pwc-archive/papers-with-abstracts huggingface dataset:
Proactive Islanding of the Power Grid to Mitigate High-Impact Low-Frequency Events
Date: 2019-11-11 00:00:00

This paper proposes a methodology for enhancing power systems resiliency by
proactively splitting an interconnected grid into small self-sustaining islands
in preparation for extreme events. The idea is to posture the system so that
cascading outages can be bound within affected areas, preventing the
propagation of disturbances to the rest of the system. This mitigation strategy
will prove especially useful when advance notification of a threat is available
but its nature not well understood. In our method, islands are determined using
a constrained hierarchical spectral clustering technique. We further check the
viability of the resultant islands using steady-state AC power flow. The
performance of the approach is illustrated using a detailed PSS/E model of the
heavily meshed transmission network operated by PJM Interconnection in the
eastern USA. Representative cases from different seasons show that variations
in power flow patterns influence island configuration.
iGLU 1.0: An Accurate Non-Invasive Near-Infrared Dual Short Wavelengths Spectroscopy based Glucometer for Smart Healthcare
Date: 2019-11-10 00:00:00
Tasks: regression

In the case of diabetes, fingertip pricking for a blood sample is
inconvenient for glucose measurement. Invasive approaches like laboratory test
and one-touch glucometer enhance the risk of blood-related infections. To
mitigate this important issue, in the current paper, we propose a novel
Internet-of-Medical-Things (IoMT) enabled edge-device for precise, non-invasive
blood glucose measurement. In this work, a near-infrared (NIR) spectroscopic
technique using two wavelengths (940 nm, 1300 nm) is taken to detect the
glucose molecule from human blood. The novel device called iGLU is based on NIR
spectroscopy and machine learning (ML) models of high accuracy. An optimal
multiple polynomial regression model and deep neural network (DNN) model have
been presented for precise measurement. The proposed device is validated and
blood glucose values are stored on the cloud using open IoT platform for remote
monitoring by an endocrinologist. For device validation, the estimated blood
glucose values have been compared with blood glucose values obtained from the
invasive device. It has been observed that mean absolute relative difference
(MARD) and average error (AvgE) are found 4.66 % and 4.61 % respectively from
predicted blood glucose concentration values. The regression coefficient is
found 0.81. The proposed spectroscopic non-invasive device provides accurate
and cost-effective solution for smart healthcare.
Identification in discrete choice models with imperfect information
Authors: Cristina Gualdani, Shruti Sinha
Date: 2019-11-11 00:00:00
Tasks: counterfactual, Discrete Choice Models

We study identification of preferences in static single-agent discrete choice models where decision makers may be imperfectly informed about the state of the world. We leverage the notion of one-player Bayes Correlated Equilibrium by Bergemann and Morris (2016) to provide a tractable characterization of the sharp identified set. We develop a procedure to practically construct the sharp identified set following a sieve approach, and provide sharp bounds on counterfactual outcomes of interest. We use our methodology and data on the 2017 UK general election to estimate a spatial voting model under weak assumptions on agents' information about the returns to voting. Counterfactual exercises quantify the consequences of imperfect information on the well-being of voters and parties.
Modulation of oculomotor control during reading of mirrored and inverted texts
Date: 2019-11-11 00:00:00

The interplay between cognitive and oculomotor processes during reading can
be explored when the spatial layout of text deviates from the typical display.
In this study, we investigate various eye-movement measures during reading of
text with experimentally manipulated layout (word-wise and letter-wise
mirrored-reversed text as well as inverted and scrambled text). While typical
findings (e.g., longer mean fixation times, shorter mean saccades lengths) in
reading manipulated texts compared to normal texts were reported in earlier
work, little is known about changes of oculomotor targeting observed in
within-word landing positions under the above text layouts. Here we carry out
precise analyses of landing positions and find substantial changes in the
so-called launch-site effect in addition to the expected overall slow-down of
reading performance. Specifically, during reading of our manipulated text
conditions with reversed letter order (against overall reading direction), we
find a reduced launch-site effect, while in all other manipulated text
conditions, we observe an increased launch-site effect. Our results clearly
indicate that the oculomotor system is highly adaptive when confronted with
unusual reading conditions.
Mode-Multiplexed Full-Field Reconstruction Using Direct and Phase Retrieval Detection
Date: 2019-11-12 00:00:00
Tasks: Retrieval

We realize mode-multiplexed full-field reconstruction over six spatial and
polarization modes after 30-km multimode fiber transmission using
intensity-only measurements without any optical carrier or local oscillator at
the receiver or transmitter. The receiver's capabilities to cope with modal
dispersion and mode dependent loss are experimentally demonstrated.
Global Convergence of Policy Gradient for Sequential Zero-Sum Linear Quadratic Dynamic Games
Date: 2019-11-12 00:00:00

We propose projection-free sequential algorithms for linear-quadratic
dynamics games. These policy gradient based algorithms are akin to Stackelberg
leadership model and can be extended to model-free settings. We show that if
the leader performs natural gradient descent/ascent, then the proposed
algorithm has a global sublinear convergence to the Nash equilibrium. Moreover,
if the leader adopts a quasi-Newton policy, the algorithm enjoys a global
quadratic convergence. Along the way, we examine and clarify the intricacies of
adopting sequential policy updates for LQ games, namely, issues pertaining to
stabilization, indefinite cost structure, and circumventing projection steps.
Extended MinP Tests for Global and Multiple testing
Authors: Zeng-Hua Lu
Date: 2019-11-12 00:00:00

Empirical economic studies often involve multiple propositions or hypotheses, with researchers aiming to assess both the collective and individual evidence against these propositions or hypotheses. To rigorously assess this evidence, practitioners frequently employ tests with quadratic test statistics, such as $F$-tests and Wald tests, or tests based on minimum/maximum type test statistics. This paper introduces a combination test that merges these two classes of tests using the minimum $p$-value principle. The proposed test capitalizes on the global power advantages of both constituent tests while retaining the benefits of the stepdown procedure from minimum/maximum type tests.
A Simple Estimator for Quantile Panel Data Models Using Smoothed Quantile Regressions
Date: 2019-11-12 00:00:00
Tasks: valid

Canay (2011)'s two-step estimator of quantile panel data models, due to its
simple intuition and low computational cost, has been widely used in empirical
studies in recent years. In this paper, we revisit the estimator of Canay
(2011) and point out that in his asymptotic analysis the bias of his estimator
due to the estimation of the fixed effects is mistakenly omitted, and that such
omission will lead to invalid inference on the coefficients. To solve this
problem, we propose a similar easy-to-implement estimator based on smoothed
quantile regressions. The asymptotic distribution of the new estimator is
established and the analytical expression of its asymptotic bias is derived.
Based on these results, we show how to make asymptotically valid inference
based on both analytical and split-panel jackknife bias corrections. Finally,
finite sample simulations are used to support our theoretical analysis and to
illustrate the importance of bias correction in quantile regressions for panel
data.
MRI denoising using Deep Learning and Non-local averaging
Date: 2019-11-18 00:00:00
Tasks: Deep Learning, Denoising

This paper proposes a novel method for automatic MRI denoising that exploits
last advances in deep learning feature regression and self-similarity
properties of the MR images. The proposed method is a two-stage approach. In
the first stage, an overcomplete patch-based convolutional neural network
blindly removes the noise without specific estimation of the local noise
variance to produce a preliminary estimation of the noise-free image. The
second stage uses this preliminary denoised image as a guide image within a
rotationally invariant non-local means filter to robustly denoise the original
noisy image. The proposed approach has been compared with related
state-of-the-art methods and showed competitive results in all the studied
cases while being much faster than comparable filters. We present a denoising
method that can be blindly applied to any type of MR image since it can
automatically deal with both stationary and spatially varying noise patterns.
Genetic assignment of illegally trafficked neotropical primates and implications for reintroduction programs
Date: 2019-11-12 00:00:00
Tasks: Management

The black and gold howler monkey (Alouatta caraya) is a neotropical primate
that faces the highest capture pressure for illegal trade in Argentina. We
evaluate the applicability of genetic assignment tests based on microsatellite
genotypic data to accurately assign individuals to their site of origin. The
search was conducted on a genetic database to determine the nearest sampled
population or to associate them to three clusters described here for the
Argentinean populations of A. caraya. We correctly assign 73% of the
individuals in the database to nearest population of origin, and 93.3% to their
cluster of origin. With this database, we were able to determine the probable
origin of 17 confiscated individuals, 12 of which were reintroduced in the
province of Misiones and 5 confiscated individuals reintroduced in the province
of Santa Fe. Moreover, we also determined the probable origin of 3 individuals
found dead in cities in northern Argentina. This approach highlights the
relevance of generating genotype indexing databases of species to assist with
in-situ and ex-situ conservation and management programs. Our results
underscore the importance of knowing the origin of individuals for
reintroduction and/or species recovery programs and to pinpoint the hotspots of
illegal capture of various species.
Active interfacial dynamic transport of fluid in fibrous connective tissues and a hypothesis of interstitial fluid circulatory system
Date: 2019-11-25 00:00:00

Fluid in interstitial spaces accounts for ~20% of an adult body weight. Does
it circulate around the body like vascular circulations besides a diffusive and
short-ranged transport? This bold conjecture has been debated for decades. As a
conventional physiological concept, interstitial space was the space between
cells and a micron-sized space. Fluid in interstitial spaces is thought to be
entrapped within interstitial matrix. However, our serial data have further
defined an interfacial transport zone on a solid fiber of interstitial matrix.
Within this fine space that is probably nanosized, fluid can transport along a
fiber under a driving power. Since 2006, our imaging data from volunteers and
cadavers have revealed a long-distance extravascular pathway for interstitial
fluid flow, comprising four types of anatomic distributions at least. The
framework of each extravascular pathway contains the longitudinally assembled
and oriented fibers, working as a fibrous guiderail for fluid flow.
Interestingly, our data showed that the movement of fluid in a fibrous pathway
is in response to a dynamic driving source and named as dynamotaxis. By
analysis of some representative studies and our experimental results, a
hypothesis of interstitial fluid circulatory system is proposed.
Target Tracking with Electrical Impedance Tomography
Date: 2019-11-12 00:00:00
Tasks: Geophysics

Electrical impedance tomography (EIT) has been successfully applied to
several important application domains such as medicine, geophysics and
industrial imaging. EIT offers a high temporal resolution, which allows to
track the location of a moving target on a conductive surface accurately.
Existing EIT methods are geared towards high image quality instead of smooth
target trajectories, which makes them suboptimal for target tracking. We
combine EIT methods with hidden Markov models for tracking moving targets on a
conductive surface. Numerical experiments indicate that the proposed method
outperforms existing EIT methods in target tracking accuracy.
Communication in Plants: Comparison of Multiple Action Potential and Mechanosensitive Signals with Experiments
Date: 2019-11-12 00:00:00

Both action potentials and mechanosensitive signalling are an important
communication mechanisms in plants. Considering an information theoretic
framework, this paper explores the effective range of multiple action
potentials for a long chain of cells (i.e., up to 100) in different
configurations, and introduces the study of multiple mechanosensitive
activation signals (generated due to a mechanical stimulus) in plants. For both
these signals, we find that the mutual information per cell and information
propagation speed tends to increase up to a certain number of receiver cells.
However, as the number of cells increase beyond 10 to 12, the mutual
information per cell starts to decrease. To validate our model and results, we
include an experimental verification of the theoretical model, using a
PhytlSigns biosignal amplifier, allowing us to measure the magnitude of the
voltage associated with the multiple AP and mechanosensitive activation signals
induced by different stimulus in plants. Experimental data is used to calculate
the mutual information and information propagation speed, which is compared
with corresponding numerical results. Since these signals are used for a
variety of important tasks within the plant, understanding them may lead to new
bioengineering methods for plants.
Parameterization of Retrofit Controllers
Date: 2020-05-25 00:00:00

This study investigates a parameterization of all retrofit controllers.
Retrofit control can accomplish modular design of control systems, i.e.,
independent design of subcontrollers only with its corresponding subsystem
model in a dynamical network system. In the retrofit control framework, the
network system to be controlled is regarded as an interconnected system
composed of the subsystem of interest and an environment. Existing studies have
revealed that all retrofit controllers can be characterized as a constrained
Youla parameterization under the technical assumption that the subsystem of
interest is stable. It has also been discovered that all of the retrofit
controllers that belong to a particular tractable class have a distinctive
internal structure composed of a rectifier and an internal controller under the
additional technical assumption that the interconnection signal from the
environment to the subsystem of interest is measurable or that the internal
state of the subsystem of interest is measurable. Due to the internal
structure, the retrofit controller design problem under the assumptions can be
reduced to a standard controller design problem to which existing controller
synthesis techniques can be applied. The aim of this paper is to extend the
above results without the technical assumptions. It is found that the existing
developments can naturally be generalized through the Youla parameterization
for unstable systems and an explicit description of the inverse system with the
geometric control theory. The result leads to the conclusion that retrofit
controllers can readily be designed even in the general case.
On the nature of explanations offered by network science: A perspective from and for practicing neuroscientists
Date: 2019-11-12 00:00:00

Network neuroscience represents the brain as a collection of regions and
inter-regional connections. Given its ability to formalize systems-level
models, network neuroscience has generated unique explanations of neural
function and behavior. The mechanistic status of these explanations and how
they can contribute to and fit within the field of neuroscience as a whole has
received careful treatment from philosophers. However, these philosophical
contributions have not yet reached many neuroscientists. Here we complement
formal philosophical efforts by providing an applied perspective from and for
neuroscientists. We discuss the mechanistic status of the explanations offered
by network neuroscience and how they contribute to, enhance, and interdigitate
with other types of explanations in neuroscience. In doing so, we rely on
philosophical work concerning the role of causality, scale, and mechanisms in
scientific explanations. In particular, we make the distinction between an
explanation and the evidence supporting that explanation, and we argue for a
scale-free nature of mechanistic explanations. In the course of these
discussions, we hope to provide a useful applied framework in which network
neuroscience explanations can be exercised across scales and combined with
other fields of neuroscience to gain deeper insights into the brain and
behavior.
Index Tracking with Cardinality Constraints: A Stochastic Neural Networks Approach
Authors: Yu Zheng, Bowei Chen, Timothy M. Hospedales, Yongxin Yang
Date: 2019-11-12 00:00:00
Tasks: Evolutionary Algorithms

Partial (replication) index tracking is a popular passive investment strategy. It aims to replicate the performance of a given index by constructing a tracking portfolio which contains some constituents of the index. The tracking error optimisation is quadratic and NP-hard when taking the L0 constraint into account so it is usually solved by heuristic methods such as evolutionary algorithms. This paper introduces a simple, efficient and scalable connectionist model as an alternative. We propose a novel reparametrisation method and then solve the optimisation problem with stochastic neural networks. The proposed approach is examined with S&P 500 index data for more than 10 years and compared with widely used index tracking approaches such as forward and backward selection and the largest market capitalisation methods. The empirical results show our model achieves excellent performance. Compared with the benchmarked models, our model has the lowest tracking error, across a range of portfolio sizes. Meanwhile it offers comparable performance to the others on secondary criteria such as volatility, Sharpe ratio and maximum drawdown.
Disconnection-aware Attack Detection in Networked Control Systems
Date: 2020-02-27 00:00:00

This study deals with security issues in dynamical networked control systems.
The goal is to establish a unified framework of the attack detection stage,
which includes the four processes of monitoring the system state, making a
decision based on the monitored signal, disconnecting the corrupted subsystem,
and operating the remaining system during restoration. This paper, in
particular, considers a disconnection-aware attack detector design problem.
Traditionally, observer-based attack detectors are designed based on the system
model with a fixed network topology and cannot cope with a change of the
topology caused by disconnection. The disconnection-aware design problem is
mathematically formulated and a solution is proposed in this paper. A numerical
example demonstrates the effectiveness of the proposed detector through an
inverter-based voltage control system in a benchmark model.
A two-player portfolio tracking game
Authors: Moritz Voß
Date: 2019-11-12 00:00:00
Tasks: Vocal Bursts Valence Prediction

We study the competition of two strategic agents for liquidity in the benchmark portfolio tracking setup of Bank, Soner, Vo{\ss} (2017). Specifically, both agents track their own stochastic running trading targets while interacting through common aggregated temporary and permanent price impact \`a la Almgren and Chriss (2001). The resulting stochastic linear quadratic differential game with terminal state constraints allows for a unique and explicitly available open-loop Nash equilibrium. Our results reveal how the equilibrium strategies of the two players take into account the other agent's trading targets: either in an exploitative intent or by providing liquidity to the competitor, depending on the relation between temporary and permanent price impact. As a consequence, different behavioral patterns can emerge as optimal in equilibrium. These insights complement and extend existing studies in the literature on predatory trading models examined in the context of optimal portfolio liquidation games.
Lightning Impulse Current Tests on some Electroconductive Fabrics
Authors: Jorge A. Cristancho C., Carlos A. Rivera G., Jorge E. Rodriguez M., John J. Pantoja A., Liz K. Herrera Q., Francisco Roman
Date: 2019-11-12 00:00:00

On the search of lightweight lightning protection materials that can be used as part of lightning protection systems, we investigate some types of electroconductive fabrics by applying several lightning impulse currents in laboratory. Samples of four commercially available electroconductive textiles were analyzed, two rip-stop, a plain-weave, a nonwoven, and additionally a carbon-impregnated polymeric film. Under laboratory conditions, each sample was subjected to several lengthwise subsequent lightning-like currents of 8/20 us standard waveform, recording both voltage and current signals. Optical and scanning electron microscope observations were performed after tests, revealing some patterns or morphological changes on the fabric surface. Despite these changes, the investigated conductive textiles withstand the several lightning impulse currents applied. Results suggest that some conductive fabrics could be used in personal mobile shelters, to protect human beings against the earth potential rise caused by a close lightning discharge.
Beveridgean Unemployment Gap
Authors: Pascal Michaillat, Emmanuel Saez
Date: 2019-11-13 00:00:00

This paper develops a sufficient-statistic formula for the unemployment gap -- the difference between the actual unemployment rate and the efficient unemployment rate. While lowering unemployment puts more people into work, it forces firms to post more vacancies and to devote more resources to recruiting. This unemployment-vacancy tradeoff, governed by the Beveridge curve, determines the efficient unemployment rate. Accordingly, the unemployment gap can be measured from three sufficient statistics: elasticity of the Beveridge curve, social cost of unemployment, and cost of recruiting. Applying this formula to the United States, 1951--2019, we find that the efficient unemployment rate averages 4.3%, always remains between 3.0% and 5.4%, and has been stable between 3.8% and 4.6% since 1990. As a result, the unemployment gap is countercyclical, reaching 6 percentage points in slumps. The US labor market is therefore generally inefficient and especially inefficiently slack in slumps. In turn, the unemployment gap is a crucial statistic to design labor-market and macroeconomic policies.