pwc-archive/papers-with-abstracts huggingface dataset:
Geometric PID Controller for Stabilization of Nonholonomic Mechanical Systems on Lie Groups
Authors: Rama Seshan, Ravi N Banavar, D. H. S. Maithripala, Arun D. Mahindrakar
Date: 2021-11-13 00:00:00
Tasks: Point Tracking

The PID controller is an elegant and versatile controller for set point tracking in double integrator systems of which mechanical systems evolving on Euclidean space constitute a large class. But since mechanical systems are typically constrained interconnections of rigid bodies whose configuration space is $SE(3)$, which is not even topologically Euclidean, a geometric PID controller has been developed for mechanical systems evolving on Lie groups. In this work, we extend the framework to such systems which have nonholonomic constraints. It encompasses many practically applicable mechanical systems encountered in robotics as robots are constrained interconnections of rigid bodies where the constraints could either be holonomic or nonholonomic.
Selection of the Speed Command Distance for Improved Performance of a Rule-Based VSL and Lane Change Control
Authors: Tianchen Yuan, Faisal Alasiri, Petros A. Ioannou
Date: 2021-11-13 00:00:00

Variable Speed Limit (VSL) control has been one of the most popular techniques with the potential of smoothing traffic flow, maximizing throughput at bottlenecks, and improving mobility and safety. Despite the substantial research efforts in the application of VSL control, few studies have looked into the effect of the VSL sign distance from the point of an accident or a bottleneck. In this paper, we show that this distance has a significant impact on the effectiveness and performance of VSL control. We propose a rule-based VSL strategy that matches the outflow of the upstream VSL zone with the bottleneck capacity based on a multi-section Cell Transmission Model (CTM). Then, we consider the distance of the upstream VSL zone as a control variable and perform a comprehensive analysis of its impact on the performance of the closed-loop traffic control system based on the multi-section CTM. We develop a lower bound that this distance needs to satisfy in order to guarantee homogeneous traffic density across sections and reduce bottleneck congestion. The bound is verified analytically and demonstrated using microscopic simulation of traffic on I-710 in Southern California. The simulations are used to quantify the benefits on mobility, safety and emissions obtained by selecting the upstream VSL zone distance to satisfy the analytical lower bound. The developed lower bound is a design tool which can be used to tune and improve the performance of VSL controllers.
Control Barrier Function Augmentation in Sampling-based Control Algorithm for Sample Efficiency
Authors: Chuyuan Tao, Hunmin Kim, HyungJin Yoon, Naira Hovakimyan, Petros Voulgaris
Date: 2021-11-12 00:00:00

For a nonlinear stochastic path planning problem, sampling-based algorithms generate thousands of random sample trajectories to find the optimal path while guaranteeing safety by Lagrangian penalty methods. However, the sampling-based algorithm can perform poorly in obstacle-rich environments because most samples might violate safety constraints, invalidating the corresponding samples. To improve the sample efficiency of sampling-based algorithms in cluttered environments, we propose an algorithm based on model predictive path integral control and control barrier functions. The proposed algorithm needs fewer samples and time-steps and has a better performance in cluttered environments compared to the original model predictive path integral control algorithm.
Adaptive and quasi-sliding control of shimmy in landing gears
Authors: Daniel A. Burbano-Lombana, Marco Coraggio, Mario di Bernardo, Franco Garofalo, Michele Pugliese
Date: 2021-11-12 00:00:00

Shimmy is a dangerous phenomenon that occurs when aircraft's nose landing gears oscillate in a rapid and uncontrollable fashion. In this paper, we propose the use of two nonlinear control approaches (zero average control and model reference adaptive control based on minimal control synthesis) as simple yet effective strategies to suppress undesired oscillations, even in the presence of uncertainties and partial state measurements. Numerical results are presented to validate the proposed control approaches.
Synchronization of networks of piecewise-smooth systems
Authors: Marco Coraggio, Pietro DeLellis, S. John Hogan, Mario di Bernardo
Date: 2021-11-12 00:00:00

We study convergence in networks of piecewise-smooth (PWS) systems that commonly arise in applications to model dynamical systems whose evolution is affected by macroscopic events such as switches and impacts. Existing approaches were typically oriented toward guaranteeing global bounded synchronizability, local stability of the synchronization manifold, or achieving synchronization by exerting a control action on each node. Here we start by generalizing existing results on QUAD systems to the case of PWS systems, accounting for a large variety of nonlinear coupling laws. Then, we propose that a discontinuous coupling can be used to guarantee global synchronizability of a network of N PWS agents under mild assumptions on the individual dynamics. We provide extensive numerical simulations to gain insights on larger networks.
Neuronal avalanches and critical dynamics of brain waves
Authors: Vitaly L. Galinsky, Lawrence R. Frank
Date: 2021-11-15 00:00:00

Analytical expressions for scaling of brain wave spectra derived from the general nonlinear wave Hamiltonian form show excellent agreement with experimental "neuronal avalanche" data. The theory of the weakly evanescent nonlinear brain wave dynamics reveals the underlying collective processes hidden behind the phenomenological statistical description of the neuronal avalanches and connects together the whole range of brain activity states, from oscillatory wave-like modes, to neuronal avalanches, to incoherent spiking, showing that the neuronal avalanches are just the manifestation of the different nonlinear side of wave processes abundant in cortical tissue. In a more broad way these results show that a system of wave modes interacting through all possible combinations of the third order nonlinear terms described by a general wave Hamiltonian necessarily produces anharmonic wave modes with temporal and spatial scaling properties that follow scale free power laws. To the best of our knowledge this was never reported in the physical literature and may be applicable to many physical systems that involve wave processes and not just to neuronal avalanches.
A continuum mathematical model of substrate-mediated tissue growth
Authors: Maud El-Hachem, Scott W McCue, Matthew J Simpson
Date: 2021-11-15 00:00:00

We consider a continuum mathematical model of biological tissue formation inspired by recent experiments describing thin tissue growth in 3D-printed bioscaffolds. The continuum model involves a partial differential equation describing the density of tissue, $\hat{u}(\hat{\mathbf{x}},\hat{t})$, that is coupled to the concentration of an immobile extracellular substrate, $\hat{s}(\hat{\mathbf{x}},\hat{t})$. Cell migration is modelled with a nonlinear diffusion term, where the diffusive flux is proportional to $\hat{s}$, while a logistic growth term models cell proliferation. The extracellular substrate $\hat{s}$ is produced by cells, and undergoes linear decay. Preliminary numerical simulations show that this mathematical model, which we call the \textit{substrate model}, is able to recapitulate key features of recent tissue growth experiments, including the formation of sharp fronts. To provide a deeper understanding of the model we then analyse travelling wave solutions of the substrate model, showing that the model supports both sharp-fronted travelling wave solutions that move with a minimum wave speed, $c = c_{\rm{min}}$, as well as smooth-fronted travelling wave solutions that move with a faster travelling wave speed, $c > c_{\rm{min}}$. We provide a geometric interpretation that explains the difference between smooth- and sharp-fronted travelling wave solutions that is based on a slow manifold reduction of the desingularised three-dimensional phase space. In addition to exploring the nature of the smooth- and sharp-fronted travelling waves, we also develop and test a series of useful approximations that describe the shape of the travelling wave solutions in various limits. These approximations apply to both the sharp-fronted travelling wave solutions, and the smooth-fronted travelling wave solutions. Software to implement all calculations is available on GitHub.
Symmetry-driven network reconstruction through pseudobalanced coloring optimization
Authors: Ian Leifer, David Phillips, Francesco Sorrentino, Hernán A. Makse
Date: 2021-11-15 00:00:00
Tasks: Link Prediction

Symmetries found through automorphisms or graph fibrations provide important insights in network analysis. Symmetries identify clusters of robust synchronization in the network which improves the understanding of the functionality of complex biological systems. Network symmetries can be determined by finding a {\it balanced coloring} of the graph, which is a node partition in which each cluster of nodes receives the same information (color) from the rest of the graph. In recent work we saw that biological networks such as gene regulatory networks, metabolic networks and neural networks in organisms ranging from bacteria to yeast and humans are rich in fibration symmetries related to the graph balanced coloring. Networks based on real systems, however, are built on experimental data which are inherently incomplete, due to missing links, collection errors, and natural variations within specimens of the same biological species. Therefore, it is fair to assume that some of the existing symmetries were not detected in our analysis. For that reason, a method to find pseudosymmetries and repair networks based on those symmetries is important when analyzing real world networks. In this paper we introduce the {\it pseudobalanced coloring} \eqref{eq:mainip} problem, and provide an integer programming formulation which (a) calculates a pseudobalanced coloring of the graph taking into account the missing data, and (b) optimally repairs the graph with the minimal number of added/removed edges to maximize the symmetry of the graph. We apply our formulation to the {\it C. elegans} connectome to find pseudocoloring and the optimal graph repair. Our solution compares well with a manually curated ground-truth {\it C. elegans} graph as well as solutions generated by other methods of missing link prediction.
Predicting the Directional Transport of Multivalent Cargo from Position Dependent Binding and Unbinding Rates
Authors: Lewis Scott Mosby, Anne Straube, Marco Polin
Date: 2021-11-13 00:00:00
Tasks: Position

Multivalent cargo that can interact with substrates via multiple interaction sites exhibit shared characteristics despite being found in different systems at different length-scales. Here, a general analytical model has been developed that can describe the motion of multivalent cargo as a response to position dependence in the binding and unbinding rates of their interaction sites. Cargo exhibit both an effective diffusivity and velocity, which acts in the direction of increasing cargo-substrate binding rate and decreasing cargo-substrate unbinding rate. This model can reproduce previously published experimental findings using only the binding and unbinding rate distributions of cargo interaction sites, and without any further parameter fitting. Extension of the cargo binding model to two dimensions reveals an effective velocity with the same properties as that derived for the $1$D case.
Instant tissue field and magnetic susceptibility mapping from MR raw phase using Laplacian enabled deep neural networks
Authors: Yang Gao, Zhuang Xiong, Amir Fazlollahi, Peter J Nestor, Viktor Vegh, Fatima Nasrallah, Craig Winter, G. Bruce Pike, Stuart Crozier, Feng Liu, Hongfu Sun
Date: 2021-11-15 00:00:00

Quantitative susceptibility mapping (QSM) is a valuable MRI post-processing technique that quantifies the magnetic susceptibility of body tissue from phase data. However, the traditional QSM reconstruction pipeline involves multiple non-trivial steps, including phase unwrapping, background field removal, and dipole inversion. These intermediate steps not only increase the reconstruction time but amplify noise and errors. This study develops a large-stencil Laplacian preprocessed deep learning-based neural network for near instant quantitative field and susceptibility mapping (i.e., iQFM and iQSM) from raw MR phase data. The proposed iQFM and iQSM methods were compared with established reconstruction pipelines on simulated and in vivo datasets. In addition, experiments on patients with intracranial hemorrhage and multiple sclerosis were also performed to test the generalization of the novel neural networks. The proposed iQFM and iQSM methods yielded comparable results to multi-step methods in healthy subjects while dramatically improving reconstruction accuracies on intracranial hemorrhages with large susceptibilities. The reconstruction time was also substantially shortened from minutes using multi-step methods to only 30 milliseconds using the trained iQFM and iQSM neural networks.
Nachhaltige Strategien gegen die COVID-19-Pandemie in Deutschland im Winter 2021/2022
Authors: Viola Priesemann, Eberhard Bodenschatz, Sandra Ciesek, Eva Grill, Emil N. Iftekhar, Christian Karagiannidis, André Karch, Mirjam Kretzschmar, Berit Lange, Sebastian A. Müller, Kai Nagel, Armin Nassehi, Mathias W. Pletz, Barbara Prainsack, Ulrike Protzer, Leif Erik Sander, Andreas Schuppert, Anita Schöbel, Klaus Überla, Carsten Watzl, Hajo Zeeb
Date: 2021-11-10 00:00:00
Tasks: Position

In this position paper, a large group of interdisciplinary experts outlines response strategies against the spread of SARS-CoV-2 in the winter of 2021/2022 in Germany. We review the current state of the COVID-19 pandemic, from incidence and vaccination efficacy to hospital capacity. Building on this situation assessment, we illustrate various possible scenarios for the winter, and detail the mechanisms and effectiveness of the non-pharmaceutical interventions, vaccination, and booster. With this assessment, we want to provide orientation for decision makers about the progress and mitigation of COVID-19.
Time-Transformed Test for the Explosive Bubbles under Non-stationary Volatility
Authors: Eiji Kurozumi, Anton Skrobotov, Alexey Tsarev
Date: 2020-12-27 00:00:00
Tasks: Time Series, Time Series Analysis

This paper is devoted to testing for the explosive bubble under time-varying non-stationary volatility. Because the limiting distribution of the seminal Phillips et al. (2011) test depends on the variance function and usually requires a bootstrap implementation under heteroskedasticity, we construct the test based on a deformation of the time domain. The proposed test is asymptotically pivotal under the null hypothesis and its limiting distribution coincides with that of the standard test under homoskedasticity, so that the test does not require computationally extensive methods for inference. Appealing finite sample properties are demonstrated through Monte-Carlo simulations. An empirical application demonstrates that the upsurge behavior of cryptocurrency time series in the middle of the sample is partially explained by the volatility change.
Dynamic Network Quantile Regression Model
Authors: Xiu Xu, Weining Wang, Yongcheol Shin, Chaowen Zheng
Date: 2021-11-15 00:00:00
Tasks: model, quantile regression, regression

We propose a dynamic network quantile regression model to investigate the quantile connectedness using a predetermined network information. We extend the existing network quantile autoregression model of Zhu et al. (2019b) by explicitly allowing the contemporaneous network effects and controlling for the common factors across quantiles. To cope with the endogeneity issue due to simultaneous network spillovers, we adopt the instrumental variable quantile regression (IVQR) estimation and derive the consistency and asymptotic normality of the IVQR estimator using the near epoch dependence property of the network process. Via Monte Carlo simulations, we confirm the satisfactory performance of the IVQR estimator across different quantiles under the different network structures. Finally, we demonstrate the usefulness of our proposed approach with an application to the dataset on the stocks traded in NYSE and NASDAQ in 2016.
When Can We Ignore Measurement Error in the Running Variable?
Authors: Yingying Dong, Michal Kolesár
Date: 2021-11-14 00:00:00
Tasks: valid

In many applications of regression discontinuity designs, the running variable used by the administrator to assign treatment is only observed with error. We show that, provided the observed running variable (i) correctly classifies the treatment assignment, and (ii) affects the conditional means of the potential outcomes smoothly, ignoring the measurement error nonetheless yields an estimate with a causal interpretation: the average treatment effect for units whose observed running variable equals to the cutoff. We show that, possibly after doughnut trimming, these assumptions accommodate a variety of settings where support of the measurement error is not too wide. We propose to conduct inference using bias-aware methods, which remain valid even when discreteness or irregular support in the observed running variable may lead to partial identification. We illustrate the results for both sharp and fuzzy designs in an empirical application.
Rational AI: A comparison of human and AI responses to triggers of economic irrationality in poker
Authors: C. Grace Haaf, Devansh Singh, Cinny Lin, Scofield Zou
Date: 2021-11-14 00:00:00
Tasks: Decision Making

Humans exhibit irrational decision-making patterns in response to environmental triggers, such as experiencing an economic loss or gain. In this paper we investigate whether algorithms exhibit the same behavior by examining the observed decisions and latent risk and rationality parameters estimated by a random utility model with constant relative risk-aversion utility function. We use a dataset consisting of 10,000 hands of poker played by Pluribus, the first algorithm in the world to beat professional human players and find (1) Pluribus does shift its playing style in response to economic losses and gains, ceteris paribus; (2) Pluribus becomes more risk-averse and rational following a trigger but the humans become more risk-seeking and irrational; (3) the difference in playing styles between Pluribus and the humans on the dimensions of risk-aversion and rationality are particularly differentiable when both have experienced a trigger. This provides support that decision-making patterns could be used as "behavioral signatures" to identify human versus algorithmic decision-makers in unlabeled contexts.
Large Order-Invariant Bayesian VARs with Stochastic Volatility
Authors: Joshua C. C. Chan, Gary Koop, Xuewen Yu
Date: 2021-11-14 00:00:00

Many popular specifications for Vector Autoregressions (VARs) with multivariate stochastic volatility are not invariant to the way the variables are ordered due to the use of a Cholesky decomposition for the error covariance matrix. We show that the order invariance problem in existing approaches is likely to become more serious in large VARs. We propose the use of a specification which avoids the use of this Cholesky decomposition. We show that the presence of multivariate stochastic volatility allows for identification of the proposed model and prove that it is invariant to ordering. We develop a Markov Chain Monte Carlo algorithm which allows for Bayesian estimation and prediction. In exercises involving artificial and real macroeconomic data, we demonstrate that the choice of variable ordering can have non-negligible effects on empirical results. In a macroeconomic forecasting exercise involving VARs with 20 variables we find that our order-invariant approach leads to the best forecasts and that some choices of variable ordering can lead to poor forecasts using a conventional, non-order invariant, approach.
Asymmetric Conjugate Priors for Large Bayesian VARs
Authors: Joshua C. C. Chan
Date: 2021-11-13 00:00:00

Large Bayesian VARs are now widely used in empirical macroeconomics. One popular shrinkage prior in this setting is the natural conjugate prior as it facilitates posterior simulation and leads to a range of useful analytical results. This is, however, at the expense of modeling flexibility, as it rules out cross-variable shrinkage -- i.e., shrinking coefficients on lags of other variables more aggressively than those on own lags. We develop a prior that has the best of both worlds: it can accommodate cross-variable shrinkage, while maintaining many useful analytical results, such as a closed-form expression of the marginal likelihood. This new prior also leads to fast posterior simulation -- for a BVAR with 100 variables and 4 lags, obtaining 10,000 posterior draws takes less than half a minute on a standard desktop. We demonstrate the usefulness of the new prior via a structural analysis using a 15-variable VAR with sign restrictions to identify 5 structural shocks.
On Risk and Time Pressure: When to Think and When to Do
Authors: Christoph Carnehl, Johannes Schneider
Date: 2021-11-14 00:00:00

We study the tradeoff between fundamental risk and time. A time-constrained agent has to solve a problem. She dynamically allocates effort between implementing a risky initial idea and exploring alternatives. Discovering an alternative implies progress that has to be converted to a solution. As time runs out, the chances of converting it in time shrink. We show that the agent may return to the initial idea after having left it in the past to explore alternatives. Our model helps explain so-called false starts. To finish fast, the agent delays exploring alternatives reducing the overall success probability.
Averaging plus Learning Models and Their Asymptotics
Authors: Ionel Popescu, Tushar Vaidya
Date: 2019-04-17 00:00:00

We develop original models to study interacting agents in financial markets and in social networks. Within these models randomness is vital as a form of shock or news that decays with time. Agents learn from their observations and learning ability to interpret news or private information in time-varying networks. Under general assumption on the noise, a limit theorem is developed for the generalised DeGroot framework for certain type of conditions governing the learning. In this context, the agents beliefs (properly scaled) converge in distribution that is not necessarily normal. Fresh insights are gained not only from proposing a new setting for social learning models but also from using different techniques to study discrete time random linear dynamical systems.