pwc-archive/papers-with-abstracts huggingface dataset:
Quantitative Cost and Schedule Risk Analysis of Nuclear Waste Storage
Date: 2019-01-13 00:00:00

This study provides an independent, outside-in estimate of the cost and
schedule risks of nuclear waste storage projects. Based on a reference class of
216 past, comparable projects, risk of cost overrun was found to be 202% or
less, with 80% certainty, i.e., 20% risk of an overrun above 202%. Based on a
reference class of 200 past, comparable projects, risk of schedule overrun was
found to be 104% or less, with 80% certainty, i.e., 20% risk of overrun above
104%. Cost risk and schedule risk are both substantial for nuclear waste
storage projects.
Proteomic and metagenomic insights into prehistoric Spanish Levantine Rock Art
Date: 2019-01-24 00:00:00

The Iberian Mediterranean Basin is home to one of the largest groups of
prehistoric rock art sites in Europe. Despite the cultural relevance of
prehistoric Spanish Levantine rock art, pigment composition remains partially
unknown, and the nature of the binders used for painting has yet to be
disclosed. In this work, we present the first omic analysis applied to one of
the flagship Levantine rock art sites: the Valltorta ravine (Castell{\'o}n,
Spain). We used high-throughput sequencing to provide the first description of
the bacterial communities colonizing the rock art patina, which proved to be
dominated by Firmicutes species and might have a protective effect on the
paintings. Proteomic analysis was also performed on rock art microsamples in
order to determine the organic binders present in Levantine prehistoric rock
art pigments. This information could shed light on the controversial dating of
this UNESCO Cultural Heritage, and contribute to defining the chrono-cultural
framework of the societies responsible for these paintings.
Compressive Initial Access and Beamforming Training for Millimeter-Wave Cellular Systems
Date: 2019-01-31 00:00:00
Tasks: PICO

Initial access (IA) is a fundamental physical layer procedure in cellular
systems where user equipment (UE) detects nearby base station (BS) as well as
acquire synchronization. Due to the necessity of using antenna array in
millimeter-wave (mmW) IA, the channel spatial information can also be inferred.
The state-of-the-art directional IA (DIA) uses sector sounding beams with
limited angular resolution, and thus requires additional dedicated radio
resources, access latency and overhead for refined beam training. To remedy the
problem of access latency and overhead in DIA, this work proposes to use a
quasi-omni pseudorandom sounding beam for IA, and develops a novel algorithm
for joint initial access and fine resolution initial beam training without
requiring extra radio resources. We provide the analysis of the proposed
algorithm miss detection rate under synchronization error, and further derive
Cram\'er-Rao lower bound of angular estimation under frequency offset. Using
QuaDRiGa simulator with mmMAGIC model at 28 GHz, the numerical results show
that the proposed approach is advantageous to DIA with hierarchical beam
training. The proposed algorithm offers up to two order of magnitude access
latency saving compared to DIA, when the same discovery, post training SNR, and
overhead performance are targeted. This conclusion holds true in various
propagation environments and 3D locations of a mmW pico-cell with up to 140m
radius.
Ligand-induced oligomerization of the human GPCR neurotensin receptor 1 monitored in living HEK293T cells
Date: 2019-01-31 00:00:00

The human neurotensin receptor 1 (NTSR1) is a G protein-coupled receptor that
can be expressed in HEK293T cells by stable transfection. Its ligand is a
13-amino-acid peptide that binds with nanomolar affinity from the extracellular
side to NTSR1. Ligand binding induces conformational changes that trigger the
intracellular signaling processes. Recent single-molecule studies revealed a
dynamic monomer - dimer equilibrium of the receptor in vitro. Here we report on
the oligomerization state of the human NTSR1 in the plasma membrane of HEK293T
cells in vivo. We fused different fluorescent marker proteins mRuby3 or
mNeonGreen to the C-terminus of NTSR1 and mutated a tetracysteine motif into
intracellular loop 3 (ICL3) for subsequent FlAsH labeling. Oligomerization of
NTSR1 was monitored before and after stimulation of the receptor with its
ligand by FLIM and homoFRET microscopy (i.e. Forster resonance energy transfer
between identical fluorophores detected by fluorescence anisotropy), by
colocalization microscopy and by time-lapse imaging using structured
illumination microscopy (SIM).
Taxation of a GMWB Variable Annuity in a Stochastic Interest Rate Model
Authors: Andrea Molent
Date: 2019-01-31 00:00:00

Modeling taxation of Variable Annuities has been frequently neglected but accounting for it can significantly improve the explanation of the withdrawal dynamics and lead to a better modeling of the financial cost of these insurance products. The importance of including a model for taxation has first been observed by Moenig and Bauer (2016) while considering a GMWB Variable Annuity. In particular, they consider the simple Black-Scholes dynamics to describe the underlying security. Nevertheless, GMWB are long term products and thus accounting for stochastic interest rate has relevant effects on both the financial evaluation and the policy holder behavior, as observed by Gouden\`ege et al. (2018). In this paper we investigate the outcomes of these two elements together on GMWB evaluation. To this aim, we develop a numerical framework which allows one to efficiently compute the fair value of a policy. Numerical results show that accounting for both taxation and stochastic interest rate has a determinant impact on the withdrawal strategy and on the cost of GMWB contracts. In addition, it can explain why these products are so popular with people looking for a protected form of investment for retirement.
Condensation of nucleoid in Escherichia coli cell as a result of prolonged starvation
Date: 2019-01-31 00:00:00

Electron microscopy and X-ray diffraction studies of dormant E. coli cells
revealed several forms of nucleoid condensation: quasi - nanocrystalline, quasi
-liquid crystalline (or spore-like) and folded nucleosome - like structure. Of
particular interest is the third type of structure since it was described here
for the first time: the folded nucleosome-like. Such a structure has no
relation to the toroidal DNA organization, which are the intermediate form in
the formation of the quasi - nanocrystalline structure. Results observed here
shed a new light both on the phenomenon of nucleoid condensation in prokaryotic
cells and on the general problem of developing a response to stress. It was
found out that there is no single mechanism for nucleoid condensation in the
population of a dormant cell; diversity in their number, shape and packing has
been seen. According to the recognized concept of a bacterial population as a
multicellular organism, its heterogeneity allows to respond flexibly to the
environmental changes and to survive in stressful situations. That is the
reason why we observed at least three types of nucleoid condensation in dormant
E. coli cells. Heterogeneity of dormant cells increases the ability of the
whole population to survive under various stress conditions. For better
understanding of the nucleoid condensation mechanism, it is necessary to study
the reverse transition of the nucleoid in bacteria from the dormant to the
functional state.
Waves of desertification in a competitive ecosystem
Date: 2019-01-31 00:00:00
Tasks: Management

We study a mathematical model inspired by a common scenario in northern
Patagonia consisting of humans and their livestock living together with native
wildlife. The main production system in this area is sheep farming, which due
to wrong historical management has led to desertification of the habitat, with
impact on both native wildlife and livestock. Particularly the largest native
herbivore, the guanaco, has reflected in their numbers this alteration induced
in the environment. We analyze a mathematical model that captures the main
characteristics of the interaction between sheep and guanaco: the hierarchical
competition and the advantage granted by humans to the herds, and also
incorporates a dynamic for the habitat. Using the metapopulation formalism, the
trophic web of two herbivores is extended over a patchy landscape that
considers two characteristic times for the dynamics of the resource. Our study
stresses the dependence of the metapopulations dynamics on the recovery time of
the resource. These results are backed up by a deterministic mean field model
which shares some similarities with the stochastic and spatially extended one.
We find different regimes depending on the parameters considered: coexistence
of both species, survival of a single species and extinction of the other, and
extinction of both. Remarkably, in some regions of parameters space we detect
the presence of periodical spatio-temporal patterns, with persistent
oscillations of constant amplitude. Based on these results, we perform a
characterization of the observed scenarios in order to gain insight about the
system.
An InfoVis Tool for Interactive Component-Based Evaluation
Authors: Rocco Giacomo, Silvello Gianmaria
Date: 2019-01-31 00:00:00

In this paper, we present an InfoVis tool based on Sankey diagrams for the
exploration of large combinatorial combinations of IR components - the Grid of
Points (GoP). The goal of this tool is to ease the comprehension of the
behavior of single IR components within fully functioning off-the-shelf IR
systems without recurring to complex statistical tools.
Battery selection for optimal grid-outage resilient photovoltaic and battery systems
Date: 2019-01-22 00:00:00

The first and most important purpose of the current research work is to
investigate the effects that different battery types have on the optimal
configuration of photovoltaic (PV) and battery systems, from both economic and
resilience perspectives. Many industry reports, as well as research papers,
have already highlighted the crucial role that storage systems have in the
coming years in the electricity sector, especially when combined with renewable
energy systems (RES). Given the high cost of storage technologies, there is an
urgent need for optimizing such integrated energy systems. In this paper, a
simulation-based method is adopted and improved, in order to compare different
battery types based on their characteristics by considering projected trends in
the future. After the introduction of four different battery types, i.e.
lead-acid, sodium sulphur, vanadium redox and lithium-ion, the mathematical
model for the optimization problem is presented, along with the required
explanations. Subsequently, a case study is described and the numerical
assumptions are defined. Therein, our specific focus addresses the different
values that the four battery types possess in three critical parameters, i.e.
battery cost, efficiency and depth of discharge (DoD). Finally, results and
discussion are provided in an illustrative and informative way. This model
provides a useful guide for relevant future work in the area, and also serves
as a baseline for more comprehensive methodologies regarding optimal sizing of
photovoltaic and battery systems.
On the Theoretical Limits of Beam Steering in mmWave Massive MIMO Channels
Date: 2019-01-30 00:00:00

Analog Beamsteering (ABS) has emerged as a low complexity, power efficient
solution for MillimeterWave (mmWave) massive Multiple Input Multiple Output
(MIMO) systems. Moreover, driven by the low spatial correlation between the
User Terminals (UTs) with high number of transmit antennas (massive MIMO) at
the Base Station (BS), ABS can be used to support Multi User (MU) MIMO
scenarios instead of digital or Hybrid Beamforming (HBF). However, we show in
this paper, that even with high number of transmit antennas, the HBF can
achieve better Spectral Efficiency (SE) compared to the MU ABS even in pure
Line of Sight (LoS) channels. Moreover, we prove that the MU ABS saturates to a
constant SE at high transmit Signal to Noise Ratio (SNR) and we theoretically
derive an approximation to that saturation bound in this paper. On the other
hand, we highlight that the HBF's SE scales with the transmit SNR even in high
SNR regime. Finally, given the same power consumption and hardware complexity
as the MU ABS case, we show that HBF asymptotically achieves the optimal SE
(ideal non interference scenario) when increasing the number of transmit
antennas.
Optimal Sampling for Dynamic Complex Networks with Graph-Bandlimited Initialization
Date: 2019-04-27 00:00:00

Many engineering, social, and biological complex systems consist of dynamical
elements connected via a large-scale network. Monitoring the network's dynamics
is essential for a variety of maintenance and scientific purposes. Whilst we
understand how to optimally sample a single dynamic element or a non-dynamic
graph, we do not possess a theory on how to optimally sample networked
dynamical elements. Here, we study nonlinear dynamic graph signals on a fixed
complex network. We define the necessary conditions for optimal sampling in the
combining time- and graph-domain to fully recover the networked dynamics. We
firstly interpret the networked dynamics into a linearized matrix. Then, we
prove that the dynamic signals can be sampled and fully recovered if the
networked dynamics is stable and their initialization is bandlimited in the
graph spectral domain. This new theory directly maps optimal sampling locations
and rates to the graph properties and governing nonlinear dynamics. This can
inform the optimal placement of experimental probes and sensors on dynamical
networks as well as inform the design of each sensor's optimal sampling rate.
We motivate the reader with two examples of recovering the networked dynamics
for: social population growth and networked protein biochemical interactions
with both bandlimited and arbitrary initialization.
A Real-Time Optimal Eco-driving for Autonomous Vehicles Crossing Multiple Signalized Intersections
Date: 2019-01-31 00:00:00
Tasks: Autonomous Vehicles

This paper develops an optimal acceleration/speed profile for a single
autonomous vehicle crossing multiple signalized intersections without stopping
in free flow mode. The design objective is to produce both time and energy
efficient acceleration profiles of autonomous vehicles based on vehicle to
infrastructure communication. Our design approach differs from most existing
approaches based on numerical calculations: it begins with identifying the
structure of the optimal acceleration profile and then showing that it is
characterized by several parameters, which are used for design optimization.
Therefore, the infinite dimensional optimal control problem is transformed into
a finite dimensional parametric optimization problem, which enables a real-time
online analytical solution. The simulation results show quantitatively the
advantages of considering multiple intersections jointly rather than dealing
with them individually. Based on mild assumptions, the optimal eco-driving
algorithm is readily extended to include interfering traffic.
Sparse Array DFT Beamformers for Wideband Sources
Date: 2019-01-31 00:00:00

Sparse arrays are popular for performance optimization while keeping the
hardware and computational costs down. In this paper, we consider sparse arrays
design method for wideband source operating in a wideband jamming environment.
Maximizing the signal-to-interference plus noise ratio (MaxSINR) is adopted as
an optimization objective for wideband beamforming. Sparse array design problem
is formulated in the DFT domain to process the source as parallel narrowband
sources. The problem is formulated as quadratically constraint quadratic
program (QCQP) alongside the weighted mixed $l_{1-\infty}$-norm squared
penalization of the beamformer weight vector. The semidefinite relaxation (SDR)
of QCQP promotes sparse solutions by iteratively re-weighting beamformer based
on previous iteration. It is shown that the DFT approach reduces the
computational cost considerably as compared to the delay line approach, while
efficiently utilizing the degrees of freedom to harness the maximum output SINR
offered by the given array aperture.
Descriptor system techniques and software tools
Authors: Andreas Varga
Date: 2019-01-31 00:00:00

The role of the descriptor system representation as basis for reliable numerical computations for system analysis and synthesis, and in particular, for the manipulation of rational matrices, is discussed and available robust numerical software tools are described.
Image Reconstruction from Undersampled Confocal Microscopy Data using Multiresolution Based Maximum Entropy Regularization
Date: 2019-02-17 00:00:00
Tasks: Compressive Sensing, Image Reconstruction

We consider the problem of reconstructing 2D images from randomly
under-sampled confocal microscopy samples. The well known and widely celebrated
total variation regularization, which is the L1 norm of derivatives, turns out
to be unsuitable for this problem; it is unable to handle both noise and
under-sampling together. This issue is linked with the notion of phase
transition phenomenon observed in compressive sensing research, which is
essentially the break-down of total variation methods, when sampling density
gets lower than certain threshold. The severity of this breakdown is determined
by the so-called mutual incoherence between the derivative operators and
measurement operator. In our problem, the mutual incoherence is low, and hence
the total variation regularization gives serious artifacts in the presence of
noise even when the sampling density is not very low. There has been very few
attempts in developing regularization methods that perform better than total
variation regularization for this problem. We develop a multi-resolution based
regularization method that is adaptive to image structure. In our approach, the
desired reconstruction is formulated as a series of coarse-to-fine
multi-resolution reconstructions; for reconstruction at each level, the
regularization is constructed to be adaptive to the image structure, where the
information for adaption is obtained from the reconstruction obtained at
coarser resolution level. This adaptation is achieved by using maximum entropy
principle, where the required adaptive regularization is determined as the
maximizer of entropy subject to the information extracted from the coarse
reconstruction as constraints. We demonstrate the superiority of the proposed
regularization method over existing ones using several reconstruction examples.
Trading beams for bandwidth: Imaging with randomized beamforming
Date: 2019-01-31 00:00:00

We study the problem of actively imaging a range-limited far-field scene
using an antenna array. We describe how the range limit imposes structure in
the measurements across multiple wavelengths. This structure allows us to
introduce a novel trade-off: the number of spatial array measurements (i.e.,
beams that have to be formed) can be reduced significantly lower than the
number array elements if the scene is illuminated with a broadband source. To
take advantage of this trade-off, we use a small number of "generic" linear
combinations of the array outputs, instead of the phase offsets used in
conventional beamforming. We provide theoretical justification for the proposed
trade-off without making any strong structural assumptions on the target scene
(such as sparsity) except that it is range limited. In proving our theoretical
results, we take inspiration from the sketching literature. We also provide
simulation results to establish the merit of the proposed signal acquisition
strategy. Our proposed method results in a reduction in the number of required
spatial measurements in an array imaging system and hence can directly impact
their speed and cost of operation.
Entropy-Based Learning of Sensing Matrices
Date: 2019-03-29 00:00:00
Tasks: compressed sensing

This paper proposes a learning method to construct an efficient sensing
(measurement) matrix, having orthogonal rows, for compressed sensing of a class
of signals. The learning scheme identifies the sensing matrix by maximizing the
entropy of measurement vectors. The bounds on the entropy of the measurement
vector necessary for the unique recovery of a signal are also proposed. A
comparison of the performance of the designed sensing matrix and the sensing
matrices constructed using other existing methods is also presented. The
simulation results on the recovery of synthetic, speech, and image signals,
compressively sensed using the sensing matrix identified, shows an improvement
in the accuracy of recovery. The reconstruction quality is better, using less
number of measurements, than those measured using sensing matrices identified
by other methods.
A Quantum-Inspired Method for Three-Dimensional Ligand-Based Virtual Screening
Date: 2019-01-28 00:00:00
Tasks: Drug Discovery

Measuring similarity between molecules is an important part of virtual
screening (VS) experiments deployed during the early stages of drug discovery.
Most widely used methods for evaluating the similarity of molecules use
molecular fingerprints to encode structural information. While similarity
methods using fingerprint encodings are efficient, they do not consider all the
relevant aspects of molecular structure. In this paper, we describe a
quantum-inspired graph-based molecular similarity (GMS) method for ligand-based
VS. The GMS method is formulated as a quadratic unconstrained binary
optimization problem that can be solved using a quantum annealer, providing the
opportunity to take advantage of this nascent and potentially groundbreaking
technology. In this study, we consider various features relevant to
ligand-based VS, such as pharmacophore features and three-dimensional atomic
coordinates, and include them in the GMS method. We evaluate this approach on
various datasets from the DUD_LIB_VS_1.0 library. Our results show that using
three-dimensional atomic coordinates as features for comparison yields higher
early enrichment values. In addition, we evaluate the performance of the GMS
method against conventional fingerprint approaches. The results demonstrate
that the GMS method outperforms fingerprint methods for most of the datasets,
presenting a new alternative in ligand-based VS with the potential for future
enhancement.
Tutorials on X-ray Phase Contrast Imaging: Some Fundamentals and Some Conjectures on Future Developments
Date: 2019-02-27 00:00:00
Tasks: Retrieval

These tutorials introduce some basics of imaging with coherent X-rays,
focusing on phase contrast. We consider the transport-of-intensity equation, as
one particular method for X-ray phase contrast imaging among many, before
passing on to the inverse problem of phase retrieval. These ideas are applied
to two-dimensional and three-dimensional propagation-based phase-contrast
imaging using coherent X-rays. We then consider the role of partial coherence,
and sketch a generic means by which partially coherent X-ray imaging scenarios
may be modelled, using the space-frequency description of partial coherence.
Besides covering fundamental concepts in both theory and practice, we also give
opinions on future trends in X-ray phase contrast imaging including X-ray
tomography, and comparison of different phase contrast imaging methods. These
tutorials will be accessible to those with a basic background in optics (e.g.
wave equation, Maxwell equations, Fresnel and Fraunhofer diffraction, and the
basics of Fourier and vector analysis) and interactions of X-rays with matter
(e.g. attenuation mechanisms and complex refractive index).
Bayesian Elicitation
Authors: Mark Whitmeyer
Date: 2019-02-03 00:00:00

We study how a decision-maker can acquire more information from an agent by reducing her own ability to observe what the agent transmits. In a large class of binary-action games, opacity design is just as good as full commitment to actions and also guarantees that ex ante information acquisition always benefits the receiver, even though without opacity design this learning might actually lower the receiver's expected payoff.