pwc-archive/papers-with-abstracts huggingface dataset:
Time-lagged Ordered Lasso for network inference
Date: 2018-12-10 00:00:00
Tasks: regression

Accurate gene regulatory networks can be used to explain the emergence of
different phenotypes, disease mechanisms, and other biological functions. Many
methods have been proposed to infer networks from gene expression data but have
been hampered by problems such as low sample size, inaccurate constraints, and
incomplete characterizations of regulatory dynamics. Since expression
regulation is dynamic, time-course data can be used to infer causality, but
these datasets tend to be short or sparsely sampled. In addition, temporal
methods typically assume that the expression of a gene at a time point depends
on the expression of other genes at only the immediately preceding time point,
while other methods include additional time points without any constraints to
account for their temporal distance. These limitations can contribute to
inaccurate networks with many missing and anomalous links.
  We adapted the time-lagged Ordered Lasso, a regularized regression method
with temporal monotonicity constraints, for \textit{de novo} reconstruction. We
also developed a semi-supervised method that embeds prior network information
into the Ordered Lasso to discover novel regulatory dependencies in existing
pathways. We evaluated these approaches on simulated data for a repressilator,
time-course data from past DREAM challenges, and a HeLa cell cycle dataset to
show that they can produce accurate networks subject to the dynamics and
assumptions of the time-lagged Ordered Lasso regression.
Using routinely collected patient data to support clinical trials research in accountable care organizations
Authors: McMurry Andrew J, Zhang Richen, Foxman Alex, Reiter Lawrence, Schnel Ronny, Brandman DeLeys
Date: 2018-06-25 00:00:00
Tasks: De-identification, Specificity

Background: More than half (57%) of pharma clinical research spend is in
support of clinical trials. One reason is that Electronic Health Record (EHR)
systems and HIPAA privacy rules often limit how broadly patient information can
be shared, resulting in laborious human efforts to manually collect,
de-identify, and summarize patient information for use in clinical studies.
  Purpose: Conduct feasibility study for a Rheumatoid Arthritis (RA) clinical
trial in an Accountable Care Organization. Measure prevalence of RA and related
conditions matching study criteria. Evaluate automation of patient
de-identification and summarization to support patient cohort development for
clinical studies.
  Methods: Collect original clinical documentation directly from the provider
EHR system and extract clinical concepts necessary for matching study criteria.
Automatically de-identify Protected Health Information (PHI) protect patient
privacy and promote sharing. Leverage existing physician expert knowledge
sources to enable analysis of patient populations.
  Results: Prevalence of RA was four percent (4%) in the study population (mean
age 53 years, 52% female, 48% male). Clinical documentation for 3500 patient
were extracted from three (3) EHR systems. Grouped diagnosis codes revealed
high prevalence of diabetes and diseases of the circulatory system, as
expected. De-identification accurately removed 99% of PHI identifiers with 99%
sensitivity and 99% specificity.
  Conclusions: Results suggest the approach can improve automation and
accelerate planning and construction of new clinical studies in the ACO
setting. De-identification accuracy was better than previously approved
requirements defined by four (4) hospital Institutional Review Boards.
Weakest-link control of invasive species: Impacts of memory, bounded rationality and network structure in repeated cooperative games
Date: 2018-06-29 00:00:00
Tasks: Decision Making, Management

The nature of dispersal of many invasive pests and pathogens in agricultural
and forestry makes it necessary to consider how the actions of one manager
affect neighbouring properties. In addition to the direct effects of a
potential spread of a pest and the resulting economic loss, there are also
indirect consequences that affect whole regions and that require coordinated
actions to manage and/or to eradicate it (like movement restrictions). In this
paper we address the emergence and stability of cooperation among agents who
respond to a threat of an invasive pest or disease. The model, based on the
weakest-link paradigm, uses repeated multi-participant coordination games where
players' pay-offs depend on management decisions to prevent the invasion on
their own land as well as of their neighbours on a network. We show that for
the basic cooperation game agents select the risk-dominant strategy of a Stag
hunt game over the pay-off dominant strategy of implementing control measures.
However, cooperation can be achieved by the social planner offering a
biosecurity payment. The critical level of this payment depends on the details
of the decision-making process, with higher trust (based on a reputation of
other agents reflecting their past performance) allowing a significant
reduction in necessary payments and slowing down decay in cooperation when the
payment is low. We also find that allowing for uncertainty in decision-making
process can enhance cooperation for low levels of payments. Finally, we show
the importance of industry structure to the emergence of cooperation, with
increase in the average coordination number of network nodes leading to
increase in the critical biosecurity payment.
hep-th
Authors: He Yang-Hui, Jejjala Vishnu, Nelson Brent D.
Date: 2018-06-27 00:00:00
Tasks: Binary Classification, Math

We apply techniques in natural language processing, computational
linguistics, and machine-learning to investigate papers in hep-th and four
related sections of the arXiv: hep-ph, hep-lat, gr-qc, and math-ph. All of the
titles of papers in each of these sections, from the inception of the arXiv
until the end of 2017, are extracted and treated as a corpus which we use to
train the neural network Word2Vec. A comparative study of common n-grams,
linear syntactical identities, word cloud and word similarities is carried out.
We find notable scientific and sociological differences between the fields. In
conjunction with support vector machines, we also show that the syntactic
structure of the titles in different sub-fields of high energy and mathematical
physics are sufficiently different that a neural network can perform a binary
classification of formal versus phenomenological sections with 87.1% accuracy,
and can perform a finer five-fold classification across all sections with 65.1%
accuracy.
Optimal investment and consumption with forward preferences and uncertain parameters
Authors: Wing Fung Chong, Gechun Liang
Date: 2018-07-03 00:00:00

This paper studies robust forward investment and consumption preferences within a zero-volatility context. Different from previous works, we consider an incomplete financial market model due to general investment portfolio constraints. We provide a new PDE characterization and a novel semi-explicit saddle-point construction of forward preferences and optimal strategies. We further present a more detailed construction of forward preferences and optimal strategies under constant relative risk aversion (CRRA). Key findings emphasize the necessity of a specific relationship between the initial investment preference and the forward consumption preference, indicating a long-term decreasing trend in forward consumption preference behavior.
Deep neural networks for non-linear model-based ultrasound reconstruction
Date: 2018-09-28 00:00:00

Ultrasound reflection tomography is widely used to image large complex
specimens that are only accessible from a single side, such as well systems and
nuclear power plant containment walls. Typical methods for inverting the
measurement rely on delay-and-sum algorithms that rapidly produce
reconstructions but with significant artifacts. Recently, model-based
reconstruction approaches using a linear forward model have been shown to
significantly improve image quality compared to the conventional approach.
However, even these techniques result in artifacts for complex objects because
of the inherent non-linearity of the ultrasound forward model.
  In this paper, we propose a non-iterative model-based reconstruction method
for inverting measurements that are based on non-linear forward models for
ultrasound imaging. Our approach involves obtaining an approximate estimate of
the reconstruction using a simple linear back-projection and training a deep
neural network to refine this to the actual reconstruction. We apply our method
to simulated ultrasound data and demonstrate dramatic improvements in image
quality compared to the delay-and-sum approach and the linear model-based
reconstruction approach.
Tonic activation of extrasynaptic NMDA receptors decreases intrinsic excitability and promotes bistability in a model of neuronal activity
Date: 2020-01-31 00:00:00

NMDA receptors (NMDA-R) typically contribute to excitatory synaptic
transmission in the central nervous system. While calcium influx through NMDA-R
plays a critical role in synaptic plasticity, experimental evidence indicates
that NMDAR-mediated calcium influx also modifies neuronal excitability through
the activation of calcium-activated potassium channels. This mechanism has not
yet been studied theoretically. Our theoretical model provides a simple
description of neuronal electrical activity that takes into account the tonic
activity of extrasynaptic NMDA receptors and a cytosolic calcium compartment.
We show that calcium influx mediated by the tonic activity of NMDA-R can be
coupled directly to the activation of calcium-activated potassium channels,
resulting in an overall inhibitory effect on neuronal excitability.
Furthermore, the presence of tonic NMDA-R activity promotes bistability in
electrical activity by dramatically increasing the stimulus interval where both
a stable steady state and repetitive firing can coexist. These results could
provide an intrinsic mechanism for the constitution of memory traces in
neuronal circuits. They also shed light on the way by which $\beta$-amyloids
can alter neuronal activity when interfering with NMDA-R in Alzheimer's disease
and cerebral ischemia.
Tensor Decomposition for EEG Signal Retrieval
Date: 2019-06-29 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Retrieval, Tensor Decomposition

Prior studies have proposed methods to recover multi-channel
electroencephalography (EEG) signal ensembles from their partially sampled
entries. These methods depend on spatial scenarios, yet few approaches aiming
to a temporal reconstruction with lower loss. The goal of this study is to
retrieve the temporal EEG signals independently which was overlooked in data
pre-processing. We considered EEG signals are impinging on tensor-based
approach, named nonlinear Canonical Polyadic Decomposition (CPD). In this
study, we collected EEG signals during a resting-state task. Then, we defined
that the source signals are original EEG signals and the generated tensor is
perturbed by Gaussian noise with a signal-to-noise ratio of 0 dB. The sources
are separated using a basic non-negative CPD and the relative errors on the
estimates of the factor matrices. Comparing the similarities between the source
signals and their recovered versions, the results showed significantly high
correlation over 95%. Our findings reveal the possibility of recoverable
temporal signals in EEG applications.
On the Identifying Content of Instrument Monotonicity
Date: 2019-10-16 00:00:00

This paper studies the identifying content of the instrument monotonicity
assumption of Imbens and Angrist (1994) on the distribution of potential
outcomes in a model with a binary outcome, a binary treatment and an exogenous
binary instrument. Specifically, I derive necessary and sufficient conditions
on the distribution of the data under which the identified set for the
distribution of potential outcomes when the instrument monotonicity assumption
is imposed can be a strict subset of that when it is not imposed.
Data-Driven Learning-Based Optimization for Distribution System State Estimation
Date: 2019-03-22 00:00:00
Tasks: Hybrid Machine Learning, State Estimation

Distribution system state estimation (DSSE) is a core task for monitoring and
control of distribution networks. Widely used algorithms such as Gauss-Netwon
perform poorly with the limited number of measurements typically available for
DSSE, often require many iterations to obtain reasonable results, and sometimes
fail to converge. DSSE is a non-convex problem, and working with a limited
number of measurements further aggravate the situation, as indeterminacy
induces multiple global (in addition to local) minima. Gauss-Newton is also
known to be sensitive to initialization. Hence, the situation is far from
ideal. It is therefore natural to ask if there is a smart way of initializing
Gauss-Newton that will avoid these DSSE-specific pitfalls. This paper proposes
using historical or simulation-derived data to train a shallow neural network
to `learn to initialize' -- that is, map the available measurements to a point
in the neighborhood of the true latent states (network voltages), which is used
to initialize Gauss-Newton. It is shown that this hybrid machine learning /
optimization approach yields superior performance in terms of stability,
accuracy, and runtime efficiency, compared to conventional optimization-only
approaches. It is also shown that judicious design of the neural network
training cost function helps to improve the overall DSSE performance.
Option Pricing with Heavy-Tailed Distributions of Logarithmic Returns
Authors: Lasko Basnarkov, Viktor Stojkoski, Zoran Utkovski, Ljupco Kocarev
Date: 2018-07-04 00:00:00

A growing body of literature suggests that heavy tailed distributions represent an adequate model for the observations of log returns of stocks. Motivated by these findings, here we develop a discrete time framework for pricing of European options. Probability density functions of log returns for different periods are conveniently taken to be convolutions of the Student's t-distribution with three degrees of freedom. The supports of these distributions are truncated in order to obtain finite values for the options. Within this framework, options with different strikes and maturities for one stock rely on a single parameter -- the standard deviation of the Student's t-distribution for unit period. We provide a study which shows that the distribution support width has weak influence on the option prices for certain range of values of the width. It is furthermore shown that such family of truncated distributions approximately satisfies the no-arbitrage principle and the put-call parity. The relevance of the pricing procedure is empirically verified by obtaining remarkably good match of the numerically computed values by our scheme to real market data.
Mechanisms of Calcium Leak from Cardiac Sarcoplasmic Reticulum Revealed by Statistical Mechanics
Authors: Anna Maltsev, Michael Stern, Victor Maltsev
Date: 2018-07-04 00:00:00

Heart muscle contraction is normally activated by a synchronized Ca release from sarcoplasmic reticulum (SR), a major intracellular Ca store. However, under abnormal conditions Ca leaks from the SR, decreasing heart contraction amplitude and increasing risk of life-threatening arrhythmia. The mechanisms and regimes of SR operation generating the abnormal Ca leak remain unclear. Here we employed both numerical and analytical modeling to get mechanistic insights into the emergent Ca leak phenomenon. Our numerical simulations using a detailed realistic model of Ca release unit (CRU) reveal sharp transitions resulting in Ca leak. The emergence of leak is closely mapped mathematically to the Ising model from statistical mechanics. The system steady-state behavior is determined by two aggregate parameters: the analogues of magnetic field ($h$) and the inverse temperature ($\beta$) in the Ising model, for which we have explicit formulas in terms of SR Ca and release channel opening/closing rates. The classification of leak regimes takes the shape of a phase $\beta$-$h$ diagram, with the regime boundaries occurring at $h$=0 and a critical value of $\beta$ ($\beta*$) which we estimate using a classical Ising model and mean field theory. Our theory predicts that a synchronized Ca leak will occur when $h$>0 and $\beta>\beta*$ and a disordered leak occurs when $\beta<\beta*$ and $h$ is not too negative. The disorder leak is distinguished from synchronized leak (in long-lasting sparks) by larger Peierls contour lengths, an output parameter reflecting degree of disorder. Thus, in addition to our detailed numerical model approach we also offer an instantaneous computational tool using analytical formulas of the Ising model for respective RyR parameters and SR Ca load that describe and classify phase transitions and leak emergence.
Disentangling basal and accrued height-for-age for cross-population comparisons
Date: 2019-08-17 00:00:00

Objectives: Current standards for comparing stunting across human populations
assume a universal model of child growth. Such comparisons ignore population
differences that are independent of deprivation and health outcomes. This paper
partitions variation in height-for-age that is specifically associated with
deprivation and health outcomes to provide a basis for cross-population
comparisons.
  Materials & Methods: Using a multi-level model with a sigmoid relationship of
resources and growth, we partition variation in height-for-age z-scores (HAZ)
from 1,522,564 children across 70 countries into two components: 1) "accrued
HAZ" shaped by environmental inputs (e.g., undernutrition, infectious disease,
inadequate sanitation, poverty), and 2) a country-specific "basal HAZ"
independent of such inputs. We validate these components against
population-level infant mortality rates, and assess how these basal differences
may affect cross-population comparisons of stunting.
  Results: Basal HAZ differs reliably across countries (range of 1.5 SD) and is
independent of measures of infant mortality. By contrast, accrued HAZ captures
stunting as impaired growth due to deprivation and is more closely associated
with infant mortality than observed HAZ. Ranking populations by accrued HAZ
suggest that populations in West Africa and the Caribbean suffer much greater
levels of stunting than suggested by observed HAZ.
  Discussion: Current universal standards may dramatically underestimate
stunting in populations with taller basal HAZ. Relying on observed HAZ rather
than accrued HAZ may also lead to inappropriate cross-population comparisons,
such as concluding that Haitian children enjoy better conditions for growth
than do Indian or Guatemalan children.
A theory for combinations of risk measures
Authors: Marcelo Brutti Righi
Date: 2018-07-05 00:00:00

We study combinations of risk measures under no restrictive assumption on the set of alternatives. We develop and discuss results regarding the preservation of properties and acceptance sets for the combinations of risk measures. One of the main results is the representation of resulting risk measures from the properties of both alternative functionals and combination functions. We build on developing a dual representation for an arbitrary mixture of convex risk measures. In this case, we obtain a penalty that recalls the notion of inf-convolution under theoretical measure integration. We develop results related to this specific context. We also explore features of individual interest generated by our frameworks, such as the preservation of continuity properties and the representation of worst-case risk measures.
Modelling Aspects of Planar Multi-Mode Antennas for Direction-of-Arrival Estimation
Date: 2019-05-29 00:00:00
Tasks: Direction of Arrival Estimation

Multi-mode antennas are an alternative to classical antenna arrays, and hence
a promising emerging sensor technology for a vast variety of applications in
the areas of array signal processing and digital communications. An unsolved
problem is to describe the radiation pattern of multi-mode antennas in closed
analytic form based on calibration measurements or on electromagnetic field
(EMF) simulation data. As a solution, we investigate two modeling methods: One
is based on the array interpolation technique (AIT), the other one on wavefield
modeling (WM). Both methods are able to accurately interpolate quantized EMF
data of a given multi-mode antenna, in our case a planar four-port antenna
developed for the 6-8.5 GHz range. Since the modeling methods inherently depend
on parameter sets, we investigate the influence of the parameter choice on the
accuracy of both models. Furthermore, we evaluate the impact of modeling errors
for coherent maximum-likelihood direction-of-arrival (DoA) estimation given
different model parameters. Numerical results are presented for a single
polarization component. Simulations reveal that the estimation bias introduced
by model errors is subject to the chosen model parameters. Finally, we provide
optimized sets of AIT and WM parameters for the multi-mode antenna under
investigation. With these parameter sets, EMF data samples can be reproduced in
interpolated form with high angular resolution.
Fixed Effects and the Generalized Mundlak Estimator
Authors: Dmitry Arkhangelsky, Guido Imbens
Date: 2018-07-05 00:00:00
Tasks: regression

We develop a new approach for estimating average treatment effects in observational studies with unobserved group-level heterogeneity. We consider a general model with group-level unconfoundedness and provide conditions under which aggregate balancing statistics -- group-level averages of functions of treatments and covariates -- are sufficient to eliminate differences between groups. Building on these results, we reinterpret commonly used linear fixed-effect regression estimators by writing them in the Mundlak form as linear regression estimators without fixed effects but including group averages. We use this representation to develop Generalized Mundlak Estimators (GMEs) that capture group differences through group averages of (functions of) the unit-level variables and adjust for these group differences in flexible and robust ways in the spirit of the modern causal literature.
V-CNN: When Convolutional Neural Network encounters Data Visualization
Authors: Yang Mao, Li Bo, Feng Guanxiong, Yan Zhongjiang
Date: 2018-06-12 00:00:00
Tasks: Data Visualization, Deep Learning, Intrusion Detection, Network Intrusion Detection

In recent years, deep learning poses a deep technical revolution in almost
every field and attracts great attentions from industry and academia.
Especially, the convolutional neural network (CNN), one representative model of
deep learning, achieves great successes in computer vision and natural language
processing. However, simply or blindly applying CNN to the other fields results
in lower training effects or makes it quite difficult to adjust the model
parameters. In this poster, we propose a general methodology named V-CNN by
introducing data visualizing for CNN. V-CNN introduces a data visualization
model prior to CNN modeling to make sure the data after processing is fit for
the features of images as well as CNN modeling. We apply V-CNN to the network
intrusion detection problem based on a famous practical dataset: AWID.
Simulation results confirm V-CNN significantly outperforms other studies and
the recall rate of each invasion category is more than 99.8%.
Generalization of Doob's Inequality and A Tighter Estimate on Look-back Option Price
Date: 2018-07-13 00:00:00

In this short note, we will strengthen the classic Doob's $L^p$ inequality
for sub-martingale processes. Because this inequality is of fundamental
importance to the theory of stochastic process, we believe this generalization
will find many interesting applications.