pwc-archive/papers-with-abstracts huggingface dataset:
Searchers adjust their eye movement dynamics to the target characteristics in natural scenes
Date: 2018-02-12 00:00:00
Tasks: Specificity

When searching a target in a natural scene, both the target's visual
properties and similarity to the background influence whether (and how fast)
humans are able to find it. However, thus far it has been unclear whether
searchers adjust the dynamics of their eye movements (e.g., fixation durations,
saccade amplitudes) to the target they search for. In our experiment
participants searched natural scenes for six artificial targets with different
spatial frequency throughout eight consecutive sessions. High-spatial frequency
targets led to smaller saccade amplitudes and shorter fixation durations than
low-spatial frequency targets if target identity was known before the trial. If
a saccade was programmed in the same direction as the previous saccade
(saccadic momentum), fixation durations and successive saccade amplitudes were
not influenced by target type. Visual saliency and empirical density at the
endpoints of saccadic momentum saccades were comparatively low, indicating that
these saccades were less selective. Our results demonstrate that searchers
adjust their eye movement dynamics to the search target in a sensible fashion,
since low-spatial frequencies are visible farther into the periphery than
high-spatial frequencies. Additionally, the saccade direction specificity of
our effects suggests a separation of saccades into a default scanning mechanism
and a selective, target-dependent mechanism.
The Coupled TuFF-BFF Algorithm for Automatic 3D Segmentation of Microglia
Date: 2018-02-12 00:00:00
Tasks: Segmentation

We propose an automatic 3D segmentation algorithm for multiphoton microscopy
images of microglia. Our method is capable of segmenting tubular and blob-like
structures from noisy images. Current segmentation techniques and software fail
to capture the fine processes and soma of the microglia cells, useful for the
study of the microglia role in the brain during healthy and diseased states.
Our coupled tubularity flow field (TuFF)-blob flow field (BFF) method evolves a
level set toward the object boundary using the directional tubularity and
blobness measure of 3D images. Our method found a 20% performance increase
against state of the art segmentation methods on a dataset of 3D images of
microglia even in images with intensity heterogeneity throughout the object.
The coupled TuFF-BFF segmentation results also yielded 40% improvement in
accuracy for the ramification index of the processes, which displays the
efficacy of our method.
On Random-Matrix Bases, Ghost Imaging and X-ray Phase Contrast Computational Ghost Imaging
Date: 2018-04-27 00:00:00

A theory of random-matrix bases is presented, including expressions for
orthogonality, completeness and the random-matrix synthesis of arbitrary
matrices. This is applied to ghost imaging as the realization of a random-basis
reconstruction, including an expression for the resulting signal-to-noise
ratio. Analysis of conventional direct imaging and ghost imaging leads to a
criterion which, when satisfied, implies reduced dose for computational ghost
imaging. We also propose an experiment for x-ray phase contrast computational
ghost imaging, which enables differential phase contrast to be achieved in an
x-ray ghost imaging context. We give a numerically robust solution to the
associated inverse problem of decoding differential phase contrast x-ray ghost
images, to yield a quantitative map of the projected thickness of the sample.
Spatial dynamics of flower organ formation
Date: 2018-01-29 00:00:00

Understanding the emergence of biological structures and their changes is a
complex problem. On a biochemical level, it is based on gene regulatory
networks (GRN) consisting on interactions between the genes responsible for
cell differentiation and coupled in a greater scale with external factors. In
this work we provide a systematic methodological framework to construct
Waddington's epigenetic landscape of the GRN involved in cellular determination
during the early stages of development of angiosperms. As a specific example we
consider the flower of the plant \textit{Arabidopsis thaliana}. Our model,
which is based on experimental data, recovers accurately the spatial
configuration of the flower during cell fate determination, not only for the
wild type, but for its homeotic mutants as well. The method developed in this
project is general enough to be used in the study of the relationship between
genotype-phenotype in other living organisms.
A Direct Method for the Transient Stability Analysis of Transmission Switching Events
Date: 2018-02-12 00:00:00

In this paper, we propose an energy-based method for the transient stability
analysis of a power system transmission switching event. In this method the
exit point of pseudo-fault trajectory is used to determine a relevant
controlling unstable equilibrium point (CUEP) for a switching event, the
stability of the switching event is then assessed based on the energy margin
between the computed relevant CUEP and the post-switching initial point. The
effectiveness of the method is demonstrated on switching events in the
structure-preserving models of a heavily loaded version of the WSCC 9-bus
3-machine system, and the base case IEEE 145-bus 50-machine system. A scheme
for the detailed analysis of power system switching events is then proposed.
Statistical Sensor Fusion of a 9-DoF MEMS IMU for Indoor Navigation
Date: 2018-02-12 00:00:00
Tasks: Pose Tracking, Position, Sensor Fusion

Sensor fusion of a MEMS IMU with a magnetometer is a popular system design,
because such 9-DoF (degrees of freedom) systems are capable of achieving
drift-free 3D orientation tracking. However, these systems are often vulnerable
to ambient magnetic distortions and lack useful position information; in the
absence of external position aiding (e.g. satellite/ultra-wideband positioning
systems) the dead-reckoned position accuracy from a 9-DoF MEMS IMU deteriorates
rapidly due to unmodelled errors. Positioning information is valuable in many
satellite-denied geomatics applications (e.g. indoor navigation, location-based
services, etc.). This paper proposes an improved 9-DoF IMU indoor pose tracking
method using batch optimization. By adopting a robust in-situ user
self-calibration approach to model the systematic errors of the accelerometer,
gyroscope, and magnetometer simultaneously in a tightly-coupled post-processed
least-squares framework, the accuracy of the estimated trajectory from a 9-DoF
MEMS IMU can be improved. Through a combination of relative magnetic
measurement updates and a robust weight function, the method is able to
tolerate a high level of magnetic distortions. The proposed auto-calibration
method was tested in-use under various heterogeneous magnetic field conditions
to mimic a person walking with the sensor in their pocket, a person checking
their phone, and a person walking with a smartwatch. In these experiments, the
presented algorithm improved the in-situ dead-reckoning orientation accuracy by
79.8 - 89.5% and the dead-reckoned positioning accuracy by 72.9 - 92.8%, thus
reducing the relative positioning error from metre-level to decimetre-level
after ten seconds of integration, without making assumptions about the user's
dynamics.
What is the Sharpe Ratio, and how can everyone get it wrong?
Date: 2018-02-13 00:00:00

The Sharpe ratio is the most widely used risk metric in the quantitative
finance community - amazingly, essentially everyone gets it wrong. In this
note, we will make a quixotic effort to rectify the situation.
A General Method for Demand Inversion
Date: 2018-02-27 00:00:00

This paper describes a numerical method to solve for mean product qualities
which equates the real market share to the market share predicted by a discrete
choice model. The method covers a general class of discrete choice model,
including the pure characteristics model in Berry and Pakes(2007) and the
random coefficient logit model in Berry et al.(1995) (hereafter BLP). The
method transforms the original market share inversion problem to an
unconstrained convex minimization problem, so that any convex programming
algorithm can be used to solve the inversion. Moreover, such results also imply
that the computational complexity of inverting a demand model should be no more
than that of a convex programming problem. In simulation examples, I show the
method outperforms the contraction mapping algorithm in BLP. I also find the
method remains robust in pure characteristics models with near-zero market
shares.
Phased Microphone Array for Sound Source Localization with Deep Learning
Date: 2018-02-13 00:00:00
Tasks: Computational Efficiency, Deep Learning, Sound Source Localization

To phased microphone array for sound source localization, algorithm with both
high computational efficiency and high precision is a persistent pursuit. In
this paper convolutional neural network (CNN) a kind of deep learning is
preliminarily applied as a new algorithm. At high frequency CNN can reconstruct
the sound localizations with excellent spatial resolution as good as DAMAS,
within a very short time as short as conventional beamforming. This exciting
result means that CNN perfectly finds source distribution directly from
cross-spectral matrix without given propagation function in advance, and thus
CNN deserves to be further explored as a new algorithm.
A polymer model for the quantitative reconstruction of 3d chromosome architecture from Hi-C and GAM data
Date: 2020-04-28 00:00:00

It is widely believed that the folding of the chromosome in the nucleus has a
major effect on genetic expression. For example co-regulated genes in several
species have been shown to colocalize in space despite being far away on the
DNA sequence. In this manuscript, we present a new method to model the
three-dimensional structure of the chromosome in live cells, based on DNA-DNA
interactions measured in high-throughput chromosome conformation capture
experiments (Hi-C) and genome architecture mapping experiments (GAM). Our
approach incorporates a polymer model, and directly uses the contact
probabilities measured in Hi-C and GAM experiments rather than estimates of
average distances between genomic loci. Specifically, we model the chromosome
as a Gaussian polymer with harmonic interactions and extract the coupling
coefficients best reproducing the experimental contact probabilities. In
contrast to existing methods, we give an exact expression of the contact
probabilities at thermodynamic equilibrium. The Gaussian effective model (GEM)
reconstructed with our method reproduces experimental contacts with high
accuracy. We also show how Brownian Dynamics simulations of our reconstructed
GEM can be used to study chromatin organization, and possibly give some clue
about its dynamics.
Authentication of Satellite Navigation Signals by Wiretap Coding and Artificial Noise
Date: 2018-04-18 00:00:00

In order to combat the spoofing of global navigation satellite system (GNSS)
signals we propose a novel approach for satellite signal authentication based
on information-theoretic security. In particular we superimpose to the
navigation signal an authentication signal containing a secret message
corrupted by artificial noise (AN), still transmitted by the satellite. We
impose the following properties: a) the authentication signal is synchronous
with the navigation signal, b) the authentication signal is orthogonal to the
navigation signal and c) the secret message is undecodable by the attacker due
to the presence of the AN. The legitimate receiver synchronizes with the
navigation signal and stores the samples of the authentication signal with the
same synchronization. After the transmission of the authentication signal,
through a separate public asynchronous authenticated channel (e.g., a secure
Internet connection) additional information is made public allowing the
receiver to a) decode the secret message, thus overcoming the effects of AN,
and b) verify the secret message. We assess the performance of the proposed
scheme by the analysis of both the secrecy capacity of the authentication
message and the attack success probability, under various attack scenarios. A
comparison with existing approaches shows the effectiveness of the proposed
scheme.
Blue mussel shell shape plasticity and natural environments: a quantitative approach
Date: 2018-02-13 00:00:00

Shape variability represents an important direct response of organisms to
selective environments. Here, we use a combination of geometric morphometrics
and generalised additive mixed models (GAMMs) to identify spatial patterns of
natural shell shape variation in the North Atlantic and Arctic blue mussels,
Mytilus edulis and M. trossulus, with environmental gradients of temperature,
salinity and food availability across 3980 km of coastlines. New statistical
methods and multiple study systems at various geographical scales allowed the
uncoupling of the developmental and genetic contributions to shell shape and
made it possible to identify general relationships between blue mussel shape
variation and environment that are independent of age and species influences.
We find salinity had the strongest effect on the latitudinal patterns of
Mytilus shape, producing shells that were more elongated, narrower and with
more parallel dorsoventral margins at lower salinities. Temperature and food
supply, however, were the main drivers of mussel shape heterogeneity. Our
findings revealed similar shell shape responses in Mytilus to less favourable
environmental conditions across the different geographical scales analysed. Our
results show how shell shape plasticity represents a powerful indicator to
understand the alterations of blue mussel communities in rapidly changing
environments.
Bond graph modelling of the cardiac action potential: Implications for drift and non-unique steady states
Date: 2018-02-13 00:00:00

Mathematical models of cardiac action potentials have become increasingly
important in the study of heart disease and pharmacology, but concerns linger
over their robustness during long periods of simulation, in particular due to
issues such as model drift and non-unique steady states. Previous studies have
linked these to violation of conservation laws, but only explored those issues
with respect to charge conservation in specific models. Here, we propose a
general and systematic method of identifying conservation laws hidden in models
of cardiac electrophysiology by using bond graphs, and develop a bond graph
model of the cardiac action potential to study long-term behaviour. Bond graphs
provide an explicit energy-based framework for modelling physical systems,
which makes them well-suited for examining conservation within
electrophysiological models. We find that the charge conservation laws derived
in previous studies are examples of the more general concept of a "conserved
moiety". Conserved moieties explain model drift and non-unique steady states,
generalising the results from previous studies. The bond graph approach
provides a rigorous method to check for drift and non-unique steady states in a
wide range of cardiac action potential models, and can be extended to examine
behaviours of other excitable systems.
Lattice Functions for the Analysis of Analog-to-Digital Conversion
Date: 2019-03-28 00:00:00
Tasks: Quantization

Analog-to-digital (A/D) converters are the common interface between analog
signals and the domain of digital discrete-time signal processing. In essence,
this domain simultaneously incorporates quantization both in amplitude and
time, i.e. amplitude quantization and uniform time sampling. Thus, we view A/D
conversion as a sampling process in both the time and amplitude domains based
on the observation that the underlying continuous-time signals representing
digital sequences can be sampled in a lattice---i.e. at points restricted to
lie on a uniform grid both in time and amplitude. We refer to them as lattice
functions. This is in contrast with the traditional approach based on the
classical sampling theorem and quantization error analysis. The latter has been
mainly addressed with the help of probabilistic models, or deterministic ones
either confined to very particular scenarios or considering worst-case
assumptions. In this paper, we provide a deterministic theoretical analysis and
framework for the functions involved in digital discrete-time processing. We
show that lattice functions possess a rich analytic structure in the context of
integral-valued entire functions of exponential type. We derive set and
spectral properties of this class of functions. This allows us to prove in a
deterministic way and for general bandlimited functions a fundamental lower
bound on the maximum frequency component introduced by quantization that is
independent of the resolution of the quantizer.
Broadband MIMO Couplers Characterization and Comparison
Date: 2018-02-13 00:00:00

This paper focuses on MIMO Power Line Communication (PLC) to provide
increased performance. In MIMO PLC, appropriate coupling methods are necessary
in order to enable the effective injection of the signal through the broad band
PLC channel so that high data rates can be achieved. In this paper, we want to
analytically characterize strengths and weaknesses of each coupler design
(topology) from an electrical circuit perspective. We dwell on the description
and analysis of the three main and common configurations used for the MIMO
couplers: star (S), triangle ($\Delta$) and T.
Asset Price Volatility and Price Extrema
Date: 2018-07-28 00:00:00

The relationship between price volatilty and a market extremum is examined
using a fundamental economics model of supply and demand. By examining
randomness through a microeconomic setting, we obtain the implications of
randomness in the supply and demand, rather than assuming that price has
randomness on an empirical basis. Within a very general setting the volatility
has an extremum that precedes the extremum of the price. A key issue is that
randomness arises from the supply and demand, and the variance in the
stochastic differential equation govening the logarithm of price must reflect
this. Analogous results are obtained by further assuming that the supply and
demand are dependent on the deviation from fundamental value of the asset.
The Quotient of Normal Random Variables And Application to Asset Price Fat Tails
Date: 2018-02-13 00:00:00

The quotient of random variables with normal distributions is examined and
proven to have have power law decay, with density $f\left( x\right) \simeq
f_{0}x^{-2}$, with the coefficient depending on the means and variances of the
numerator and denominator and their correlation. We also obtain the conditional
probability densities for each of the four quadrants given by the signs of the
numerator and denominator for arbitrary correlation $\rho \in\lbrack-1,1).$ For
$\rho=-1$ we obtain a particularly simple closed form solution for all $x\in$
$\mathbb{R}$. The results are applied to a basic issue in economics and
finance, namely the density of relative price changes. Classical finance
stipulates a normal distribution of relative price changes, though empirical
studies suggest a power law at the tail end. By considering the supply and
demand in a basic price change model, we prove that the relative price change
has density that decays with an $x^{-2}$ power law. Various parameter limits
are established.
Gain control with A-type potassium current: IA as a switch between divisive and subtractive inhibition
Date: 2018-02-13 00:00:00

Neurons process information by transforming barrages of synaptic inputs into
spiking activity. Synaptic inhibition suppresses the output firing activity of
a neuron, and is commonly classified as having a subtractive or divisive effect
on a neuron's output firing activity. Subtractive inhibition can narrow the
range of inputs that evoke spiking activity by eliminating responses to
non-preferred inputs. Divisive inhibition is a form of gain control: it
modifies firing rates while preserving the range of inputs that evoke firing
activity. Since these two "modes" of inhibition have distinct impacts on neural
coding, it is important to understand the biophysical mechanisms that
distinguish these response profiles.
  We use simulations and mathematical analysis of a neuron model to find the
specific conditions for which inhibitory inputs have subtractive or divisive
effects. We identify a novel role for the A-type Potassium current (IA). In our
model, this fast-activating, slowly- inactivating outward current acts as a
switch between subtractive and divisive inhibition. If IA is strong (large
maximal conductance) and fast (activates on a time-scale similar to spike
initiation), then inhibition has a subtractive effect on neural firing. In
contrast, if IA is weak or insufficiently fast-activating, then inhibition has
a divisive effect on neural firing. We explain these findings using dynamical
systems methods to define how a spike threshold condition depends on synaptic
inputs and IA.
  Our findings suggest that neurons can "self-regulate" the gain control
effects of inhibition via combinations of synaptic plasticity and/or modulation
of the conductance and kinetics of A-type Potassium channels. This novel role
for IA would add flexibility to neurons and networks, and may relate to recent
observations of divisive inhibitory effects on neurons in the nucleus of the
solitary tract.
Adapting the CVA model to Leland's framework
Date: 2018-02-13 00:00:00
Tasks: model

We consider the framework proposed by Burgard and Kjaer (2011) that derives
the PDE which governs the price of an option including bilateral counterparty
risk and funding. We extend this work by relaxing the assumption of absence of
transaction costs in the hedging portfolio by proposing a cost proportional to
the amount of assets traded and the traded price. After deriving the nonlinear
PDE, we prove the existence of a solution for the corresponding
initial-boundary value problem. Moreover, we develop a numerical scheme that
allows to find the solution of the PDE by setting different values for each
parameter of the model. To understand the impact of each variable within the
model, we analyze the Greeks of the option and the sensitivity of the price to
changes in all the risk factors.
Efficient Discovery of Variable-length Time Series Motifs with Large Length Range in Million Scale Time Series
Authors: Gao Yifeng, Lin Jessica
Date: 2018-02-13 00:00:00
Tasks: Time Series, Time Series Analysis

Detecting repeated variable-length patterns, also called variable-length
motifs, has received a great amount of attention in recent years. Current
state-of-the-art algorithm utilizes fixed-length motif discovery algorithm as a
subroutine to enumerate variable-length motifs. As a result, it may take hours
or days to execute when enumeration range is large. In this work, we introduce
an approximate algorithm called HierarchIcal based Motif Enumeration (HIME) to
detect variable-length motifs with a large enumeration range in million-scale
time series. We show in the experiments that the scalability of the proposed
algorithm is significantly better than that of the state-of-the-art algorithm.
Moreover, the motif length range detected by HIME is considerably larger than
previous sequence-matching based approximate variable-length motif discovery
approach. We demonstrate that HIME can efficiently detect meaningful
variable-length motifs in long, real world time series.