pwc-archive/papers-with-abstracts huggingface dataset:
Spatial gene drives and pushed genetic waves
Date: 2017-08-22 00:00:00

Gene drives have the potential to rapidly replace a harmful wild-type allele
with a gene drive allele engineered to have desired functionalities. However,
an accidental or premature release of a gene drive construct to the natural
environment could damage an ecosystem irreversibly. Thus, it is important to
understand the spatiotemporal consequences of the super-Mendelian population
genetics prior to potential applications. Here, we employ a reaction-diffusion
model for sexually reproducing diploid organisms to study how a locally
introduced gene drive allele spreads to replace the wild-type allele, even
though it possesses a selective disadvantage $s>0$. Using methods developed by
N. Barton and collaborators, we show that socially responsible gene drives
require $0.5<s<0.697$, a rather narrow range. In this "pushed wave" regime, the
spatial spreading of gene drives will be initiated only when the initial
frequency distribution is above a threshold profile called "critical
propagule", which acts as a safeguard against accidental release. We also study
how the spatial spread of the pushed wave can be stopped by making gene drives
uniquely vulnerable ("sensitizing drive") in a way that is harmless for a
wild-type allele. Finally, we show that appropriately sensitized drives in two
dimensions can be stopped even by imperfect barriers perforated by a series of
gaps.
Exploring the relationship between technological improvement and innovation diffusion: An empirical test
Date: 2018-05-31 00:00:00

Different technological domains have significantly different rates of
performance improvement. Prior theory indicates that such differing rates
should influence the relative speed of diffusion of the products embodying the
different technologies since improvement in performance during the diffusion
process increases the desirability of the product diffusing. However, there has
not been a broad empirical attempt to examine this effect and to clarify the
underlying cause. Therefore, this paper reviews the theoretical basis and
focuses upon empirical tests of this effect across multiple products and their
underlying technologies. The results for 18 different diffusing products show
the expected relationship-faster diffusion for products based on more rapidly
improving technological domains- between technological improvement and
diffusion with strong statistical significance. The empirical examination also
demonstrates that technological improvement does not slow down in the latter
parts of diffusion when penetration does slow down. This finding indicates that
diffusion slow down in the latter stages is due to market saturation effects
and is not due to slowdown of performance improvement.
Loklak - A Distributed Crawler and Data Harvester for Overcoming Rate Limits
Authors: Singanamalla Sudheesh, Christen Michael Peter
Date: 2017-04-12 00:00:00

Modern social networks have become sources for vast quantities of data.
Having access to such big data can be very useful for various researchers and
data scientists. In this paper we describe Loklak, an open source distributed
peer to peer crawler and scraper for supporting such research on platforms like
Twitter, Weibo and other social networks. Social networks such as Twitter and
Weibo pose various limitations to the user on the rate at which one could
freely collect such data for research. Our crawler enables researchers to
continuously collect data while overcoming the barriers of authentication and
rate limits imposed to provide a repository of open data as a service.
How brains are built: Principles of computational neuroscience
Date: 2017-03-29 00:00:00
Tasks: Astronomy

'If I cannot build it, I do not understand it.' So said Nobel laureate
Richard Feynman, and by his metric, we understand a bit about physics, less
about chemistry, and almost nothing about biology.
  When we fully understand a phenomenon, we can specify its entire sequence of
events, causes, and effects so completely that it is possible to fully simulate
it, with all its internal mechanisms intact. Achieving that level of
understanding is rare. It is commensurate with constructing a full design for a
machine that could serve as a stand-in for the thing being studied. To
understand a phenomenon sufficiently to fully simulate it is to understand it
computationally.
  'Computation' does not refer to computers per se. Rather, it refers to the
underlying principles and methods that make them work. As Turing Award
recipient Edsger Dijkstra said, computational science 'is no more about
computers than astronomy is about telescopes.' Computational science is the
study of the hidden rules underlying complex phenomena from physics to
psychology.
  Computational neuroscience, then, has the aim of understanding brains
sufficiently well to be able to simulate their functions, thereby subsuming the
twin goals of science and engineering: deeply understanding the inner workings
of our brains, and being able to construct simulacra of them. As simple robots
today substitute for human physical abilities, in settings from factories to
hospitals, so brain engineering will construct stand-ins for our mental
abilities, and possibly even enable us to fix our brains when they break.
Multivariate Multiscale Dispersion Entropy of Biomedical Times Series
Date: 2017-04-12 00:00:00
Tasks: Time Series, Time Series Analysis

Objective: Due to the non-linearity of numerous biomedical signals,
non-linear analysis of multi-channel time series, notably multivariate
multiscale entropy (mvMSE), has been extensively used in biomedical signal
processing. However, mvMSE has three drawbacks: 1) mvMSE values are either
undefined or unreliable for short signals; 2) mvMSE is not fast enough for
real-time applications; and 3) the computation of mvMSE for signals with a
large number of channels requires the storage of a huge number of elements.
Methods: To deal with these problems and improve the stability of mvMSE, we
introduce multivariate multiscale dispersion entropy (MDE - mvMDE) as an
extension of our recently developed MDE, to quantify the complexity of
multivariate time series. Results: We assess mvMDE, in comparison with mvMSE
and multivariate multiscale fuzzy entropy (mvMFE), on correlated and
uncorrelated multi-channel noise signals, bivariate autoregressive processes,
and three biomedical datasets. The results show that mvMDE takes into account
dependencies in patterns across both the time and spatial domains. The mvMDE,
mvMSE, and mvMFE methods are consistent in that they lead to similar
conclusions about the underlying physiological conditions. However, the
proposed mvMDE discriminates various physiological states of the biomedical
recordings better than mvMSE and mvMFE. In addition, for both the short and
long time series, the mvMDE-based results are noticeably more stable than the
mvMSE- and mvMFE-based ones. Conclusion: For short multivariate time series,
mvMDE, unlike mvMSE, does not result in undefined values. Furthermore, mvMDE is
noticeably faster than mvMFE and mvMSE and also needs to store a considerably
smaller number of elements. Significance: Due to its ability to detect
different kinds of dynamics of multivariate signals, mvMDE has great potential
to analyse various physiological signals.
Efficient and Effective Tail Latency Minimization in Multi-Stage Retrieval Systems
Authors: Mackenzie Joel, Culpepper J. Shane, Blanco Roi, Crane Matt, Clarke Charles L. A., Lin Jimmy
Date: 2017-04-20 00:00:00
Tasks: Retrieval

Scalable web search systems typically employ multi-stage retrieval
architectures, where an initial stage generates a set of candidate documents
that are then pruned and re-ranked. Since subsequent stages typically exploit a
multitude of features of varying costs using machine-learned models, reducing
the number of documents that are considered at each stage improves latency. In
this work, we propose and validate a unified framework that can be used to
predict a wide range of performance-sensitive parameters which minimize
effectiveness loss, while simultaneously minimizing query latency, across all
stages of a multi-stage search architecture. Furthermore, our framework can be
easily applied in large-scale IR systems, can be trained without explicitly
requiring relevance judgments, and can target a variety of different
efficiency-effectiveness trade-offs, making it well suited to a wide range of
search scenarios. Our results show that we can reliably predict a number of
different parameters on a per-query basis, while simultaneously detecting and
minimizing the likelihood of tail-latency queries that exceed a pre-specified
performance budget. As a proof of concept, we use the prediction framework to
help alleviate the problem of tail-latency queries in early stage retrieval. On
the standard ClueWeb09B collection and 31k queries, we show that our new hybrid
system can reliably achieve a maximum query time of 200 ms with a 99.99%
response time guarantee without a significant loss in overall effectiveness.
The solutions presented are practical, and can easily be used in large-scale
distributed search engine deployments with a small amount of additional
overhead.
Cardiac re-entry dynamics & self-termination in DT-MRI based model of Human Foetal Heart
Date: 2017-08-19 00:00:00
Tasks: Anatomy

The effect of heart geometry and anisotropy on cardiac re-entry dynamics and
self-termination is studied here in anatomically realistic computer simulations
of human foetal heart. 20 weeks of gestational age human foetal heart isotropic
and anisotropic anatomy models from diffusion tensor MRI data sets are used in
the computer simulations. The fibre orientation angles of the heart were
obtained from the DT-MRI primary eigenvalues. In a spatially homogeneous
electrophysiological mono domain model with the DT-MRI based heart geometries,
we initiate simplified Fitz-Hugh-Nagumo kinetics cardiac re-entry at a
prescribed location in a 2D slice, and in the full 3D anatomy model. In a slice
of the heart, the MRI based fibre anisotropy changes the re-entry dynamics from
pinned to anatomical re-entry. In the full 3D MRI based model, the foetal heart
fibre anisotropy changes the re-entry dynamics from a persistent re-entry to
the re-entry self-termination.
Split probabilities and species tree inference under the multispecies coalescent model
Date: 2017-04-13 00:00:00

Using topological summaries of gene trees as a basis for species tree
inference is a promising approach to obtain acceptable speed on genomic-scale
datasets, and to avoid some undesirable modeling assumptions. Here we study the
probabilities of splits on gene trees under the multispecies coalescent model,
and how their features might inform species tree inference. After investigating
the behavior of split consensus methods, we investigate split invariants ---
that is, polynomial relationships between split probabilities. These invariants
are then used to show that, even though a split is an unrooted notion, split
probabilities retain enough information to identify the rooted species tree
topology for trees of more than 5 taxa, with one possible 6-taxon exception.
An empirical behavioural order-driven model with price limit rules
Date: 2017-04-14 00:00:00

We develop an empirical behavioural order-driven (EBOD) model, which consists
of an order placement process and an order cancellation process. Price limit
rules are introduced in the definition of relative price. The order placement
process is determined by several empirical regularities: the long memory in
order directions, the long memory in relative prices, the asymmetric
distribution of relative prices, and the nonlinear dependence of the average
order size and its standard deviation on the relative price. Order cancellation
follows a Poisson process with the arrival rate determined from real data and
the cancelled order is determined according to the empirical distributions of
relative price level and relative position at the same price level. All these
ingredients of the model are derived based on the empirical microscopic
regularities in the order flows of stocks on the Shenzhen Stock Exchange. The
model is able to produce the main stylized facts in real markets. Computational
experiments uncover that asymmetric setting of price limits will cause the
stock price diverging exponentially when the up price limit is higher than the
down price limit and vanishing vice versus. We also find that asymmetric price
limits have influences on stylized facts. Our EBOD model provides a suitable
computational experiment platform for academics, market participants and policy
makers.
Crude oil market and geopolitical events: an analysis based on information-theory-based quantifiers
Date: 2017-04-14 00:00:00

This paper analyzes the informational efficiency of oil market during the
last three decades, and examines changes in informational efficiency with major
geopolitical events, such as terrorist attacks, financial crisis and other
important events. The series under study is the daily prices of West Texas
Intermediate (WTI) in USD/BBL, commonly used as a benchmark in oil pricing. The
analysis is performed using information-theory-derived quantifiers, namely
permutation entropy and permutation statistical complexity. These metrics allow
capturing the hidden structure in the market dynamics, and allow discriminating
different degrees of informational efficiency. We find that some geopolitical
events impact on the underlying dynamical structure of the market.
Label-free three-dimensional (3D) structural imaging in live cells using intrinsic optical refractive index
Date: 2017-04-14 00:00:00

Here we report a method for visualization of volumetric structural
information of live biological samples with no exogenous contrast agents. The
process is made possible through a technique that involves generation,
synthesis and analysis of three-dimensional (3D) Fourier components of light
diffracted by the sample. This leads to the direct recovery of quantitative
cellular morphology with no iterative procedures for reduced computational
complexity. Combing with the fact that the technique is easily adaptive to any
imaging platform and requires minimum sample preparation, our proposed method
is particularly promising for observing fast, volumetric and dynamic events
previously only accessible through staining methods.
Simplifying credit scoring rules using LVQ+PSO
Date: 2017-04-14 00:00:00
Tasks: Decision Making

One of the key elements in the banking industry rely on the appropriate
selection of customers. In order to manage credit risk, banks dedicate special
efforts in order to classify customers according to their risk. The usual
decision making process consists in gathering personal and financial
information about the borrower. Processing this information can be time
consuming, and presents some difficulties due to the heterogeneous structure of
data. We offer in this paper an alternative method that is able to classify
customers' profiles from numerical and nominal attributes. The key feature of
our method, called LVQ+PSO, is the finding of a reduced set of classifying
rules. This is possible, due to the combination of a competitive neural network
with an optimization technique. These rules constitute a predictive model for
credit risk approval. The reduced quantity of rules makes this method not only
useful for credit officers aiming to make quick decisions about granting a
credit, but also could act as borrower's self selection. Our method was applied
to an actual database of a credit consumer financial institution in Ecuador. We
obtain very satisfactory results. Future research lines are exposed.
Simulated Ablation for Detection of Cells Impacting Paracrine Signalling in Histology Analysis
Date: 2017-04-14 00:00:00

Intra-tumour phenotypic heterogeneity limits accuracy of clinical diagnostics
and hampers the efficiency of anti-cancer therapies. Dealing with this cellular
heterogeneity requires adequate understanding of its sources, which is
extremely difficult, as phenotypes of tumour cells integrate hardwired
(epi)mutational differences with the dynamic responses to microenvironmental
cues. The later come in form of both direct physical interactions, as well as
inputs from gradients of secreted signalling molecules. Furthermore, tumour
cells can not only receive microenvironmental cues, but also produce them.
Despite high biological and clinical importance of understanding spatial
aspects of paracrine signaling, adequate research tools are largely lacking.
Here, a partial differential equation (PDE) based mathematical model is
developed that mimics the process of cell ablation. This model suggests how
each cell might contribute to the microenvironment by either absorbing or
secreting diffusible factors, and quantifies the extent to which observed
intensities can be explained via diffusion mediated signalling. The model
allows for the separation of phenotypic responses to signalling gradients
within tumour microenvironments from the combined influence of responses
mediated by direct physical contact and hardwired (epi)genetic differences. The
differential equation is solved around cell membrane outlines using a finite
element method (FEM). The method is applied to a multi-channel
immunofluorescence in situ hybridization (iFISH) stained breast cancer
histological specimen and correlations are investigated between: HER2 gene
amplification; HER2 protein expression; and cell interaction with the
diffusible microenvironment. This approach allows partial deconvolution of the
complex inputs...
Model Uncertainty, Recalibration, and the Emergence of Delta-Vega Hedging
Date: 2017-04-14 00:00:00

We study option pricing and hedging with uncertainty about a Black-Scholes
reference model which is dynamically recalibrated to the market price of a
liquidly traded vanilla option. For dynamic trading in the underlying asset and
this vanilla option, delta-vega hedging is asymptotically optimal in the limit
for small uncertainty aversion. The corresponding indifference price
corrections are determined by the disparity between the vegas, gammas, vannas,
and volgas of the non-traded and the liquidly traded options.
Trilobite "pelotons": Possible hydrodynamic drag effects between leading and following trilobites in trilobite queues
Date: 2017-04-18 00:00:00
Tasks: Form

Energy saving mechanisms in nature allow following organisms to expend less
energy than leaders. Queues, or ordered rows of individuals, may form when
organisms exploit the available energy saving mechanism while travelling at
near-maximal sustainable metabolic capacities; compact clusters form when group
members travel well below maximal sustainable metabolic capacities. The group
size range, given here as the ratio of the difference between the size of the
largest and smallest group members, and the size of the largest member (as a
per cent), has been hypothesized to correspond proportionately to the energy
saving quantity because weaker, smaller, individuals sustain speeds of
stronger, larger, individuals by exploiting the energy saving mechanism (as a
per cent). During migration, small individuals outside this range may perish,
or form sub-groups, or simply not participate in migratory behavior. We
approximate drag forces for leading and following individuals in queues of the
late Devonian (approx. 370 Ma) trilobite Trimerocephalus chopini. Applying data
from literature of R. herculea, a living crustacean, we approximate the
hypothetical walking speed and maximal speeds for T. chopini. Findings
reasonably support the hypothesis: among the population of fossilized queues of
T. chopini reported by Kin and Blazejowski (2013), trilobite size range was 75%
while the size range within queues, was 63%; this corresponds reasonably with
drag reductions in following positions that permit approx. 61.5% energy saving
for trilobites following others in optimal low-drag positions. We model
collective trilobite behavior associated with hydrodynamic drafting.
Model of Brain Activation Predicts the Neural Collective Influence Map of the Brain
Date: 2017-04-15 00:00:00

Efficient complex systems have a modular structure, but modularity does not
guarantee robustness, because efficiency also requires an ingenious interplay
of the interacting modular components. The human brain is the elemental
paradigm of an efficient robust modular system interconnected as a network of
networks (NoN). Understanding the emergence of robustness in such modular
architectures from the interconnections of its parts is a long-standing
challenge that has concerned many scientists. Current models of dependencies in
NoN inspired by the power grid express interactions among modules with fragile
couplings that amplify even small shocks, thus preventing functionality.
Therefore, we introduce a model of NoN to shape the pattern of brain
activations to form a modular environment that is robust. The model predicts
the map of neural collective influencers (NCIs) in the brain, through the
optimization of the influence of the minimal set of essential nodes responsible
for broadcasting information to the whole-brain NoN. Our results suggest new
intervention protocols to control brain activity by targeting influential
neural nodes predicted by network theory.
Root Mean Square Error of Neural Spike Train Sequence Matching with Optogenetics
Date: 2017-08-21 00:00:00

Optogenetics is an emerging field of neuroscience where neurons are
genetically modified to express light-sensitive receptors that enable external
control over when the neurons fire. Given the prominence of neuronal signaling
within the brain and throughout the body, optogenetics has significant
potential to improve the understanding of the nervous system and to develop
treatments for neurological diseases. This paper uses a simple optogenetic
model to compare the timing distortion between a randomly-generated target
spike sequence and an externally-stimulated neuron spike sequence. The
distortion is measured by filtering each sequence and finding the root mean
square error between the two filter outputs. The expected distortion is derived
in closed form when the target sequence generation rate is sufficiently low.
Derivations are verified via simulations.
Excitable behaviors
Date: 2017-04-14 00:00:00
Tasks: Sensitivity

This chapter revisits the concept of excitability, a basic system property of
neurons. The focus is on excitable systems regarded as behaviors rather than
dynamical systems. By this we mean open systems modulated by specific
interconnection properties rather than closed systems classified by their
parameter ranges. Modeling, analysis, and synthesis questions can be formulated
in the classical language of circuit theory. The input-output characterization
of excitability is in terms of the local sensitivity of the current-voltage
relationship. It suggests the formulation of novel questions for non-linear
system theory, inspired by questions from experimental neurophysiology.
Measurement of Economic Growth, Development and Under Development: New Model and Application
Date: 2017-04-17 00:00:00

This paper presents a simple model to measure the relative economic growth of
economic systems. The model considers S-Shaped patterns of economic growth
that, represented with a linear model, measure how an economic system grows in
comparison with another one. In particular, this model introduces an approach
which indicates if the economic system has a process of economic growth,
development or under development. The application of the model is provided for
regions and macro regions of the Italian economic system.