pwc-archive/papers-with-abstracts huggingface dataset:
Power Minimizer Symbol-Level Precoding: A Closed-Form Sub-Optimal Solution
Date: 2018-07-27 00:00:00
Tasks: Form

In this letter, we study the optimal solution of the multiuser symbol-level
precoding (SLP) for minimization of the total transmit power under given
signal-to-interference-plus-noise ratio (SINR) constraints. Adopting the
distance preserving constructive interference regions (DPCIR), we first derive
a simplified reformulation of the problem. Then, we analyze the structure of
the optimal solution using the Karush-Kuhn-Tucker (KKT) optimality conditions,
thereby we obtain the necessary and sufficient condition under which the power
minimizer SLP is equivalent to the conventional zero-forcing beamforming
(ZFBF). This further leads us to a closed-form sub-optimal SLP solution
(CF-SLP) for the original problem. Simulation results show that CF-SLP provides
significant gains over ZFBF, while performing quite close to the optimal SLP in
scenarios with rather small number of users. The results further indicate that
the CF-SLP method has a reduction of order $10^3$ in computational time
compared to the optimal solution.
Multiparametric qualimetric microsurgical scanning chip-lancet model: theoretical metrological and biomedical considerations
Authors: Oleg V. Gradov, Arthur G. Jablokov
Date: 2018-07-23 00:00:00

The construction of a novel surgical instrument is considered, which is also a probing device providing a signal to the measuring equipment, which after its interpretation allows to obtain useful information about the section quality and the biomaterial properties. We propose here some formalized considerations on the possibility of its implementation for different variables registration. The idea is also extrapolated into the field of micrurgy which refers to the microelectrode techniques and the local potential registration in situ.
On the use of Singular Spectrum Analysis
Date: 2018-07-27 00:00:00
Tasks: Time Series, Time Series Analysis

Singular Spectrum Analysis (SSA) or Singular Value Decomposition (SVD) are
often used to de-noise univariate time series or to study their spectral
profile. Both techniques rely on the eigendecomposition of the cor- relation
matrix estimated after embedding the signal into its delayed coordi- nates. In
this work we show that the eigenvectors can be used to calculate the
coefficients of a set of filters which form a filter bank. The properties of
these filters are derived. In particular we show that their outputs can be
grouped according to their frequency response. Furthermore, the fre- quency at
the maximum of each frequency response and the corresponding eigenvalue can
provide a power spectrum estimation of the time series. Two different
applications illustrate how both characteristics can be applied to analyze
wideband signals in order to achieve narrow-band signals or to infer their
frequency occupation.
A hybrid automated detection of epileptic seizures in EEG based on wavelet and machine learning techniques
Date: 2018-07-13 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Seizure Detection

Epilepsy is a neurological condition such that it affects the brain and the
nervous system. It is characterized by recurrent seizures, which are physical
reactions to sudden, usually brief, excessive electrical discharges in a group
of brain cells. Hence, seizure identification has great importance in clinical
therapy of epileptic patients. Electroencephalogram (EEG) is one of the main
biomarker that can measure voltage fluctuations of the brain and EEG data
analysis helps to investigate the patient with epilepsy syndrome as epilepsy
leaves their signature in EEG signals. In this paper, the Discrete Wavelet
Transform (DWT) is applied to EEG signals to pre-processing, decompose it till
the 4th level of decomposition tree.Various features like Entropy, Min, Max,
Mean, Median, Standard deviation, Variance, Skewness, Energy and Relative Wave
Energy (RWE) were computed in terms of detailed coefficients and the
approximation coefficients of the last decomposition level.Then, the extracted
features are evaluated by three modern machine-learning classifiers such as
Radial Basis Function based Support Vector Machine (SVMRBF), k-Nearest Neighbor
(KNN) and Naive Bayes (NB). The experimental results demonstrate that the
highest classification accuracy (100\%) for normal subject data versus
epileptic data is obtained by SVMRBF. the corresponding accuracy between normal
subject data and epileptic data using KNN and NB is obtained as 99.50\% and
99\% for the eyes open and eyes closed conditions, respectively. The similar
accuracies, while comparing the interictal and ictal data, are obtained as
99\%, 97.50\% and 98.50\% using the SVMRBF, KNN and NB classifiers,
respectively. These accuracies are quite higher than earlier results published.
Dictionary Learning in Fourier Transform Scanning Tunneling Spectroscopy
Authors: Cheung Sky C., Shin John Y., Lau Yenson, Chen Zhengyu, Sun Ju, Zhang Yuqian, Wright John N., Pasupathy Abhay N.
Date: 2018-07-19 00:00:00
Tasks: Dictionary Learning

Modern high-resolution microscopes, such as the scanning tunneling
microscope, are commonly used to study specimens that have dense and aperiodic
spatial structure. Extracting meaningful information from images obtained from
such microscopes remains a formidable challenge. Fourier analysis is commonly
used to analyze the underlying structure of fundamental motifs present in an
image. However, the Fourier transform fundamentally suffers from severe phase
noise when applied to aperiodic images. Here, we report the development of a
new algorithm based on nonconvex optimization, applicable to any microscopy
modality, that directly uncovers the fundamental motifs present in a real-space
image. Apart from being quantitatively superior to traditional Fourier
analysis, we show that this novel algorithm also uncovers phase sensitive
information about the underlying motif structure. We demonstrate its usefulness
by studying scanning tunneling microscopy images of a Co-doped iron arsenide
superconductor and prove that the application of the algorithm allows for the
complete recovery of quasiparticle interference in this material. Our phase
sensitive quasiparticle interference imaging results indicate that the pairing
symmetry in optimally doped NaFeAs is consistent with a sign-changing s+- order
parameter.
Combined Mutiplicative-Heston Model for Stochastic Volatility
Date: 2018-07-27 00:00:00
Tasks: model

We consider a model of stochastic volatility which combines features of the
multiplicative model for large volatilities and of the Heston model for small
volatilities. The steady-state distribution in this model is a Beta Prime and
is characterized by the power-law behavior at both large and small
volatilities. We discuss the reasoning behind using this model as well as
consequences for our recent analyses of distributions of stock returns and
realized volatility.
Estimating Diffeomorphic Mappings between Templates and Noisy Data: Variance Bounds on the Estimated Canonical Volume Form
Date: 2018-09-17 00:00:00
Tasks: Anatomy, Form

Anatomy is undergoing a renaissance driven by availability of large digital
data sets generated by light microscopy. A central computational task is to map
individual data volumes to standardized templates. This is accomplished by
regularized estimation of a diffeomorphic transformation between the coordinate
systems of the individual data and the template, building the transformation
incrementally by integrating a smooth flow field. The canonical volume form of
this transformation is used to quantify local growth, atrophy, or cell density.
While multiple implementations exist for this estimation, less attention has
been paid to the variance of the estimated diffeomorphism for noisy data.
Notably, there is an infinite dimensional un-observable space defined by those
diffeomorphisms which leave the template invariant. These form the stabilizer
subgroup of the diffeomorphic group acting on the template. The corresponding
flat directions in the energy landscape are expected to lead to increased
estimation variance. Here we show that a least-action principle used to
generate geodesics in the space of diffeomorphisms connecting the subject brain
to the template removes the stabilizer. This provides reduced-variance
estimates of the volume form. Using simulations we demonstrate that the
asymmetric large deformation diffeomorphic mapping methods (LDDMM), which
explicitly incorporate the asymmetry between idealized template images and
noisy empirical images, provide lower variance estimators than their
symmetrized counterparts (cf. ANTs). We derive Cramer-Rao bounds for the
variances in the limit of small deformations. Analytical results are shown for
the Jacobian in terms of perturbations of the vector fields and divergence of
the vector field.
Multiple Access for Transmissions Over Independent Fading Channels
Date: 2018-07-28 00:00:00

We propose to employ a multilevel detection (MLDT) technique to allow
multiple users which respectively transmit messages over independent fading
channels to share the same resource, e.g., the same signature sequence in the
CDMA (code division multiple access) system. The users are separated by the
different channel gains including amplitudes and phases resultant from the
independent fading channels. In a CDMA system with a fixed amount of available
signature sequences, the number of users can be doubled or tripled by using
MLDT although there is the cost of some BER performance degradation.
Corrected XVA Modelling Framework and Formulae for KVA and MVA
Date: 2018-07-28 00:00:00
Tasks: regression

We discuss and clarify the XVA modelling framework specified in the paper
"MVA by replication and regression" (Risk Magazine, May 2015) for including
bilateral credit risk and funding costs in derivative pricing, and in doing so
we rectify two key errors in the valuation adjustments accounting for costs of
capital and initial margin, and present corrected formulae for KVA and MVA.
Decimation analysis in the signal processing of current to detect broken bars in induction machine
Date: 2018-07-28 00:00:00
Tasks: Fault Diagnosis

This paper presents a study on the reduction of the sampling frequency of the
current signals of an induction motor, the reductions are performed by means
time-decimation technique for digital signal processing. We have used the Fast
Fourier Transform to obtain the fault signal spectrum of broken bars. The
results have shown how the decimation technique significantly reduces the
number of operations and the time required to calculate the Fast Fourier
Transform without loss of information. This approach provides a better
performance of embedded systems for fault diagnosis based on characteristic of
amplitude signal modulation.
Analysing Shortcomings of Statistical Parametric Speech Synthesis
Date: 2018-07-28 00:00:00
Tasks: Speech Synthesis

Output from statistical parametric speech synthesis (SPSS) remains noticeably
worse than natural speech recordings in terms of quality, naturalness, speaker
similarity, and intelligibility in noise. There are many hypotheses regarding
the origins of these shortcomings, but these hypotheses are often kept vague
and presented without empirical evidence that could confirm and quantify how a
specific shortcoming contributes to imperfections in the synthesised speech.
Throughout speech synthesis literature, surprisingly little work is dedicated
towards identifying the perceptually most important problems in speech
synthesis, even though such knowledge would be of great value for creating
better SPSS systems.
  In this book chapter, we analyse some of the shortcomings of SPSS. In
particular, we discuss issues with vocoding and present a general methodology
for quantifying the effect of any of the many assumptions and design choices
that hold SPSS back. The methodology is accompanied by an example that
carefully measures and compares the severity of perceptual limitations imposed
by vocoding as well as other factors such as the statistical model and its use.
Resilience of airborne networks
Date: 2018-07-28 00:00:00
Tasks: BIG-bench Machine Learning

Networked flying platforms can be used to provide cellular coverage and
capacity. Given that 5G and beyond networks are expected to be always available
and highly reliable, resilience and reliability of these networks must be
investigated. This paper introduces the specific features of airborne networks
that influence their resilience. We then discuss how machine learning and
blockchain technologies can enhance the resilience of networked flying
platforms.
Modeling Human Decision-making: An Overview of the Brussels Quantum Approach
Date: 2018-07-29 00:00:00
Tasks: Decision Making, Logical Reasoning, Negation

We present the fundamentals of the quantum theoretical approach we have
developed in the last decade to model cognitive phenomena that resisted
modeling by means of classical logical and probabilistic structures, like
Boolean, Kolmogorovian and, more generally, set theoretical structures. We
firstly sketch the operational-realistic foundations of conceptual entities,
i.e. concepts, conceptual combinations, propositions, decision-making entities,
etc. Then, we briefly illustrate the application of the quantum formalism in
Hilbert space to represent combinations of natural concepts, discussing its
success in modeling a wide range of empirical data on concepts and their
conjunction, disjunction and negation. Next, we naturally extend the quantum
theoretical approach to model some long-standing `fallacies of human
reasoning', namely, the `conjunction fallacy' and the `disjunction effect'.
Finally, we put forward an explanatory hypothesis according to which human
reasoning is a defined superposition of `emergent reasoning' and `logical
reasoning', where the former generally prevails over the latter. The quantum
theoretical approach explains human fallacies as the consequence of genuine
quantum structures in human reasoning, i.e. `contextuality', `emergence',
`entanglement', `interference' and `superposition'. As such, it is alternative
to the Kahneman-Tversky research programme, which instead aims to explain human
fallacies in terms of `individual heuristics and biases'.
CoHSI II; The average length of proteins, evolutionary pressure and eukaryotic fine structure
Authors: Les Hatton, Gregory Warr
Date: 2018-07-29 00:00:00

The CoHSI (Conservation of Hartley-Shannon Information) distribution is at the heart of a wide-class of discrete systems, defining (amongst other properties) the length distribution of their components. Discrete systems such as the known proteome, computer software and texts are all known to fit this distribution accurately. In a previous paper, we explored the properties of this distribution in detail. Here we will use these properties to show why the average length of components in general and proteins in particular is highly conserved, howsoever measured, demonstrating this on various aggregations of proteins taken from the UniProt database. We will go on to define departures from this equilibrium state, identifying fine structure in the average length of eukaryotic proteins that result from evolutionary processes.
Sparse Representation of 3D Images for Piecewise Dimensionality Reduction with High Quality Reconstruction
Date: 2019-06-10 00:00:00
Tasks: Dimensionality Reduction

Sparse representation of 3D images is considered within the context of data
reduction. The goal is to produce high quality approximations of 3D images
using fewer elementary components than the number of intensity points in the 3D
array. This is achieved by means of a highly redundant dictionary and a
dedicated pursuit strategy especially designed for low memory requirements. The
benefit of the proposed framework is illustrated in the first instance by
demonstrating the gain in dimensionality reduction obtained when approximating
true color images as very thin 3D arrays, instead of performing an independent
channel by channel approximation. The full power of the approach is further
exemplified by producing high quality approximations of hyper-spectral images
with a reduction of up to 371 times the number of data points in the
representation.
Relative kinematics of an anchorless network
Date: 2018-07-30 00:00:00

Estimating the location of N coordinates in a P dimensional Euclidean space
from pairwise distances (or proximity measurements), is a principal challenge
in a wide variety of fields. Conventionally, when localizing a static network
of immobile nodes, non-linear dimensional reduction techniques are applied on
the measured Euclidean distance matrix (EDM) to obtain the relative coordinates
upto a rotation and translation. In this article, we focus on an anchorless
network of mobile nodes, where the distance measurements between the mobile
nodes are time-varying in nature. Furthermore, in an anchorless network the
absolute knowledge of any node positions, motion or reference frame is absent.
We derive a novel data model which relates the time-varying EDMs to the
time-varying relative positions of an anchorless network. Using this data
model, we estimate the relative position, relative velocity and higher order
derivatives, which are collectively termed as the relative kinematics of the
anchorless network. The derived data model is inherently ill-posed, however can
be solved using certain relative immobility constraints. We propose elegant
closed form solutions to recursively estimate the relative kinematics of the
network. For the sake of completeness, estimators are also proposed to find the
absolute kinematics of the nodes, given known reference anchors. Cramer-Rao
bounds are derived for the new data model and simulations are performed to
analyze the performance of the proposed solutions.
Are theoretical results 'Results'?
Authors: Raymond E. Goldstein
Date: 2018-07-30 00:00:00

Yes.
A factor-model approach for correlation scenarios and correlation stress-testing
Date: 2019-01-28 00:00:00

In 2012, JPMorgan accumulated a USD~6.2 billion loss on a credit derivatives
portfolio, the so-called `London Whale', partly as a consequence of
de-correlations of non-perfectly correlated positions that were supposed to
hedge each other. Motivated by this case, we devise a factor model for
correlations that allows for scenario-based stress testing of correlations. We
derive a number of analytical results related to a portfolio of homogeneous
assets. Using the concept of Mahalanobis distance, we show how to identify
adverse scenarios of correlation risk. In addition, we demonstrate how
correlation and volatility stress tests can be combined. As an example, we
apply the factor-model approach to the "London Whale" portfolio and determine
the value-at-risk impact from correlation changes. Since our findings are
particularly relevant for large portfolios, where even small correlation
changes can have a large impact, a further application would be to stress test
portfolios of central counterparties, which are of systemically relevant size.
Polarization under rising inequality and economic decline
Date: 2020-04-24 00:00:00

Social and political polarization is a significant source of conflict and
poor governance in many societies. Thus, understanding its causes has become a
priority of scholars across many disciplines. Here we demonstrate that shifts
in socialization strategies analogous to political polarization and identity
politics can arise as a locally-beneficial response to both rising wealth
inequality and economic decline. Adopting a perspective of cultural evolution,
we develop a framework to study the emergence of polarization under shifting
economic environments. In many contexts, interacting with diverse out-groups
confers benefits from innovation and exploration greater than those that arise
from interacting exclusively with a homogeneous in-group. However, when the
economic environment favors risk-aversion, a strategy of seeking low-risk
interactions can be important to maintaining individual solvency. To capture
this dynamic, we assume that in-group interactions have a lower expected
outcome, but a more certain one. Thus in-group interactions are less risky than
out-group interactions. Our model shows that under conditions of economic
decline or increasing wealth inequality, some members of the population benefit
from adopting a risk-averse, in-group favoring strategy. Moreover, we show that
such in-group polarization can spread rapidly to the whole population and
persist even when the conditions that produced it have reversed. Finally we
offer empirical support for the role of income inequality as a driver of
affective polarization in the United States, mirroring findings on a panel of
developed democracies. Our work provides a framework for studying how disparate
forces interplay, via cultural evolution, to shape patterns of identity, and
unifies what are often seen as conflicting explanations for political
polarization: identity threat versus economic anxiety.
Sub-Nyquist Radar Systems: Temporal, Spectral and Spatial Compression
Date: 2018-07-30 00:00:00
Tasks: compressed sensing

Conventional radar transmits electromagnetic waves towards the targets of
interest. In between the outgoing pulses, the radar measures the signal
reflected from the targets to determine their presence, range, velocity and
other characteristics. Radar systems face multiple challenges, generating many
trade-offs such as bandwidth versus range resolution and dwell time versus
Doppler resolution. In MIMO radar, high resolution requires a large aperture
and high number of antennas, increasing hardware and processing requirements.
  Recently, novel approaches in sampling theory and radar signal processing
have been proposed to allow target detection and parameter recovery from
samples obtained below the Nyquist rate. These techniques exploit the sparsity
of the target scene in order to reduce the required number of samples, pulses
and antennas, breaking the link between bandwidth, dwell time and number of
antennas on the one hand and range, Doppler and azimuth resolution,
respectively, on the other.
  This review introduces this so-called sub-Nyquist radar paradigm and
describes the corresponding sampling and recovery algorithms, that leverage
compressed sensing techniques to perform time and spatial compression. We focus
on non radar imaging applications and survey many recent compressed radar
systems. Our goal is to review the main impacts of compressed radar on
parameter resolution as well as digital and analog complexity. The survey
includes fast and slow time compression schemes as well as spatial compression
approaches. We show that beyond substantial rate reduction, compression may
also enable communication and radar spectrum sharing. Throughout the paper, we
consider both theoretical and practical aspects of compressed radar, and
present hardware prototype implementations, demonstrating real-time target
parameter recovery from low rate samples in pulse-Doppler and MIMO radars.
Microarrays denoising via smoothing of coefficients in wavelet domain
Date: 2018-07-10 00:00:00
Tasks: Denoising

We describe a novel method for removing noise (in wavelet domain) of unknown
variance from microarrays. The method is based on a smoothing of the
coefficients of the highest subbands. Specifically, we decompose the noisy
microarray into wavelet subbands, apply smoothing within each highest subband,
and reconstruct a microarray from the modified wavelet coefficients. This
process is applied a single time, and exclusively to the first level of
decomposition, i.e., in most of the cases, it is not necessary a
multirresoltuion analysis. Denoising results compare favorably to the most of
methods in use at the moment.