pwc-archive/papers-with-abstracts huggingface dataset:
Promotion through Connections: Favors or Information?
Authors: Yann Bramoullé, Kenan Huremović
Date: 2017-08-23 00:00:00

Connections appear to be helpful in many contexts, such as obtaining a job, a promotion, a grant, a loan, or publishing a paper. This may be due either to favoritism or to information conveyed by connections. Attempts at identifying both effects have relied on measures of true quality, generally built from data collected long after promotion. Building on earlier work on discrimination, we propose a new method to identify favors and information from data collected at the time of promotion. Under weak assumptions, we show that promotion decisions for connected candidates look more random to the econometrician due to the information channel. We derive new identification results and estimate the strength of the two effects. We adapt the control function approach to address the issue of the selection into connections. Applying our methodology to academic promotions in Spain and Italy, as well as political advancements in China, we find evidence that connections may both convey information and attract favors.
Effective Blog Pages Extractor for Better UGC Accessing
Authors: Zhao Kui, Wang Yi, Hu Xia, Wang Can
Date: 2017-08-26 00:00:00

Blog is becoming an increasingly popular media for information publishing.
Besides the main content, most of blog pages nowadays also contain noisy
information such as advertisements etc. Removing these unrelated elements can
improves user experience, but also can better adapt the content to various
devices such as mobile phones. Though template-based extractors are highly
accurate, they may incur expensive cost in that a large number of template need
to be developed and they will fail once the template is updated. To address
these issues, we present a novel template-independent content extractor for
blog pages. First, we convert a blog page into a DOM-Tree, where all elements
including the title and body blocks in a page correspond to subtrees. Then we
construct subtree candidate set for the title and the body blocks respectively,
and extract both spatial and content features for elements contained in the
subtree. SVM classifiers for the title and the body blocks are trained using
these features. Finally, the classifiers are used to extract the main content
from blog pages. We test our extractor on 2,250 blog pages crawled from nine
blog sites with obviously different styles and templates. Experimental results
verify the effectiveness of our extractor.
Generative Models for Network Neuroscience: Prospects and Promise
Date: 2017-08-26 00:00:00

Network neuroscience is the emerging discipline concerned with investigating
the complex patterns of interconnections found in neural systems, and to
identify principles with which to understand them. Within this discipline, one
particularly powerful approach is network generative modeling, in which wiring
rules are algorithmically implemented to produce synthetic network
architectures with the same properties as observed in empirical network data.
Successful models can highlight the principles by which a network is organized
and potentially uncover the mechanisms by which it grows and develops. Here we
review the prospects and promise of generative models for network neuroscience.
We begin with a primer on network generative models, with a discussion of
compressibility and predictability, utility in intuiting mechanisms, and a
short history on their use in network science broadly. We then discuss
generative models in practice and application, paying particular attention to
the critical need for cross-validation. Next, we review generative models of
biological neural networks, both at the cellular and large-scale level, and
across a variety of species including \emph{C. elegans}, \emph{Drosophila},
mouse, rat, cat, macaque, and human. We offer a careful treatment of a few
relevant distinctions, including differences between generative models and null
models, sufficiency and redundancy, inferring and claiming mechanism, and
functional and structural connectivity. We close with a discussion of future
directions, outlining exciting frontiers both in empirical data collection
efforts as well as in method and theory development that, together, further the
utility of the generative network modeling approach for network neuroscience.
A Simple Algorithm for Solving Ramsey Optimal Policy with Exogenous Forcing Variables
Date: 2017-08-26 00:00:00

This algorithm extends Ljungqvist and Sargent (2012) algorithm of Stackelberg
dynamic game to the case of dynamic stochastic general equilibrium models
including exogenous forcing variables. It is based Anderson, Hansen, McGrattan,
Sargent (1996) discounted augmented linear quadratic regulator. It adds an
intermediate step in solving a Sylvester equation. Forward-looking variables
are also optimally anchored on forcing variables. This simple algorithm calls
for already programmed routines for Ricatti, Sylvester and Inverse matrix in
Matlab and Scilab. A final step using a change of basis vector computes a
vector auto regressive representation including Ramsey optimal policy rule
function of lagged observable variables, when the exogenous forcing variables
are not observable.
Effects of mindfulness on perceived stress levels and heart rate variability
Date: 2017-08-26 00:00:00
Tasks: Heart Rate Variability

Mindfulness has become increasingly popular as a method for building
resilience against stress in both clinical and healthy populations. This study
sought to investigate the effects of mindfulness training on perceived levels
of stress and heart rate variability in students.
A Trigger Sequence in a Leucine Zipper Aids its Dimerization; Simulation Results
Date: 2017-08-27 00:00:00

Leucine zippers are alpha helical monomers dimerized to a coiled coil
structure. Various scenarios for dimerization span interaction of unstructured
monomers that form alpha helices in the process of dimerization to preformed
alpha helical monomers dimerizing.Another suggested possibility, a trigger
sequence hypothesis (M. O. Steinmetz et al., Proc. Natl. Acad. Sci. U. S. A.
2007, 104, 7062-7067), is that a C terminal (CT) part of each monomer is a
trigger sequence that is dimerized and, subsequently, the remainder N terminal
(NT) part of each monomer (that is partially ordered) zips together to form the
coiled coil. In this work, methods are developed to computationally explore the
trigger sequence hypothesis based on an extension of a previously introduced
(R. I. Cukier, J. Chem. Phys. 2011, 134, 045104) Hamiltonian Temperature
Replica Exchange Method Mean Square (HTREM_MS) procedure, which scales the
Hamiltonian in both potential and kinetic energies, to enhance sampling
generically, along with additional restraints to enhance sampling in desired
regions of conformational space. The method is applied to a 31-residue
truncation of the 33-residue leucine zipper (GCN4-p1) of the yeast
transcriptional activator GCN4. Using a variety of HTREM_MS simulations, we
find that the NT of one monomer of the dimer becomes disordered when the NT
parts of the monomers are separated. In contrast, when the CT parts of the
monomers are separated, both monomers remain alpha helical. These simulations
suggest that the CT does act as a trigger sequence and are consistent with a
disordered NT. We also investigate whether the disordered NT can be induced to
re-form the dimerized leucine zipper. It does, but with some lack of recovery
of alpha helical hydrogen bonding structure in the NT.
Quantifying and Visualizing Hidden Preferential Aggregations Amid Heterogeneity
Date: 2017-08-27 00:00:00

Biological systems often exhibit a heterogeneous arrangement of objects, such
as assorted nuclear chromatin patterns in a tumor, assorted species of bacteria
in biofilms, or assorted aggregates of subcellular particles. Principle
Component Analysis (PCA) and Multiple Component Analysis (MCA) provide
information about which features in multidimensional data aggregate, but do not
provide in situ spatial information about these aggregations. This paper
outlines the Numericized Histogram Score (NHS) algorithm, which converts the
histogram distribution of shortest distances between objects into a continuous
variable that can be represented as a spatial heatmap. A histogram can be
transformed into an intensity value by assigning a weighting factor to each
sequential bin. Each object in an image can be replaced by its NHS value, which
when calibrated to a color scale results in a heatmap. These spatial heatmaps
reveal regions of aggregation amid heterogeneity that would otherwise mask
loco-regional spatial associations, which will be especially useful in the
field of digital pathology. In addition to visualizing aggregations as
heatmaps, the ability to calculate degrees of recurring patterns of aggregation
allows investigators to stratify samples for further insights into clinical
outcome, response to treatment, or omic subtypes (genomic, transcriptomic,
proteomic, metabolomic, etc.).
MSM lag time cannot be used for variational model selection
Date: 2017-08-27 00:00:00
Tasks: Model Selection

The variational principle for conformational dynamics has enabled the
systematic construction of Markov state models through the optimization of
hyperparameters by approximating the transfer operator. In this note we discuss
why lag time of the operator being approximated must be held constant in the
variational approach.
Determining Positive Cancer Rescue Mutations in p53 Based Cancers by using Artificial Intelligence
Date: 2017-08-28 00:00:00

A mutation in a protein-coding gene in DNA can alter the protein structure
coded by the same gene. Structurally altered proteins usually lose their
functions and sometimes gain an undesirable function instead. These types of
mutations and their effects can result in genetic diseases or antibiotic
resistant bacteria, among other health issues. Important curing methods have
been developed for detecting mutations against AIDS as well as genetic
diseases. Another example is the influenza virus. The reasons why a vaccination
developed to fight against influenza does not work the following year are (a)
the mutation of its DNA and (b) the outbreak of the virus after it has been
mutated especially if it is a virus that escaped the vaccinations target. Due
to such reasons, it is highly important to know in advance the location of a
potential mutation in a protein as well as the problems it might cause the
medical sciences. In this study we have used artificial neural networks, which
are one of the latest artificial intelligence technologies, to determine the
effects of cancer mutations. The model we developed has given more successful
results compared to other methods. We foresee that our model will bring a new
dimension to medical research and the medical industry.
An equilibrium-conserving taxation scheme for income from capital
Date: 2017-08-28 00:00:00

Under conditions of market equilibrium, the distribution of capital income
follows a Pareto power law, with an exponent that characterizes the given
equilibrium. Here, a simple taxation scheme is proposed such that the post-tax
capital income distribution remains an equilibrium distribution, albeit with a
different exponent. This taxation scheme is shown to be progressive, and its
parameters can be simply derived from (i) the total amount of tax that will be
levied, (ii) the threshold selected above which capital income will be taxed
and (iii) the total amount of capital income. The latter can be obtained either
by using Piketty's estimates of the capital/labor income ratio or by fitting
the initial Pareto exponent. Both ways moreover provide a check on the amount
of declared income from capital.
Isotachophoresis applied to chemical reactions
Date: 2017-08-28 00:00:00
Tasks: Cell Detection

This review discusses research developments and applications of
isotachophoresis (ITP) to the initiation, control, and acceleration of chemical
reactions, emphasizing reactions involving biomolecular reactants such as
nucleic acids, proteins, and live cells. ITP is a versatile technique which
requires no specific geometric design or material, and is compatible with a
wide range of microfluidic and automated platforms. Though ITP has
traditionally been used as a purification and separation technique, recent
years have seen its emergence as a method to automate and speed up chemical
reactions. ITP has been used to demonstrate up to 14,000-fold acceleration of
nucleic acid assays, and has been used to enhance lateral flow and other
immunoassays, and even whole bacterial cell detection assays. We here classify
these studies into two categories: homogeneous (all reactants in solution) and
heterogeneous (at least one reactant immobilized on a solid surface) assay
configurations. For each category, we review and describe physical modeling and
scaling of ITP-aided reaction assays, and elucidate key principles in ITP assay
design. We summarize experimental advances, and identify common threads and
approaches which researchers have used to optimize assay performance. Lastly,
we propose unaddressed challenges and opportunities that could further improve
these applications of ITP.
Default Contagion with Domino Effect , A First Passage Time Approach
Date: 2017-08-28 00:00:00

The present paper introduces a structural framework to model dependent
defaults, with a particular interest in their contagion.
It's Time to Consider "Time" when Evaluating Recommender-System Algorithms [Proposal]
Authors: Beel Joeran
Date: 2017-10-29 00:00:00
Tasks: Position, Recommendation Systems, Time Series, Time Series Analysis

In this position paper, we question the current practice of calculating
evaluation metrics for recommender systems as single numbers (e.g. precision
p=.28 or mean absolute error MAE = 1.21). We argue that single numbers express
only average effectiveness over a usually rather long period (e.g. a year or
even longer), which provides only a vague and static view of the data. We
propose that recommender-system researchers should instead calculate metrics
for time-series such as weeks or months, and plot the results in e.g. a line
chart. This way, results show how algorithms' effectiveness develops over time,
and hence the results allow drawing more meaningful conclusions about how an
algorithm will perform in the future. In this paper, we explain our reasoning,
provide an example to illustrate our reasoning and present suggestions for what
the community should do next.
How to disentangle psychobiological stress reactivity and recovery: A comparison of model-based and non-compartmental analyses of cortisol concentrations
Date: 2018-02-20 00:00:00

This article seeks to address the prevailing issue of how to measure specific
process components of psychobiological stress responses. Particularly the
change of cortisol secretion due to stress exposure has been discussed as an
endophenotype of many psychosomatic health outcomes. To assess its process
components, a large variety of non-compartmental parameters (i.e., composite
measures of substance concentrations at different points in time) like the area
under the concentration-time curve (AUC) are utilized. However, a systematic
evaluation and validation of these parameters based on a physiologically
plausible model of cortisol secretion has not been performed so far. Thus, a
population pharmacokinetic (mixed-effects SDE) model was developed and fitted
to densely sampled salivary cortisol data of 10 males from Montreal, Canada,
and sparsely sampled data of 200 mixed-sex participants from Dresden, Germany,
who completed the Trier Social Stress Test (TSST). Besides the two major
process components representing (1) stress-related cortisol secretion
(reactivity) and (2) cortisol elimination (recovery), the model incorporates
two additional, often disregarded components: (3) the secretory delay after
stress onset, and (4) deviations from the projected steady-state concentration.
The fitted model (R2 = 99%) was thereafter used to investigate the correlation
structure of the four individually varying, and readily interpretable model
parameters and eleven popular non-compartmental parameters. Based on these
analyses, we recommend to use the minimum-maximum cortisol difference and the
minimum concentration as proxy measures of reactivity and recovery,
respectively. Finally, statistical power analyses of the reactivity-related sex
effect illustrate the consequences of using impure non-compartmental measures
of the different process components that underlie the cortisol stress response.
Spontaneous Chiral Symmetry Breaking in Planar Polarized Epithelia
Date: 2017-08-28 00:00:00

Most animal body plans have some degree of left-right asymmetry. This
chirality at the tissue and organ level is often assumed to originate from the
intrinsic handedness of biological molecules. How this handedness might be
transferred from molecules to tissues during development, however, is not well
understood. Here we explore an alternative paradigm where tissue chirality
results from spontaneous symmetry breaking at the cellular scale, with
molecular chirality acting only as a weak bias that ensures that one handedness
predominates over the other. Specifically, we show that systems capable of
generating planar polarity, found in many epithelial tissues, can also
generically break left-right symmetry, and we identify the key interaction
parameters that must be varied to access the chiral phase. In addition to a
chiral polar phase corresponding to one found in liquid crystal films, a
two-dimensional chiral nematic phase with no liquid crystal analog is also
possible. Our results have clear implications for the interpretation of mutant
phenotypes, especially in certain Drosophila epithelia.
Identifying relationship lending in the interbank market: A network approach
Date: 2018-04-27 00:00:00

Relationship lending is broadly interpreted as a strong partnership between a
lender and a borrower. Nevertheless, we still lack consensus regarding how to
quantify the strength of a lending relationship, while simple statistics such
as the frequency and volume of loans have been used as proxies in previous
studies. Here, we propose statistical tests to identify relationship lending as
a significant tie between banks. Application of the proposed method to the
Italian interbank networks reveals that the fraction of relationship lending
among all bilateral trades has been quite stable and that the relationship
lenders tend to impose high interest rates at the time of financial distress.
Measurement of Common Risk Factors: A Panel Quantile Regression Model for Returns
Date: 2017-08-29 00:00:00
Tasks: quantile regression, regression

This paper investigates how to measure common market risk factors using newly
proposed Panel Quantile Regression Model for Returns. By exploring the fact
that volatility crosses all quantiles of the return distribution and using
penalized fixed effects estimator we are able to control for otherwise
unobserved heterogeneity among financial assets. Direct benefits of the
proposed approach are revealed in the portfolio Value-at-Risk forecasting
application, where our modeling strategy performs significantly better than
several benchmark models according to both statistical and economic comparison.
In particular Panel Quantile Regression Model for Returns consistently
outperforms all the competitors in the 5\% and 10\% quantiles. Sound
statistical performance translates directly into economic gains which is
demonstrated in the Global Minimum Value-at-Risk Portfolio and Markowitz-like
comparison. Overall results of our research are important for correct
identification of the sources of systemic risk, and are particularly attractive
for high dimensional applications.
Synchrony-induced modes of oscillation of a neural field model
Date: 2017-08-29 00:00:00

We investigate the modes of oscillation of heterogeneous ring-networks of
quadratic integrate-and-fire neurons with non-local, space-dependent coupling.
Perturbations of the equilibrium state with a particular wave number produce
transient standing waves with a specific frequency, analogous to those in a
tense string. In the neuronal network, the equilibrium corresponds to a
spatially homogeneous, asynchronous state. Perturbations of this state excite
the network's oscillatory modes, which reflect the interplay of episodes of
synchronous spiking with the excitatory-inhibitory spatial interactions. In the
thermodynamic limit, an exact low-dimensional neural field model describing the
macroscopic dynamics of the network is derived. This allows us to obtain
formulas for the Turing eigenvalues of the spatially-homogeneous state, and
hence to obtain its stability boundary. We find that the frequency of each
Turing mode depends on the corresponding Fourier coefficient of the synaptic
pattern of connectivity. The decay rate instead, is identical for all
oscillation modes as a consequence of the heterogeneity-induced
desynchronization of the neurons. Finally, we numerically compute the spectrum
of spatially-inhomogeneous solutions branching from the Turing bifurcation,
showing that similar oscillatory modes operate in neural bump states, and are
maintained away from onset.
Changing the Direction of the Economic and Demographic Research
Date: 2017-08-29 00:00:00

A simple but useful method of reciprocal values is introduced, explained and
illustrated. This method simplifies the analysis of hyperbolic distributions,
which are causing serious problems in the demographic and economic research. It
allows for a unique identification of hyperbolic distributions and for
unravelling components of more complicated trajectories. This method is
illustrated by a few examples. They show that fundamental postulates of the
demographic and economic research are contradicted by data, even by precisely
the same data, which are used in this research. The generally accepted
postulates are based on the incorrect understanding of hyperbolic
distributions, which characterise the historical growth of population and the
historical economic growth. In particular, data used, but never analysed,
during the formulation of the Unified Growth Theory show that this theory is
based on fundamentally incorrect premises and thus is fundamentally defective.
Application of this simple method of analysis points to new directions in the
demographic and economic research. It suggests simpler interpretations of the
mechanism of growth. The concept or the evidence of the past primitive and
difficult living conditions, which might be perhaps described as some kind of
stagnation, is not questioned or disputed. It is only demonstrated that
trajectories of the past economic growth and of the growth of population were
not reflecting any form of stagnation and thus that they were not shaped by
these primitive and difficult living conditions. The concept or evidence of an
explosion in technology, medicine, education and in the improved living
conditions is not questioned or disputed. It is only demonstrated that this
possible explosion is not reflected in the trajectories of the economic growth
and of the growth of population.