pwc-archive/papers-with-abstracts huggingface dataset:
Field Trial of Alien Wavelengths on GARR Optical Network
Date: 2018-05-11 00:00:00

GARR optical network is composed of two separate optical network domains at
the national level. With the aim to integrate them we implemented alien
wavelengths to deliver high-performance services improving the overall network
efficiency. This paper describes the path followed to deploy the alien
wavelengths in the GARR operational infrastructure from the planning phase to
the production network.
Network Sensitivity of Systemic Risk
Authors: Amanah Ramadiah, Domenico Di Gangi, D. Ruggiero Lo Sardo, Valentina Macchiati, Tuan Pham Minh, Francesco Pinotti, Mateusz Wilinski, Paolo Barucca, Giulio Cimini
Date: 2018-05-11 00:00:00
Tasks: Sensitivity

A growing body of studies on systemic risk in financial markets has emphasized the key importance of taking into consideration the complex interconnections among financial institutions. Much effort has been put in modeling the contagion dynamics of financial shocks, and to assess the resilience of specific financial markets - either using real network data, reconstruction techniques or simple toy networks. Here we address the more general problem of how shock propagation dynamics depends on the topological details of the underlying network. To this end we consider different realistic network topologies, all consistent with balance sheets information obtained from real data on financial institutions. In particular, we consider networks of varying density and with different block structures, and diversify as well in the details of the shock propagation dynamics. We confirm that the systemic risk properties of a financial network are extremely sensitive to its network features. Our results can aid in the design of regulatory policies to improve the robustness of financial markets.
Nonparametric optimization of short-term confidence bands for wind power generation
Date: 2018-05-10 00:00:00
Tasks: Scheduling

The increasing rate of penetration of non-conventional renewable energies is
affecting the traditional assumption of controllability over energy sources.
Power dispatching scheduling methods need to integrate the intrinsic randomness
of some new sources, among which, wind energy is particularly difficult to
treat. This work aims on the construction of confidence bands around wind
energy forecasts. Complementarily, the proposed technique can be extended to
integrate multiple forecasts into a single one, whose band's width is narrower
at the same level of confidence. The work is based upon a real-world
application case, developed for the Uruguayan Electricity Market, a world
leader country in the penetration of renewable energies.
Reconstruction of multiple non-line-of-sight objects using back projection based on ellipsoid mode decomposition
Date: 2018-05-11 00:00:00

Non-line-of-sight imaging has attracted more attentions for its wide
applications.Even though ultrasensitive cameras or detectors with high
time-resolution are available, current back-projection methods are still
powerless to acquire a satisfying reconstruction of multiple hidden objects due
to severe aliasing artifacts. Here, a novel back-projection method is developed
to reconstruct multiple hidden objects. Our method considers decomposing all
the ellipsoids in a confidence map into several clusters belonging to different
objects (namely ellipsoid mode decomposition), and then reconstructing the
objects individually from their ellipsoid modes by filtering and thresholding,
respectively. Importantly, the simulated and experimental results demonstrate
that this method can effectively eliminate the impacts of aliasing artifacts
and exhibits potential advantages in separating, locating and recovering
multiple hidden objects, which might be a good base for reconstructing complex
non-line-ofsight scenes.
Construction of Forward Performance Processes in Stochastic Factor Models and an Extension of Widder's Theorem
Date: 2018-05-11 00:00:00

We consider the problem of optimal portfolio selection under forward
investment performance criteria in an incomplete market. Given multiple traded
assets, the prices of which depend on multiple observable stochastic factors,
we construct a large class of forward performance processes with power-utility
initial data, as well as the corresponding optimal portfolios. This is done by
solving the associated non-linear parabolic partial differential equations
(PDEs) posed in the "wrong" time direction, for stock-factor correlation
matrices with eigenvalue equality (EVE) structure, which we introduce here.
Along the way we establish on domains an explicit form of the generalized
Widder's theorem of Nadtochiy and Tehranchi [NT15, Theorem 3.12] and rely
hereby on the Laplace inversion in time of the solutions to suitable linear
parabolic PDEs posed in the "right" time direction.
Theory of circadian metabolism
Date: 2018-05-11 00:00:00

Many organisms repartition their proteome in a circadian fashion in response
to the daily nutrient changes in their environment. A striking example is
provided by cyanobacteria, which perform photosynthesis during the day to fix
carbon. These organisms not only face the challenge of rewiring their proteome
every 12 hours, but also the necessity of storing the fixed carbon in the form
of glycogen to fuel processes during the night. In this manuscript, we extend
the framework developed by Hwa and coworkers (Scott et al., Science 330, 1099
(2010)) for quantifying the relatinship between growth and proteome composition
to circadian metabolism. We then apply this framework to investigate the
circadian metabolism of the cyanobacterium Cyanothece, which not only fixes
carbon during the day, but also nitrogen during the night, storing it in the
polymer cyanophycin. Our analysis reveals that the need to store carbon and
nitrogen tends to generate an extreme growth strategy, in which the cells
predominantly grow during the day, as observed experimentally. This strategy
maximizes the growth rate over 24 hours, and can be quantitatively understood
by the bacterial growth laws. Our analysis also shows that the slow relaxation
of the proteome, arising from the slow growth rate, puts a severe constraint on
implementing this optimal strategy. Yet, the capacity to estimate the time of
the day, enabled by the circadian clock, makes it possible to anticipate the
daily changes in the environment and mount a response ahead of time. This
significantly enhances the growth rate by counteracting the detrimental effects
of the slow proteome relaxation.
WU-NEAT: A clinically validated, open- source MATLAB toolbox for limited-channel neonatal EEG analysis
Date: 2018-05-11 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Goal: Limited-channel EEG research in neonates is hindered by lack of open,
accessible analytic tools. To overcome this limitation, we have created the
Washington University- Neonatal EEG Analysis Toolbox (WU-NEAT), containing two
of the most commonly used tools, provided in an open-source,
clinically-validated package running within MATLAB. Methods: The first
algorithm is the amplitude-integrated EEG (aEEG), which is generated by
filtering, rectifying and time-compressing the original EEG recording, with
subsequent semi-logarithmic display. The second algorithm is the spectral edge
frequency (SEF), calculated as the critical frequency below which a user-
defined proportion of the EEG spectral power is located. The aEEG algorithm was
validated by three experienced reviewers. Reviewers evaluated aEEG recordings
of fourteen preterm/term infants, displayed twice in random order, once using a
reference algorithm and again using the WU-NEAT aEEG algorithm. Using standard
methodology, reviewers assigned a background pattern classification.
Inter/intra-rater reliability was assessed. For the SEF, calculations were made
using the same fourteen recordings, first with the reference and then with the
WU-NEAT algorithm. Results were compared using Pearson's correlation
coefficient. Results: For the aEEG algorithm, intra- and inter-rater
reliability was 100% and 98%, respectively. For the SEF, the mean (SD) Pearson
correlation coefficient between algorithms was 0.96 (0.04). Conclusion: We have
demonstrated a clinically-validated toolbox for generating the aEEG as well as
calculating the SEF from EEG data. Open-source access will enable widespread
use of common analytic algorithms which are device-independent and not subject
to obsolescence, thereby facilitating future collaborative research in neonatal
EEG.
Choice of adaptive sampling strategy impacts state discovery, transition probabilities, and the apparent mechanism of conformational changes
Date: 2018-05-11 00:00:00

Interest in equilibrium-based sampling methods has grown with recent advances
in computational hardware and Markov state modeling (MSM) methods, yet
outstanding questions remain that hinder widespread adoption. Namely, how do
sampling strategies explore conformational space and how might this influence
predictions? Here, we seek to answer these questions for four commonly used
sampling methods: 1) a long simulation, 2) many short simulations, 3) adaptive
sampling, and 4) FAST. We first develop a theoretical framework for
analytically calculating the probability of discovering states and uncover the
drastic effects of varying the number and length of simulations. We then use
kinetic Monte Carlo simulations on a variety of physically inspired landscapes
to characterize state discovery and transition pathways. Consistently, we find
that FAST simulations discover target states with the highest probability and
traverse realistic pathways. Furthermore, we uncover the pathology that short
parallel simulations sometimes predict an incorrect transition pathway by
crossing large energy barriers that long simulations would typically
circumnavigate, which we refer to as pathway tunneling. To protect against
tunneling, we introduce FAST-string, which samples along the highest-flux
transition paths to refine an MSMs transition probabilities and discriminate
between competing pathways. Additionally, we compare MSM estimators in
describing thermodynamics and kinetics. For adaptive sampling, we recommend
normalizing the transition counts out of each state after adding pseudo-counts
to avoid creating sources or sinks. Lastly, we evaluate our insights from
simple landscapes with all-atom molecular dynamics simulations of the folding
of the {\lambda}-repressor protein. We find that FAST-contacts predicts the
same folding pathway as long simulations but with orders of magnitude less
simulation time.
Category learning can alter perception and its neural correlates
Date: 2019-12-10 00:00:00
Tasks: ERP

Learned Categorical Perception (CP) occurs when the members of different
categories come to look more dissimilar (between-category separation) and/or
members of the same category come to look more similar (within-category
compression) after a new category has been learned. To measure learned CP and
its physiological correlates we compared dissimilarity judgments and Event
Related Potentials (ERPs) before and after learning to sort multi-featured
visual textures into two categories by trial and error with corrective
feedback. With the same number of trials and feedback, about half the
participants succeeded in learning the categories (learners: criterion 80%
accuracy) and the rest did not (non-learners). At both lower and higher levels
of difficulty, successful learners showed significant between-category
separation in pairwise dissimilarity judgments after learning compared to
before; their late parietal ERP positivity (LPC, usually interpreted as
decisional) also increased and their occipital negativity (N1) (usually
interpreted as perceptual) decreased. LPC increased with response accuracy and
N1 amplitude decreased with between-category separation for the Learners.
Non-learners showed no significant changes in dissimilarity judgments, LPC or
N1, within or between categories. This is behavioral and physiological evidence
that category learning can alter perception. We sketch a neural net model for
this effect.
An Inner SOCP Approximate Algorithm for Robust Adaptive Beamforming for General-Rank Signal Model
Date: 2018-05-12 00:00:00
Tasks: CPU

The worst-case robust adaptive beamforming problem for general-rank signal
model is considered. Its formulation is to maximize the worst-case
signal-to-interference-plus-noise ratio (SINR), incorporating a positive
semidefinite constraint on the actual covariance matrix of the desired signal.
In the literature, semidefinite program (SDP) techniques, together with others,
have been applied to approximately solve this problem. Herein an inner
second-order cone program (SOCP) approximate algorithm is proposed to solve it.
In particular, a sequence of SOCPs are constructed and solved, while the SOCPs
have the nondecreasing optimal values and converge to a locally optimal value
(it is in fact a globally optimal value through our extensive simulations). As
a result, our algorithm does not use computationally heavy SDP relaxation
technique. To validate our inner approximation results, simulation examples are
presented, and they demonstrate the improved performance of the new robust
beamformer in terms of the averaged cpu-time (indicating how fast the
algorithms converge) in a high signal-to-noise region.
New Embedded Representations and Evaluation Protocols for Inferring Transitive Relations
Authors: Subramanian Sandeep, Chakrabarti Soumen
Date: 2018-05-12 00:00:00
Tasks: Question Answering, Relation, Word Embeddings

Beyond word embeddings, continuous representations of knowledge graph (KG)
components, such as entities, types and relations, are widely used for entity
mention disambiguation, relation inference and deep question answering. Great
strides have been made in modeling general, asymmetric or antisymmetric KG
relations using Gaussian, holographic, and complex embeddings. None of these
directly enforce transitivity inherent in the is-instance-of and is-subtype-of
relations. A recent proposal, called order embedding (OE), demands that the
vector representing a subtype elementwise dominates the vector representing a
supertype. However, the manner in which such constraints are asserted and
evaluated have some limitations. In this short research note, we make three
contributions specific to representing and inferring transitive relations.
First, we propose and justify a significant improvement to the OE loss
objective. Second, we propose a new representation of types as
hyper-rectangular regions, that generalize and improve on OE. Third, we show
that some current protocols to evaluate transitive relation inference can be
misleading, and offer a sound alternative. Rather than use black-box deep
learning modules off-the-shelf, we develop our training networks using
elementary geometric considerations.
Bitcoin Risk Modeling with Blockchain Graphs
Date: 2018-05-12 00:00:00

A key challenge for Bitcoin cryptocurrency holders, such as startups using
ICOs to raise funding, is managing their FX risk. Specifically, a misinformed
decision to convert Bitcoin to fiat currency could, by itself, cost USD
millions.
  In contrast to financial exchanges, Blockchain based crypto-currencies expose
the entire transaction history to the public. By processing all transactions,
we model the network with a high fidelity graph so that it is possible to
characterize how the flow of information in the network evolves over time. We
demonstrate how this data representation permits a new form of microstructure
modeling - with the emphasis on the topological network structures to study the
role of users, entities and their interactions in formation and dynamics of
crypto-currency investment risk. In particular, we identify certain sub-graphs
('chainlets') that exhibit predictive influence on Bitcoin price and
volatility, and characterize the types of chainlets that signify extreme
losses.
A New Method for Epileptic Seizure Classification in EEG Using Adapted Wavelet Packets
Date: 2018-05-12 00:00:00
Tasks: Classification, EEG, Electroencephalogram (EEG)

Electroencephalography (EEG), as the most common tool for epileptic seizure
classification, contains useful information about different physiological
states of the brain. Seizure related features in EEG signals can be better
identified when localized in time frequency basis projections. In this work, a
novel method for epileptic seizure classification based on wavelet packets
(WPs) is presented in which both mother wavelet function and WP bases are
adapted a posteriori to improve the seizure classification. A support vector
machine (SVM) as classifier is used for seizure versus non-seizure EEG segment
classification. In order to evaluate the proposed algorithm, a publicly
available dataset containing different groups patient with epilepsy and healthy
individuals are used. The obtained results indicate that the proposed method
outperforms some previously proposed algorithms in epileptic seizure
classification.
The Heston stochastic volatility model with piecewise constant parameters - efficient calibration and pricing of window barrier options
Authors: Daniel Guterding, Wolfram Boenkost
Date: 2018-05-12 00:00:00

The Heston stochastic volatility model is a standard model for valuing financial derivatives, since it can be calibrated using semi-analytical formulas and captures the most basic structure of the market for financial derivatives with simple structure in time-direction. However, extending the model to the case of time-dependent parameters, which would allow for a parametrization of the market at multiple timepoints, proves more challenging. We present a simple and numerically efficient approach to the calibration of the Heston stochastic volatility model with piecewise constant parameters. We show that semi-analytical formulas can also be derived in this more complex case and combine them with recent advances in computational techniques for the Heston model. Our numerical scheme is based on the calculation of the characteristic function using Gauss-Kronrod quadrature with an additional control variate that stabilizes the numerical integrals. We use our method to calibrate the Heston model with piecewise constant parameters to the foreign exchange (FX) options market. Finally, we demonstrate improvements of the Heston model with piecewise constant parameters upon the standard Heston model in selected cases.
Effects of a Price limit Change on Market Stability at the Intraday Horizon in the Korean Stock Market
Date: 2018-05-12 00:00:00

This paper investigates the effects of a price limit change on the volatility
of the Korean stock market's (KRX) intraday stock price process. Based on the
most recent transaction data from the KRX, which experienced a change in the
price limit on June 15, 2015, we examine the change in realized variance after
the price limit change to investigate the overall effects of the change on the
intraday market volatility. We then analyze the effects in more detail by
applying the discrete Fourier transform (DFT) to the data set. We find evidence
that the market becomes more volatile in the intraday horizon because of the
increase in the amplitudes of the low-frequency components of the price
processes after the price limit change. Therefore, liquidity providers are in a
worse situation than they were prior to the change.
On-the-fly Table Generation
Authors: Zhang Shuo, Balog Krisztian
Date: 2018-05-13 00:00:00

Many information needs revolve around entities, which would be better
answered by summarizing results in a tabular format, rather than presenting
them as a ranked list. Unlike previous work, which is limited to retrieving
existing tables, we aim to answer queries by automatically compiling a table in
response to a query. We introduce and address the task of on-the-fly table
generation: given a query, generate a relational table that contains relevant
entities (as rows) along with their key properties (as columns). This problem
is decomposed into three specific subtasks: (i) core column entity ranking,
(ii) schema determination, and (iii) value lookup. We employ a feature-based
approach for entity ranking and schema determination, combining deep semantic
features with task-specific signals. We further show that these two subtasks
are not independent of each other and can assist each other in an iterative
manner. For value lookup, we combine information from existing tables and a
knowledge base. Using two sets of entity-oriented queries, we evaluate our
approach both on the component level and on the end-to-end table generation
task.
An Enhanced MPPT Method based on ANN-assisted Sequential Monte Carlo and Quickest Change Detection
Date: 2018-05-13 00:00:00
Tasks: Change Detection, Point Tracking, State Estimation

The performance of a photovoltaic system is subject to varying environmental
conditions, and it becomes more challenging to track the maximum power point
(MPP) and maintain the optimal performance when partial shading occurs. In this
paper, we propose an enhanced maximum power point tracking (MPPT) method
utilizing the state estimation by the sequential Monte Carlo (SMC) filtering
which is assisted by the prediction of MPP via an artificial neural network
(ANN). A state-space model for the sequential estimation of MPP is proposed in
the framework of incremental conductance (I-C) MPPT approach, and the ANN model
based on the observed voltage and current or irradiance data predicts the
global MPP (GMPP) to refine the estimation by SMC. Moreover, a quick
irrandiance change detection method is applied, such that the SMC-based MPPT
method resorts to the assistance from ANN only when partial shading is
detected. Simulation results show that the proposed enhanced MPPT method
achieves high efficiency and is robust to rapid irradiance change under
different noise levels.
Multimodal Cross-registration and Quantification of Metric Distortions in Whole Brain Histology of Marmoset using Diffeomorphic Mappings
Date: 2019-04-17 00:00:00

Whole brain neuroanatomy using tera-voxel light-microscopic data sets is of
much current interest. A fundamental problem in this field is the mapping of
individual brain data sets to a reference space. Previous work has not
rigorously quantified the distortions in brain geometry from in-vivo to ex-vivo
brains due to the tissue processing, which will be important when computing
properties such as local cell and process densities at the voxel level in
creating reference brain maps. Further, existing approaches focus on
registering uni-modal volumetric data; however, given the increasing interest
in the marmoset model for neuroscience research, it is necessary to
cross-register multi-modal data sets including MRIs and multiple histological
series that can help address individual variations in brain architecture. Here
we present a computational approach for same-subject multimodal MRI guided
reconstruction of a histological series, jointly with diffeomorphic mapping to
a reference atlas. We quantify the scale change during the different stages of
histological processing of the brains using the Jacobian determinant of the
diffeomorphic transformations involved. There are two major steps in the
histology process with associated scale distortions (a) brain perfusion (b)
histological sectioning and reassembly. By mapping the final image stacks to
the ex-vivo post fixation MRI, we show that tape-transfer histology can be
reassembled accurately into 3D volumes with a local scale change of 2.0 $\pm$
0.4% per axis dimension. In contrast, the perfusion step, as assessed by
mapping the in-vivo MRIs to the ex-vivo post fixation MRIs, shows a larger
local scale change of 6.9 $\pm$ 2.1% per axis dimension. This is the first
systematic quantification of the local metric distortions associated with
whole-brain histological processing, and we expect that the results will
generalize to other species.