pwc-archive/papers-with-abstracts huggingface dataset:
Extending the linear-noise approximation to biochemical systems influenced by intrinsic noise and slow lognormally distributed extrinsic noise
Authors: Emma M. Keizer, Bjorn Bastian, Robert W. Smith, Ramon Grima, Christian Fleck
Date: 2018-11-14 00:00:00

It is well known that the kinetics of an intracellular biochemical network is stochastic. This is due to intrinsic noise arising from the random timing of biochemical reactions in the network as well as due to extrinsic noise stemming from the interaction of unknown molecular components with the network and from the cell's changing environment. While there are many methods to study the effect of intrinsic noise on the system dynamics, few exist to study the influence of both types of noise. Here we show how one can extend the conventional linear-noise approximation to allow for the rapid evaluation of the molecule numbers statistics of a biochemical network influenced by intrinsic noise and by slow lognormally distributed extrinsic noise. The theory is applied to simple models of gene regulatory networks and its validity confirmed by comparison with exact stochastic simulations. In particular we show how extrinsic noise modifies the dependence of the variance of the molecule number fluctuations on the rate constants, the mutual information between input and output signalling molecules and the robustness of feed-forward loop motifs.
Speech Coding, Speech Interfaces and IoT - Opportunities and Challenges
Date: 2018-11-14 00:00:00

Recent speech and audio coding standards such as 3GPP Enhanced Voice Services
match the foreseeable needs and requirements in transmission of speech and
audio, when using current transmission infrastructure and applications. Trends
in Internet-of-Things technology and development in personal digital assistants
(PDAs) however begs us to consider future requirements for speech and audio
codecs. The opportunities and challenges are here summarized in three concepts:
collaboration, unification and privacy. First, an increasing number of devices
will in the future be speech-operated, whereby the ability to focus voice
commands to a specific devices becomes essential. We therefore need methods
which allows collaboration between devices, such that ambiguities can be
resolved. Second, such collaboration can be achieved with a unified and
standardized communication protocol between voice-operated devices. To achieve
such collaboration protocols, we need to develop distributed speech coding
technology for ad-hoc IoT networks. Finally however, collaboration will
increase the demand for privacy protection in speech interfaces and it is
therefore likely that technologies for supporting privacy and generating trust
will be in high demand.
Stochastic Algorithmic Differentiation of (Expectations of) Discontinuous Functions (Indicator Functions)
Date: 2019-11-11 00:00:00

In this paper, we present a method for the accurate estimation of the
derivative (aka.~sensitivity) of expectations of functions involving an
indicator function by combining a stochastic algorithmic differentiation and a
regression.
  The method is an improvement of the approach presented in [Risk Magazine
April 2018].
  The finite difference approximation of a partial derivative of a Monte-Carlo
integral of a discontinuous function is known to exhibit a high Monte-Carlo
error. The issue is evident since the Monte-Carlo approximation of a
discontinuous function is just a finite sum of discontinuous functions and as
such, not even differentiable.
  The algorithmic differentiation of a discontinuous function is problematic. A
natural approach is to replace the discontinuity by continuous functions. This
is equivalent to replacing a path-wise automatic differentiation by a (local)
finite difference approximation.
  We present an improvement (in terms of variance reduction) by decoupling the
integration of the Dirac delta and the remaining conditional expectation and
estimating the two parts by separate regressions. For the algorithmic
differentiation, we derive an operator that can be injected seamlessly - with
minimal code changes - into the algorithm resulting in the exact result.
Automatic event detection in microblogs using incremental machine learning
Authors: Bandaragoda Tharindu Rukshan, De Silva Daswin, Alahakoon Damminda
Date: 2018-10-16 00:00:00
Tasks: BIG-bench Machine Learning, Diversity, Event Detection, Sensitivity

The global popularity of microblogs has led to an increasing accumulation of
large volumes of text data on microblogging platforms such as Twitter. These
corpora are untapped resources to understand social expressions on diverse
subjects. Microblog analysis aims to unlock the value of such expressions by
discovering insights and events of significance hidden among swathes of text.
Besides velocity; diversity of content, brevity, absence of structure and
time-sensitivity are key challenges in microblog analysis. In this paper, we
propose an unsupervised incremental machine learning and event detection
technique to address these challenges. The proposed technique separates a
microblog discussion into topics to address the key problem of diversity. It
maintains a record of the evolution of each topic over time. Brevity,
time-sensitivity and unstructured nature are addressed by these individual
topic pathways which contribute to generate a temporal, topic-driven structure
of a microblog discussion. The proposed event detection method continuously
monitors these topic pathways using multiple domain-independent event
indicators for events of significance. The autonomous nature of topic
separation, topic pathway generation, new topic identification and event
detection, appropriates the proposed technique for extensive applications in
microblog analysis. We demonstrate these capabilities on tweets containing
#microsoft and tweets containing #obama.
A Multimodal Approach towards Emotion Recognition of Music using Audio and Lyrical Content
Authors: Bhattacharya Aniruddha, Kadambari K. V.
Date: 2018-10-10 00:00:00
Tasks: Emotion Recognition, Word Embeddings

We propose MoodNet - A Deep Convolutional Neural Network based architecture
to effectively predict the emotion associated with a piece of music given its
audio and lyrical content.We evaluate different architectures consisting of
varying number of two-dimensional convolutional and subsampling layers,followed
by dense layers.We use Mel-Spectrograms to represent the audio content and word
embeddings-specifically 100 dimensional word vectors, to represent the textual
content represented by the lyrics.We feed input data from both modalities to
our MoodNet architecture.The output from both the modalities are then fused as
a fully connected layer and softmax classfier is used to predict the category
of emotion.Using F1-score as our metric,our results show excellent performance
of MoodNet over the two datasets we experimented on-The MIREX Multimodal
dataset and the Million Song Dataset.Our experiments reflect the hypothesis
that more complex models perform better with more training data.We also observe
that lyrics outperform audio as a better expressed modality and conclude that
combining and using features from multiple modalities for prediction tasks
result in superior performance in comparison to using a single modality as
input.
Open-source platforms for fast room acoustic simulations in complex structures
Date: 2018-11-13 00:00:00

This article presents new numerical simulation tools, respectively developed
in Matlab and Blender softwares. Available in open-source under the GPL 3.0
license, it uses a ray-tracing/image-sources hybrid method to calculate the
room acoustics for large meshes. Performances are optimized to solve problems
of significant size (typically more than 100,000 surface elements and about a
million of rays). For this purpose, a Divide and Conquer approach with a
recursive binary tree structure has been implemented to reduce the quadratic
complexity of the ray/element interactions to near-linear. Thus, execution
times are less sensitive to the mesh density, which allows simulations of
complex geometry. After ray propagation, a hybrid method leads to
image-sources, which can be visually analyzed to localize sound map. Finally,
impulse responses are constructed from the image-sources and FIR filters are
proposed natively over 8 octave bands, taking into account material absorption
properties and propagation medium. This algorithm is validated by comparisons
with theoretical test cases. Furthermore, an example on a quite complex case,
namely the ancient theater of Orange is presented.
The role of short-term immigration on disease dynamics: An SIR model with age-structure
Date: 2018-12-15 00:00:00

We formulate an age-structured nonlinear partial differential equation model
that features short-term immigration effects in a population. Individuals can
immigrate into the population as any of the three stages in the model:
susceptible, infected or recovered}. Global stability of the immigration-free
and infection-free equilibria is discussed. A generalized numerical framework
is established and specific short-term immigration scenarios are explored.
Navigating the Cryptocurrency Landscape: An Islamic Perspective
Date: 2018-11-14 00:00:00
Tasks: RAG, TAG

Almost a decade on from the launch of Bitcoin, cryptocurrencies continue to
generate headlines and intense debate. What started as an underground
experiment by a rag tag group of programmers armed with a Libertarian manifesto
has now resulted in a thriving $230 billion ecosystem, with constant on-going
innovation. Scholars and researchers alike are realizing that cryptocurrencies
are far more than mere technical innovation; they represent a distinct and
revolutionary new economic paradigm tending towards decentralization.
Unfortunately, this bold new universe is little explored from the perspective
of Islamic economics and finance. Our work aims to address these deficiencies.
Our paper makes the following distinct contributions We significantly expand
the discussion on whether cryptocurrencies qualify as "money" from an Islamic
perspective and we argue that this debate necessitates rethinking certain
fundamental definitions. We conclude that the cryptocurrency phenomenon, with
its radical new capabilities, may hold considerable opportunity which merits
deeper investigation.
Pulse radar with FPGA range compression for real time displacement and vibration monitoring
Date: 2018-11-14 00:00:00

This paper aims at presenting the basic functionality of a radar platform for
real-time monitoring of displacement and vibration. The real time capabilities
make the radar platform useful when live monitoring of targets is required. The
system is based on the RF analog front-end of an USRP, and the range
compression (time-domain cross-correlation) is implemented on the FPGA included
in the USRP. Further processing is performed on the host computer to plot real
time range profiles, displacements, vibration frequencies spectra and
spectrograms (waterfall plots) for long term monitoring. The system is
currently in experimental form and the present paper aims at proving its
functionality. The precision of this system is estimated at 0.6 mm for
displacement measurements and 1.8 mm for vibration amplitude measurements.
DNA methylation markers to assess biological age
Date: 2018-11-14 00:00:00

Among the different biomarkers of aging based on omics and clinical data, DNA
methylation clocks stand apart providing unmatched accuracy in assessing the
biological age of both humans and animal models of aging. Here, we discuss
robustness of DNA methylation clocks and bounds on their out-of-sample
performance and review computational strategies for development of the clocks.
A Fast Method for Array Response Adjustment with Phase-Only Constraint
Date: 2018-11-14 00:00:00

In this paper, we propose a fast method for array response adjustment with
phase-only constraint. This method can precisely and rapidly adjust the array
response of a given point by only varying the entry phases of a pre-assigned
weight vector. We show that phase-only array response adjustment can be
formulated as a polygon construction problem, which can be solved by edge
rotation in complex plain. Unlike the existing approaches, the proposed
algorithm provides an analytical solution and guarantees a precise phase-only
adjustment without pattern distortion. Moreover, the proposed method is
suitable for an arbitrarily given weight vector and has a low computational
complexity. Representative examples are presented to demonstrate the
effectiveness of the proposed algorithm.
Arterial Blood Pressure Feature Estimation Using Photoplethysmography
Date: 2018-11-14 00:00:00

Continuous and noninvasive monitoring of blood pressure has numerous clinical
and fitness applications. Current methods of continuous measurement of blood
pressure are either invasive and/or require expensive equipment. Therefore, we
investigated a new method for the continuous estimation of two main features of
blood pressure waveform: systolic and diastolic pressures. The estimates were
obtained from a photoplethysmography signal as input to the fifth order
autoregressive moving average models. The performance of the method was
evaluated using beat-to-beat full-wave blood pressure measurements from 15
young subjects, with no known cardiovascular disorder, in supine position as
they breathed normally and also while they performed a breath-hold maneuver.
The level of error in the estimates, as measured by the root mean square of the
model residuals, was less than 5 mmHg during normal breathing and less than 8
mmHg during the breath-hold maneuver. The mean of model residuals both during
normal breathing and breath-hold maneuvers was considered to be less than 3.2
mmHg. The dependency of the accuracy of the estimates on the subject data was
assessed by comparing the modeling errors for the 15 subjects. Less than 1% of
the models showed significant differences (p < 0.05) from the other models,
which indicates a high level of consistency among the models.
Hybrid Generative-Discriminative Models for Inverse Materials Design
Authors: Nguyen Phuoc, Tran Truyen, Gupta Sunil, Rana Santu, Venkatesh Svetha
Date: 2018-10-31 00:00:00
Tasks: Density Estimation

Discovering new physical products and processes often demands enormous
experimentation and expensive simulation. To design a new product with certain
target characteristics, an extensive search is performed in the design space by
trying out a large number of design combinations before reaching to the target
characteristics. However, forward searching for the target design becomes
prohibitive when the target is itself moving or only partially understood. To
address this bottleneck, we propose to use backward prediction by leveraging
the rich data generated during earlier exploration and construct a machine
learning framework to predict the design parameters for any target in a single
step. This poses two technical challenges: the first caused due to one-to-many
mapping when learning the inverse problem and the second caused due to an user
specifying the target specifications only partially. To overcome the
challenges, we formulate this problem as conditional density estimation under
high-dimensional setting with incomplete input and multimodal output. We solve
the problem through a deep hybrid generative-discriminative model, which is
trained end-to-end to predict the optimum design.
Measuring Knowledge for Recognition and Knowledge Entropy
Date: 2018-11-15 00:00:00

People employ their knowledge to recognize things. This paper is concerned
with how to measure people's knowledge for recognition and how it changes. The
discussion is based on three assumptions. Firstly, we construct two evolution
process equations, of which one is for uncertainty and knowledge, and the other
for uncertainty and ignorance. Secondly, by solving the equations, formulas for
measuring the levels of knowledge and the levels of ignorance are obtained in
two particular cases. Thirdly, a new concept of knowledge entropy is
introduced. Its similarity with Boltzmann's entropy and its difference with
Shannon's Entropy are examined. Finally, it is pointed out that the obtained
formulas of knowledge and knowledge entropy reflect two fundamental principles:
(1) The knowledge level of a group is not necessarily a simple sum of the
individuals' knowledge levels; and (2) An individual's knowledge entropy never
increases if the individual's thirst for knowledge never decreases.
Comprehensive evaluation of statistical speech waveform synthesis
Date: 2018-12-11 00:00:00

Statistical TTS systems that directly predict the speech waveform have
recently reported improvements in synthesis quality. This investigation
evaluates Amazon's statistical speech waveform synthesis (SSWS) system. An
in-depth evaluation of SSWS is conducted across a number of domains to better
understand the consistency in quality. The results of this evaluation are
validated by repeating the procedure on a separate group of testers. Finally,
an analysis of the nature of speech errors of SSWS compared to hybrid unit
selection synthesis is conducted to identify the strengths and weaknesses of
SSWS. Having a deeper insight into SSWS allows us to better define the focus of
future work to improve this new technology.
The effects of non-tariff measures on agri-food trade: a review and meta-analysis of empirical evidence
Date: 2018-11-15 00:00:00

The increasing policy interests and the vivid academic debate on non-tariff
measures (NTMs) has stimulated a growing literature on how NTMs affect agrifood
trade. The empirical literature provides contrasting and heterogeneous
evidence, with some studies supporting the standards as catalysts view, and
others favouring the standards as barriers explanation. To the extent that NTMs
can influence trade, understanding the prevailing effect, and the motivations
behind one effect or the other, is a pressing issue. We review a large body of
empirical evidence on the effect of NTMs on agri-food trade and conduct a
meta-analysis to disentangle potential determinants of heterogeneity in
estimates. Our findings show the role played by the publication process and by
study-specific assumptions. Some characteristics of the studies are correlated
with positive significant estimates, others covary with negative significant
estimates. Overall, we found that the effects of NTMs vary across types of
NTMs, proxy for NTMs, and levels of details of studies. Not negligible is the
influence of methodological issues and publication process.
On approximations of Value at Risk and Expected Shortfall involving kurtosis
Authors: Matyas Barczy, Adam Dudas, Jozsef Gall
Date: 2018-11-15 00:00:00

We derive new approximations for the Value at Risk and the Expected Shortfall at high levels of loss distributions with positive skewness and excess kurtosis, and we describe their precisions for notable ones such as for exponential, Pareto type I, lognormal and compound (Poisson) distributions. Our approximations are motivated by that kind of extensions of the so-called Normal Power Approximation, used for approximating the cumulative distribution function of a random variable, which incorporate not only the skewness but the kurtosis of the random variable in question as well. We show the performance of our approximations in numerical examples and we also give comparisons with some known ones in the literature.
Staff dimensioning in homecare services with uncertain demands
Authors: Rodriguez C.  LIMOS, CIS-ENSMSE, Garaix Thierry  LIMOS, CIS-ENSMSE, Xie X., Augusto V.
Date: 2018-10-29 00:00:00

The problem addressed in this paper is how to calculate the amount of
personnel required to ensure the activity of a home health care (HHC) center on
a tactical horizon. Design of quantitative approaches for this question is
challenging. The number of caregivers has to be determined for each profession
in order to balance the coverage of patients in a region and the workforce cost
over several months. Unknown demand in care and spatial dimensions, combination
of skills to cover a care and individual trips visiting patients make the
underlaying optimization problem very hard. Few studies are dedicated to staff
dimensioning for HHC compared to patient to nurses assignment/sequencing and
centers location problems. We propose an original two-stage approach based on
integer linear stochastic programming, that exploits historical medical data.
The first stage calculates (near-)optimal levels of resources for possible
demand scenarios , while the second stage computes the optimal number of
caregiver for each profession to meet a target coverage indicator. For
decision-makers, our algorithm gives the number of employees for each category
required to satisfy the demand without any recourse (overtime, external
resources) with fixed probability and confidence interval. The approach has
been tested on various instances built from data of the French agency of
hospitalization data (ATIH).
Modeling car-following behavior on urban expressways in Shanghai: A naturalistic driving study
Authors: Zhu Meixin, Wang Xuesong, Tarko Andrew P., Fang Shou'en
Date: 2018-11-11 00:00:00

Five car-following models were calibrated, validated and cross-compared. The
intelligent driver model performed best among the evaluated models.
Considerable behavioral differences between different drivers were found.
Calibrated model parameters may not be numerically equivalent with observed
ones.
Age-at-harvest models as monitoring and harvest management tools for Wisconsin carnivores
Date: 2018-11-15 00:00:00
Tasks: Management, State Space Models

Quantifying and estimating wildlife population sizes is a foundation of
wildlife management. However, many carnivore species are cryptic, leading to
innate difficulties in estimating their populations. We evaluated the potential
for using more rigorous statistical models to estimate the populations of black
bears (Ursus americanus) in Wisconisin, and their applicability to other
furbearers such as bobcats (Lynx rufus). To estimate black bear populations, we
developed an AAH state-space model in a Bayesian framework based on Norton
(2015) that can account for variation in harvest and population demographics
over time. Our state-space model created an accurate estimate of the black bear
population in Wisconsin based on age-at-harvest data and improves on previous
models by using little demographic data, no auxiliary data, and not being
sensitive to initial population size. The increased accuracy of the AAH
state-space models should allow for better management by being able to set
accurate quotas to ensure a sustainable harvest for the black bear population
in each zone. We also evaluated the demography and annual harvest data of
bobcats in Wisconsin to determine trends in harvest, method, and hunter
participation in Wisconsin. Each trait of harvested bobcats that we tested
(mass, male:female sex ratio, and age) changed over time, and because these
were interrelated, and we inferred that harvest selection for larger size
biased harvests in favor of a) larger, b) older, and c) male bobcats by hound
hunters. We also found an increase in the proportion of bobcats that were
harvested by hound hunting compared to trapping, and that hound hunters were
more likely to create taxidermy mounts than trappers. We also found that
decreasing available bobcat tags and increasing success have occurred over
time, and correlate with substantially increasing both hunter populations and
hunter interest.