pwc-archive/papers-with-abstracts huggingface dataset:
Dependence of technological improvement on artifact interactions
Date: 2016-02-23 00:00:00

Empirical research has shown performance improvement of many different
technological domains occurs exponentially but with widely varying improvement
rates. What causes some technologies to improve faster than others do? Previous
quantitative modeling research has identified artifact interactions, where a
design change in one component influences others, as an important determinant
of improvement rates. The models predict that improvement rate for a domain is
proportional to the inverse of the domain interaction parameter. However, no
empirical research has previously studied and tested the dependence of
improvement rates on artifact interactions. A challenge to testing the
dependence is that any method for measuring interactions has to be applicable
to a wide variety of technologies. Here we propose a patent-based method that
is both technology domain-agnostic and less costly than alternative methods. We
use textual content from patent sets in 27 domains to find the influence of
interactions on improvement rates. Qualitative analysis identified six specific
keywords that signal artifact interactions. Patent sets from each domain were
then examined to determine the total count of these 6 keywords in each domain,
giving an estimate of artifact interactions in each domain. It is found that
improvement rates are positively correlated with the inverse of the total count
of keywords with correlation coefficient of +0.56 with a p-value of 0.002. The
empirical results agree with model predictions and support the suggestion that
domains with higher number of artifacts interactions (higher complexity) will
improve at a slower pace.
Dynamic signal tracking in a simple V1 spiking model
Date: 2016-01-12 00:00:00

This work is part of an effort to understand the neural basis for our visual
system's ability, or failure, to accurately track moving visual signals. We
consider here a ring model of spiking neurons, intended as a simplified
computational model of a single hypercolumn of the primary visual cortex.
Signals that consist of edges with time-varying orientations localized in space
are considered. Our model is calibrated to produce spontaneous and driven
firing rates roughly consistent with experiments, and our two main findings,
for which we offer dynamical explanation on the level of neuronal interactions,
are the following: (1) We have documented consistent transient overshoots in
signal perception following signal switches due to emergent interactions of the
E- and I-populations, and (2) for continuously moving signals, we have found
that accuracy is considerably lower at reversals of orientation than when
continuing in the same direction (as when the signal is a rotating bar). To
measure performance, we use two metrics, called fidelity and reliability, to
compare signals reconstructed by the system to the ones presented, and to
assess trial-to-trial variability. We propose that the same population
mechanisms responsible for orientation selectivity also impose constraints on
dynamic signal tracking that manifest in perception failures consistent with
psychophysical observations.
Predicting the three-dimensional folding of cis-regulatory regions in mammalian genomes using bioinformatic data and polymer models
Date: 2016-01-12 00:00:00

The three-dimensional organisation of chromosomes can be probed using methods
such as Capture-C. However it is unclear how such population level data relates
to the organisation within a single cell, and the mechanisms leading to the
observed interactions are still largely obscure. We present a polymer modelling
scheme based on the assumption that chromosome architecture is maintained by
protein bridges which form chromatin loops. To test the model we perform FISH
experiments and also compare with Capture-C data. Starting merely from the
locations of protein binding sites, our model accurately predicts the
experimentally observed chromatin interactions, revealing a population of 3D
conformations.
Social Network Extraction: Superficial Method and Information Retrieval
Authors: Nasution Mahyuddin K. M., Noah Shahrul Azman Mohd., Saad Saidah
Date: 2016-01-12 00:00:00
Tasks: Information Retrieval, Retrieval

Social network has become one of the themes of government issues, mainly
dealing with the chaos. The use of web is steadily gaining ground in these
issues. However, most of the web documents are unstructured and lack of
semantic. In this paper we proposed an Information Retrieval driven method for
dealing with heterogeneity of features in the web. The proposed solution is to
compare some approaches have shown the capacity to extract social relation:
strength relations and relations based on online academic database.
Spatial self-organization in hybrid models of multicellular adhesion
Date: 2016-01-12 00:00:00

Spatial self-organization emerges in distributed systems exhibiting local
interactions when nonlinearities and the appropriate propagation of signals are
at work. These kinds of phenomena can be modeled with different frameworks,
typically cellular automata or reaction-diffusion systems. A different class of
dynamical processes involves the correlated movement of agents over space,
which can be mediated through chemotactic movement or minimization of cell-cell
interaction energy. A classic example of the latter is given by the formation
of spatially segregated assemblies when cells display differential adhesion.
Here we consider a new class of dynamical models, involving cell adhesion among
two stochastically exchangeable cell states as a minimal model capable of
exhibiting well-defined, ordered spatial patterns. Our results suggest that a
whole space of pattern-forming rules is hosted by the combination of physical
differential adhesion and the value of probabilities modulating cell phenotypic
switching, showing that Turing-like patterns can be obtained without resorting
to reaction-diffusion processes. If the model is expanded allowing cells to
proliferate and die in an environment where diffusible nutrient and toxic waste
are at play, different phases are observed, characterized by regularly spaced
patterns. The analysis of the parameter space reveals that certain phases reach
higher population levels than other modes of organization. A detailed
exploration of the mean-field theory is also presented. Finally we let
populations of cells with different adhesion matrices compete for reproduction,
showing that, in our model, structural organization can improve the fitness of
a given cell population. The implications of these results for ecological and
evolutionary models of pattern formation and the emergence of multicellularity
are outlined.
Safe and complete contig assembly via omnitigs
Date: 2016-08-16 00:00:00

Contig assembly is the first stage that most assemblers solve when
reconstructing a genome from a set of reads. Its output consists of contigs --
a set of strings that are promised to appear in any genome that could have
generated the reads. From the introduction of contigs 20 years ago, assemblers
have tried to obtain longer and longer contigs, but the following question was
never solved: given a genome graph $G$ (e.g. a de Bruijn, or a string graph),
what are all the strings that can be safely reported from $G$ as contigs? In
this paper we finally answer this question, and also give a polynomial time
algorithm to find them. Our experiments show that these strings, which we call
omnitigs, are 66% to 82% longer on average than the popular unitigs, and 29% of
dbSNP locations have more neighbors in omnitigs than in unitigs.
An Efficient Coding Theory for a Dynamic Trajectory Predicts non-Uniform Allocation of Grid Cells to Modules in the Entorhinal Cortex
Date: 2016-01-12 00:00:00
Tasks: Decoder, Position

Grid cells in the entorhinal cortex encode the position of an animal in its
environment using spatially periodic tuning curves of varying periodicity.
Recent experiments established that these cells are functionally organized in
discrete modules with uniform grid spacing. Here we develop a theory for
efficient coding of position, which takes into account the temporal statistics
of the animal's motion. The theory predicts a sharp decrease of module
population sizes with grid spacing, in agreement with the trends seen in the
experimental data. We identify a simple scheme for readout of the grid cell
code by neural circuitry, that can match in accuracy the optimal Bayesian
decoder of the spikes. This readout scheme requires persistence over varying
timescales, ranging from ~1ms to ~1s, depending on the grid cell module. Our
results suggest that the brain employs an efficient representation of position
which takes advantage of the spatiotemporal statistics of the encoded variable,
in similarity to the principles that govern early sensory coding.
Memory Recall and Spike Frequency Adaptation
Date: 2016-03-14 00:00:00
Tasks: Retrieval

The brain can reproduce memories from partial data; this ability is critical
for memory recall. The process of memory recall has been studied using
auto-associative networks such as the Hopfield model. This kind of model
reliably converges to stored patterns which contain the memory. However, it is
unclear how the behavior is controlled by the brain so that after convergence
to one configuration, it can proceed with recognition of another one. In the
Hopfield model this happens only through unrealistic changes of an effective
global temperature that destabilizes all stored configurations. Here we show
that spike frequency adaptation (SFA), a common mechanism affecting neuron
activation in the brain, can provide state dependent control of pattern
retrieval. We demonstrate this in a Hopfield network modified to include SFA,
and also in a model network of biophysical neurons. In both cases SFA allows
for selective stabilization of attractors with different basins of attraction,
and also for temporal dynamics of attractor switching that is not possible in
standard auto-associative schemes. The dynamics of our models give a plausible
account of different sorts of memory retrieval.
Credit risk: Taking fluctuating asset correlations into account
Date: 2016-01-12 00:00:00

In structural credit risk models, default events and the ensuing losses are
both derived from the asset values at maturity. Hence it is of utmost
importance to choose a distribution for these asset values which is in
accordance with empirical data. At the same time, it is desirable to still
preserve some analytical tractability. We achieve both goals by putting forward
an ensemble approach for the asset correlations. Consistently with the data, we
view them as fluctuating quantities, for which we may choose the average
correlation as homogeneous. Thereby we can reduce the number of parameters to
two, the average correlation between assets and the strength of the
fluctuations around this average value. Yet, the resulting asset value
distribution describes the empirical data well. This allows us to derive the
distribution of credit portfolio losses. With Monte-Carlo simulations for the
Value at Risk and Expected Tail Loss we validate the assumptions of our
approach and demonstrate the necessity of taking fluctuating correlations into
account.
Hidden Thermodynamic Information in Protein Amino Acid Mutation Tables
Date: 2016-01-11 00:00:00

We combine the standard 1992 20x20 substitution matrix based on block
alignment, BLOSUM62, with the standard 1982 amino acid hydropathicity scale KD
as well as the modern 2007 hydropathicity scale MZ, and compare the results.
The 20-parameter KD and MZ hydropathicity scales have different thermodynamic
character, corresponding to first and second order transitions. The KD and MZ
comparisons show that the mutation rates reflect quantitative iteration of
qualitative amino acid phobic and philic binary 2x10 properties that define
quaternary 4x5 subgroups (but not quinary 5x4 subgroups), with the modern MZ
bioinformatic scale giving much better results. The quaternary 5 mer MZ 4x5
subgroups are called mutons (Mu5).
Iontophoretic transdermal drug delivery: a multi-layered approach
Date: 2016-11-11 00:00:00

We present a multi-layer mathematical model to describe the the transdermal
drug release from an iontophoretic system. The Nernst-Planck equation describe
the basic convection-diffusion process, with the electric potential obtained by
Laplace equation. These equations are complemented with suitable interface and
boundary conditions in a multi-domain. The stability of the mathematical
problem is discussed in different scenarios and a finite-difference method is
used to solve the coupled system. Numerical experiments are included to
illustrate the drug dynamics under different conditions.
On a law of large numbers for insurance risks
Date: 2016-01-13 00:00:00

This note presents a kind of the strong law of large numbers for an insurance
risk caused by a single catastrophic event rather than by an accumulation of
independent and identically distributed risks. We derive this result by a large
diversification effect resulting from optimal allocation of the risk to many
reinsurers or investors.
A modified galactose network model with implications for growth
Date: 2016-01-13 00:00:00

The yeast galactose network has provided many insights into how eukaryotic
gene circuits regulate metabolic function. However, there is currently no
consensus model of the network that incorporates protein dilution due to
cellular growth. We address this by adapting a well-known model and having it
account for growth benefit and burden due to expression of the network
proteins. Modifying the model to incorporate galactose transport and basal
Gal1p production allows us to better reproduce experimental observations.
Incorporating the growth rate effect demonstrates how the native network can
optimize growth in different galactose environments. These findings advance our
quantitative understanding of this gene network, and implement a general
approach for analysing the balance between growth costs and benefits in a range
of metabolic control networks.
Bursty and persistent properties of large-scale brain networks revealed with a point-based method for dynamic functional connectivity
Date: 2016-01-13 00:00:00
Tasks: Functional Connectivity, Sensitivity, Time Series, Time Series Analysis

In this paper, we present a novel and versatile method to study the dynamics
of resting-state fMRI brain connectivity with a high temporal sensitivity.
Whereas most existing methods often rely on dividing the time-series into
larger segments of data (i.e. so called sliding window techniques), the
point-based method (PBM) proposed here provides an estimate of brain
connectivity at the level of individual sampled time-points. The achieved
increase in temporal sensitivity, together with temporal graph network theory
allowed us to study functional integration between, as well as within,
resting-state networks. Our results show that functional integrations between
two resting-state networks predominately occurs in bursts of activity with
intermittent periods of less connectivity, whereas the functional connectivity
within resting-state networks is characterized by a tonic/periodic connectivity
pattern. Moreover, the point-based approach allowed us to estimate the
persistency of brain connectivity, i.e. the duration of the intrinsic trace or
memory of resting-state connectivity patterns. The described point-based method
of dynamic resting-state functional connectivity allows for a detailed and
expanded view on the temporal dynamics of resting-state connectivity that
provides novel insights into how neuronal information processing is integrated
in the human brain at the level of large-scale networks.
Growth against entropy in bacterial metabolism: the phenotypic trade-off behind empirical growth rate distributions in E. coli
Date: 2016-05-27 00:00:00

The solution space of genome-scale models of cellular metabolism provides a
map between physically viable flux configurations and cellular metabolic
phenotypes described, at the most basic level, by the corresponding growth
rates. By sampling the solution space of E. coli's metabolic network, we show
that empirical growth rate distributions recently obtained in experiments at
single-cell resolution can be explained in terms of a trade-off between the
higher fitness of fast-growing phenotypes and the higher entropy of
slow-growing ones. Based on this, we propose a minimal model for the evolution
of a large bacterial population that captures this trade-off. The scaling
relationships observed in experiments encode, in such frameworks, for the same
distance from the maximum achievable growth rate, the same degree of growth
rate maximization, and/or the same rate of phenotypic change. Being grounded on
genome-scale metabolic network reconstructions, these results allow for
multiple implications and extensions in spite of the underlying conceptual
simplicity.
A Novel Matrix Representation of Discrete Biomedical Signals
Date: 2016-01-13 00:00:00
Tasks: Seizure Detection

In this work we propose a novel symmetric square matrix representation of one
or more digital signals of finite equal length. For appropriate window length
and sliding paradigm this matrix contains useful information about the signals
in a two dimensional image form. Then this representation can be treated either
as an algebraic matrix or as a geometric image. We have shown applications of
both on human multichannel intracranial electroencephalogram (iEEG). In the
first application we have shown that for certain patients the highest
eigenvalue of the matrix obtained from the epileptic focal channels goes up
during a seizure. The focus of this paper is on an application of the second
concept, by which we have come up with an automatic seizure detection algorithm
on a publicly available benchmark data. Except for delay in detection in all
other aspects the new algorithm outperformed the detection performance based on
a support vector machine based algorithm. We have also indicated how this
sparse random matrix representation of brain electrical signals can encode the
activities of the brain.
Estimating intrinsic and extrinsic noise from single-cell gene expression measurements
Date: 2016-01-13 00:00:00

Gene expression is stochastic and displays variation ("noise") both within
and between cells. Intracellular (intrinsic) variance can be distinguished from
extracellular (extrinsic) variance by applying the law of total variance to
data from two-reporter assays that probe expression of identical gene pairs in
single-cells. We examine established formulas for the estimation of intrinsic
and extrinsic noise and provide interpretations of them in terms of a
hierarchical model. This allows us to derive corrections that minimize the mean
squared error, an objective that may be important when sample sizes are small.
The statistical framework also highlights the need for quantile normalization,
and provides justification for the use of the sample correlation between the
two reporter expression levels to estimate the percent contribution of
extrinsic noise to the total noise. Finally, we provide a geometric
interpretation of these results that clarifies the current interpretation.
Identifier Namespaces in Mathematical Notation
Authors: Grigorev Alexey
Date: 2016-01-13 00:00:00

In this thesis, we look at the problem of assigning each identifier of a
document to a namespace. At the moment, there does not exist a special dataset
where all identifiers are grouped to namespaces, and therefore we need to
create such a dataset ourselves.
  To do that, we need to find groups of documents that use identifiers in the
same way. This can be done with cluster analysis methods. We argue that
documents can be represented by the identifiers they contain, and this approach
is similar to representing textual information in the Vector Space Model.
Because of this, we can apply traditional document clustering techniques for
namespace discovery.
  Because the problem is new, there is no gold standard dataset, and it is hard
to evaluate the performance of our method. To overcome it, we first use Java
source code as a dataset for our experiments, since it contains the namespace
information. We verify that our method can partially recover namespaces from
source code using only information about identifiers.
  The algorithms are evaluated on the English Wikipedia, and the proposed
method can extract namespaces on a variety of topics. After extraction, the
namespaces are organized into a hierarchical structure by using existing
classification schemes such as MSC, PACS and ACM. We also apply it to the
Russian Wikipedia, and the results are consistent across the languages.
  To our knowledge, the problem of introducing namespaces to mathematics has
not been studied before, and prior to our work there has been no dataset where
identifiers are grouped into namespaces. Thus, our result is not only a good
start, but also a good indicator that automatic namespace discovery is
possible.
Quantile hedging on markets with proportional transaction costs
Date: 2016-01-13 00:00:00

In the paper a problem of risk measures on a discrete-time market model with
transaction costs is studied. Strategy effectiveness and shortfall risk is
introduced. This paper is a generalization of quantile hedging presented in
[4].
Large losses - probability minimizing approach
Date: 2016-01-13 00:00:00

The probability minimizing problem of large losses of portfolio in discrete
and continuous time models is studied. This gives a generalization of quantile
hedging presented in [3].
Modeling Eating Behaviors: the Role of Environment and Positive Food Association Learning via a Ratatouille Effect
Date: 2015-12-13 00:00:00
Tasks: Nutrition

Eating behaviors among a large population of children are studied as a
dynamic process driven by nonlinear interactions in the sociocultural school
environment. The impact of food association learning on diet dynamics, inspired
by a pilot study conducted among Arizona children in Pre-Kindergarten to 8th
grades, is used to build simple population-level learning models.
Qualitatively, mathematical studies are used to highlight the possible
ramifications of instruction, learning in nutrition, and health at the
community level. Model results suggest that nutrition education programs at the
population-level have minimal impact on improving eating behaviors, findings
that agree with prior field studies. Hence, the incorporation of food
association learning may be a better strategy for creating resilient
communities of healthy and non-healthy eaters. A \textit{Ratatouille} effect
can be observed when food association learners become food preference learners,
a potential sustainable behavioral change, which in turn, may impact the
overall distribution of healthy eaters. In short, this work evaluates the
effectiveness of population-level intervention strategies and the importance of
institutionalizing nutrition programs that factor in economical, social,
cultural, and environmental elements that mesh well with the norms and values
in the community.
Question Answering on Linked Data: Challenges and Future Directions
Authors: Shekarpour Saeedeh, Lukovnikov Denis, Kumar Ashwini Jaya, Endris Kemele, Singh Kuldeep, Thakkar Harsh, Lange Christoph
Date: 2016-02-16 00:00:00
Tasks: Question Answering

Question Answering (QA) systems are becoming the inspiring model for the
future of search engines. While recently, underlying datasets for QA systems
have been promoted from unstructured datasets to structured datasets with
highly semantic-enriched metadata, but still question answering systems involve
serious challenges which cause to be far beyond desired expectations. In this
paper, we raise the challenges for building a Question Answering (QA) system
especially with the focus of employing structured data (i.e. knowledge graph).
This paper provide an exhaustive insight of the known challenges, so far. Thus,
it helps researchers to easily spot open rooms for the future research agenda.