pwc-archive/papers-with-abstracts huggingface dataset:
Angle-Constrained Formation Control for Circular Mobile Robots
Date: 2020-05-10 00:00:00
Tasks: Collision Avoidance

In this letter, we investigate the formation control problem of mobile robots
moving in the plane where, instead of assuming robots to be simple points, each
robot is assumed to have the form of a disk with equal radius. Based on
interior angle measurements of the neighboring robots' disk, which can be
obtained from low-cost vision sensors, we propose a gradient-based distributed
control law and show the exponential convergence property of the associated
error system. By construction, the proposed control law has the appealing
property of ensuring collision avoidance between neighboring robots. We also
present simulation results for {a team} of four circular mobile robots forming
a rectangular shape.
A Compositional Sheaf-Theoretic Framework for Event-Based Systems (Extended Version)
Authors: Gioele Zardini, David I. Spivak, Andrea Censi, Emilio Frazzoli
Date: 2020-05-10 00:00:00

A compositional sheaf-theoretic framework for the modeling of complex event-based systems is presented. We show that event-based systems are machines, with inputs and outputs, and that they can be composed with machines of different types, all within a unified, sheaf-theoretic formalism. We take robotic systems as an exemplar of complex systems and rigorously describe actuators, sensors, and algorithms using this framework.
Power and Accuracy of Multi-Layer Perceptrons (MLPs) under Reduced-voltage FPGA BRAMs Operation
Authors: Behzad Salami, Osman Unsal, Adrian Cristal
Date: 2020-05-10 00:00:00

In this paper, we exploit the aggressive supply voltage underscaling technique in Block RAMs (BRAMs) of Field Programmable Gate Arrays (FPGAs) to improve the energy efficiency of Multi-Layer Perceptrons (MLPs). Additionally, we evaluate and improve the resilience of this accelerator. Through experiments on several representative FPGA fabrics, we observe that until a minimum safe voltage level, i.e., Vmin the MLP accuracy is not affected. This safe region involves a large voltage guardband. Also, it involves a narrower voltage region where faults start to appear in memories due to the increased circuit delay, but these faults are masked by MLP, and thus, its accuracy is not affected. However, further undervolting causes significant accuracy loss as a result of the fast-increasing high fault rates. Based on the characterization of these undervolting faults, we propose fault mitigation techniques that can effectively improve the resilience behavior of such accelerator. Our evaluation is based on four FPGA platforms. On average, we achieve >90% energy saving with a negligible accuracy loss of up to 0.1%.
Statistical inference for the EU portfolio in high dimensions
Authors: Taras Bodnar, Solomiia Dmytriv, Yarema Okhrin, Nestor Parolya, Wolfgang Schmid
Date: 2020-05-10 00:00:00
Tasks: Vocal Bursts Intensity Prediction

In this paper, using the shrinkage-based approach for portfolio weights and modern results from random matrix theory we construct an effective procedure for testing the efficiency of the expected utility (EU) portfolio and discuss the asymptotic behavior of the proposed test statistic under the high-dimensional asymptotic regime, namely when the number of assets $p$ increases at the same rate as the sample size $n$ such that their ratio $p/n$ approaches a positive constant $c\in(0,1)$ as $n\to\infty$. We provide an extensive simulation study where the power function and receiver operating characteristic curves of the test are analyzed. In the empirical study, the methodology is applied to the returns of S\&P 500 constituents.
River environmental restoration based on random observations of a non-smooth stochastic dynamical system
Date: 2020-05-11 00:00:00

Earth and soils are indispensable elements of river environment.
Dam-downstream environment and ecosystems have been severely affected by
reduced or even stopped sediment supply from the upstream. Replenishing earth
and soils from outside the river has been considered as an effective way to
mitigate this issue. However, its cost-effective implementation has not been
considered from a theoretical side. This paper presents a tractable new
stochastic control model to deal with this issue. The sediment dynamics in the
river environment follow non-smooth and continuous-time piecewise deterministic
dynamics. The model assumes that the observation of the sediment dynamics is
carried out only randomly and discretely, and that the sediment can be
replenished at each observation time with cost. This partial observation
assumption is consistent with the fact that continuously obtaining the
environmental information is difficult in applications. The performance index
to penalize the sediment depletion has a non-smooth term as well. We
demonstrate that these non-smoothness factors harmonize with a dynamic
programming principle, and derive the optimality equation in a degenerate
elliptic form governing the most cost-efficient sediment replenishment policy.
We analytically derive and verify an exact solution under a simplified
condition for a discounted case, an Ergodic case, and a complete information
case. A more realistic case is handled using a high-resolution finite
difference scheme. We then provide the optimal sediment replenishment policy
numerically.
Nonparametric Expected Shortfall Forecasting Incorporating Weighted Quantiles
Authors: Giuseppe Storti, Chao Wang
Date: 2020-05-11 00:00:00
Tasks: quantile regression, Time Series, Time Series Analysis

A new semi-parametric Expected Shortfall (ES) estimation and forecasting framework is proposed. The proposed approach is based on a two-step estimation procedure. The first step involves the estimation of Value-at-Risk (VaR) at different quantile levels through a set of quantile time series regressions. Then, the ES is computed as a weighted average of the estimated quantiles. The quantiles weighting structure is parsimoniously parameterized by means of a Beta weight function whose coefficients are optimized by minimizing a joint VaR and ES loss function of the Fissler-Ziegel class. The properties of the proposed approach are first evaluated with an extensive simulation study using two data generating processes. Two forecasting studies with different out-of-sample sizes are then conducted, one of which focuses on the 2008 Global Financial Crisis (GFC) period. The proposed models are applied to 7 stock market indices and their forecasting performances are compared to those of a range of parametric, non-parametric and semi-parametric models, including GARCH, Conditional AutoRegressive Expectile (CARE), joint VaR and ES quantile regression models and simple average of quantiles. The results of the forecasting experiments provide clear evidence in support of proposed models.
Posterior Probabilities for Lorenz and Stochastic Dominance of Australian Income Distributions
Authors: David Gunawan, William E. Griffiths, Duangkamon Chotikapanich
Date: 2020-05-11 00:00:00

Using HILDA data for the years 2001, 2006, 2010, 2014 and 2017, we compute posterior probabilities for dominance for all pairwise comparisons of income distributions in these years. The dominance criteria considered are Lorenz dominance and first and second order stochastic dominance. The income distributions are estimated using an infinite mixture of gamma density functions, with posterior probabilities computed as the proportion of Markov chain Monte Carlo draws that satisfy the inequalities that define the dominance criteria. We find welfare improvements from 2001 to 2006 and qualified improvements from 2006 to the later three years. Evidence of an ordering between 2010, 2014 and 2017 cannot be established.
The Biophysical Micro-Environment's Influence on Cell Fate Decisions During Macrophage Activation and Somatic Cell Reprogramming
Authors: Zachary Reitz
Date: 2020-05-11 00:00:00

Cells are known to sense and respond to their mechanical microenvironment in profound ways. Various evidence has implicated the adhesome, a body of adhesion associated proteins, in several cell fate decisions, including differentiation and proliferation. Furthermore, recent work has demonstrated that substrate topography can modulate the epigenetic state of fibroblasts, facilitate cell reprogramming, and temper inflammation. However, the effectors responsible for such phenomena remain poorly understood. Here we explore the effect of biomaterial design, adhesion, and intracellular forces of the epigenetic and transcriptional regulation of cell state during macrophage activation and somatic cell reprogramming.
Non-Orthogonal Multiple Access in the Presence of Additive Generalized Gaussian Noise
Authors: Lina Bariah, Sami Muhaidat, Paschalis C. Sofotasios, Sanjeev Gurugopinath, Walaa Hamouda, Halim Yanikomeroglu
Date: 2020-05-11 00:00:00
Tasks: Diversity

In this letter, we investigate the performance of non-orthogonal multiple access (NOMA), under the assumption of generalized Gaussian noise (GGN), over Rayleigh fading channels. Specifically, we consider a NOMA system with $L$ users, each of which is equipped with a single antenna, and derive an exact expression for the pairwise error probability (PEP). The derived PEP expression is subsequently utilized to derive a union bound on the bit error rate (BER) and to quantify the diversity orders realized by NOMA users in the presence of additive white (AW) GGN. Capitalizing on the derived PEP expression and the union bound, the error rate performance of NOMA users is further evaluated for different special cases of AWGGN. The derived analytical results, corroborated by simulation results, show that the shaping parameter of the GGN $(\alpha)$ has negligible effect on the diversity gains of NOMA users, particularly for large $\alpha$ values. Accordingly, as in the case of additive white Gaussian noise (AWGN), the maximum achievable diversity order is determined by the user's order.
Macroeconomic Forecasting with Fractional Factor Models
Date: 2020-05-11 00:00:00

We combine high-dimensional factor models with fractional integration methods
and derive models where nonstationary, potentially cointegrated data of
different persistence is modelled as a function of common fractionally
integrated factors. A two-stage estimator, that combines principal components
and the Kalman filter, is proposed. The forecast performance is studied for a
high-dimensional US macroeconomic data set, where we find that benefits from
the fractional factor models can be substantial, as they outperform univariate
autoregressions, principal components, and the factor-augmented
error-correction model.
Beamwidth Optimization and Resource Partitioning Scheme for Localization Assisted mm-wave Communications
Authors: Gourab Ghatak, Remun Koirala, Antonio De Domenico, Benoit Denis, Davide Dardari, Bernard Uguen, Marceau Coupechoux
Date: 2020-05-11 00:00:00

We study a mm-wave wireless network deployed along the roads of an urban area, to support localization and communication services simultaneously for outdoor mobile users. In this network, we propose a mm-wave initial beam-selection scheme based on localization-bounds, which greatly reduces the initial access delay as compared to traditional initial access schemes for standalone mm-wave base station (BS). Then, we introduce a downlink transmission protocol, in which the radio frames are partitioned into three phases, namely, initial access, data, and localization, respectively. We establish a trade-off between the localization and communication performance of mm-wave systems, and show how enhanced localization can actually improve the data-communication performance. Our results suggest that dense BS deployments enable to allocate more resources to the data phase while still maintaining appreciable localization performance. Furthermore, for the case of sparse deployments and large beam dictionary size (i.e., with thinner beams), more resources must be allotted to the localization phase for optimizing the rate coverage. Based on our results, we provide several system design insights and dimensioning rules for the network operators that will deploy the first generation of mm-wave BSs.
First Experimental Demonstration of a 3-Dimensional Simplex Modulation Format Showing Improved OSNR Performance Compared to DP-BPSK
Authors: Annika Dochhan, Helmut Griesser, Michael Eiselt
Date: 2020-05-11 00:00:00

We experimentally demonstrate a novel 3-dimensional modulation format with 1.2-dB OSNR tolerance improvement potential compared to DP-BPSK, as verified for the linear case. The non-linearity tolerance is evaluated in single-span transmission over 300km SSMF.
A Bandwidth Scalable Millimetre Wave Over-The-Air Test System with Low Complexity
Authors: Erich Zöchmann, Terje Mathiesen, Thomas Blazek, Herbert Groll, Golsa Ghiaasi
Date: 2020-05-11 00:00:00

In this work, we show the design and validation of a testbed for over-the-air testing of millimetre waves equipments. We have extended the frequency capabilities of a baseband channel emulator which is capable of emulating non-stationary channels, to higher frequencies. To assure that the propagation between devices-under-test and the RF-frontend of the emulator is only a line-of-sight link, we have isolated the devices-under-test in two anechoic chambers. We characterize the testbed at 57 GHz by means of frequency sweeps for two artificial cases: when the emulator is replicating a one-tap channel and a two-tap channel. Finally we demonstrate the system functionality through reproduction of channels which were acquired during a vehicular millimetre wave measurement campaign conducted in Vienna in 2018.
Effects of Controller Heterogeneity on Autonomous Vehicle Traffic
Date: 2020-05-11 00:00:00
Tasks: Autonomous Vehicles

Interactions between road users are both highly non-linear and profoundly
complex, and there is no reason to expect that interactions between autonomous
vehicles will be any different. Given the recent rapid development of
autonomous vehicle technologies, we need to understand how these interactions
are likely to present themselves, and what their implications might be. This
paper looks into the impact of autonomous vehicles with differing controllers,
focusing specifically on the effects of changing the mean and heterogeneity of
controller parameters on three key performance metrics: throughput, passenger
safety and comfort. Towards this end, we develop a method for systematically
sampling vehicle controllers as a function of parameter heterogeneity. In
addition to evaluating the impact of heterogeneity on performance, we quantify
the relative impacts of controller input parameters on the output performance
metrics by means of sensitivity analyses. The MovSim traffic simulator was used
to simulate a realistic traffic system, whilst recording maximum throughput, as
well as lane change frequencies and mean absolute accelerations as proxies for
safety and comfort. Our results reveal that traffic performance is primarily
affected by the heterogeneity of vehicle target velocities, as well as by the
mean values of a very small subset of the parameters, of which the target
velocity is by far the most significant.
Simultaneous State and Unknown Input Estimation for Continuous-discrete Stochastic Systems
Date: 2020-05-11 00:00:00

This paper considers the simultaneous state and unknown input estimation for
continuous-discrete stochastic systems. Two types of approaches (with and
without modeling of unknown inputs) which can address this issue are
investigated. A novel continuous recursive four-step Kalman filter is proposed
and its asymptotic stability condition is established. A novel one-step unknown
input Kalman filter is proposed and has guaranteed stability when the number of
unknown inputs is equal to that of the measurements. The design of unknown
input Kalman filters and observers is unified. Furthermore, an adaptive
augmented Kalman filter which requires the modeling of unknown inputs is
introduced. The estimation error covariance of the recursive four-step Kalman
filter and the adaptive augmented Kalman filter is analyzed and compared.
Finally, simulation results demonstrate the effectiveness of the proposed
approaches.
Reinforcement Learning Based on Real-Time Iteration NMPC
Date: 2020-05-11 00:00:00
Tasks: Model Predictive Control, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement Learning (RL) has proven a stunning ability to learn optimal
policies from data without any prior knowledge on the process. The main
drawback of RL is that it is typically very difficult to guarantee stability
and safety. On the other hand, Nonlinear Model Predictive Control (NMPC) is an
advanced model-based control technique which does guarantee safety and
stability, but only yields optimality for the nominal model. Therefore, it has
been recently proposed to use NMPC as a function approximator within RL. While
the ability of this approach to yield good performance has been demonstrated,
the main drawback hindering its applicability is related to the computational
burden of NMPC, which has to be solved to full convergence. In practice,
however, computationally efficient algorithms such as the Real-Time Iteration
(RTI) scheme are deployed in order to return an approximate NMPC solution in
very short time. In this paper we bridge this gap by extending the existing
theoretical framework to also cover RL based on RTI NMPC. We demonstrate the
effectiveness of this new RL approach with a nontrivial example modeling a
challenging nonlinear system subject to stochastic perturbations with the
objective of optimizing an economic cost.
COVID-19 mitigation strategies and overview on results from relevant studies in Europe
Authors: Philipp Heider
Date: 2020-05-11 00:00:00

In December 2019, the first patients in Wuhan, China were diagnosed with a primary atypical pneumonia, which showed to be unknown and contagious. Since then, known as COVID-19 disease, the responsible viral pathogen, SARS-CoV-2, has spread around the world in a pandemic. Decisions on how to deal with the crisis are often based on simulations of the pandemic spread of the virus. The results of some of these, as well as their methodology and possibilities for improvement, will be described in more detail in this paper in order to inform beyond the current public health dogma called "flatten-the-curve". There are several ways to model an epidemic in order to simulate the spread of diseases. Depending on the timeliness, scope and quality of the associated real data, these multivariable models differ in the value of used parameters, but also in the selection of considered influencing factors. It was exemplarily shown that epidemics in their course are simulated more realistically by models that assume subexponential growth. Furthermore, various simulations of the COVID-19 pandemic were presented in an European perspective, compared against each other and discussed in more detail. It is difficult to estimate how credible the simulations of the pandemic models currently are, so it remains to be seen whether the spread of the pandemic can be effectively reduced by the measures taken. Whether a model works well in reality is largely determined by the quality and scope of its underlying data. Past studies have shown that countermeasures are able to reduce reproduction numbers or transmission rates in epidemics. In addition to that, the presented modelling study provides a good framework for the creation of subexponential-growth-models for assessing the spread of COVID-19.
Fractional trends and cycles in macroeconomic time series
Date: 2020-05-23 00:00:00
Tasks: Common Sense Reasoning, Time Series, Time Series Analysis

We develop a generalization of correlated trend-cycle decompositions that
avoids prior assumptions about the long-run dynamic characteristics by
modelling the permanent component as a fractionally integrated process and
incorporating a fractional lag operator into the autoregressive polynomial of
the cyclical component. The model allows for an endogenous estimation of the
integration order jointly with the other model parameters and, therefore, no
prior specification tests with respect to persistence are required. We relate
the model to the Beveridge-Nelson decomposition and derive a modified Kalman
filter estimator for the fractional components. Identification, consistency,
and asymptotic normality of the maximum likelihood estimator are shown. For US
macroeconomic data we demonstrate that, unlike $I(1)$ correlated unobserved
components models, the new model estimates a smooth trend together with a cycle
hitting all NBER recessions. While $I(1)$ unobserved components models yield an
upward-biased signal-to-noise ratio whenever the integration order of the
data-generating mechanism is greater than one, the fractionally integrated
model attributes less variation to the long-run shocks due to the fractional
trend specification and a higher variation to the cycle shocks due to the
fractional lag operator, leading to more persistent cycles and smooth trend
estimates that reflect macroeconomic common sense.
Doppler Radar for the Extraction of Biomechanical Parameters in Gait Analysis
Authors: Ann-Kathrin Seifert, Martin Grimmer, Abdelhak M. Zoubir
Date: 2020-05-11 00:00:00
Tasks: Privacy Preserving, Rhythm

The applicability of Doppler radar for gait analysis is investigated by quantitatively comparing the measured biomechanical parameters to those obtained using motion capturing and ground reaction forces. Nineteen individuals walked on a treadmill at two different speeds, where a radar system was positioned in front of or behind the subject. The right knee angle was confined by an adjustable orthosis in five different degrees. Eleven gait parameters are extracted from radar micro-Doppler signatures. Here, new methods for obtaining the velocities of individual lower limb joints are proposed. Further, a new method to extract individual leg flight times from radar data is introduced. Based on radar data, five spatiotemporal parameters related to rhythm and pace could reliably be extracted. Further, for most of the considered conditions, three kinematic parameters could accurately be measured. The radar-based stance and flight time measurements rely on the correct detection of the time instant of maximal knee velocity during the gait cycle. This time instant is reliably detected when the radar has a back view, but is underestimated when the radar is positioned in front of the subject. The results validate the applicability of Doppler radar to accurately measure a variety of medically relevant gait parameters. Radar has the potential to unobtrusively diagnose changes in gait, e.g., to design training in prevention and rehabilitation. As contact-less and privacy-preserving sensor, radar presents a viable technology to supplement existing gait analysis tools for long-term in-home examinations.