pwc-archive/papers-with-abstracts huggingface dataset:
An Enhanced Middleware for Collaborative Privacy in IPTV Recommender Services
Authors: Elmisery Ahmed M., Botvich Dmitri
Date: 2017-11-21 00:00:00

One of the concerns users have to confronted when using IPTV system is the
information overload that makes it difficult for them to find a suitable
content according to their personal preferences. Recommendation service is one
of the most widely adopted technologies for alleviating this problem, these
services intend to provide people with referrals of items they will appreciate
based on their preferences. IPTV users must ensure their sensitive preferences
collected by any recommendation service are properly secured. In this work, we
introduce a framework for private recommender service based on Enhanced
Middleware for Collaborative Privacy (EMCP). EMCP executes a two-stage
concealment process that gives the user a complete control on the privacy level
of his/her profile. We utilize trust mechanism to augment the accuracy and
privacy of the recommendations. Trust heuristic spot users who are trustworthy
with respect to the user requesting the recommendation. Later, the neighborhood
formation is calculated using proximity metrics based on these trustworthy
users. Finally, Users submit their profiles in an obfuscated form without
revealing any information about their data, and the computation of
recommendations proceeds over the obfuscated data using secure multiparty
computation protocol. We expand the obfuscation scope from single obfuscation
level for all users to arbitrary obfuscation levels based on trustworthy
between users. In other words, we correlate the obfuscation level with
different trust levels, so the more trusted a target user is the less
obfuscation copy of profile he can access. We also provide an IPTV network
scenario and experimentation results. Our results and analysis show that our
two-stage concealment process not only protects the privacy of users but also
can maintain the recommendations accuracy.
The Lower Bound Error for polynomial NARMAX using an Arbitrary Number of Natural Interval Extensions
Date: 2017-11-21 00:00:00

The polynomial NARMAX (Nonlinear AutoRegressive Moving Average model with
eXogenous input) is a model that represents the dynamics of physical systems.
This polynomial contains information from the past of the inputs and outputs of
the process, that is, it is a recursive model. In digital computers this
generates the propagation of the rounding error. Our procedure is based on the
estimation of the maximum value of the lower bound error considering an
arbitrary number of pseudo-orbits produced from different natural interval
extensions, and a posterior Lyapunov exponent calculation. We applied
successfully our technique for two identified models of the systems: sine map
and Duffing-Ueda oscillator
Statistical properties of market collective responses
Date: 2017-11-21 00:00:00

We empirically analyze the price and liquidity responses to trade signs,
traded volumes and signed traded volumes. Utilizing the singular value
decomposition, we explore the interconnections of price responses and of
liquidity responses across the whole market. The statistical characteristics of
their singular vectors are well described by the $t$ location-scale
distribution. Furthermore, we discuss the relation between prices and liquidity
with respect to their overlapping factors. The factors of price and liquidity
changes are non-random when these factors are related to the traded volumes.
This means that the traded volumes play a critical role in the price change
induced by the liquidity change. In contrast, the two kinds of factors are
weakly overlapping when they are related to the trade signs and signed traded
volumes. Hence, an imbalance of liquidity is related to the price change.
Price Optimisation for New Business
Date: 2017-11-21 00:00:00

This contribution is concerned with price optimisation of the new business
for a non-life product. Due to high competition in the insurance market,
non-life insurers are interested in increasing their conversion rates on new
business based on some profit level. In this respect, we consider the
competition in the market to model the probability of accepting an offer for a
specific customer. We study two optimisation problems relevant for the insurer
and present some algorithmic solutions for both continuous and discrete case.
Finally, we provide some applications to a motor insurance dataset.
Beyond Accuracy Optimization: On the Value of Item Embeddings for Student Job Recommendations
Authors: Lacic Emanuel, Kowald Dominik, Reiter-Haas Markus, Slawicek Valentin, Lex Elisabeth
Date: 2017-11-21 00:00:00
Tasks: Diversity

In this work, we address the problem of recommending jobs to university
students. For this, we explore the utilization of neural item embeddings for
the task of content-based recommendation, and we propose to integrate the
factors of frequency and recency of interactions with job postings to combine
these item embeddings. We evaluate our job recommendation system on a dataset
of the Austrian student job portal Studo using prediction accuracy, diversity
and an adapted novelty metric. This paper demonstrates that utilizing frequency
and recency of interactions with job postings for combining item embeddings
results in a robust model with respect to accuracy and diversity, which also
provides the best adapted novelty results.
Efficient and fast algorithms to generate holograms for optical tweezers
Date: 2017-11-20 00:00:00

We discuss and compare three algorithms for generating holograms: simple
rounding, Floyd-Steinberg error diffusion dithering, and mixed region amplitude
freedom (MRAF). The methods are optimised for producing large arrays of tightly
focused optical tweezers for trapping particles. The algorithms are compared in
terms of their speed, efficiency, and accuracy, for periodic arrangements of
traps; an arrangement of particular interest in the field of quantum computing.
We simulate the image formation using each of a binary amplitude modulating
digital mirror device (DMD) and a phase modulating spatial light modulator
(PSLM) as the display element. While a DMD allows for fast frame rates, the
slower PSLM is more efficient and provides higher accuracy with a
quasi-continuous variation of phase. We discuss the relative merits of each
algorithm for use with both a DMD and a PSLM, allowing one to choose the ideal
approach depending on the circumstances.
Temporal precision of regulated gene expression
Date: 2017-11-21 00:00:00

Important cellular processes such as migration, differentiation, and
development often rely on precise timing. Yet, the molecular machinery that
regulates timing is inherently noisy. How do cells achieve precise timing with
noisy components? We investigate this question using a first-passage-time
approach, for an event triggered by a molecule that crosses an abundance
threshold and that is regulated by either an accumulating activator or a
diminishing repressor. We find that the optimal strategy corresponds to a
nonlinear increase in the amount of the target molecule over time. Optimality
arises from a tradeoff between minimizing the extrinsic timing noise of the
regulator, and minimizing the intrinsic timing noise of the target molecule
itself. Although either activation or repression outperforms an unregulated
strategy, when we consider the effects of cell division, we find that
repression outperforms activation if division occurs late in the process. Our
results explain the nonlinear increase and low noise of mig-1 gene expression
in migrating neuroblast cells during Caenorhabditis elegans development, and
suggest that mig-1 regulation is dominated by repression for maximal temporal
precision. These findings suggest that dynamic regulation may be a simple and
powerful strategy for precise cellular timing.
Evolution at the edge of expanding populations
Date: 2019-04-25 00:00:00

Predicting evolution of expanding populations is critical to control
biological threats such as invasive species and cancer metastasis. Expansion is
primarily driven by reproduction and dispersal, but nature abounds with
examples of evolution where organisms pay a reproductive cost to disperse
faster. When does selection favor this 'survival of the fastest?' We searched
for a simple rule, motivated by evolution experiments where swarming bacteria
evolved into an hyperswarmer mutant which disperses $ \sim 100\%$ faster but
pays a growth cost of $\sim 10 \%$ to make many copies of its flagellum. We
analyzed a two-species model based on the Fisher equation to explain this
observation: the population expansion rate ($v$) results from an interplay of
growth ($r$) and dispersal ($D$) and is independent of the carrying capacity:
$v=2\sqrt{rD}$. A mutant can take over the edge only if its expansion rate
($v_2$) exceeds the expansion rate of the established species ($v_1$); this
simple condition ($v_2 > v_1$) determines the maximum cost in slower growth
that a faster mutant can pay and still be able to take over. Numerical
simulations and time-course experiments where we tracked evolution by imaging
bacteria suggest that our findings are general: less favorable conditions delay
but do not entirely prevent the success of the fastest. Thus, the expansion
rate defines a traveling wave fitness, which could be combined with trade-offs
to predict evolution of expanding populations.
Efficient low-dimensional approximation of continuous attractor networks
Date: 2017-11-21 00:00:00

Continuous "bump" attractors are an established model of cortical working
memory for continuous variables and can be implemented using various neuron and
network models. Here, we develop a generalizable approach for the approximation
of bump states of continuous attractor networks implemented in networks of both
rate-based and spiking neurons. The method relies on a low-dimensional
parametrization of the spatial shape of firing rates, allowing to apply
efficient numerical optimization methods. Using our theory, we can establish a
mapping between network structure and attractor properties that allows the
prediction of the effects of network parameters on the steady state firing rate
profile and the existence of bumps, and vice-versa, to fine-tune a network to
produce bumps of a given shape.
Polynomial Jump-Diffusion Models
Authors: Damir Filipović, Martin Larsson
Date: 2017-11-21 00:00:00

We develop a comprehensive mathematical framework for polynomial jump-diffusions in a semimartingale context, which nest affine jump-diffusions and have broad applications in finance. We show that the polynomial property is preserved under polynomial transformations and L\'evy time change. We present a generic method for option pricing based on moment expansions. As an application, we introduce a large class of novel financial asset pricing models with excess log returns that are conditional L\'evy based on polynomial jump-diffusions.
Assessing Mortality of Blunt Trauma with Co-morbidity
Date: 2017-11-17 00:00:00

Objectives: To obtain a better estimate of the mortality of individuals
suffering from blunt force trauma, including co-morbidity. Methodology: The
Injury severity Score (ISS) is the default world standard for assessing the
severity of multiple injuries. ISS is a mathematical fit to empirical field
data. It is demonstrated that ISS is proportional to the Gibbs/Shannon Entropy.
A new Entropy measure of morbidity from blunt force trauma including
co-morbidity is derived based on the von Neumann Entropy, called the
Abbreviated Morbidity Scale (AMS). Results: The ISS trauma measure has been
applied to a previously published database, and good correlation has been
achieved. Here the existing trauma measure is extended to include the
co-morbidity of disease by calculating an Abbreviated Morbidity Score (AMS),
which encapsulates the disease co-morbidity in a manner analogous to AIS, and
on a consistent Entropy base. Applying Entropy measures to multiple injuries,
highlights the role of co-morbidity and that the elderly die at much lower
levels of injury than the general population, as a consequence of co-morbidity.
These considerations lead to questions regarding current new car assessment
protocols, and how well they protect the most vulnerable road users. Keywords:
Blunt Force Trauma, Injury Severity Score, Co-morbidity, Entropy.
Effect of sodium chloride (NaCl) on the growth of six Acacia species
Date: 2017-10-30 00:00:00

Background: Salinity is one of the major abiotic stresses affecting plant
production in arid and semi-arid regions. It causes reduction of cultivable
area and combined with other factors, presents a serious threat to food
stability in these areas. Context: In front of this problem, the selection of
salt tolerant species and varieties remains the best economic approach for
exploitation and rehabilitation of salt-affected regions. Objective: The
purpose of this study was to assess and compare the seed germination response
of six Acacia species under different NaCl concentrations in order to explore
opportunities for selection and breeding salt tolerant genotypes. Methods: The
salinity effect was examined by measuring some agro-morphological parameters in
controlled growth environment using five treatment levels: 0, 100, 200, 300 and
400 mM of NaCl. Results: The analyzed data revealed significant variability in
salt response within and between species. All growth parameters were
progressively reduced by increased NaCl concentrations. Growth in height, leaf
number and total plant dry weight were considered as the most sensitive
parameters. However, the growth reduction varied among species in accordance
with their tolerance level. It is important to note that all species survived
at the highest salinity (400 mM). Whereas A. horrida and A. raddiana were
proved to be often the best tolerant, they recorded the lowest reduction
percentage at this stage. Conclusion: The genetic variability found in the
studied species at seedling stage may be used to select genotypes particularly
suitable for rehabilitation and exploitation of lands affected by salinity.
Scalable Inference of Ordinary Differential Equation Models of Biochemical Processes
Date: 2018-10-11 00:00:00

Ordinary differential equation models have become a standard tool for the
mechanistic description of biochemical processes. If parameters are inferred
from experimental data, such mechanistic models can provide accurate
predictions about the behavior of latent variables or the process under new
experimental conditions. Complementarily, inference of model structure can be
used to identify the most plausible model structure from a set of candidates,
and thus gain novel biological insight. Several toolboxes can infer model
parameters and structure for small- to medium-scale mechanistic models out of
the box. However, models for highly multiplexed datasets can require hundreds
to thousands of state variables and parameters. For the analysis of such
large-scale models, most algorithms require intractably high computation times.
This chapter provides an overview of state-of-the-art methods for parameter and
model inference, with an emphasis on scalability.
Turbo EP-based Equalization: a Filter-Type Implementation
Date: 2018-12-21 00:00:00
Tasks: Decoder, Vocal Bursts Type Prediction

This manuscript has been submitted to Transactions on Communications on
September 7, 2017; revised on January 10, 2018 and March 27, 2018; and accepted
on April 25, 2018
  We propose a novel filter-type equalizer to improve the solution of the
linear minimum-mean squared-error (LMMSE) turbo equalizer, with computational
complexity constrained to be quadratic in the filter length. When high-order
modulations and/or large memory channels are used the optimal BCJR equalizer is
unavailable, due to its computational complexity. In this scenario, the
filter-type LMMSE turbo equalization exhibits a good performance compared to
other approximations. In this paper, we show that this solution can be
significantly improved by using expectation propagation (EP) in the estimation
of the a posteriori probabilities. First, it yields a more accurate estimation
of the extrinsic distribution to be sent to the channel decoder. Second,
compared to other solutions based on EP the computational complexity of the
proposed solution is constrained to be quadratic in the length of the finite
impulse response (FIR). In addition, we review previous EP-based turbo
equalization implementations. Instead of considering default uniform priors we
exploit the outputs of the decoder. Some simulation results are included to
show that this new EP-based filter remarkably outperforms the turbo approach of
previous versions of the EP algorithm and also improves the LMMSE solution,
with and without turbo equalization.
Optimal Power Control in Decentralized Gaussian Multiple Access Channels
Date: 2019-12-18 00:00:00

We consider the decentralized power optimization problem for Gaussian
fast-fading multiple access channel (MAC) so that the average sum-throughput is
maximized. In our MAC setup, each transmitter has access to only its own fading
coefficient or channel state information (CSI) while the receiver has full CSI
available at all instants. Unlike centralized MAC (full CSIT MAC) where the
optimal powers are known explicitly, the analytical solution for optimal
decentralized powers does not seem feasible. In this letter, we specialize
alternating-maximization (AM) method for numerically computing the optimal
powers and ergodic capacity of the decentralized MAC for general fading
statistics and average power constraints. For illustration, we apply our AM
method to compute the capacity of MAC channels with fading distributions such
as Rayleigh, Rician etc.
Asymmetric return rates and wealth distribution influenced by the introduction of technical analysis into a behavioral agent based model
Date: 2017-11-22 00:00:00
Tasks: Decision Making

Behavioral Finance has become a challenge to the scientific community. Based
on the assumption that behavioral aspects of investors may explain some
features of the Stock Market, we propose an agent based model to study
quantitatively this relationship. In order to approximate the simulated market
to the complexity of real markets, we consider that the investors are connected
among them through a small world network; each one has its own psychological
profile (Imitation, Anti-Imitation, Random); two different strategies for
decision making: one of them is based on the trust neighborhood of the investor
and the other one considers a technical analysis, the momentum of the market
index technique. We analyze the market index fluctuations, the wealth
distribution of the investors according to their psychological profiles and the
rate of return distribution. Moreover, we analyze the influence of changing the
psychological profile of the hub of the network and report interesting results
which show how and when anti-imitation becomes the most profitable strategy for
investment. Besides this, an intriguing asymmetry of the return rate
distribution is explained considering the behavioral aspect of the investors.
This asymmetry is quite robust being observed even when a completely different
algorithm to calculate the decision making of the investors was applied to it,
a remarkable result which, up to our knowledge, has never been reported before.
Subthreshold signal encoding in coupled FitzHugh-Nagumo neurons
Date: 2017-11-22 00:00:00

Despite intensive research, the mechanisms underlying how neurons encode
external inputs remain poorly understood. Recent work has focused on the
response of a single neuron to a weak, subthreshold periodic signal. By
simulating the FitzHugh-Nagumo stochastic model and then using a symbolic
method to analyze the firing activity of the neuron, preferred and infrequent
spike patterns (defined by the relative timing of the spikes) were detected,
whose probabilities encode information about the signal. As not individual
neurons in isolation but neuronal populations are responsible for the emergence
of complex behaviors, a relevant question is whether this coding mechanism is
robust when the neuron is not isolated. We study how a second neuron, which
does not perceive the subthreshold signal, affects the detection and the
encoding of the signal, done by the first neuron. Through simulations of two
coupled FitzHugh-Nagumo neurons we show that the coding mechanism is indeed
robust, as the neuron that perceives the signal fires a spike train that has
symbolic patterns whose probabilities depend on the features of the signal.
Moreover, we show that the second neuron facilitates the detection of the
signal, by lowering the firing threshold of the first neuron. This in turn
decreases the internal noise level need to fire the spikes that encode the
signal. We also show that the probabilities of the symbolic patterns achieve
maximum or minimum values when the period of the external signal is close to
(or is half of) the mean firing period of the neuron.
Valuation of equity warrants for uncertain financial market
Date: 2017-11-23 00:00:00

In this paper, within the framework of uncertainty theory, the valuation of
equity warrants is investigated. Different from the methods of probability
theory, the equity warrants pricing problem is solved by using the method of
uncertain calculus. Based on the assumption that the firm price follows an
uncertain differential equation, the equity warrants pricing formula is
obtained for uncertain stock model.
Mixture-of-tastes Models for Representing Users with Diverse Interests
Authors: Kula Maciej
Date: 2018-01-29 00:00:00

Most existing recommendation approaches implicitly treat user tastes as
unimodal, resulting in an average-of-tastes representations when multiple
distinct interests are present. We show that appropriately modelling the
multi-faceted nature of user tastes through a mixture-of-tastes model leads to
large increases in recommendation quality. Our result holds both for deep
sequence-based and traditional factorization models, and is robust to careful
selection and tuning of baseline models. In sequence-based models, this
improvement is achieved at a very modest cost in model complexity, making
mixture-of-tastes models a straightforward improvement on existing baselines.
Data-Driven Nonparametric Existence and Association Problems
Date: 2017-11-22 00:00:00

We investigate two closely related nonparametric hypothesis testing problems.
In the first problem (i.e., the existence problem), we test whether a testing
data stream is generated by one of a set of composite distributions. In the
second problem (i.e., the association problem), we test which one of the
multiple distributions generates a testing data stream. We assume that some
distributions in the set are unknown with only training sequences generated by
the corresponding distributions are available. For both problems, we construct
the generalized likelihood (GL) tests, and characterize the error exponents of
the maximum error probabilities. For the existence problem, we show that the
error exponent is mainly captured by the Chernoff information between the set
of composite distributions and alternative distributions. For the association
problem, we show that the error exponent is captured by the minimum Chernoff
information between each pair of distributions as well as the KL divergences
between the approximated distributions (via training sequences) and the true
distributions. We also show that the ratio between the lengths of training and
testing sequences plays an important role in determining the error decay rate.
Detection Theory for Union of Subspaces
Date: 2019-01-19 00:00:00

The focus of this paper is on detection theory for union of subspaces (UoS).
To this end, generalized likelihood ratio tests (GLRTs) are presented for
detection of signals conforming to the UoS model and detection of the
corresponding "active" subspace. One of the main contributions of this paper is
bounds on the performances of these GLRTs in terms of geometry of subspaces
under various assumptions on the observation noise. The insights obtained
through geometrical interpretation of the GLRTs are also validated through
extensive numerical experiments on both synthetic and real-world data.