pwc-archive/papers-with-abstracts huggingface dataset:
Power Cycling Test Bench for Accelerated Life Testing for Reliability Assessment of SiC-MOSFET in Extreme Offshore Environment
Date: 2020-01-15 00:00:00
Tasks: Band Gap

The reliability of power semiconductor switches is important when considering
their vital role in power electronic converters for downhole subsea
applications. Respect to technology advancements in material sciences, power
MOSFETs with wide band gap materials have been proposed such as silicon carbide
(SiC) and gallium nitride (GaN) as an alternative to existing silicon (Si)
based MOSFETs and IGBTs. However, reliability analysis should be performed
before substituting SiC-MOSFETs in the place of existing Si-MOSFETs and IGBTs.
Due to costly equipment of experimental test setup for accelerated life test, a
good reliable and precise simulation-based test bench should be used to test
the life test procedure before implementing actual hardware. Therefore, this
paper introduces a power cycle (PC) test bench for accelerated life testing for
reliability assessment of SiC-MOSFET in harsh offshore environment. The
introduced test bench is a simulation-based of power switch in SimScape and
LTspice and has been validated with datasheet of 1.2 kV SiC-MOSFET,
CAS300M12BM2 by CREE. Preliminary hardware circuits are also shown for further
experimental tests. The captured data from the Device-Under-Test (DUT) in
different ambient temperatures are envisioned and provide critical information
about the failure mechanisms and lifetime characteristics of power devices. The
provided lifetime characteristics data of SiC-MOSFET can be used to
statistically estimate the Remaining-Useful-Lifetime (RUL) of component in a
real application such as downhole motor drives.
Arbitrarily Strong Utility-Privacy Tradeoff in Multi-Agent Systems
Date: 2020-08-11 00:00:00

Each agent in a network makes a local observation that is linearly related to
a set of public and private parameters. The agents send their observations to a
fusion center to allow it to estimate the public parameters. To prevent leakage
of the private parameters, each agent first sanitizes its local observation
using a local privacy mechanism before transmitting it to the fusion center. We
investigate the utility-privacy tradeoff in terms of the Cram\'er-Rao lower
bounds for estimating the public and private parameters. We study the class of
privacy mechanisms given by linear compression and noise perturbation, and
derive necessary and sufficient conditions for achieving arbitrarily strong
utility-privacy tradeoff in a multi-agent system for both the cases where prior
information is available and unavailable, respectively. We also provide a
method to find the maximum estimation privacy achievable without compromising
the utility and propose an alternating algorithm to optimize the
utility-privacy tradeoff in the case where arbitrarily strong utility-privacy
tradeoff is not achievable.
Semi-Static Radio Frame Configuration for URLLC Deployments in 5G Macro TDD Networks
Date: 2020-01-16 00:00:00

Dynamic time division duplexing (TDD) is one of the major novelties of the 5G
new radio standard. It notably improves the network resource utilization with
sporadic directional packet arrivals. Although, the feasibility of the
ultra-reliable and low-latency communications (URLLC) within such deployments
is critically challenged, mainly due to the cross-link interference (CLI). In
this work, we propose a semi-static and computationally-efficient TDD radio
frame adaptation algorithm for 5G macro deployments. Particularly, we first
identify the quasi-static variance of the cross-cell traffic buffering
performance, with various CLI co-existence conditions. Accordingly, a common
radio frame pattern is dynamically estimated based on the filtered multi-cell
traffic statistics. Our system-level simulation results show that the proposed
solution achieves a highly improved URLLC outage performance, i.e., offering
40% reduction gain of the achievable URLLC outage latency compared to perfect
static-TDD, and approaching the optimal interference-free flexible-TDD case;
though, with a significantly lower control overhead size.
Cross-Link Interference Suppression By Orthogonal Projector For 5G Dynamic TDD URLLC Systems
Date: 2020-01-16 00:00:00

Dynamic time division duplexing (TDD) is envisioned as a vital transmission
technology of the 5G new radio, due to its reciprocal propagation
characteristics. However, the potential cross-link interference (CLI) imposes a
fundamental limitation against the feasibility of the ultra-reliable and low
latency communications (URLLC) in dynamic-TDD systems. In this work, we propose
a near-optimal and complexity-efficient CLI suppression scheme using orthogonal
spatial projection, while the signaling overhead is limited to B-bit, over the
back-haul links. Compared to the state-of-the-art dynamic-TDD studies, proposed
solution offers a significant improvement of the URLLC outage latency, e.g.,
-199% reduction, while boosting the achievable capacity per the URLLC packet by
+156%.
Performance of Wireless Optical Communication With Reconfigurable Intelligent Surfaces and Random Obstacles
Date: 2020-01-16 00:00:00

It is difficult for free space optical communication to be applied in mobile
communication due to the obstruction of obstacles in the environment, which is
expected to be solved by reconfigurable intelligent surface technology. The
reconfigurable intelligent surface is a new type of digital coding
meta-materials, which can reflect, compute and program electromagnetic and
optical waves in real time. We purpose a controllable multi-branch wireless
optical communication system based on the optical reconfigurable intelligent
surface technology. By setting up multiple optical reconfigurable intelligent
surface in the environment, multiple artificial channels are built to improve
system performance and to reduce the outage probability. Three factors
affecting channel coefficients are investigated in this paper, which are beam
jitter, jitter of the reconfigurable intelligent surface and the probability of
obstruction. Based on the model, we derive the closed-form probability density
function of channel coefficients, the asymptotic system's average bit error
rate and outage probability for systems with single and multiple branches. It
is revealed that the probability density function contains an impulse function,
which causes irreducible error rate and outage probability floors. Numerical
results indicate that compared with free-space optical communication systems
with single direct path, the performance of the multi-branch system is improved
and the outage probability is reduced.
Boundary stabilization of systems of high order PDEs arising from flexible robotics
Date: 2020-01-16 00:00:00

The problem of stabilization of a system of coupled PDEs of the forth-order
by means of boundary control is investigated. The considered setup arises from
the classical Euler-Bernoulli beam model, and constitutes a generalization of
flexible mechanical systems. A linear feedback controller is proposed, and
using an abstract formulation based on operator semigroup theory, we are able
to prove the well-posedness and the stability of the closed-loop system. The
performances of the proposed controller are illustrated by means of numerical
simulations.
Distributed LQR-based observer design for large-scale multi-agent networks
Date: 2020-01-16 00:00:00

In this paper, network of agents with identical dynamics is considered. The
agents are assumed to be fed by self and neighboring output measurements, while
the states are not available for measuring. Viewing distributed estimation as
dual to the distributed LQR problem, a distributed observer is proposed by
exploiting two complementary distributed LQR methods. The first consists of a
bottom-up approach in which optimal interactions between self-stabilizing
agents are defined so as to minimize an upper bound of the global LQR
criterion. In the second (top-down) approach, the centralized optimal LQR
controller is approximated by a distributed control scheme whose stability is
guaranteed by the stability margins of LQR control. In this paper, distributed
observer which minimizes an upper bound of a deterministic performance
criterion, is proposed by solving a dual LQR problem using bottom-up approach.
The cost function is defined by considering minimum-energy estimation theory
where the weighting matrices have deterministic interpretation. The presented
results are useful for designing optimal or near-optimal distributed
control/estimation schemes.
RadioWeaves for efficient connectivity: analysis andimpact of constraints in actual deployments
Date: 2020-01-16 00:00:00

We present a new type of wireless access infras-tructure consisting of a
fabric of dispersed electronic circuitsand antennas that collectively function
as a massive, distributed antenna array. We have chosen to name this new
wireless infrastructure 'RadioWeaves' and anticipate they can be integrated
into indoor and outdoor walls, furniture, and otherobjects, rendering them a
natural part of the environment. Technologically, RadioWeaves will deploy
distributed arrays to create both favorable propagation and antenna array
interaction. The technology leverages on the ideas of large-scale intelligent
surfaces and cell-free wireless access. Offering close to the service
connectivity and computing, new grades in energy efficiency,reliability, and
low latency can be reached. The new concept moreover can be scaled up easily to
offer a very high capacity inspecific areas demanding so. In this paper we
anticipate how two different demanding use cases can be served well by a
dedicated RadioWeaves deployment: a crowd scenario and a highly reflective
factory environment. A practical approach towards a RadioWeaves prototype,
integrating dispersed electronics invisibly in a room environment, is
introduced. We outline diverse R\&D challenges that need to be addressed to
realize the great potential of the RadioWeaves technology.
Quadratic Hedging and Optimization of Option Exercise Policies
Authors: Nicola Secomandi
Date: 2020-01-16 00:00:00

Quadratic hedging of option payoffs generates the variance optimal martingale measure. When an option features an exercise policy and its cash flows are hedged according to this approach, it may be tempting to optimize such a policy under this measure. Because the variance optimal martingale measure may not be an equivalent probability measure, focusing on American options we show that the resulting exercise policy may be unappealing. This drawback can sometimes be remedied by imposing time consistency on exercise policies, but in general persists even in this case, which compounds the familiar issue that valuing an option using this measure may not result in an arbitrage free value. An alternative and known approach bypasses both of these pitfalls by optimizing option exercise policies under any given equivalent martingale measure and anchoring quadratic hedging to the resulting value of this policy. Additional research may assess on realistic applications the magnitude of the limitations associated with optimizing option exercise policies based on the variance optimal martingale measure.
Model-based interfacing of large-scale metrology instruments
Date: 2020-01-16 00:00:00
Tasks: Management, model

Metrology assisted assembly systems constitute cyber physical production
systems relying on in-process sensor data as input to model-based control
loops. These range from local, physical control loops, e.g. for robots to
closed-loop product lifecycles including quality management. The variety and
amount of involved sensors, actors and data sources require a distinct
infrastructure to ensure efficient, reliable and secure implementation. Within
the paradigm of Internet of Production a reference architecture for such an
infrastructure is established by four layers: Raw data (1), provisioning of
proprietary systems (2), data aggregation and brokering (3) and decision
support (4). In modern metrology assisted assembly systems, a virtual reference
frame is constituted by one or multiple predominantly optical Large-Scale
Metrology instruments, e.g. laser trackers, indoor GPS or multilateration based
on ultra wideband communication. An economically efficient implementation of
the reference frame can be achieved using cooperative data fusion, both by
increasing the operative volume with existing systems and by optimizing the
utilization of highly precise and therefor typically cost-intensive
instruments. Herewith a harmonization is required as well from a physical
perspective as in terms of communication interfaces to the raw data provided by
the individual instruments. The authors propose a model-based approach to
obtain a protocol-agnostic interface description, viewing a Large-Scale
Metrology instrument as an abstract object oriented system consisting of one or
multiple base units and mobile entities.
Supermartingale deflators in the absence of a numéraire
Authors: Philipp Harms, Chong Liu, Ariel Neufeld
Date: 2020-01-16 00:00:00

In this paper we study arbitrage theory of financial markets in the absence of a num\'eraire both in discrete and continuous time. In our main results, we provide a generalization of the classical equivalence between no unbounded profits with bounded risk (NUPBR) and the existence of a supermartingale deflator. To obtain the desired results, we introduce a new approach based on disintegration of the underlying probability space into spaces where the market crashes at deterministic times.
A Unified Bayesian View on Spatially Informed Source Separation and Extraction based on Independent Vector Analysis
Date: 2020-01-16 00:00:00
Tasks: blind source separation

Signal separation and extraction are important tasks for devices recording
audio signals in real environments which, aside from the desired sources, often
contain several interfering sources such as background noise or concurrent
speakers. Blind Source Separation (BSS) provides a powerful approach to address
such problems. However, BSS algorithms typically treat all sources equally and
do not resolve uncertainty regarding the ordering of the separated signals at
the output of the algorithm, i.e., the outer permutation problem. This paper
addresses this problem by incorporating prior knowledge into the adaptation of
the demixing filters, e.g., the position of the sources, in a Bayesian
framework. We focus here on methods based on Independent Vector Analysis (IVA)
as it elegantly and successfully deals with the internal permutation problem.
By including a background model, i.e., a model for sources we are not
interested to separate, we enable the algorithm to extract the sources of
interest in overdetermined and underdetermined scenarios at a low computational
complexity. The proposed framework allows to incorporate prior knowledge about
the demixing filters in a generic way and unifies several known and newly
proposed algorithms using a Bayesian view. For all algorithmic variants, we
provide efficient update rules based on the iterative projection principle. The
performance of a large variety of representative algorithmic variants,
including very recent algorithms, is compared using measured room impulse
responses.
Examining the correlation of the level of wage inequality with labor market institutions
Date: 2020-01-24 00:00:00

Technological change is responsible for major changes in the labor market.
One of the offspring of technological change is the SBTC, which is for many
economists the leading cause of the increasing wage inequality. However,
despite that the technological change affected similarly the majority of the
developed countries, nevertheless, the level of the increase of wage inequality
wasn't similar. Following the predictions of the SBTC theory, the different
levels of inequality could be due to varying degrees of skill inequality
between economies, possibly caused by variations in the number of skilled
workers available. However, recent research shows that the difference mentioned
above can explain a small percentage of the difference between countries.
Therefore, most of the resulting inequality could be due to the different ways
in which the higher level of skills is valued in each labor market. The
position advocated in this article is that technological change is largely
given for all countries without much scope to reverse. Therefore, in order to
illustrate the changes in the structure of wage distribution that cause wage
inequality, we need to understand how technology affects labor market
institutions.In this sense, the pay inequality caused by technological progress
is not a phenomenon we passively accept. On the contrary, recognizing that the
structure and the way labor market institutions function is largely influenced
by the way institutions respond to technological change, we can understand and
maybe reverse this underlying wage inequality.
Non-binary universal tree-based networks
Date: 2020-01-14 00:00:00

A tree-based network $N$ on $X$ is called universal if every phylogenetic
tree on $X$ is a base tree for $N$. Recently, binary universal tree-based
networks have attracted great attention in the literature and their existence
has been analyzed in various studies. In this note, we extend the analysis to
non-binary networks and show that there exist both a rooted and an unrooted
non-binary universal tree-based network with $n$ leaves for all positive
integers $n$.
Distributional synthetic controls
Authors: Florian Gunsilius
Date: 2020-01-17 00:00:00
Tasks: Causal Inference, counterfactual, quantile regression

This article extends the widely-used synthetic controls estimator for evaluating causal effects of policy changes to quantile functions. The proposed method provides a geometrically faithful estimate of the entire counterfactual quantile function of the treated unit. Its appeal stems from an efficient implementation via a constrained quantile-on-quantile regression. This constitutes a novel concept of independent interest. The method provides a unique counterfactual quantile function in any scenario: for continuous, discrete or mixed distributions. It operates in both repeated cross-sections and panel data with as little as a single pre-treatment period. The article also provides abstract identification results by showing that any synthetic controls method, classical or our generalization, provides the correct counterfactual for causal models that preserve distances between the outcome distributions. Working with whole quantile functions instead of aggregate values allows for tests of equality and stochastic dominance of the counterfactual- and the observed distribution. It can provide causal inference on standard outcomes like average- or quantile treatment effects, but also more general concepts such as counterfactual Lorenz curves or interquartile ranges.
A guided network propagation approach to identify disease genes that combines prior and new information
Authors: Borislav H. Hristov, Bernard Chazelle, Mona Singh
Date: 2020-01-17 00:00:00

A major challenge in biomedical data science is to identify the causal genes underlying complex genetic diseases. Despite the massive influx of genome sequencing data, identifying disease-relevant genes remains difficult as individuals with the same disease may share very few, if any, genetic variants. Protein-protein interaction networks provide a means to tackle this heterogeneity, as genes causing the same disease tend to be proximal within networks. Previously, network propagation approaches have spread signal across the network from either known disease genes or genes that are newly putatively implicated in the disease (e.g., found to be mutated in exome studies or linked via genome-wide association studies). Here we introduce a general framework that considers both sources of data within a network context. Specifically, we use prior knowledge of disease-associated genes to guide random walks initiated from genes that are newly identified as perhaps disease-relevant. In large-scale testing across 24 cancer types, we demonstrate that our approach for integrating both prior and new information not only better identifies cancer driver genes than using either source of information alone but also readily outperforms other state-of-the-art network-based approaches. To demonstrate the versatility of our approach, we also apply it to genome-wide association data to identify genes functionally relevant for several complex diseases. Overall, our work suggests that guided network propagation approaches that utilize both prior and new data are a powerful means to identify disease genes.
Neural coincidence detection strategies during perception of multi-pitch musical tones
Date: 2020-01-17 00:00:00

Multi-pitch perception is investigated in a listening test using 30
recordings of musical sounds with two tones played simultaneously, except for
two gong sounds with inharmonic overtone spectrum, judging roughness and
separateness as the ability to tell the two tones in each recording apart. 13
sounds were from a Western guitar playing all 13 intervals in one octave, the
other sounds were mainly from non-Western instruments comparing familiar with
unfamiliar instrument sounds for Western listeners. Additionally the sounds
were processed in a cochlear model transferring the mechanical basilar membrane
motion into neural spikes followed by a post-processing simulating different
degrees of coincidence detection. Separateness perception showed a clear
distinction between familiar and unfamiliar sounds, while roughness perception
did not. By correlating perception with simulation different perception
strategies were found. Familiar sounds correlated strongly positive with high
degrees of coincidence detection, where only 3-5 periodicities were left, while
unfamiliar sounds correlated with low coincidence levels. This corresponds to
an attention to pitch and timbre respectively. Additionally, separateness
perception shows an opposite correlation between perception and neural
correlates between familiar and unfamiliar sounds. This correlates with the
perceptional finding of the distinction between familiar and unfamiliar sounds
with separateness.
Designing unimodular sequence with good auto-correlation properties via Block Majorization-Minimization method
Date: 2020-01-17 00:00:00

Constant modulus sequence having lower side-lobe levels in its
auto-correlation function plays an important role in the applications like
SONAR, RADAR and digital communication systems. In this paper, we consider the
problem of minimizing the Integrated Sidelobe Level (ISL) metric, to design a
complex unimodular sequence of any length. The underlying optimization problem
is solved iteratively using the Block Majorization-Minimization(MM) technique,
which ensures that the resultant algorithm to be monotonic. We also show a
computationally efficient way to implement the algorithm using Fast Fourier
Transform (FFT) and Inverse Fast Fourier Transform (IFFT) operations. Numerical
experiments were conducted to compare the proposed algorithm with the
state-of-the art algorithms and was found that the proposed algorithm performs
better in terms of computational complexity and speed of convergence.
Corporate Governance, Noise Trading and Liquidity of Stocks
Authors: Jianhao Su
Date: 2020-01-17 00:00:00

Our main task is to study the effect of corporate governance on the market liquidity of listed companies' stocks. We establish a theoretical model that contains the heterogeneity of investors' beliefs to explain the mechanisms by which corporate governance improves liquidity of the corporate stocks. In this process we found that the existence of noise traders who are semi-informed in the market is an important condition for corporate governance to have the effect of improving liquidity of the stocks. We further find that the strength of this effect is affected by the degree of noise traders' participation in market transactions. Our model reveals that corporate governance and the degree of noise traders' participation in transactions have a synergistic effect on improving the liquidity of the stocks.