pwc-archive/papers-with-abstracts huggingface dataset:
Risk management with machine-learning-based algorithms
Authors: Simon Fécamp, Joseph Mikael, Xavier Warin
Date: 2019-02-14 00:00:00
Tasks: BIG-bench Machine Learning, Management

We propose some machine-learning-based algorithms to solve hedging problems in incomplete markets. Sources of incompleteness cover illiquidity, untradable risk factors, discrete hedging dates and transaction costs. The proposed algorithms resulting strategies are compared to classical stochastic control techniques on several payoffs using a variance criterion. One of the proposed algorithm is flexible enough to be used with several existing risk criteria. We furthermore propose a new moment-based risk criteria.
Online Supervisory Control and Resource Management for Energy Harvesting BS Sites Empowered with Computation Capabilities
Date: 2019-02-14 00:00:00
Tasks: Edge-computing, energy management, Management

The convergence of communication and computing has lead to the emergence of
Multi-access Edge Computing (MEC), where computing resources (supported by
Virtual Machines (VMs)) are distributed at the edge of the Mobile Network (MN),
i.e., in Base Stations (BSs), with the aim of ensuring reliable and ultra-low
latency services. Moreover, BSs equipped with Energy Harvesting (EH) systems
can decrease the amount of energy drained from the power grid resulting in
energetically self-sufficient MNs. The combination of these paradigms is
considered here. Specifically, we propose an online optimization algorithm,
called ENergy Aware and Adaptive Management (ENAAM), based on foresighted
control policies exploiting (short-term) traffic load and harvested energy
forecasts, where BSs and VMs are dynamically switched on/off towards energy
savings and QoS provisioning. Our numerical results reveal that ENAAM achieves
energy savings with respect to the case where no energy management is applied,
ranging from 57% and 69%. Moreover, the extension of ENAAM within a cluster of
BSs provides a further gain ranging from 9% to 16% in energy savings with
respect to the optimization performed in isolation for each BS.
Automated Image Analysis and Contiguity Estimation for Liquid Phase Sintered Tungsten Heavy Alloys
Date: 2019-01-29 00:00:00

In this study an automated software model using digital image processing
techniques is proposed for extracting the image characteristics and contiguity
of liquid phase sintered tungsten heavy alloys. The developed model takes a
typical image as input and processes it with no human intervention and provides
the corresponding image characteristics and contiguity value. The image
processing algorithm includes segmentation, binding point extraction, phase
connection, particle count and contiguity estimation stages. For the output,
microstructural parameters such as tungsten particle size, amount of tungsten
phase and contiguity are determined. The model is implemented by using 6
different scanning electron microscope images of liquid phase sintered
90W-7Ni-3Fe and 93W-4.9Ni-2.1Fe allloys. The results indicate that relative to
the manual measurements, the automated model can correctly estimate the
contiguity with an error in the vicinity of 5.6% - 2.9% for these two alloys.
The developed software can easily be adapted to be used for other
microstructures. It is also provided as open-source and available for other
researchers.
Market Impact: A Systematic Study of the High Frequency Options Market
Authors: Emilio Said, Ahmed Bel Hadj Ayed, Damien Thillou, Jean-Jacques Rabeyrin, Frédéric Abergel
Date: 2019-02-13 00:00:00
Tasks: Vocal Bursts Intensity Prediction

This paper deals with a fundamental subject that has seldom been addressed in recent years, that of market impact in the options market. Our analysis is based on a proprietary database of metaorders-large orders that are split into smaller pieces before being sent to the market on one of the main Asian markets. In line with our previous work on the equity market [Said et al., 2018], we propose an algorithmic approach to identify metaorders, based on some implied volatility parameters, the at the money forward volatility and at the money forward skew. In both cases, we obtain results similar to the now well understood equity market: Square-root law, Fair Pricing Condition and Market Impact Dynamics.
Micro-UAV Detection with a Low-Grazing Angle Millimeter Wave Radar
Date: 2019-02-14 00:00:00

Millimeter wave radars are popularly used in last-mile radar-based defense
systems. Detection of low-altitude airborne target using these radars at
low-grazing angles is an important problem in the field of electronic warfare,
which becomes challenging due to the significant effects of clutters in the
terrain. This paper provides both experimental and analytical investigation of
micro unmanned aerial vehicle (UAV) detection in a rocky terrain using a low
grazing angle, surface-sited 24 GHz dual polarized frequency modulated
continuous wave (FMCW) radar. The radar backscatter signal from the UAV is
polluted by land clutters which is modeled using a uniform Weibull
distribution. A constant false alarm rate (CFAR) detector which employs
adaptive thresholding is designed to detect the UAV in the rich clutter
background. In order to further enhance the discrimination of the UAV from the
clutter, the micro-Doppler signature of the rotating propellers and bulk
trajectory of the UAV are extracted and plotted in the time-frequency domain.
Online Resource Management in Energy Harvesting BS Sites through Prediction and Soft-Scaling of Computing Resources
Date: 2019-02-14 00:00:00
Tasks: Edge-computing, energy management, Management

Multi-Access Edge Computing (MEC) is a paradigm for handling delay sensitive
services that require ultra-low latency at the access network. With it,
computing and communications are performed within one Base Station (BS) site,
where the computation resources are in the form of Virtual Machines (VMs)
(computer emulators) in the MEC server. MEC and Energy Harvesting (EH) BSs,
i.e., BSs equipped with EH equipments, are foreseen as a key towards
next-generation mobile networks. In fact, EH systems are expected to decrease
the energy drained from the electricity grid and facilitate the deployment of
BSs in remote places, extending network coverage and making energy
self-sufficiency possible in remote/rural sites. In this paper, we propose an
online optimization algorithm called ENergy Aware and Adaptive Management
(ENAAM), for managing remote BS sites through foresighted control policies
exploiting (short-term) traffic load and harvested energy forecasts. Our
numerical results reveal that ENAAM achieves energy savings with respect to the
case where no energy management is applied, ranging from 56% to 66% through the
scaling of computing resources, and keeps the server utilization factor between
30% and 96% over time (with an average of 75%). Notable benefits are also found
against heuristic energy management techniques.
Influence of nicotine and alcohol on sleep latency: the reward-attention circuit model
Authors: Karine Guimarães
Date: 2019-02-14 00:00:00

We previously developed a neurocomputational model called the reward-attention circuit, which has been used to investigate the separate effects of nicotine and alcohol on attention focus. The model is based on known circuits linking midbrain dopaminergic neurons to the thalamocortical loop and is sufficiently versatile to capture diverse phenomena. In this work, we used the model to study the influence of both nicotine and alcohol on sleep latency (time to fall asleep). In line with findings from other studies, our simulations suggest that nicotine can have a stimulating effect that increases sleep latency, promoting wakefulness, and, in contrast, alcohol can have a sedating effect that induces sleep. Together, our results suggest insights into alterations in mesothalamic dopamine activity and sleep. Also, they raise speculations about aspects of insomnia and low mesothalamic dopamine.
Iterative Joint Ptychography-Tomography with Total Variation Regularization
Date: 2019-02-15 00:00:00

In order to determine the 3D structure of a thick sample, researchers have
recently combined ptychography (for high resolution) and tomography (for 3D
imaging) in a single experiment. 2-step methods are usually adopted for
reconstruction, where the ptychography and tomography problems are often solved
independently. In this paper, we provide a novel model and ADMM-based algorithm
to jointly solve the ptychography-tomography problem iteratively, also
employing total variation regularization. The proposed method permits large
scan stepsizes for the ptychography experiment, requiring less measurements and
being more robust to noise with respect to other strategies, while achieving
higher reconstruction quality results.
Constrained Risk Budgeting Portfolios: Theory, Algorithms, Applications & Puzzles
Date: 2019-02-15 00:00:00

This article develops the theory of risk budgeting portfolios, when we would
like to impose weight constraints. It appears that the mathematical problem is
more complex than the traditional risk budgeting problem. The formulation of
the optimization program is particularly critical in order to determine the
right risk budgeting portfolio. We also show that numerical solutions can be
found using methods that are used in large-scale machine learning problems.
Indeed, we develop an algorithm that mixes the method of cyclical coordinate
descent (CCD), alternating direction method of multipliers (ADMM), proximal
operators and Dykstra's algorithm. This theoretical body is then applied to
some investment problems. In particular, we show how to dynamically control the
turnover of a risk parity portfolio and how to build smart beta portfolios
based on the ERC approach by improving the liquidity of the portfolio or
reducing the small cap bias. Finally, we highlight the importance of the
homogeneity property of risk measures and discuss the related scaling puzzle.
Comparison of SVM and Spectral Embedding in Promoter Biobricks' Categorizing and Clustering
Authors: Shangjie Zou
Date: 2019-02-15 00:00:00
Tasks: BIG-bench Machine Learning, Clustering

Background: In organisms' genomes, promoters are short DNA sequences on the upstream of structural genes, with the function of controlling genes' transcription. Promoters can be roughly divided into two classes: constitutive promoters and inducible promoters. Promoters with clear functional annotations are practical synthetic biology biobricks. Many statistical and machine learning methods have been introduced to predict the functions of candidate promoters. Spectral Eigenmap has been proved to be an effective clustering method to classify biobricks, while support vector machine (SVM) is a powerful machine learning algorithm, especially when dataset is small. Methods: The two algorithms: spectral embedding and SVM are applied to the same dataset with 375 prokaryotic promoters. For spectral embedding, a Laplacian matrix is built with edit distance, followed by K-Means Clustering. The sequences are represented by numeric vector to serve as dataset for SVM trainning. Results: SVM achieved a high predicting accuracy of 93.07% in 10-fold cross validation for classification of promoters' transcriptional functions. Laplacian eigenmap (spectral embedding) based on editing distance may not be capable for extracting discriminative features for this task.
Sum Rate Fairness Trade-off-based Resource Allocation Technique for MISO NOMA Systems
Date: 2019-02-15 00:00:00
Tasks: Fairness

In this paper, we propose a beamforming design that jointly considers two
conflicting performance metrics, namely the sum rate and fairness, for a
multiple-input single-output non-orthogonal multiple access system. Unlike the
conventional rate-aware beamforming designs, the proposed approach has the
flexibility to assign different weights to the objectives (i.e., sum rate and
fairness) according to the network requirements and the channel conditions. In
particular, the proposed design is first formulated as a multi-objective
optimization problem, and subsequently mapped to a single objective
optimization (SOO) problem by exploiting the weighted sum approach combined
with a prior articulation method. As the resulting SOO problem is non-convex,
we use the sequential convex approximation technique, which introduces multiple
slack variables, to solve the overall problem. Simulation results are provided
to demonstrate the performance and the effectiveness of the proposed approach
along with detailed comparisons with conventional rate-aware-based beamforming
designs.
Critical Transitions in Intensive Care Units: A Sepsis Case Study
Date: 2019-10-23 00:00:00
Tasks: Time Series, Time Series Analysis

The progression of complex human diseases is associated with critical
transitions across dynamical regimes. These transitions often spawn
early-warning signals and provide insights into the underlying disease-driving
mechanisms. In this paper, we propose a computational method based on surprise
loss (SL) to discover data-driven indicators of such transitions in a
multivariate time series dataset of septic shock and non-sepsis patient cohorts
(MIMIC-III database). The core idea of SL is to train a mathematical model on
time series in an unsupervised fashion and to quantify the deterioration of the
model's forecast (out-of-sample) performance relative to its past (in-sample)
performance. Considering the highest value of the moving average of SL as a
critical transition, our retrospective analysis revealed that critical
transitions occurred at a median of over 35 hours before the onset of septic
shock, which suggests the applicability of our method as an early-warning
indicator. Furthermore, we show that clinical variables at critical-transition
regions are significantly different between septic shock and non-sepsis
cohorts. Therefore, our paper contributes a critical-transition-based
data-sampling strategy that can be utilized for further analysis, such as
patient classification. Moreover, our method outperformed other indicators of
critical transition in complex systems, such as temporal autocorrelation and
variance.
Comprehensive review of models and methods for inferences in bio-chemical reaction networks
Date: 2019-02-15 00:00:00
Tasks: State Estimation

Key processes in biological and chemical systems are described by networks of
chemical reactions. From molecular biology to biotechnology applications,
computational models of reaction networks are used extensively to elucidate
their non-linear dynamics. Model dynamics are crucially dependent on parameter
values which are often estimated from observations. Over past decade, the
interest in parameter and state estimation in models of (bio-)chemical reaction
networks (BRNs) grew considerably. Statistical inference problems are also
encountered in many other tasks including model calibration, discrimination,
identifiability and checking as well as optimum experiment design, sensitivity
analysis, bifurcation analysis and other. The aim of this review paper is to
explore developments of past decade to understand what BRN models are commonly
used in literature, and for what inference tasks and inference methods. Initial
collection of about 700 publications excluding books in computational biology
and chemistry were screened to select over 260 research papers and 20 graduate
theses concerning estimation problems in BRNs. The paper selection was
performed as text mining using scripts to automate search for relevant keywords
and terms. The outcome are tables revealing the level of interest in different
inference tasks and methods for given models in literature as well as recent
trends. In addition, a brief survey of general estimation strategies is
provided to facilitate understanding of estimation methods which are used for
BRNs. Our findings indicate that many combinations of models, tasks and methods
are still relatively sparse representing new research opportunities to explore
those that have not been considered - perhaps for a good reason. The paper
concludes by discussing future research directions including research problems
which cannot be directly deduced from presented tables.
Assessing the Level of Autonomic Nervous Activity for Effective Biofeedback Training
Date: 2019-02-15 00:00:00

This paper proposes a prototype of a new biofeedback training based on
mathematical models of cardiovascular control. For this purpose we develop a
low-cost device that is able to record and process arterial pulse wave via
photoplethysmograph and skin temperature on the peripheral part of the arm. A
benchmark analysis of our device against a registered cardiovascular
measurement system (Biopac MP35 from Biopac Inc., USA) shows that heart rate
and skin temperature values delivered by our device are acceptable with a very
low residual error (+/-1 beat/min, +/-1 oC).
  Both measured signals are feed into a mathematical model which estimates the
level of activity of both sympathetic and parasympathetic branches of the
autonomic nervous system. That information is used jointly with other
biological parameters for investigating the stress score of the subject. The
data is processed in real-time and continuously displayed to the user for
effective biofeedback training.
  The complete solution was preliminary tested on three volunteers who used the
displayed biofeedback information in order to regulate their emotional state
successfully during Biofeedback training. They exhibited a significant
reduction in stress score compared to three control subjects who did not used
our solution during biofeedback training. This supports the benefits of
biofeedback training with autonomic nervous tone assessment as effective
holistic healing method.
Energy Efficient Beamforming Design for MISO Non-Orthogonal Multiple Access Systems
Date: 2019-02-15 00:00:00

When considering the future generation wireless networks, non-orthogonal
multiple access (NOMA) represents a viable multiple access technique for
improving the spectral efficiency. The basic performance of NOMA is often
enhanced using downlink beamforming and power allocation techniques. Although
downlink beamforming has been previously studied with different performance
criteria, such as sum-rate and max-min rate, it has not been studied in the
multiuser, multiple-input single-output (MISO) case, particularly with the
energy efficiency criteria. In this paper, we investigate the design of an
energy efficient beamforming technique for downlink transmission in the context
of a multiuser MISO-NOMA system. In particular, this beamforming design is
formulated as a global energy efficiency (GEE) maximization problem with
minimum user rate requirements and transmit power constraints. By using the
sequential convex approximation (SCA) technique and the Dinkelbach's algorithm
to handle the non-convex nature of the GEE-Max problem, we propose two novel
algorithms for solving the downlink beamforming problem for the MISO-NOMA
system. Our evaluation of the proposed algorithms shows that they offer similar
optimal designs and are effective in offering substantial energy efficiencies
compared to the designs based on conventional methods.
Gamma band oscillations reflect sensory and affective dimensions of pain
Date: 2019-02-15 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Pain is a multidimensional process, which can be modulated by emotions,
however, the mechanisms underlying this modulation are unknown. We used
pictures with different emotional valence (negative, positive, neutral) as
primes and applied electrical painful stimuli as targets to healthy
participants. We assessed pain intensity and unpleasantness ratings and
recorded electroencephalograms (EEG). We found that pain unpleasantness, and
not pain intensity ratings were modulated by emotion, with increased ratings
for negative and decreased for positive pictures. We also found two consecutive
gamma band oscillations (GBOs) related to pain processing from time frequency
analyses of the EEG signals. An early GBO had a cortical distribution
contralateral to the painful stimulus, and its amplitude was positively
correlated with intensity and unpleasantness ratings, but not with prime
valence. The late GBO had a centroparietal distribution and its amplitude was
larger for negative compared to neutral and positive pictures. The emotional
modulation effect (negative versus positive) of the late GBO amplitude was
positively correlated with pain unpleasantness. The early GBO might reflect the
overall pain perception, possibly involving the thalamocortical circuit, while
the late GBO might be related to the affective dimension of pain and top-down
related processes.
Disruption of the prefrontal cortex improves implicit contextual memory-guided attention: combined behavioural and electrophysiological evidence
Date: 2019-02-15 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Many studies have shown that the dorsolateral prefrontal cortex (DLPFC) plays
an important role in top-down cognitive control over intentional and deliberate
behavior. However, recent studies have reported that DLPFC-mediated top-down
control interferes with implicit forms of learning. Here we used continuous
theta burst stimulation (cTBS) combined with electroencephalography (EEG) to
investigate the causal role of DLPFC in implicit contextual memory-guided
attention. We aimed to test whether transient disruption of the DLPFC would
interfere with implicit learning performance and related electrical brain
activity. We applied neuronavigation-guided cTBS to the DLPFC or to the vertex
as a control region prior to the performance of an implicit contextual learning
task. We found that cTBS applied over the DLPFC significantly improved
performance during implicit contextual learning. We also noted that beta-band
(13-19 Hz) oscillatory power was reduced at fronto-central channels about 140
to 370 ms after visual stimulus onset in cTBS DLPFC compared with cTBS vertex.
Taken together, our results provide evidence that DLPFC-mediated top-down
control interferes with contextual memory-guided attention and beta-band
oscillatory activity.
Ion Condensation onto Ribozyme is Site-Specific and Fold-Dependent
Date: 2019-04-13 00:00:00
Tasks: Position

The highly charged RNA molecules, with each phosphate carrying a single
negative charge, cannot fold into well-defined architectures with tertiary
interactions, in the absence of ions. For ribozymes, divalent cations are known
to be more efficient than monovalent ions in driving them to a compact state
although Mg$^{2+}$ ions are needed for catalytic activities. Therefore, how
ions interact with RNA is relevant in understanding RNA folding. It is often
thought that most of the ions are territorially and non-specifically bound to
the RNA, as predicted by the counterion condensation (CIC) theory. Here, we
show using simulations of ${\it Azoarcus}$ ribozyme, based on an accurate
coarse-grained Three Site Interaction (TIS) model, with explicit divalent and
monovalent cations, that ion condensation is highly specific and depends on the
nucleotide position. The regions with high coordination between the phosphate
groups and the divalent cations are discernible even at very low Mg$^{2+}$
concentrations when the ribozyme does not form tertiary interactions.
Surprisingly, these regions also contain the secondary structural elements that
nucleate subsequently in the self-assembly of RNA, implying that ion
condensation is determined by the architecture of the folded state. These
results are in sharp contrast to interactions of ions (monovalent and divalent)
with rigid charged rods in which ion condensation is uniform and position
independent. The differences are explained in terms of the dramatic
non-monotonic shape fluctuations in the ribozyme as it folds with increasing
Mg$^{2+}$ or Ca$^{2+}$ concentration.