pwc-archive/papers-with-abstracts huggingface dataset:
Cache-Aided Non-Orthogonal Multiple Access for 5G-Enabled Vehicular Networks
Date: 2019-06-14 00:00:00

The increasing demand for rich multimedia services and the emergence of the
Internet-of-Things (IoT) pose challenging requirements for the next generation
vehicular networks. Such challenges are largely related to high spectral
efficiency and low latency requirements in the context of massive content
delivery and increased connectivity. In this respect, caching and
non-orthogonal multiple access (NOMA) paradigms have been recently proposed as
potential solutions to effectively address some of these key challenges. In the
present contribution, we introduce cache-aided NOMA as an enabling technology
for vehicular networks. In this context, we first consider the full file
caching case, where each vehicle caches and requests entire files using the
NOMA principle. Without loss of generality, we consider a two-user vehicular
network communication scenario under double Nakagami$-m$ fading conditions and
propose an optimum power allocation policy. To this end, an optimization
problem that maximizes the overall probability of successful decoding of files
at each vehicle is formulated and solved. Furthermore, we consider the case of
split file caching, where each file is divided into two parts. A joint power
allocation optimization problem is formulated, where power allocation across
vehicles and cached split files is investigated. The offered analytic results
are corroborated by extensive results from computer simulations and interesting
insights are developed. Indicatively, it is shown that the proposed
caching-aided NOMA outperforms the conventional NOMA technique.
FPGA-based real-time autocorrelator and its application in dynamic light scattering
Date: 2019-06-14 00:00:00

Digital correlators play a significant role in dynamic light scattering (DLS)
technology, which characterizes particle size distribution. We present a field
programmable gate array (FPGA)-based digital correlator that can be applied to
process DLS data. To satisfy the DLS requirements in the FPGA logic with
limited resources, a multiple lag time period (multi-{\tau} ) method is
employed that does not require storing the full data set in memory. Moreover,
the device directly accepts the transistor-transistor logic (TTL) signal from
the photon counting detector by measuring the time intervals between photon
events and calculates the autocorrelation functions in real time. Furthermore,
we derive estimates for the error arising from the use of the multi-{\tau}
correlator. We implement all the necessary operations in a single Xilinx FPGA
chip with a lag time from 10 ns to 45 min, including a highly optimized photon
counter.
How tree-based is my network? Proximity measures for unrooted phylogenetic networks
Date: 2020-01-16 00:00:00

Tree-based networks are a class of phylogenetic networks that attempt to
formally capture what is meant by "tree-like" evolution. A given non-tree-based
phylogenetic network, however, might appear to be very close to being
tree-based, or very far. In this paper, we formalise the notion of proximity to
tree-based for unrooted phylogenetic networks, with a range of proximity
measures. These measures also provide characterisations of tree-based networks.
One measure in particular, related to the nearest neighbour interchange
operation, allows us to define the notion of "tree-based rank". This provides a
subclassification within the tree-based networks themselves, identifying those
networks that are "very" tree-based. Finally, we prove results relating
tree-based networks in the settings of rooted and unrooted phylogenetic
networks, showing effectively that an unrooted network is tree-based if and
only if it can be made a rooted tree-based network by rooting it and orienting
the edges appropriately. This leads to a clarification of the contrasting
decision problems for tree-based networks, which are polynomial in the rooted
case but NP complete in the unrooted.
Model Risk in Credit Risk
Date: 2019-06-14 00:00:00
Tasks: model

The issue of model risk in default modeling has been known since inception of
the Academic literature in the field. However, a rigorous treatment requires a
description of all the possible models, and a measure of the distance between a
single model and the alternatives, consistent with the applications. This is
the purpose of the current paper. We first analytically describe all possible
joint models for default, in the class of finite sequences of exchangeable
Bernoulli random variables. We then measure how the model risk of choosing or
calibrating one of them affects the portfolio loss from default, using two
popular and economically sensible metrics, Value-at-Risk (VaR) and Expected
Shortfall (ES).
Control-Aware Scheduling for Low Latency Wireless Systems with Deep Learning
Date: 2019-10-30 00:00:00
Tasks: Deep Learning, Scheduling

We consider the problem of scheduling transmissions over low-latency wireless
communication links to control various control systems. Low-latency
requirements are critical in developing wireless technology for industrial
control and Tactile Internet, but are inherently challenging to meet while also
maintaining reliable performance. An alternative to ultra reliable low latency
communications is a framework in which reliability is adapted to control system
demands. We formulate the control-aware scheduling problem as a constrained
statistical optimization problem in which the optimal scheduler is a function
of current control and channel states. The scheduler is parameterized with a
deep neural network, and the constrained problem is solved using techniques
from primal-dual learning, which have a necessary model-free property in that
they do not require explicit knowledge of channels models, performance metrics,
or system dynamics to execute. The resulting control-aware deep scheduler is
evaluated in empirical simulations and strong performance is shown relative to
other model-free heuristic scheduling methods.
Multi-Carrier Agile Phased Array Radar
Date: 2020-02-23 00:00:00
Tasks: compressed sensing

Modern radar systems are expected to operate reliably in congested
environments. A candidate technology for meeting these demands is frequency
agile radar (FAR), which randomly changes its carrier frequencies. FAR is known
to improve the electronic counter-countermeasures (ECCM) performance while
facilitating operation in congested setups. To enhance the target recovery
performance of FAR in complex electromagnetic environments, we propose two
radar schemes extending FAR to multi-carrier waveforms. The first is Wideband
Multi-carrier Agile Radar (WMAR), which transmits/receives wideband waveforms
simultaneously with every antenna. To mitigate the demanding hardware
requirements associated with wideband waveforms used by WMAR, we next propose
multi-Carrier AgilE phaSed Array Radar (CAESAR). CAESAR uses narrowband
monotone waveforms, thus facilitating ease of implementation of the system,
while introducing {\em spatial agility}. We characterize the transmitted and
received signals of the proposed schemes, and develop an algorithm for
recovering the targets, based on concepts from compressed sensing to estimate
the range-Doppler parameters of the targets. We then derive conditions which
guarantee their accurate reconstruction. Our numerical study demonstrates that
both multi-carrier schemes improve performance compared to FAR while
maintaining its practical benefits. We also demonstrate that the performance of
CAESAR, which uses monotone waveforms, is within a small gap from the wideband
radar.
Variants of the Smith-Wilson method with a view towards applications
Date: 2019-06-14 00:00:00

We propose two variants of the Smith-Wilson method for practical application
in the insurance industry. Our first variant relaxes the Smith-Wilson energy
and can be used to incorporate less reliable market data with a certain weight
rather than disregarding it completely. This is particularly useful for
deriving yield curves in the IFRS 17 accounting regime, where there is a
mandate to incorporate all available market data.
  A second variant incorporates the requirement to reach the ultimate forward
rate at a prescribed term into the problem formulation. This provides a natural
way to fulfil the Solvency II convergence requirement and is more elegant than
the current methodology adapting the term-scale parameter to control
convergence.
Nonreciprocal Yagi-Uda Filtering Antennas
Date: 2019-06-14 00:00:00

This paper proposes a novel and compact nonreciprocal filtering antenna based
on temporal modulation. The device is composed of a third order filtering
section integrated into a Yagi-Uda planar printed antenna. Strong
nonreciprocity in transmission and reception is achieved at the same operation
frequency by time-modulating the resonators of the filtering section. These
resonators are implemented as quarter wavelength microstrip lines terminated
with varactors located on the ground plane. Such plane is also employed as a
reflector for the Yagi-Uda antenna and to host the coplanar waveguides that
feed low-frequency signals to the varactors, thus leading to a very compact
design. A prototype is manufactured and successfully tested at 2.4 GHz, showing
isolation greater than 20 dB between transmission and reception modes in both
E- and H- planes for all directions in space and a gain drop of only 3.5 dB
compared to a reference antenna. The proposed devices can easily be integrated
with other electronic circuits and may find exciting applications in
communication, radar, and sensing systems.
Calibration of Local-Stochastic Volatility Models by Optimal Transport
Authors: Ivan Guo, Gregoire Loeper, Shiyi Wang
Date: 2019-06-15 00:00:00

In this paper, we study a semi-martingale optimal transport problem and its application to the calibration of Local-Stochastic Volatility (LSV) models. Rather than considering the classical constraints on marginal distributions at initial and final time, we optimise our cost function given the prices of a finite number of European options. We formulate the problem as a convex optimisation problem, for which we provide a PDE formulation along with its dual counterpart. Then we solve numerically the dual problem, which involves a fully non-linear Hamilton-Jacobi-Bellman equation. The method is tested by calibrating a Heston-like LSV model with simulated data and foreign exchange market data.
Option Pricing via Multi-path Autoregressive Monte Carlo Approach
Authors: Wei-Cheng Chen, Wei-Ho Chung
Date: 2019-06-15 00:00:00

The pricing of financial derivatives, which requires massive calculations and close-to-real-time operations under many trading and arbitrage scenarios, were largely infeasible in the past. However, with the advancement of modern computing, the efficiency has substantially improved. In this work, we propose and design a multi-path option pricing approach via autoregression (AR) process and Monte Carlo Simulations (MCS). Our approach learns and incorporates the price characteristics into AR process, and re-generates the price paths for options. We apply our approach to price weekly options underlying Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and compare the results with prior practiced models, e.g., Black-Scholes-Merton and Binomial Tree. The results show that our approach is comparable with prior practiced models.
A Simple Local Minimal Intensity Prior and An Improved Algorithm for Blind Image Deblurring
Date: 2020-10-29 00:00:00
Tasks: Blind Image Deblurring, Computational Efficiency, Deblurring, Image Deblurring

Blind image deblurring is a long standing challenging problem in image
processing and low-level vision. Recently, sophisticated priors such as dark
channel prior, extreme channel prior, and local maximum gradient prior, have
shown promising effectiveness. However, these methods are computationally
expensive. Meanwhile, since these priors involved subproblems cannot be solved
explicitly, approximate solution is commonly used, which limits the best
exploitation of their capability. To address these problems, this work firstly
proposes a simplified sparsity prior of local minimal pixels, namely patch-wise
minimal pixels (PMP). The PMP of clear images is much more sparse than that of
blurred ones, and hence is very effective in discriminating between clear and
blurred images. Then, a novel algorithm is designed to efficiently exploit the
sparsity of PMP in deblurring. The new algorithm flexibly imposes sparsity
inducing on the PMP under the MAP framework rather than directly uses the half
quadratic splitting algorithm. By this, it avoids non-rigorous approximation
solution in existing algorithms, while being much more computationally
efficient. Extensive experiments demonstrate that the proposed algorithm can
achieve better practical stability compared with state-of-the-arts. In terms of
deblurring quality, robustness and computational efficiency, the new algorithm
is superior to state-of-the-arts. Code for reproducing the results of the new
method is available at https://github.com/FWen/deblur-pmp.git.
Ensemble Consider Kalman Filtering
Date: 2019-06-16 00:00:00
Tasks: State Estimation

In this paper, the ensemble consider Kalman filter is proposed to mitigate
the negative effects of uncertain parameters in nonlinear dynamic and
measurement models. The ensemble Kalman filter can avoid using the Jacobian
matrices and reduce the computational complexity, the unknown parameters of the
models still are not considered. By incorporating the statistics of the
uncertain parameters into the state estimation formulations and using an
augmented-state approach, the ensemble integration is reset by resampling the
ensemble members in the new step, and the EnCKF algorithm is derived. Two
numerical simulations show that the presented EnCKF can mitigate the negative
effects of the uncertain parameters.
Detecting p-hacking
Authors: Graham Elliott, Nikolay Kudrin, Kaspar Wuthrich
Date: 2019-06-16 00:00:00

We theoretically analyze the problem of testing for $p$-hacking based on distributions of $p$-values across multiple studies. We provide general results for when such distributions have testable restrictions (are non-increasing) under the null of no $p$-hacking. We find novel additional testable restrictions for $p$-values based on $t$-tests. Specifically, the shape of the power functions results in both complete monotonicity as well as bounds on the distribution of $p$-values. These testable restrictions result in more powerful tests for the null hypothesis of no $p$-hacking. When there is also publication bias, our tests are joint tests for $p$-hacking and publication bias. A reanalysis of two prominent datasets shows the usefulness of our new tests.
Shape Matters: Evidence from Machine Learning on Body Shape-Income Relationship
Date: 2019-06-16 00:00:00
Tasks: BIG-bench Machine Learning

We study the association between physical appearance and family income using
a novel data which has 3-dimensional body scans to mitigate the issue of
reporting errors and measurement errors observed in most previous studies. We
apply machine learning to obtain intrinsic features consisting of human body
and take into account a possible issue of endogenous body shapes. The
estimation results show that there is a significant relationship between
physical appearance and family income and the associations are different across
the gender. This supports the hypothesis on the physical attractiveness premium
and its heterogeneity across the gender.
Adaptive mitigation of the Air-Time pressure in LoRa multi-gateway architectures
Authors: Francesca Cuomo, Manuel Campo, Enrico Bassetti, Lorenzo Cartella, Federica Sole, Giuseppe Bianchi
Date: 2019-06-16 00:00:00

LoRa is a promising technology in the current Internet of Things market, which operates in un-licensed bands achieving long-range communications and with ultra power devices. In this work we capitalize on the idea introduced in [1], i.e. balance the Air-Time of the different modulation spreading factors (SF), and adapt it to operate in a typical metropolitan scenario comprising multiple gateways (GWs) interconnected to a same network server. Our proposed approach, named ADaptive Mitigation of the AIr-time pressure in lORa (AD MAIORA), relies on a suitable measure of the per-spreading-factor load at each GW - quantified by means of a so-called pressure table -, and on a relevant heuristic algorithm which attempts to balance such a per-SF-pressure. Especially in cases of very loaded scenarios, where a high number of nodes insist on the same GWs, the use of AD MAIORA shows significant performance gains, up to a factor of 5 improvements with respect to the legacy LoRaWAN's Adaptive Data Rate.
General equilibrium in a heterogeneous-agent incomplete-market economy with many consumption goods and a risk-free bond
Authors: Bar Light
Date: 2019-06-17 00:00:00

We study a pure-exchange incomplete-market economy with heterogeneous agents. In each period, the agents choose how much to save (i.e., invest in a risk-free bond), how much to consume, and which bundle of goods to consume while their endowments are fluctuating. We focus on a competitive stationary equilibrium (CSE) in which the wealth distribution is invariant, the agents maximize their expected discounted utility, and both the prices of consumption goods and the interest rate are market-clearing. Our main contribution is to extend some general equilibrium results to an incomplete-market Bewley-type economy with many consumption goods. Under mild conditions on the agents' preferences, we show that the aggregate demand for goods depends only on their relative prices and that the aggregate demand for savings is homogeneous of degree in prices, and we prove the existence of a CSE. When the agents' preferences can be represented by a CES (constant elasticity of substitution) utility function with an elasticity of substitution that is higher than or equal to one, we prove that the CSE is unique. Under the same preferences, we show that a higher inequality of endowments does not change the equilibrium prices of goods, and decreases the equilibrium interest rate. Our results shed light on the impact of market incompleteness on the properties of general equilibrium models.
Nested partitions from hierarchical clustering statistical validation
Date: 2019-06-17 00:00:00
Tasks: Clustering

We develop a greedy algorithm that is fast and scalable in the detection of a
nested partition extracted from a dendrogram obtained from hierarchical
clustering of a multivariate series. Our algorithm provides a $p$-value for
each clade observed in the hierarchical tree. The $p$-value is obtained by
computing a number of bootstrap replicas of the dissimilarity matrix and by
performing a statistical test on each difference between the dissimilarity
associated with a given clade and the dissimilarity of the clade of its parent
node. We prove the efficacy of our algorithm with a set of benchmarks generated
by using a hierarchical factor model. We compare the results obtained by our
algorithm with those of Pvclust. Pvclust is a widely used algorithm developed
with a global approach originally motivated by phylogenetic studies. In our
numerical experiments we focus on the role of multiple hypothesis test
correction and on the robustness of the algorithms to inaccuracy and errors of
datasets. We also apply our algorithm to a reference empirical dataset. We
verify that our algorithm is much faster than Pvclust algorithm and has a
better scalability both in the number of elements and in the number of records
of the investigated multivariate set. Our algorithm provides a hierarchically
nested partition in much shorter time than currently widely used algorithms
allowing to perform a statistically validated cluster analysis detection in
very large systems.
Decomposition formula for jump diffusion models
Authors: Raul Merino, Jan Pospíšil, Tomáš Sobotka, Josep Vives
Date: 2019-06-17 00:00:00

In this paper we derive a generic decomposition of the option pricing formula for models with finite activity jumps in the underlying asset price process (SVJ models). This is an extension of the well-known result by Alos (2012) for Heston (1993) SV model. Moreover, explicit approximation formulas for option prices are introduced for a popular class of SVJ models - models utilizing a variance process postulated by Heston (1993). In particular, we inspect in detail the approximation formula for the Bates (1996) model with log-normal jump sizes and we provide a numerical comparison with the industry standard - Fourier transform pricing methodology. For this model, we also reformulate the approximation formula in terms of implied volatilities. The main advantages of the introduced pricing approximations are twofold. Firstly, we are able to significantly improve computation efficiency (while preserving reasonable approximation errors) and secondly, the formula can provide an intuition on the volatility smile behaviour under a specific SVJ model.
A Chance-Constrained Stochastic Electricity Market
Date: 2019-12-18 00:00:00

Efficiently accommodating uncertain renewable resources in wholesale
electricity markets is among the foremost priorities of market regulators in
the US, UK and EU nations. However, existing deterministic market designs fail
to internalize the uncertainty and their scenario-based stochastic extensions
are limited in their ability to simultaneously maximize social welfare and
guarantee non-confiscatory market outcomes in expectation and per each
scenario. This paper proposes a chance-constrained stochastic market design,
which is capable of producing a robust competitive equilibrium and
internalizing uncertainty of the renewable resources in the price formation
process. The equilibrium and resulting prices are obtained for different
uncertainty assumptions, which requires using either linear (restrictive
assumptions) or second-order conic (more general assumptions) duality in the
price formation process. The usefulness of the proposed stochastic market
design is demonstrated via the case study carried out on the 8-zone ISO New
England testbed.
Invariance in ecological pattern
Date: 2019-08-20 00:00:00

The abundance of different species in a community often follows the log
series distribution. Other ecological patterns also have simple forms. Why does
the complexity and variability of ecological systems reduce to such simplicity?
Common answers include maximum entropy, neutrality, and convergent outcome from
different underlying biological processes. This article proposes a more general
answer based on the concept of invariance, the property by which a pattern
remains the same after transformation. Invariance has a long tradition in
physics. For example, general relativity emphasizes the need for the equations
describing the laws of physics to have the same form in all frames of
reference. By bringing this unifying invariance approach into ecology, we show
that the log series pattern dominates when the consequences of processes acting
on abundance are invariant to the addition or multiplication of abundance by a
constant. The lognormal pattern dominates when the processes acting on net
species growth rate obey rotational invariance (symmetry) with respect to the
summing up of the individual component processes. Recognizing how these
invariances connect pattern to process leads to a synthesis of previous
approaches. First, invariance provides a simpler and more fundamental maximum
entropy derivation of the log series distribution. Second, invariance provides
a simple derivation of the key result from neutral theory: the log series at
the metacommunity scale and a clearer form of the skewed lognormal at the local
community scale. The invariance expressions are easy to understand because they
uniquely describe the basic underlying components that shape pattern.