: 2015-05-11 00:00:00

Biological information processing networks consist of many components, which
are coupled by an even larger number of complex multivariate interactions.
However, analyses of data sets from fields as diverse as neuroscience,
molecular biology, and behavior have reported that observed statistics of
states of some biological networks can be approximated well by maximum entropy
models with only pairwise interactions among the components. Based on
simulations of random Ising spin networks with $p$-spin ($p>2$) interactions,
here we argue that this reduction in complexity can be thought of as a natural
property of densely interacting networks in certain regimes, and not
necessarily as a special property of living systems. By connecting our analysis
to the theory of random constraint satisfaction problems, we suggest a reason
for why some biological systems may operate in this regime.
Neutral dynamics with environmental noise: age-size statistics and species lifetimes
Date: 2015-05-12 00:00:00

Neutral dynamics, where taxa are assumed to be demographically equivalent and
their abundance is governed solely by the stochasticity of the underlying
birth-death process, has proved itself as an important minimal model that
accounts for many empirical datasets in genetics and ecology. However, the
restriction of the model to demographic [${\cal{O}} ({\sqrt N})$] noise yields
relatively slow dynamics that appears to be in conflict with both short-term
and long-term characteristics of the observed systems. Here we analyze two of
these problems - age size relationships and species extinction time - in the
framework of a neutral theory with both demographic and environmental
stochasticity. It turns out that environmentally induced variations of the
demographic rates control the long-term dynamics and modify dramatically the
predictions of the neutral theory with demographic noise only, yielding much
better agreement with empirical data. We consider two prototypes of "zero mean"
environmental noise, one which is balanced with regard to the arithmetic
abundance, another balanced in the logarithmic (fitness) space, study their
species lifetime statistics and discuss their relevance to realistic models of
community dynamics.
Ontology Based Document Clustering Using MapReduce
Authors: Elsayed Abdelrahman, Mokhtar Hoda M. O., Ismail Osama
Date: 2015-05-12 00:00:00
Tasks: Clustering

Nowadays, document clustering is considered as a data intensive task due to
the dramatic, fast increase in the number of available documents. Nevertheless,
the features that represent those documents are also too large. The most common
method for representing documents is the vector space model, which represents
document features as a bag of words and does not represent semantic relations
between words. In this paper we introduce a distributed implementation for the
bisecting k-means using MapReduce programming model. The aim behind our
proposed implementation is to solve the problem of clustering intensive data
documents. In addition, we propose integrating the WordNet ontology with
bisecting k-means in order to utilize the semantic relations between words to
enhance document clustering results. Our presented experimental results show
that using lexical categories for nouns only enhances internal evaluation
measures of document clustering; and decreases the documents features from
thousands to tens features. Our experiments were conducted using Amazon Elastic
MapReduce to deploy the Bisecting k-means algorithm.
Statistical analysis of NOMAO customer votes for spots of France
Authors: Palovics Robert, Daroczy Balint, Benczur Andras, Pap Julia, Ermann Leonardo, Phan Samuel, Chepelianskii Alexei D., Shepelyansky Dima L.
Date: 2015-05-12 00:00:00
Tasks: Imputation

We investigate the statistical properties of votes of customers for spots of
France collected by the startup company NOMAO. The frequencies of votes per
spot and per customer are characterized by a power law distributions which
remain stable on a time scale of a decade when the number of votes is varied by
almost two orders of magnitude. Using the computer science methods we explore
the spectrum and the eigenvalues of a matrix containing user ratings to
geolocalized items. Eigenvalues nicely map to large towns and regions but show
certain level of instability as we modify the interpretation of the underlying
matrix. We evaluate imputation strategies that provide improved prediction
performance by reaching geographically smooth eigenvectors. We point on
possible links between distribution of votes and the phenomenon of
self-organized criticality.
Frappe: Understanding the Usage and Perception of Mobile App Recommendations In-The-Wild
Authors: Baltrunas Linas, Church Karen, Karatzoglou Alexandros, Oliver Nuria
Date: 2015-05-12 00:00:00
Tasks: Recommendation Systems

This paper describes a real world deployment of a context-aware mobile app
recommender system (RS) called Frappe. Utilizing a hybrid-approach, we
conducted a large-scale app market deployment with 1000 Android users combined
with a small-scale local user study involving 33 users. The resulting usage
logs and subjective feedback enabled us to gather key insights into (1)
context-dependent app usage and (2) the perceptions and experiences of
end-users while interacting with context-aware mobile app recommendations.
While Frappe performs very well based on usage-centric evaluation metrics
insights from the small-scale study reveal some negative user experiences. Our
results point to a number of actionable lessons learned specifically related to
designing, deploying and evaluating mobile context-aware RS in-the-wild with
real users.
Efficient Similarity Indexing and Searching in High Dimensions
Authors: Zhong Yu
Date: 2015-05-12 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Efficient indexing and searching of high dimensional data has been an area of
active research due to the growing exploitation of high dimensional data and
the vulnerability of traditional search methods to the curse of dimensionality.
This paper presents a new approach for fast and effective searching and
indexing of high dimensional features using random partitions of the feature
space. Experiments on both handwritten digits and 3-D shape descriptors have
shown the proposed algorithm to be highly effective and efficient in indexing
and searching real data sets of several hundred dimensions. We also compare its
performance to that of the state-of-the-art locality sensitive hashing
algorithm.
The case for absolute ligand discrimination : modeling information processing and decision by immune T cells
Date: 2015-12-20 00:00:00

Some cells have to take decision based on the quality of surroundings
ligands, almost irrespective of their quantity, a problem we name "absolute
discrimination". An example of absolute discrimination is recognition of
not-self by immune T Cells. We show how the problem of absolute discrimination
can be solved by a process called "adaptive sorting". We review several
implementations of adaptive sorting, as well as its generic properties such as
antagonism. We show how kinetic proofreading with negative feedback implements
an approximate version of adaptive sorting in the immune context. Finally, we
revisit the decision problem at the cell population level, showing how
phenotypic variability and feedbacks between population and single cells are
crucial for proper decision.
Firing Rate Dynamics in Recurrent Spiking Neural Networks with Intrinsic and Network Heterogeneity
Date: 2016-11-21 00:00:00
Tasks: Descriptive, Dimensionality Reduction

Heterogeneity of neural attributes has recently gained a lot of attention and
is increasing recognized as a crucial feature in neural processing. Despite its
importance, this physiological feature has traditionally been neglected in
theoretical studies of cortical neural networks. Thus, there is still a lot
unknown about the consequences of cellular and circuit heterogeneity in spiking
neural networks. In particular, combining network or synaptic heterogeneity and
intrinsic heterogeneity has yet to be considered systematically despite the
fact that both are known to exist and likely have significant roles in neural
network dynamics. In a canonical recurrent spiking neural network model, we
study how these two forms of heterogeneity lead to different distributions of
excitatory firing rates. To analytically characterize how these types of
heterogeneities affect the network, we employ a dimension reduction method that
relies on a combination of Monte Carlo simulations and probability density
function equations. We find that the relationship between intrinsic and network
heterogeneity has a strong effect on the overall level of heterogeneity of the
firing rates. Specifically, this relationship can lead to amplification or
attenuation of firing rate heterogeneity, and these effects depend on whether
the recurrent network is firing asynchronously or rhythmically firing. These
observations are captured with the aforementioned reduction method, and
furthermore simpler analytic descriptions based on this dimension reduction
method are developed. The final analytic descriptions provide compact and
descriptive formulas for how the relationship between intrinsic and network
heterogeneity determines the firing rate heterogeneity dynamics in various
settings.
Identification of the Zeo1 Protein as a Candidate Structural Homolog of {\alpha}-Synuclein in Budding Yeast
Date: 2015-05-23 00:00:00

Human {\alpha}-synuclein (SNCA) is a 140-amino-acid protein belonging to the
three-member synuclein family. It has been extensively studied due to its
misfolding/aggregation in and genetic linkage to neurodegenerative diseases,
especially Parkinson's disease (PD). To better understand its biology, models
of SNCA toxicity have been developed in budding yeast over the past decade,
which have yielded insights into the protein's modes of action in specific
pathways and potential therapeutic targets. Given that the synuclein gene
family is not present in yeast, an extensive homology search was undertaken to
determine if any yeast protein may possess structural homology to SNCA and
whose native biology may shed more light on SNCA's pathomechanism in
eukaryotes. We identified Zeo1, a membrane-associated protein involved in the
cell wall integrity (CWI) pathway, as a candidate structural homolog. We show
that Zeo1 overexpression is toxic in yeast and, similar to SNCA, localizes to
lipid membranes. A number of biochemical similarities between Zeo1 and SNCA
also become apparent in light of this potential structural connection.
Moreover, the yeast PKC1 gene, a kinase acting as a downstream signaling hub in
the CWI pathway, rescues both SNCA- and Zeo1-induced toxicities. Using the same
homology search methods that identified Zeo1, we show that Pkc1 has a hybrid
structural similarity to PINK1 and PARIS, two mitochondrial PD-implicated
proteins not generally linked directly to synuclein-specific pathobiology.
Overall, this proof-of-concept study shows the potential utility of hitherto
uncharacterized cross-species structural homologs, identified using comparative
proteome-wide structure prediction algorithms, in shedding light on abstruse
connections among disease-relevant proteins.
Mechanistic Model to Replace Hodgkin-Huxley Equations
Date: 2015-05-13 00:00:00
Tasks: model

In this paper we construct a mathematical model for excitable membranes by
introducing circuit characteristics for ion pump, ion current activation, and
voltage-gating. The model is capable of reestablishing the Nernst resting
potentials, all-or-nothing action potentials, absolute refraction, anode break
excitation, and spike bursts. We propose to replace the Hodgkin-Huxley model by
our model as the basis template for neurons and excitable membranes.
Stochastic Choice and Optimal Sequential Sampling
Date: 2015-05-13 00:00:00

We model the joint distribution of choice probabilities and decision times in
binary choice tasks as the solution to a problem of optimal sequential
sampling, where the agent is uncertain of the utility of each action and pays a
constant cost per unit time for gathering information. In the resulting optimal
policy, the agent's choices are more likely to be correct when the agent
chooses to decide quickly, provided that the agent's prior beliefs are correct.
For this reason it better matches the observed correlation between decision
time and choice probability than does the classical drift-diffusion model,
where the agent is uncertain which of two actions is best but knows the utility
difference between them
Exact and approximate moment closures for non-Markovian network epidemics
Date: 2015-05-13 00:00:00
Tasks: Triplet

Moment-closure techniques are commonly used to generate low-dimensional
deterministic models to approximate the average dynamics of stochastic systems
on networks. The quality of such closures is usually difficult to asses and the
relationship between model assumptions and closure accuracy are often
difficult, if not impossible, to quantify. Here we carefully examine some
commonly used moment closures, in particular a new one based on the concept of
maximum entropy, for approximating the spread of epidemics on networks by
reconstructing the probability distributions over triplets based on those over
pairs. We consider various models (SI, SIR, SEIR and Reed-Frost-type) under
Markovian and non-Markovian assumption characterising the latent and infectious
periods. We initially study two special networks, namely the open triplet and
closed triangle, for which we can obtain analytical results. We then explore
numerically the exactness of moment closures for a wide range of larger motifs,
thus gaining understanding of the factors that introduce errors in the
approximations, in particular the presence of a random duration of the
infectious period and the presence of overlapping triangles in a network. We
also derive a simpler and more intuitive proof than previously available
concerning the known result that pair-based moment closure is exact for the
Markovian SIR model on tree-like networks under pure initial conditions. We
also extend such a result to all infectious models, Markovian and
non-Markovian, in which susceptibles escape infection independently from each
infected neighbour and for which infectives cannot regain susceptible status,
provided the network is tree-like and initial conditions are pure. This works
represent a valuable step in deepening understanding of the assumptions behind
moment closure approximations and for putting them on a more rigorous
mathematical footing.
Hedging of defaultable claims in a structural model using a locally risk-minimizing approach
Date: 2015-05-13 00:00:00

In the context of a locally risk-minimizing approach, the problem of hedging
defaultable claims and their Follmer-Schweizer decompositions are discussed in
a structural model. This is done when the underlying process is a finite
variation Levy process and the claims pay a predetermined payout at maturity,
contingent on no prior default. More precisely, in this particular framework,
the locally risk-minimizing approach is carried out when the underlying process
has jumps, the derivative is linked to a default event, and the probability
measure is not necessarily risk-neutral.
The Emergence of Cooperation from a Single Mutant during Microbial Life-Cycles
Date: 2015-06-14 00:00:00

Cooperative behavior is widespread in nature, even though cooperating
individuals always run the risk to be exploited by free-riders. Population
structure effectively promotes cooperation given that a threshold in the level
of cooperation was already reached. However, the question how cooperation can
emerge from a single mutant, which cannot rely on a benefit provided by other
cooperators, is still puzzling. Here, we investigate this question for a
well-defined but generic situation based on typical life-cycles of microbial
populations where individuals regularly form new colonies followed by growth
phases. We analyze two evolutionary mechanisms favoring cooperative behavior
and study their strength depending on the inoculation size and the length of a
life-cycle. In particular, we find that population bottlenecks followed by
exponential growth phases strongly increase the survival and fixation
probabilities of a single cooperator in a free-riding population.