pwc-archive/papers-with-abstracts huggingface dataset:
Cooperative Audio Source Separation and Enhancement Using Distributed Microphone Arrays and Wearable Devices
Date: 2019-12-10 00:00:00
Tasks: Audio Source Separation

Augmented listening devices such as hearing aids often perform poorly in
noisy and reverberant environments with many competing sound sources. Large
distributed microphone arrays can improve performance, but data from remote
microphones often cannot be used for delay-constrained real-time processing. We
present a cooperative audio source separation and enhancement system that
leverages wearable listening devices and other microphone arrays spread around
a room. The full distributed array is used to separate sound sources and
estimate their statistics. Each listening device uses these statistics to
design real-time binaural audio enhancement filters using its own local
microphones. The system is demonstrated experimentally using 10 speech sources
and 160 microphones in a large, reverberant room.
Stability and Robustness of Disturbance Observer based Motion Control Systems
Date: 2019-12-10 00:00:00

This paper analyzes the robustness and stability of a disturbance observer
(DOB) and a reaction torque observer (RTOB) based robust motion control
systems. Conventionally, a DOB is analyzed by using an ideal velocity
measurement that is obtained without using a low-pass-filter (LPF); however, it
is impractical due to noise constraints. An LPF of velocity measurement changes
the robustness of a DOB significantly and puts a new design constraint on the
bandwidth of a DOB. An RTOB, which is used to estimate environmental impedance,
is an application of a DOB. The stability of an RTOB based robust force control
system has not been reported yet since its oversimplified model is derived by
assuming that an RTOB has a feed-forward control structure. However, in
reality, it has a feed-back control structure; therefore, not only the
performance but also the stability is affected by the design parameters of a
RTOB. A new practical stability analysis method is proposed for a RTOB based
robust force control system. Besides that novel and practical design methods,
which improve the robustness of a DOB and the stability and performance of an
RTOB based robust force control system, are proposed by using the new analysis
methods. The validity of the proposals is verified by simulation and
experimental results.
Analysis of Cardiovascular Changes Caused by Epileptic Seizures in Human Photoplethysmogram Signal
Date: 2019-12-11 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Heart Rate Variability, Seizure Detection

Objectives: This study examines human Photoplethysmogram (PPG) along with
Electrocardiogram (ECG) signals to study cardiac autonomic imbalance in
epileptic seizures. The significance and the prevalence of changes in PPG
morphological parameters have been investigated to find common patterns among
subjects. Alterations in cardiovascular parameters measured by PPG/ECG signals
are used to train a neural network based on LSTM for automatic seizure
detection. Methods: Electroencephalogram (EEG), ECG, and PPG signals from 12
different subjects ( 8 males;4 females;age 34.3$\pm$ 13.8) were recorded
including 57 seizures and 101 hours of inter-ictal data. 12 PPG features
significantly changing due to epileptic seizures were extracted and normalized
based on a proposed z-score metric. 7 feature are heart rate variability
related and 5 features hemodynamic related. Results: A consistent pattern of
ictal change was observed for all the features across the subjects/seziures.
The proposed seizure detector is subject independent and works for both
nocturnal and diurnal seizures. With an average of 0.52 false alarms per hour,
positive predictive value of $43\%$ and sensitivity of $92\%$, the new proposed
hemodynamic based seizure detector shows improvement over the the heart rate
variability based detector. Conclusion: The cardiac autonomic imbalance due to
seizure manifests itself in variations of peripheral hemodynamics measured by
PPG signal, suggesting vasoconstriction in limbs. These variations can be used
on a consumer seizure detecting devices with optical sensors for seizure
detection. Significance: The stereotyped pattern is common among all the
subjects which can help understand the mechanism of cardiac autonomic imbalance
induced by epileptic seizures.
Third-Degree Price Discrimination Versus Uniform Pricing
Authors: Dirk Bergemann, Francisco Castro, Gabriel Weintraub
Date: 2019-12-11 00:00:00

We compare the profit of the optimal third-degree price discrimination policy against a uniform pricing policy. A uniform pricing policy offers the same price to all segments of the market. Our main result establishes that for a broad class of third-degree price discrimination problems with concave profit functions (in the price space) and common support, a uniform price is guaranteed to achieve one half of the optimal monopoly profits. This profit bound holds for any number of segments and prices that the seller might use under third-degree price discrimination. We establish that these conditions are tight and that weakening either common support or concavity can lead to arbitrarily poor profit comparisons even for regular or monotone hazard rate distributions.
Accurate Measurement of Propagation Delay in a Multi-Span Optical Link
Date: 2019-12-11 00:00:00

The principle of Correlation Optical Time Domain Reflectometry (C-OTDR) is
proposed to accurately measure the propagation delay over a multi-span optical
fiber link. The delay of the transmission fiber is measured in the reflective
mode, while uni-directional node components are measured in a transmissive
mode. Delimiting reflectors are required between the sections for accurate
demarcation.
On the Radiality Constraints for Distribution System Restoration and Reconfiguration Problems
Date: 2020-03-15 00:00:00
Tasks: Computational Efficiency

Radiality constraints are involved in both distribution system restoration
and reconfiguration problems. However, a set of widely used radiality
constraints, i.e., the spanning tree (ST) constraints, has its limitations
which have not been well recognized. In this letter, the limitation of the ST
constraints is analyzed and an effective set of constraints, referred to as the
single-commodity flow constraints, is presented. Furthermore, a combined set of
constraints is proposed and case studies indicate that the combined constraints
can gain computational efficiency in the reconfiguration problem.
Recommendations on the use of radiality constraints are also provided.
An Iterative Heuristic Method to Determine Radial Topology for Distribution System Restoration
Authors: Jiayu Liu, Qiqi Zhang, Jiaxu Li, Ying Wang
Date: 2019-12-11 00:00:00

Coordinating multiple local power sources can restore critical loads after the major outages caused by extreme events. A radial topology is needed for distribution system restoration, while determining a good topology in real-time for online use is a challenge. In this paper, a graph theory-based heuristic considering power flow state is proposed to fast determine the radial topology. The loops of distribution network are eliminated by iteration. The proposed method is validated by one snapshot and multi-period critical load restoration models on different cases. The case studies indicate that the proposed method can determine radial topology in a few seconds and ensure the restoration capacity.
Risk of Bitcoin Market: Volatility, Jumps, and Forecasts
Authors: Junjie Hu, Wolfgang Karl Härdle, Weiyu Kuo
Date: 2019-12-11 00:00:00

Cryptocurrency, the most controversial and simultaneously the most interesting asset, has attracted many investors and speculators in recent years. The visibly significant market capitalization of cryptos also motivates modern financial instruments such as futures and options. Those will depend on the dynamics, volatility, or even the jumps of cryptos. We provide a comprehensive investigation of the risk dynamics of the Bitcoin Market from a realized volatility perspective. The Bitcoin market is extremely risky in the sense of volatility, entangled jumps, and extensive consecutive jumps, which reflect the major incidents worldwide. Empirical study shows that the lagged realized variance increases the future realized variance, while the jumps, especially positive ones, significantly reduce future realized variance. The out-of-sample forecasting model reveals that, in terms of forecasting accuracy and utility gain, investors interested in the long-term realized variance benefit from explicitly modelling the jumps and signed estimators, which is unnecessary for the short-term realized variance forecast.
Sparse Joint Transmission for Cell-Free Massive MIMO: A Sparse PCA Approach
Authors: Deokhwan Han, Jeonghun Park, Namyoon Lee
Date: 2019-12-11 00:00:00

Cell-free massive multiple-input multiple-output (MIMO) is a promising cellular network. In this network, a large number of distributed and multi-antenna access points (APs) jointly serve many single antenna users using the same time-frequency resource. Consequently, it possibly provides a uniform service experience to users regardless of the users' locations by eliminating interference at cell boundaries via user-centric joint transmission. This joint transmission, however, requires extremely high signaling overheads for data sharing via backhaul links and causes a high network-wide power consumption. To resolve these problems, in this paper, we present a novel joint transmission method, which is referred to as sparse joint transmission (sparse-JT), for cell-free massive MIMO networks with finite backhaul capacity constraints. Sparse-JT jointly identifies the user-centric cooperative APs sets, precoding vectors for beamforming and compression, and power allocation that maximizes a lower bound of the sum-spectral efficiency under the constraint that a total number of active APs per the joint transmission is sparse. The proposed algorithm guarantees to identify a local-optimal solution for a relaxed sum-spectral maximization problem. By simulations, we show that sparse-JT achieves higher ergodic spectral efficiencies than those attained by multi-cell zero-forcing precoding with the user-centric AP clustering algorithm in all system configurations.
Systemic Risk: Fire-Walling Financial Systems Using Network-Based Approaches
Authors: V. Sasidevan, Nils Bertschinger
Date: 2019-12-11 00:00:00

The latest financial crisis has painfully revealed the dangers arising from a globally interconnected financial system. Conventional approaches based on the notion of the existence of equilibrium and those which rely on statistical forecasting have seen to be inadequate to describe financial systems in any reasonable way. A more natural approach is to treat financial systems as complex networks of claims and obligations between various financial institutions present in an economy. The generic framework of complex networks has been successfully applied across several disciplines, e.g., explaining cascading failures in power transmission systems and epidemic spreading. Here we review various network models addressing financial contagion via direct inter-bank contracts and indirectly via overlapping portfolios of financial institutions. In particular, we discuss the implications of the "robust-yet-fragile" nature of financial networks for cost-effective regulation of systemic risk.
Vine disease detection in UAV multispectral images with deep learning segmentation approach
Date: 2019-12-11 00:00:00
Tasks: Image Registration, Management

One of the major goals of tomorrow's agriculture is to increase agricultural
productivity but above all the quality of production while significantly
reducing the use of inputs. Meeting this goal is a real scientific and
technological challenge. Smart farming is among the promising approaches that
can lead to interesting solutions for vineyard management and reduce the
environmental impact. Automatic vine disease detection can increase efficiency
and flexibility in managing vineyard crops, while reducing the chemical inputs.
This is needed today more than ever, as the use of pesticides is coming under
increasing scrutiny and control. The goal is to map diseased areas in the
vineyard for fast and precise treatment, thus guaranteeing the maintenance of a
healthy state of the vine which is very important for yield management. To
tackle this problem, a method is proposed here for vine disease detection using
a deep learning segmentation approach on Unmanned Aerial Vehicle (UAV) images.
The method is based on the combination of the visible and infrared images
obtained from two different sensors. A new image registration method was
developed to align visible and infrared images, enabling fusion of the
information from the two sensors. A fully convolutional neural network approach
uses this information to classify each pixel according to different instances,
namely, shadow, ground, healthy and symptom. The proposed method achieved more
than 92% of detection at grapevine-level and 87% at leaf level, showing
promising perspectives for computer aided disease detection in vineyards.
Human Electromagnetic Field Exposure in Wearable Communications: A Review
Date: 2019-12-11 00:00:00

The concern on human health is often overseen while wearable technologies
attract exploding interests. Mainly due to the extreme proximity or a direct
physical contact to the human skin, wearable communications devices are
acknowledged to cause higher levels of specific absorption rate (SAR) at the
skin surface. Unfortunately, so far, we have found no study encompassing all
the aspects that the general public needs to understand about wearable
technologies--i.e., the analytical and experimental backgrounds, and report of
SAR levels generated from commercial wearable devices. In this context, this
paper provides an extensive review on SAR from various commercial wearable
devices that are currently sold in the market, as well as the analytical
framework and the current measurement methodologies for standard compliance
tests. Moreover, considering the present interest in millimeter wave (mmW),
this paper sheds light on the SAR evaluated at 60 GHz and also compares the SAR
to that measured at 2.4 GHz. We expect that this paper will be of value in
informing the general public of the safety in using the currently sold wearable
devices, and in igniting further study of the exact biological consequences
from electromagnetic field (EMF) exposure due to wearable devices.
Breaking the Limits of Redundancy Systems Analysis
Date: 2019-12-11 00:00:00

Redundancy mechanisms such as triple modular redundancy protect
safety-critical components by replication and thus improve systems fault
tolerance. However, the gained fault tolerance comes along with costs to be
invested, e.g., increasing execution time, energy consumption, or packaging
size, for which constraints have to be obeyed during system design. This turns
the question of finding suitable combinations of components to be protected
into a challenging task as the number of possible protection combinations grows
exponentially in the number of components. We propose family-based approaches
to tackle the combinatorial blowup in redundancy systems modeling and analysis
phases. Based on systems designed in SIMULINK we show how to obtain models that
include all possible protection combinations and present a tool chain that,
given a probabilistic error model, generates discrete Markov chain families.
Using symbolic techniques that enable concise family representation and
analysis, we show how SIMULINK models of realistic size can be protected and
analyzed with a single family-based analysis run while a one-by-one analysis of
each protection combination would clearly exceed any realistic time
constraints.
Application of Mathematical Epidemiology to crop vector-borne diseases. The cassava mosaic virus disease case
Date: 2019-12-11 00:00:00
Tasks: Epidemiology

In this chapter, an application of Mathematical Epidemiology to crop
vector-borne diseases is presented to investigate the interactions between
crops, vectors, and virus. The main illustrative example is the cassava mosaic
disease (CMD). The CMD virus has two routes of infection: through vectors and
also through infected crops. In the field, the main tool to control CMD
spreading is roguing. The presented biological model is sufficiently generic
and the same methodology can be adapted to other crops or crop vector-borne
diseases. After an introduction where a brief history of crop diseases and
useful information on Cassava and CMD is given, we develop and study a
compartmental temporal model, taking into account the crop growth and the
vector dynamics. A brief qualitative analysis of the model is provided,i.e.,
existence and uniqueness of a solution,existence of a disease-free equilibrium
and existence of an endemic equilibrium. We also provide conditions for local
(global) asymptotic stability and show that a Hopf Bifurcation may occur, for
instance, when diseased plants are removed. Numerical simulations are provided
to illustrate all possible behaviors. Finally, we discuss the theoretical and
numerical outputs in terms of crop protection.
On the difference between the volatility swap strike and the zero vanna implied volatility
Authors: Elisa Alos, Frido Rolloos, Kenichiro Shiraya
Date: 2019-12-11 00:00:00

In this paper, Malliavin calculus is applied to arrive at exact formulas for the difference between the volatility swap strike and the zero vanna implied volatility for volatilities driven by fractional noise. To the best of our knowledge, our estimate is the first to derive the rigorous relationship between the zero vanna implied volatility and the volatility swap strike. In particular, we will see that the zero vanna implied volatility is a better approximation for the volatility swap strike than the ATMI.
Overview of quantitative susceptibility mapping using deep learning -- Current status, challenges and opportunities
Date: 2019-12-11 00:00:00
Tasks: Deep Learning

Quantitative susceptibility mapping (QSM) has gained broad interests in the
field by extracting biological tissue properties, predominantly myelin, iron
and calcium from magnetic resonance imaging (MRI) phase measurements in vivo.
Thereby, QSM can reveal pathological changes of these key components in a
variety of diseases. QSM requires multiple processing steps such as phase
unwrapping, background field removal and field-to-source-inversion. Current
state of the art techniques utilize iterative optimization procedures to solve
the inversion and background field correction, which are computationally
expensive and require a careful choice of regularization parameters. With the
recent success of deep learning using convolutional neural networks for solving
ill-posed reconstruction problems, the QSM community also adapted these
techniques and demonstrated that the QSM processing steps can be solved by
efficient feed forward multiplications not requiring iterative optimization nor
the choice of regularization parameters. Here, we review the current status of
deep learning based approaches for processing QSM, highlighting limitations and
potential pitfalls, and discuss the future directions the field may take to
exploit the latest advances in deep learning for QSM.
Distortion matrix approach for ultrasound imaging of random scattering media
Date: 2020-06-12 00:00:00
Tasks: Seismic Imaging

Focusing waves inside inhomogeneous media is a fundamental problem for
imaging. Spatial variations of wave velocity can strongly distort propagating
wavefronts and degrade image quality. Adaptive focusing can compensate for such
aberration, but is only effective over a restricted field of view. Here, we
introduce a full-field approach to wave imaging based on the concept of the
distortion matrix. This operator essentially connects any focal point inside
the medium with the distortion that a wavefront, emitted from that point,
experiences due to heterogeneities. A time-reversal analysis of the distortion
matrix enables the estimation of the transmission matrix that links each sensor
and image voxel. Phase aberrations can then be unscrambled for any point,
providing a full-field image of the medium with diffraction-limited resolution.
Importantly, this process is particularly efficient in random scattering media,
where traditional approaches such as adaptive focusing fail. Here, we first
present an experimental proof-of-concept on a tissue mimicking phantom, and
then apply the method to in vivo imaging of human soft tissues. While
introduced here in the context of acoustics, this approach can also be extended
to optical microscopy, radar or seismic imaging.
Computational resolution limit: a theory towards super-resolution
Date: 2020-01-21 00:00:00
Tasks: Super-Resolution

Given an image generated by the convolution of point sources with a
band-limited function, the deconvolution problem is to reconstruct the source
number, positions, and amplitudes. Rayleigh investigated the limit of this
problem and formulated the Rayleigh limit, for the case of two sources with
identical amplitudes. On the other hand, many numerical experiments demonstrate
that a stable recovery of the sources is possible even if the sources are
separated below the Rayleigh limit. This is so-called "super-resolution". In
this paper, a new mathematical theory for super-resolution will be developed.
The theory will address the issue when one can recover the source number
exactly. The key is a new concept "computational resolution limit" which is
defined to be the minimum separation distance between the sources such that
exact recovery of the source number is possible. This new resolution limit is
determined by the signal-to-noise ratio and the sparsity of sources, in
addition to the cutoff frequency of the image. Sharp upper bound for this limit
is derived, which reveals the importance of the sparsity as well as the
signal-to-noise ratio to the recovery problem. The stability for recovering the
source positions is further derived when the separation distance is beyond the
upper bound. Moreover, a MUSIC-type algorithm is proposed to recover the source
number and to verify our theoretical results on the computational resolution
limit. Its performance in the super-resolution regime when the sources are
separated below the Rayleigh limit is analyzed both theoretically and
numerically. The results are based on a multipole expansion method and a novel
non-linear approximation theory in Vandermonde space.
Closed form optimal exercise boundary of the American put option
Authors: Yerkin Kitapbayev
Date: 2019-12-11 00:00:00
Tasks: Form

We present three models of stock price with time-dependent interest rate, dividend yield, and volatility, respectively, that allow for explicit forms of the optimal exercise boundary of the finite maturity American put option. The optimal exercise boundary satisfies the nonlinear integral equation of Volterra type. We choose time-dependent parameters of the model so that the integral equation for the exercise boundary can be solved in the closed form. We also define the contracts of put type with time-dependent strike price that support the explicit optimal exercise boundary.
Sub-sampling and other considerations for efficient risk estimation in large portfolios
Authors: Michael B. Giles, Abdul-Lateef Haji-Ali
Date: 2019-12-11 00:00:00

Computing risk measures of a financial portfolio comprising thousands of derivatives is a challenging problem because (a) it involves a nested expectation requiring multiple evaluations of the loss of the financial portfolio for different risk scenarios and (b) evaluating the loss of the portfolio is expensive and the cost increases with its size. In this work, we look at applying Multilevel Monte Carlo (MLMC) with adaptive inner sampling to this problem and discuss several practical considerations. In particular, we discuss a sub-sampling strategy whose computational complexity does not increase with the size of the portfolio. We also discuss several control variates that significantly improve the efficiency of MLMC in our setting.