pwc-archive/papers-with-abstracts huggingface dataset:
Performance Analysis and Optimization of RIS-Assisted Networks in Nakagami-m Environment
Authors: Monjed H. Samuh, Anas M. Salhab, Ahmed H. Abd El-Malek
Date: 2020-10-15 00:00:00
Tasks: Diversity, Form

This work studies and optimizes the performance of reconfigurable intelligent surface (RIS)-aided networks in Nakagami-m fading environment. First, accurate closed-form approximations for the channel distributions are derived. Then, closed-form formulas for the system outage probability, average symbol error probability (ASEP), and the channel capacity are obtained. Furthermore, we provide three different optimization approaches for finding the optimum number of reflecting elements to achieve a target outage probability. At the high signal-to-noise ratio (SNR) regime, a closed-form expression for the asymptotic outage probability is obtained to give more insights into the system performance. Results show that the considered system can achieve a diversity order of (a+1)/2, where a is a function of the Nakagami-m fading parameter m and the number of reflecting elements N. Moreover, findings show that m is more influential on the diversity order than N. Finally, the achieved expressions are applicable to non-integer values of m and any number of meta-surface elements N.
Network Topology Inference with Graphon Spectral Penalties
Authors: T. Mitchell Roddenberry, Madeline Navarro, Santiago Segarra
Date: 2020-10-15 00:00:00
Tasks: valid

We consider the problem of inferring the unobserved edges of a graph from data supported on its nodes. In line with existing approaches, we propose a convex program for recovering a graph Laplacian that is approximately diagonalizable by a set of eigenvectors obtained from the second-order moment of the observed data. Unlike existing work, we incorporate prior knowledge about the distribution from where the underlying graph was drawn. In particular, we consider the case where the graph was drawn from a graphon model, and we supplement our convex optimization problem with a provably-valid regularizer on the spectrum of the graph to be recovered. We present the cases where the graphon model is assumed to be known and the more practical setting where the relevant features of the model are inferred from auxiliary network observations. Numerical experiments on synthetic and real-world data illustrate the advantage of leveraging the proposed graphon prior, even when the prior is imperfect.
The Shift to 6G Communications: Vision and Requirements
Authors: Muhammad Waseem Akhtar, Syed Ali Hassan, Rizwan Ghaffar, Haejoon Jung, Sahil Garg, M. Shamim Hossain
Date: 2020-10-15 00:00:00
Tasks: BIG-bench Machine Learning, Edge-computing, Quantum Machine Learning

The sixth-generation (6G) wireless communication network is expected to integrate the terrestrial, aerial, and maritime communications into a robust network which would be more reliable, fast, and can support a massive number of devices with ultra-low latency requirements. The researchers around the globe are proposing cutting edge technologies such as artificial intelligence (AI)/machine learning (ML), quantum communication/quantum machine learning (QML), blockchain, tera-Hertz and millimeter waves communication, tactile Internet, non-orthogonal multiple access (NOMA), small cells communication, fog/edge computing, etc., as the key technologies in the realization of beyond 5G (B5G) and 6G communications. In this article, we provide a detailed overview of the 6G network dimensions with air interface and associated potential technologies. More specifically, we highlight the use cases and applications of the proposed 6G networks in various dimensions. Furthermore, we also discuss the key performance indicators (KPI) for the B5G/6G network, challenges, and future research opportunities in this domain.
The measure of model risk in credit capital requirements
Authors: Roberto Baviera
Date: 2020-10-15 00:00:00

Credit capital requirements in Internal Rating Based approaches require the calibration of two key parameters: the probability of default and the loss-given-default. This letter considers the uncertainty about these two parameters and models this uncertainty in an elementary way: it shows how this estimation risk can be computed and properly taken into account in regulatory capital. We analyse two standard real datasets: one composed by all corporates rated by Moody's and one limited only to the speculative grade ones. We statistically test model hypotheses on both marginal distributions and parameter dependency. We compute the estimation risk impact and observe that parameter dependency raises substantially the tail risk in capital requirements. The results are striking with a required increase in regulatory capital in the range $38\%$-$66\%$.
Background risk and small-stakes risk aversion
Authors: Xiaosheng Mu, Luciano Pomatto, Philipp Strack, Omer Tamuz
Date: 2020-10-15 00:00:00

We show that under plausible levels of background risk, no theory of choice under risk -- such as expected utility theory, prospect theory, or rank dependent utility -- can simultaneously satisfy the following three economic postulates: (i) Decision makers are risk-averse over small gambles, (ii) they respect stochastic dominance, and (iii) they account for background risk.
Revisiting Shao and Sokal's $B_2$ index of phylogenetic balance
Authors: François Bienvenu, Gabriel Cardona, Celine Scornavacca
Date: 2020-10-16 00:00:00

Measures of phylogenetic balance, such as the Colless and Sackin indices, play an important role in phylogenetics. Unfortunately, these indices are specifically designed for phylogenetic trees, and do not extend naturally to phylogenetic networks (which are increasingly used to describe reticulate evolution). This led us to consider a lesser-known balance index, whose definition is based on a probabilistic interpretation that is equally applicable to trees and to networks. This index, known as the $B_2$ index, was first proposed by Shao and Sokal in 1990. Surprisingly, it does not seem to have been studied mathematically since. Likewise, it is used only sporadically in the biological literature, where it tends to be viewed as arcane. In this paper, we study mathematical properties of $B_2$ such as its expectation and variance under the most common models of random trees and its extremal values over various classes of phylogenetic networks. We also assess its relevance in biological applications, and find it to be comparable to that of the Colless and Sackin indices. Altogether, our results call for a reevaluation of the status of this somewhat forgotten measure of phylogenetic balance.
Joint Optimization for Coordinated Charging Control of Commercial Electric Vehicles Under Distributed Hydrogen Energy Supply
Authors: Teng Long, Qing-Shan Jia
Date: 2020-10-16 00:00:00

The transition to the zero-carbon power system is underway accelerating recently. Hydrogen energy and electric vehicles (EVs) are promising solutions on the supply and demand sides. This paper presents a novel architecture that includes hydrogen production stations (HPSs), fast charging stations (FCSs), and commercial EVs. The proposed architecture jointly optimizes the distributed hydrogen energy dispatch and the EV charging location selection, and is formulated by a time-varying bi-level bipartite graph (T-BBG) model for real-time operation. We develop a bi-level iteration optimization method combining linear programming (LP) and Kuhn-Munkres (KM) algorithm to solve the joint problem whose optimality is proved theoretically. The effectiveness of the proposed architecture on reducing the operating cost is verified via case studies in Shanghai. The proposed method outperforms other strategies and improves the performance by at least 13% which shows the potential economic benefits of the joint architecture. The convergence and impact of the pile number, battery capacity, EV speed and penalty factor are assessed.
An Application of Hölder's Inequality to Economics
Authors: James Otterson
Date: 2020-10-16 00:00:00

We use H\"older's inequality to get simple derivations of certain economic formulas involving CES, Armington, or $n$-stage Armington functions.
Model-based Decentralized Bayesian Algorithm for Distributed Compressed Sensing
Authors: Razieh Torkamani, Hadi Zayyani, Ramazan Ali Sadeghzadeh
Date: 2020-10-16 00:00:00
Tasks: compressed sensing, Compressive Sensing

In this paper, a novel model-based distributed compressive sensing (DCS) algorithm is proposed. DCS exploits the inter-signal correlations and has the capability to jointly recover multiple sparse signals. Proposed approach is a Bayesian decentralized algorithm which uses the type 1 joint sparsity model (JSM-1) and exploits the intra-signal correlations, as well as the inter-signal correlations. Compared to the conventional DCS algorithm, which only exploit the joint sparsity of the signals, the proposed approach takes the intra- and inter-scale dependencies among the wavelet coefficients into account to enable the utilization of the individual signal structure. Furthermore, the Bessel K-form (BKF) is used as the prior distribution which has a sharper peak at zero and heavier tails than the Gaussian distribution. The variational Bayesian (VB) inference is employed to perform the posterior distributions and acquire a closed-form solution for model parameters. Simulation results demonstrate that the proposed algorithm have good recovery performance in comparison with state-of the-art techniques.
Statistical Graph Signal Recovery Using Variational Bayes
Authors: Razieh Torkamani, Hadi Zayyani
Date: 2020-10-16 00:00:00

This paper investigates the problem of graph signal recovery (GSR) when the topology of the graph is not known in advance. In this paper, the elements of the weighted adjacency matrix is statistically related to normal distribution and the graph signal is assumed to be Gaussian Markov Random Field (GMRF). Then, the problem of GSR is solved by a Variational Bayes (VB) algorithm in a Bayesian manner by computing the posteriors in a closed form. The posteriors of the elements of weighted adjacency matrix are proved to have a new distribution which we call it generalized compound confluent hypergeometric (GCCH) distribution. Moreover, the variance of the noise is estimated by calculating its posterior via VB. The simulation results on synthetic and real-world data shows the superiority of the proposed Bayesian algorithm over some state-of-the-art algorithms in recovering the graph signal.
Unconventional Policies Effects on Stock Market Volatility: A MAP Approach
Authors: Demetrio Lacava, Giampiero M. Gallo, Edoardo Otranto
Date: 2020-10-16 00:00:00

Taking the European Central Bank unconventional policies as a reference, we suggest a class of Multiplicative Error Models (MEM) taylored to analyze the impact such policies have on stock market volatility. The new set of models, called MEM with Asymmetry and Policy effects (MAP), keeps the base volatility dynamics separate from a component reproducing policy effects, with an increase in volatility on announcement days and a decrease unfolding implementation effects. When applied to four Eurozone markets, a Model Confidence Set approach finds a significant improvement of the forecasting power of the proxy after the Expanded Asset Purchase Programme implementation; a multi--step ahead forecasting exercise estimates the duration of the effect, and, by shocking the policy variable, we are able to quantify the reduction in volatility which is more marked for debt--troubled countries.
Partial Discharge Direction of Arrival Estimation in Air-insulated Substation by UHF Wireless Array and RSSI Maximum Likelihood Estimator
Authors: Bei Han, Lingen Luo, Gehao Sheng, Xiuchen Jiang
Date: 2020-10-16 00:00:00
Tasks: Clustering, Direction of Arrival Estimation

The quick detection and localization of partial discharge (PD) in an air-insulated substation (AIS) based on ultrahigh-frequency (UHF) sensor arrays are efficient for power equipment monitoring. The adopted UHF PD time difference of arrival (TDOA) methods mainly use the time difference of electromagnetic wave signals. Thus, the system requires both a high sampling rate and time synchronization accuracy, leading to a high cost and large size. In this study, the feasibility and accuracy of PD DOA in an AIS were investigated using a UHF wireless sensor array and the received signal strength indicator. First, the power pattern of the designed UHF wireless sensor array was obtained via an offline experiment. Then, a statistical approach to the PD DOA method based on the maximum likelihood estimator was employed to obtain the preliminary DOA result. Finally, interpolation and clustering algorithms were used to improve the accuracy of the DOA. A laboratory test was conducted. The average error of the PD DOA was less than 6{\deg}, and the cost-effectiveness and portability were clearly improved.
Experimental Evaluation of Vibration Influence on a Resonant MEMS Scanning System for Automotive Lidars
Authors: Han Woong Yoo, Rene Riegler, David Brunner, Stephan Albert, Thomas Thurner, Georg Schitter
Date: 2020-10-16 00:00:00

This paper demonstrates a vibration test for a resonant MEMS scanning system in operation to evaluate the vibration immunity for automotive lidar applications. The MEMS mirror has a reinforcement structure on the backside of the mirror, causing vibration coupling by a mismatch between the center of mass and the rotation axis. An analysis of energy variation is proposed, showing direction dependency of vibration coupling. Vibration influences are evaluated by transient vibration response and vibration frequency sweep using a single tone vibration for translational y- and z- axis. The measurement results demonstrate standard deviation (STD) amplitude and frequency errors are up to 1.64 % and 0.26 %, respectively, for 2 grms single tone vibrations on y axis. The simulation results also show a good agreement with both measurements, proving the proposed vibration coupling mechanism of the MEMS mirror. The phased locked loop (PLL) improves the STD amplitude and frequency errors to 0.91 % and 0.15 % for y axis vibration, corresponding to 44.4 % and 43.0 % reduction, respectively, showing the benefit of a controlled MEMS mirror for reliable automotive MEMS lidars.
Towards more realistic models of genomes in populations: the Markov-modulated sequentially Markov coalescent
Authors: Julien Y. Dutheil
Date: 2020-10-16 00:00:00

The development of coalescent theory paved the way to statistical inference from population genetic data. In the genomic era, however, coalescent models are limited due to the complexity of the underlying ancestral recombination graph. The sequentially Markov coalescent (SMC) is a heuristic that enables the modelling of complete genomes under the coalescent framework. While it empowers the inference of detailed demographic history of a population from as few as one diploid genome, current implementations of the SMC make unrealistic assumptions about the homogeneity of the coalescent process along the genome, ignoring the intrinsic spatial variability of parameters such as the recombination rate. Here, I review the historical developments of SMC models and discuss the evidence for parameter heterogeneity. I then survey approaches to handle this heterogeneity, focusing on a recently developed extension of the SMC.
A review of mass concentrations of Bramblings Fringilla montifringilla: implications for assessment of large numbers of birds
Date: 2020-10-24 00:00:00

Mass concentrations of birds, or lack of such, is a phenomenon of great
ecological and domestic significance. Apart from being and indicator for e.g.
food availability, ecological change and population size, it is also a source
of conflict between humans and birds. Moreover, massive gatherings or colonies
of birds also get the attention of the public -- either as a spectacular
phenomenon or as an unwelcome pest -- thereby forming the public perception of
birds and their abundance. In the context of the mass concentration of
bramblings (Fringilla montifringilla) in Sweden the winter 2019-2020, this work
reviews the literature on this striking phenomenon. Winter roosts are found to
amount to on the order of one million birds per hectare of roost area, but the
spread between reports is significant. Support for roosts of up to around 15
million birds was found, but much larger numbers are frequently recited in the
literature. It is argued that these larger numbers are the result of
overestimation or, in some cases, even completely unfounded (potentially
typos). While the difficulties related to the count of large numbers of birds
can explain this state, it is unfortunate that "high numbers" sometimes
displace proper numbers. Since incorrect data, and its persistence, may result
in that incorrect conclusions are drawn from new observations, this matter
deserves attention. As the Brambling is a well-studied species, the matter also
raises concerns regarding numbers for mass concentrations of other species. It
is recommended that very large numbers of birds should be recited and used with
care: underlying data and methods of the original sources should be
scrutinized. Analogously, reporters of large numbers of birds are advised to
describe and document counting methods. In particular, number estimates based
on flock volume and bird density should be avoided.
Classification of Manifest Huntington Disease using Vowel Distortion Measures
Date: 2020-10-19 00:00:00
Tasks: Classification

Huntington disease (HD) is a fatal autosomal dominant neurocognitive disorder
that causes cognitive disturbances, neuropsychiatric symptoms, and impaired
motor abilities (e.g., gait, speech, voice). Due to its progressive nature, HD
treatment requires ongoing clinical monitoring of symptoms. Individuals with
the gene mutation which causes HD may exhibit a range of speech symptoms as
they progress from premanifest to manifest HD. Differentiating between
premanifest and manifest HD is an important yet understudied problem, as this
distinction marks the need for increased treatment. Speech-based passive
monitoring has the potential to augment clinical assessments by continuously
tracking manifestation symptoms. In this work we present the first
demonstration of how changes in connected speech can be measured to
differentiate between premanifest and manifest HD. To do so, we focus on a key
speech symptom of HD: vowel distortion. We introduce a set of vowel features
which we extract from connected speech. We show that our vowel features can
differentiate between premanifest and manifest HD with 87% accuracy.
Long-term behaviours of Autocatalytic Sets
Authors: Alessandro Ravoni
Date: 2020-10-16 00:00:00

Autocatalytic Sets are reaction networks theorised as networks at the basis of life. Their main feature is the ability of spontaneously emerging and self-reproducing. The Reflexively and Food-generated theory provides a formal definition of Autocatalytic Sets in terms of graphs with peculiar topological properties. This formalisation has been proved to be a powerful tool for the study of the chemical networks underlying life, and it was able to identify autocatalytic structures in real metabolic networks. However, the dynamical behaviour of such networks has not been yet complitely clarified. In this work, we present a first attempt to connect the topology of an Autocatalytic Set with its dynamics. For this purpose, we represent Autocatalytic Sets in terms of Chemical Reaction Networks, and we use the Chemical Reaction Network theory to detect motifs in the networks'structure, that allow us to determine the long-term behaviour of the system.
Non-intrusive speech intelligibility prediction using automatic speech recognition derived measures
Authors: Mahdie Karbasi, Stefan Bleeck, Dorothea Kolossa
Date: 2020-10-16 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Speech Enhancement, speech-recognition, Speech Recognition

The estimation of speech intelligibility is still far from being a solved problem. Especially one aspect is problematic: most of the standard models require a clean reference signal in order to estimate intelligibility. This is an issue of some significance, as a reference signal is often unavailable in practice. In this work, therefore a non-intrusive speech intelligibility estimation framework is presented. In it, human listeners' performance in keyword recognition tasks is predicted using intelligibility measures that are derived from models trained for automatic speech recognition (ASR). One such ASR-based and one signal-based measure are combined into a full framework, the proposed NO-Reference Intelligibility (Nori) estimator, which is evaluated in predicting the performance of both normal-hearing and hearing-impaired listeners in multiple noise conditions. It is shown that the Nori framework even outperforms the widely used reference-based (or intrusive) short-term objective intelligibility (STOI) measure in most considered scenarios, while being applicable in fully blind scenarios with no reference signal or transcription, creating perspectives for online and personalized optimization of speech enhancement systems.
Information Coefficient as a Performance Measure of Stock Selection Models
Authors: Feng Zhang, Ruite Guo, Honggao Cao
Date: 2020-10-16 00:00:00

Information coefficient (IC) is a widely used metric for measuring investment managers' skills in selecting stocks. However, its adequacy and effectiveness for evaluating stock selection models has not been clearly understood, as IC from a realistic stock selection model can hardly be materially different from zero and is often accompanies with high volatility. In this paper, we investigate the behavior of IC as a performance measure of stick selection models. Through simulation and simple statistical modeling, we examine the IC behavior both statically and dynamically. The examination helps us propose two practical procedures that one may use for IC-based ongoing performance monitoring of stock selection models.
Adaptive Gradient Search Beamforming for Full-Duplex mmWave MIMO Systems
Authors: Elyes Balti
Date: 2020-10-16 00:00:00

In this work, we present a framework analysis of full-duplex (FD) systems for Millimeter Wave (mmWave) analog architecture. Given that FD systems can double the ergodic capacity, such systems experience large losses caused by the loopback self-interference (SI). In addition, systems with analog architecture also suffer from other forms of losses mainly incurred by the constant amplitude (CA) constraint. For this purpose, we propose the projected Gradient Ascent algorithm to maximize the sum rate under the unit-norm and CA constraints. Unlike previous works, our approach achieves the best spectral efficiency while minimizing the losses incurred by the CA constraint. We also consider an adaptive step size to compensate for the perturbations that may affect the cost function during the optimization. The results will show that the proposed algorithm converges to the same optimal value for different initializations while the number of iterations required for the convergence changes for each case. In this context, we primarily consider the gradient search method for a two-nodes FD systems and then we extend the analysis for a dual-hop FD relaying systems. Finally, we evaluate the robustness of our method in terms of rate and outage probability and compare with previous approaches.