pwc-archive/papers-with-abstracts huggingface dataset:
A Comparison of Economic Agent-Based Model Calibration Methods
Date: 2019-02-15 00:00:00

Interest in agent-based models of financial markets and the wider economy has
increased consistently over the last few decades, in no small part due to their
ability to reproduce a number of empirically-observed stylised facts that are
not easily recovered by more traditional modelling approaches. Nevertheless,
the agent-based modelling paradigm faces mounting criticism, focused
particularly on the rigour of current validation and calibration practices,
most of which remain qualitative and stylised fact-driven. While the literature
on quantitative and data-driven approaches has seen significant expansion in
recent years, most studies have focused on the introduction of new calibration
methods that are neither benchmarked against existing alternatives nor
rigorously tested in terms of the quality of the estimates they produce. We
therefore compare a number of prominent ABM calibration methods, both
established and novel, through a series of computational experiments in an
attempt to determine the respective strengths and weaknesses of each approach
and the overall quality of the resultant parameter estimates. We find that
Bayesian estimation, though less popular in the literature, consistently
outperforms frequentist, objective function-based approaches and results in
reasonable parameter estimates in many contexts. Despite this, we also find
that agent-based model calibration techniques require further development in
order to definitively calibrate large-scale models. We therefore make
suggestions for future research.
Anatomical Region-Specific In Vivo Wireless Communication Channel Characterization
Date: 2019-02-15 00:00:00
Tasks: Diagnostic

In vivo wireless body area networks (WBANs) and their associated technologies
are shaping the future of healthcare by providing continuous health monitoring
and noninvasive surgical capabilities, in addition to remote diagnostic and
treatment of diseases. To fully exploit the potential of such devices, it is
necessary to characterize the communication channel which will help to build
reliable and high-performance communication systems. This paper presents an in
vivo wireless communication channel characterization for male torso both
numerically and experimentally (on a human cadaver) considering various organs
at 915 MHz and 2.4 GHz. A statistical path loss (PL) model is introduced, and
the anatomical region-specific parameters are provided. It is found that the
mean PL in dB scale exhibits a linear decaying characteristic rather than an
exponential decaying profile inside the body, and the power decay rate is
approximately twice at 2.4 GHz as compared to 915 MHz. Moreover, the variance
of shadowing increases significantly as the in vivo antenna is placed deeper
inside the body since the main scatterers are present in the vicinity of the
antenna. Multipath propagation characteristics are also investigated to
facilitate proper waveform designs in the future wireless healthcare systems,
and a root-mean-square (RMS) delay spread of 2.76 ns is observed at 5 cm depth.
Results show that the in vivo channel exhibit different characteristics than
the classical communication channels, and location dependency is very critical
for accurate, reliable, and energy-efficient link budget calculations.
The impact of adaptive guards for 5G and beyond
Date: 2019-02-15 00:00:00
Tasks: Scheduling

The next generation communication systems are evolving towards an increased
flexibility in different aspects. Enhanced flexibility is the key in order to
address diverse requirements. This paper presents the significance of adaptive
guards considering a windowed-OFDM system which supports a variety of services
operating asynchronously under the same network. The windowing approach
requires a guard duration to suppress the out-of-band emissions (OOBE), and the
guard band is required to handle the adjacent channel interference (ACI) along
with the windowing. The guards in both time and frequency domains are optimized
with respect to the use case and power offset between the users. To fully
exploit and further increase the potential of adaptive guards, an
interference-based scheduling algorithm is proposed as well. The results show
that the precise design that facilitates such flexibility reduce the guards
significantly and boost the spectral efficiency.
Non-Stationary Dividend-Price Ratios
Authors: Vassilis Polimenis, Ioannis Neokosmidis
Date: 2019-02-16 00:00:00

Dividend yields have been widely used in previous research to relate stock market valuations to cash flow fundamentals. However, this approach relies on the assumption that dividend yields are stationary. Due to the failure to reject the hypothesis of a unit root in the classical dividend-price ratio for the US stock market, Polimenis and Neokosmidis (2016) proposed the use of a modified dividend price ratio (mdp) as the deviation between d and p from their long run equilibrium, and showed that mdp provides substantially improved forecasting results over the classical dp ratio. Here, we extend that paper by performing multivariate regressions based on the Campbell-Shiller approximation, by utilizing a dynamic econometric procedure to estimate the modified dp, and by testing the modified ratios against reinvested dividend-yields. By comparing the performance of mdp and dp in the period after 1965, we are not only able to enhance the robustness of the findings, but also to debunk a possible false explanation that the enhanced mdp performance in predicting future returns comes from a capacity to predict the risk-free return component. Depending on whether one uses the recursive or population methodology to measure the performance of mdp, the Out-of-Sample performance gain is between 30% to 50%.
Optimal Measurement Times for a Small Number of Measures of a Brownian Motion over a Finite Period
Date: 2019-02-16 00:00:00

The measure timetable plays a critical role for the accuracy of the
estimator. This article deals with the optimization of the schedule of measures
for observing a random process in time using a Kalman filter, when the length
of the process is finite and fixed, and a fixed number of measures are
available. The measuring devices are allowed to differ. The mean variance of
the estimator is chosen as criterion for optimality. The cases of $1$ or $2$
measures are studied in detail, and analytical formulas are provided.
Weak Identification and Estimation of Social Interaction Models
Date: 2019-02-16 00:00:00

The identification of the network effect is based on either group size
variation, the structure of the network or the relative position in the
network. I provide easy-to-verify necessary conditions for identification of
undirected network models based on the number of distinct eigenvalues of the
adjacency matrix. Identification of network effects is possible; although in
many empirical situations existing identification strategies may require the
use of many instruments or instruments that could be strongly correlated with
each other. The use of highly correlated instruments or many instruments may
lead to weak identification or many instruments bias. This paper proposes
regularized versions of the two-stage least squares (2SLS) estimators as a
solution to these problems. The proposed estimators are consistent and
asymptotically normal. A Monte Carlo study illustrates the properties of the
regularized estimators. An empirical application, assessing a local government
tax competition model, shows the empirical relevance of using regularization
methods.
Robust and fast heart rate variability analysis of long and noisy electrocardiograms using neural networks and images
Date: 2019-02-16 00:00:00
Proceeding: arXiv:1902.06151 Search... Help | Advanced Search 2019 2
Tasks: Heart Rate Variability, Time Series, Time Series Analysis

Heart rate variability studies depend on the robust calculation of the
tachogram, the heart rate times series, usually by the detection of R peaks in
the electrocardiogram (ECG). ECGs however are subject to a number of sources of
noise which are difficult to filter and therefore reduce the tachogram
accuracy. We describe a pipeline for fast calculation of tachograms from noisy
ECGs of several hours' length. The pipeline consists of three stages. A neural
network (NN) trained to detect R peaks and distinguish these from noise; a
measure to robustly detect false positives (FPs) and negatives (FNs) produced
by the NN; a simple "alarm" algorithm for automatically removing FPs and
interpolating FNs. In addition, we introduce the approach of encoding ECGs,
tachograms and other cardiac time series in the form of raster images, which
greatly speeds and eases their visual inspection and analysis.
Robust Sub-meter Level Indoor Localization - A Logistic Regression Approach
Date: 2019-02-17 00:00:00
Tasks: Deep Learning, Indoor Localization, regression

Indoor localization becomes a raising demand in our daily lives. Due to the
massive deployment in the indoor environment nowadays, WiFi systems have been
applied to high accurate localization recently. Although the traditional model
based localization scheme can achieve sub-meter level accuracy by fusing
multiple channel state information (CSI) observations, the corresponding
computational overhead is significant. To address this issue, the model-free
localization approach using deep learning framework has been proposed and the
classification based technique is applied. In this paper, instead of using
classification based mechanism, we propose to use a logistic regression based
scheme under the deep learning framework, which is able to achieve sub-meter
level accuracy (97.2cm medium distance error) in the standard laboratory
environment and maintain reasonable online prediction overhead under the single
WiFi AP settings. We hope the proposed logistic regression based scheme can
shed some light on the model-free localization technique and pave the way for
the practical deployment of deep learning based WiFi localization systems.
Afferent Fiber Activity-Induced Cytoplasmic Calcium Signaling in Parvalbumin-Positive Inhibitory Interneurons of the Spinal Cord Dorsal Horn
Date: 2019-02-17 00:00:00

Neuronal calcium (Ca2+) signaling represents a molecular trigger for diverse
central nervous system adaptations and maladaptions. The altered function of
dorsal spinal inhibitory interneurons is strongly implicated in the mechanisms
underlying central sensitization in chronic pain. Surprisingly little is known,
however, about the characteristics and consequences of Ca2+ signaling in these
cells, including whether and how they are changed following a peripheral insult
or injury and how such alterations might influence maladaptive pain plasticity.
As a first step towards clarifying the precise role of Ca2+ signaling in dorsal
spinal inhibitory neurons for central sensitization, we established methods for
characterizing Ca2+ signals in genetically defined populations of these cells.
In particular, we employed recombinant adeno-associated viral vectors to
deliver subcellularly targeted, genetically encoded Ca2+ indicators into
parvalbumin-positive spinal inhibitory neurons. Using wide-field microscopy, we
observed both spontaneous and afferent fiber activity triggered Ca2+ signals in
these cells. We propose that these methods may be adapted in future studies for
the precise characterization and manipulation of Ca2+ signaling in diverse
spinal inhibitory neuron subtypes, thereby enabling the clarification of its
role in the mechanisms underlying pain chronicity and opening the door for
possibly novel treatment directions.
Spatial Channel Covariance Estimation for Hybrid Architectures Based on Tensor Decompositions
Date: 2019-02-17 00:00:00
Tasks: Compressive Sensing, Tensor Decomposition

Spatial channel covariance information can replace full instantaneous channel
state information for the analog precoder design in hybrid analog/digital
architectures. Obtaining spatial channel covariance estimation, however, is
challenging in the hybrid structure due to the use of fewer radio frequency
(RF) chains than the number of antennas. In this paper, we propose a spatial
channel covariance estimation method based on higher-order tensor decomposition
for spatially sparse time-varying frequency-selective channels. The proposed
method leverages the fact that the channel can be represented as a low-rank
higher-order tensor. We also derive the Cram\'er-Rao lower bound on the
estimation accuracy of the proposed method. Numerical results and theoretical
analysis show that the proposed tensor-based approach achieves higher
estimation accuracy in comparison with prior compressive-sensing-based
approaches or conventional angle-of-arrival estimation approaches. Simulation
results reveal that the proposed approach becomes more beneficial at low
signal-to-noise (SNR) region.
Towards peptide strings pharmacology
Authors: Razvan Tudor Radulescu
Date: 2019-02-18 00:00:00

Peptide strings have been developed as a concept for the past fourteen years. They are proposed to basically entail various quantum states engendered by the physical interactions of proteins containing peptide portions that are similar to one another and, moreover, amino acid sequences that are sterically complementary to each other. In this survey, additional insights are presented that support the notion that peptide strings are likely a biophysical phenomenon that warrants further investigations. Specifically, these putative peptide strings traits are the capacity for wave-like interferences and electric current-like properties. Therefore, future experimental validation and quantification of these predicted features as well as application of this potential energy that is stored in the distinct shapes of proteins and peptides may prove beneficial in addressing challenges in physics and medicine. Within the latter field, the key discipline of pharmacology should be most fundamentally advanced by the peptide strings approach.
Correlation Patterns in Foreign Exchange Markets
Date: 2019-02-28 00:00:00

The value of an asset in a financial market is given in terms of another
asset known as numeraire. The dynamics of the value is non-stationary and
hence, to quantify the relationships between different assets, one requires
convenient measures such as the means and covariances of the respective log
returns. Here, we develop transformation equations for these means and
covariances when one changes the numeraire. The results are verified by a
thorough empirical analysis capturing the dynamics of numerous assets in a
foreign exchange market. We show that the partial correlations between pairs of
assets are invariant under the change of the numeraire. This observable
quantifies the relationship between two assets, while the influence of the rest
is removed. As such the partial correlations uncover intriguing observations
which may not be easily noticed in the ordinary correlation analysis.
Options on CPPI with guaranteed minimum equity exposure
Date: 2019-02-18 00:00:00

In the present paper we provide a two-step principal protection strategy
obtained by combining a modification of the Constant Proportion Portfolio
Insurance (CPPI) algorithm and a classical Option Based Portfolio Insurance
(OBPI) mechanism. Such a novel approach consists in assuming that the
percentage of wealth invested in stocks cannot go under a fixed level, called
guaranteed minimum equity exposure, and using such an adjusted CPPI portfolio
as the underlying of an option. The first stage ensures to overcome the so
called cash-in risk, typically related to a standard CPPI technique, while the
second one guarantees the equity market participation. To show the
effectiveness of our proposal we provide a detailed computational analysis
within the Heston-Vasicek framework, numerically comparing the evaluation of
the price of European plain vanilla options when the underlying is either a
purely risky asset, a standard CPPI portfolio and a CPPI with guaranteed
minimum equity exposure.
Using sequencing coverage statistics to identify sex chromosomes in minke whales
Date: 2019-02-18 00:00:00

The ever-increasing number of genome sequencing and resequencing projects is
a central source of insights into the ecology and evolution of non-model
organisms. An important aspect of genomics is the elucidation of sex
determination systems and identifying genes on sex chromosomes. This not only
helps reveal mechanisms behind sex determination in the species under study,
but their characteristics make sex chromosomes a unique tool for studying the
mechanisms and effects of recombination and genomic rearrangements and how they
affect adaption and selection. Despite this, many sequencing projects omit such
investigations. Here, we apply a simple method using sequencing coverage
statistics to identify scaffolds belonging to the sex chromosomes of minke
whale, and show how the sex chromosome system can be determined using coverage
statistics alone. Using publicly available data, we identify the previously
unknown sex of an Antarctic minke whale as female. We further investigate
public sequence data from the different species and sub-species of minke whale,
and classify genomic scaffolds from a published minke whale assembly as X or Y
chromosomal sequences. Our findings are consistent with previous results that
identified a handful of scaffolds as sex chromosomal, but we are able to
identify a much larger set of scaffolds, likely to represent close to the
complete sex chromosomal sequences for the minke whale. Sequence coverage
statistics provides a readily available tool for investigating the sex
determination system and locate genes on sex chromosomes. This analysis is
straightforward and can often be performed with existing resources.
Theory-plus-code documentation of the DEPAM workflow for soundscape description
Date: 2019-02-14 00:00:00

In the Big Data era, the community of PAM faces strong challenges, including
the need for more standardized processing tools accross its different
applications in oceanography, and for more scalable and high-performance
computing systems to process more efficiently the everly growing datasets. In
this work we address conjointly both issues by first proposing a detailed
theory-plus-code document of a classical analysis workflow to describe the
content of PAM data, which hopefully will be reviewed and adopted by a maximum
of PAM experts to make it standardized. Second, we transposed this workflow
into the Scala language within the Spark/Hadoop frameworks so it can be
directly scaled out on several node cluster.
Securing Voice-driven Interfaces against Fake (Cloned) Audio Attacks
Date: 2019-02-18 00:00:00
Tasks: Speech Synthesis, Voice Cloning

Voice cloning technologies have found applications in a variety of areas
ranging from personalized speech interfaces to advertisement, robotics, and so
on. Existing voice cloning systems are capable of learning speaker
characteristics and use trained models to synthesize a person's voice from only
a few audio samples. Advances in cloned speech generation technologies are
capable of generating perceptually indistinguishable speech from a bona-fide
speech. These advances pose new security and privacy threats to voice-driven
interfaces and speech-based access control systems. The state-of-the-art speech
synthesis technologies use trained or tuned generative models for cloned speech
generation. Trained generative models rely on linear operations, learned
weights, and excitation source for cloned speech synthesis. These systems leave
characteristic artifacts in the synthesized speech. Higher-order spectral
analysis is used to capture differentiating attributes between bona-fide and
cloned audios. Specifically, quadrature phase coupling (QPC) in the estimated
bicoherence, Gaussianity test statistics, and linearity test statistics are
used to capture generative model artifacts. Performance of the proposed method
is evaluated on cloned audios generated using speaker adaptation- and speaker
encoding-based approaches. Experimental results for a dataset consisting of 126
cloned speech and 8 bona-fide speech samples indicate that the proposed method
is capable of detecting bona-fide and cloned audios with close to a perfect
detection rate.
Large-scale directed network inference with multivariate transfer entropy and hierarchical statistical testing
Date: 2019-07-30 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Specificity, Time Series, Time Series Analysis

Network inference algorithms are valuable tools for the study of large-scale
neuroimaging datasets. Multivariate transfer entropy is well suited for this
task, being a model-free measure that captures nonlinear and lagged
dependencies between time series to infer a minimal directed network model.
Greedy algorithms have been proposed to efficiently deal with high-dimensional
datasets while avoiding redundant inferences and capturing synergistic effects.
However, multiple statistical comparisons may inflate the false positive rate
and are computationally demanding, which limited the size of previous
validation studies. The algorithm we present---as implemented in the IDTxl
open-source software---addresses these challenges by employing hierarchical
statistical tests to control the family-wise error rate and to allow for
efficient parallelisation. The method was validated on synthetic datasets
involving random networks of increasing size (up to 100 nodes), for both linear
and nonlinear dynamics. The performance increased with the length of the time
series, reaching consistently high precision, recall, and specificity (>98% on
average) for 10000 time samples. Varying the statistical significance threshold
showed a more favourable precision-recall trade-off for longer time series.
Both the network size and the sample size are one order of magnitude larger
than previously demonstrated, showing feasibility for typical EEG and MEG
experiments.
In Vivo Communications: Steps Toward the Next Generation of Implantable Devices
Date: 2019-02-19 00:00:00

In vivo wireless medical devices have the potential to play a vital role in
future healthcare technologies by improving the quality of human life. In order
to fully exploit the capabilities of such devices, it is necessary to
characterize and model the in vivo wireless communication channel. Utilization
of this model will have a significant role in improving the communication
performance of embedded medical devices in terms of power, reliability and
spectral efficiency. In this paper, the state of the art in this field is
presented to provide a comprehensive understanding of current models. Such
knowledge will be used to optimize the design and selection of various in vivo
wireless communication methods, operational frequencies, and antenna design.
Finally, open research areas are discussed for future studies.
Performance of MIMO channel estimation with a physical model
Date: 2019-02-19 00:00:00

Channel estimation is challenging in multi-antenna communication systems,
because of the large number of parameters to estimate. One way of facilitating
this task is to use a physical model describing the multiple paths constituting
the channel, in the hope of reducing the number of unknowns in the problem. The
achievable performance of estimation using this kind of physical model is
studied theoretically. It is found that adjusting the number of estimated paths
leads to a bias-variance tradeoff which is characterized. Moreover, computing
the Fisher information matrix of the model allows to identify orthogonal
parameters, ultimately leading to fast and asymptotically optimal algorithms as
a byproduct.