pwc-archive/papers-with-abstracts huggingface dataset:
Smoothed SVD-based Beamforming for FBMC/OQAM Systems Based on Frequency Spreading
Date: 2018-06-19 00:00:00

The combination of singular value decomposition (SVD)-based beamforming and
filter bank multicarrier with offset quadrature amplitude modulation
(FBMC/OQAM) has not been successful to date. The difficulty of this combination
is that, the beamformers may experience significant changes between adjacent
subchannels, therefore destroy the orthogonality among FBMC/OQAM real-valued
symbols, even under channels with moderate frequency selectivity. In this
paper, we address this problem from two aspects: i) an SVD-FS-FBMC architecture
is adopted to support beamforming with finer granularity in frequency domain,
based on the frequency spreading FBMC (FS-FBMC) structure, i.e., beamforming on
FS-FBMC tones rather than on subchannels; ii) criterion and methods are
proposed to smooth the beamformers from tone to tone. The proposed finer
beamforming and smoothing greatly improve the smoothness of beamformers,
therefore effectively suppress the leaked ICI/ISI. Simulations are conducted
under the scenario of IEEE 802.11n wireless LAN. Results show that the proposed
SVD-FS-FBMC system shares close BER performance with its orthogonal frequency
division multiplexing (OFDM) counterpart under the frequency selective
channels.
Impact of Building-Level Motor Protection on Power System Transient Behaviors
Date: 2018-06-19 00:00:00

Protection strategies for transmission and distribution systems have been
extensively investigated to facilitate better coordination of physical
protection devices. A diverse range of functional motors with dedicated
protection schemes are being used more and more in commercial, residential, and
industrial buildings. This paper focuses on simulating several of the most
popular protection schemes using the ElectroMagnetic Transient Program (EMTP)
model for three-phase and single-phase induction motors in existing commercial
buildings connected to typical distribution feeders. To investigate the
behaviors of single-phase motors stalling, the actions of motor protection and
reconnections, and the impacts of device-level protection on system-level
dynamics, we imposed voltage depressions at the head of a feeder fully loaded
with functional induction motors. Several distribution feeders are represented
in a standard IEEE 39-bus transmission system to simulate fault-induced delayed
voltage recovery (FIDVR) and explore mitigation strategies by optimally
configuring the building-level motor protection settings.
Portfolio Choice with Market-Credit Risk Dependencies
Date: 2018-06-19 00:00:00

We study an optimal investment/consumption problem in a model capturing
market and credit risk dependencies. Stochastic factors drive both the default
intensity and the volatility of the stocks in the portfolio. We use the
martingale approach and analyze the recursive system of nonlinear
Hamilton-Jacobi-Bellman equations associated with the dual problem. We
transform such a system into an equivalent system of semi-linear PDEs, for
which we establish existence and uniqueness of a bounded global classical
solution. We obtain explicit representations for the optimal strategy,
consumption path and wealth process, in terms of the solution to the recursive
system of semi-linear PDEs. We numerically analyze the sensitivity of the
optimal investment strategies to risk aversion, default risk and volatility.
Dynamical anchoring of distant Arrhythmia Sources by Fibrotic Regions via Restructuring of the Activation Pattern
Date: 2018-06-19 00:00:00

Rotors are functional reentry sources identified in clinically relevant
cardiac arrhythmias, such as ventricular and atrial fibrillation. Ablation
targeting rotor sites has resulted in arrhythmia termination. Recent clinical,
experimental and modelling studies demonstrate that rotors are often anchored
around fibrotic scars or regions with increased fibrosis. However the
mechanisms leading to abundance of rotors at these locations are not clear. The
current study explores the hypothesis whether fibrotic scars just serve as
anchoring sites for the rotors or whether there are other active processes
which drive the rotors to these fibrotic regions. Rotors were induced at
different distances from fibrotic scars of various sizes and degree of
fibrosis. Simulations were performed in a 2D model of human ventricular tissue
and in a patient-specific model of the left ventricle of a patient with remote
myocardial infarction. In both the 2D and the patient-specific model we found
that without fibrotic scars, the rotors were stable at the site of their
initiation. However, in the presence of a scar, rotors were eventually
dynamically anchored from large distances by the fibrotic scar via a process of
dynamical reorganization of the excitation pattern. This process coalesces with
a change from polymorphic to monomorphic ventricular tachycardia.
On the relation between Sion's minimax theorem and existence of Nash equilibrium in asymmetric multi-players zero-sum game with only one alien
Date: 2018-06-17 00:00:00

We consider the relation between Sion's minimax theorem for a continuous
function and a Nash equilibrium in an asymmetric multi-players zero-sum game in
which only one player is different from other players, and the game is
symmetric for the other players. Then,
  1. The existence of a Nash equilibrium, which is symmetric for players other
than one player, implies Sion's minimax theorem for pairs of this player and
one of other players with symmetry for the other players.
  2. Sion's minimax theorem for pairs of one player and one of other players
with symmetry for the other players implies the existence of a Nash equilibrium
which is symmetric for the other players.
  Thus, they are equivalent.
End-to-End Neural Ranking for eCommerce Product Search: an application of task models and textual embeddings
Authors: Brenner Eliot, Zhao Jun, Kutiyanawala Aliasgar, Yan Zheng
Date: 2018-06-19 00:00:00
Tasks: Benchmarking

We consider the problem of retrieving and ranking items in an eCommerce
catalog, often called SKUs, in order of relevance to a user-issued query. The
input data for the ranking are the texts of the queries and textual fields of
the SKUs indexed in the catalog. We review the ways in which this problem both
resembles and differs from the problems of IR in the context of web search. The
differences between the product-search problem and the IR problem of web search
necessitate a different approach in terms of both models and datasets. We first
review the recent state-of-the-art models for web search IR, distinguishing
between two distinct types of model which we call the distributed type and the
local-interaction type. The different types of relevance models developed for
IR have complementary advantages and disadvantages when applied to eCommerce
product search. Further, we explain why the conventional methods for dataset
construction employed in the IR literature fail to produce data which suffices
for training or evaluation of models for eCommerce product search. We explain
how our own approach, applying task modeling techniques to the click-through
logs of an eCommerce site, enables the construction of a large-scale dataset
for training and robust benchmarking of relevance models. Our experiments
consist of applying several of the models from the IR literature to our own
dataset. Empirically, we have established that, when applied to our dataset,
certain models of local-interaction type reduce ranking errors by one-third
compared to the baseline tf-idf. Applied to our dataset, the distributed models
fail to outperform the baseline. As a basis for a deployed system, the
distributed models have several advantages, computationally, over the
local-interaction models. This motivates an ongoing program of work, which we
outline at the conclusion of the paper.
Improving Protein Gamma-Turn Prediction Using Inception Capsule Networks
Date: 2018-06-11 00:00:00
Tasks: BIG-bench Machine Learning, Experimental Design, Prediction

Protein gamma-turn prediction is useful in protein function studies and
experimental design. Several methods for gamma-turn prediction have been
developed, but the results were unsatisfactory with Matthew correlation
coefficients (MCC) around 0.2-0.4. One reason for the low prediction accuracy
is the limited capacity of the methods; in particular, the traditional
machine-learning methods like SVM may not extract high-level features well to
distinguish between turn or non-turn. Hence, it is worthwhile exploring new
machine-learning methods for the prediction. A cutting-edge deep neural
network, named Capsule Network (CapsuleNet), provides a new opportunity for
gamma-turn prediction. Even when the number of input samples is relatively
small, the capsules from CapsuleNet are very effective to extract high-level
features for classification tasks. Here, we propose a deep inception capsule
network for gamma-turn prediction. Its performance on the gamma-turn benchmark
GT320 achieved an MCC of 0.45, which significantly outperformed the previous
best method with an MCC of 0.38. This is the first gamma-turn prediction method
utilizing deep neural networks. Also, to our knowledge, it is the first
published bioinformatics application utilizing capsule network, which will
provides a useful example for the community.
Learning Unit State Recognition Based on Multi-channel Data Fusion
Authors: Tian Feng, Yue Jia, Wan Xing, Chao Kuo-Min, Zheng Qinghua
Date: 2018-05-25 00:00:00

Despite recent advances in MOOC, the current e-learning systems have
advantages of alleviating barriers by time differences, and geographically
spatial separation between teachers and students. However, there has been a
'lack of supervision' problem that e-learner's learning unit state(LUS) can't
be supervised automatically. In this paper, we present a fusion framework
considering three channel data sources: 1) videos/images from a camera, 2) eye
movement information tracked by a low solution eye tracker and 3) mouse
movement. Based on these data modalities, we propose a novel approach of
multi-channel data fusion to explore the learning unit state recognition. We
also propose a method to build a learning state recognition model to avoid
manually labeling image data. The experiments were carried on our designed
online learning prototype system, and we choose CART, Random Forest and GBDT
regression model to predict e-learner's learning state. The results show that
multi-channel data fusion model have a better recognition performance in
comparison with single channel model. In addition, a best recognition
performance can be reached when image, eye movement and mouse movement features
are fused.
Distinguishing Refracted Features using Light Field Cameras with Application to Structure from Motion
Authors: Tsai Dorian, Dansereau Donald G, Peynot Thierry, Corke Peter
Date: 2018-05-31 00:00:00

Robots must reliably interact with refractive objects in many applications;
however, refractive objects can cause many robotic vision algorithms to become
unreliable or even fail, particularly feature-based matching applications, such
as structure-from-motion. We propose a method to distinguish between refracted
and Lambertian image features using a light field camera. Specifically, we
propose to use textural cross-correlation to characterise apparent feature
motion in a single light field, and compare this motion to its Lambertian
equivalent based on 4D light field geometry. Our refracted feature
distinguisher has a 34.3% higher rate of detection compared to state-of-the-art
for light fields captured with large baselines relative to the refractive
object. Our method also applies to light field cameras with much smaller
baselines than previously considered, yielding up to 2 times better detection
for 2D-refractive objects, such as a sphere, and up to 8 times better for
1D-refractive objects, such as a cylinder. For structure from motion, we
demonstrate that rejecting refracted features using our distinguisher yields up
to 42.4% lower reprojection error, and lower failure rate when the robot is
approaching refractive objects. Our method lead to more robust robot vision in
the presence of refractive objects.
Kernel Methods for Nonlinear Connectivity Detection
Date: 2018-06-19 00:00:00
Tasks: Time Series, Time Series Analysis

In this paper, we show that the presence of nonlinear coupling between time
series may be detected employing kernel feature space representations alone
dispensing with the need to go back to solve the pre-image problem to gauge
model adequacy. As a consequence, the canonical methodology for model
construction, diagnostics, and Granger connectivity inference applies with no
change other than computation using kernels in lieu of second-order moments.
Turbulence correction with artificial neural networks
Date: 2018-06-19 00:00:00
Tasks: BIG-bench Machine Learning

We design an optical feedback network making use of machine learning
techniques and demonstrate via simulations its ability to correct for the
effects of turbulent propagation on optical modes. This artificial neural
network scheme only relies on measuring the intensity profile of the distorted
modes, making the approach simple and robust. The network results in the
generation of various mode profiles at the transmitter that, after propagation
through turbulence, closely resemble the desired target mode. The corrected
optical mode profiles at the receiver are found to be nearly identical to the
desired profiles, with near-zero mean square error indices. We are hopeful that
the present results combining the fields of machine learning and optical
communications will greatly enhance the robustness of free-space optical links.
Gradient Sensing via Cell Communication
Authors: Dallas Foster, Collin Victor, Brian Frost, Juan M. Restrepo
Date: 2018-06-19 00:00:00

Experimental evidence lends support to the conjecture that the ability of chains of cells to sense the gradient of an external chemical concentration could rely on cell-to-cell communication. This is the basis for the gradient sensing nature of a specific model type of the Local Excitation, Global Inhibition (LEGI) principle, wherein the strength of the external chemical field is sensed through a comparison between a local exciting species and a global inhibitor that is shared via intra-cellular reactions in the cell chain. In this study we generalize the nearest neighbor communication mechanism in the above-mentioned LEGI model in order to explore how the chemical sensing characteristics depend on the parameterization of the communication itself, cell size, and the radius of influence of neighboring cells. It was found that the radius of influence was less important than the approximating model for communication. Higher order approximations to the communication mechanism were better able to sense an external gradient. However, an analysis of the signal to noise ratio established that higher order models for communication were more prone to noise and thus have a lower signal to noise ratio. The generalization as well as the tools used in the analysis of the dynamics can be extended to more heterogeneous networks and can thus prove useful in using models and observations in the process of understanding chemical gradient via LEGI models with a communication component.
Optimal Dividend Distribution Under Drawdown and Ratcheting Constraints on Dividend Rates
Date: 2019-03-22 00:00:00

We consider the optimal dividend problem under a habit formation constraint
that prevents the dividend rate to fall below a certain proportion of its
historical maximum, the so-called drawdown constraint. This is an extension of
the optimal Duesenberry's ratcheting consumption problem, studied by Dybvig
(1995) [Review of Economic Studies 62(2), 287-313], in which consumption is
assumed to be nondecreasing. Our problem differs from Dybvig's also in that the
time of ruin could be finite in our setting, whereas ruin was impossible in
Dybvig's work. We formulate our problem as a stochastic control problem with
the objective of maximizing the expected discounted utility of the dividend
stream until bankruptcy, in which risk preferences are embodied by power
utility. We semi-explicitly solve the corresponding Hamilton-Jacobi-Bellman
variational inequality, which is a nonlinear free-boundary problem. The optimal
(excess) dividend rate $c^*_t$ - as a function of the company's current surplus
$X_t$ and its historical running maximum of the (excess) dividend rate $z_t$ -
is as follows: There are constants $0 < w_{\alpha} < w_0 < w^*$ such that (1)
for $0 < X_t \le w_{\alpha} z_t$, it is optimal to pay dividends at the lowest
rate $\alpha z_t$, (2) for $w_{\alpha} z_t < X_t < w_0 z_t$, it is optimal to
distribute dividends at an intermediate rate $c^*_t \in (\alpha z_t, z_t)$, (3)
for $w_0 z_t < X_t < w^* z_t$, it is optimal to distribute dividends at the
historical peak rate $z_t$, (4) for $X_t > w^* z_t$, it is optimal to increase
the dividend rate above $z_t$, and (5) it is optimal to increase $z_t$ via
singular control as needed to keep $X_t \le w^* z_t$. Because, the maximum
(excess) dividend rate will eventually be proportional to the running maximum
of the surplus, "mountains will have to move" before we increase the dividend
rate beyond its historical maximum.
Comparative Analysis of Hybrid DC Breaker and Assembly HVDC Breaker
Date: 2018-06-20 00:00:00
Tasks: Decision Making

Voltage Source Converters (VSC) are becoming more common in modern High
Voltage DC (HVDC) transmission systems. One of the major challenges in a
multi-terminal VSCHVDC is protection against DC side faults. Two major designs,
namely, the hybrid DC breaker and the assembly HVDC breaker, are compared for
operational behavior, speed of operation and current carrying capability. The
Dual Modular Redundancy (DMR) technique is utilized for decision making of a
fault scenario. This uses a dual voting system when one result contradicts the
other. This helps in the design of a fail-safe mechanism for the operation of
both types of breakers. Current threshold combined with directional change is
considered for the breaker operation. A three-terminal bipolar VSC HVDC system
is designed in PSCAD/EMTDC and simulation results are utilized to draw a
comparison of the two different designs of DC breakers.
Is VIX still the investor fear gauge? Evidence for the US and BRIC markets
Date: 2018-07-23 00:00:00

We investigate the relationships of the VIX with US and BRIC markets. In
detail, we pick up the analysis from the point left off by (Sarwar, 2012), and
we focus on the period: Jan 2007 - Feb 2018, thus capturing the relations
before, during and after the 2008 financial crisis. Results pinpoint frequent
structural breaks in the VIX and suggest an enhancement around 2008 of the fear
transmission in response to negative market moves; largely depending on
overlaps in trading hours, this has become even stronger post-crisis for the
US, while for BRIC countries has gone back towards pre-crisis levels.
Modeling continuous levels of resistance to multidrug therapy in cancer
Date: 2018-06-20 00:00:00

Multidrug resistance consists of a series of genetic and epigenetic
alternations that involve multifactorial and complex processes, which are a
challenge to successful cancer treatments. Accompanied by advances in
biotechnology and high-dimensional data analysis techniques that are bringing
in new opportunities in modeling biological systems with continuous phenotypic
structured models, we study a cancer cell population model that considers a
multi-dimensional continuous resistance trait to multiple drugs to investigate
multidrug resistance. We compare our continuous resistance trait model with
classical models that assume a discrete resistance state and classify the cases
when the continuum and discrete models yield different dynamical patterns in
the emerging heterogeneity in response to drugs. We also compute the maximal
fitness resistance trait for various continuum models and study the effect of
epimutations. Finally, we demonstrate how our approach can be used to study
tumor growth regarding the turnover rate and the proliferating fraction, and
show that a continuous resistance level may result in a different dynamics when
compared with the predictions of other discrete models.
An Analytics Tool for Exploring Scientific Software and Related Publications
Authors: Hoppe Anett, Hagen Jascha, Holzmann Helge, Kniesel Günter, Ewerth Ralph
Date: 2018-06-20 00:00:00

Scientific software is one of the key elements for reproducible research.
However, classic publications and related scientific software are typically not
(sufficiently) linked, and it lacks tools to jointly explore these artefacts.
In this paper, we report on our work on developing an analytics tool for
jointly exploring software and publications. The presented prototype, a concept
for automatic code discovery, and two use cases demonstrate the feasibility and
usefulness of the proposal.
  Submitted to TPDL 2018 as Demonstration Paper.
Back-end Electronics for Low Background and Medium Scale Physics Experiments Based on an Asymmetric Network
Date: 2018-06-20 00:00:00

The detector readout architecture introduced in this paper is intended for
small to medium size physics experiments that have moderate bandwidth needs,
and applications that require an ultimately low background radioactivity for
the parts close to the detector. The first idea to simplify the readout system
and minimize material budget is to use a common fanout structure to transport
from the off-detector back-end electronics all the traffic required for the
synchronization, configuration and readout of the front-end electronics. The
second idea is to use between each front-end card and the back-end electronics
a point-to-point link that runs at the relatively low speed that suffices for
the target application. This broadens the possible choices for the physical
media of the communication links, e.g. glass fiber, plastic optical fiber, or
copper. This paper presents a communication protocol adequate for the proposed
asymmetric network and shows the design of a back-end unit capable of
controlling 32 front-end units at up to 12.8 Gbps of aggregate bandwidth using
an inexpensive commercial FPGA module where the large number of regular I/O
pins interface to the front-end links, while the few available multi-gigabit
per second capable transceivers are affected to the communication with the
upper stage of the DAQ system.
Measuring the response of gold prices to uncertainty: An analysis beyond the mean
Date: 2018-06-20 00:00:00
Tasks: quantile regression

This paper provides an innovative perspective on the role of gold as a hedge
and safe haven. We use a quantile-on-quantile regression approach to capture
the dependence structure between gold returns and changes in uncertainty under
different gold market conditions, while considering the nuances of uncertainty
levels. To capture the core uncertainty effects on gold returns, a dynamic
factor model is used. This technique allows summarizing the impact of six
different indexes (namely economic, macroeconomic, microeconomic, monetary
policy, financial and political uncertainties) within one aggregate measure of
uncertainty. In doing so, we show that the gold's role as a hedge and safe
haven cannot be assumed to hold at all times. This ability seems to be
sensitive to the gold's various market states (bearish, normal or bullish) and
to whether the uncertainty is low, middle or high. Interestingly, we find a
positive and strong relationship between gold returns and the uncertainty
composite indicator when the uncertainty attains its highest level and under
normal gold market scenario. This suggests that holding a diversified portfolio
composed of gold could help protecting against exposure to uncertain risks.