pwc-archive/papers-with-abstracts huggingface dataset:
Methodology for identifying study sites in scientific corpus
Authors: Kergosien Eric  GERIICO, Bessagnet Marie-Noëlle  LIUPPA, Teisseire Maguelonne  UMR TETIS, Schöpfel Joachim  GERIICO, Farvardin Mohammad Amin  LAMSADE, Chaudiron Stéphane  GERIICO, Jacquemin Bernard  GERIICO, Parc-Lacayrelle Annig Le  LIUPPA, Roche Mathieu  UMR TETIS, Sallaberry Christian  LIUPPA, Tonneau Jean-Philippe  UMR TETIS, Bessagnet Marie-Noelle  LIUPPA, Farvardin Amin  LAMSADE, Lacayrelle Annig
Date: 2018-08-13 00:00:00
Tasks: Articles, Information Retrieval, Retrieval

The TERRE-ISTEX project aims at identifying the evolution of research working
relation to study areas, disciplinary crossings and concrete research methods
based on the heterogeneous digital content available in scientific corpora. The
project is divided into three main actions: (1) to identify the periods and
places which have been the subject of empirical studies, and which reflect the
publications resulting from the corpus analyzed, (2) to identify the thematics
addressed in these works and (3) to develop a web-based geographical
information retrieval tool (GIR). The first two actions involve approaches
combining Natural languages processing patterns with text mining methods. By
crossing the three dimensions (spatial, thematic and temporal) in a GIR engine,
it will be possible to understand what research has been carried out on which
territories and at what time. In the project, the experiments are carried out
on a heterogeneous corpus including electronic thesis and scientific articles
from the ISTEX digital libraries and the CIRAD research center.
Critical neuronal models with relaxed timescales separation
Date: 2018-07-17 00:00:00

Power laws in nature are considered to be signatures of complexity. The
theory of self-organized criticality (SOC) was proposed to explain their
origins. A longstanding principle of SOC is the \emph{separation of timescales}
axiom. It dictates that external input is delivered to the system at a much
slower rate compared to the timescale of internal dynamics. The statistics of
neural avalanches in the brain was demonstrated to follow a power law,
indicating closeness to a critical state. Moreover, criticality was shown to be
a beneficial state for various computations leading to the hypothesis, that the
brain is a SOC system. However, for neuronal systems that are constantly
bombarded by incoming signals, separation of timescales assumption is
unnatural. Recently it was experimentally demonstrated that a proper correction
of the avalanche detection algorithm to account for the increased drive during
task performance leads to a change of the power-law exponent from $1.5$ to
approximately $1.3$, but there is so far no theoretical explanation for this
change. Here we investigate the importance of timescales separation, by partly
abandoning it in various models. We achieve it by allowing for external input
during the avalanche, without compromising the separation of avalanches. We
develop an analytic treatment and provide numerical simulations of a simple
neuronal model.
Connecting Sharpe ratio and Student t-statistic, and beyond
Date: 2019-05-14 00:00:00
Tasks: Asset Management, Management

Sharpe ratio is widely used in asset management to compare and benchmark
funds and asset managers. It computes the ratio of the excess return over the
strategy standard deviation. However, the elements to compute the Sharpe ratio,
namely, the expected returns and the volatilities are unknown numbers and need
to be estimated statistically. This means that the Sharpe ratio used by funds
is subject to be error prone because of statistical estimation error. Lo
(2002), Mertens (2002) derive explicit expressions for the statistical
distribution of the Sharpe ratio using standard asymptotic theory under several
sets of assumptions (independent normally distributed - and identically
distributed returns). In this paper, we provide the exact distribution of the
Sharpe ratio for independent normally distributed return. In this case, the
Sharpe ratio statistic is up to a rescaling factor a non centered Student
distribution whose characteristics have been widely studied by statisticians.
The asymptotic behavior of our distribution provide the result of Lo (2002). We
also illustrate the fact that the empirical Sharpe ratio is asymptotically
optimal in the sense that it achieves the Cramer Rao bound. We then study the
empirical SR under AR(1) assumptions and investigate the effect of compounding
period on the Sharpe (computing the annual Sharpe with monthly data for
instance). We finally provide general formula in this case of
heteroscedasticity and autocorrelation.
A Structural Model For Simulating the Mechanical Response of Fingertip to Tactile Stimuli
Date: 2018-08-30 00:00:00

Response of the mechanoreceptors underlying the skin is greatly affected by
its mechanical properties. Knowledge of this response is essential in designing
artificial tactile devices such as minimally invasive tools and tactile
displays. The purpose of present research is to simulate the biomechanics of
tactile sensation during indentation tests. A two-dimensional finite element
model has been used for the analysis incorporating the essential anatomical
structures of a finger (i.e., skin, subcutaneous tissue, bone, and nail). The
skin and subcutaneous tissue are assumed to be hyperelastic and viscoelastic.
We obtained the stress, strain and deformation fields in indentation tests. It
was noted that the skin away from the indenter was experiencing considerable
amount of strain energy density. Also, the response of SAI afferents could be
predicted from the proposed model. These results were in agreement with the
results mentioned in other published experimental data.
Turnpike Property and Convergence Rate for an Investment and Consumption Model
Date: 2018-08-13 00:00:00

We discuss the turnpike property for optimal investment and consumption
problems. We find there exists a threshold value that determines the turnpike
property for investment policy. The threshold value only depends on the Sharpe
ratio, the riskless interest rate and the discount rate. We show that if
utilities behave asymptotically like power utilities and satisfy some simple
relations with the threshold value, then the turnpike property for investment
holds. There is in general no turnpike property for consumption policy. We also
provide the rate of convergence and illustrate the main results with examples
of power and non-HARA utilities and numerical tests.
Automatic Playlist Continuation through a Composition of Collaborative Filters
Authors: Teinemaa Irene, Tax Niek, Bentes Carlos
Date: 2018-08-13 00:00:00

The RecSys Challenge 2018 focused on automatic playlist continuation, i.e.,
the task was to recommend additional music tracks for playlists based on the
playlist's title and/or a subset of the tracks that it already contains. The
challenge is based on the Spotify Million Playlist Dataset (MPD), containing
the tracks and the metadata from one million real-life playlists. This paper
describes the automatic playlist continuation solution of team Latte, which is
based on a composition of collaborative filters that each capture different
aspects of a playlist, where the optimal combination of those collaborative
filters is determined using a Tree-structured Parzen Estimator (TPE). The
solution obtained the 12th place out of 112 participating teams in the final
leaderboard. Team Latte participated in the main track of the challenge of the
RecSys Challenge 2018.
Noncoherent Multiantenna Receivers for Cognitive Backscatter System with Multiple RF Sources
Date: 2018-08-15 00:00:00

Cognitive backscattering, an integration of cognitive radio and backsatter
modulation, is emerging as a potential candidate for green Internet of Things
(IoT). In cognitive backscatter systems, the backscatter device (BD) shares not
only the same spectrum, but also the same radio-frequency (RF) source with the
legacy system. In this paper, we investigate the signal transmission problem,
in which a basic transmission model is considered which consists of K RF
sources, one BD and one reader equipped with M antennas. A non-cooperative
scenario is considered, where there is no cooperation between the legacy
systems and the backscatter system, and no pilots are transmitted from the RF
sources or BD to the reader. The on-off keying differential modulation is
adopted to achieve noncoherent transmission. Firstly, through the capacity
analyses, we point out that high-throughput backscatter transmission can be
achieved when the number of the receive antennas satisfies M>K. The Chernoff
Information (CI) is also derived to predict the detection performance. Next, we
address the signal detection problem at the reader. The optimal soft decision
(SD) and suboptimal hard decision (HD) detectors are designed based on the
maximum likelihood criterion. To tackle the non-cooperation challenge, a fully
blind channel estimation method is proposed to learn the detection-required
parameters based on clustering. Extensive numerical results verify the
effectiveness of the proposed detectors and the channel estimation method. In
addition, it is illustrated that the increase of K may not necessarily lead to
performance degradation when multiple receive antennas are exploited.
MUFold-BetaTurn: A Deep Dense Inception Network for Protein Beta-Turn Prediction
Date: 2018-08-13 00:00:00
Tasks: Experimental Design, Feature Engineering, Prediction

Beta-turn prediction is useful in protein function studies and experimental
design. Although recent approaches using machine-learning techniques such as
SVM, neural networks, and K-NN have achieved good results for beta-turn
pre-diction, there is still significant room for improvement. As previous
predictors utilized features in a sliding window of 4-20 residues to capture
interactions among sequentially neighboring residues, such feature engineering
may result in incomplete or biased features, and neglect interactions among
long-range residues. Deep neural networks provide a new opportunity to address
these issues. Here, we proposed a deep dense inception network (DeepDIN) for
beta-turn prediction, which takes advantages of the state-of-the-art deep
neural network design of the DenseNet and the inception network. A test on a
recent BT6376 benchmark shows that the DeepDIN outperformed the previous best
BetaTPred3 significantly in both the overall prediction accuracy and the
nine-type beta-turn classification. A tool, called MUFold-BetaTurn, was
developed, which is the first beta-turn prediction tool utilizing deep neural
networks. The tool can be downloaded at
http://dslsrv8.cs.missouri.edu/~cf797/MUFoldBetaTurn/download.html.
Discriminative multi-view Privileged Information learning for image re-ranking
Authors: Li Jun, Xu Chang, Yang Wankou, Sun Changyin, Tao Dacheng, Zhang Hong
Date: 2018-07-26 00:00:00
Tasks: Re-Ranking, Retrieval

Conventional multi-view re-ranking methods usually perform asymmetrical
matching between the region of interest (ROI) in the query image and the whole
target image for similarity computation. Due to the inconsistency in the visual
appearance, this practice tends to degrade the retrieval accuracy particularly
when the image ROI, which is usually interpreted as the image objectness,
accounts for a smaller region in the image. Since Privileged Information (PI),
which can be viewed as the image prior, enables well characterizing the image
objectness, we are aiming at leveraging PI for further improving the
performance of the multi-view re-ranking accuracy in this paper. Towards this
end, we propose a discriminative multi-view re-ranking approach in which both
the original global image visual contents and the local auxiliary PI features
are simultaneously integrated into a unified training framework for generating
the latent subspaces with sufficient discriminating power. For the on-the-fly
re-ranking, since the multi-view PI features are unavailable, we only project
the original multi-view image representations onto the latent subspace, and
thus the re-ranking can be achieved by computing and sorting the distances from
the multi-view embeddings to the separating hyperplane. Extensive experimental
evaluations on the two public benchmarks Oxford5k and Paris6k reveal our
approach provides further performance boost for accurate image re-ranking,
whilst the comparative study demonstrates the advantage of our method against
other multi-view re-ranking methods.
Computational Modeling of the Effects of Inflammatory Response and Granulation Tissue Properties on Human Bone Fracture Healing
Date: 2018-08-13 00:00:00

Bone healing process includes four phases: inflammatory response, soft callus
formation, hard callus development, and remodeling. Mechanobiological models
have been used to investigate the role of various mechanical and biological
factors on the bone healing. However, the initial phase of healing, which
includes the inflammatory response, the granulation tissue formation and the
initial callus formation during the first few days post-fracture, are generally
neglected in such studies. In this study, we developed a finite-element-based
model to simulate different levels of diffusion coefficient for mesenchymal
stem cell (MSC) migration, Young's modulus of granulation tissue, callus
thickness and interfragmentary gap size to understand the modulatory effects of
these initial phase parameters on bone healing. The results showed that faster
MSC migration, stiffer granulation tissue, thicker callus and smaller
interfragmentary gap enhanced healing to some extent. After a certain
threshold, a state of saturation was reached for MSC migration rate,
granulation tissue stiffness and callus thickness. Therefore, a parametric
study was performed to verify that the callus formed at the initial phase, in
agreement with experimental observations, has an ideal range of geometry and
material properties to have the most efficient healing time. Findings from this
paper quantified the effects of the healing initial phase on healing outcome to
better understand the biological and mechanobiological mechanisms and their
utilization in the design and optimization of treatment strategies. Simulation
outcomes also demonstrated that for fractures, where bone segments are in close
proximity, callus development is not required. This finding is consistent with
the concepts of primary and secondary bone healing.
Hybrid Powerline/Wireless Diversity for Smart Grid Communications: Design Challenges and Real-time Implementation
Date: 2018-08-14 00:00:00
Tasks: Diversity, Management

The demand for energy is growing at an unprecedented pace that is much higher
than the energy generation capacity growth rate using both conventional and
green technologies.In particular, the electric power sector is consistently
rated among the most dynamic growth markets over all other energy markets.
Distributed (decentralized) energy generation based on renewable energy sources
is an efficient and reliable solution to serve such huge energy demand growth
[1]. However, to manage dynamic and complex distributed systems, a massive
amount of data has to be measured, collected, exchanged and processed in real
time. Smart grids manage an intelligent energy delivery network enabled two-way
communications between data concentrators operated by utility companies and
smart meters installed at the end users. In particular, dynamic power-grid
loading and peak load management are the two main driving forces for
bidirectional communications over the grid. Narrowband power line
communications (NB-PLC) and wireless communications in the unlicensed frequency
band (sub-1 GHz or 2.4 GHz) are the two main communications systems adopted to
support the growing smart grid applications. Moreover, since NB-PLC and
unlicensed wireless links experience channel and interference with markedly
different statistics, transmitting the same information signal concurrently
over both links significantly enhances the smart grid communications
reliability. In this article, we compare various diversity combining schemes
for simultaneous power line and wireless transmissions. Furthermore, we
developed a real-time testbed for the hybrid PLC/wireless system to demonstrate
the performance enhancement achieved by PLC/wireless diversity combining over a
single link performance.
Effect of filter shape on excess noise performance in continuous variable quantum key distribution with Gaussian modulation
Date: 2018-08-14 00:00:00

An attractive implementation for quantum cryptography is the continuous
variable variation, as it relies on standard telecommunication components.
Modulating the quantum signal using a Gaussian format is attractive since it
has been proven to be secure. This work investigates the effect of the roll-off
of a root raised cosine pulse shaping and matched filter on the excess noise
performance of a Gaussian modulated quantum key distribution system in a
simulated back to back configuration. Contrary to intuition, it is found that
the roll-off parameter does not significantly impact the performance of the
system.
AFEL-REC: A Recommender System for Providing Learning Resource Recommendations in Social Learning Environments
Authors: Kowald Dominik, Lacic Emanuel, Theiler Dieter, Lex Elisabeth
Date: 2018-08-14 00:00:00
Tasks: Recommendation Systems

In this paper, we present preliminary results of AFEL-REC, a recommender
system for social learning environments. AFEL-REC is build upon a scalable
software architecture to provide recommendations of learning resources in near
real-time. Furthermore, AFEL-REC can cope with any kind of data that is present
in social learning environments such as resource metadata, user interactions or
social tags. We provide a preliminary evaluation of three recommendation use
cases implemented in AFEL-REC and we find that utilizing social data in form of
tags is helpful for not only improving recommendation accuracy but also
coverage. This paper should be valuable for both researchers and practitioners
interested in providing resource recommendations in social learning
environments.
A note on representation of BSDE-based dynamic risk measures and dynamic capital allocations
Date: 2018-08-14 00:00:00

In this paper, we provide a representation theorem for dynamic capital
allocation under It{\^o}-L{\'e}vy model. We consider the representation of
dynamic risk measures defined under Backward Stochastic Differential Equations
(BSDE) with generators that grow quadratic-exponentially in the control
variables. Dynamic capital allocation is derived from the differentiability of
BSDEs with jumps. The results are illustrated by deriving a capital allocation
representation for dynamic entropic risk measure and static coherent risk
measure.
Efficient Sum-of-Sinusoids based Spatial Consistency for the 3GPP New-Radio Channel Model
Date: 2018-08-14 00:00:00

Spatial consistency was proposed in the 3GPP TR 38.901 channel model to
ensure that closely spaced mobile terminals have similar channels. Future
extensions of this model might incorporate mobility at both ends of the link.
This requires that all random variables in the model must be correlated in 3
(single-mobility) and up to 6 spatial dimensions (dual-mobility). Existing
filtering methods cannot be used due to the large requirements of memory and
computing time. The sum-of-sinusoids model promises to be an efficient
solution. To use it in the 3GPP channel model, we extended the existing model
to a higher number of spatial dimensions and propose a new method to calculate
the sinusoid coefficients in order to control the shape of the autocorrelation
function. The proposed method shows good results for 2, 3, and 6 dimensions and
achieves a four times better approximation accuracy compared to the existing
model. This provides a very efficient implementation of the 3GPP proposal and
enables the simulation of many communication scenarios that were thought to be
impossible to realize with geometry-based stochastic channel models.
Regime-Switching Temperature Dynamics Model for Weather Derivatives
Date: 2018-08-13 00:00:00
Tasks: model

Weather is a key production factor in agricultural crop production and at the
same time the most significant and least controllable source of peril in
agriculture. These effects of weather on agricultural crop production have
triggered a widespread support for weather derivatives as a means of mitigating
the risk associated with climate change on agriculture. However, these products
are faced with basis risk as a result of poor design and modelling of the
underlying weather variable (temperature). In order to circumvent these
problems, a novel time-varying mean-reversion L\'evy regime-switching model is
used to model the dynamics of the deseasonalized temperature dynamics. Using
plots and test statistics, it is observed that the residuals of the
deseasonalized temperature data are not normally distributed. To model the
non-normality in the residuals, we propose using the hyperbolic distribution to
capture the semi-heavy tails and skewness in the empirical distributions of the
residuals for the shifted regime. The proposed regime-switching model has a
mean-reverting heteroskedastic process in the base regime and a L\'evy process
in the shifted regime. By using the Expectation-Maximization algorithm, the
parameters of the proposed model are estimated. The proposed model is flexible
as it modelled the deseasonalized temperature data accurately.
Dynamic programming for optimal stopping via pseudo-regression
Date: 2019-04-25 00:00:00
Tasks: regression

We introduce new variants of classical regression-based algorithms for
optimal stopping problems based on computation of regression coefficients by
Monte Carlo approximation of the corresponding $L^2$ inner products instead of
the least-squares error functional. Coupled with new proposals for simulation
of the underlying samples, we call the approach "pseudo regression". A detailed
convergence analysis is provided and it is shown that the approach
asymptotically leads to less computational cost for a pre-specified error
tolerance, hence to lower complexity. The method is justified by numerical
examples.
A Novel Method for Determining DOA from Far-Field TDOA or FDOA
Date: 2018-08-14 00:00:00

Passive source localization is often performed using time difference of
arrival (TDOA) measurements, frequency difference of arrival (FDOA)
measurements, direction of arrival (DOA) measurements, or a combination of all
of these. For a source in the far-field, DOA can be extracted from the TDOA and
FDOA measurements due to simplifications that arise in the far-field
approximation. This paper presents this relationship and the corresponding DOA
estimation method. Utilizing TDOA and FDOA measurements for computation of
signal DOA requires only a linear solve, which makes the corresponding source
localization technique very efficient. Additionally, the method provides an
inherent de-noising of receiver measurements, since they are being projected
onto the range of the receiver differencing matrix.
Finding morphology points of electrocardiographic signal waves using wavelet analysis
Date: 2018-08-14 00:00:00
Tasks: Sensitivity

A new algorithm has been developed for delineation of significant points of
various electrocardiographic signal (ECG) waves, taking into account
information from all available leads and providing similar or higher accuracy
in comparison with other modern technologies. The test results for the QT
database show a sensitivity above 97% when detecting ECG wave peaks and 96% for
their onsets and offsets, as well as better positive predictive value compared
to the previously known algorithms. In contrast to the previously published
algorithms, the proposed approach also allows one to determine the morphology
of waves. The segmentation mean errors of all significant points are below the
tolerances defined by the Committee of General Standards for
Electrocardiography (CSE).