pwc-archive/papers-with-abstracts huggingface dataset:
Development and cytotoxic response of two proliferative MDA- MB-231 and non-proliferative SUM1315 three-dimensional cell culture models of triple-negative basal-like breast cancer cell lines
Date: 2017-11-29 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction, Prognosis

Triple-Negative Basal-Like tumors, representing 15 to 20% of breast cancers,
are very aggressive and with poor prognosis. Targeted therapies have been
developed extensively in preclinical and clinical studies to open the way for
new treatment strategies. The present study has focused on developing 3D cell
cultures from SUM1315 and MDA-MB-231, two triple-negative basal-like (TNBL)
breast cancer cell lines, using the liquid overlay technique. Extracellular
matrix concentration, cell density, proliferation, cell viability, topology and
ultrastructure parameters were determined. The results showed that for both
cell lines, the best conditioning regimen for compact and homogeneous spheroid
formation was to use 1000 cells per well and 2% Geltrex. This conditioning
regimen highlighted two 3D cell models: non-proliferative SUM1315 spheroids and
proliferative MDA-MB-231 spheroids. In both cell lines, the comparison of 2D vs
3D cell culture viability in the presence of increasing concentrations of
chemotherapeutic agents i.e. cisplatin, docetaxel and epirubicin, showed that
spheroids were clearly less sensitive than monolayer cell cultures. Moreover, a
proliferative or non-proliferative 3D cell line property would enable
determination of cytotoxic and/or cytostatic drug activity. 3D cell culture
could be an excellent tool in addition to the arsenal of techniques currently
used in preclinical studies. http://www.impactjournals.com/oncotarget/
Oncotarget, Advance Publications 2017
A Random Attention Model
Date: 2019-08-29 00:00:00
Tasks: model

This paper illustrates how one can deduce preference from observed choices
when attention is not only limited but also random. In contrast to earlier
approaches, we introduce a Random Attention Model (RAM) where we abstain from
any particular attention formation, and instead consider a large class of
nonparametric random attention rules. Our model imposes one intuitive
condition, termed Monotonic Attention, which captures the idea that each
consideration set competes for the decision-maker's attention. We then develop
revealed preference theory within RAM and obtain precise testable implications
for observable choice probabilities. Based on these theoretical findings, we
propose econometric methods for identification, estimation, and inference of
the decision maker's preferences. To illustrate the applicability of our
results and their concrete empirical content in specific settings, we also
develop revealed preference theory and accompanying econometric methods under
additional nonparametric assumptions on the consideration set for binary choice
problems. Finally, we provide general purpose software implementation of our
estimation and inference results, and showcase their performance using
simulations.
Enhancing Binomial and Trinomial Equity Option Pricing Models
Date: 2017-12-10 00:00:00

We extend the classical Cox-Ross-Rubinstein binomial model in two ways. We
first develop a binomial model with time-dependent parameters that equate all
moments of the pricing tree increments with the corresponding moments of the
increments of the limiting It\^o price process. Second, we introduce a new
trinomial model in the natural (historical) world, again fitting all moments of
the pricing tree increments to the corresponding geometric Brownian motion. We
introduce the risk-neutral trinomial tree and derive a hedging strategy based
on an additional perpetual derivative used as a second asset for hedging in any
node of the trinomial pricing tree.
SneakPeek: Interest Mining of Images based on User Interaction
Authors: Shahrokhian Daniyal, de Juan Alejandro Vera
Date: 2017-12-10 00:00:00

Nowadays, eye tracking is the most used technology to detect areas of
interest. This kind of technology requires specialized equipment recording
user's eyes. In this paper, we propose SneakPeek, a different approach to
detect areas of interest on images displayed in web pages based on the zooming
and panning actions of the users through the image. We have validated our
proposed solution with a group of test subjects that have performed a test in
our on-line prototype. Being this the first iteration of the algorithm, we have
found both good and bad results, depending on the type of image. In specific,
SneakPeek works best with medium/big objects in medium/big sized images. The
reason behind it is the limitation on detection when smartphone screens keep
getting bigger and bigger. SneakPeek can be adapted to any website by simply
adapting the controller interface for the specific case.
Precluding Oscillations in Michaelis-Menten Approximations of Dual-site Phosphorylation Systems
Date: 2018-10-31 00:00:00

Oscillations play a major role in a number of biological systems, from
predator-prey models of ecology to circadian clocks. In this paper we focus on
the question of whether oscillations exist within dual-site phosphorylation
systems. Previously, Wang and Sontag showed, using monotone systems theory,
that the Michaelis-Menten (MM) approximation of the distributive and sequential
dual-site phosphorylation system lacks oscillations. However, biological
systems are generally not purely distributive; there is generally some
processive behavior as well. Accordingly, this paper focuses on the MM
approximation of a general sequential dual-site phosphorylation system that
contains both processive and distributive components, termed the composite
system. Expanding on the methods of Bozeman and Morales, we preclude
oscillations in the MM approximation of the composite system. This implies the
lack of oscillations in the MM approximations of the processive and
distributive systems, shown previously, as well as in the MM approximation of
the partially processive and partially distributive mixed-mechanism system.
Interactions between Health Searchers and Search Engines
Authors: Philipp George, White Ryen W.
Date: 2017-12-11 00:00:00

The Web is an important resource for understanding and diagnosing medical
conditions. Based on exposure to online content, people may develop undue
health concerns, believing that common and benign symptoms are explained by
serious illnesses. In this paper, we investigate potential strategies to mine
queries and searcher histories for clues that could help search engines choose
the most appropriate information to present in response to exploratory medical
queries. To do this, we performed a longitudinal study of health search
behavior using the logs of a popular search engine. We found that query
variations which might appear innocuous (e.g. "bad headache" vs "severe
headache") may hold valuable information about the searcher which could be used
by search engines to improve performance. Furthermore, we investigated how
medically concerned users respond differently to search engine result pages
(SERPs) and find that their disposition for clicking on concerning pages is
pronounced, potentially leading to a self-reinforcement of concern. Finally, we
studied to which degree variations in the SERP impact future search and
real-world health-seeking behavior and obtained some surprising results (e.g.,
viewing concerning pages may lead to a short-term reduction of real-world
health seeking).
Revisiting the determinacy on New Keynesian Models: A survey
Authors: Alberto F. Boix, Adrián Segura Moreiras
Date: 2017-12-11 00:00:00
Tasks: Survey

The goal of this paper is to review some analytic techniques that are potentially useful to shed light on the determinacy question that arises in New Keynesian models as result of a combination of several monetary policy rules; in these models, we provide conditions to guarantee existence and uniqueness of equilibrium by means of results that are obtained from theoretical analysis. In particular, these methods confirm the well known fact that Taylor--like rules in interest rate setting are not the only way to reach determinacy of the rational expectations equilibrium in the New Keynesian setting. The key technical tool we use for that purposes is the so--called Budan--Fourier Theorem, that we review along the paper. All the ideas and techniques presented have been already used, our contribution that might be original here are the organization and emphasis.
Forecasting of a Hierarchical Functional Time Series on Example of Macromodel for Day and Night Air Pollution in Silesia Region: A Critical Overview
Date: 2017-11-25 00:00:00
Tasks: Time Series, Time Series Analysis

In economics we often face a system, which intrinsically imposes a structure
of hierarchy of its components, i.e., in modelling trade accounts related to
foreign exchange or in optimization of regional air protection policy.
  A problem of reconciliation of forecasts obtained on different levels of
hierarchy has been addressed in the statistical and econometric literature for
many times and concerns bringing together forecasts obtained independently at
different levels of hierarchy.
  This paper deals with this issue in case of a hierarchical functional time
series. We present and critically discuss a state of art and indicate
opportunities of an application of these methods to a certain environment
protection problem. We critically compare the best predictor known from the
literature with our own original proposal. Within the paper we study a
macromodel describing a day and night air pollution in Silesia region divided
into five subregions.
Rigorous mathematical analysis of the quasispecies model: From Manfred Eigen to the recent developments
Date: 2017-12-11 00:00:00

We review the major progress in the rigorous analysis of the classical
quasispecies model that usually comes in two related but different forms: the
Eigen model and the Crow--Kimura model. The model itself was formulated almost
50 years ago, and in its stationary form represents an easy to formulate
eigenvalue problem. Notwithstanding the simplicity of the problem statement, we
still lack full understanding of the behavior of the mean population fitness
and the quasispecies distribution for an arbitrary fitness landscape. Our main
goal in this review is two-fold: First, to highlight a number of impressive
mathematical results, including some of the recent ones, which pertain to the
mathematical development of the quasispecies theory. Second, to emphasize that,
despite these 50 years of vigorous research, there are still very natural both
biological and mathematical questions that remain to be addressed within the
quasispecies framework. Our hope is that at least some of the approaches we
review in this text can be of help for anyone embarking on further analysis of
the quasispecies model.
The impact of hydrodynamic interactions on protein folding rates depends on temperature
Date: 2017-12-12 00:00:00
Tasks: Friction, Protein Folding

We investigated the impact of hydrodynamic interactions (HI) on protein
folding using a coarse-grained model. The extent of the impact of hydrodynamic
interactions, whether it accelerates, retards, or has no effect on protein
folding, has been controversial. Together with a theoretical framework of the
energy landscape theory (ELT) for protein folding that describes the dynamics
of the collective motion with a single reaction coordinate across a folding
barrier, we compared the kinetic effects of HI on the folding rates of two
protein models that use a chain of single beads with distinctive topologies: a
64-residue alpha/beta chymotrypsin inhibitor 2 (CI2) protein, and a 57-residue
beta-barrel alpha-spectrin src-Homology 3 domain (SH3) protein. When comparing
the protein folding kinetics simulated with Brownian dynamics in the presence
of HI to that in the absence of HI, we find that the effect of HI on protein
folding appears to have a crossover behavior about the folding temperature.
Meaning that at a temperature greater than the folding temperature, the
enhanced friction from the hydrodynamic solvents between the beads in an
unfolded configuration results in lowered folding rate; conversely, at a
temperature lower than the folding temperature, HI accelerates folding by the
backflow of solvent toward the native folded state. Additionally, the extent of
acceleration depends on the topology of a protein: for a protein like CI2,
where its folding nucleus is rather diffuse in a transition state, HI channels
the formation of contacts by favoring a major folding pathway in a complex free
energy landscape, thus accelerating folding. For a protein like SH3, where its
folding nucleus is already specific and less diffuse, HI matters less at a
temperature lower than the folding temperature. Our findings provide further
theoretical insight to protein folding kinetic experiments and simulations.
Interdependence Theory of Tissue Failure: Bulk and Boundary Effects
Date: 2017-12-11 00:00:00

The mortality rate of many complex multicellular organisms increase with age,
which suggests that net aging damage is accumulative, despite remodeling
processes. But how exactly do little mishaps in the cellular level accumulate
and spread to become a systemic catastrophe? To address this question we
present experiments with synthetic tissues, an analytical model consistent with
experiments, and a number of implications that follow the analytical model. Our
theoretical framework describes how shape, curvature and density influences the
propagation of failure in a tissue subject to oxidative damage. We propose that
aging is an emergent property governed by interaction between cells, and that
intercellular processes play a role that are at least as important as
intracellular ones.
Fast Nearest-Neighbor Classification using RNN in Domains with Large Number of Classes
Authors: Singh Gautam, Dasgupta Gargi, Deng Yu
Date: 2017-12-11 00:00:00
Tasks: General Classification, text-classification, Text Classification

In scenarios involving text classification where the number of classes is
large (in multiples of 10000s) and training samples for each class are few and
often verbose, nearest neighbor methods are effective but very slow in
computing a similarity score with training samples of every class. On the other
hand, machine learning models are fast at runtime but training them adequately
is not feasible using few available training samples per class. In this paper,
we propose a hybrid approach that cascades 1) a fast but less-accurate
recurrent neural network (RNN) model and 2) a slow but more-accurate
nearest-neighbor model using bag of syntactic features.
  Using the cascaded approach, our experiments, performed on data set from IT
support services where customer complaint text needs to be classified to return
top-$N$ possible error codes, show that the query-time of the slow system is
reduced to $1/6^{th}$ while its accuracy is being improved. Our approach
outperforms an LSH-based baseline for query-time reduction. We also derive a
lower bound on the accuracy of the cascaded model in terms of the accuracies of
the individual models. In any two-stage approach, choosing the right number of
candidates to pass on to the second stage is crucial. We prove a result that
aids in choosing this cutoff number for the cascaded system.
Learning Based Segmentation of CT Brain Images: Application to Post-Operative Hydrocephalic Scans
Date: 2017-12-11 00:00:00
Tasks: Anatomy, Brain Segmentation, Dictionary Learning, Segmentation

Objective: Hydrocephalus is a medical condition in which there is an abnormal
accumulation of cerebrospinal fluid (CSF) in the brain. Segmentation of brain
imagery into brain tissue and CSF (before and after surgery, i.e. pre-op vs.
postop) plays a crucial role in evaluating surgical treatment. Segmentation of
pre-op images is often a relatively straightforward problem and has been well
researched. However, segmenting post-operative (post-op) computational
tomographic (CT)-scans becomes more challenging due to distorted anatomy and
subdural hematoma collections pressing on the brain. Most intensity and feature
based segmentation methods fail to separate subdurals from brain and CSF as
subdural geometry varies greatly across different patients and their intensity
varies with time. We combat this problem by a learning approach that treats
segmentation as supervised classification at the pixel level, i.e. a training
set of CT scans with labeled pixel identities is employed. Methods: Our
contributions include: 1.) a dictionary learning framework that learns class
(segment) specific dictionaries that can efficiently represent test samples
from the same class while poorly represent corresponding samples from other
classes, 2.) quantification of associated computation and memory footprint, and
3.) a customized training and test procedure for segmenting post-op
hydrocephalic CT images. Results: Experiments performed on infant CT brain
images acquired from the CURE Children's Hospital of Uganda reveal the success
of our method against the state-of-the-art alternatives. We also demonstrate
that the proposed algorithm is computationally less burdensome and exhibits a
graceful degradation against number of training samples, enhancing its
deployment potential.
Physical epistatic landscape of antibody binding affinity
Date: 2017-12-11 00:00:00
Tasks: Specificity

Affinity maturation produces antibodies that bind antigens with high
specificity by accumulating mutations in the antibody sequence. Mapping out the
antibody-antigen affinity landscape can give us insight into the accessible
paths during this rapid evolutionary process. By developing a carefully
controlled null model for noninteracting mutations, we characterized epistasis
in affinity measurements of a large library of antibody variants obtained by
Tite-Seq, a recently introduced Deep Mutational Scan method yielding physical
values of the binding constant. We show that representing affinity as the
binding free energy minimizes epistasis. Yet, we find that epistatically
interacting sites contribute substantially to binding. In addition to negative
epistasis, we report a large amount of beneficial epistasis, enlarging the
space of high-affinity antibodies as well as their mutational accessibility.
These properties suggest that the degeneracy of antibody sequences that can
bind a given antigen is enhanced by epistasis - an important property for
vaccine design.
The Calculus of Democratization and Development
Date: 2017-12-12 00:00:00

In accordance with "Democracy's Effect on Development: More Questions than
Answers", we seek to carry out a study in following the description in the
'Questions for Further Study.' To that end, we studied 33 countries in the
Sub-Saharan Africa region, who all went through an election which should signal
a "step-up" for their democracy, one in which previously homogenous regimes
transfer power to an opposition party that fairly won the election. After doing
so, liberal-democracy indicators and democracy indicators were evaluated in the
five years prior to and after the election took place, and over that ten-year
period, we examine the data for trends. If we see positive or negative trends
over this time horizon, we are able to conclude that it was the recent increase
in the quality of their democracy which led to it. Having investigated examples
of this in depth, there seem to be three main archetypes which drive the
results. Countries with positive results to their democracy from the election
have generally positive effects on their development, countries with more
"plateau" like results also did well, but countries for whom the descent to
authoritarianism was continued by this election found more negative results.
On the existence of a cherry-picking sequence
Date: 2017-12-12 00:00:00

Recently, the minimum number of reticulation events that is required to
simultaneously embed a collection P of rooted binary phylogenetic trees into a
so-called temporal network has been characterized in terms of cherry-picking
sequences. Such a sequence is a particular ordering on the leaves of the trees
in P. However, it is well-known that not all collections of phylogenetic trees
have a cherry-picking sequence. In this paper, we show that the problem of
deciding whether or not P has a cherry-picking sequence is NP-complete for when
P contains at least eight rooted binary phylogenetic trees. Moreover, we use
automata theory to show that the problem can be solved in polynomial time if
the number of trees in P and the number of cherries in each such tree are
bounded by a constant.
Identifiability of tree-child phylogenetic networks under a probabilistic recombination-mutation model of evolution
Date: 2017-12-12 00:00:00

Phylogenetic networks are an extension of phylogenetic trees which are used
to represent evolutionary histories in which reticulation events (such as
recombination and hybridization) have occurred. A central question for such
networks is that of identifiability, which essentially asks under what
circumstances can we reliably identify the phylogenetic network that gave rise
to the observed data? Recently, identifiability results have appeared for
networks relative to a model of sequence evolution that generalizes the
standard Markov models used for phylogenetic trees. However, these results are
quite limited in terms of the complexity of the networks that are considered.
In this paper, by introducing an alternative probabilistic model for evolution
along a network that is based on some ground-breaking work by Thatte for
pedigrees, we are able to obtain an identifiability result for a much larger
class of phylogenetic networks (essentially the class of so-called tree-child
networks). To prove our main theorem, we derive some new results for
identifying tree-child networks combinatorially, and then adapt some techniques
developed by Thatte for pedigrees to show that our combinatorial results imply
identifiability in the probabilistic setting. We hope that the introduction of
our new model for networks could lead to new approaches to reliably construct
phylogenetic networks.
A geometric model of multi-scale orientation preference maps via Gabor functions
Date: 2017-12-12 00:00:00

In this paper we present a new model for the generation of orientation
preference maps in the primary visual cortex (V1), considering both orientation
and scale features. First we undertake to model the functional architecture of
V1 by interpreting it as a principal fiber bundle over the 2-dimensional
retinal plane by introducing intrinsic variables orientation and scale. The
intrinsic variables constitute a fiber on each point of the retinal plane and
the set of receptive profiles of simple cells is located on the fiber. Each
receptive profile on the fiber is mathematically interpreted as a rotated Gabor
function derived from an uncertainty principle. The visual stimulus is lifted
in a 4-dimensional space, characterized by coordinate variables, position,
orientation and scale, through a linear filtering of the stimulus with Gabor
functions. Orientation preference maps are then obtained by mapping the
orientation value found from the lifting of a noise stimulus onto the
2-dimensional retinal plane. This corresponds to a Bargmann transform in the
reducible representation of the $\text{SE}(2)=\mathbb{R}^2\times S^1$ group. A
comparison will be provided with a previous model based on the Bargman
transform in the irreducible representation of the $\text{SE}(2)$ group,
outlining that the new model is more physiologically motivated. Then we present
simulation results related to the construction of the orientation preference
map by using Gabor filters with different scales and compare those results to
the relevant neurophysiological findings in the literature.