pwc-archive/papers-with-abstracts huggingface dataset:
Complex synchronization patterns in the human connectome network
Date: 2016-03-15 00:00:00

A major challenge in neuroscience is posed by the need for relating the
emerging dynamical features of brain activity with the underlying modular
structure of neural connections, hierarchically organized throughout several
scales. The spontaneous emergence of coherence and synchronization across such
scales is crucial to neural function, while its anomalies often relate to
pathological conditions. Here we provide a numerical study of synchronization
dynamics in the human connectome network. Our purpose is to provide a detailed
characterization of the recently uncovered broad dynamic regime, interposed
between order and disorder, which stems from the hierarchical modular
organization of the human connectome. In this regime -similar in essence to a
Griffiths phase- synchronization dynamics are trapped within metastable
attractors of local coherence. Here we explore the role of noise, as an
effective description of external perturbations, and discuss how its presence
accounts for the ability of the system to escape intermittently from such
attractors and explore complex dynamic repertoires of locally coherent states,
in analogy with experimentally recorded patterns of cerebral activity.
Activity of any neuron with delayed feedback stimulated with Poisson stream is non-Markov
Date: 2015-03-11 00:00:00

For a class of excitatory spiking neuron models with delayed feedback fed
with a Poisson stochastic process, it is proven that the stream of output
interspike intervals cannot be presented as a Markov process of any order.
  Keywords: spiking neuron; Poisson stochastic process; probability density
function; delayed feedback; non-Markov stochastic process
Neural mechanism to simulate a scale-invariant future timeline
Date: 2015-07-24 00:00:00
Tasks: Future prediction, Hippocampus, Translation

Predicting future events, and their order, is important for efficient
planning. We propose a neural mechanism to non-destructively translate the
current state of memory into the future, so as to construct an ordered set of
future predictions. This framework applies equally well to translations in time
or in one-dimensional position. In a two-layer memory network that encodes the
Laplace transform of the external input in real time, translation can be
accomplished by modulating the weights between the layers. We propose that
within each cycle of hippocampal theta oscillations, the memory state is swept
through a range of translations to yield an ordered set of future predictions.
We operationalize several neurobiological findings into phenomenological
equations constraining translation. Combined with constraints based on physical
principles requiring scale-invariance and coherence in translation across
memory nodes, the proposition results in Weber-Fechner spacing for the
representation of both past (memory) and future (prediction) timelines. The
resulting expressions are consistent with findings from phase precession
experiments in different regions of the hippocampus and reward systems in the
ventral striatum. The model makes several experimental predictions that can be
tested with existing technology.
On Eigen's quasispecies model, two-valued fitness landscapes, and isometry groups acting on finite metric spaces
Date: 2016-06-13 00:00:00

A two-valued fitness landscape is introduced for the classical Eigen's
quasispecies model. This fitness landscape can be considered as a direct
generalization of the so-called single or sharply peaked landscape. A general,
non permutation invariant quasispecies model is studied, therefore the
dimension of the problem is $2^N\times 2^N$, where $N$ is the sequence length.
It is shown that if the fitness function is equal to $w+s$ on a $G$-orbit $A$
and is equal to $w$ elsewhere, then the mean population fitness can be found as
the largest root of an algebraic equation of degree at most $N+1$. Here $G$ is
an arbitrary isometry group acting on the metric space of sequences of zeroes
and ones of the length $N$ with the Hamming distance. An explicit form of this
exact algebraic equation is given in terms of the spherical growth function of
the $G$-orbit $A$. Sufficient conditions for the so-called error threshold for
sequences of orbits are given. Motivated by the analysis of the two-valued
fitness landscapes an abstract generalization of Eigen's model is introduced
such that the sequences are identified with the points of a finite metric space
$X$ together with a group of isometries acting transitively on $X$. In
particular, a simplicial analogue of the original quasispecies model is
discussed, which can be considered as a mathematical model of the switching of
the antigenic variants for some bacteria.
Contributions to the Theory of Thermostated Systems II: Least Dissipation of Helmholtz Free Energy in Nano-Biology
Date: 2015-03-12 00:00:00

In this paper, we develop further the theory of thermostated systems along
the lines of our earlier paper. Two results are highlighted: 1) in the Markov
limit of the contracted description, a least dissipation of Helmholtz free
energy principle is established; and 2) a detailed account of the
appropriateness of this principle for nano-biology, including the evolution of
life, is presented.
Population Stability and Momentum
Date: 2015-02-10 00:00:00

A new approach is developed to understand stability of a population and
further understanding of population momentum.
An optimal control model of mosquito reduction management in a dengue endemic region
Date: 2017-11-30 00:00:00
Tasks: Management

Aedes aegypti is known as the responsible vector transmitting dengue
flavivirus. Unavailability of medication to cure the transmission of the virus
in the human blood becomes a global health issue in recent decades. World
epidemiologists are encouraged to focus on the investigation over the effective
and inexpensive way to prevent dengue transmission, i.e. mosquito control. In
this paper, we present a model depicting the dynamics of mosquito population
based on indoor-outdoor life cycle classification. The basic mosquito offspring
number was obtained and analysis of equilibria was shown. We brought along a
discussion on the application of optimal control to the model in which two
simultaneous schemes were introduced. The first scheme is done by disseminating
chemical like temephos in spots where eggs and larvae develop, meanwhile the
second scheme is done by deploying fumigation through areas where adult
mosquitoes prevalently nest, indoor as well as outdoor. A version of the
gradient-based method was presented to set up a workflow in minimizing the
objective functional with respect to some control variables. Numerical results
from the analysis of the basic mosquito offspring number with constant control
and from that with optimal control suggested that the application of fumigation
is preferable over that of temephos. It was also suggested that applying both
control schemes simultaneously gives the most significant reduction in the
population.
Statistical Properties and Pre-hit Dynamics of Price Limit Hits in the Chinese Stock Markets
Date: 2015-03-12 00:00:00

Price limit trading rules are adopted in some stock markets (especially
emerging markets) trying to cool off traders' short-term trading mania on
individual stocks and increase market efficiency. Under such a microstructure,
stocks may hit their up-limits and down-limits from time to time. However, the
behaviors of price limit hits are not well studied partially due to the fact
that main stock markets such as the US markets and most European markets do not
set price limits. Here, we perform detailed analyses of the high-frequency data
of all A-share common stocks traded on the Shanghai Stock Exchange and the
Shenzhen Stock Exchange from 2000 to 2011 to investigate the statistical
properties of price limit hits and the dynamical evolution of several important
financial variables before stock price hits its limits. We compare the
properties of up-limit hits and down-limit hits. We also divide the whole
period into three bullish periods and three bearish periods to unveil possible
differences during bullish and bearish market states. To uncover the impacts of
stock capitalization on price limit hits, we partition all stocks into six
portfolios according to their capitalizations on different trading days. We
find that the price limit trading rule has a cooling-off effect (object to the
magnet effect), indicating that the rule takes effect in the Chinese stock
markets. We find that price continuation is much more likely to occur than
price reversal on the next trading day after a limit-hitting day, especially
for down-limit hits, which has potential practical values for market
practitioners.
Correlations between promoter activity and its nucleotide positions in spacing region
Date: 2015-03-12 00:00:00

Transcription is one of the essential processes for cells to read genetic
information encoded in genes, which is initiated by the binding of RNA
polymerase to related promoter. Experiments have found that the nucleotide
sequence of promoter has great influence on gene expression strength, or
promoter activity. In synthetic biology, one interesting question is how we can
synthesize a promoter with given activity, and which positions of promoter
sequence are important for determining its activity. In this study, based on
recent experimental data, correlations between promoter activity and its
sequence positions are analyzed by various methods. Our results show that,
except nucleotides in the two highly conserved regions, $-35$ box and $-10$
box, influences of nucleotides in other positions are also not neglectable. For
example, modifications of nucleotides around position $-19$ in spacing region
may change promoter activity in a large scale. The results of this study might
be helpful to our understanding of biophysical mechanism of gene transcription,
and may also be helpful to the design of synthetic cell factory.
Coalescent histories for lodgepole species trees
Date: 2015-03-12 00:00:00

Coalescent histories are combinatorial structures that describe for a given
gene tree and species tree the possible lists of branches of the species tree
on which the gene tree coalescences take place. Properties of the number of
coalescent histories for gene trees and species trees affect a variety of
probabilistic calculations in mathematical phylogenetics. Exact and asymptotic
evaluations of the number of coalescent histories, however, are known only in a
limited number of cases. Here we introduce a particular family of species
trees, the \emph{lodgepole} species trees $(\lambda_n)_{n\geq 0}$, in which
tree $\lambda_n$ has $m=2n+1$ taxa. We determine the number of coalescent
histories for the lodgepole species trees, in the case that the gene tree
matches the species tree, showing that this number grows with $m!!$ in the
number of taxa $m$. This computation demonstrates the existence of tree
families in which the growth in the number of coalescent histories is faster
than exponential. Further, it provides a substantial improvement on the lower
bound for the ratio of the largest number of matching coalescent histories to
the smallest number of matching coalescent histories for trees with $m$ taxa,
increasing a previous bound of $(\sqrt{\pi} / 32)[(5m-12)/(4m-6)] m \sqrt{m}$
to $[ \sqrt{m-1}/(4 \sqrt{e}) ]^{m}$. We discuss the implications of our
enumerative results for phylogenetic computations.
Profitable forecast of prices of stock options on real market data via the solution of an ill-posed problem for the Black-Scholes equation
Date: 2015-03-12 00:00:00

A new mathematical model for the Black-Scholes equation is proposed to
forecast option prices. This model includes new interval for the price of the
underlying stock as well as new initial and boundary conditions. Conventional
notions of maturity time and strike prices are not used. The Black-Scholes
equation is solved as a parabolic equation with the reversed time, which is an
ill-posed problem. Thus, a regularization method is used to solve it. This idea
is verified on real market data for twenty liquid options. A trading strategy
is proposed. This strategy indicates that our method is profitable on at least
those twenty options. We conjecture that our method might lead to significant
profits of those financial institutions which trade large amounts of options.
We caution, however, that detailed further studies are necessary to verify this
conjecture.
A divisive hierarchical clustering-based method for indexing image information
Authors: Izadpanah Najva
Date: 2015-03-12 00:00:00
Tasks: Clustering, Image Retrieval, Retrieval

In most practical applications of image retrieval, high-dimensional feature
vectors are required, but current multi-dimensional indexing structures lose
their efficiency with growth of dimensions. Our goal is to propose a divisive
hierarchical clustering-based multi-dimensional indexing structure which is
efficient in high-dimensional feature spaces. A projection pursuit method has
been used for finding a component of the data, which data's projections onto it
maximizes the approximation of negentropy for preparing essential information
in order to partitioning of the data space. Various tests and experimental
results on high-dimensional datasets indicate the performance of proposed
method in comparison with others.
Geo-SAGE: A Geographical Sparse Additive Generative Model for Spatial Item Recommendation
Authors: Wang Weiqing, Yin Hongzhi, Chen Ling, Sun Yizhou, Sadiq Shazia, Zhou Xiaofang
Date: 2015-03-12 00:00:00
Tasks: Recommendation Systems

With the rapid development of location-based social networks (LBSNs), spatial
item recommendation has become an important means to help people discover
attractive and interesting venues and events, especially when users travel out
of town. However, this recommendation is very challenging compared to the
traditional recommender systems. A user can visit only a limited number of
spatial items, leading to a very sparse user-item matrix. Most of the items
visited by a user are located within a short distance from where he/she lives,
which makes it hard to recommend items when the user travels to a far away
place. Moreover, user interests and behavior patterns may vary dramatically
across different geographical regions. In light of this, we propose Geo-SAGE, a
geographical sparse additive generative model for spatial item recommendation
in this paper. Geo-SAGE considers both user personal interests and the
preference of the crowd in the target region, by exploiting both the
co-occurrence pattern of spatial items and the content of spatial items. To
further alleviate the data sparsity issue, Geo-SAGE exploits the geographical
correlation by smoothing the crowd's preferences over a well-designed spatial
index structure called spatial pyramid. We conduct extensive experiments to
evaluate the performance of our Geo-SAGE model on two real large-scale
datasets. The experimental results clearly demonstrate our Geo-SAGE model
outperforms the state-of-the-art in the two tasks of both out-of-town and
home-town recommendations.
Capturing, Documenting and Visualizing Search Contexts for building Multimedia Corpora
Authors: Fernando Zeon Trevor
Date: 2015-03-12 00:00:00

In Social Science research, multimedia documents are often collected to
answer particular research questions like: "Which of the aesthetic properties
of a photo are considered important on the web" or "How has Street Art
developed over the past 50 years". Therefore, a researcher generally issues
multiple queries to a number of search engines. This activity may span over
long time intervals and results in a collection which can be further analyzed.
Documenting the collection building process which includes the context of the
carried out searches is imperative for social scientists to reproduce their
research. Such context documentation consists of several user actions and
search attributes like: the issued queries; the results clicked and saved;
duration a particular result was viewed for; the set of results that was
displayed but neither clicked, nor saved; as well as user annotations like
comments or tags. In this work we will describe a search process tracking
module and a search history visualization module. These modules can be
integrated into keyword based search systems through a REST API which was
developed to help capture, document and revisit past search contexts while
building a web corpora. Finally, we detail the implementation of how the module
was integrated into the LearnWeb2.0 platform - a multimedia web2.0 search and
sharing application which can obtain resources from various web2.0 tools such
as Youtube, Bing, Flickr, etc using keyword search.
Breast Cancer Data Analytics With Missing Values: A study on Ethnic, Age and Income Groups
Date: 2015-03-12 00:00:00
Tasks: Missing Values

An analysis of breast cancer incidences in women and the relationship between
ethnicity and survival rate has been an ongoing study with recorded incidences
of missing values in the secondary data. In this paper, we study and report the
results of breast cancer survival rate by ethnicity, age and income groups from
the dataset collected for 53593 patients in South East England between the
years 1998 and 2003. In addition to this, we also predict the missing values
for the ethnic groups in the dataset. The principle findings in our study
suggest that: 1) women of white ethnicity in South East England have a highest
percentage of survival rate when compared to the black ethnicity, 2) High
income groups have higher survival rates to that of lower income groups and 3)
Age groups between 80-95 have lower percentage of survival rate.
Bounds for randomly shared risk of heavy-tailed loss factors
Date: 2016-04-11 00:00:00

For a risk vector $V$, whose components are shared among agents by some
random mechanism, we obtain asymptotic lower and upper bounds for the
individual agents' exposure risk and the aggregated risk in the market. Risk is
measured by Value-at-Risk or Conditional Tail Expectation. We assume Pareto
tails for the components of $V$ and arbitrary dependence structure in a
multivariate regular variation setting. Upper and lower bounds are given by
asymptotically independent and fully dependent components of $V$ with respect
to the tail index $\alpha$ being smaller or larger than 1. Counterexamples,
where for non-linear aggregation functions no bounds are available, complete
the picture.
From Group Recommendations to Group Formation
Authors: Roy Senjuti Basu, Lakshmanan Laks V. S., Liu Rui
Date: 2015-03-11 00:00:00
Tasks: Recommendation Systems

There has been significant recent interest in the area of group
recommendations, where, given groups of users of a recommender system, one
wants to recommend top-k items to a group that maximize the satisfaction of the
group members, according to a chosen semantics of group satisfaction. Examples
semantics of satisfaction of a recommended itemset to a group include the
so-called least misery (LM) and aggregate voting (AV). We consider the
complementary problem of how to form groups such that the users in the formed
groups are most satisfied with the suggested top-k recommendations. We assume
that the recommendations will be generated according to one of the two group
recommendation semantics - LM or AV. Rather than assuming groups are given, or
rely on ad hoc group formation dynamics, our framework allows a strategic
approach for forming groups of users in order to maximize satisfaction. We show
that the problem is NP-hard to solve optimally under both semantics.
Furthermore, we develop two efficient algorithms for group formation under LM
and show that they achieve bounded absolute error. We develop efficient
heuristic algorithms for group formation under AV. We validate our results and
demonstrate the scalability and effectiveness of our group formation algorithms
on two large real data sets.
Identifying relevant positions in proteins by Critical Variable Selection
Date: 2016-01-19 00:00:00
Tasks: Multiple Sequence Alignment, Variable Selection

Evolution in its course found a variety of solutions to the same optimisation
problem. The advent of high-throughput genomic sequencing has made available
extensive data from which, in principle, one can infer the underlying structure
on which biological functions rely. In this paper, we present a new method
aimed at extracting sites encoding structural and func- tional properties from
a set of protein primary sequences, namely a Multiple Sequence Alignment. The
method, called Critical Variable Selection, is based on the idea that subsets
of relevant sites cor- respond to subsequences that occur with a particularly
broad frequency distribution in the dataset. By applying this algorithm to in
silico sequences, to the Response Regulator Receiver and to the Voltage Sensor
Domain of Ion Channels, we show that this procedure recovers not only
information encoded in single site statistics and pairwise correlations but it
also captures dependencies going beyond pairwise correlations. The method
proposed here is complementary to Statistical Coupling Analysis, in that the
most relevant sites predicted by the two methods markedly differ. We find
robust and consistent results for datasets as small as few hundred sequences,
that reveal a hidden hierarchy of sites that is consistent with present
knowledge on biologically relevant sites and evo- lutionary dynamics. This
suggests that Critical Variable Selection is able to identify in a Multiple
Sequence Alignment a core of sites encoding functional and structural
information.
Fusing Text and Image for Event Detection in Twitter
Authors: Alqhtani Samar M., Luo Suhuai, Regan Brian
Date: 2015-03-13 00:00:00
Tasks: Event Detection

In this contribution, we develop an accurate and effective event detection
method to detect events from a Twitter stream, which uses visual and textual
information to improve the performance of the mining process. The method
monitors a Twitter stream to pick up tweets having texts and images and stores
them into a database. This is followed by applying a mining algorithm to detect
an event. The procedure starts with detecting events based on text only by
using the feature of the bag-of-words which is calculated using the term
frequency-inverse document frequency (TF-IDF) method. Then it detects the event
based on image only by using visual features including histogram of oriented
gradients (HOG) descriptors, grey-level cooccurrence matrix (GLCM), and color
histogram. K nearest neighbours (Knn) classification is used in the detection.
The final decision of the event detection is made based on the reliabilities of
text only detection and image only detection. The experiment result showed that
the proposed method achieved high accuracy of 0.94, comparing with 0.89 with
texts only, and 0.86 with images only.
Spatial organization of bacterial transcription and translation
Date: 2016-08-29 00:00:00
Tasks: Translation

In bacteria such as $\textit{Escherichia coli}$, DNA is compacted into a
nucleoid near the cell center, while ribosomes$-$molecular complexes that
translate messenger RNAs (mRNAs) into proteins$-$are mainly localized at the
poles. We study the impact of this spatial organization using a minimal
reaction-diffusion model for the cellular transcriptional-translational
machinery. Our model predicts that $\sim 90\%$ of mRNAs are segregated to the
poles and reveals a "circulation" of ribosomes driven by the flux of mRNAs,
from synthesis in the nucleoid to degradation at the poles. To address the
existence of non-specific, transient interactions between ribosomes and mRNAs,
we developed a novel method to efficiently incorporate such transient
interactions into reaction-diffusion equations, which allowed us to quantify
the biological implications of such non-specific interactions, e.g. for
ribosome efficiency.