pwc-archive/papers-with-abstracts huggingface dataset:
Multi-task WaveNet: A Multi-task Generative Model for Statistical Parametric Speech Synthesis without Fundamental Frequency Conditions
Date: 2018-06-22 00:00:00
Tasks: Multi-Task Learning, Prediction, Speech Synthesis

This paper introduces an improved generative model for statistical parametric
speech synthesis (SPSS) based on WaveNet under a multi-task learning framework.
Different from the original WaveNet model, the proposed Multi-task WaveNet
employs the frame-level acoustic feature prediction as the secondary task and
the external fundamental frequency prediction model for the original WaveNet
can be removed. Therefore the improved WaveNet can generate high-quality speech
waveforms only conditioned on linguistic features. Multi-task WaveNet can
produce more natural and expressive speech by addressing the pitch prediction
error accumulation issue and possesses more succinct inference procedures than
the original WaveNet. Experimental results prove that the SPSS method proposed
in this paper can achieve better performance than the state-of-the-art approach
utilizing the original WaveNet in both objective and subjective preference
tests.
Cross-layer framework and optimization for efficient use of the energy budget of IoT Nodes
Date: 2020-03-20 00:00:00

Both physical and MAC-layer need to be jointly optimized to maximize the
autonomy of IoT devices. Therefore, a cross-layer design is imperative to
effectively realize Low Power Wide Area networks (LPWANs). In the present
paper, a cross-layer assessment framework including power modeling is proposed.
Through this simulation framework, the energy consumption of IoT devices,
currently deployed in LoRaWAN networks, is evaluated. We demonstrate that a
cross-layer approach significantly improves energy efficiency and overall
throughput. Two major contributions are made. First, an open-source LPWAN
assessment framework has been conceived. It allows testing and evaluating
hypotheses and schemes. Secondly, as a representative case, the LoRaWAN
protocol is assessed. The findings indicate how a cross-layer approach can
optimize LPWANs in terms of energy efficiency and throughput. For instance, it
is shown that the use of larger payloads can reduce up to three times the
energy consumption on quasi-static channels yet may bring an energy penalty
under adverse dynamic conditions.
Mathematical models for chemotaxis and their applications in self-organisation phenomena
Date: 2018-06-25 00:00:00

Chemotaxis is a fundamental guidance mechanism of cells and organisms,
responsible for attracting microbes to food, embryonic cells into developing
tissues, immune cells to infection sites, animals towards potential mates, and
mathematicians into biology. The Patlak-Keller-Segel (PKS) system forms part of
the bedrock of mathematical biology, a go-to-choice for modellers and analysts
alike. For the former it is simple yet recapitulates numerous phenomena; the
latter are attracted to these rich dynamics. Here I review the adoption of PKS
systems when explaining self-organisation processes. I consider their
foundation, returning to the initial efforts of Patlak and Keller and Segel,
and briefly describe their patterning properties. Applications of PKS systems
are considered in their diverse areas, including microbiology, development,
immunology, cancer, ecology and crime. In each case a historical perspective is
provided on the evidence for chemotactic behaviour, followed by a review of
modelling efforts; a compendium of the models is included as an Appendix.
Finally, a half-serious/half-tongue-in-cheek model is developed to explain how
cliques form in academia. Assumptions in which scholars alter their research
line according to available problems leads to clustering of academics and the
formation of "hot" research topics.
Resolution and accuracy of non-linear regression of PSF with artificial neural networks
Date: 2018-06-21 00:00:00
Tasks: regression

In a previous work we have demonstrated a novel numerical model for the point
spread function (PSF) of an optical system that can efficiently model both
experimental measurements and lens design simulations of the PSF. The novelty
lies in the portability and the parameterization of this model, which allows
for completely new ways to validate optical systems, which is especially
interesting for mass production optics like in the automotive industry, but
also for ophtalmology. The numerical basis for this model is a non-linear
regression of the PSF with an artificial neural network (ANN). In this work we
examine two important aspects of this model: the spatial resolution and the
accuracy of the model. Measurement and simulation of a PSF can have a much
higher resolution then the typical pixel size used in current camera sensors,
especially those for the automotive industry. We discuss the influence this has
on on the topology of the ANN and the final application where the modeled PSF
is actually used. Another important influence on the accuracy of the trained
ANN is the error metric which is used during training. The PSF is a distinctly
non-linear function, which varies strongly over field and defocus, but
nonetheless exhibits strong symmetries and spatial relations. Therefore we
examine different distance and similarity measures and discuss its influence on
the modeling performance of the ANN.
Quasiconvex risk measures with markets volatility
Date: 2019-06-24 00:00:00

Since the quasiconvex risk measures is a bigger class than the well known
convex risk measures, the study of quasiconvex risk measures makes sense
especially in the financial markets with volatility. In this paper, we will
study the quasiconvex risk measures defined on a special space $L^{p(\cdot)}$
where the variable exponent $p(\cdot)$ is no longer a given real number like
the space $L^{p}$, but a random variable, which reflects the possible
volatility of the financial markets. The dual representation for this
quasiconvex risk measures will also provided.
Determining water mass flow control strategies for a turbocharged SI engine using a two-stage calculation method
Date: 2018-06-21 00:00:00
Tasks: Friction

Reduction of heat and friction losses is a proven approach to increase the
engine efficiency. Therefore, and due to a stabilized, robust combustion, a
specific adjustment of component temperatures is desirable in highly transient
conditions. In this paper, a turbocharged SI engine is investigated numerically
concerning the potential regulation of temperatures, including heat fluxes,
only by controlling the water mass flow rate. Using two independent models, a
simplified lumped capacity model and a detailed three-dimensional CFD-CHT
simulation, an efficient, two-stage calculation method is suggested for an
optimized determination of control strategies and their parameters. This
complements existing published works which usually control more than one
parameter, but use one model. Different control strategies, like feed forward
or feedback controllers, are proposed and compared. In addition, a more
holistic approach is presented performing a Monte Carlo simulation which
evaluates temperatures, as well as hydraulic pumping losses. Using expedient
control strategies and parameters, it could be shown that the engine
temperatures can be effectively regulated within a wide range. The two
different models show partly similar results, and the efficient, two-stage
optimization method has proven its worth. However, there are some significant
differences between simplified and detailed modelling which are worth
mentioning.
CoHSI I; Detailed properties of the Canonical Distribution for Discrete Systems such as the Proteome
Authors: Les Hatton, Gregory Warr
Date: 2018-06-21 00:00:00

The CoHSI (Conservation of Hartley-Shannon Information) distribution is at the heart of a wide-class of discrete systems, defining the length distribution of their components amongst other global properties. Discrete systems such as the known proteome where components are proteins, computer software, where components are functions and texts where components are books, are all known to fit this distribution accurately. In this short paper, we explore its solution and its resulting properties and lay the foundation for a series of papers which will demonstrate amongst other things, why the average length of components is so highly conserved and why long components occur so frequently in these systems. These properties are not amenable to local arguments such as natural selection in the case of the proteome or human volition in the case of computer software, and indeed turn out to be inevitable global properties of discrete systems devolving directly from CoHSI and shared by all. We will illustrate this using examples from the Uniprot protein database as a prelude to subsequent studies.
An Annealed Sequential Monte Carlo Method for Bayesian Phylogenetics
Date: 2019-03-13 00:00:00

We describe an "embarrassingly parallel" method for Bayesian phylogenetic
inference, annealed Sequential Monte Carlo, based on recent advances in the
Sequential Monte Carlo literature such as adaptive determination of annealing
parameters. The algorithm provides an approximate posterior distribution over
trees and evolutionary parameters as well as an unbiased estimator for the
marginal likelihood. This unbiasedness property can be used for the purpose of
testing the correctness of posterior simulation software. We evaluate the
performance of phylogenetic annealed Sequential Monte Carlo by reviewing and
comparing with other computational Bayesian phylogenetic methods, in
particular, different marginal likelihood estimation methods. Unlike previous
Sequential Monte Carlo methods in phylogenetics, our annealed method can
utilize standard Markov chain Monte Carlo tree moves and hence benefit from the
large inventory of such moves available in the literature. Consequently, the
annealed Sequential Monte Carlo method should be relatively easy to incorporate
into existing phylogenetic software packages based on Markov chain Monte Carlo
algorithms. We illustrate our method using simulation studies and real data
analysis.
Pansharpening via Detail Injection Based Convolutional Neural Networks
Date: 2018-06-23 00:00:00
Tasks: Pansharpening

Pansharpening aims to fuse a multispectral (MS) image with an associated
panchromatic (PAN) image, producing a composite image with the spectral
resolution of the former and the spatial resolution of the latter. Traditional
pansharpening methods can be ascribed to a unified detail injection context,
which views the injected MS details as the integration of PAN details and
band-wise injection gains. In this work, we design a detail injection based CNN
(DiCNN) framework for pansharpening, with the MS details being directly
formulated in end-to-end manners, where the first detail injection based CNN
(DiCNN1) mines MS details through the PAN image and the MS image, and the
second one (DiCNN2) utilizes only the PAN image. The main advantage of the
proposed DiCNNs is that they provide explicit physical interpretations and can
achieve fast convergence while achieving high pansharpening quality.
Furthermore, the effectiveness of the proposed approaches is also analyzed from
a relatively theoretical point of view. Our methods are evaluated via
experiments on real-world MS image datasets, achieving excellent performance
when compared to other state-of-the-art methods.
A New All-Digital Background Calibration Technique for Time-Interleaved ADC Using First Order Approximation FIR Filters
Date: 2018-06-24 00:00:00
Tasks: All

This paper describes a new all-digital technique for calibration of the
mismatches in time-interleaved analog-to-digital converters (TIADCs) to reduce
the circuit area. The proposed technique gives the first order approximation of
the gain mismatches and sample-time mismatches, and employs first order
approximation FIR filter banks to calibrate the sampled signal, which do not
need large number of FIR taps. In the case of a two-channel 12-bit TIADC, the
proposed technique improves SINAD of simulated data from 45dB to 69dB, and
improves SINAD of measured data from 47dB to 53dB, while the number of FIR taps
is only 30. In the case of slight mismatches, 24-bit FIR coefficient is
sufficient to correct 12-bit signals, which makes it easy to implement this
technique in hardware. In addition, this technique is not limited by the number
of sub-ADC channels and can be calculated in parallel in hardware, these
features enable this technique to be versatile and capable of real-time
calibration.
Channel Estimation for Massive MIMO Communication System Using Deep Neural Network
Date: 2018-06-24 00:00:00

In this paper we consider the problem of sparse signal recovery in Multiple
Measurement Vectors (MMVs) case. Recently, ample researches have been conducted
to solve this problem and diverse methods are proposed, one of which is deep
neural network approach. Here, employing deep neural networks we have provided
two new greedy algorithms in order to solve MMV problems. In the first
algorithm, we create a stacked vector of measurement matrix columns and a new
measurement matrix, which can be assumed as the Kronecker product of the
primary compressive sampling matrix and a unitary matrix. Afterwards, in order
to reconstruct sparse vectors corresponding to this new set of equations, a
four-layer feed-forward neural network is applied.
How do simple evolutionary strategies and investment optimizations affect ecological patterns? The case of generalized Taylor's Law
Date: 2018-06-24 00:00:00

Taylor's Law (TL) relates the variance to the mean of a random variable via
power law. In ecology it applies to populationsand it is a common empirical
pattern shared among different ecosystems. Measurements give power law exponent
to be between 1 and 2, and more often to cluster around 2, whereas theoretical
models predict TL exponent can assume any real value. In this paper, adopting
the framework of multiplicative growth models in a Markovian environment, we
investigate the possibility of evolutionary strategies to be responsible for TL
exponent to be in a finite range. We implement three different strategies the
individuals can follow and for each strategy set two different optimization
investment objectives. In all the studied cases we find TL exponent can assume
any real value due to the existence of regions of the model parameters in which
the exponent can diverge. Furthermore, under natural hypothesis on the dynamics
of the environment, the shapes of these regions do not depend on different
strategies adopted and nor on the optimization objective. Thus the introduction
of strategies dose not affect the range of TL exponent in the model. In our
theoretical framework rare events are shaping the value of the TL exponent,
suggesting, as hinted by previous works, that empirical values may be a
statistical artifact following from under sampling.
Perceptually Relevant Preservation of Interaural Time Differences in Binaural Hearing Aids
Date: 2018-06-24 00:00:00

This work presents a noise reduction method with perceptually relevant
preservation of the interaural time difference (ITD) of the residual noise in
binaural hearing aids. The interaural coherence (IC) concept, previously
applied to the Multichannel Wiener Filter (MWF) for preservation of the spatial
subjective sensation of diffuse noise fields, is proposed here to both preserve
and emphasize the ITD binaural cues of a directional acoustic noise source. It
is demonstrated that the previously developed MWF-ITD technique may decrease
the original IC magnitude of the processed noise, consequently increasing the
variance of the interaural phase difference (IPD) of the output signals. It is
shown that the MWF-IC technique concomitantly minimizes a nonlinear function of
the difference between input and output IPD, which is strictly related to ITD,
and preserves the natural coherence of the directional noise captured by the
reference microphones. Objective measures and psychoacoustic experiments
corroborate the theoretical findings, showing the MWF-IC technique provides
relevant noise reduction, while preserving the original ITD subjective
perception and original lateralization for a directional noise source. These
results are especially relevant for hearing aid designers, since they indicate
the MWF-IC as a noise reduction technique that provides resid-ual noise spatial
preservation for both diffuse and directional noise sources in frequencies
below 1.5 kHz.
A Second Look at Post Crisis Pricing of Derivatives - Part I: A Note on Money Accounts and Collateral
Authors: Hovik Tumasyan
Date: 2018-06-24 00:00:00

The paper reviews origins of the approach to pricing derivatives post-crisis by following three papers that have received wide acceptance from practitioners as the theoretical foundations for it - [Piterbarg 2010], [Burgard and Kjaer 2010] and [Burgard and Kjaer 2013]. The review reveals several conceptual and technical inconsistencies with the approaches taken in these papers. In particular, a key component of the approach - prescription of cost components to a risk-free money account, generates derivative prices that are not cleared by the markets that trade the derivative and its underlying securities. It also introduces several risk-free positions (accounts) that accrue at persistently non-zero spreads with respect to each other and the risk free rate. In the case of derivatives with counterparty default risk [Burgard and Kjaer 2013] introduces an approach referred to as semi-replication, which through the choice of cost components in the money account results in derivative prices that carry arbitrage opportunities in the form of holding portfolio of counterparty's bonds versus a derivative position with it. This paper derives no-arbitrage expressions for default-risky derivative contracts with and without collateral, avoiding these inconsistencies.
Local migration quantification method for scratch assays
Date: 2018-06-24 00:00:00

Motivation: The scratch assay is a standard experimental protocol used to
characterize cell migration. It can be used to identify genes that regulate
migration and evaluate the efficacy of potential drugs that inhibit cancer
invasion. In these experiments, a scratch is made on a cell monolayer and
recolonisation of the scratched region is imaged to quantify cell migration
rates. A drawback of this methodology is the lack of its reproducibility
resulting in irregular cell-free areas with crooked leading edges. Existing
quantification methods deal poorly with such resulting irregularities present
in the data. Results: We introduce a new quantification method that can analyse
low quality experimental data. By considering in-silico and in-vitro data, we
show that the method provides a more accurate statistical classification of the
migration rates than two established quantification methods. The application of
this method will enable the quantification of migration rates of scratch assay
data previously unsuitable for analysis. Availability and Implementation: The
source code and the implementation of the algorithm as a GUI along with an
example dataset and user instructions, are available in
https://bitbucket.org/anavictoria-ponce/local_migration_quantification_scratch_assays/src/master/.
The datasets are available in
https://ganymed.math.uni-heidelberg.de/~victoria/publications.shtml.
EMPHASIS: An Emotional Phoneme-based Acoustic Model for Speech Synthesis System
Date: 2018-06-26 00:00:00
Tasks: Emotional Speech Synthesis, Parameter Prediction, regression, Speech Synthesis

We present EMPHASIS, an emotional phoneme-based acoustic model for speech
synthesis system. EMPHASIS includes a phoneme duration prediction model and an
acoustic parameter prediction model. It uses a CBHG-based regression network to
model the dependencies between linguistic features and acoustic features. We
modify the input and output layer structures of the network to improve the
performance. For the linguistic features, we apply a feature grouping strategy
to enhance emotional and prosodic features. The acoustic parameters are
designed to be suitable for the regression task and waveform reconstruction.
EMPHASIS can synthesize speech in real-time and generate expressive
interrogative and exclamatory speech with high audio quality. EMPHASIS is
designed to be a multi-lingual model and can synthesize Mandarin-English speech
for now. In the experiment of emotional speech synthesis, it achieves better
subjective results than other real-time speech synthesis systems.
Semiparametrically Point-Optimal Hybrid Rank Tests for Unit Roots
Date: 2018-06-25 00:00:00
Tasks: valid

We propose a new class of unit root tests that exploits invariance properties
in the Locally Asymptotically Brownian Functional limit experiment associated
to the unit root model. The invariance structures naturally suggest tests that
are based on the ranks of the increments of the observations, their average,
and an assumed reference density for the innovations. The tests are
semiparametric in the sense that they are valid, i.e., have the correct
(asymptotic) size, irrespective of the true innovation density. For a correctly
specified reference density, our test is point-optimal and nearly efficient.
For arbitrary reference densities, we establish a Chernoff-Savage type result,
i.e., our test performs as well as commonly used tests under Gaussian
innovations but has improved power under other, e.g., fat-tailed or skewed,
innovation distributions. To avoid nonparametric estimation, we propose a
simplified version of our test that exhibits the same asymptotic properties,
except for the Chernoff-Savage result that we are only able to demonstrate by
means of simulations.
Evaluation of Information Retrieval Systems Using Structural Equation Modelling
Authors: Melucci Massimo
Date: 2018-06-25 00:00:00
Tasks: Information Retrieval, Retrieval

The interpretation of the experimental data collected by testing systems
across input datasets and model parameters is of strategic importance for
system design and implementation. In particular, finding relationships between
variables and detecting the latent variables affecting retrieval performance
can provide designers, engineers and experimenters with useful if not necessary
information about how a system is performing. This paper discusses the use of
Structural Equation Modelling (SEM) in providing an in-depth explanation of
evaluation results and an explanation of failures and successes of a system; in
particular, we focus on the case of Information Retrieval.
Measuring Integrated Information: Comparison of Candidate Measures in Theory and Simulation
Date: 2018-06-25 00:00:00
Tasks: Diversity

Integrated Information Theory (IIT) is a prominent theory of consciousness
that has at its centre measures that quantify the extent to which a system
generates more information than the sum of its parts. While several candidate
measures of integrated information (`$\Phi$') now exist, little is known about
how they compare, especially in terms of their behaviour on non-trivial network
models. In this article we provide clear and intuitive descriptions of six
distinct candidate measures. We then explore the properties of each of these
measures in simulation on networks consisting of eight interacting nodes,
animated with Gaussian linear autoregressive dynamics. We find a striking
diversity in the behaviour of these measures -- no two measures show consistent
agreement across all analyses. Further, only a subset of the measures appear to
genuinely reflect some form of dynamical complexity, in the sense of
simultaneous segregation and integration between system components. Our results
help guide the operationalisation of IIT and advance the development of
measures of integrated information that may have more general applicability.