pwc-archive/papers-with-abstracts huggingface dataset:
Bayesian prediction of jumps in large panels of time series data
Authors: Angelos Alexopoulos, Petros Dellaportas, Omiros Papaspiliopoulos
Date: 2019-03-28 00:00:00
Tasks: Time Series, Time Series Analysis

We take a new look at the problem of disentangling the volatility and jumps processes of daily stock returns. We first provide a computational framework for the univariate stochastic volatility model with Poisson-driven jumps that offers a competitive inference alternative to the existing tools. This methodology is then extended to a large set of stocks for which we assume that their unobserved jump intensities co-evolve in time through a dynamic factor model. To evaluate the proposed modelling approach we conduct out-of-sample forecasts and we compare the posterior predictive distributions obtained from the different models. We provide evidence that joint modelling of jumps improves the predictive ability of the stochastic volatility models.
A generalized kinetic framework applied to whole-cell catalysis in biofilm flow reactors clarifies performance enhancements
Date: 2019-04-10 00:00:00
Tasks: Benchmarking

A common kinetic framework for studies of whole-cell catalysis is vital for
understanding and optimizing bioflow reactors. In this work, we demonstrate the
applicability of a flow-adapted version of Michaelis-Menten kinetics to a
catalytic bacterial biofilm. A three-electrode microfluidic electrochemical
flow cell measured increased turnover rates by as much as 50% from a Geobacter
sulfurreducens biofilm as flow rate was varied. Based on parameters from the
applied kinetic framework, flow-induced increases to turnover rate, catalytic
efficiency and device reaction capacity could be linked to an increase in
catalytic biomass. This study demonstrates that a standardized kinetic
framework is critical for quantitative measurements of new living catalytic
systems in flow cells and for benchmarking against well-studied catalytic
systems such as enzymes.
Optimal excess-of-loss reinsurance for stochastic factor risk models
Date: 2019-04-10 00:00:00

We study the optimal excess-of-loss reinsurance problem when both the
intensity of the claims arrival process and the claim size distribution are
influenced by an exogenous stochastic factor. We assume that the insurer's
surplus is governed by a marked point process with dual-predictable projection
affected by an environmental factor and that the insurance company can borrow
and invest money at a constant real-valued risk-free interest rate $r$. Our
model allows for stochastic risk premia, which take into account risk
fluctuations. Using stochastic control theory based on the
Hamilton-Jacobi-Bellman equation, we analyze the optimal reinsurance strategy
under the criterion of maximizing the expected exponential utility of the
terminal wealth. A verification theorem for the value function in terms of
classical solutions of a backward partial differential equation is provided.
Finally, some numerical results are discussed.
Doppler Fluctuation Spectroscopy of Intracellular Dynamics in Living Tissue
Date: 2019-04-10 00:00:00

Intracellular dynamics in living tissue are dominated by active transport
driven by bioenergetic processes far from thermal equilibrium. Intracellular
constituents typically execute persistent walks. In the limit of long mean-free
paths, the persistent walks are ballistic, exhibiting a "Doppler edge" in light
scattering fluctuation spectra. At shorter transport lengths, the fluctuations
are described by lifetime-broadened Doppler spectra. Dynamic light scattering
from transport in the ballistic, diffusive or the cross-over regime is derived
analytically, including the derivation of autocorrelation functions through a
driven damped harmonic oscillator analog for light scattering from persistent
walks. The theory is validated through Monte Carlo simulations. Experimental
evidence for the Doppler edge in 3D living tissue is obtained using biodynamic
imaging based on low-coherence interferometry and digital holography.
ASVspoof 2019: Future Horizons in Spoofed and Fake Audio Detection
Date: 2019-04-14 00:00:00
Tasks: Speaker Verification

ASVspoof, now in its third edition, is a series of community-led challenges
which promote the development of countermeasures to protect automatic speaker
verification (ASV) from the threat of spoofing. Advances in the 2019 edition
include: (i) a consideration of both logical access (LA) and physical access
(PA) scenarios and the three major forms of spoofing attack, namely synthetic,
converted and replayed speech; (ii) spoofing attacks generated with
state-of-the-art neural acoustic and waveform models; (iii) an improved,
controlled simulation of replay attacks; (iv) use of the tandem detection cost
function (t-DCF) that reflects the impact of both spoofing and countermeasures
upon ASV reliability. Even if ASV remains the core focus, in retaining the
equal error rate (EER) as a secondary metric, ASYspoof also embraces the
growing importance of fake audio detection. ASVspoof 2019 attracted the
participation of 63 research teams, with more than half of these reporting
systems that improve upon the performance of two baseline spoofing
countermeasures. This paper describes the 2019 database, protocols and
challenge results. It also outlines major findings which demonstrate the real
progress made in protecting against the threat of spoofing and fake audio.
Improved visual function in a case of ultra-low vision following ischemic encephalopathy following transcranial electrical stimulation; A case study
Date: 2019-04-10 00:00:00

Cortical visual impairment is amongst the key pathological causes of
pediatric visual abnormalities predominantly resulting from hypoxic-ischemic
brain injury. Such an injury results in profound visual impairments which
severely impairs the patient's quality of life. Given the nature of the
pathology, treatments are mostly limited to rehabilitation strategies such as
transcranial electrical stimulation and visual rehabilitation therapy. Here, we
discussed an 11 year-old girl case with cortical visual impairment who
underwent concurrent visual rehabilitation therapy and transcranial electrical
stimulation resulting in her improved visual function. This novel and
noninvasive therapeutic intervention has shown potential for application in
neuro-visual rehabilitation therapy (nVRT).
Theory of Cryptocurrency Interest Rates
Date: 2019-12-17 00:00:00

A term structure model in which the short rate is zero is developed as a
candidate for a theory of cryptocurrency interest rates. The price processes of
crypto discount bonds are worked out, along with expressions for the
instantaneous forward rates and the prices of interest-rate derivatives. The
model admits functional degrees of freedom that can be calibrated to the
initial yield curve and other market data. Our analysis suggests that strict
local martingales can be used for modelling the pricing kernels associated with
virtual currencies based on distributed ledger technologies.
Capacity and Energy-Efficiency of Delayed Access Scheme for Small Cell Networks
Date: 2019-04-17 00:00:00

Data applications may typically tolerate a moderate delay before packet
transmission between user equipment (UE) and cell begins. This delay can be
taken advantage to reduce the communication distance, improve coverage
probability, and increase overall energy-efficiency of the small cell network.
To demonstrate such merits, we suggest a simple access scheme and analyze the
distribution of coverage probability and throughput as a function of delay and
transmit distance. Sufficient number of small base stations (SBSs) handle the
peak traffic load. To improve energy-efficiency of the network, a number of
SBSs are switched off at low traffic periods. Energy-efficiency can be further
improved by turning all of the SBSs on and off, rather than selecting a subset
and leaving them off. By doing so, coverage probability and bit-rate can be
improved by delaying their transmissions and waiting for a closer SBS to become
available. Results show that by turning SBSs on and off continuously and taking
advantage of initial delay to connect a SBS yield an order of magnitude
improvement in energy-efficiency, improves the coverage probability
significantly at low signal to interference and noise (SINR) regime.
Clustering Based Hybrid Precoding Design for Multi-User Massive MIMO Systems
Date: 2019-04-11 00:00:00
Tasks: Clustering

Hybrid precoding has been recognized as a promising technology to combat the
path loss of millimeter wave signals in massive multiple-input multiple-output
(MIMO) systems. However, due to the joint optimization of the digital and
analog precoding matrices as well as extra constraints for the analog part, the
hybrid precoding design is still a tough issue in current research. In this
paper, we adopt the thought of clustering in unsupervised learning and provide
design schemes for fully-connected hybrid precoding (FHP) and
adaptively-connected hybrid precoding (AHP) in multi-user massive MIMO systems.
For FHP, we propose the hierarchical-agglomerative-clustering-based (HAC-based)
scheme to explore the relevance among RF chains in optimal hybrid procoding
design. The similar RF chains are merged into an individual RF chain when
insufficient RF chains are equipped at the base station (BS). For AHP, we
propose the modified-K-means-based (MKM-based) scheme to explore the relevance
among antennas at the BS. The similar antennas are supported by the same RF
chain to make full use of the flexible connection in AHP. Particularly, in
proposed MKM-based AHP design, the clustering centers are updated by
alternating-optimum-based (AO-based) scheme with a special initialization
method, which is capable to individually provide feasible sub-connected hybrid
precoding (SHP) design. Simulation results highlight the superior spectrum
efficiency of proposed HAC-based FHP scheme, and the high power efficiency of
proposed MKM-based AHP scheme. Moreover, all the proposed schemes are clarified
to effectively handle the inter-user interference and outperform the existing
work.
Enhancing Bridge Deck Delamination Detection Based on Aerial Thermography Through Grayscale Morphologic Reconstruction: A Case Study
Date: 2019-04-11 00:00:00
Tasks: Clustering, Quantization

Environmental-induced temperature variations across the bridge deck were one
of the major factors that degraded the performance of delamination detection
through thermography. The non-uniformly distributed thermal background yields
the assumption of most conventional quantitative methods used in practice such
as global thresholding and k-means clustering. This study proposed a
pre-processing method to estimate the thermal background through iterative
grayscale morphologic reconstruction based on a pre-selected temperature
contrast. After the estimation of the background, the thermal feature of
delamination was kept in the residual image. A UAV-based nondestructive survey
was carried out on an in-service bridge for a case study and two delamination
quantization methods (threshold-based and clustering-based) were applied on
both raw and residual thermal image. Results were compared and evaluated based
on the hammer sounding test on the same bridge. The performance of
detectability was noticeably improved while direct implementation of
post-processing on raw image exhibited over- and under-estimation of
delamination. The selection of pre-defined temperature contrast and stopping
criterion of iteration were discussed. The study concluded the usefulness of
the proposed method for the case study and further evaluation and parameter
tuning are expected to generalize the method and procedure.
Identification of similarity amongst eucalyptus planted areas based on leaf-cutting ant nest sizes
Date: 2019-04-16 00:00:00
Tasks: Management

Techniques for leaf-cutting ant control have been investigated in literature
due to the importance of the damage they cause to agriculture. The
effectiveness of different forms of control is explored in researches aimed at
identifying the balance between pest control efficiency and environmental
damage caused by the treatments applied. Plantations with large territorial
extensions, which can be contiguous or not, are usually subdivided into local
administration that collects data to determine the frequencies of areas size
occupied by ant nests. The purpose of this work is to build a relationship of
similarities among different geographical regions using the frequency data of
nests size occurrence by applying Information Bottleneck (IB) method and
Principal Component Analysis (PCA). IB allows simultaneous clustering of each
region with the ant nest size distribution, while PCA is used to reduce the
variable dimensionalities into a three-dimensional representation of the
results. The approach was applied to data of leaf-cutting ants Atta spp.
(Hymenoptera: Formicidae) in cultivated Eucalyptus spp forests in Sao Paulo,
Brazil. The results suggest information acquired by the method can help
coordinate pest management, such as the allocation of baits, material and
personnel.
Deep-learning based numerical BSDE method for barrier options
Authors: Bing Yu, Xiaojing Xing, Agus Sudjianto
Date: 2019-04-11 00:00:00
Tasks: Deep Learning

As is known, an option price is a solution to a certain partial differential equation (PDE) with terminal conditions (payoff functions). There is a close association between the solution of PDE and the solution of a backward stochastic differential equation (BSDE). We can either solve the PDE to obtain option prices or solve its associated BSDE. Recently a deep learning technique has been applied to solve option prices using the BSDE approach. In this approach, deep learning is used to learn some deterministic functions, which are used in solving the BSDE with terminal conditions. In this paper, we extend the deep-learning technique to solve a PDE with both terminal and boundary conditions. In particular, we will employ the technique to solve barrier options using Brownian motion bridges.
Secure mmWave Communications in Cognitive Radio Networks
Date: 2019-04-12 00:00:00

In this letter, the secrecy performance in cognitive radio networks (CRNs)
over fluctuating two-ray (FTR) channels, which is used to model the millimetre
wave channel, is investigated in terms of the secrecy outage probability (SOP).
Specifically, we consider the case where a source (S) transmits confidential
messages to a destination (D), and an eavesdropper wants to wiretap the
information from S to D. In a CRN framework, we assume that the primary user
shares its spectrum with S, where S adopts the underlay strategy to control its
transmit power without impairing the quality of service of the primary user.
After some mathematical manipulations, an exact analytical expression for the
SOP is derived. In order to get physical and technical insights into the effect
of the channel parameters on the SOP, we derive an asymptotic formula for the
SOP in the high signal-to-noise ratio region of the S--D link. We finally show
some selected Monte-Carlo simulation results to validate the correctness of our
derived analytical expressions.
Distribution Regression in Duration Analysis: an Application to Unemployment Spells
Authors: Miguel A. Delgado, Andrés García-Suaza, Pedro H. C. Sant'Anna
Date: 2019-04-12 00:00:00
Tasks: regression

This article proposes inference procedures for distribution regression models in duration analysis using randomly right-censored data. This generalizes classical duration models by allowing situations where explanatory variables' marginal effects freely vary with duration time. The article discusses applications to testing uniform restrictions on the varying coefficients, inferences on average marginal effects, and others involving conditional distribution estimates. Finite sample properties of the proposed method are studied by means of Monte Carlo experiments. Finally, we apply our proposal to study the effects of unemployment benefits on unemployment duration.
Model Predictive Control of Shallow Drowsiness: Improving Productivity of Office Workers
Date: 2019-04-11 00:00:00
Tasks: Model Predictive Control, Scheduling

This paper proposes a methodology of model predictive control for alleviating
shallow drowsiness of office workers and thus improving their productivity. The
methodology is based on dynamically scheduling setting values for air
conditioning and lighting to minimize drowsiness level of office workers on the
basis of a prediction model that represents the relation between future
drowsiness level and combination of indoor temperature and ambient illuminance.
The prediction model can be identified by utilizing state-of-the-art drowsiness
estimation method. The proposed methodology was evaluated in regard to a real
routine task (performed by six subjects over five workdays), and the evaluation
results demonstrate that the proposed methodology improved the processing speed
of the task by 8.3% without degrading comfort of the workers.
A Neural Network Prediction Based Adaptive Mode Selection Scheme in Full-Duplex Cognitive Networks
Date: 2019-04-12 00:00:00
Tasks: Prediction

We propose a neural network (NN) predictor and an adaptive mode selection
scheme for the purpose of both improving secondary user's (SU's) throughput and
reducing collision probability to the primary user (PU) in full-duplex (FD)
cognitive networks. SUs can adaptively switch between FD
transmission-and-reception (TR) and transmission-and-sensing (TS) modes based
on the NN prediction results for each transmission duration. The prediction
performance is then analysed in terms of prediction error probability. We also
compare the performance of our proposed scheme with conventional TR and TS
modes in terms of SUs average throughput and collision probability,
respectively. Simulation results show that our proposed scheme achieves even
better SUs average throughput compared with TR mode. Meanwhile, the collision
probability can be reduced close to the level of TS mode.
Dynamic investment model of the life cycle of a company under the influence of factors in a competitive environment
Date: 2019-04-21 00:00:00

Modelling all possible life cycles of a company in a highly competitive
economic environment gives a significant advantage to the owner in his business
investment activities. This article proposes and analyses a dynamic model of a
company's life cycle with known action costs and transition probabilities, that
can be affected by an outside influence. For this task, the Markov model was
utilized. The proposed model is illustrated on a task of determining an
advertising policy for a car dealership, that would increase the stock equity
of a company. The result demonstrates the usefulness of a model for use in
determining future actions of a company. We also review multiple models of the
influence of outside factors on a company's total capitalization.
Modeling tumorspheres reveals cancer stem cell niche building and plasticity
Authors: L. Benítez, L. Barberis, C. A. Condat
Date: 2019-04-09 00:00:00

Cancer stem cells have been shown to be critical to the development of a variety of solid cancers. The precise interplay mechanisms between cancer stem cells and the rest of a tissue are still not elucidated. To shed light on the interactions between stem and non-stem cancer cell populations we develop a two-population mathematical model, which is suitable to describe tumorsphere growth. Both interspecific and intraspecific interactions, mediated by the microenvironment, are included. We show that there is a tipping point, characterized by a transcritical bifurcation, where a purely non-stem cell attractor is replaced by a new attractor that contains both stem and differentiated cancer cells. The model is then applied to describe the outcome of a recent experiment. This description reveals that, while the intraspecific interactions are inhibitory, the interspecific interactions stimulate growth. This can be understood in terms of stem cells needing differentiated cells to reinforce their niches, and phenotypic plasticity favoring the de-differentiation of differentiated cells into cancer stem cells. We posit that this is a consequence of the deregulation of the quorum sensing that maintains homeostasis in healthy tissues.
Chronic stress may disrupt covariant fluctuations of vitamin D and cortisol plasma levels in pregnant sheep during the last trimester: a preliminary report
Date: 2019-08-12 00:00:00

Psychosocial stress during pregnancy is a known contributor to preterm birth,
but also has been increasingly appreciated as an in utero insult acting
long-term on prenatal and postnatal neurodevelopmental trajectories. These
events impact many information molecules, including both vitamin D and
cortisol. Both have been linked to low birth premature babies. Cortisol tends
to be further elevated in women, while vitamin D tends to be decreased from
their normal levels during pregnancy. One facilitates labor in part by
elevating placental CRH, the other by limiting CRH in placental tissue. Both
are linked to managing adversity. Studies in large animal models with high
resemblance to human physiology are sparse to model the changes induced by such
stress exposure. Using an established pregnant sheep model of stress during
human development, here we focused on measuring the changes in maternal Vitamin
D and cortisol responses due to chronic inescapable stress mimicking daily
challenges in the last trimester of human pregnancy. The present pilot data
show that chronic maternal stress during pregnancy results in endocrine and
metabolic chronic habituation paralleled by sensitization to acute stress
challenges. Chronic stress appears to disrupt a physiological relationship
between oscillations of vitamin D and cortisol. These speculations need to be
explored in future studies.
Shedding light on the dark matter of the biomolecular structural universe: Progress in RNA 3D structure prediction
Date: 2019-04-13 00:00:00
Tasks: RNA 3D STRUCTURE PREDICTION

Structured RNA plays many functionally relevant roles in molecular life.
Structural information, while required to understand the functional cycles in
detail, is challenging to gather. Computational methods promise to complement
experimental efforts by predicting three-dimensional RNA models. Here, we
provide a concise view of the state of the art methodologies with a focus on
the strengths and the weaknesses of the different approaches. Furthermore, we
analyzed the recent developments regarding the use of coevolutionary
information and how it can boost the prediction performances. We finally
discuss some open perspectives and challenges for the near future in the RNA
structural stability field.
Costly Attention and Retirement
Authors: Jamie Hentall-MacCuish
Date: 2019-04-13 00:00:00
Tasks: Friction, Single Particle Analysis

In UK data, I document the prevalence of misbeliefs regarding the State Pension eligibility age (SPA) and these misbeliefs' predictivity of retirement. Exploiting policy variation, I estimate a lifecycle model of retirement in which rationally inattentive households learning about uncertain pension policy endogenously generates misbeliefs. Endogenous misbeliefs explain 43\%-88\% of the excessive (given financial incentives) drop in employment at SPA. To achieve this, I develop a solution method for dynamic rational inattention models with history-dependent beliefs. Costly attention makes the SPA up to 15\% less effective at increasing old-age employment. Information letters improve welfare and increase employment.