pwc-archive/papers-with-abstracts huggingface dataset:
Downlink channel spatial covariance estimation in realistic FDD massive MIMO systems
Date: 2018-10-25 00:00:00

The knowledge of the downlink (DL) channel spatial covariance matrix at the
BS is of fundamental importance for large-scale array systems operating in
frequency division duplexing (FDD) mode. In particular, this knowledge plays a
key role in the DL channel state information (CSI) acquisition. In the massive
MIMO regime, traditional schemes based on DL pilots are severely limited by the
covariance feedback and the DL training overhead. To overcome this problem,
many authors have proposed to obtain an estimate of the DL spatial covariance
based on uplink (UL) measurements. However, many of these approaches rely on
simple channel models, and they are difficult to extend to more complex models
that take into account important effects of propagation in 3D environments and
of dual-polarized antenna arrays. In this study we propose a novel technique
that takes into account the aforementioned effects, in compliance with the
requirements of modern 4G and 5G system designs. Numerical simulations show the
effectiveness of our approach.
Improving the Representation and Conversion of Mathematical Formulae by Considering their Textual Context
Authors: Schubotz Moritz, Greiner-Petter Andre, Scharpf Philipp, Meuschke Norman, Cohl Howard, Gipp Bela
Date: 2018-04-13 00:00:00
Tasks: Information Retrieval, Retrieval

Mathematical formulae represent complex semantic information in a concise
form. Especially in Science, Technology, Engineering, and Mathematics,
mathematical formulae are crucial to communicate information, e.g., in
scientific papers, and to perform computations using computer algebra systems.
Enabling computers to access the information encoded in mathematical formulae
requires machine-readable formats that can represent both the presentation and
content, i.e., the semantics, of formulae. Exchanging such information between
systems additionally requires conversion methods for mathematical
representation formats. We analyze how the semantic enrichment of formulae
improves the format conversion process and show that considering the textual
context of formulae reduces the error rate of such conversions. Our main
contributions are: (1) providing an openly available benchmark dataset for the
mathematical format conversion task consisting of a newly created test
collection, an extensive, manually curated gold standard and task-specific
evaluation metrics; (2) performing a quantitative evaluation of
state-of-the-art tools for mathematical format conversions; (3) presenting a
new approach that considers the textual context of formulae to reduce the error
rate for mathematical format conversions. Our benchmark dataset facilitates
future research on mathematical format conversions as well as research on many
problems in mathematical information retrieval. Because we annotated and linked
all components of formulae, e.g., identifiers, operators and other entities, to
Wikidata entries, the gold standard can, for instance, be used to train methods
for formula concept discovery and recognition. Such methods can then be applied
to improve mathematical information retrieval systems, e.g., for semantic
formula search, recommendation of mathematical content, or detection of
mathematical plagiarism.
Language Recognition using Time Delay Deep Neural Network
Date: 2018-04-13 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), regression, speech-recognition, Speech Recognition

This work explores the use of a monolingual Deep Neural Network (DNN) model
as an universal background model (UBM) to address the problem of Language
Recognition (LR) in I-vector framework. A Time Delay Deep Neural Network
(TDDNN) architecture is used in this work, which is trained as an acoustic
model in an English Automatic Speech Recognition (ASR) task. A logistic
regression model is trained to classify the I-vectors. The proposed system is
tested with fourteen languages with various confusion pairs and it can be
easily extended to include a new language by just retraining the last simple
logistic regression model. The architectural flexibility is the major advantage
of the proposed system compared to the single DNN classifier based approach.
Higher order molecular organisation as a source of biological function
Date: 2018-09-20 00:00:00

Molecular interactions have widely been modelled as networks. The local
wiring patterns around molecules in molecular networks are linked with their
biological functions. However, networks model only pairwise interactions
between molecules and cannot explicitly and directly capture the higher order
molecular organisation, such as protein complexes and pathways. Hence, we ask
if hypergraphs (hypernetworks), that directly capture entire complexes and
pathways along with protein-protein interactions (PPIs), carry additional
functional information beyond what can be uncovered from networks of pairwise
molecular interactions. The mathematical formalism of a hypergraph has long
been known, but not often used in studying molecular networks due to the lack
of sophisticated algorithms for mining the underlying biological information
hidden in the wiring patterns of molecular systems modelled as hypernetworks.
  We propose a new, multi-scale, protein interaction hypernetwork model that
utilizes hypergraphs to capture different scales of protein organization,
including PPIs, protein complexes and pathways. In analogy to graphlets, we
introduce hypergraphlets, small, connected, non-isomorphic, induced
sub-hypergraphs of a hypergraph, to quantify the local wiring patterns of these
multi-scale molecular hypergraphs and to mine them for new biological
information. We apply them to model the multi-scale protein networks of baker
yeast and human and show that the higher order molecular organisation captured
by these hypergraphs is strongly related to the underlying biology.
Importantly, we demonstrate that our new models and data mining tools reveal
different, but complementary biological information compared to classical PPI
networks. We apply our hypergraphlets to successfully predict biological
functions of uncharacterised proteins.
The Determinants of Home Bias in Stock Portfolio: An Emerging and Developed Markets Study
Date: 2018-03-26 00:00:00
Tasks: Econometrics

The objective of this paper is to measure the degree of home bias (HB) within
holdings portfolio and to identify their determining factors. By following
literature and an international capital asset pricing model, we have chosen
quite a number of susceptible factors that impact HB. This model is, hence,
estimated for 20 countries, with cross-section econometrics, between 2008 and
2013. Our results show that all countries have recorded a high level of HB in
their holdings portfolio. After that, we test if the HB of the emerging markets
and that of the developed markets react differently to the determining factors.
The volatility of the exchange rate is statistically significant with emerging
markets, while it is hardly remarkable for the developed countries.
Co-variance, size, distance, language, legal framework and foreign organization
stocks prevents American investors to invest abroad.
Object Detection and Geometric Profiling through Dirty Water Media Using Asymmetry Properties of Backscattered Signals
Authors: Wu Chensheng, Lee Robert, Davis Christopher C.
Date: 2018-04-13 00:00:00
Tasks: object-detection, Object Detection

The scattering of light observed through the turbid underwater channel is
often regarded as the leading challenge when designing underwater
electro-optical imaging systems. There have been many approaches to address the
effects of scattering such as using pulsed laser sources to reject scattered
light temporally, or using intensity modulated waveforms and matched filters to
remove the scattered light spectrally. In this paper, a new method is proposed
which primarily uses the backscattering asymmetry property for object detection
and geometric profiling. In our approach, two parallel and identical continuous
wave (CW) laser beams with narrow beam widths (~2mm) are used as active
illumination sources. The two beams also have controllable spacing and aiming
angle, as well as initial phase difference for convenience of scanning and
profiling a target. Through theory and experimental results, it will be shown
that when an object leans or tilts towards one of the beam's central
trajectory, the asymmetry in the backscattered signals can be used to indicate
the location or slope of the target's surface, respectively. By varying the
spacing or aiming angle of the two beams, a number of surface samples can be
collected to reconstruct the object's shape geometrically. The resolution and
range limit of our approach are also measured and reported in this work. In
application, our proposed method provides an economic solution to perform
imaging through turbid underwater environments. Additionally, the idea can be
combined with the pulsed or modulated laser signals for enhanced imaging
results.
A survey and a new selection criterion for statistical home range estimation
Date: 2018-04-13 00:00:00
Tasks: Open-Ended Question Answering

The home range of a specific animal describes the geographic area where this
individual spends most of the time while carrying out its usual activities
(eating, resting, reproduction, ...). Although a well-established definition of
this concept is lacking, there is a variety of home range estimators. The first
objective of this work is to review and categorize the statistical
methodologies proposed in the literature to approximate the home range of an
animal, based on a sample of observed locations. The second aim is to address
the open question of choosing the "best" home range from a collection of them
based on the same sample. We introduce a numerical index, based on a
penalization criterion, to rank the estimated home ranges. The key idea is to
balance the excess area covered by the estimator (with respect to the original
sample) and a shape descriptor measuring the over-adjustment of the home range
to the data. To our knowledge, apart from computing the home range area, our
ranking procedure is the first one which is both applicable to real data and to
any type of home range estimator. Further, the optimization of the selection
index provides in fact a way to select the smoothing parameter for the kernel
home range estimator. For clarity of exposition, we have applied all the
estimation procedures and our selection proposal to a set of real locations of
a Mongolian wolf using R as the statistical software. As a byproduct, this
review contains a thorough revision of the implementation of home range
estimators in the R language.
How reliable are the Powell-Wetherall plot method and the maximum-length approach? Implications for length-based studies of growth and mortality
Date: 2018-04-14 00:00:00

Length-based methods are the cornerstone of many population studies and stock
assessments. This study tested two widely used methods: the Powell-Wetherall
(P-W) plot and the Lmax approach, i.e., estimating Linf directly from Lmax. In
most simulations, P-W estimates of the ratio total mortality / growth (Z/K
ratio) were biased beyond acceptable limits (bias > 30%). Bias in Z/K showed a
complex behavior, without possible corrections. Estimates of asymptotic length
(Linf) were less biased than Z/K, but were very sensible to intra-cohort
variability in growth and to changes in the occurrence of large individuals in
the sample. Exclusion of the largest size classes during the regression
procedure or weighing by abundance does not solve these issues. Perfect
linearization of the data and absurdly narrow confidence intervals for Z/K will
lead users to erroneous overconfidence in outputs. Clearly, the P-W method is
not suitable for the assessment of Z/K ratios of natural populations.
Estimation of Linf may be tentatively possible under very specific conditions,
with necessary external verifications. Also, this study demonstrates that there
is no way to estimate Linf directly from Lmax, since there is no particular
relationship to expect a priori between Linf and Lmax. Errors in estimating
Linf directly affect the estimate of the growth constant K and all other
subsequent calculations in population studies, stock assessments and ecosystem
models. New approaches are urgently needed for length-based studies of body
growth (e.g., unconstrained curve fit with subsequent bootstrapping), that
consider the inherent uncertainty regarding the underlying data and processes.
Amino-acid network clique analysis of protein mutation correlation effects: a case study of lysozme
Date: 2018-04-14 00:00:00

Optimizing amino-acid mutations has been a most challenging task in modern
bio- industrial enzyme designing. It is well known that many successful designs
often hinge on extensive correlations among mutations at different sites within
the enzyme, however, the underpinning mechanism for these correlations is far
from clear. Here, we present a topology-based model to quantitively
characterize correlation effects between mutations. The method is based on the
molecular dynamic simulations and the amino-acid network clique analysis that
simply examines if two single mutation sites belong to some 3-clique. We
analyzed 13 dual mutations of T4 phage lysozyme and found that the clique-based
model successfully distinguishes highly correlated or non-additive double-site
mutations from those with less correlation or additive mutations. We also
applied the model to the protein Eglin c whose topology is significantly
distinct from that of T4 phage lysozyme, and found that the model can, to some
extension, still identify non-additive mutations from additive ones. Our
calculations showed that mutation correlation effects may heavily depend on
topology relationship among mutation sites, which can be quantitatively
characterized using amino-acid network k-cliques. We also showed that
double-site mutation correlations can be significantly altered by exerting a
third mutation, indicating that more detailed physico-chemistry interactions
might be considered with the network model for better understanding of the
elusive mutation-correlation principle.
Parcels of Universe or why Schr\"odinger and Fourier are so relatives?
Date: 2018-06-18 00:00:00
Tasks: Quantization

This paper is about the surprising connection between the Fourier heat
equation and the Schr\"odinger wave equation. In fact, if the independent
"time" variable in the heat equation is replaced by the time variable
multiplied by $i=\sqrt{-1}$, the heat equation becomes the Schr\"odinger
equation. Two quite different physical phenomena are put in close connection:
the heat diffusion in a material and the probability amplitude of particles in
an atom. It is a fact of life that the movements of a small particle floating
randomly in a fluid, the well-known Brownian motion, is regulated by the
Fourier equation while the probabilistic behavior of the matter around us, the
quantum world, is driven by the Schr\"odinger equation but no known stochastic
process seems at work here. The apparent simplicity of the formal connection by
a "time-rotation", a Wick rotation as it is commonly known, seems to point
otherwise. Why this connection? Is there any physical intuitive explanation? Is
there any practical value? In this paper, the authors attempt to shed some
light on the above questions. The recent concept of volume quantization in
noncommutative geometry, due to Connes, Chamseddine and Mukhanov, points again
to stochastic processes also underlying the quantum world making Fourier and
Schr\"oodinger strict relatives.
Navigating temporal networks
Date: 2018-04-14 00:00:00

Navigation on graphs is the problem how an agent walking on the graph can get
from a source to a target with limited information about the graph. The
information and the way to exploit it can vary. In this paper, we study
navigation on temporal networks -- networks where we have explicit information
about the time of the interaction, not only who interacts with whom. We
contrast a type of greedy navigation -- where agents follow paths that would
have worked well in the past -- with two strategies that do not exploit the
additional information. We test these on empirical temporal network data sets.
The greedy navigation is indeed more efficient than the reference strategies,
meaning that there are correlations in the real temporal networks that can be
exploited. We find that the navigability for individual nodes is most strongly
correlated with degree and burstiness, i.e., both topological and temporal
structures affect the navigation efficiency.
Robust entropy requires strong and balanced excitatory and inhibitory synapses
Date: 2018-04-14 00:00:00

It is widely appreciated that well-balanced excitation and inhibition are
necessary for proper function in neural networks. However, in principle, such
balance could be achieved by many possible configurations of excitatory and
inhibitory strengths, and relative numbers of excitatory and inhibitory
neurons. For instance, a given level of excitation could be balanced by either
numerous inhibitory neurons with weak synapses, or few inhibitory neurons with
strong synapses. Among the continuum of different but balanced configurations,
why should any particular configuration be favored? Here we address this
question in the context of the entropy of network dynamics by studying an
analytically tractable network of binary neurons. We find that entropy is
highest at the boundary between excitation-dominant and inhibition-dominant
regimes. Entropy also varies along this boundary with a trade-off between high
and robust entropy: weak synapse strengths yield high network entropy which is
fragile to parameter variations, while strong synapse strengths yield a lower,
but more robust, network entropy. In the case where inhibitory and excitatory
synapses are constrained to have similar strength, we find that a small, but
non-zero fraction of inhibitory neurons, like that seen in mammalian cortex,
results in robust and relatively high entropy.
Distributions of Historic Market Data -- Implied and Realized Volatility
Date: 2018-04-14 00:00:00

We undertake a systematic comparison between implied volatility, as
represented by VIX (new methodology) and VXO (old methodology), and realized
volatility. We compare visually and statistically distributions of realized and
implied variance (volatility squared) and study the distribution of their
ratio. We find that the ratio is best fitted by heavy-tailed -- lognormal and
fat-tailed (power-law) -- distributions, depending on whether preceding or
concurrent month of realized variance is used. We do not find substantial
difference in accuracy between VIX and VXO. Additionally, we study the variance
of theoretical realized variance for Heston and multiplicative models of
stochastic volatility and compare those with realized variance obtained from
historic market data.
A non-linear convex cost model for economic dispatch in microgrids
Date: 2018-04-15 00:00:00

This paper proposes a convex non-linear cost saving model for optimal
economic dispatch in a microgrid. The mod-el incorporates energy storage
degradation cost and intermittent renewable generation. Cell degradation cost
being a non-linear model, its incorporation in an objective function alters the
convexity of the optimization problem and stochastic algorithms are required
for its solution. This paper builds on the scope for usage of macroscopically
semi-empirical models for degradation cost in economic dispatch problems and
proves that these cost models derived from the existing semi-empirical capacity
fade equations for LiFePO4 cells are convex under some operating condi-tions.
The proposed non-linear model was tested on two data sets of varying size which
portray different trends of seasonality. The results show that the model
reflects the trends of seasonality existing in the data sets and it mini-mizes
the total fuel cost globally when compared to conventional systems of economic
dispatch. The results thus indicate that the model achieves a more accurate
estimate of fuel cost in the system and can be effectively utilized for cost
analysis in power system applications.
Shapley Value Methods for Attribution Modeling in Online Advertising
Date: 2018-04-15 00:00:00
Tasks: Computational Efficiency

This paper re-examines the Shapley value methods for attribution analysis in
the area of online advertising. As a credit allocation solution in cooperative
game theory, Shapley value method directly quantifies the contribution of
online advertising inputs to the advertising key performance indicator (KPI)
across multiple channels. We simplify its calculation by developing an
alternative mathematical formulation. The new formula significantly improves
the computational efficiency and therefore extends the scope of applicability.
Based on the simplified formula, we further develop the ordered Shapley value
method. The proposed method is able to take into account the order of channels
visited by users. We claim that it provides a more comprehensive insight by
evaluating the attribution of channels at different stages of user conversion
journeys. The proposed approaches are illustrated using a real-world online
advertising campaign dataset.
Intertrochanteric Fracture Visualization and Analysis Using a Map Projection Technique
Date: 2018-10-13 00:00:00
Tasks: Prognosis

Understanding intertrochanteric fracture distribution is an important topic
in orthopaedics due to its high morbidity and mortality. The intertrochanteric
fracture can contain high-dimensional information including complicated 3D
fracture lines, which often make it difficult to visualize or to obtain
valuable statistics for clinical diagnosis and prognosis applications. This
paper proposed a map projection technique to map the high-dimensional
information into a 2D parametric space. This method can preserve the 3D
proximal femur surface and structure while visualizing the entire fracture line
with a single plot/view. Using this method and a standardization technique, a
total of 100 patients with different ages and genders are studied based on the
original radiographs acquired by CT scan. The comparison shows that the
proposed map projection representation is more efficient and rich in
information visualization than the conventional heat map technique. Using the
proposed method, a fracture probability can be obtained at any location in the
2D parametric space, from which the most probable fracture region can be
accurately identified. The study shows that age and gender have significant
influences on intertrochanteric fracture frequency and fracture line
distribution.
The Italian Pension Gap: a Stochastic Optimal Control Approach
Date: 2018-04-15 00:00:00

We study the gap between the state pension provided by the Italian pension
system pre-Dini reform and post-Dini reform. The goal is to fill the gap
between the old and the new pension by joining a defined contribution pension
scheme and adopting an optimal investment strategy that is target-based. We
find that it is possible to cover, at least partially, this gap with the
additional income of the pension scheme, especially in the presence of late
retirement and in the presence of stagnant career. Workers with dynamic career
and workers who retire early are those who are most penalised by the reform.
Results are intuitive and in line with previous studies on the subject.