pwc-archive/papers-with-abstracts huggingface dataset:
Law-invariant functionals on general spaces of random variables
Authors: Fabio Bellini, Pablo Koch-Medina, Cosimo Munari, Gregor Svindland
Date: 2018-08-02 00:00:00

We establish general versions of a variety of results for quasiconvex, lower-semicontinuous, and law-invariant functionals. Our results extend well-known results from the literature to a large class of spaces of random variables. We sometimes obtain sharper versions, even for the well-studied case of bounded random variables. Our approach builds on two fundamental structural results for law-invariant functionals: the equivalence of law invariance and Schur convexity, i.e., monotonicity with respect to the convex stochastic order, and the fact that a law-invariant functional is fully determined by its behaviour on bounded random variables. We show how to apply these results to provide a unifying perspective on the literature on law-invariant functionals, with special emphasis on quantile-based representations, including Kusuoka representations, dilatation monotonicity, and infimal convolutions.
Brief increases in corticosterone affect morphology, stress responses, and telomere length, but not post-fledging movements, in a wild songbird
Date: 2018-07-31 00:00:00

Organisms are frequently exposed to challenges during development, such as
poor weather and food shortage. Such challenges can initiate the hormonal
stress response, which involves secretion of glucocorticoids. Although the
hormonal stress response helps organisms deal with challenges, long-term
exposure to high levels of glucocorticoids can have morphological, behavioral,
and physiological consequences, especially during development. Glucocorticoids
are also associated with reduced survival and telomere shortening. To
investigate whether brief, acute exposures to glucocorticoids can also produce
these phenotypic effects in free-living birds, we exposed wild tree swallow
(Tachycineta bicolor) nestlings to a brief exogenous dose of cort once per day
for five days and then measured their morphology, baseline and stress-induced
corticosterone levels, and telomere length. We also deployed radio tags on a
subset of nestlings, which allowed us to determine the age at which tagged
nestlings left the nest (fledged) and their pattern of presence and absence at
the natal site during the post-breeding period. Corticosterone-treated
nestlings had lower mass, higher baseline and stress-induced corticosterone,
and reduced telomeres; other metrics of morphology were affected weakly or not
at all. Our treatment resulted in no significant effect on survival to
fledging, fledge age, or age at first departure from the natal site, and we
found no negative effect of corticosterone on inter-annual return rate. These
results show that brief acute corticosterone exposure during development can
have measurable effects on phenotype in free-living tree swallows.
Corticosterone may therefore mediate correlations between rearing environment
and phenotype in developing organisms, even in the absence of prolonged
stressors.
Infinite dimensional portfolio representation as applied to model points selection in life insurance
Authors: Enrico Ferri
Date: 2018-08-02 00:00:00

We consider the problem of seeking an optimal set of model points associated to a fixed portfolio of life insurance policies. Such an optimal set is characterized by minimizing a certain risk functional, which gauges the average discrepancy with the fixed portfolio in terms of the fluctuation of the interest rate term structure within a given time horizon. We prove a representation theorem which provides two alternative formulations of the risk functional and which may be understood in connection with the standard approaches for the portfolio immunization based on sensitivity analysis. For this purpose, a general framework concerning some techniques of stochastic integration in Banach space and Malliavin calculus is introduced. A numerical example is discussed when considering a portfolio of whole life policies.
Adaptive l1-regularization for short-selling control in portfolio selection
Date: 2018-07-27 00:00:00

We consider the l1-regularized Markowitz model, where a l1-penalty term is
added to the objective function of the classical mean-variance one to stabilize
the solution process, promoting sparsity in the solution. The l1-penalty term
can also be interpreted in terms of short sales, on which several financial
markets have posed restrictions. The choice of the regularization parameter
plays a key role to obtain optimal portfolios that meet the financial
requirements. We propose an updating rule for the regularization parameter in
Bregman iteration to control both the sparsity and the number of short
positions. We show that the modified scheme preserves the properties of the
original one. Numerical tests are reported, which show the effectiveness of the
approach.
Parameter identifiability of a respiratory mechanics model in an idealized preterm infant
Date: 2020-01-15 00:00:00
Tasks: parameter estimation, Sensitivity

The complexity of mathematical models describing respiratory mechanics has
grown in recent years to integrate with cardiovascular models and incorporate
nonlinear dynamics. However, additional model complexity has rarely been
studied in the context of patient-specific observable data. This study
investigates parameter identification of a previously developed nonlinear
respiratory mechanics model (Ellwein Fix, PLoS ONE 2018) tuned to the
physiology of 1 kg preterm infant, using local deterministic sensitivity
analysis, subset selection, and gradient-based optimization. The model consists
of 4 differential state equations with 31 parameters to predict airflow and
dynamic pulmonary volumes and pressures generated under six simulation
conditions. The relative sensitivity solutions of the model state equations
with respect to each of the parameters were calculated with finite differences
and a sensitivity ranking was created for each parameter and simulation. Subset
selection identified a set of independent parameters that could be estimated
for all six simulations. The combination of these analyses produced a subset of
6 independent sensitive parameters that could be estimated given idealized
clinical data. All optimizations performed using pseudo-data with perturbed
nominal parameters converged within 40 iterations and estimated parameters
within ~8% of nominal values on average. This analysis indicates the
feasibility of performing parameter estimation on real patient-specific data
set described by a nonlinear respiratory mechanics model for studying dynamics
in preterm infants.
Efficient Two-Dimensional Line Spectrum Estimation Based on Decoupled Atomic Norm Minimization
Date: 2018-10-11 00:00:00
Tasks: Astronomy, Super-Resolution

This paper presents an efficient optimization technique for gridless {2-D}
line spectrum estimation, named decoupled atomic norm minimization (D-ANM). The
framework of atomic norm minimization (ANM) is considered, which has been
successfully applied in 1-D problems to allow super-resolution frequency
estimation for correlated sources even when the number of snapshots is highly
limited. The state-of-the-art 2-D ANM approach vectorizes the 2-D measurements
to their 1-D equivalence, which incurs huge computational cost and may become
too costly for practical applications. We develop a novel decoupled approach of
2-D ANM via semi-definite programming (SDP), which introduces a new matrix-form
atom set to naturally decouple the joint observations in both dimensions
without loss of optimality. Accordingly, the original large-scale 2-D problem
is equivalently reformulated via two decoupled one-level Toeplitz matrices,
which can be solved by simple 1-D frequency estimation with pairing. Compared
with the conventional vectorized approach, the proposed D-ANM technique reduces
the computational complexity by several orders of magnitude with respect to the
problem size. It also retains the benefits of ANM in terms of precise signal
recovery, small number of required measurements, and robustness to source
correlation. The complexity benefits are particularly attractive for
large-scale antenna systems such as massive MIMO, radar signal processing and
radio astronomy.
Cable Diagnostics with Power Line Modems for Smart Grid Monitoring
Date: 2019-02-22 00:00:00

Remote monitoring of electrical cable conditions is an essential
characteristic of the next-generation smart grid, which features the ability to
consistently surveil and control the grid infrastructure. In this paper, we
propose a technique that harnesses power line modems (PLMs) as sensors for
monitoring cable health. We envisage that all or most of these PLMs have
already been deployed for data communication purposes and focus on the
distribution grid or neighborhood area networks in the smart grid. For such a
setting, we propose a machine learning (ML) based framework for automatic cable
diagnostics by continuously monitoring the cable status to identify, assess,
and locate possible degradations. As part of our technique, we also synthesize
state-of-the-art reflectometry methods within the PLMs to extract beneficial
features for effective performance of our proposed ML solution. Simulation
results demonstrate the effectiveness of our solution under different aging
conditions and varying load configurations. Finally, we reflect on our proposed
diagnostics method by evaluating its robustness and comparing it with existing
alternatives.
The role of industry, occupation, and location specific knowledge in the survival of new firms
Date: 2018-07-23 00:00:00

How do regions acquire the knowledge they need to diversify their economic
activities? How does the migration of workers among firms and industries
contribute to the diffusion of that knowledge? Here we measure the industry,
occupation, and location-specific knowledge carried by workers from one
establishment to the next using a dataset summarizing the individual work
history for an entire country. We study pioneer firms--firms operating in an
industry that was not present in a region--because the success of pioneers is
the basic unit of regional economic diversification. We find that the growth
and survival of pioneers increase significantly when their first hires are
workers with experience in a related industry, and with work experience in the
same location, but not with past experience in a related occupation. We compare
these results with new firms that are not pioneers and find that
industry-specific knowledge is significantly more important for pioneer than
non-pioneer firms. To address endogeneity we use Bartik instruments, which
leverage national fluctuations in the demand for an activity as shocks for
local labor supply. The instrumental variable estimates support the finding
that industry-related knowledge is a predictor of the survival and growth of
pioneer firms. These findings expand our understanding of the micro-mechanisms
underlying regional economic diversification events.
Built to Last: Functional and structural mechanisms in the moth olfactory network mitigate effects of neural injury
Date: 2020-09-11 00:00:00

Most organisms suffer neuronal damage throughout their lives, which can
impair performance of core behaviors. Their neural circuits need to maintain
function despite injury, which in particular requires preserving key system
outputs. In this work, we explore whether and how certain structural and
functional neuronal network motifs act as injury mitigation mechanisms.
Specifically, we examine how (i) Hebbian learning, (ii) high levels of noise,
and (iii) parallel inhibitory and excitatory connections contribute to the
robustness of the olfactory system in the Manduca sexta moth. We simulate
injuries on a detailed computational model of the moth olfactory network
calibrated to in vivo data. The injuries are modeled on focal axonal swellings,
a ubiquitous form of axonal pathology observed in traumatic brain injuries and
other brain disorders. Axonal swellings effectively compromise spike train
propagation along the axon, reducing the effective neural firing rate delivered
to downstream neurons. All three of the network motifs examined significantly
mitigate the effects of injury on readout neurons, either by reducing injury's
impact on readout neuron responses or by restoring these responses to
pre-injury levels. These motifs may thus be partially explained by their value
as adaptive mechanisms to minimize the functional effects of neural injury.
More generally, robustness to injury is a vital design principle to consider
when analyzing neural systems.
A Deep Learning based Joint Segmentation and Classification Framework for Glaucoma Assesment in Retinal Color Fundus Images
Authors: Chakravarty Arunava, Sivswamy Jayanthi
Date: 2018-07-29 00:00:00
Tasks: Diagnostic

Automated Computer Aided diagnostic tools can be used for the early detection
of glaucoma to prevent irreversible vision loss. In this work, we present a
Multi-task Convolutional Neural Network (CNN) that jointly segments the Optic
Disc (OD), Optic Cup (OC) and predicts the presence of glaucoma in color fundus
images. The CNN utilizes a combination of image appearance features and
structural features obtained from the OD-OC segmentation to obtain a robust
prediction. The use of fewer network parameters and the sharing of the CNN
features for multiple related tasks ensures the good generalizability of the
architecture, allowing it to be trained on small training sets. The
cross-testing performance of the proposed method on an independent validation
set acquired using a different camera and image resolution was found to be good
with an average dice score of 0.92 for OD, 0.84 for OC and AUC of 0.95 on the
task of glaucoma classification illustrating its potential as a mass screening
tool for the early detection of glaucoma.
Coverage Error Optimal Confidence Intervals for Local Polynomial Regression
Authors: Sebastian Calonico, Matias D. Cattaneo, Max H. Farrell
Date: 2018-08-04 00:00:00
Tasks: regression

This paper studies higher-order inference properties of nonparametric local polynomial regression methods under random sampling. We prove Edgeworth expansions for $t$ statistics and coverage error expansions for interval estimators that (i) hold uniformly in the data generating process, (ii) allow for the uniform kernel, and (iii) cover estimation of derivatives of the regression function. The terms of the higher-order expansions, and their associated rates as a function of the sample size and bandwidth sequence, depend on the smoothness of the population regression function, the smoothness exploited by the inference procedure, and on whether the evaluation point is in the interior or on the boundary of the support. We prove that robust bias corrected confidence intervals have the fastest coverage error decay rates in all cases, and we use our results to deliver novel, inference-optimal bandwidth selectors. The main methodological results are implemented in companion \textsf{R} and \textsf{Stata} software packages.
Steady State Reduction of generalized Lotka-Volterra systems in the microbiome
Date: 2019-02-11 00:00:00

The generalized Lotka-Volterra (gLV) equations, a classic model from
theoretical ecology, describe the population dynamics of a set of interacting
species. As the number of species in these systems grow in number, their
dynamics become increasingly complex and intractable. We introduce Steady State
Reduction (SSR), a method that reduces a gLV system of many ecological species
into two-dimensional (2D) subsystems that each obey gLV dynamics and whose
basis vectors are steady states of the high-dimensional model. We apply this
method to an experimentally-derived model of the gut microbiome in order to
observe the transition between "healthy" and "diseased" microbial states.
Specifically, we use SSR to investigate how fecal microbiota transplantation, a
promising clinical treatment for dysbiosis, can revert a diseased microbial
state to health.
Frontiers in Pigment Cell and Melanoma Research
Date: 2018-08-24 00:00:00
Tasks: Epidemiology

We identify emerging frontiers in clinical and basic research of melanocyte
biology and its associated biomedical disciplines. We describe challenges and
opportunities in clinical and basic research of normal and diseased melanocytes
that impact current approaches to research in melanoma and the dermatological
sciences. We focus on four themes: (1) clinical melanoma research, (2) basic
melanoma research, (3) clinical dermatology, and (4) basic pigment cell
research, with the goal of outlining current highlights, challenges, and
frontiers associated with pigmentation and melanocyte biology. Significantly,
this document encapsulates important advances in melanocyte and melanoma
research including emerging frontiers in melanoma immunotherapy, medical and
surgical oncology, dermatology, vitiligo, albinism, genomics and systems
biology, epidemiology, pigment biophysics and chemistry, and evolution.
An analysis of high-frequency cryptocurrencies prices dynamics using permutation-information-theory quantifiers
Date: 2018-07-26 00:00:00

This paper discusses the dynamics of intraday prices of twelve
cryptocurrencies during last months' boom and bust. The importance of this
study lies on the extended coverage of the cryptoworld, accounting for more
than 90\% of the total daily turnover. By using the complexity-entropy
causality plane, we could discriminate three different dynamics in the data
set. Whereas most of the cryptocurrencies follow a similar pattern, there are
two currencies (ETC and ETH) that exhibit a more persistent stochastic
dynamics, and two other currencies (DASH and XEM) whose behavior is closer to a
random walk. Consequently, similar financial assets, using blockchain
technology, are differentiated by market participants.
Computational modeling approaches in gonadotropin signaling
Date: 2018-07-31 00:00:00
Tasks: Drug Discovery

Follicle-stimulating hormone (FSH) and luteinizing hormone (LH) play
essential roles in animal reproduction. They exert their function through
binding to their cognate receptors, which belong to the large family of G
protein-coupled receptors (GPCRs). This recognition at the plasma membrane
triggers a plethora of cellular events, whose processing and integration
ultimately lead to an adapted biological response. Understanding the nature and
the kinetics of these events is essential for innovative approaches in drug
discovery. The study and manipulation of such complex systems requires the use
of computational modeling approaches combined with robust in vitro functional
assays for calibration and validation. Modeling brings a detailed understanding
of the system and can also be used to understand why existing drugs do not work
as well as expected, and how to design more efficient ones.
Human Luteinizing Hormone and Chorionic Gonadotropin Display Biased Agonism at the LH and LH/CG Receptors
Date: 2018-07-31 00:00:00

Human luteinizing hormone (LH) and chorionic gonadotropin (hCG) have been
considered biologically equivalent because of their structural similarities and
their binding to the same receptor; the LH/CGR. However, accumulating evidence
suggest that LH/CGR differentially responds to the two hormones triggering
differential intracellular signaling and steroidogenesis. The mechanistic basis
of such differential responses remains mostly unknown. Here, we compared the
abilities of recombinant rhLH and rhCG to elicit cAMP, \beta-arrestin 2
activation, and steroidogenesis in HEK293 cells and mouse Leydig tumor cells
(mLTC-1). For this, BRET and FRET technologies were used allowing quantitative
analyses of hormone activities in real-time and in living cells. Our data
indicate that rhLH and rhCG differentially promote cell responses mediated by
LH/CGR revealing interesting divergences in their potencies, efficacies and
kinetics: rhCG was more potent than rhLH in both HEK293 and mLTC-1 cells.
Interestingly, partial effects of rhLH were found on \beta-arrestin recruitment
and on progesterone production compared to rhCG. Such a link was further
supported by knockdown experiments. These pharmacological differences
demonstrate that rhLH and rhCG act as natural biased agonists. The discovery of
novel mechanisms associated with gonadotropin-specific action may ultimately
help improve and personalize assisted reproduction technologies
Follicle-stimulating hormone receptor: Advances and remaining challenges
Date: 2018-07-31 00:00:00

Follicle-stimulating hormone (FSH) is produced in the pituitary and is
essential for reproduction. It specifically binds to a membrane receptor (FSHR)
expressed in somatic cells of the gonads. The FSH/FSHR system presents many
peculiarities compared to classical G protein-coupled receptors (GPCRs). FSH is
a large naturally heterogeneous heterodimeric glycoprotein. The FSHR is
characterized by a very large NH2-terminal extracellular domain, which binds
the FSH and participates to the activation/inactivation switch of the receptor.
Once activated, the FSHR couples to G{\alpha}s and, in some instances, to other
G{\alpha} subunits. G protein-coupled receptor kinases and \beta-arrestins are
also recruited to the FSHR and account for its desensitization, the control of
its trafficking and its intracellular signalling. Of note, the FSHR
internalization and recycling are very fast and involve very early endosomes
instead of early endosomes. All the transduction mechanisms triggered upon FSH
stimulation lead to the activation of a complex signalling network that
controls gene expression by acting at multiple levels. The integration of these
mechanisms leads to context-adapted responses from the target gonadal cells,
but also indirectly affects the fate of germ cells. Depending of the
physiological/developmental stage, FSH elicits proliferation, differentiation
or apoptosis in order to maintain the homeostasis of the reproductive system.
Pharmacological tools targeting FSHR recently came to the fore and open
promising prospects both for basic research and therapeutic applications. This
paper provides an updated review of the most salient aspects and peculiarities
of FSHR biology and pharmacology.
Discovering Latent Information By Spreading Activation Algorithm For Document Retrieval
Authors: Ngo Vuong M.
Date: 2018-07-29 00:00:00
Tasks: Information Retrieval, Retrieval

Syntactic search relies on keywords contained in a query to find suitable
documents. So, documents that do not contain the keywords but contain
information related to the query are not retrieved. Spreading activation is an
algorithm for finding latent information in a query by exploiting relations
between nodes in an associative network or semantic network. However, the
classical spreading activation algorithm uses all relations of a node in the
network that will add unsuitable information into the query. In this paper, we
propose a novel approach for semantic text search, called
query-oriented-constrained spreading activation that only uses relations
relating to the content of the query to find really related information.
Experiments on a benchmark dataset show that, in terms of the MAP measure, our
search engine is 18.9% and 43.8% respectively better than the syntactic search
and the search using the classical constrained spreading activation.
  KEYWORDS: Information Retrieval, Ontology, Semantic Search, Spreading
Activation