pwc-archive/papers-with-abstracts huggingface dataset:
A mathematical model of the metabolism of a cell. Self-organization and chaos
Authors: V. I. Grytsay, I. V. Musatenko
Date: 2018-01-31 00:00:00

Using the classical tools of nonlinear dynamics, we study the process of self-organization and the appearance of the chaos in the metabolic process in a cell with the help of a mathematical model of the transformation of steroids by a cell Arthrobacter globiformis. We constructed the phase-parametric diagrams obtained under a variation of the dissipation of the kinetic membrane potential. The oscillatory modes obtained are classified as regular and strange attractors. We calculated the bifurcations, by which the self-organization and the chaos occur in the system, and the transitions "chaos-order", "order-chaos", "order-order", and "chaos-chaos" arise. Feigenbaum's scenarios and the intermittences are found. For some selected modes, the projections of the phase portraits of attractors, Poincar\'e sections, and Poincar\'e maps are constructed. The total spectra of Lyapunov indices for the modes under study are calculated. The structural stability of the attractors is demonstrated. A general scenario of the formation of regular and strange attractors in the given metabolic process in a cell is found. The physical nature of their appearance in the metabolic process is studied.
Clustering Gene Expression Time Series with Coregionalization: Speed propagation of ALS
Date: 2018-02-12 00:00:00
Tasks: Clustering, Time Series, Time Series Analysis

Clustering of gene expression time series gives insight into which genes may
be coregulated, allowing us to discern the activity of pathways in a given
microarray experiment. Of particular interest is how a given group of genes
varies with different model conditions or genetic background. Amyotrophic
lateral sclerosis (ALS), an irreversible diverse neurodegenerative disorder
showed consistent phenotypic differences and the disease progression is
heterogeneous with significant variability. This paper demonstrated about
finding some significant gene expression profiles and its associated or
co-regulated cluster of gene expressions from four groups of data with
different genetic background or models conditions. Gene enrichment score
analysis and pathway analysis of judicially selected clusters lead toward
identifying features underlying the differential speed of disease progression.
Gene ontology overrepresentation analysis showed clusters from the proposed
method are less likely to be clustered just by chance. In this paper, we
develop a new clustering method that allows each cluster to be parameterised
according to whether the behaviour of the genes across conditions is correlated
or anti-correlated. Our proposed method unveil the potency of latent
information shared between multiple model conditions and their replicates
during modelling gene expression data.
Evolution of the Science Fiction Writer's Capacity to Imagine the Future
Date: 2019-03-13 00:00:00

Drawing upon a body of research on the evolution of creativity, this paper
proposes a theory of how, when, and why the forward-thinking story-telling
abilities of humans evolved, culminating in the visionary abilities of science
fiction writers. The ability to recursively chain thoughts together evolved
approximately two million years ago. Language abilities, and the ability to
shift between different modes of thought, evolved approximately 100,000 years
ago. Science fiction dates to at least the second Century AD. It is suggested
that well before this time, but after 100,000 years ago, and concurrent with
the evolution of a division of labour between creators and imitators there
arose a division of labour between past, present, and future thinkers.
Agent-based model research suggests there are social benefits to the evolution
of individual differences in creativity such that there is a balance between
novelty-generating creators and continuity-perpetuating imitators. A balance
between individuals focused on the past, present, and future would be expected
to yield similar adaptive benefits.
In a One-Bit Rush: Low-Latency Wireless Spectrum Monitoring with Binary Sensor Arrays
Date: 2018-04-30 00:00:00
Tasks: Decision Making

Detecting the presence of a random wireless source with minimum latency
utilizing an array of radio sensors is considered. The problem is studied under
the constraint that the analog-to-digital conversion at each sensor is
restricted to reading the sign of the analog received signal. We formulate the
resulting digital signal processing task as a sequential hypothesis test in
simple form. To circumvent the intractable probabilistic model of the
multivariate binary array data, a reduced model representation within the
exponential family in conjunction with a log-likelihood ratio approximation is
employed. This approach allows us to design a likelihood-based sequential test
and to analyze its analytic performance along Wald's classical arguments. In
the context of wireless spectrum monitoring for satellite-based navigation and
synchronization systems, we study the achievable processing latency,
characterized by the average sample number, as a function of the binary sensors
in use. The practical feasibility and potential of the discussed low-complexity
sensing and decision-making technology is demonstrated via simulations.
Visualizing Treasury Issuance Strategy
Authors: Christopher Cameron
Date: 2018-02-09 00:00:00

We introduce simple cost and risk proxy metrics that can be attached to Treasury issuance strategy to complement analysis of the resulting portfolio weighted-average maturity (WAM). These metrics are based on mapping issuance fractions to their long-term, asymptotic portfolio implications for cost and risk under mechanical debt-rolling dynamics. The resulting mapping enables one to visualize tradeoffs involved in contemplated issuance reallocation, and identify an efficient frontier and optimal tenor. Historical Treasury issuance strategy is analyzed empirically using these cost and risk metrics to illustrate how changes in issuance needs and strategy have translated into structural shifts in the cost and risk stance of Treasury issuance.
Evolutionary value of collective sensing
Date: 2018-02-10 00:00:00
Tasks: Decision Making

We propose a mathematical model for collective sensing in a population
growing in a stochastically varying environment. In the population, individuals
use an information channel for sensing the environment, and two channels for
signal production and comprehension to communicate among themselves. We show
that existence of such system has a positive effect on population growth, hence
can have a positive evolutionary effect. We show that the gain in growth due to
the collective sensing is related to information theoretic entities, which can
be considered as the information content of this system from the environment.
We further show that heterogeneity in communication resulted from network or
spatial structure increases growth. We compute the growth rate of a population
residing on a lattice and show that growth rate near the maximum noise level in
observation or communication, increases exponentially as noise decreases. This
exponential effect makes the emergence of collective observation an easy
outcome in an evolutionary process. Furthermore, we are able to quantify
interesting effects such as accelerated growth, and simplification of decision
making due to information amplification by communication. Finally, we show that
an amount of noise in representation formation has more disadvantageous effect
compared to the same noise in signal production.
Document Classification Using Distributed Machine Learning
Authors: Aydin Galip, Hallac Ibrahim Riza
Date: 2018-02-10 00:00:00
Tasks: BIG-bench Machine Learning, Classification, Document Classification, General Classification

In this paper, we investigate the performance and success rates of Na\"ive
Bayes Classification Algorithm for automatic classification of Turkish news
into predetermined categories like economy, life, health etc. We use Apache Big
Data technologies such as Hadoop, HDFS, Spark and Mahout, and apply these
distributed technologies to Machine Learning.
Double Minimum Variance Beamforming Method to Enhance Photoacoustic Imaging
Date: 2018-02-11 00:00:00

One of the common algorithms used to reconstruct photoacoustic (PA) images is
the non-adaptive Delay-and-Sum (DAS) beamformer. However, the quality of the
reconstructed PA images obtained by DAS is not satisfying due to its high level
of sidelobes and wide mainlobe. In contrast, adaptive beamformers, such as
minimum variance (MV), result in an improved image compared to DAS. In this
paper, a novel beamforming method, called Double MV (D-MV) is proposed to
enhance the image quality compared to the MV. It is shown that there is a
summation procedure between the weighted subarrays in the output of the MV
beamformer. This summation can be interpreted as the non-adaptive DAS
beamformer. It is proposed to replace the existing DAS with the MV algorithm to
reduce the contribution of the off-axis signals caused by the DAS beamformer
between the weighted subarrays. The numerical results show that the proposed
technique improves the full-width-half-maximum (FWHM) and signal-to-noise ratio
(SNR) for about 28.83 \mu m and 4.8 dB in average, respectively, compared to MV
beamformer. Also, quantitative evaluation of the experimental results indicates
that the proposed D-MV leads to 0.15 mm and 1.96 dB improvement in FWHM and
SNR, in comparison with MV beamformer.
Photoacoustic Image Formation Based on Sparse Regularization of Minimum Variance Beamformer
Date: 2018-02-11 00:00:00

Delay-and-Sum (DAS) is the most common algorithm used in photoacoustic (PA)
image formation. However, this algorithm results in a reconstructed image with
a wide mainlobe and high level of sidelobes. Minimum variance (MV), as an
adaptive beamformer, overcomes these limitations and improves the image
resolution and contrast. In this paper, a novel algorithm, named
modified-sparse-MV (MS-MV) is proposed in which a L1-norm constraint is added
to the MV minimization problem after some modifications, in order to suppress
the sidelobes more efficiently, compared to MV. The added constraint can be
interpreted as the sparsity of the output of the MV beamformed signals. Since
the final minimization problem is convex, it can be solved efficiently using a
simple iterative algorithm. The numerical results show that the proposed
method, MS-MV beamformer, improves the signal-to-noise (SNR) about 19.48 dB, in
average, compared to MV. Also, the experimental results, using a wire-target
phantom, show that MS-MV leads to SNR improvement of about 2.64 dB in
comparison with the MV.
New Proposals of a Stress Measure in a Capital and its Robust Estimator
Date: 2018-02-11 00:00:00

In this paper a novel approach for a measurement of stresses in a capital,
which induce the capital flows between economic systems, is proposed. The
proposals appeal to an apparatus offered by the statistical theory of shape. We
propose a stress functional basing on a concept of mean shape determined by
representative particles of a capital carrier. We also propose methods of
describing changes in an amount and a structure of stresses in a capital
appealing, among others, to a Bookstein's pair of thin plain spline
deformation, and a measure of a shape variability. We apply our approach to an
indirect verification of the hypothesis according to which a capital flow
between economic systems is related to an activity of an inner force related to
stresses in a capital. We indicate, that the stresses create a phenomenon
analogous to the heat, which may be interpreted in terms of a positive economic
external effect, which attracts a capital from environment of a system to the
system. For empirical studies we propose robust approach to estimate the stress
functional basing on the data depth concept. In the empirical research we use
data on five branch stock indexes from Warsaw Stock Exchange. The studied
period involves the financial crisis of 2007.
Uncertainty Principles Associated with the Offset Linear Canonical Transform
Date: 2018-02-11 00:00:00

As a time-shifted and frequency-modulated version of the linear canonical
transform (LCT), the offset linear canonical transform (OLCT) provides a more
general framework of most existing linear integral transforms in signal
processing and optics. To study simultaneous localization of a signal and its
OLCT, the classical Heisenberg's uncertainty principle has been recently
generalized for the OLCT. In this paper, we complement it by presenting another
two uncertainty principles, i.e., Donoho-Stark's uncertainty principle and
Amrein-Berthier-Benedicks's uncertainty principle, for the OLCT. Moreover, we
generalize the short-time LCT to the short-time OLCT. We likewise present
Lieb's uncertainty principle for the short-time OLCT and give a lower bound for
its essential support.
A new convolution theorem associated with the linear canonical transform
Date: 2018-07-18 00:00:00

In this paper, we first introduce a new notion of canonical convolution
operator, and show that it satisfies the commutative, associative, and
distributive properties, which may be quite useful in signal processing.
Moreover, it is proved that the generalized convolution theorem and generalized
Young's inequality are also hold for the new canonical convolution operator
associated with the LCT. Finally, we investigate the sufficient and necessary
conditions for solving a class of convolution equations associated with the
LCT.
Integrated Millimeter Wave and Sub-6 GHz Wireless Networks: A Roadmap for Joint Mobile Broadband and Ultra-Reliable Low-Latency Communications
Date: 2018-10-30 00:00:00
Tasks: Autonomous Vehicles, Management

Next-generation wireless networks must enable emerging technologies such as
augmented reality and connected autonomous vehicles via wide range of wireless
services that span enhanced mobile broadband (eMBB), as well as ultra-reliable
low-latency communication (URLLC). Existing wireless systems that solely rely
on the scarce sub-6 GHz, microwave ($\mu$W) frequency bands will be unable to
meet such stringent and mixed service requirements for future wireless services
due to spectrum scarcity. Meanwhile, operating at high-frequency millimeter
wave (mmWave) bands is seen as an attractive solution, primarily due to the
bandwidth availability and possibility of large-scale multi-antenna
communication. However, mmWave communication is inherently unreliable due to
its susceptibility to blockage, high path loss, and channel uncertainty. Hence,
to provide URLLC and high-speed wireless access, it is desirable to seamlessly
integrate the reliability of $\mu$W networks with the high capacity of mmWave
networks. To this end, in this paper, the first comprehensive tutorial for
\emph{integrated mmWave-$\mu$W} communications is introduced. This envisioned
integrated design will enable wireless networks to achieve URLLC along with
eMBB by leveraging the best of two worlds: reliable, long-range communications
at the $\mu$W bands and directional high-speed communications at the mmWave
frequencies. To achieve this goal, key solution concepts are discussed that
include new architectures for the radio interface, URLLC-aware frame structure
and resource allocation methods along with mobility management, to realize the
potential of integrated mmWave-$\mu$W communications. The opportunities and
challenges of each proposed scheme are discussed and key results are presented
to show the merits of the developed integrated mmWave-$\mu$W framework.
High-Resolution Angle Tracking for Mobile Wideband Millimeter-Wave Systems with Antenna Array Calibration
Date: 2018-02-12 00:00:00

Millimeter-wave (mmWave) systems use directional beams to support high-rate
data communications. Small misalignment between the transmit and receive beams
(e.g., due to the mobility) can result in significant drop of the received
signal quality especially in line-of-sight communication channels. In this
paper, we propose and evaluate high-resolution angle tracking strategies for
wideband mmWave systems with mobility. We custom design pairs of auxiliary
beams as the tracking beams, and use them to capture the angle variations,
towards which the steering directions of the data beams are adjusted. Different
from conventional beam tracking designs, the proposed framework neither depends
on the angle variation model nor requires an on-grid assumption. For practical
implementation of the proposed methods, we examine the impact of the array
calibration errors on the auxiliary beam pair design. Numerical results reveal
that by employing the proposed methods, good angle tracking performance can be
achieved under various antenna array configurations, channel models, and
mobility conditions.
A Novel Sub-Nyquist Multiband Signal Detection Algorithm for Cognitive Radio
Date: 2018-02-12 00:00:00

Wideband spectrum sensing (WSS) is an essential technology for cognitive
radio. However, the sampling rate is still a bottleneck of WSS. Several
sub-Nyquist sensing methods have been proposed. These technologies deteriorate
in the low signal to noise ratio (SNR) regime or suffer high computational
complexity. In this paper, we propose a novel sub-Nyquist WSS method based on
Multi-coset (MC) sampling. We design a simple SNR-robust and low-complexity
multiband signal detection algorithm. In particular, the proposed method
differs the commonly used detection algorithms which are based on energy
detection (ED), matched filter (MF) or cyclostationary detection (CD). We
exploit the linear recurrent relation between the locations of nonzero
frequencies and the DFT of the arithmetic-shifted subsampled signals. These
relations can be uniquely expressed by a series of the so-called frequency
locator polynomials (FLPs). The scalar of the relations is related to the
bandwidths of the subsignals. Basing on this, we propose a detector for sparse
multiband signals along with the method estimating carrier frequency and
bandwidth. The detector does not require priori knowledge about the frequency
locations of the signals of interest. Moreover, it has lower complexity of both
samples and computation compared to CD in sparse case. Experimental results
show the detector outperforms ED in the sub-Nyquist regime especially in low
SNRs.
An Improved and More Accurate Expression for a PDF Related to Eigenvalue-Based Spectrum Sensing
Date: 2018-02-12 00:00:00

Cooperative spectrum sensing based on the limiting eigenvalue ratio of the
covariance matrix offers superior detection performance and overcomes the noise
uncertainty problem. While an exact expression exists, it is complex and
multiple useful approximate expressions have been published in the literature.
An improved, more accurate, integral solution for the probability density
function of the ratio is derived using order statistical analysis to remove the
simplifying, but incorrect, independence assumption. Thereby, the letter makes
an advance in the rigorous theory of eigenvalue-based spectrum sensing.
UAV-Enabled Mobile Edge Computing: Offloading Optimization and Trajectory Design
Date: 2018-02-12 00:00:00
Tasks: Edge-computing

With the emergence of diverse mobile applications (such as augmented
reality), the quality of experience of mobile users is greatly limited by their
computation capacity and finite battery lifetime. Mobile edge computing (MEC)
and wireless power transfer are promising to address this issue. However, these
two techniques are susceptible to propagation delay and loss. Motivated by the
chance of short-distance line-of-sight achieved by leveraging unmanned aerial
vehicle (UAV) communications, an UAV-enabled wireless powered MEC system is
studied. A power minimization problem is formulated subject to the constraints
on the number of the computation bits and energy harvesting causality. The
problem is non-convex and challenging to tackle. An alternative optimization
algorithm is proposed based on sequential convex optimization. Simulation
results show that our proposed design is superior to other benchmark schemes
and the proposed algorithm is efficient in terms of the convergence.
A 1Mbps Real-time NLOS UV Scattering Communication System with Receiver Diversity over 1km
Date: 2018-02-12 00:00:00
Tasks: Diversity

In the non-line of sight (NLOS) ultraviolet (UV) scattering communication,
the received signals exhibit the characteristics of discrete photoelectrons due
to the extremely large path loss. We design and demonstrate an NLOS UV
scattering communication system in this work, where the receiver-side signal
detection is designed based on a discrete-time Poisson channel model. In our
system, a laser and multiple photomultiplier tubes are employed as the optical
transmitter and detector, respectively. Furthermore, we design algorithms for
pulse-counting, synchronization, channel estimation and $LLR$ computation for
hardware realization in FPGA board. Simulation results are provided to evaluate
the proposed system design and specify the system key parameters. We perform
field tests for real-time communication with the transmission range over $1$km,
where the system throughput reaches $1$Mbps.
Spontaneous domain formation in disordered copolymers as a mechanism for chromosome structuring
Date: 2018-02-12 00:00:00

Motivated by the problem of domain formation in chromosomes, we studied a
co--polymer model where only a subset of the monomers feel attractive
interactions. These monomers are displaced randomly from a regularly-spaced
pattern, thus introducing some quenched disorder in the system. Previous work
has shown that in the case of regularly-spaced interacting monomers this chain
can fold into structures characterized by multiple distinct domains of
consecutive segments. In each domain, attractive interactions are balanced by
the entropy cost of forming loops. We show by advanced replica-exchange
simulations that adding disorder in the position of the interacting monomers
further stabilizes these domains. The model suggests that the partitioning of
the chain into well-defined domains of consecutive monomers is a spontaneous
property of heteropolymers. In the case of chromosomes, evolution could have
acted on the spacing of interacting monomers to modulate in a simple way the
underlying domains for functional reasons.
Towards an Open Science Platform for the Evaluation of Data Fusion
Authors: Huang Weinan, Chen Junyi, Meng Lei, Lillis David
Date: 2018-02-12 00:00:00

Combining the results of different search engines in order to improve upon
their performance has been the subject of many research papers. This has become
known as the "Data Fusion" task, and has great promise in dealing with the vast
quantity of unstructured textual data that is a feature of many Big Data
scenarios. However, no universally-accepted evaluation methodology has emerged
in the community. This makes it difficult to make meaningful comparisons
between the various proposed techniques from reading the literature alone.
Variations in the datasets, metrics, and baseline results have all contributed
to this difficulty.
  This paper argues that a more unified approach is required, and that a
centralised software platform should be developed to aid researchers in making
comparisons between their algorithms and others. The desirable qualities of
such a system have been identified and proposed, and an early prototype has
been developed. Re-implementing algorithms published by other researchers is a
great burden on those proposing new techniques. The prototype system has the
potential to greatly reduce this burden and thus encourage more comparable
results being generated and published more easily.