pwc-archive/papers-with-abstracts huggingface dataset:
Enhancing performance of coherent OTDR systems with polarization diversity complementary codes
Date: 2018-04-25 00:00:00
Tasks: Diversity, Sensitivity

Monitoring the optical phase change in a fiber enables a wide range of
applications where fast phase variations are induced by acoustic signals or
vibrations in general. However, the quality of the estimated fiber response
strongly depends on the method used to modulate the light sent to the fiber and
capture the variations of the optical field. In this paper, we show that
distributed optical fiber sensing systems can advantageously exploit techniques
from the telecommunication domain, as those used in coherent optical
transmission, to enhance their performance in detecting mechanical events,
while jointly offering a simpler setup than widespread pulse-cloning or
spectral-sweep based schemes with acousto-optic modulators. We periodically
capture an overall fiber Jones matrix estimate thanks to a novel probing
technique using two mutually orthogonal complementary (Golay) pairs of binary
sequences applied simultaneously in phase and quadrature on two orthogonal
polarization states. A perfect channel response estimation of the sensor array
is achieved, subject to conditions detailed in the paper, thus enhancing the
sensitivity and bandwidth of coherent phase-OTDR systems. High sensitivity,
linear response, and bandwidth coverage up to 18 kHz are demonstrated with a
sensor array composed of 10 fiber Bragg gratings (FBGs).
On the iterated estimation of dynamic discrete choice games
Date: 2020-05-24 00:00:00

We study the asymptotic properties of a class of estimators of the structural
parameters in dynamic discrete choice games. We consider K-stage policy
iteration (PI) estimators, where K denotes the number of policy iterations
employed in the estimation. This class nests several estimators proposed in the
literature such as those in Aguirregabiria and Mira (2002, 2007), Pesendorfer
and Schmidt-Dengler (2008), and Pakes et al. (2007). First, we establish that
the K-PML estimator is consistent and asymptotically normal for all K. This
complements findings in Aguirregabiria and Mira (2007), who focus on K=1 and K
large enough to induce convergence of the estimator. Furthermore, we show under
certain conditions that the asymptotic variance of the K-PML estimator can
exhibit arbitrary patterns as a function of K. Second, we establish that the
K-MD estimator is consistent and asymptotically normal for all K. For a
specific weight matrix, the K-MD estimator has the same asymptotic distribution
as the K-PML estimator. Our main result provides an optimal sequence of weight
matrices for the K-MD estimator and shows that the optimally weighted K-MD
estimator has an asymptotic distribution that is invariant to K. The invariance
result is especially unexpected given the findings in Aguirregabiria and Mira
(2007) for K-PML estimators. Our main result implies two new corollaries about
the optimal 1-MD estimator (derived by Pesendorfer and Schmidt-Dengler (2008)).
First, the optimal 1-MD estimator is optimal in the class of K-MD estimators.
In other words, additional policy iterations do not provide asymptotic
efficiency gains relative to the optimal 1-MD estimator. Second, the optimal
1-MD estimator is more or equally asymptotically efficient than any K-PML
estimator for all K. Finally, the appendix provides appropriate conditions
under which the optimal 1-MD estimator is asymptotically efficient.
Cellular switches orchestrate rhythmic circuits
Date: 2018-02-19 00:00:00

Small inhibitory neuronal circuits have long been identified as key neuronal
motifs to generate and modulate the coexisting rhythms of various motor
functions. Our paper highlights the role of a cellular switching mechanism to
orchestrate such circuits. The cellular switch makes the circuits
reconfigurable, robust, adaptable, and externally controllable. Without this
cellular mechanism, the circuits rhythms entirely rely on specific tunings of
the synaptic connectivity, which makes them rigid, fragile, and difficult to
control externally. We illustrate those properties on the much studied
architecture of a small network controlling both the pyloric and gastric
rhythms of crabs. The cellular switch is provided by a slow negative
conductance often neglected in mathematical modeling of central pattern
generators. We propose that this conductance is simple to model and key to
computational studies of rhythmic circuit neuromodulation.
Achieving perfect coordination amongst agents in the co-action minority game
Date: 2018-05-24 00:00:00

We discuss the strategy that rational agents can use to maximize their
expected long-term payoff in the co-action minority game. We argue that the
agents will try to get into a cyclic state, where each of the $(2N +1)$ agent
wins exactly $N$ times in any continuous stretch of $(2N+1)$ days. We propose
and analyse a strategy for reaching such a cyclic state quickly, when any
direct communication between agents is not allowed, and only the publicly
available common information is the record of total number of people choosing
the first restaurant in the past. We determine exactly the average time
required to reach the periodic state for this strategy. We show that it varies
as $(N/\ln 2) [1 + \alpha \cos (2 \pi \log_2 N)$], for large $N$, where the
amplitude $\alpha$ of the leading term in the log-periodic oscillations is
found be $\frac{8 \pi^2}{(\ln 2)^2} \exp{(- 2 \pi^2/\ln 2)} \approx
{\color{blue}7 \times 10^{-11}}$.
Theory of Morphogenesis
Date: 2018-02-19 00:00:00

A model of morphogenesis is proposed based on seven explicit postulates. The
mathematical import and biological significance of the postulates are explored
and discussed.
Seasonal Web Search Query Selection for Influenza-Like Illness (ILI) Estimation
Authors: Hansen Niels Dalum, Mølbak Kåre, Cox Ingemar J., Lioma Christina
Date: 2018-02-19 00:00:00
Tasks: Re-Ranking

Influenza-like illness (ILI) estimation from web search data is an important
web analytics task. The basic idea is to use the frequencies of queries in web
search logs that are correlated with past ILI activity as features when
estimating current ILI activity. It has been noted that since influenza is
seasonal, this approach can lead to spurious correlations with features/queries
that also exhibit seasonality, but have no relationship with ILI. Spurious
correlations can, in turn, degrade performance. To address this issue, we
propose modeling the seasonal variation in ILI activity and selecting queries
that are correlated with the residual of the seasonal model and the observed
ILI signal. Experimental results show that re-ranking queries obtained by
Google Correlate based on their correlation with the residual strongly favours
ILI-related queries.
Range-Only Localization in n-Dimensional Networks With Arbitrary Anchor Placement
Date: 2018-02-19 00:00:00
Tasks: Network Embedding

This paper considers node localization in static sensor networks using
range-only measurements. Similar to state- of-the-art algorithms, such as ECHO
and DILOC, we rely on barycentric coordinates of the nodes to transform the
non-convex node localization problem into a linear system of equations. The
main contribution of this paper is a simple closed-form expression for
generalized barycentric coordinates, which extends existing algorithms from two
to n dimensions and allows arbitrary anchor-node configurations. The result
relies on a connection between the Cayley-Menger bi-determinants of subsets of
n+1 neighbor nodes and the signed volume of the simplices defined by these
neighbor nodes. Hence, for noise-free measurements, the proposed method
computes the optimal sensor network embedding as the solution of a linear
system with coefficients obtained from the generalized barycentric node
coordinates. Using simulations, we provide comparisons with DILOC and Matlab's
MDS implementation. We also show that it is possible to improve our algorithm
run time using fewer subsets of neighbor nodes.
Real World Evaluation of Approaches to Research Paper Recommendation
Authors: Dinesh Siddharth
Date: 2018-02-19 00:00:00
Tasks: Recommendation Systems

In this work, we have identified the need for choosing baseline approaches
for research-paper recommendation systems. Following a literature survey of all
research paper recommendation approaches described over the last four years, we
framed criteria that makes for a well-rounded set of baselines. These are
implemented on Mr. DLib a literature recommendation platform. User click data
was collected as part of an ongoing experiment in collaboration with our
partner Gesis. We reported the results from our evaluation for the experiments.
We will be able to draw clearer conclusions as time passes. We find that a term
based similarity search performs better than keyword based approaches. These
results are a good starting point in finding performance improvements for
related document searches.
ElasticPath2Path: Automated morphological classification of neurons by elastic path matching
Date: 2018-02-20 00:00:00
Tasks: Classification

In the study of neurons, morphology influences function. The complexity in
the structure of neurons poses a challenge in the identification and analysis
of similar and dissimilar neuronal cells. Existing methodologies carry out
structural and geometrical simplifications, which substantially change the
morphological statistics. Using digitally-reconstructed neurons, we extend the
work of Path2Path as ElasticPath2Path, which seamlessly integrates the
graph-theoretic and differential-geometric frameworks. By decomposing a neuron
into a set of paths, we derive graph metrics, which are path concurrence and
path hierarchy. Next, we model each path as an elastic string to compute the
geodesic distance between the paths of a pair of neurons. Later, we formulate
the problem of finding the distance between two neurons as a path assignment
problem with a cost function combining the graph metrics and the geodesic
deformation of paths. ElasticPath2Path is shown to have superior performance
over the state of the art.
Thyristor Voltage Equalizing Network for Crowbar Application
Date: 2018-04-25 00:00:00

Many high voltage applications are realized with series connected thyristors.
Voltage imbalance among series connected thyristors during steady state as well
as in transients is one of the major concerns. This voltage imbalance is
mitigated by using static and dynamic balancing network. Dynamic balancing
networks are typically designed based on reverse recovery charge of the
thyristor during turn-off, which suits many applications. But this is not the
case for a crowbar application, where turn-off of the thyristor is not a major
circuit constraint. This paper proposes the design method for dynamic balancing
network considering gate turn-on delay time and the balancing network component
tolerances. The paper derives two models for the dynamic balancing network
based on its charge-discharge cycle. The importance of charge-discharge cycle
in the design of dynamic balancing network during high di/dt operation is
emphasized. Influence of dynamic balancing resistance and crowbar current
limiting inductance on voltage imbalance, charging current and discharging
current is studied using the analytical model. The proposed design method also
offers flexibility to incorporate differences in propagation delays among the
thyristor drivers that are used to trigger individual thyristors. Such delays
cannot be directly incorporated in the conventional balancing network design
method based on reverse recovery. Further, it is also analytically shown that
designing the dynamic balancing network based on reverse recovery charge makes
the balancing network lossy and bulky for crowbar application. Simulation
studies and experimental results on a 12kV , 1kA crowbar consisting of six
series connected thyristors confirms the theoretical analysis and validates the
proposed approach for crowbar applications.
Small Signal Audiosusceptibility Model for Series Resonant Converter
Date: 2018-02-20 00:00:00
Tasks: Unity

Models that accurately predict the output voltage ripple magnitude are
essential for applications with stringent performance target for it. Impact of
dc input ripple on the output ripple for a Series Resonant Converter (SRC)
using discrete domain exact discretization modelling method is analysed in this
paper. A novel discrete state space model along with a small signal model for
SRC considering 3 state variables is presented. The audiosusceptibility (AS)
transfer function which relates the input to output ripple is derived from the
small signal model. Analysis of the AS transfer function indicates a resonance
peak and an expression is derived connecting the AS resonance frequency for
input ripple with different SRC component values. Further analysis is done to
show that a set of values for SRC parameter exists, which forms a design
region, for which the normalized gain offered by the SRC for input ripple is
less than unity at any input ripple frequency. A test setup to introduce the
variable frequency ripple at the input of SRC for the experimental evaluation
of AS transfer function is also proposed. Influence of stray parameters on AS
gain, AS resonance frequency and on SRC tank resonance frequency is addressed.
An SRC is designed at a power level of 10kW. The analysis using the derived
model, simulations, and experimental results are found to be closely matching.
Nanosecond-precision Time-of-Arrival Estimation for Aircraft Signals with low-cost SDR Receivers
Date: 2018-02-20 00:00:00

Precise Time-of-Arrival (TOA) estimations of aircraft and drone signals are
important for a wide set of applications including aircraft/drone tracking, air
traffic data verification, or self-localization. Our focus in this work is on
TOA estimation methods that can run on low-cost software-defined radio (SDR)
receivers, as widely deployed in Mode S / ADS-B crowdsourced sensor networks
such as the OpenSky Network. We evaluate experimentally classical TOA
estimation methods which are based on a cross-correlation with a reconstructed
message template and find that these methods are not optimal for such signals.
We propose two alternative methods that provide superior results for real-world
Mode S / ADS-B signals captured with low-cost SDR receivers. The best method
achieves a standard deviation error of 1.5 ns.
Discovering Hidden Topical Hubs and Authorities in Online Social Networks
Authors: Lee Roy Ka-Wei, Hoang Tuan-Anh, Lim Ee-Peng
Date: 2018-02-20 00:00:00
Tasks: Topic Models

Finding influential users in online social networks is an important problem
with many possible useful applications. HITS and other link analysis methods,
in particular, have been often used to identify hub and authority users in web
graphs and online social networks. These works, however, have not considered
topical aspect of links in their analysis. A straightforward approach to
overcome this limitation is to first apply topic models to learn the user
topics before applying the HITS algorithm. In this paper, we instead propose a
novel topic model known as Hub and Authority Topic (HAT) model to combine the
two process so as to jointly learn the hub, authority and topical interests. We
evaluate HAT against several existing state-of-the-art methods in two aspects:
(i) modeling of topics, and (ii) link recommendation. We conduct experiments on
two real-world datasets from Twitter and Instagram. Our experiment results show
that HAT is comparable to state-of-the-art topic models in learning topics and
it outperforms the state-of-the-art in link recommendation task.
On the non-detectability of spiked large random tensors
Date: 2018-02-20 00:00:00

This paper addresses the detection of a low rank high-dimensional tensor
corrupted by an additive complex Gaussian noise. In the asymptotic regime where
all the dimensions of the tensor converge towards $+\infty$ at the same rate,
existing results devoted to rank 1 tensors are extended. It is proved that if a
certain parameter depending on the low rank tensor is below a threshold, then
the null hypothesis and the presence of the low rank tensor are
undistinguishable hypotheses in the sense that no test performs better than a
random choice.
A Closed-loop controller to improve the Stability of Cascaded DC/DC Converters
Date: 2018-02-20 00:00:00

Study of the buck converter and cascaded system considering the voltage mode
controller has been done. First the small signal analysis of a buck dc/dc
converter is presented and its mathematical representation has been showed.
Then, the cascaded converter model regarding close loop impedances and voltage
gain has been studied. The controller for this converter is proposed to
stabilize the performance of the plant. The effectiveness of the proposed
controller has been tested on a typical buck converter.
Performance analysis of a novel hybrid FSO / RF communication system
Date: 2018-11-20 00:00:00

In this paper, a novel dual-hop relay-assisted hybrid Free Space Optical /
Radio Frequency (FSO / RF) communication system is presented. In this structure
an access point connects users within the building to the Base Station via a
hybrid parallel FSO / RF link, this link is proposed firstly. Parallel
combination of FSO and RF links and use of an access point, will increase
capacity, reliability and data rate of the system. It is the first time that
the effect of number of users on the performance of a dual-hop relay-assisted
hybrid parallel FSO / RF system is investigated. FSO link is considered in
Gamma-Gamma atmospheric turbulence with the effect of pointing error and RF
link is considered in Rayleigh fading. For the first time, closed-form
expressions are derived for Bit Error Rate (BER) and Outage Probability (P_out)
of the proposed system. Derived expressions are verified through MATLAB
simulations. It is shown that the performance of the proposed system is almost
independent of atmospheric turbulence intensity, thereby when atmospheric
turbulence strengthens, low power consumption is required for maintenance of
the system performance. Hence the proposed structure is particularly suitable
for mobile communication systems in which a small mobile battery supplies
transmitter power. Also the proposed system performance of the system is
preferable even at low signal to noise ratio (SNR). Therefore, proposed
structure significantly reduces power consumption while maintaining performance
of the system.
Comparison of threshold-based algorithms for sparse signal recovery
Date: 2018-02-18 00:00:00
Tasks: Compressive Sensing

Intensively growing approach in signal processing and acquisition, the
Compressive Sensing approach, allows sparse signals to be recovered from small
number of randomly acquired signal coefficients. This paper analyses some of
the commonly used threshold-based algorithms for sparse signal reconstruction.
Signals satisfy the conditions required by the Compressive Sensing theory. The
Orthogonal Matching Pursuit, Iterative Hard Thresholding and Single Iteration
Reconstruction algorithms are observed. Comparison in terms of reconstruction
error and execution time is performed within the experimental part of the
paper.
Evolutionary games: natural selection of strategies
Date: 2018-02-16 00:00:00

In this paper, I model and study the process of natural selection between all
possible mixed strategies in classical two-player two-strategy games. I derive
and solve an equation that is a natural generalization of the Taylor-Jonker
replicator equation that describes dynamics of pure strategy frequencies. I
then investigate the evolution of not only frequencies of pure strategies but
also of total distribution of mixed strategies. I show that the process of
natural selection of strategies for all games obeys the dynamical Principle of
minimum of information gain.
Inference of gene loss rates after whole genome duplications at early vertebrates through ancient genome reconstructions
Date: 2018-05-22 00:00:00

The famous 2R hypothesis was first proposed by Susumu Ohno in 1970. It states
that the two whole genome duplications had shaped the genome of early
vertebrates. The most convincing evidence for 2R hypothesis comes from the 4:1
ratio chromosomal regions that have preserved both gene content and order in
vertebrates compared with closely related. However, due to the shortage of such
strict evidence, the 2R hypothesis is still under debates.
  Here, we present a combined perspective of phylogenetic and genomic homology
to revisit the hypothesis of 2R whole genome duplications. Ancestral vertebrate
genomes as well as ancient duplication events were created from 17 extant
vertebrate species. Extant descendants from the duplication events at early
vertebrates were extracted and reorganized to partial genomes. We then examined
the gene order based synteny, and projected back to phylogenetic gene trees for
examination of synteny evidence of the reconstructed early vertebrate genes. We
identified 7877 ancestral genes that were created from 3026 duplication events
at early vertebrates, and more than 50% of the duplication events show synteny
evidence. Thus, our reconstructions provide very strong evidence for the 2R
hypothesis.
  We also reconstructed the genome of early vertebrates, and built a model of
the gene gains and losses in early vertebrates. We estimated that there were
about 12,000 genes in early vertebrates before 2R, and the probability of a
random gene get lost after the first round of whole genome duplication is
around 0.45, and the probability of a random gene get lost after the second
round of whole genome duplication is around 0.55.
  This research provides convincing evidence for the 2R hypothesis, and may
provide further insights in vertebral evolution.
  Data availability: https://github.com/haimingt/Ohnologs-and-2R-WGD
Automation of gene function prediction through modeling human curators' decisions in GO phylogenetic annotation project
Date: 2018-02-20 00:00:00

The Gene Ontology Consortium launched the GO-PAINT project (Phylogenetic
Annotation and INference Tool) 9 years ago and is currently being used in the
GO Reference Genome Annotation Project to support inference of GO function
terms (molecular function, cellular component and biological process) by
homology. PAINT uses a phylogenetic model to infer gene function by homology, a
process that requires manual curation of experienced biocurators. Tremendous
amount of time and efforts have been spent on the GO-PAINT project yielding
more than 4000 fully annotated phylogenetic families with more than 170,000
annotations. These preliminary data have thus enabled potential algorithmic
representation and automatic solvation of the additional 9000 unannoated
phylogenetic families. Here we present an automated pipeline for phylogenetic
annotation and inference, which simulates the standard annotation procedures of
curators and models the curators' decisions during the manual curation process.
The pipeline has been built into the newest version of PAINT software available
at http://www.pantherdb.org/downloads/index.jsp. The standalone automation
pipeline and datasets are available at
https://github.com/haimingt/GO-PAINT-automation