pwc-archive/papers-with-abstracts huggingface dataset:
Autonomous Interference Mapping for Industrial IoT Networks over Unlicensed Bands
Authors: Simone Grimaldi, Aamir Mahmood, Syed Ali Hassan, Gerhard Petrus Hancke, Mikael Gidlund
Date: 2020-06-24 00:00:00

The limited coexistence capabilities of current Internet-of-things (IoT) wireless standards produce inefficient spectrum utilization and mutual performance impairment. The entity of the issue escalates in industrial IoT (IIoT) applications, which instead have stringent quality-of-service requirements and exhibit very-low error tolerance. The constant growth of wireless applications over unlicensed bands mandates then the adoption of dynamic spectrum access techniques, which can greatly benefit from interference mapping over multiple dimensions of the radio space. In this article, the authors analyze the critical role of real-time interference detection and classification mechanisms that rely on IIoT devices only, without the added complexity of specialized hardware. The trade-offs between classification performance and feasibility are analyzed in connection with the implementation on low-complexity IIoT devices. Moreover, the authors explain how to use such mechanisms for enabling IIoT networks to construct and maintain multidimensional interference maps at run-time in an autonomous fashion. Lastly, the authors give an overview of the opportunities and challenges of using interference maps to enhance the performance of IIoT networks under interference.
Clustering and Power Optimization for NOMA Multi-Objective Problems
Authors: Zijian Wang, Mylene Pischella, Luc Vandendorpe
Date: 2020-06-24 00:00:00
Tasks: Clustering

This paper considers uplink multiple access (MA) transmissions, where the MA technique is adaptively selected between Non Orthogonal Multiple Access (NOMA) and Orthogonal Multiple Access (OMA). Two types of users, namely Internet of Things (IoT) and enhanced mobile broadband (eMBB) coexist with different metrics to be optimized, energy efficiency (EE) for IoT and spectral efficiency (SE) for eMBB. The corresponding multi-objective power allocation problems aiming at maximizing a weighted sum of EE and SE are solved for both NOMA and OMA. Based on the identification of the best MA strategy, a clustering algorithm is then proposed to maximize the multi-objective metric per cluster as well as NOMA use. The proposed clustering, power allocation and MA selection algorithm is shown to outperform other clustering solutions and non-adaptive MA techniques.
Partial Information Sharing over Social Learning Networks
Authors: Virginia Bordignon, Vincenzo Matta, Ali H. Sayed
Date: 2020-06-24 00:00:00

This work addresses the problem of sharing partial information within social learning strategies. In traditional social learning, agents solve a distributed multiple hypothesis testing problem by performing two operations at each instant: first, agents incorporate information from private observations to form their beliefs over a set of hypotheses; second, agents combine the entirety of their beliefs locally among neighbors. Within a sufficiently informative environment and as long as the connectivity of the network allows information to diffuse across agents, these algorithms enable agents to learn the true hypothesis. Instead of sharing the entirety of their beliefs, this work considers the case in which agents will only share their beliefs regarding one hypothesis of interest, with the purpose of evaluating its validity, and draws conditions under which this policy does not affect truth learning. We propose two approaches for sharing partial information, depending on whether agents behave in a self-aware manner or not. The results show how different learning regimes arise, depending on the approach employed and on the inherent characteristics of the inference problem. Furthermore, the analysis interestingly points to the possibility of deceiving the network, as long as the evaluated hypothesis of interest is close enough to the truth.
A matlab code to compute reproduction numbers with applications to the Covid-19 outbreak
Date: 2020-06-23 00:00:00

We discuss the generation of various reproduction ratios or numbers that are
very useful to monitor an ongoing epidemic like Covid-19 and examine the
effects of intervention measures. A detailed SEIR algorithm is described for
their computation, with applications given to the current Covid-19 outbreaks in
a number of countries (Argentina, Brazil, France, Italy, Mexico, Spain, UK and
USA). The corresponding matlab script, complete and ready to use, is provided
for free downloading.
Deep Neural Network based Distance Estimation for Geometry Calibration in Acoustic Sensor Networks
Date: 2020-06-24 00:00:00

We present an approach to deep neural network based (DNN-based) distance
estimation in reverberant rooms for supporting geometry calibration tasks in
wireless acoustic sensor networks. Signal diffuseness information from acoustic
signals is aggregated via the coherent-to-diffuse power ratio to obtain a
distance-related feature, which is mapped to a source-to-microphone distance
estimate by means of a DNN. This information is then combined with
direction-of-arrival estimates from compact microphone arrays to infer the
geometry of the sensor network. Unlike many other approaches to geometry
calibration, the proposed scheme does only require that the sampling clocks of
the sensor nodes are roughly synchronized. In simulations we show that the
proposed DNN-based distance estimator generalizes to unseen acoustic
environments and that precise estimates of the sensor node positions are
obtained.
Real-time Flexibility Feedback for Closed-loop Aggregator and System Operator Coordination
Date: 2020-06-23 00:00:00
Tasks: Capacity Estimation

Aggregators have emerged as crucial tools for the coordination of
distributed, controllable loads. However, to be used effectively, aggregators
must be able to communicate the available flexibility of the loads they control
to the system operator in a manner that is both (i) concise enough to be
scalable to aggregators governing hundreds or even thousands of loads and (ii)
informative enough to allow the system operator to send control signals to the
aggregator that lead to optimization of system-level objectives, such as cost
minimization, and do not violate private constraints of the loads, such as
satisfying specific load demands. In this paper, we present the design of a
real-time flexibility feedback signal based on maximization of entropy. The
design provides a concise and informative signal that can be used by the system
operator to perform online cost minimization and real-time capacity estimation,
while provably satisfying the private constraints of the loads. In addition to
deriving analytic properties of the design, we illustrate the effectiveness of
the design using a dataset from an adaptive electric vehicle charging network.
Designing Resilient Linear Driftless Systems
Authors: Jean-Baptiste Bouvier, Melkior Ornik
Date: 2020-06-24 00:00:00

Critical systems must be designed resilient to all kinds of malfunctions. We are especially interested by the loss of control authority over actuators. This malfunction considers actuators producing uncontrolled and possibly undesirable inputs. We investigate the design of resilient linear systems capable of reaching their target even after such a malfunction. In contrast with the settings of robust control and fault-tolerant control, we consider undesirable but observable inputs of the same magnitude as controls since they are produced by a faulty actuator of the system. The control inputs can then depend on these undesirable inputs. Building on our previous work, we focus on designing resilient systems able to withstand the loss of one or multiple actuators. Since resilience refers to the existence of a control law driving the state to the target, we naturally continue with the synthesis of such a control law. We conclude with a numerical application of our theory on the ADMIRE fighter jet model.
A Holistic Framework for Parameter Coordination of Interconnected Microgrids against Disasters
Date: 2020-06-24 00:00:00

This paper proposes a holistic framework for parameter coordination of a
power electronic-interfaced microgrid interconnection against natural
disasters. The paper identifies a transient stability issue in a microgrid
interconnection. Based on recent advances in control theory, we design a
framework that can systematically coordinate system parameters, such that
post-disaster equilibrium points of microgrid interconnections are
asymptotically stable. The core of the framework is a stability assessment
algorithm using sum of squares programming. The efficacy of the proposed
framework is tested in a four-microgrid interconnection. The proposed framework
has potential to extend to microgrid interconnections with a wide range of
hierarchical control schemes.
Kleptoparasitism on carpenter ants (Camponotus spp.) by Podarcis tiliguerta (Gmelin, 1789) in Corsica and Podarcis filfolensis (Bedriaga, 1876) on the Maltese islands
Authors: Todd R. Lewis, Alex Ramsay, Arnold Sciberras, Colin Bailey
Date: 2020-06-24 00:00:00

Kleptoparasitic behavior was observed by the authors in P. tiliguerta and P. filfolensis. That this behavior has been observed in two mediterranean species of Podarcis, independently parasitizing on species of Camponotus ant, suggests there is benefit to kleptoparasitism on Camponotus spp. in Mediterranean ecosystems.
DeFi Protocols for Loanable Funds: Interest Rates, Liquidity and Market Efficiency
Authors: Lewis Gudgeon, Sam M. Werner, Daniel Perez, William J. Knottenbelt
Date: 2020-06-11 00:00:00

We coin the term *Protocols for Loanable Funds (PLFs)* to refer to protocols which establish distributed ledger-based markets for loanable funds. PLFs are emerging as one of the main applications within Decentralized Finance (DeFi), and use smart contract code to facilitate the intermediation of loanable funds. In doing so, these protocols allow agents to borrow and save programmatically. Within these protocols, interest rate mechanisms seek to equilibrate the supply and demand for funds. In this paper, we review the methodologies used to set interest rates on three prominent DeFi PLFs, namely Compound, Aave and dYdX. We provide an empirical examination of how these interest rate rules have behaved since their inception in response to differing degrees of liquidity. We then investigate the market efficiency and inter-connectedness between multiple protocols, examining first whether Uncovered Interest Parity holds within a particular protocol and second whether the interest rates for a particular token market show dependence across protocols, developing a Vector Error Correction Model for the dynamics.
Asset Prices and Capital Share Risks: Theory and Evidence
Date: 2020-06-24 00:00:00

An asset pricing model using long-run capital share growth risk has recently
been found to successfully explain U.S. stock returns. Our paper adopts a
recursive preference utility framework to derive an heterogeneous asset pricing
model with capital share risks.While modeling capital share risks, we account
for the elevated consumption volatility of high income stockholders. Capital
risks have strong volatility effects in our recursive asset pricing model.
Empirical evidence is presented in which capital share growth is also a source
of risk for stock return volatility. We uncover contrasting unconditional and
conditional asset pricing evidence for capital share risks.
Online Graph-Based Change Point Detection in Multiband Image Sequences
Authors: Ricardo Augusto Borsoi, Cédric Richard, André Ferrari, Jie Chen, José Carlos Moreira Bermudez
Date: 2020-06-24 00:00:00
Tasks: Change Point Detection

The automatic detection of changes or anomalies between multispectral and hyperspectral images collected at different time instants is an active and challenging research topic. To effectively perform change-point detection in multitemporal images, it is important to devise techniques that are computationally efficient for processing large datasets, and that do not require knowledge about the nature of the changes. In this paper, we introduce a novel online framework for detecting changes in multitemporal remote sensing images. Acting on neighboring spectra as adjacent vertices in a graph, this algorithm focuses on anomalies concurrently activating groups of vertices corresponding to compact, well-connected and spectrally homogeneous image regions. It fully benefits from recent advances in graph signal processing to exploit the characteristics of the data that lie on irregular supports. Moreover, the graph is estimated directly from the images using superpixel decomposition algorithms. The learning algorithm is scalable in the sense that it is efficient and spatially distributed. Experiments illustrate the detection and localization performance of the method.
Optimizing Voting Order on Sequential Juries: A Median Voter Theorem and Beyond
Authors: Steve Alpern, Bo Chen
Date: 2020-06-24 00:00:00

We consider an odd-sized "jury", which votes sequentially between two states of Nature (say A and B, or Innocent and Guilty) with the majority opinion determining the verdict. Jurors have private information in the form of a signal in [-1,+1], with higher signals indicating A more likely. Each juror has an ability in [0,1], which is proportional to the probability of A given a positive signal, an analog of Condorcet's p for binary signals. We assume that jurors vote honestly for the alternative they view more likely, given their signal and prior voting, because they are experts who want to enhance their reputation (after their vote and actual state of Nature is revealed). For a fixed set of jury abilities, the reliability of the verdict depends on the voting order. For a jury of size three, the optimal ordering is always as follows: middle ability first, then highest ability, then lowest. For sufficiently heterogeneous juries, sequential voting is more reliable than simultaneous voting and is in fact optimal (allowing for non-honest voting). When average ability is fixed, verdict reliability is increasing in heterogeneity. For medium-sized juries, we find through simulation that the median ability juror should still vote first and the remaining ones should have increasing and then decreasing abilities.
Option Pricing: Channels, Target Zones and Sideways Markets
Authors: Zura Kakushadze
Date: 2020-06-25 00:00:00

After a market downturn, especially in an uncertain economic environment such as the current state, there can be a relatively long period with a sideways market, where indexes, stocks, etc., move in channels with support and resistance levels. We discuss option pricing in such scenarios, in both cases of unattainable as well as attainable boundaries, and obtain closed-form option pricing formulas. Our results also apply to FX rates in target zones without interest rate pegging (USD/HKD, digital currencies, etc.).
Shut and re-open: the role of schools in the spread of COVID-19 in Europe
Authors: Helena B. Stage, Joseph Shingleton, Sanmitra Ghosh, Francesca Scarabel, Lorenzo Pellis, Thomas Finnie
Date: 2020-06-25 00:00:00

We investigate the effect of school closure and subsequent reopening on the transmission of COVID-19, by considering Denmark, Norway, Sweden, and German states as case studies. By comparing the growth rates in daily hospitalisations or confirmed cases under different interventions, we provide evidence that the effect of school closure is visible as a reduction in the growth rate approximately 9 days after implementation. Limited school attendance, such as older students sitting exams or the partial return of younger year groups, does not appear to significantly affect community transmission. A large-scale reopening of schools while controlling or suppressing the epidemic appears feasible in countries such as Denmark or Norway, where community transmission is generally low. However, school reopening can contribute to significant increases in the growth rate in countries like Germany, where community transmission is relatively high. Our findings underscore the need for a cautious evaluation of reopening strategies that ensure low classroom occupancy and a solid infrastructure to quickly identify and isolate new infections.
Cointegration in large VARs
Authors: Anna Bykhovskaya, Vadim Gorin
Date: 2020-06-25 00:00:00

The paper analyses cointegration in vector autoregressive processes (VARs) for the cases when both the number of coordinates, $N$, and the number of time periods, $T$, are large and of the same order. We propose a way to examine a VAR of order $1$ for the presence of cointegration based on a modification of the Johansen likelihood ratio test. The advantage of our procedure over the original Johansen test and its finite sample corrections is that our test does not suffer from over-rejection. This is achieved through novel asymptotic theorems for eigenvalues of matrices in the test statistic in the regime of proportionally growing $N$ and $T$. Our theoretical findings are supported by Monte Carlo simulations and an empirical illustration. Moreover, we find a surprising connection with multivariate analysis of variance (MANOVA) and explain why it emerges.
Convex Incremental Dissipativity Analysis of Nonlinear Systems - Extended version
Authors: Chris Verhoek, Patrick J. W. Koelewijn, Sofie Haesaert, Roland Tóth
Date: 2020-06-25 00:00:00

Efficiently computable stability and performance analysis of nonlinear systems becomes increasingly more important in practical applications. Dissipativity can express stability and performance jointly, but existing results are limited to the regions around the equilibrium points of these nonlinear systems. The incremental framework, based on the convergence of the system trajectories, removes this limitation. We investigate how stability and performance characterizations of nonlinear systems in the incremental framework are linked to dissipativity, and how general performance characterization beyond the $\mathcal{L}_2$-gain concept can be understood in this framework. This paper presents a matrix inequalities-based convex incremental dissipativity analysis for nonlinear systems via quadratic storage and supply functions. The proposed dissipativity analysis links the notions of incremental, differential, and general dissipativity. We show that through differential dissipativity, incremental and general dissipativity of the nonlinear system can be guaranteed. These results also lead to the incremental extensions of the $\mathcal{L}_2$-gain, the generalized $\mathcal{H}_2$-norm, the $\mathcal{L}_\infty$-gain, and passivity of nonlinear systems.
Favorable Propagation with User Cluster Sharing
Authors: Chelsea Miller, Peter J. Smith, Pawel A. Dmochowski, Harsh Tataria, Andreas F. Molisch
Date: 2020-06-25 00:00:00

We examine the favorable propagation (FP) behavior of a massive multi-user multiple-input-multiple-output (MU-MIMO) system equipped with a uniform linear array (ULA), horizontal uniform rectangular array (HURA) or uniform circular array (UCA) using a ray-based channel model with user cluster sharing. We demonstrate FP for these systems and provide analytical expressions for the mean-squared distance (MSD) of the FP metric from its large-system limit for each of the aforementioned topologies. We use these results to examine the detrimental effects of user cluster sharing on FP behavior, and demonstrate the superior performance of the ULA as compared to the UCA and the HURA with equal inter-element spacing. Although cluster sharing has a negative impact on FP for finite arrays, we additionally examine the asymptotic rate of convergence to FP as a function of array size and show that this rate is unchanged with or without user cluster sharing.
Matching Multidimensional Types: Theory and Application
Date: 2020-06-25 00:00:00

Becker (1973) presents a bilateral matching model in which scalar types
describe agents. For this framework, he establishes the conditions under which
positive sorting between agents' attributes is the unique market outcome.
Becker's celebrated sorting result has been applied to address many economic
questions. However, recent empirical studies in the fields of health,
household, and labor economics suggest that agents have multiple
outcome-relevant attributes. In this paper, I study a matching model with
multidimensional types. I offer multidimensional generalizations of concordance
and supermodularity to construct three multidimensional sorting patterns and
two classes of multidimensional complementarities. For each of these sorting
patterns, I identify the sufficient conditions which guarantee its optimality.
In practice, we observe sorting patterns between observed attributes that are
aggregated over unobserved characteristics. To reconcile theory with practice,
I establish the link between production complementarities and the aggregated
sorting patterns. Finally, I examine the relationship between agents' health
status and their spouses' education levels among U.S. households within the
framework for multidimensional matching markets. Preliminary analysis reveals a
weak positive association between agents' health status and their spouses'
education levels. This weak positive association is estimated to be a product
of three factors: (a) an attraction between better-educated individuals, (b) an
attraction between healthier individuals, and (c) a weak positive association
between agents' health status and their education levels. The attraction
channel suggests that the insurance risk associated with a two-person family
plan is higher than the aggregate risk associated with two individual policies.
Dialogue Enhancement in Object-based Audio -- Evaluating the Benefit on People above 65
Date: 2020-06-25 00:00:00
Tasks: blind source separation

Due to age-related hearing loss, elderly people often struggle with following
the language on TV. Because they form an increasing part of the audience, this
problem will become even more important in the future and needs to be addressed
by research and development. Object-based audio is a promising approach to
solve this issue as it offers the possibility of customizable dialogue
enhancement (DE). For this thesis an Adjustment / Satisfaction Test (A/ST) was
conducted to evaluate the preferred loudness difference (LD) between speech and
background in people above 65. Two different types of DE were tested: DE with
separately available audio components (speech and background) and DE with
components created by blind source separation (BSS). The preferred LDs compared
to the original, differences of the preferred LDs between the two DE methods
and the listener satisfaction were tested. It was observed that the preferred
LDs were larger than the original LDs, that customizable DE increases listener
satisfaction and that the two DE methods performed comparably well in terms of
preferred LD and listener satisfaction. Based on the results, it can be assumed
that elderly viewers above 65 will benefit equally from user-adjustable DE by
available components and by dialogue separation.
Dynamic User Pairing for Non-Orthogonal Multiple Access in Downlink Networks
Authors: Kha-Hung Nguyen, Hieu V. Nguyen, Van-Phuc Bui, Oh-Soon Shin
Date: 2020-06-25 00:00:00
Tasks: Heuristic Search

This paper considers a downlink (DL) system where non-orthogonal multiple access (NOMA) beamforming and dynamic user pairing are jointly optimized to maximize the minimum throughput of all DL users. The resulting problem belongs to a class of mixed-integer non-convex optimization. To solve the problem, we first relax the binary variables to continuous ones, and then devise an iterative algorithm based on the inner approximation method which provides at least a local optimal solution. Numerical results verify that the proposed algorithm outperforms other ones, such as conventional beamforming, NOMA with random-pairing and heuristic-search strategies.