pwc-archive/papers-with-abstracts huggingface dataset:
A High-Speed, Real-Time Vision System for Texture Tracking and Thread Counting
Date: 2018-12-10 00:00:00
Tasks: Motion Estimation, Template Matching, Translation

In garment manufacturing, an automatic sewing machine is desirable to reduce
cost. To accomplish this, a high speed vision system is required to track
fabric motions and recognize repetitive weave patterns with high accuracy, from
a micro perspective near a sewing zone. In this paper, we present an innovative
framework for real-time texture tracking and weave pattern recognition. Our
framework includes a module for motion estimation using blob detection and
feature matching. It also includes a module for lattice detection to facilitate
the weave pattern recognition. Our lattice detection algorithm utilizes blob
detection and template matching to assess pair-wise similarity in blobs'
appearance. In addition, it extracts information of dominant orientations to
obtain a global constraint in the topology. By incorporating both constraints
in the appearance similarity and the global topology, the algorithm determines
a lattice that characterizes the topological structure of the repetitive weave
pattern, thus allowing for thread counting. In our experiments, the proposed
thread-based texture tracking system is capable of tracking denim fabric with
high accuracy (e.g., 0.03 degree rotation and 0.02 weave-thread' translation
errors) and high speed (3 frames per second), demonstrating its high potential
for automatic real-time textile manufacturing.
Influence of Multiple Sequence Alignment Depth on Potts Statistical Models of Protein Covariation
Date: 2019-01-19 00:00:00
Tasks: Multiple Sequence Alignment

Potts statistical models have become a popular and promising way to analyze
mutational covariation in protein Multiple Sequence Alignments (MSAs) in order
to understand protein structure, function and fitness. But the statistical
limitations of these models, which can have millions of parameters and are fit
to MSAs of only thousands or hundreds of effective sequences using a procedure
known as inverse Ising inference, are incompletely understood. In this work we
predict how model quality degrades as a function of the number of sequences
$N$, sequence length $L$, amino-acid alphabet size $q$, and the degree of
conservation of the MSA, in different applications of the Potts models: In
"fitness" predictions of individual protein sequences, in predictions of the
effects of single-point mutations, in "double mutant cycle" predictions of
epistasis, and in 3-d contact prediction in protein structure. We show how as
MSA depth $N$ decreases an "overfitting" effect occurs such that sequences in
the training MSA have overestimated fitness, and we predict the magnitude of
this effect and discuss how regularization can help correct for it, use a
regularization procedure motivated by statistical analysis of the effects of
finite sampling. We find that as $N$ decreases the quality of point-mutation
effect predictions degrade least, fitness and epistasis predictions degrade
more rapidly, and contact predictions are most affected. However, overfitting
becomes negligible for MSA depths of more than a few thousand effective
sequences, as often used in practice, and regularization becomes less
necessary. We discuss the implications of these results for users of Potts
covariation analysis.
Scale Selective Extended Local Binary Pattern for Texture Classification
Date: 2018-12-11 00:00:00
Tasks: Classification, Texture Classification

In this paper, we propose a new texture descriptor, scale selective extended
local binary pattern (SSELBP), to characterize texture images with scale
variations. We first utilize multi-scale extended local binary patterns (ELBP)
with rotation-invariant and uniform mappings to capture robust local micro- and
macro-features. Then, we build a scale space using Gaussian filters and
calculate the histogram of multi-scale ELBPs for the image at each scale.
Finally, we select the maximum values from the corresponding bins of
multi-scale ELBP histograms at different scales as scale-invariant features. A
comprehensive evaluation on public texture databases (KTH-TIPS and UMD) shows
that the proposed SSELBP has high accuracy comparable to state-of-the-art
texture descriptors on gray-scale-, rotation-, and scale-invariant texture
classification but uses only one-third of the feature dimension.
Completed Local Derivative Pattern for Rotation Invariant Texture Classification
Date: 2018-12-11 00:00:00
Tasks: Classification, Texture Classification

In this paper, we propose a new texture descriptor, completed local
derivative pattern (CLDP). In contrast to completed local binary pattern
(CLBP), which involves only local differences at each scale, CLDP encodes the
directional variation of the local differences of two scales as a complementary
component to local patterns in CLBP. The new component in CLDP, with regarded
as the directional derivative pattern, reflects the directional smoothness of
local textures without increasing computation complexity. Experimental results
on the Outex database show that CLDP, as a uni-scale pattern, outperforms
uni-scale state-of-the-art texture descriptors on texture classification and
has comparable performance with multi-scale texture descriptors.
Influence of High-Speed Railway System on Inter-city Travel Behavior in Vietnam
Date: 2018-12-11 00:00:00

To analyze the influence of introducing the High-Speed Railway (HSR) system
on business and non-business travel behavior, this study develops an integrated
inter-city travel demand model to represent trip generations, destination
choice, and travel mode choice behavior. The accessibility calculated from the
RP/SP (Revealed Preference/Stated Preference) combined nested logit model of
destination and mode choices is used as an explanatory variable in the trip
frequency models. One of the important findings is that additional travel would
be induced by introducing HSR. Our simulation analyses also reveal that HSR and
conventional airlines will be the main modes for middle distances and long
distances, respectively. The development of zones may highly influence the
destination choices for business purposes, while prices of HSR and Low-Cost
Carriers affect choices for non-business purposes. Finally, the research
reveals that people on non-business trips are more sensitive to changes in
travel time, travel cost and regional attributes than people on business trips.
Sparse Channel Estimation with Gradient-Based Algorithms: A comparative Study
Date: 2018-12-11 00:00:00

Channel state information (CSI) is very crucial for any wireless
communication systems. Typically, CSI can be characterized at the receiver side
using channel impulse response (CIR). Many observations have shown that the CIR
of broadband multipath wireless channels are often sparse. To this point, the
family of least mean square (LMS)-based algorithms have been widely used to
estimate the CIR, unfortunately, the performance of LMS family is not much
accurate in terms of sparse channel estimation. The Least Mean Mixed Norm
(LMMN) algorithm combines the advantages of both the Least Mean square (LMS)
and the Least Mean Fourth (LMF)algorithm, which makes this algorithm stands in
a very special position among the family members in terms of convergence and
steady state error. In this paper, we held a fair comparative study between the
LMMN and a number of the LMS-based algorithms, such as the LMS algorithm, the
zero-attracting (ZA-LMS) algorithm, and the normalized (NLMS) algorithm.
Simulation results are carried out to compare the performance of all these
algorithms with the LMMN algorithm. The results show that the LMMN algorithm
outperforms the rest of these algorithms in the identification of sparse
systems in terms of both fast convergence and the steady state error.
Proceedings of the 17th Dutch-Belgian Information Retrieval Workshop
Authors: Brandsen Alex, Dirkson Anne, Kraaij Wessel, Lamers Wout, Verberne Suzan, de Vos Hugo, Wiggers Gineke
Date: 2018-12-11 00:00:00
Tasks: Information Retrieval, Retrieval

This volume contains the papers presented at DIR 2018: 17th Dutch-Belgian
Information Retrieval Workshop (DIR) held on November 23, 2018 in Leiden. DIR
aims to serve as an international platform (with a special focus on the
Netherlands and Belgium) for exchange and discussions on research &
applications in the field of information retrieval and related fields.
  The committee accepted 4 short papers presenting novel work, 3 demo
proposals, and 8 compressed contributions (summaries of papers recently
published in international journals and conferences). Each submission was
reviewed by at least 3 programme committee members.
A Numerical Analysis of the Modified Kirk's Formula and Applications to Spread Option Pricing Approximations a numerical analysis of the modified kirk's formula and applications to spread option pricing approximations
Authors: Suren Harutyunyan, AdriÀ Masip BorrÀs
Date: 2018-12-11 00:00:00

In this paper we study recent developments in the approximation of the spread option pricing. As the Kirk\'s Approximation is extremely flawed in the cases when the correlation is very high, we explore a recent development that allows approximating with simplicity and accuracy the option price. To assess the goodness of fit of the new method, we increase dramatically the number of simulations and scenarios to test the new method and compare it with the original Kirk\'s formula. The simulations confirmed that the Modified Kirk\'s Approximation method is extremely accurate, improving Kirk\'s approach for two-asset spread options.
A finite state projection method for steady-state sensitivity analysis of stochastic reaction networks
Date: 2018-12-11 00:00:00
Tasks: Sensitivity

Consider the standard stochastic reaction network model where the dynamics is
given by a continuous-time Markov chain over a discrete lattice. For such
models, estimation of parameter sensitivities is an important problem, but the
existing computational approaches to solve this problem usually require
time-consuming Monte Carlo simulations of the reaction dynamics. Therefore
these simulation-based approaches can only be expected to work over finite
time-intervals, while it is often of interest in applications to examine the
sensitivity values at the steady-state after the Markov chain has relaxed to
its stationary distribution. The aim of this paper is to present a
computational method for the estimation of steady-state parameter
sensitivities, which instead of using simulations, relies on the recently
developed stationary Finite State Projection (sFSP) algorithm [J. Chem. Phys.
147, 154101 (2017)] that provides an accurate estimate of the stationary
distribution at a fixed set of parameters. We show that sensitivity values at
these parameters can be estimated from the solution of a Poisson equation
associated with the infinitesimal generator of the Markov chain. We develop an
approach to numerically solve the Poisson equation and this yields an efficient
estimator for steady-state parameter sensitivities. We illustrate this method
using several examples.
Phase inpainting in time-frequency plane
Date: 2018-12-11 00:00:00

We propose a new problem of missing data reconstruction in the time-frequency
plane. This problem called phase inpainting, consists in reconstructing a
signal from time-frequency observations where all amplitudes and some phases
are known while the remaining phases are missing. A mathematical formulation of
this problem is given. We propose three alternatives of existing algorithms. An
iterative algorithm: Griffin and Lim and two semidefinite programming
optimization algorithms: PhaseLift and PhaseCut. The obtained results show that
knowledge of certain phases improves the reconstruction's quality.
Faster-than-fast NMF using random projections and Nesterov iterations
Date: 2018-12-11 00:00:00

Random projections have been recently implemented in Nonnegative Matrix
Factorization (NMF) to speed-up the NMF computations, with a negligible loss of
performance. In this paper, we investigate the effects of such projections when
the NMF technique uses the fast Nesterov gradient descent (NeNMF). We
experimentally show the randomized subspace iteration to significantly speed-up
NeNMF.
Monetary Measures of Risk
Date: 2018-12-11 00:00:00
Tasks: Survey

This survey gives an introduction to monetary measures of risk as monotone
and cash additive functions on spaces of univariate random variables. Primal
and dual representation results as well as several examples are discussed.
Principal ways to construct risk measures are given and extensions to more
general situations indicated.
Bacterial growth: a statistical physicist's guide
Date: 2018-12-11 00:00:00

Bacterial growth presents many beautiful phenomena that pose new theoretical
challenges to statistical physicists, and are also amenable to laboratory
experimentation. This review provides some of the essential biological
background, discusses recent applications of statistical physics in this field,
and highlights the potential for future research.
Observability and Structural Identifiability of Nonlinear Biological Systems
Date: 2018-12-11 00:00:00

Observability is a modelling property that describes the possibility of
inferring the internal state of a system from observations of its output. A
related property, structural identifiability, refers to the theoretical
possibility of determining the parameter values from the output. In fact,
structural identifiability becomes a particular case of observability if the
parameters are considered as constant state variables. It is possible to
simultaneously analyse the observability and structural identifiability of a
model using the conceptual tools of differential geometry. Many complex
biological processes can be described by systems of nonlinear ordinary
differential equations, and can therefore be analysed with this approach. The
purpose of this review article is threefold: (I) to serve as a tutorial on
observability and structural identifiability of nonlinear systems, using the
differential geometry approach for their analysis; (II) to review recent
advances in the field; and (III) to identify open problems and suggest new
avenues for research in this area.
PrecoG: an efficient unitary split preconditioner for the transform-domain LMS filter via graph Laplacian regularization
Authors: Tamal Batabyal, Daniel S. Weller, Jaideep Kapur, Scott T. Acton
Date: 2018-12-11 00:00:00

Transform-domain least mean squares (LMS) adaptive filters encompass the class of algorithms where the input data are subjected to a data-independent unitary transform followed by a power normalization stage as preprocessing steps. Because conventional transformations are not data-dependent, this preconditioning procedure was shown theoretically to improve the convergence of the LMS filter only for certain classes of input data. However, in reality if the class of input data is not known beforehand, it is difficult to decide which transformation to use. Thus, there is a need to devise a learning framework to obtain such a preconditioning transformation using input data prior to applying on the input data. It is hypothesized that the underlying topology of the data affects the selection of the transformation. With the input modeled as a weighted graph that mimics neuronal interactions, PrecoG obtains the desired transform by recursive estimation of the graph Laplacian matrix. Additionally, we show the efficacy of the transform as a generalized split preconditioner on a linear system of equations and in Hebb-LMS settings. In terms of the improvement of the condition number after applying the transformation, PrecoG performs significantly better than the existing state-of-the-art techniques that involve unitary and non-unitary transforms.
Game-Theoretic Optimal Portfolios for Jump Diffusions
Authors: Alex Garivaltis
Date: 2018-12-11 00:00:00

This paper studies a two-person trading game in continuous time that generalizes Garivaltis (2018) to allow for stock prices that both jump and diffuse. Analogous to Bell and Cover (1988) in discrete time, the players start by choosing fair randomizations of the initial dollar, by exchanging it for a random wealth whose mean is at most 1. Each player then deposits the resulting capital into some continuously-rebalanced portfolio that must be adhered to over $[0,t]$. We solve the corresponding `investment $\phi$-game,' namely the zero-sum game with payoff kernel $\mathbb{E}[\phi\{\textbf{W}_1V_t(b)/(\textbf{W}_2V_t(c))\}]$, where $\textbf{W}_i$ is player $i$'s fair randomization, $V_t(b)$ is the final wealth that accrues to a one dollar deposit into the rebalancing rule $b$, and $\phi(\bullet)$ is any increasing function meant to measure relative performance. We show that the unique saddle point is for both players to use the (leveraged) Kelly rule for jump diffusions, which is ordinarily defined by maximizing the asymptotic almost-sure continuously-compounded capital growth rate. Thus, the Kelly rule for jump diffusions is the correct behavior for practically anybody who wants to outperform other traders (on any time frame) with respect to practically any measure of relative performance.
Geometry of epithelial cells provides a robust method for image based inference of stress within tissues
Date: 2018-12-11 00:00:00
Tasks: ARC

Cellular mechanics plays an important role in epithelial morphogenesis, a
process wherein cells reshape and rearrange to produce tissue-scale
deformations. However, the study of tissue-scale mechanics is impaired by the
difficulty of direct measurement of stress in-vivo. Alternative, image-based
inference schemes aim to estimate stress from snapshots of cellular geometry
but are challenged by sensitivity to fluctuations and measurement noise as well
as the dependence on boundary conditions. Here we overcome these difficulties
by introducing a new variational approach - the Geometrical Variation Method
(GVM) - which exploits the fundamental duality between stress and cellular
geometry that exists in the state of mechanical equilibrium of discrete
mechanical networks that approximate cellular tissues. In the Geometrical
Variation Method, the two dimensional apical geometry of an epithelial tissue
is approximated by a 2D tiling with Circular Arc Polygons (CAP) in which the
arcs represent intercellular interfaces defined by the balance of local line
tension and pressure differentials between adjacent cells. We take advantage of
local constraints that mechanical equilibrium imposes on CAP geometry to define
a variational procedure that extracts the best fitting equilibrium
configuration from images of epithelial monolayers. The GVM-based stress
inference algorithm has been validated by the comparison of the predicted
cellular and mesoscopic scale stress and measured myosin II patterns in the
epithelial tissue during Drosophila embryogenesis. GVM prediction of mesoscopic
stress tensor correlates at the 80% level with the measured myosin distribution
and reveals that most of the myosin II activity is involved in a static
internal force balance within the epithelial layer. Lastly, this study provides
a practical method for non-destructive estimation of stress in live epithelial
tissues.
Inter-Core Crosstalk in Multicore Fibers: Impact on 56-Gbaud/{\lambda}/Core PAM-4 Transmission
Date: 2018-11-13 00:00:00

We experimentally demonstrate the impact of inter-core crosstalk in multicore
fibers on 56Gbaud PAM-4 signal quality after 2.5-km transmission over a
weakly-coupled and uncoupled sevencore fibers, revealing the crosstalk
dependence on carrier central wavelength in range of 1540-1560 nm.
Deep Neural Networks Meet CSI-Based Authentication
Authors: Abyaneh Amirhossein Yazdani, Foumani Ali Hosein Gharari, Pourahmadi Vahid
Date: 2018-11-26 00:00:00

The first step of a secure communication is authenticating legible users and
detecting the malicious ones. In the last recent years, some promising schemes
proposed using wireless medium network's features, in particular, channel state
information (CSI) as a means for authentication. These schemes mainly compare
user's previous CSI with the new received CSI to determine if the user is in
fact what it is claiming to be. Despite high accuracy, these approaches lack
the stability in authentication when the users rotate in their positions. This
is due to a significant change in CSI when a user rotates which mislead the
authenticator when it compares the new CSI with the previous ones. Our approach
presents a way of extracting features from raw CSI measurements which are
stable towards rotation. We extract these features by the means of a deep
neural network. We also present a scenario in which users can be {efficiently}
authenticated while they are at certain locations in an environment (even if
they rotate); and, they will be rejected if they change their location. Also,
experimental results are presented to show the performance of the proposed
scheme.
Experimental Comparison of Hardware-Amenable Spike Detection Algorithms for iBMIs
Date: 2018-12-12 00:00:00

This paper presents an experiment based comparison of absolute threshold (AT)
and non-linear energy operator (NEO) spike detection algorithms in
Intra-cortical Brain Machine Interfaces (iBMIs). Results show an average
increase in decoding performance of approx. 5% in monkey A across 28 sessions
recorded over 6 days and approx. 2% in monkey B across 35 sessions recorded
over 8 days when using NEO over AT. To the best of our knowledge, this is the
first ever reported comparison of spike detection algorithms in an iBMI
experimental framework involving two monkeys. Based on the improvements
observed in an experimental setting backed by previously reported improvements
in simulation studies, we advocate switching from state of the art spike
detection technique - AT to NEO.
Weak comonotonicity
Date: 2019-09-11 00:00:00

The classical notion of comonotonicity has played a pivotal role when solving
diverse problems in economics, finance, and insurance. In various practical
problems, however, this notion of extreme positive dependence structure is
overly restrictive and sometimes unrealistic. In the present paper, we put
forward a notion of weak comonotonicity, which contains the classical notion of
comonotonicity as a special case, and gives rise to necessary and sufficient
conditions for a number of optimization problems, such as those arising in
portfolio diversification, risk aggregation, and premium calculation. In
particular, we show that a combination of weak comonotonicity and weak
antimonotonicity with respect to some choices of measures is sufficient for the
maximization of Value-at-Risk aggregation, and weak comonotonicity is necessary
and sufficient for the Expected Shortfall aggregation. Finally, with the help
of weak comonotonicity acting as an intermediate notion of dependence between
the extreme cases of no dependence and strong comonotonicity, we give a natural
solution to a risk-sharing problem.
A Low-Rank and Joint-Sparse Model for Ultrasound Signal Reconstruction
Date: 2018-12-12 00:00:00

With the introduction of very dense sensor arrays in ultrasound (US) imaging,
data transfer rate and data storage became a bottleneck in ultrasound system
design. To reduce the amount of sampled channel data, we propose to use a
low-rank and joint-sparse model to represent US signals and exploit the
correlations between adjacent receiving channels. Results show that the
proposed method is adapted to the ultrasound signals and can recover high
quality image approximations from as low as 10% of the samples.