pwc-archive/papers-with-abstracts huggingface dataset:
Approximate State Space Modelling of Unobserved Fractional Components
Date: 2020-05-16 00:00:00

We propose convenient inferential methods for potentially nonstationary
multivariate unobserved components models with fractional integration and
cointegration. Based on finite-order ARMA approximations in the state space
representation, maximum likelihood estimation can make use of the EM algorithm
and related techniques. The approximation outperforms the frequently used
autoregressive or moving average truncation, both in terms of computational
costs and with respect to approximation quality. Monte Carlo simulations reveal
good estimation properties of the proposed methods for processes of different
complexity and dimension.
Pan-Cancer Epigenetic Biomarker Selection from Blood Samples Using SAS
Date: 2018-12-21 00:00:00

A key focus in current cancer research is the discovery of cancer biomarkers
that allow earlier detection with high accuracy and lower costs for both
patients and hospitals. Blood samples have long been used as a health status
indicator, but DNA methylation signatures in blood have not been fully
appreciated in cancer research. Historically, analysis of cancer has been
conducted directly with the patient's tumor or related tissues. Such analyses
allow physicians to diagnose a patient's health and cancer status; however,
physicians must observe certain symptoms that prompt them to use biopsies or
imaging to verify the diagnosis. This is a post-hoc approach. Our study will
focus on epigenetic information for cancer detection, specifically information
about DNA methylation in human peripheral blood samples in cancer discordant
monozygotic twin-pairs. This information might be able to help us detect cancer
much earlier, before the first symptom appears. Several other types of
epigenetic data can also be used, but here we demonstrate the potential of
blood DNA methylation data as a biomarker for pan-cancer using SAS 9.3 and SAS
EM. We report that 55 methylation CpG sites measurable in blood samples can be
used as biomarkers for early cancer detection and classification.
Dynamics and computation in mixed networks containing neurons that accelerate towards spiking
Date: 2019-08-15 00:00:00

Networks in the brain consist of different types of neurons. Here we
investigate the influence of neuron diversity on the dynamics, phase space
structure and computational capabilities of spiking neural networks. We find
that already a single neuron of a different type can qualitatively change the
network dynamics and that mixed networks may combine the computational
capabilities of ones with a single neuron type. We study inhibitory networks of
concave leaky (LIF) and convex "anti-leaky" (XIF) integrate-and-fire neurons
that generalize irregularly spiking non-chaotic LIF neuron networks. Endowed
with simple conductance-based synapses for XIF neurons, our networks can
generate a balanced state of irregular asynchronous spiking as well. We
determine the voltage probability distributions and self-consistent firing
rates assuming Poisson input with finite size spike impacts. Further, we
compute the full spectrum of Lyapunov exponents (LEs) and the covariant
Lyapunov vectors (CLVs) specifying the corresponding perturbation directions.
We find that there is approximately one positive LE for each XIF neuron. This
indicates in particular that a single XIF neuron renders the network dynamics
chaotic. A simple mean-field approach, which can be justified by properties of
the CLVs, explains the finding. As an application, we propose a spike-based
computing scheme where our networks serve as computational reservoirs and their
different stability properties yield different computational capabilities.
Wikipedia Text Reuse: Within and Without
Authors: Alshomary Milad, Völske Michael, Licht Tristan, Wachsmuth Henning, Stein Benno, Hagen Matthias, Potthast Martin
Date: 2018-12-21 00:00:00
Tasks: Retrieval

We study text reuse related to Wikipedia at scale by compiling the first
corpus of text reuse cases within Wikipedia as well as without (i.e., reuse of
Wikipedia text in a sample of the Common Crawl). To discover reuse beyond
verbatim copy and paste, we employ state-of-the-art text reuse detection
technology, scaling it for the first time to process the entire Wikipedia as
part of a distributed retrieval pipeline. We further report on a pilot analysis
of the 100 million reuse cases inside, and the 1.6 million reuse cases outside
Wikipedia that we discovered. Text reuse inside Wikipedia gives rise to new
tasks such as article template induction, fixing quality flaws due to
inconsistencies arising from asynchronous editing of reused passages, or
complementing Wikipedia's ontology. Text reuse outside Wikipedia yields a
tangible metric for the emerging field of quantifying Wikipedia's influence on
the web. To foster future research into these tasks, and for reproducibility's
sake, the Wikipedia text reuse corpus and the retrieval pipeline are made
freely available.
Growth, Industrial Externality, Prospect Dynamics, and Well-being on Markets
Date: 2020-07-28 00:00:00

Functions or 'functionings' enable to give a structure to any activity and
their combinations constitute the capabilities which characterize economic
assets such as work utility. The basic law of supply and demand naturally
emerges from that structure while integrating this utility within frames of
reference in which conditions of growth and associated inflation are identified
in the exchange mechanisms. Growth sustainability is built step by step taking
into account functional and organizational requirements which are followed
through a project up to a product delivery with different levels of
externalities. Entering the market through that structure leads to designing
basic equations of its dynamics and to finding canonical solutions, or
particular equilibria, after specifying the notion of maturity introduced in
order to refine the basic model. This approach allows to tackle behavioral
foundations of Prospect Theory through a generalization of its probability
weighting function for rationality analyses which apply to Western, Educated,
Industrialized, Rich, and Democratic societies as well as to the poorest ones.
The nature of reality and well-being appears then as closely related to the
relative satisfaction reached on the market, as it can be conceived by an
agent, according to business cycles; this reality being the result of the
complementary systems that govern human mind as structured by rational
psychologists. The final concepts of growth integrate and extend the maturity
part of the behavioral model into virtuous or erroneous sustainability.
Network structure of cascading neural systems predicts stimulus propagation and recovery
Date: 2019-11-10 00:00:00

Many neural systems display cascading behavior characterized by uninterrupted
sequences of neuronal firing. This gap precludes an understanding of how
variations in network structure manifest in neural dynamics and either support
or impinge upon information processing. Here, we develop a theoretical
understanding of how network structure supports information processing through
network dynamics, and we validate our theory with empirical data. Using a
generalized spiking model and mathematical tools from linear systems theory,
network control theory, and information theory, we show how network structure
can be designed to temporally extend the propagation and recovery of certain
stimulus patterns. Moreover, we observe cycles as structural and dynamic motifs
that are prevalent in such networks. Broadly, our results demonstrate how
cascading neural networks could contribute to cognitive faculties that require
lasting activation of neuronal patterns, such as working memory or attention.
The case for emulating insect brains using anatomical "wiring diagrams" equipped with biophysical models of neuronal activity
Date: 2019-10-27 00:00:00

Developing whole-brain emulation (WBE) technology would provide immense
benefits across neuroscience, biomedicine, artificial intelligence, and
robotics. At this time, constructing a simulated human brain lacks feasibility
due to limited experimental data and limited computational resources. However,
I suggest that progress towards this goal might be accelerated by working
towards an intermediate objective, namely insect brain emulation (IBE). More
specifically, this would entail creating biologically realistic simulations of
entire insect nervous systems along with more approximate simulations of
non-neuronal insect physiology to make "virtual insects." I argue that this
could be realistically achievable within the next 20 years. I propose that
developing emulations of insect brains will galvanize the global community of
scientists, businesspeople, and policymakers towards pursuing the loftier goal
of emulating the human brain. By demonstrating that WBE is possible via IBE,
simulating mammalian brains and eventually the human brain may no longer be
viewed as too radically ambitious to deserve substantial funding and resources.
Furthermore, IBE will facilitate dramatic advances in cognitive neuroscience,
artificial intelligence, and robotics through studies performed using virtual
insects.
Poverty, Income Inequality and Growth in Bangladesh: Revisited Karl-Marx
Date: 2018-12-21 00:00:00

This study tries to find the relationship among poverty inequality and
growth. The major finding of this study is poverty has reduced significantly
from 2000 to 2016, which is more than 100 percent but in recent time poverty
reduction has slowed down. Slower and unequal household consumption growth
makes sloth the rate of poverty reduction. Average annual consumption fell from
1.8 percent to 1.4 percent from 2010 to 2016 and poorer households experienced
slower consumption growth compared to richer households.
Many Average Partial Effects: with An Application to Text Regression
Authors: Harold D. Chiang
Date: 2018-12-21 00:00:00
Tasks: regression, valid

We study estimation, pointwise and simultaneous inference, and confidence intervals for many average partial effects of lasso Logit. Focusing on high-dimensional, cluster-sampling environments, we propose a new average partial effect estimator and explore its asymptotic properties. Practical penalty choices compatible with our asymptotic theory are also provided. The proposed estimator allow for valid inference without requiring oracle property. We provide easy-to-implement algorithms for cluster-robust high-dimensional hypothesis testing and construction of simultaneously valid confidence intervals using a multiplier cluster bootstrap. We apply the proposed algorithms to the text regression model of Wu (2018) to examine the presence of gendered language on the internet.
An Enhanced Initial Margin Methodology to Manage Warehoused Credit Risk
Date: 2018-12-21 00:00:00

The use of CVA to cover credit risk is widely spread, but has its
limitations. Namely, dealers face the problem of the illiquidity of instruments
used for hedging it, hence forced to warehouse credit risk. As a result,
dealers tend to offer a limited OTC derivatives market to highly risky
counterparties. Consequently, those highly risky entities rarely have access to
hedging services precisely when they need them most. In this paper we propose a
method to overcome this limitation. We propose to extend the CVA risk-neutral
framework to compute an initial margin (IM) specific to each counterparty,
which depends on the credit quality of the entity at stake, transforming the
effective credit rating of a given netting set to AAA, regardless of the credit
rating of the counterparty. By transforming CVA requirement into IM ones, as
proposed in this paper, an institution could rely on the existing mechanisms
for posting and calling of IM, hence ensuring the operational viability of this
new form of managing warehoused risk. The main difference with the currently
standard framework is the creation of a Specific Initial Margin, that depends
in the credit rating of the counterparty and the characteristics of the netting
set in question. In this paper we propose a methodology for such transformation
in a sound manner, and hence this method overcomes some of the limitations of
the CVA framework.
Mesoscopic population equations for spiking neural networks with synaptic short-term plasticity
Date: 2018-12-21 00:00:00

Coarse-graining microscopic models of biological neural networks to obtain
mesoscopic models of neural activities is an essential step towards multi-scale
models of the brain. Here, we extend a recent theory for mesoscopic population
dynamics with static synapses to the case of dynamic synapses exhibiting
short-term plasticity (STP). Under the assumption that spike arrivals at
synapses have Poisson statistics, we derive analytically stochastic mean-field
dynamics for the effective synaptic coupling between finite-size populations
undergoing Tsodyks-Markram STP. The novel mean-field equations account for both
finite number of synapses and correlations between the neurotransmitter release
probability and the fraction of available synaptic resources. Comparisons with
Monte Carlo simulations of the microscopic model show that in both feedforward
and recurrent networks the mesoscopic mean-field model accurately reproduces
stochastic realizations of the total synaptic input into a postsynaptic neuron
and accounts for stochastic switches between Up and Down states as well as for
population spikes. The extended mesoscopic population theory of spiking neural
networks with STP may be useful for a systematic reduction of detailed
biophysical models of cortical microcircuits to efficient and mathematically
tractable mean-field models.
On asymptotic characterization of destabilizing switching signals for switched linear systems
Date: 2018-12-22 00:00:00

This paper deals with classes of (de)stabilizing switching signals for
switched systems. Most of the available conditions for stability of switched
systems are sufficient in nature, and consequently, their violation does not
conclude instability of a switched system. The study of instability is,
however, important for obvious reasons. Our contributions are twofold: Firstly,
we propose a class of switching signals under which a continuous-time switched
linear system is unstable. Our characterization of instability depends solely
on the asymptotic behaviour of frequency of switching, frequency of transition
between subsystems, and fraction of activation of subsystems. Secondly, we show
that our class of destabilizing switching signals is a strict subset of the
class of switching signals that does not satisfy asymptotic characterization of
stability recently proposed in the literature. This observation identifies a
gap between asymptotic characterizations of stabilizing and destabilizing
switching signals for switched linear systems. The main apparatus for our
analysis is multiple Lyapunov-like functions.
Certainty based Reduced Sparse Solution for Dense Array EEG Source Localization
Date: 2018-12-22 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

The EEG source localization is an ill-posed problem. It involves estimation
of the sources which outnumbers the number of measurements. For a given
measurement at given time all sources are not active which makes the problem as
sparse inversion problem. This paper presents a new approach for dense array
EEG source localization. This paper aims at reducing the solution space to only
most certain sources and thereby reducing the problem of ill-posedness. This
employs a two-stage method where the first stage finds the most certain sources
that are likely to produce the observed EEG by using a statistical measure of
sources, the second stage solves the inverse problem by restricting the
solution space to only most certain sources and their neighbors. This reduces
the solution space for other source localization methods hence improvise their
accuracy in localizing the active neurological sources in the brain which is
the main goal. This method has been validated and applied to real 256 channel
data and the results were analyzed.
Robust Tests for Convergence Clubs
Date: 2018-12-22 00:00:00

In many applications common in testing for convergence the number of
cross-sectional units is large and the number of time periods are few. In these
situations asymptotic tests based on an omnibus null hypothesis are
characterised by a number of problems. In this paper we propose a multiple
pairwise comparisons method based on an a recursive bootstrap to test for
convergence with no prior information on the composition of convergence clubs.
Monte Carlo simulations suggest that our bootstrap-based test performs well to
correctly identify convergence clubs when compared with other similar tests
that rely on asymptotic arguments. Across a potentially large number of
regions, using both cross-country and regional data for the European Union, we
find that the size distortion which afflicts standard tests and results in a
bias towards finding less convergence, is ameliorated when we utilise our
bootstrap test.
Kinetic modelling of competition and depletion of shared miRNAs by competing endogenous RNAs
Date: 2018-12-22 00:00:00
Tasks: Translation

Non-conding RNAs play a key role in the post-transcriptional regulation of
mRNA translation and turnover in eukaryotes. miRNAs, in particular, interact
with their target RNAs through protein-mediated, sequence-specific binding,
giving rise to extended and highly heterogeneous miRNA-RNA interaction
networks. Within such networks, competition to bind miRNAs can generate an
effective positive coupling between their targets. Competing endogenous RNAs
(ceRNAs) can in turn regulate each other through miRNA-mediated crosstalk.
Albeit potentially weak, ceRNA interactions can occur both dynamically,
affecting e.g. the regulatory clock, and at stationarity, in which case ceRNA
networks as a whole can be implicated in the composition of the cell's
proteome. Many features of ceRNA interactions, including the conditions under
which they become significant, can be unraveled by mathematical and in silico
models. We review the understanding of the ceRNA effect obtained within such
frameworks, focusing on the methods employed to quantify it, its role in the
processing of gene expression noise, and how network topology can determine its
reach.
Selection of cyanobacteria over green algae in a photo-sequencing batch bioreactor fed with wastewater
Date: 2018-12-22 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

A strategy based on photo sequencing batch operation was used to select
cyanobacteria over unsettled green algae in a wastewater treatment system,
evaluating for the first time the effect of hydraulic regimes on nutritional
dynamics and microorganisms competition. During 30 days of operation, an
initial microalgae mixed consortia dominated by the green microalgae
Scenedesmus sp. was cultivated in two different photo-sequencing batch reactors
operated at hydraulic retention time (HRT) of 6 days (PSBR6) and 4 days (PSBR4)
at a theoretical solids retention time (SRT) of 10 d. Both reactors were
compared with a semi-continuous reactor (SC10) operated at 10 d of HRT and 10
days of SRT (used as a control). The results indicated that PSBR6 and PSBR4
decreased Scenedesmus sp. population by 88% and 48%, respectively. However,
only PSBR6 provided suitable conditions to select cyanobacteria from an initial
green algae dominated culture. These conditions included volumetric loads of
11.72 mg TN L-1d-1, 2.04 mg TP L-1d-1 and 53.31 mg TOC L-1d-1. The remaining
nutrients in the culture led also to a phosphorus limiting N:P ratio (34:1)
that improved the increase of cyanobacteria from an initial 2% until 70% of the
total population. In addition, PSBR6 reached a biomass production of 0.12 g
L-1d-1, while removing TN, TP and TOC by 58%, 83% and 85%, respectively.
Conversely, the application of higher nutrients loads caused by lower HRT
(PSBR4) led to an increase of only 13% of cyanobacteria while SC10 remained
with the same biomass composition during all the experimental time. Thus, this
study showed that the dominance of cyanobacteria in microalgal based wastewater
treatment systems can be controlled by the operational and nutritional
conditions. This knowledge could contribute to control microalgae contamination
from up-scaling cyanobacterial biomass production in wastewater treatment
systems.
Channel Modeling for Over-water Communications
Date: 2018-12-22 00:00:00

Over-water communication has been an important research field of wireless
communications and radar. Since there are so many parameters in this
environment that are changing continuously in the spatial and temporal domains
such as weather and sea surface parameters. Hence, accurately modeling the
over-water propagation channel is very crucial and challenging at the same
time. In this report, we will shed some light on the parabolic equation (PE)
method which is one of the main methods used to model the radio waves
propagation over variable terrain and through the homogeneous and inhomogeneous
atmosphere. Besides ray-tracing, PE is among the most widely used approaches
nowadays for deterministically analyze over-water channels. We are trying to
pave the way for better understanding of the mathematical background for the PE
and develop a tool to perform advanced analysis for the over-water
communication channel taking into consideration its spatial and temporal
behavior.
Multicarrier Chirp-Division Multiplexing for Wireless Communications
Date: 2019-01-17 00:00:00

We propose a multicarrier chirp-division multiplexing (MCDM) system in which
orthogonal chirp waveforms are utilized as frequency subcarriers. Orthogonal
characteristics of chirp subcarriers are analyzed in respect to
cross-correlation coefficients among subcarriers. Moreover, orthogonal chirp
transform (OCT) is developed to implement MCDM systems. In addition, we design
a low complexity receiver, including packet synchronization, carrier frequency
offset compensation, channel estimation, and symbol detection. Proposed MCDM
systems have both advantages of chirp waveforms and multicarrier architectures.
Computational complexity of our proposed detector is discussed in detail. The
bit-error-rate (BER) performance of the MCDM system is evaluated in simulations
and indoor radio frequency (RF) experiments. We have demonstrated effectiveness
of MCDM systems both in simulation and experimental results, comparing to
orthogonal frequency division multiplexing (OFDM) systems. Moreover, MCDM can
be further applied to higher order modulations for enabling higher data rates
for RF wireless communications.
Hybrid Hughes-Hartogs Power Allocation Algorithms for OFDMA Systems
Date: 2018-12-22 00:00:00

This work analyzes the discrete solution of Hughes-Hartogs (HH) for the
transmission rate maximization problem with power constraint in the OFDMA
systems and explores mechanisms to reduce the computational complexity of
greedy algorithms. In addition to the solution characterization, a
computational complexity analysis is developed, considering the number of
executed operations for running time purpose. Moreover, we have compared the
system capacity via the throughput obtained with the HH solution, and its
variants combined with three complexity reduction mechanisms. These tools
consist of an initial allocation bit vector calculated by rounding the results
of the water-filling (WF) solution, the multiple subchannels per iteration
updating, and the adoption of a subchannel grouping procedure. Our findings
indicate that the update of multiple subchannels and the subcarriers grouping
techniques reduce the number of iterations required for convergence of the
original HH, with some throughput degradation. Also, the bit-allocation
mechanism based on the WF is deployed as an alternative to overcome the HH
solution, increasing the computational complexity.