pwc-archive/papers-with-abstracts huggingface dataset:
Exact Expectation Analysis of the Deficient-Length LMS Algorithm
Date: 2018-10-30 00:00:00

Stochastic models that predict adaptive filtering algorithms performance
usually employ several assumptions in order to simplify the analysis. Although
these simplifications facilitate the recursive update of the statistical
quantities of interest, they by themselves may hamper the modeling accuracy.
This paper simultaneously avoids for the first time the employment of two
ubiquitous assumptions often adopted in the analysis of the least mean squares
algorithm. The first of them is the so-called independence assumption, which
presumes statistical independence between adaptive coefficients and input data.
The second one assumes a sufficient-order configuration, in which the lengths
of the unknown plant and the adaptive filter are equal. State equations that
characterize both the mean and mean square performance of the deficient-length
configuration without using the independence assumption are provided. The
devised analysis, encompassing both transient and steady-state regimes, is not
restricted neither to white nor to Gaussian input signals and is able to
provide a proper step size upper bound that guarantees stability.
A neural field model for color perception unifying assimilation and contrast
Date: 2019-06-28 00:00:00

We address the question of color-space interactions in the brain, by
proposing a neural field model of color perception with spatial context for the
visual area V1 of the cortex. Our framework reconciles two opposing perceptual
phenomena, known as simultaneous contrast and chromatic assimilation. They have
been previously shown to act synergistically, so that at some point in an
image, the color seems perceptually more similar to that of adjacent neighbors,
while being more dissimilar from that of remote ones. Thus, their combined
effects are enhanced in the presence of a spatial pattern, and can be measured
as larger shifts in color matching experiments. Our model supposes a
hypercolumnar structure coding for colors in V1, and relies on the notion of
color opponency introduced by Hering. The connectivity kernel of the neural
field exploits the balance between attraction and repulsion in color and
physical spaces, so as to reproduce the sign reversal in the influence of
neighboring points. The color sensation at a point, defined from a steady state
of the neural activities, is then extracted as a nonlinear percept conveyed by
an assembly of neurons. It connects the cortical and perceptual levels, because
we describe the search for a color match in asymmetric matching experiments as
a mathematical projection on color sensations. We validate our color neural
field alongside this color matching framework, by performing a multi-parameter
regression to data produced by psychophysicists and ourselves. All the results
show that we are able to explain the nonlinear behavior of shifts observed
along one or two dimensions in color space, which cannot be done using a simple
linear model.
Alternating Diffusion Map Based Fusion of Multimodal Brain Connectivity Networks for IQ Prediction
Date: 2018-10-30 00:00:00
Tasks: Dimensionality Reduction, Functional Connectivity

To explain individual differences in development, behavior, and cognition,
most previous studies focused on projecting resting-state functional MRI (fMRI)
based functional connectivity (FC) data into a low-dimensional space via linear
dimensionality reduction techniques, followed by executing analysis operations.
However, linear dimensionality analysis techniques may fail to capture
nonlinearity of brain neuroactivity. Moreover, besides resting-state FC, FC
based on task fMRI can be expected to provide complementary information.
Motivated by these considerations, we nonlinearly fuse resting-state and
task-based FC networks (FCNs) to seek a better representation in this paper. We
propose a framework based on alternating diffusion map (ADM), which extracts
geometry-preserving low-dimensional embeddings that successfully parameterize
the intrinsic variables driving the phenomenon of interest. Specifically, we
first separately build resting-state and task-based FCNs by symmetric positive
definite matrices using sparse inverse covariance estimation for each subject,
and then utilize the ADM to fuse them in order to extract significant
low-dimensional embeddings, which are used as fingerprints to identify
individuals. The proposed framework is validated on the Philadelphia
Neurodevelopmental Cohort data, where we conduct extensive experimental study
on resting-state and fractal $n$-back task fMRI for the classification of
intelligence quotient (IQ). The fusion of resting-state and $n$-back task fMRI
by the proposed framework achieves better classification accuracy than any
single fMRI, and the proposed framework is shown to outperform several other
data fusion methods. To our knowledge, this paper is the first to demonstrate a
successful extension of the ADM to fuse resting-state and task-based fMRI data
for accurate prediction of IQ.
Nighttime Light, Superlinear Growth, and Economic Inequalities at the Country Level
Date: 2018-10-30 00:00:00

Research has highlighted relationships between size and scaled growth across
a large variety of biological and social organisms, ranging from bacteria,
through animals and plants, to cities an companies. Yet, heretofore,
identifying a similar relationship at the country level has proven challenging.
One reason is that, unlike the former, countries have predefined borders, which
limit their ability to grow "organically." This paper addresses this issue by
identifying and validating an effective measure of organic growth at the
country level: nighttime light emissions, which serve as a proxy of energy
allocations where more productive activity takes place. This indicator is
compared to population size to illustrate that while nighttime light emissions
are associated with superlinear growth, population size at the country level is
associated with sublinear growth. These relationships and their implications
for economic inequalities are then explored using high-resolution geospatial
datasets spanning the last three decades.
Power Factor Correction of Inductive Loads using PLC
Date: 2018-10-30 00:00:00

This paper proposes an automatic power factor correction for variable
inductive loads, most dominantly induction motors (IM) utilizing the
Programmable Logic Controllers (PLC). This hardware implementation of a 3{\O}
Inductive load system focuses on the automatic correction of power factor using
PLC. With the help of PLC, different performance parameters current level, real
power, and inductive power are obtained and logged in the PC. Using PLC
program, according to the control strategy to obtain a pre-specified power
factor a set of capacitors sized in a binary rate will be switched on or off
with the help of switching relays and contactors. This PLC control strategy
relies on a lookup table which is prepared based on two input parameters - peak
current and power factor, at a constant voltage. From these parameters, PLC
will calculate the reactive power of the system and accordingly the right
sequence of the capacitors are switched on in order to compensate for reactive
power.
Whole genome single nucleotide polymorphism genotyping of Staphylococcus aureus
Date: 2018-10-30 00:00:00

Next-generation sequencing technology enables routine detection of bacterial
pathogens for clinical diagnostics and genetic research. Whole genome
sequencing has been of importance in the epidemiologic analysis of bacterial
pathogens. However, few whole genome sequencing-based genotyping pipelines are
available for practical applications. Here, we present the whole genome
sequencing-based single nucleotide polymorphism (SNP) genotyping method and
apply to the evolutionary analysis of methicillin-resistant Staphylococcus
aureus. The SNP genotyping method calls genome variants using next-generation
sequencing reads of whole genomes and calculates the pair-wise Jaccard
distances of the genome variants. The method may reveal the high-resolution
whole genome SNP profiles and the structural variants of different isolates of
methicillin-resistant S. aureus (MRSA) and methicillin-susceptible S. aureus
(MSSA) strains. The phylogenetic analysis of whole genomes and particular
regions may monitor and track the evolution and the transmission dynamic of
bacterial pathogens. The computer programs of the whole genome sequencing-based
SNP genotyping method are available to the public at
https://github.com/cyinbox/NGS.
Wavelength-resolved neutron tomography for crystalline materials
Date: 2019-02-18 00:00:00

Wavelength-resolved (WR) neutron transmission tomography is an emerging
technique to characterize engineering materials. While tomographic
reconstruction for amorphous samples is straightforward, it is challenging to
reconstruct samples with single-crystal domains because the attenuation of the
sample varies as a function of its orientation with respect to the incident
beam due to Bragg scattering. In this paper, we present an algorithm that can
reconstruct samples with single-crystal domains from WR neutron tomographic
measurements. In particular, we use a model-based iterative reconstruction
(MBIR) technique that reconstructs the volume by identifying and leaving out
the regions of the measurement that are affected by Bragg scatter. We combine
the output of the MBIR method with an algorithm that matches the reconstruction
to the identified Bragg scatter to reconstruct a feature that corresponds to
the local crystallography of the sample being measured. Using simulated data,
we demonstrate how our algorithm can reconstruct materials with single-crystal
domains, thereby adding a powerful new capability for WR neutron imaging
instruments.
Connecting the Dots: Identifying Network Structure via Graph Signal Processing
Date: 2018-10-31 00:00:00
Tasks: Causal Inference, graph construction, Graph Learning

Network topology inference is a prominent problem in Network Science. Most
graph signal processing (GSP) efforts to date assume that the underlying
network is known, and then analyze how the graph's algebraic and spectral
characteristics impact the properties of the graph signals of interest. Such an
assumption is often untenable beyond applications dealing with e.g., directly
observable social and infrastructure networks; and typically adopted graph
construction schemes are largely informal, distinctly lacking an element of
validation. This tutorial offers an overview of graph learning methods
developed to bridge the aforementioned gap, by using information available from
graph signals to infer the underlying graph topology. Fairly mature statistical
approaches are surveyed first, where correlation analysis takes center stage
along with its connections to covariance selection and high-dimensional
regression for learning Gaussian graphical models. Recent GSP-based network
inference frameworks are also described, which postulate that the network
exists as a latent underlying structure, and that observations are generated as
a result of a network process defined in such a graph. A number of arguably
more nascent topics are also briefly outlined, including inference of dynamic
networks, nonlinear models of pairwise interaction, as well as extensions to
directed graphs and their relation to causal inference. All in all, this paper
introduces readers to challenges and opportunities for signal processing
research in emerging topic areas at the crossroads of modeling, prediction, and
control of complex behavior arising in networked systems that evolve over time.
Latent variable approach to diarization of audio recordings using ad-hoc randomly placed mobile devices
Date: 2018-10-31 00:00:00

Diarization of audio recordings from ad-hoc mobile devices using spatial
information is considered in this paper. A two-channel synchronous recording is
assumed for each mobile device, which is used to compute directional statistics
separately at each device in a frame-wise manner. The recordings across the
mobile devices are asynchronous, but a coarse synchronization is performed by
aligning the signals using acoustic events, or real-time clock. Direction
statistics computed for all the devices, are then modeled jointly using a
Dirichlet mixture model, and the posterior probability over the mixture
components is used to derive the diarization information. Experiments on real
life recordings using mobile phones show a diarization error rate of less than
14%.
SIEVE: Helping Developers Sift Wheat from Chaff via Cross-Platform Analysis
Authors: Sulistya Agus, Prana Gede Artha Azriadi, Sharma Abhishek, Lo David, Treude Christoph
Date: 2018-10-31 00:00:00
Tasks: Representation Learning, Word Embeddings

Software developers have benefited from various sources of knowledge such as
forums, question-and-answer sites, and social media platforms to help them in
various tasks. Extracting software-related knowledge from different platforms
involves many challenges. In this paper, we propose an approach to improve the
effectiveness of knowledge extraction tasks by performing cross-platform
analysis. Our approach is based on transfer representation learning and word
embeddings, leveraging information extracted from a source platform which
contains rich domain-related content. The information extracted is then used to
solve tasks in another platform (considered as target platform) with less
domain-related contents. We first build a word embeddings model as a
representation learned from the source platform, and use the model to improve
the performance of knowledge extraction tasks in the target platform. We
experiment with Software Engineering Stack Exchange and Stack Overflow as
source platforms, and two different target platforms, i.e., Twitter and
YouTube. Our experiments show that our approach improves performance of
existing work for the tasks of identifying software-related tweets and helpful
YouTube comments.
Discriminatively Re-trained i-vector Extractor for Speaker Recognition
Date: 2018-10-31 00:00:00
Tasks: Speaker Recognition, Speaker Verification

In this work we revisit discriminative training of the i-vector extractor
component in the standard speaker verification (SV) system. The motivation of
our research lies in the robustness and stability of this large generative
model, which we want to preserve, and focus its power towards any intended SV
task. We show that after generative initialization of the i-vector extractor,
we can further refine it with discriminative training and obtain i-vectors that
lead to better performance on various benchmarks representing different
acoustic domains.
Evaluation of spike sorting algorithms: Simulations and application to human Subthalamic Nucleus recordings
Date: 2018-10-31 00:00:00
Tasks: Spike Sorting

An important prerequisite for the analysis of spike synchrony in
extracellular recordings is the extraction of single unit activity from the
recorded multi unit signal. To identify single units (SUs), potential spikes
are detected and separated with respect to their potential neuronal origins
('spike sorting'). However, different sorting algorithms yield inconsistent
unit assignments which seriously influences the subsequent analyses of the
spiking activity. To evaluate the quality of spike sortings performed with
different prevalent algorithms offered by the 'Plexon Offline Sorter' we first
apply these algorithms to experimental data (ED), namely recordings in the
Subthalamic Nucleus of patients with Parkinson's disease, obtained during Deep
Brain Stimulation surgery. Since this procedure leaves us unsure about the best
sorting result we then apply all methods again to artificial data (AD) with
known ground truth (GT). AD consists of pairs of SUs with different shape
similarity embedded in the background noise of the ED. The sorting evaluation
is based on the influence of the respective methods on the SU assignments and
its effect on the resulting firing characteristics. We find a high variability
in the sorting results obtained by different algorithms that increases with SU
shape similarity. We also find significant differences in the resulting firing
characteristics of the ED. We conclude that Valley-Seeking produces the most
accurate results if the exclusion of artifacts as unsorted events is important.
If the latter is less important ('clean' data) K-Means is a better option. Our
results strongly argue for the need of standardized validation procedures for
spike sorting based on GT data. The recipe suggested here is simple enough to
become a standard procedure.
A stochastic computing architecture for iterative estimation
Date: 2018-10-31 00:00:00

Stochastic computing (SC) is a promising candidate for fault tolerant
computing in digital circuits. We present a novel stochastic computing
estimation architecture allowing to solve a large group of estimation problems
including least squares estimation as well as sparse estimation. This allows
utilizing the high fault tolerance of stochastic computing for implementing
estimation algorithms. The presented architecture is based on the recently
proposed linearized-Bregman-based Sparse Kaczmarz algorithm. To realize this
architecture, we develop a shrink function in stochastic computing and
analytically describe its error probability. We compare the stochastic
computing architecture to a fixed-point binary implementation and present
bit-true simulation results as well as synthesis results demonstrating the
feasibility of the proposed architecture for practical implementation.
Quantum Theory Methods as a Possible Alternative for the Double-Blind Gold Standard of Evidence-Based Medicine: Outlining a New Research Program
Date: 2018-10-31 00:00:00
Tasks: Decision Making, Relation

We motivate the possibility of using notions and methods derived from quantum
physics, and more specifically from the research field known as 'quantum
cognition', to optimally model different situations in the field of medicine,
its decision-making processes and ensuing practices, particularly in relation
to chronic and rare diseases. This also as a way to devise alternative
approaches to the generally adopted double-blind gold standard.
Modelling N2O dynamics of activated sludge biomass under nitrifying and denitrifying conditions: pathway contributions and uncertainty analysis
Date: 2018-10-31 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

Nitrous oxide (N2O) is a potent greenhouse gas emitted during biological
wastewater treatment. A pseudo-mechanistic model describing three biological
pathways for nitric oxide (NO) and N2O production was calibrated for mixed
culture biomass from an activated sludge process using laboratory-scale
experiments. The model (NDHA) comprehensively describes N2O producing pathways
by both autotrophic ammonium oxidizing bacteria and heterotrophic bacteria.
Extant respirometric assays and anaerobic batch experiments were designed to
calibrate endogenous and exogenous processes (heterotrophic denitrification and
autotrophic ammonium/nitrite oxidation) together with the associated net N2O
production. Ten parameters describing heterotrophic processes and seven for
autotrophic processes were accurately estimated (variance/mean < 25%). The
model predicted NO and N2O dynamics at varying dissolved oxygen, ammonium and
nitrite levels and was validated against an independent set of experiments with
the same biomass. Aerobic ammonium oxidation experiments at two oxygen levels
used for model evaluation (2 and 0.5 mg/L) indicated that both the nitrifier
denitrification (42, 64%) and heterotrophic denitrification (7, 17%) pathways
increased and dominated N2O production at high nitrite and low oxygen
concentrations; while the nitrifier nitrification pathway showed the largest
contribution at high dissolved oxygen levels (51, 19%). The uncertainty of the
biological parameter estimates was propagated to N2O model outputs via Monte
Carlo simulations as 95% confidence intervals. The accuracy of the estimated
parameters resulted in a low uncertainty of the N2O emission factors (4.6 +-
0.6% and 1.2 +- 0.1%).
Void distributions reveal structural link between jammed packings and protein cores
Date: 2018-10-31 00:00:00
Tasks: Protein Design

Dense packing of hydrophobic residues in the cores of globular proteins
determines their stability. Recently, we have shown that protein cores possess
packing fraction $\phi \approx 0.56$, which is the same as dense, random
packing of amino acid-shaped particles. In this article, we compare the
structural properties of protein cores and jammed packings of amino acid-shaped
particles in much greater depth by measuring their local and connected void
regions. We find that the distributions of surface Voronoi cell volumes and
local porosities obey similar statistics in both systems. We also measure the
probability that accessible, connected void regions percolate as a function of
the size of a spherical probe particle and show that both systems possess the
same critical probe size. By measuring the critical exponent $\tau$ that
characterizes the size distribution of connected void clusters at the onset of
percolation, we show that void percolation in packings of amino acid-shaped
particles and protein cores belong to the same universality class, which is
different from that for void percolation in jammed sphere packings. We propose
that the connected void regions of proteins are a defining feature of proteins
and can be used to differentiate experimentally observed proteins from decoy
structures that are generated using computational protein design software. This
work emphasizes that jammed packings of amino acid-shaped particles can serve
as structural and mechanical analogs of protein cores, and could therefore be
useful in modeling the response of protein cores to cavity-expanding and
-reducing mutations.
Predictive Modeling of Biomedical Signals Using Controlled Spatial Transformation
Date: 2018-10-31 00:00:00

An important paradigm in smart health is developing diagnosis tools and
monitoring a patient's heart activity through processing Electrocardiogram
(ECG) signals is a key example, sue to high mortality rate of heart-related
disease. However, current heart monitoring devices suffer from two important
drawbacks: i) failure in capturing inter-patient variability, and ii)
incapability of identifying heart abnormalities ahead of time to take effective
preventive and therapeutic interventions.
  This paper proposed a novel predictive signal processing method to solve
these issues. We propose a two-step classification framework for ECG signals,
where a global classifier recognizes severe abnormalities by comparing the
signal against a universal reference model. The seemingly normal signals are
then passed through a personalized classifier, to recognize mild but
informative signal morphology distortions. The key idea is to develop a novel
deviation analysis based on a controlled nonlinear transformation to capture
significant deviations of the signal towards any of predefined abnormality
classes. Here, we embrace the proven but overlooked fact that certain features
of ECG signals reflect underlying cardiac abnormalities before the occurrences
of cardiac disease. The proposed method achieves a classification accuracy of
96.6% and provides a unique feature of predictive analysis by providing
warnings before critical heart conditions. In particular, the chance of
observing a severe problem (a red alarm) is raised by about 5% to 10% after
observing a yellow alarm of the same type. Although we used this methodology to
provide early precaution messages to elderly and high-risk heart-patients, the
proposed method is general and applicable to similar bio-medical signal
processing applications.