pwc-archive/papers-with-abstracts huggingface dataset:
Evolutionary games under incompetence
Date: 2017-10-26 00:00:00

The adaptation process of a species to a new environment is a significant
area of study in biology. As part of natural selection, adaptation is a
mutation process which improves survival skills and reproductive functions of
species. Here, we investigate this process by combining the idea of
incompetence with evolutionary game theory. In the sense of evolution,
incompetence and training can be interpreted as a special learning process.
With focus on the social side of the problem, we analyze the influence of
incompetence on behavior of species. We introduce an incompetence parameter
into a learning function in a single-population game and analyze its effect on
the outcome of the replicator dynamics. Incompetence can change the outcome of
the game and its dynamics, indicating its significance within what are
inherently imperfect natural systems.
On Power Allocation for Distributed Detection with Correlated Observations and Linear Fusion
Date: 2017-10-26 00:00:00

We consider a binary hypothesis testing problem in an inhomogeneous wireless
sensor network, where a fusion center (FC) makes a global decision on the
underlying hypothesis. We assume sensors observations are correlated Gaussian
and sensors are unaware of this correlation when making decisions. Sensors send
their modulated decisions over fading channels, subject to individual and/or
total transmit power constraints. For parallel-access channel (PAC) and
multiple-access channel (MAC) models, we derive modified deflection coefficient
(MDC) of the test statistic at the FC with coherent reception.We propose a
transmit power allocation scheme, which maximizes MDC of the test statistic,
under three different sets of transmit power constraints: total power
constraint, individual and total power constraints, individual power
constraints only. When analytical solutions to our constrained optimization
problems are elusive, we discuss how these problems can be converted to convex
ones. We study how correlation among sensors observations, reliability of local
decisions, communication channel model and channel qualities and transmit power
constraints affect the reliability of the global decision and power allocation
of inhomogeneous sensors.
A Refined Analysis of the Gap between Expected Rate for Partial CSIT and the Massive MIMO Rate Limit
Date: 2017-10-26 00:00:00

Optimal BeamFormers (BFs) that maximize the Weighted Sum Rate (WSR) for a
Multiple-Input Multiple-Output (MIMO) interference broadcast channel (IBC)
remains an important research area. Under practical scenarios, the problem is
compounded by the fact that only partial channel state information at the
transmitter (CSIT) is available. Hence, a typical choice of the optimization
metric is the Expected Weighted Sum Rate (EWSR). However, the presence of the
expectation operator makes the optimization a daunting task. On the other hand,
for the particular, but significant, special case of massive MIMO (MaMIMO), the
EWSR converges to Expected Signal covariance Expected Interference covariance
based WSR (ESEI-WSR) and this metric is more amenable to optimization.
Recently, [1] considered a multi-user Multiple-Input Single-Output (MISO)
scenario and proposed approximating the EWSR by ESEI-WSR. They then derived a
constant bound for this approximation. This paper performs a refined analysis
of the gap between EWSR and ESEI-WSR criteria for finite antenna dimensions.
Near-Optimal Sparse Sensing for Gaussian Detection with Correlated Observations
Date: 2017-10-26 00:00:00

Detection of a signal under noise is a classical signal processing problem.
When monitoring spatial phenomena under a fixed budget, i.e., either physical,
economical or computational constraints, the selection of a subset of available
sensors, referred to as sparse sensing, that meets both the budget and
performance requirements is highly desirable. Unfortunately, the subset
selection problem for detection under dependent observations is combinatorial
in nature and suboptimal subset selection algorithms must be employed. In this
work, different from the widely used convex relaxation of the problem, we
leverage submodularity, the diminishing returns property, to provide practical
near optimal algorithms suitable for large-scale subset selection. This is
achieved by means of low-complexity greedy algorithms, which incur a reduced
computational complexity compared to their convex counterparts.
Religious Festivals and Influenza
Date: 2017-10-25 00:00:00

Objectives Influenza outbreaks have been widely studied. However, the
patterns between influenza and religious festivals remained unexplored. This
study examined the patterns of influenza and Hanukkah in Israel, and that of
influenza and Hajj in Bahrain, Egypt, Iraq, Jordan, Oman and Qatar. Method
Influenza surveillance data of these seven countries from 2009 to 2017 were
downloaded from the FluNet of the World Health Organization. Secondary data
were collected for the countries' population, and the dates of Hajj and
Hanukkah. We aggregated the weekly influenza A and B laboratory confirmations
for each country over the study period. Weekly influenza A patterns and
religious festival dates were further explored across the study period. Results
We found that influenza A peaks closely followed Hanukkah in Israel in six out
of seven years from 2010 to 2017. Aggregated influenza A peaks of the other six
Middle East countries also occurred right after Hajj every year during the
study period. Conclusions We predict that unless there is an emergence of new
influenza strain, such influenza patterns are likely to persist in future
years. Our results suggested that the optimal timing of mass influenza
vaccination should take into considerations of the dates of these religious
festivals.
Calibrated Projection in MATLAB: Users' Manual
Date: 2017-10-24 00:00:00

We present the calibrated-projection MATLAB package implementing the method
to construct confidence intervals proposed by Kaido, Molinari and Stoye (2017).
This manual provides details on how to use the package for inference on
projections of partially identified parameters. It also explains how to use the
MATLAB functions we developed to compute confidence intervals on solutions of
nonlinear optimization problems with estimated constraints.
Offset-Based Beamforming: A New Approach to Robust Downlink Transmission
Date: 2017-10-26 00:00:00

The design of a set of beamformers for the multiuser multiple-input
single-output (MISO) downlink that provides the receivers with prespecified
levels of quality-of-service (QoS) can be quite challenging when the channel
state information is not perfectly known at the base station. The constraint of
having the SINR meet or exceed a given threshold with high probability is
intractable in general, which results in problems that are fundamentally hard
to solve. In this paper, we will develop a high quality approximation of the
SINR outage constraint that, along with a semidefinite relaxation, enables us
to formulate the beamformer design problem as a convex optimization problem
that can be efficiently solved. For systems in which the uncertainty size is
small, a further approximation yields algorithms based on iterative evaluations
of closed-form expressions that have substantially lower computational cost.
Since finding the beamforming directions incurs most of the computational load
of these algorithms, analogous power loading algorithms for predefined
beamforming directions are developed and their performance is shown to be close
to optimal. When the system contains a large number of antennas, the proposed
power loading can be obtained at a computational cost that grows only linearly
in the number of antennas. The proposed power loading algorithm provides an
explicit relationship between the outage probability required and the power
consumed, which allows us to precisely control the power consumption, and
automatically identifies users who are consuming most of the power resources.
The flexibility of the proposed approach is illustrated by developing a power
loading technique that minimizes an average notion of outage.
Mixed analytical-stochastic simulation method for the recovery of a Brownian gradient source from probability fluxes to small windows
Date: 2017-10-26 00:00:00
Tasks: Position

Is it possible to recover the position of a source from the steady-state
fluxes of Brownian particles to small absorbing windows located on the boundary
of a domain? To address this question, we develop a numerical procedure to
avoid tracking Brownian trajectories in the entire infinite space. Instead, we
generate particles near the absorbing windows, computed from the analytical
expression of the exit probability. When the Brownian particles are generated
by a steady-state gradient at a single point, we compute asymptotically the
fluxes to small absorbing holes distributed on the boundary of half-space and
on a disk in two dimensions, which agree with stochastic simulations. We also
derive an expression for the splitting probability between small windows using
the matched asymptotic method. Finally, when there are more than two small
absorbing windows, we show how to reconstruct the position of the source from
the diffusion fluxes. The present approach provides a computational first
principle for the mechanism of sensing a gradient of diffusing particles, a
ubiquitous problem in cell biology.
Cell Line Classification Using Electric Cell-substrate Impedance Sensing (ECIS)
Date: 2019-11-20 00:00:00
Tasks: Classification, Time Series, Time Series Analysis

We consider cell line classification using multivariate time series data
obtained from electric cell-substrate impedance sensing (ECIS) technology. The
ECIS device, which monitors the attachment and spreading of mammalian cells in
real time through the collection of electrical impedance data, has historically
been used to study one cell line at a time. However, we show that if applied to
data from multiple cell lines, ECIS can be used to classify unknown or
potentially mislabeled cells, which may help to mitigate the current crisis of
reproducibility in the biological literature. We assess a range of approaches
to this new problem, testing different classification methods and deriving a
dictionary of 29 features to characterize ECIS data. Our analysis also makes
use of simultaneous multi-frequency ECIS data, where previous studies have
focused on only one frequency. In classification tests on fifteen mammalian
cell lines, we obtain very high out-of-sample accuracy. These preliminary
findings provide a baseline for future large-scale studies in this field.
StochSoCs: High performance biocomputing simulations for large scale Systems Biology
Date: 2017-10-26 00:00:00
Tasks: Vocal Bursts Intensity Prediction

The stochastic simulation of large-scale biochemical reaction networks is of
great importance for systems biology since it enables the study of inherently
stochastic biological mechanisms at the whole cell scale. Stochastic Simulation
Algorithms (SSA) allow us to simulate the dynamic behavior of complex kinetic
models, but their high computational cost makes them very slow for many
realistic size problems. We present a pilot service, named WebStoch, developed
in the context of our StochSoCs research project, allowing life scientists with
no high-performance computing expertise to perform over the internet stochastic
simulations of large-scale biological network models described in the SBML
standard format. Biomodels submitted to the service are parsed automatically
and then placed for parallel execution on distributed worker nodes. The workers
are implemented using multi-core and many-core processors, or FPGA accelerators
that can handle the simulation of thousands of stochastic repetitions of
complex biomodels, with possibly thousands of reactions and interacting
species. Using benchmark LCSE biomodels, whose workload can be scaled on
demand, we demonstrate linear speedup and more than two orders of magnitude
higher throughput than existing serial simulators.
Pairwise Ising model analysis of human cortical neuron recordings
Date: 2017-10-26 00:00:00

During wakefulness and deep sleep brain states, cortical neural networks show
a different behavior, with the second characterized by transients of high
network activity. To investigate their impact on neuronal behavior, we apply a
pairwise Ising model analysis by inferring the maximum entropy model that
reproduces single and pairwise moments of the neuron's spiking activity. In
this work we first review the inference algorithm introduced in Ferrari,Phys.
Rev. E (2016). We then succeed in applying the algorithm to infer the model
from a large ensemble of neurons recorded by multi-electrode array in human
temporal cortex. We compare the Ising model performance in capturing the
statistical properties of the network activity during wakefulness and deep
sleep. For the latter, the pairwise model misses relevant transients of high
network activity, suggesting that additional constraints are necessary to
accurately model the data.
Two-photon photoactivated voltage imaging in tissue with an Archaerhodopsin-derived reporter
Date: 2017-10-27 00:00:00

Robust voltage imaging in tissue remains a technical challenge. Existing
combinations of genetically encoded voltage indicators (GEVIs) and microscopy
techniques cannot simultaneously achieve sufficiently high voltage sensitivity,
background rejection, and time resolution for high-resolution mapping of
sub-cellular voltage dynamics in intact brain tissue. We developed a pooled
high-throughput screening approach to identify Archaerhodopsin mutants with
unusual photophysical properties. After screening ~105 cells, we identified a
novel GEVI, NovArch, whose 1-photon near infrared fluorescence is reversibly
enhanced by weak 2-photon excitation. Because the 2-photon excitation acts
catalytically rather than stoichiometrically, high fluorescence signals,
optical sectioning, and high time resolution are achieved simultaneously, at
modest 2- photon laser power. We developed a microscopy system optimized for
NovArch imaging in tissue. The combination of protein and optical engineering
enhanced signal contrast sufficiently to enable optical mapping of
back-propagating action potentials in dendrites in acute mouse brain slice.
Combining Aspects of Genetic Algorithms with Weighted Recommender Hybridization
Authors: Mueller Juergen
Date: 2017-10-27 00:00:00
Tasks: Recommendation Systems

Recommender systems are established means to inspire users to watch
interesting movies, discover baby names, or read books. The recommendation
quality further improves by combining the results of multiple recommendation
algorithms using hybridization methods. In this paper, we focus on the task of
combining unscored recommendations into a single ensemble. Our proposed method
is inspired by genetic algorithms. It repeatedly selects items from the
recommendations to create a population of items that will be used for the final
ensemble. We compare our method with a weighted voting method and test the
performance of both in a movie- and name-recommendation scenario. We were able
to outperform the weighted method on both datasets by 20.3 % and 31.1 % and
decreased the overall execution time by up to 19.9 %. Our results do not only
propose a new kind of hybridization method, but introduce the field of
recommender hybridization to further work with genetic algorithms.
An Automated Window Selection Procedure For DFT Based Detection Schemes To Reduce Windowing SNR Loss
Date: 2017-10-27 00:00:00

The classical spectrum analysis methods utilize window functions to reduce
the masking effect of a strong spectral component over weaker components. The
main cost of side-lobe reduction is the reduction of signal-to-noise ratio
(SNR) level of the output spectrum. We present a single snapshot method which
optimizes the selection of most suitable window function among a finite set of
candidate windows, say rectangle, Hamming, Blackman windows, for each spectral
bin. The main goal is to utilize different window functions at each spectral
output depending on the interference level encountered at that spectral bin so
as to reduce the SNR loss associated with the windowing operation. Stated
differently, the windows with strong interference suppression capabilities are
utilized only when a sufficiently powerful interferer is corrupting the
spectral bin of interest is present, i.e. only when this window is needed. The
achieved reduction in the windowing SNR loss can be important for the detection
of low SNR targets.
Unified Functorial Signal Representation III: Foundations, Redundancy, $L^0$ and $L^2$ functors
Date: 2017-10-27 00:00:00
Tasks: Translation

In this paper we propose and lay the foundations of a functorial framework
for representing signals. By incorporating additional category-theoretic
relative and generative perspective alongside the classic set-theoretic measure
theory the fundamental concepts of redundancy, compression are formulated in a
novel authentic arrow-theoretic way. The existing classic framework
representing a signal as a vector of appropriate linear space is shown as a
special case of the proposed framework.
  Next in the context of signal-spaces as a categories we study the various
covariant and contravariant forms of $L^0$ and $L^2$ functors using categories
of measurable or measure spaces and their opposites involving Boolean and
measure algebras along with partial extension. Finally we contribute a novel
definition of intra-signal redundancy using general concept of isomorphism
arrow in a category covering the translation case and others as special cases.
Through category-theory we provide a simple yet precise explanation for the
well-known heuristic of lossless differential encoding standards yielding
better compressions in image types such as line drawings, iconic image, text
etc; as compared to classic representation techniques such as JPEG which choose
bases or frames in a global Hilbert space.
Reconfigurable Power Electronics Topologies
Date: 2017-10-27 00:00:00

This paper presents two novel topologies for automatically transforming power
converter topology from three-phase 3-level cascaded H-bridge to three-phase
2-level converter design. These techniques are implemented by flicking specific
switches to rearrange circuit connections. The switches can be controlled by
signals in order to realize automation.
Elucidation of time-dependent systems biology cell response patterns with time course network enrichment
Date: 2017-10-27 00:00:00
Tasks: Time Series, Time Series Analysis

Advances in OMICS technologies emerged both massive expression data sets and
huge networks modelling the molecular interplay of genes, RNAs, proteins and
metabolites. Network enrichment methods combine these two data types to extract
subnetwork responses from case/control setups. However, no methods exist to
integrate time series data with networks, thus preventing the identification of
time-dependent systems biology responses. We close this gap with Time Course
Network Enrichment (TiCoNE). It combines a new kind of human-augmented
clustering with a novel approach to network enrichment. It finds temporal
expression prototypes that are mapped to a network and investigated for
enriched prototype pairs interacting more often than expected by chance. Such
patterns of temporal subnetwork co-enrichment can be compared between different
conditions. With TiCoNE, we identified the first distinguishing temporal
systems biology profiles in time series gene expression data of human lung
cells after infection with Influenza and Rhino virus. TiCoNE is available
online (https://ticone.compbio.sdu.dk) and as Cytoscape app in the Cytoscape
App Store (http://apps.cytoscape.org/).
Polynomial processes for power prices
Date: 2018-04-26 00:00:00

Polynomial processes have the property that expectations of polynomial
functions (of degree $n$, say) of the future state of the process conditional
on the current state are given by polynomials (of degree $\leq n$) of the
current state. Here we explore the application of polynomial processes in the
context of structural models for energy prices. We focus on the example of
Alberta power prices, derive one- and two-factor models for spot prices. We
examine their performance in numerical experiments, and demonstrate that the
richness of the dynamics they are able to generate makes them well suited for
modelling even extreme examples of energy price behaviour.
Phylogenetic Derivative: A Tool for Assessing Local Tree Reconstruction in the Presence of Recombination
Date: 2017-10-27 00:00:00

Recently, much attention has been given to understanding recombination events
along a chromosome in a variety of field. For instance, many population
genetics problems are limited by the inaccuracy of inferred evolutionary
histories of chromosomes sampled randomly from a population. This evolutionary
history differs among genomic locations as an artifact of recombination events
along a chromosome. Thus, much recent attention has been focused on identifying
these recombination points. However, many proposed methods either make
simplifying, but unrealistic, assumptions about recombination along a
chromosome, or are unable to scale to large genome-wide data like what has
become commonplace in statistical genetics. Here, we introduce a
\emph{phylogenetic derivative} to describe the relatedness of neighboring trees
along a chromosome. This phylogenetic derivative is a computationally
efficient, flexible metric that can be also be used assess the prevalence of
recombination across a chromosome. These proposed methods are tested and
perform well in analyzing both simulated data and a real mouse data set.
Single wavelength 480 Gb/s direct detection over 80km SSMF enabled by Stokes Vector Kramers Kronig transceiver
Date: 2017-10-28 00:00:00

We propose 4D modulation with directed detection employing a novel
Stokes-Vector Kramers-Kronig transceiver. It shows that employing Stokes vector
receiver, transmitted digital carrier and Kramers-Kronig detection offers an
effective way to de-rotate polarization multiplexed complex double side band
signal without using a local oscillator at receiver. The impact of system
parameters and configurations including carrier-to-signal-power ratio, guard
band of the digital carrier, oversampling ratio and real MIMO is experimentally
investigated. Finally, a record 480 Gb/s data rate over 80 km SSMF is achieved
in a 60 Gbaud PDM-16QAM single carrier experiment with a BER below the
threshold of 2.0x10-2
Jointly Tracking and Separating Speech Sources Using Multiple Features and the generalized labeled multi-Bernoulli Framework
Date: 2018-04-16 00:00:00

This paper proposes a novel joint multi-speaker tracking-and-separation
method based on the generalized labeled multi-Bernoulli (GLMB) multi-target
tracking filter, using sound mixtures recorded by microphones. Standard
multi-speaker tracking algorithms usually only track speaker locations, and
ambiguity occurs when speakers are spatially close. The proposed multi-feature
GLMB tracking filter treats the set of vectors of associated speaker features
(location, pitch and sound) as the multi-target multi-feature observation,
characterizes transitioning features with corresponding transition models and
overall likelihood function, thus jointly tracks and separates each
multi-feature speaker, and addresses the spatial ambiguity problem. Numerical
evaluation verifies that the proposed method can correctly track locations of
multiple speakers and meanwhile separate speech signals.