pwc-archive/papers-with-abstracts huggingface dataset:
Estimating Distances via Received Signal Strength and Connectivity in Wireless Sensor Networks
Date: 2018-01-29 00:00:00

Distance estimation is vital for localization and many other applications in
wireless sensor networks (WSNs). Particularly, it is desirable to implement
distance estimation as well as localization without using specific hardware in
low-cost WSNs. As such, both the received signal strength (RSS) based approach
and the connectivity based approach have gained much attention. The RSS based
approach is suitable for estimating short distances, whereas the connectivity
based approach obtains relatively good performance for estimating long
distances. Considering the complementary features of these two approaches, we
propose a fusion method based on the maximum-likelihood estimator (MLE) to
estimate the distance between any pair of neighboring nodes in a WSN through
efficiently fusing the information from the RSS and local connectivity.
Additionally, the method is reported under the practical log-normal shadowing
model, and the associated Cramer-Rao lower bound (CRLB) is also derived for
performance analysis. Both simulations and experiments based on practical
measurements are carried out, and demonstrate that the proposed method
outperforms any single approach and approaches to the CRLB as well.
First Passage Time for Tempered Stable Process and Its Application to Perpetual American Option and Barrier Option Pricing
Date: 2018-01-29 00:00:00

In this paper, we will discuss an approximation of the characteristic
function of the first passage time for a Levy process using the martingale
approach. The characteristic function of the first passage time of the tempered
stable process is provided explicitly or by an indirect numerical method. This
will be applied to the perpetual American option pricing and the barrier option
pricing. Numerical illustrations are provided for the calibrated parameters
using the market call and put prices.
Extension of the non-parametric cluster-based time-frequency statistics to the full time windows and to single condition tests
Date: 2018-01-29 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Oscillatory processes are central for the understanding of the neural bases
of cognition and behaviour. To analyse these processes, time-frequency (TF)
decomposition methods are applied and non-parametric cluster-based statistical
procedure are used for comparing two or more conditions. While this combination
is a powerful method, it has two drawbacks. One the unreliable estimation of
signals outside the cone-of-influence and the second relates to the length of
the time frequency window used for the analysis. Both impose constrains on the
non-parametric statistical procedure for inferring an effect in the TF domain.
Here we extend the method to reliably infer oscillatory differences within the
full TF map and to test single conditions. We show that it can be applied in
small time windows irrespective of the cone-of-influence and we further develop
its application to single-condition case for testing the hypothesis of the
presence or not of time-varying signals. We present tests of this new method on
real EEG and behavioural data and show that its sensitivity to single-condition
tests is at least as good as classic Fourier analysis. Statistical inference in
the full TF map is available and efficient in detecting differences between
conditions as well as the presence of time-varying signal in single condition.
Preface to Introduction to Protein Structural Bioinformatics
Authors: K. Anton Feenstra, Sanne Abeln
Date: 2018-01-29 00:00:00
Tasks: Protein Structure Prediction

While many good textbooks are available on Protein Structure, Molecular Simulations, Thermodynamics and Bioinformatics methods in general, there is no good introductory level book for the field of Structural Bioinformatics. This book aims to give an introduction into Structural Bioinformatics, which is where the previous topics meet to explore three dimensional protein structures through computational analysis. We provide an overview of existing computational techniques, to validate, simulate, predict and analyse protein structures. More importantly, it will aim to provide practical knowledge about how and when to use such techniques. We will consider proteins from three major vantage points: Protein structure quantification, Protein structure prediction, and Protein simulation & dynamics.
Similarities vs. key discrepancy between tuberculosis and cancer
Date: 2018-05-15 00:00:00

In 2015 in the United States 612,000 persons died from cancer whereas only
470 died from tuberculosis (TB), a disease which was the main cause of death
around 1900. How can one explain such a key discrepancy in treatment progress?
A statistical comparison between TB and cancer will give some clues. However,
TB and cancer also share several important features. Both TB and cancer can
affect several organs, e.g. lungs, brain, bones, intestines, skin. What in
cancer is called malignant neoplasm (tumor) is called granuloma in TB. By
isolating malignant cells (versus bacteria) from the rest of the body, such
clusters protect the host's organism but at the same time they are "secure
beachheads" from where malignant cells (versus infected macrophages) can wander
off to new locations. Thus, metastatic tumors have a TB parallel in the form of
secondary granulomas. In order to investigate more closely this parallel we use
the age-specific response of organs. Called spectrometric analysis in a
previous paper (Berrut et al. 2017), this method provides information about how
fast tumors develop and how serious they become. A convenient characterization
of the response to TB of organ j is given by the following (age-dependent)
death ratio: Tj(t)=(death by TB of type j)/(all TB deaths) The development of
cancer tumors can be described by a similar function Cj(t). We compare the
organs' responses in all cases for which specific death data are available. It
appears that for the same organ Tj(t) is similar in shape to Cj(t). For
instance, with regard to brain tumors, both TB and cancer mortality peak around
the age of 10. Such observations may bring to light vulnerabilities in the way
the immune system provides protection to various organs.
The PomXYZ Proteins Self-Organize on the Bacterial Nucleoid to Stimulate Cell Division
Date: 2018-01-29 00:00:00

Cell division site positioning is precisely regulated to generate correctly
sized and shaped daughters. We uncover a novel strategy to position the FtsZ
cytokinetic ring at midcell in the social bacterium Myxococcus xanthus. PomX,
PomY and the nucleoid-binding ParA/MinD ATPase PomZ self-assemble forming a
large nucleoid-associated complex that localizes at the division site before
FtsZ to directly guide and stimulate division. PomXYZ localization is generated
through self-organized biased random motion on the nucleoid towards midcell and
constrained motion at midcell. Experiments and theory show that PomXYZ motion
is produced by diffusive PomZ fluxes on the nucleoid into the complex. Flux
differences scale with the intracellular asymmetry of the complex and are
converted into a local PomZ concentration gradient across the complex with
translocation towards the higher PomZ concentration. At midcell, fluxes
equalize resulting in constrained motion. Flux-based mechanisms may represent a
general paradigm for positioning of macromolecular structures in bacteria.
Coactivated Clique Based Multisource Overlapping Brain Subnetwork Extraction
Date: 2018-01-26 00:00:00
Tasks: Community Detection

Subnetwork extraction using community detection methods is commonly used to
study the brain's modular structure. Recent studies indicated that certain
brain regions are known to interact with multiple subnetworks. However, most
existing methods are mainly for non-overlapping subnetwork extraction. In this
paper, we present an approach for overlapping brain subnetwork extraction using
cliques, which we defined as co-activated node groups performing multiple
tasks. We proposed a multisource subnetwork extraction approach based on the
co-activated clique, which (1) uses task co-activation and task connectivity
strength information for clique identification, (2) automatically detects
cliques of different sizes having more neuroscientific justifications, and (3)
shares the subnetwork membership, derived from a fusion of rest and task data,
among the nodes within a clique for overlapping subnetwork extraction. On real
data, compared to the commonly used overlapping community detection techniques,
we showed that our approach improved subnetwork extraction in terms of
group-level and subject-wise reproducibility. We also showed that our
multisource approach identified subnetwork overlaps within brain regions that
matched well with hubs defined using functional and anatomical information,
which enables us to study the interactions between the subnetworks and how hubs
play their role in information flow across different subnetworks. We further
demonstrated that the assignments of interacting/individual nodes using our
approach correspond with the posterior probability derived independently from
our multimodal random walker based approach.
In Praise of Artifice Reloaded: Caution with subjective image quality databases
Date: 2018-01-29 00:00:00
Tasks: Sensitivity

Subjective image quality databases are a major source of raw data on how the
visual system works in naturalistic environments. These databases describe the
sensitivity of many observers to a wide range of distortions (of different
nature and with different suprathreshold intensities) seen on top of a variety
of natural images. They seem like a dream for the vision scientist to check the
models in realistic scenarios.
  However, while these natural databases are great benchmarks for models
developed in some other way (e.g. by using the well-controlled artificial
stimuli of traditional psychophysics), they should be carefully used when
trying to fit vision models. Given the high dimensionality of the image space,
it is very likely that some basic phenomenon (e.g. sensitivity to distortions
in certain environments) are under-represented in the database. Therefore, a
model fitted on these large-scale natural databases will not reproduce these
under-represented basic phenomena that could otherwise be easily illustrated
with well selected artificial stimuli.
  In this work we study a specific example of the above statement. A
wavelet+divisive normalization layer of a sensible cascade of linear+nonlinear
layers fitted to maximize the correlation with subjective opinion of observers
on a large image quality database fails to reproduce basic crossmasking. Here
we outline a solution for this problem using artificial stimuli. Then, we show
that the resulting model is also a competitive solution for the large-scale
database. In line with Rust and Movshon (2005), our report (misrepresentation
of basic visual phenomena in subjectively-rated natural image databases) is an
additional argument in praise of artifice.
Highly-Reverberant Real Environment database: HRRE
Date: 2018-03-23 00:00:00
Tasks: speech-recognition, Speech Recognition

Speech recognition in highly-reverberant real environments remains a major
challenge. An evaluation dataset for this task is needed. This report describes
the generation of the Highly-Reverberant Real Environment database (HRRE). This
database contains 13.4 hours of data recorded in real reverberant environments
and consists of 20 different testing conditions which consider a wide range of
reverberation times and speaker-to-microphone distances. These evaluation sets
were generated by re-recording the clean test set of the Aurora-4 database
which corresponds to five loudspeaker-microphone distances in four reverberant
conditions.
Algorithms for the Construction of Incoherent Frames Under Various Design Constraints
Date: 2018-06-20 00:00:00
Tasks: compressed sensing

Unit norm finite frames are generalizations of orthonormal bases with many
applications in signal processing. An important property of a frame is its
coherence, a measure of how close any two vectors of the frame are to each
other. Low coherence frames are useful in compressed sensing applications. When
used as measurement matrices, they successfully recover highly sparse solutions
to linear inverse problems. This paper describes algorithms for the design of
various low coherence frame types: real, complex, unital (constant magnitude)
complex, sparse real and complex, nonnegative real and complex, and harmonic
(selection of rows from Fourier matrices). The proposed methods are based on
solving a sequence of convex optimization problems that update each vector of
the frame. This update reduces the coherence with the other frame vectors,
while other constraints on its entries are also imposed. Numerical experiments
show the effectiveness of the methods compared to the Welch bound, as well as
other competing algorithms, in compressed sensing applications.
Denoising Signals in Cognitive Radio Systems Using An Evolutionary Algorithm Based Adaptive Filter
Date: 2018-01-29 00:00:00
Tasks: Denoising, Evolutionary Algorithms

Noise originating from several sources in a RF environment degrades the
performance of communication systems. In wideband systems, such as cognitive
radios, noise at the receiver can originate from non-linearity present in the
RF front end, time-varying thermal noise within the receiver radio system, and
noise from adjacent network nodes. Several denoising techniques have been
proposed for cognitive radios, some of which are applied during spectrum
sensing and others to received noisy signal during communication. Examples of
some of these techniques used for noise cancellation in received signals are
least mean square (LMS) and its variants. However, these algorithms have low
performance with non-linear signals and cannot locate a global optimum solution
for noise cancellation. Therefore, application of global search optimization
techniques, such as evolutionary algorithms, is considered for noise
cancellation. In this paper, particle swarm optimization (PSO) and LMS
algorithms are implemented and their performances are evaluated. Extensive
simulations were performed where Gaussian and non-linear random noise were
added to the transmitted signal. The performance comparison was done using two
metrics: bit error rate and mean square error. The results show that PSO
outperforms LMS under both Gaussian and nonlinear random noise.
Noise Cancellation in Cognitive Radio Systems: A Performance Comparison of Evolutionary Algorithms
Date: 2018-01-29 00:00:00
Tasks: Evolutionary Algorithms

Noise cancellation is one of the important signal processing functions of any
communication system, as noise affects data integrity. In existing systems,
traditional filters are used to cancel the noise from the received signals.
These filters use fixed hardware which is capable of filtering specific
frequency or a range of frequencies. However, next generation communication
technologies, such as cognitive radio, will require the use of adaptive filters
that can dynamically reconfigure their filtering parameters for any frequency.
To this end, a few noise cancellation techniques have been proposed, including
least mean squares (LMS) and its variants. However, these algorithms are
susceptible to non-linear noise and fail to locate the global optimum solution
for de-noising. In this paper, we investigate the efficiency of two global
search optimization based algorithms, genetic algorithm and particle swarm
optimization in performing noise cancellation in cognitive radio systems. These
algorithms are implemented and their performances are compared to that of LMS
using bit error rate and mean square error as performance evaluation metrics.
Simulations are performed with additive white Gaussian noise and random
nonlinear noise. Results indicate that GA and PSO perform better than LMS for
the case of AWGN corrupted signal but for non-linear random noise PSO
outperforms the other two algorithms.
When does a disaster become a systemic event? Estimating indirect economic losses from natural disasters
Date: 2018-01-29 00:00:00
Tasks: Management

Reliable estimates of indirect economic losses arising from natural disasters
are currently out of scientific reach. To address this problem, we propose a
novel approach that combines a probabilistic physical damage catastrophe model
with a new generation of macroeconomic agent-based models (ABMs). The ABM moves
beyond the state of the art by exploiting large data sets from detailed
national accounts, census data, and business information, etc., to simulate
interactions of millions of agents representing \emph{each} natural person or
legal entity in a national economy. The catastrophe model introduces a copula
approach to assess flood losses, considering spatial dependencies of the flood
hazard. These loss estimates are used in a damage scenario generator that
provides input for the ABM, which then estimates indirect economic losses due
to the event. For the first time, we are able to link environmental and
economic processes in a computer simulation at this level of detail. We show
that moderate disasters induce comparably small but positive short- to
medium-term, and negative long-term economic impacts. Large-scale events,
however, trigger a pronounced negative economic response immediately after the
event and in the long term, while exhibiting a temporary short- to medium-term
economic boost. We identify winners and losers in different economic sectors,
including the fiscal consequences for the government. We quantify the critical
disaster size beyond which the resilience of an economy to rebuild reaches its
limits. Our results might be relevant for the management of the consequences of
systemic events due to climate change and other disasters.
Compressive Sensing: Performance Comparison Of Sparse Recovery Algorithms
Date: 2018-01-29 00:00:00
Tasks: Compressive Sensing

Spectrum sensing is an important process in cognitive radio. A number of
sensing techniques that have been proposed suffer from high processing time,
hardware cost and computational complexity. To address these problems,
compressive sensing has been proposed to decrease the processing time and
expedite the scanning process of the radio spectrum. Selection of a suitable
sparse recovery algorithm is necessary to achieve this goal. A number of sparse
recovery algorithms have been proposed. This paper surveys the sparse recovery
algorithms, classify them into categories, and compares their performances. For
the comparison, we used several metrics such as recovery error, recovery time,
covariance, and phase transition diagram. The results show that techniques
under Greedy category are faster, techniques of Convex and Relaxation category
perform better in term of recovery error, and Bayesian based techniques are
observed to have an advantageous balance of small recovery error and a short
recovery time.
Evaluation of Parallel Tempering to Accelerate Bayesian Parameter Estimation in Systems Biology
Date: 2018-01-30 00:00:00
Tasks: parameter estimation

Models of biological systems often have many unknown parameters that must be
determined in order for model behavior to match experimental observations.
Commonly-used methods for parameter estimation that return point estimates of
the best-fit parameters are insufficient when models are high dimensional and
under-constrained. As a result, Bayesian methods, which treat model parameters
as random variables and attempt to estimate their probability distributions
given data, have become popular in systems biology. Bayesian parameter
estimation often relies on Markov Chain Monte Carlo (MCMC) methods to sample
model parameter distributions, but the slow convergence of MCMC sampling can be
a major bottleneck. One approach to improving performance is parallel tempering
(PT), a physics-based method that uses swapping between multiple Markov chains
run in parallel at different temperatures to accelerate sampling. The
temperature of a Markov chain determines the probability of accepting an
unfavorable move, so swapping with higher temperatures chains enables the
sampling chain to escape from local minima. In this work we compared the MCMC
performance of PT and the commonly-used Metropolis-Hastings (MH) algorithm on
six biological models of varying complexity. We found that for simpler models
PT accelerated convergence and sampling, and that for more complex models, PT
often converged in cases MH became trapped in non-optimal local minima. We also
developed a freely-available MATLAB package for Bayesian parameter estimation
called PTempEst (http://github.com/RuleWorld/ptempest), which is closely
integrated with the popular BioNetGen software for rule-based modeling of
biological systems.
Human Echolocation in Static Situations: Auditory Models of Detection Thresholds for Distance, Pitch, Loudness and Timbre
Date: 2018-01-30 00:00:00

We investigated, by using auditory models, how three perceptual parameters,
loudness, pitch and sharpness, determine human echolocation. We used acoustic
recordings from two previous studies, both from stationary situations, and
their resulting perceptual data as input to our analysis. An initial analysis
was on the room acoustics of the recordings. The parameters of interest were
sound pressure level, autocorrelation and spectral centroid. The auditory
models were used to analyze echolocation resulting from the perceptual
variables, i.e. loudness, pitch and sharpness. Relevant auditory models were
chosen to simulate each variable. Based on these results, we calculated
psychophysical thresholds for detecting a reflecting object with constant
physical size. A non-parametric method was used to determine thresholds for
distance, loudness, pitch and sharpness. Difference thresholds were calculated
for the psychophysical variables, since a 2-Alternative-Forced-Choice Paradigm
had originally been used. We found that (1) blind persons could detect objects
at lower loudness values, lower pitch strength, different sharpness values and
at further distances than sighted persons, (2) detection thresholds based on
repetition pitch, loudness and sharpness varied and depended on room acoustics
and type of sound stimuli, (3) repetition pitch was useful for detection at
shorter distances and was determined from the peaks in the temporal profile of
the autocorrelation function, (4) loudness at shorter distances provides
echolocation information, (5) at longer distances, timbre aspects, such as
sharpness, might be used to detect objects. We also discuss binaural
information, movements and the auditory model approach. Autocorrelation was
assumed as a proper measure for pitch, but the question is raised whether a
mechanism based on strobe integration is a viable possibility.