pwc-archive/papers-with-abstracts huggingface dataset:
Data-Based Moving Horizon Estimation for Linear Discrete-Time Systems
Authors: Tobias M. Wolff, Victor G. Lopez, Matthias A. Müller
Date: 2021-11-09 00:00:00

This paper introduces a data-based moving horizon estimation (MHE) scheme for linear time-invariant discrete-time systems. The scheme solely relies on collected data without employing any system identification step. Robust global exponential stability of the data-based MHE is proven under standard assumptions for the case where the online output measurements are corrupted by some non-vanishing measurement noise. A simulation example illustrates the behavior of the data-based MHE scheme.
A Dynamic Watermarking Algorithm for Finite Markov Decision Problems
Authors: Jiacheng Tang, Jiguo Song, Abhishek Gupta
Date: 2021-11-09 00:00:00
Tasks: Intrusion Detection, Sensitivity

Dynamic watermarking, as an active intrusion detection technique, can potentially detect replay attacks, spoofing attacks, and deception attacks in the feedback channel for control systems. In this paper, we develop a novel dynamic watermarking algorithm for finite-state finite-action Markov decision processes and present upper bounds on the mean time between false alarms, and the mean delay between the time an attack occurs and when it is detected. We further compute the sensitivity of the performance of the control system as a function of the watermark. We demonstrate the effectiveness of the proposed dynamic watermarking algorithm by detecting a spoofing attack in a sensor network system.
Parameter Conditions to Prevent Voltage Oscillations Caused by LTC-Inverter Hunting on Power Distribution Grids
Authors: Jaimie Swartz, Federico Celi, Fabio Pasqualetti, Alexandra von Meier
Date: 2021-11-08 00:00:00

As more distributed energy resources (DERs) are connected to the power grid, it becomes increasingly important to ensure safe and effective coordination between legacy voltage regulation devices and inverter-based DERs. In this work, we show how a distribution circuit model, composed of two LTCs and two inverter devices, can create voltage oscillations even with reasonable choices of control parameters. By modeling the four-device circuit as a switched affine hybrid system, we analyze the system's oscillatory behavior, both during normal operation and after a cyber-physical attack. Through the analysis we determine the specific region of the voltage state space where oscillations are possible and derive conditions on the control parameters to guarantee against the oscillations. Finally, we project the derived parameter conditions onto 2D spaces, and describe the application of our problem formulation to grids with many devices.
Data-Driven Prediction with Stochastic Data: Confidence Regions and Minimum Mean-Squared Error Estimates
Authors: Mingzhou Yin, Andrea Iannelli, Roy S. Smith
Date: 2021-11-08 00:00:00
Tasks: Prediction, valid

Recently, direct data-driven prediction has found important applications for controlling unknown systems, particularly in predictive control. Such an approach provides exact prediction using behavioral system theory when noise-free data are available. For stochastic data, although approximate predictors exist based on different statistical criteria, they fail to provide statistical guarantees of prediction accuracy. In this paper, confidence regions are provided for these stochastic predictors based on the prediction error distribution. Leveraging this, an optimal predictor which achieves minimum mean-squared prediction error is also proposed to enhance prediction accuracy. These results depend on some true model parameters, but they can also be replaced with an approximate data-driven formulation in practice. Numerical results show that the derived confidence region is valid and smaller prediction errors are observed for the proposed minimum mean-squared error estimate, even with the approximate data-driven formulation.
Maximum Likelihood Signal Matrix Model for Data-Driven Predictive Control
Authors: Mingzhou Yin, Andrea Iannelli, Roy S. Smith
Date: 2020-12-08 00:00:00
Tasks: Model Predictive Control

The paper presents a data-driven predictive control framework based on an implicit input-output mapping derived directly from the signal matrix of collected data. This signal matrix model is derived by maximum likelihood estimation with noise-corrupted data. By linearizing online, the implicit model can be used as a linear constraint to characterize possible trajectories of the system in receding horizon control. The signal matrix can also be updated online with new measurements. This algorithm can be applied to large datasets and slowly time-varying systems, possibly with high noise levels. An additional regularization term on the prediction error can be introduced to enhance the predictability and thus the control performance. Numerical results demonstrate that the proposed signal matrix model predictive control algorithm is effective in multiple applications and performs better than existing data-driven predictive control algorithm.
Mobility-limiting antipredator response in the rock-paper-scissors model
Authors: J. Menezes, B. Moura
Date: 2021-11-08 00:00:00

Antipredator behavior is present in many biological systems where individuals collectively react to an imminent attack. The antipredator response may influence spatial pattern formation and ecosystem stability but requires an organism's cost to contribute to the collective effort. We investigate a nonhierarchical tritrophic system, whose predator-prey interactions are described by the rock-paper-scissors game rules. In our spatial stochastic simulations, the radius of antipredator response defines the maximum prey group size that disturbs the predator's action, determining the individual cost to participate in antipredator strategies. We consider that each organism contributes equally to the collective effort, having its mobility limited by the proportion of energy devoted to the antipredator reaction. Our outcomes show that the antipredator response leads to spiral patterns, with the segregation of organisms of the same species occupying departed spatial domains. We found that a less localized antipredator response increases the average size of the single-species patches, improving the protection of individuals against predation. Finally, our findings show that although the increase of the predation risk for a more localized antipredator response, the high mobility constraining benefits species coexistence. Our results may help ecologists understand the mechanisms leading to the stability of biological systems where locality is crucial to behavioral interactions among species.
Monitoring Atmospheric Pollutants From Ground-based Observations
Authors: Nicholas Danesi, Mayank Jain, Yee Hui Lee, Soumyabrata Dev
Date: 2021-10-21 00:00:00
Tasks: Position

Remote sensing analysts continuously monitor the amount of pollutants in the atmosphere. They are usually performed via satellite images. However, these images suffer from low temporal and low spatial resolution. Therefore, observations recorded from the ground offer us a fantastic alternative. There are low-cost sensors that continuously record the PM$_{2.5}$ and PM$_{10}$ concentration levels in the atmosphere. In this position paper, we provide an overview of the state-of-the-art techniques for pollutant forecasting. We establish the interdependence of meteorological parameters on atmospheric pollutants. Our case study in this paper is based on the island of Australia.
Optical Adaptive LMS Equalizer with an Opto-electronic Feedback Loop
Authors: Xumeng Liu, Xingyuan Xu, Arthur James Lowery
Date: 2021-11-09 00:00:00

We propose a photonic adaptive Least-Mean-Squares equalizer with an opto-electronic feedback loop to determine the updating of an optical Finite-Impulse-Response filter's weights, enabling dispersion compensation introduced by 30-km fiber based on our photonic integrated chip.
Heterogeneous popularity of metabolic reactions from evolution
Authors: Mi Jin Lee, Deok-Sun Lee
Date: 2021-11-09 00:00:00

The composition of cellular metabolism is different across species. Empirical data reveal that bacterial species contain similar numbers of metabolic reactions but that the cross-species popularity of reactions is so heterogenous that some reactions are found in all the species while others are in just few species, characterized by a power-law distribution with the exponent one. Introducing an evolutionary model concretizing the stochastic recruitment of chemical reactions into the metabolism of different species at different times and their inheritance to descendants, we demonstrate that the exponential growth of the number of species containing a reaction and the saturated recruitment rate of brand-new reactions lead to the empirically identified power-law popularity distribution. Furthermore, the structural characteristics of metabolic networks and the species' phylogeny in our simulations agree well with empirical observations.
Bounding Treatment Effects by Pooling Limited Information across Observations
Authors: Sokbae Lee, Martin Weidner
Date: 2021-11-09 00:00:00
Tasks: valid

We provide novel bounds on average treatment effects (on the treated) that are valid under an unconfoundedness assumption. Our bounds are designed to be robust in challenging situations, for example, when the conditioning variables take on a large number of different values in the observed sample, or when the overlap condition is violated. This robustness is achieved by only using limited "pooling" of information across observations. Namely, the bounds are constructed as sample averages over functions of the observed outcomes such that the contribution of each outcome only depends on the treatment status of a limited number of observations. No information pooling across observations leads to so-called "Manski bounds", while unlimited information pooling leads to standard inverse propensity score weighting. We explore the intermediate range between these two extremes and provide corresponding inference methods. We show in Monte Carlo experiments and through two empirical application that our bounds are indeed robust and informative in practice.
Optimal Decision Rules Under Partial Identification
Authors: Kohei Yata
Date: 2021-11-09 00:00:00
Tasks: counterfactual

I consider a class of statistical decision problems in which the policymaker must decide between two policies to maximize social welfare (e.g., the population mean of an outcome) based on a finite sample. The framework introduced in this paper allows for various types of restrictions on the structural parameter (e.g., the smoothness of a conditional mean potential outcome function) and accommodates settings with partial identification of social welfare. As the main theoretical result, I derive a finite-sample optimal decision rule under the minimax regret criterion. This rule has a simple form, yet achieves optimality among all decision rules; no ad hoc restrictions are imposed on the class of decision rules. I apply my results to the problem of whether to change an eligibility cutoff in a regression discontinuity setup, and illustrate them in an empirical application to a school construction program in Burkina Faso.
Pair copula constructions of point-optimal sign-based tests for predictive linear and nonlinear regressions
Authors: Kaveh Salehzadeh Nobari
Date: 2021-11-09 00:00:00
Tasks: valid

We propose pair copula constructed point-optimal sign tests in the context of linear and nonlinear predictive regressions with endogenous, persistent regressors, and disturbances exhibiting serial (nonlinear) dependence. The proposed approach entails considering the entire dependence structure of the signs to capture the serial dependence, and building feasible test statistics based on pair copula constructions of the sign process. The tests are exact and valid in the presence of heavy tailed and nonstandard errors, as well as heterogeneous and persistent volatility. Furthermore, they may be inverted to build confidence regions for the parameters of the regression function. Finally, we adopt an adaptive approach based on the split-sample technique to maximize the power of the test by finding an appropriate alternative hypothesis. In a Monte Carlo study, we compare the performance of the proposed "quasi"-point-optimal sign tests based on pair copula constructions by comparing its size and power to those of certain existing tests that are intended to be robust against heteroskedasticity. The simulation results maintain the superiority of our procedures to existing popular tests.
Do Firearm Markets Comply with Firearm Restrictions? How the Massachusetts Assault Weapons Ban Enforcement Notice Changed Firearm Sales
Authors: Meenakshi Balakrishna, Kenneth C. Wilbur
Date: 2021-11-09 00:00:00

How well do firearm markets comply with firearm restrictions? The Massachusetts Attorney General issued an Enforcement Notice in 2016 to announce a new interpretation of the key phrase "copies and duplicates" in the state's assault weapons ban. The Enforcement Notice increased assault rifle sales by 1,349 (+560%) within five days, followed by a reduction of 211 (-58%) over the next three weeks. Assault rifle sales were 64-66% lower in 2017 than in comparable earlier periods, suggesting that the Enforcement Notice reduced assault weapon sales but also that many banned weapons continued to be sold.
MIMO for MATLAB: A Toolbox for Simulating MIMO Communication Systems
Authors: Ian P. Roberts
Date: 2021-11-09 00:00:00

We present MIMO FOR MATLAB (MFM), a toolbox for MATLAB that aims to simplify the simulation of multiple-input multiple-output (MIMO) communication systems research while facilitating reproducibility, consistency, and community-driven customization. MFM offers users an object-oriented solution for simulating a variety of MIMO systems including sub-6 GHz, massive MIMO, millimeter wave, and terahertz communication. Out-of-the-box, MFM supplies users with widely used channel and path loss models from academic literature and wireless standards; if a particular channel or path loss model is not provided by MFM, users can create custom models by following a few simple rules. The complexity and overhead associated with simulating networks of multiple devices can be significantly reduced with MFM versus raw MATLAB code, especially when users want to investigate various channel models, path loss models, precoding/combining schemes, or other system-level parameters. MFM's heavy-lifting to automatically collect and distribute channel state information, aggregate interference, and report performance metrics relieves users of otherwise tedious tasks and instills confidence and consistency in the results of simulation. The use-cases of MFM vary widely from networks of hundreds of devices; to simple point-to-point communication; to serving as a channel generator; to radar, sonar, and underwater acoustic communication.
Cooperative Multiterminal Radar and Communication: A New Paradigm for 6G Mobile Networks
Authors: Leonardo Leyva, Daniel Castanheira, Adão Silva, Atílio Gameiro, Lajos Hanzo
Date: 2021-11-09 00:00:00

The impending spectrum congestion imposed by the emergence of new bandwidth-thirsty applications may be mitigated by the integration of radar and classic communications functionalities in a common system. Furthermore, the merger of a sensing component into wireless communication networks has raised interest in recent years and it may become a compelling design objective for 6G. This article presents the evolution of the hitherto separate radar and communication systems towards their amalgam known as a joint radar and communication (RADCOM) system. Explicitly, we propose to integrate a radio sensing component into 6G. We consider an ultra-dense network (UDN) scenario relying on an active multistatic radar configuration and on cooperation between the access points across the entire coverage area. The technological trends required to reach a feasible integration, the applications anticipated and the open research challenges are identified, with an emphasis on high-accuracy network synchronization. The successful integration of these technologies would facilitate centimeter-level resolution, hence supporting compelling high-resolution applications for next-generation networks, such as robotic cars and industrial assembly lines.
Multi Agent Reinforcement Learning Trajectory Design and Two-Stage Resource Management in CoMP UAV VLC Networks
Authors: Mohammad Reza Maleki, Mohammad Robat Mili, Mohammad Reza Javan, Nader Mokari, Eduard A. Jorswieck
Date: 2021-11-06 00:00:00
Tasks: Management, Multi-agent Reinforcement Learning, Multiobjective Optimization

In this paper, we consider unmanned aerial vehicles (UAVs) equipped with a visible light communication (VLC) access point and coordinated multipoint (CoMP) capability that allows users to connect to more than one UAV. UAVs can move in 3-dimensional (3D) at a constant acceleration, where a central server is responsible for synchronization and cooperation among UAVs. The effect of accelerated motion in UAV is necessary to be considered. Unlike most existing works, we examine the effects of variable speed on kinetics and radio resource allocations. For the proposed system model, we define two different time frames. In the frame, the acceleration of each UAV is specified, and in each slot, radio resources are allocated. Our goal is to formulate a multiobjective optimization problem where the total data rate is maximized, and the total communication power consumption is minimized simultaneously. To handle this multiobjective optimization, we first apply the scalarization method and then apply multi-agent deep deterministic policy gradient (MADDPG). We improve this solution method by adding two critic networks together with two-stage resources allocation. Simulation results indicate that the constant acceleration motion of UAVs shows about 8\% better results than conventional motion systems in terms of performance.
Interdigitated Columnar Representation of Personal Space and Visual Space in Human Parietal Cortex
Authors: Roger B. H. Tootell, Zahra Nasiriavanaki, Baktash Babadi, Douglas N. Greve, Shahin Nasr, Daphne J. Holt
Date: 2021-11-08 00:00:00

Personal space (PS) is the distance that people prefer to maintain between themselves and unfamiliar others. Interpersonal intrusion into a given persons PS evokes discomfort, and an urge to move further apart. We hypothesized that in parietal cortex: 1. PS processing involves a previously-described threat-sensitive brain circuit, and 2. the spatial encoding of distance is transformed accordingly, from purely sensory to PS-related. These hypotheses were tested using 7T fMRI at high spatial resolution. In response to images of different visual stimuli across a range of virtual distances, we found two categories of distance encoding in functionally corresponding columns within parietal cortex. First, PD (personal distance) columns responded selectively to moving and stationary face images presented at virtual distances nearer (but not further) than each subjects behaviorally-defined PS boundary. In the majority of PD columns, BOLD response amplitudes increased monotonically and nonlinearly with increasing virtual face proximity. In the remaining PD columns, BOLD responses decreased with increasing proximity. These fMRI response functions appear related to previously-reported variations in subjective discomfort levels, and physiologic arousal, during intrusion into but not beyond personal space. Secondly, DD (disparity distance) columns in parietal cortex responded selectively to disparity-based near or far distances, in random dot stimuli, like disparity-selective columns described previously in occipital cortex. In parietal cortex, maps of DD columns were systematically non-overlapping (interdigitated) with the PD columns. These results suggest that the transformation of spatial information, from visual to higher-order, may be computed in multiple small sites, rather than across a larger cortical gradient, in parietal cortex.
Mito-nuclear selection induces a trade-off between species ecological dominance and evolutionary lifespan
Authors: Débora Princepe, Marcus A. M. de Aguiar, Joshua B. Plotkin
Date: 2021-11-08 00:00:00
Tasks: Diversity

Mitochondrial and nuclear genomes must be co-adapted to ensure proper cellular respiration and energy production. Mito-nuclear incompatibility reduces individual fitness and induces hybrid infertility, suggesting a possible role in reproductive barriers and speciation. Here we develop a birth-death model for evolution in spatially extended populations under selection for mito-nuclear co-adaptation. Mating is constrained by physical and genetic proximity, and offspring inherit nuclear genomes from both parents, with recombination. The model predicts macroscopic patterns including a community's long-term species diversity, its species abundance distribution, speciation and extinction rates, as well as intra- and inter-specific genetic variation. We explore how these long-term outcomes depend upon the microscopic parameters of reproduction: individual fitness governed by mito-nuclear compatibility, constraints on mating compatibility, and ecological carrying capacity. We find that strong selection for mito-nuclear compatibility reduces the equilibrium number of species after a radiation, increases the species' abundances, while simultaneously increasing both speciation and extinction rates. The negative correlation between species diversity and diversification rates in our model agrees with the broad empirical pattern of lower species diversity and higher speciation/extinction rates in temperate regions, compared to the tropics. We therefore suggest that these empirical patterns may be caused in part by latitudinal variation in metabolic demands, and corresponding variation in selection on mito-nuclear function.
Bone Adaptation as a Geometric Flow
Authors: Bryce A. Besler, Tannis D. Kemp, Nils D. Forkert, Steven K. Boyd
Date: 2021-11-09 00:00:00

This paper presents bone adaptation as a geometric flow. The proposed method is based on two assumptions: first, that the bone surface is smooth (not fractal) permitting the definition of a tangent plane and, second, that the interface between marrow and bone tissue phases is orientable. This permits the analysis of bone adaptation using the well-developed mathematics of geometric flows and the numerical techniques of the level set method. Most importantly, topological changes such as holes forming in plates and rods disconnecting can be treated formally and simulated naturally. First, the relationship between biological theories of bone adaptation and the mathematical object describing geometric flow is described. This is termed the adaptation function, $F$, and is the multi-scale link described by Frost's Utah paradigm between cellular dynamics and bone structure. Second, a model of age-related bone loss termed curvature-based bone adaptation is presented. Using previous literature, it is shown that curvature-based bone adaptation is the limiting continuous equation of simulated bone atrophy, a discrete model of bone aging. Interestingly, the parameters of the model can be defined in such a way that the flow is volume-preserving. This implies that bone health can in principle change in ways that fundamentally cannot be measured by areal or volumetric bone mineral density, requiring structure-level imaging. Third, a numerical method is described and two in silico experiments are performed demonstrating the non-volume-preserving and volume-preserving cases. Taken together, recognition of bone adaptation as a geometric flow permits the recruitment of mathematical and numerical developments over the last 50 years to understanding and describing the complex surface of bone.