pwc-archive/papers-with-abstracts huggingface dataset:
Towards a Computational Framework for Automated Discovery and Modeling of Biological Rhythms from Wearable Data Streams
Authors: Runze Yan, Afsaneh Doryab
Date: 2021-09-13 00:00:00
Tasks: Rhythm, Time Series, Time Series Analysis

Modeling biological rhythms helps understand the complex principles behind the physical and psychological abnormalities of human bodies, to plan life schedules, and avoid persisting fatigue and mood and sleep alterations due to the desynchronization of those rhythms. The first step in modeling biological rhythms is to identify their characteristics, such as cyclic periods, phase, and amplitude. However, human rhythms are susceptible to external events, which cause irregular fluctuations in waveforms and affect the characterization of each rhythm. In this paper, we present our exploratory work towards developing a computational framework for automated discovery and modeling of human rhythms. We first identify cyclic periods in time series data using three different methods and test their performance on both synthetic data and real fine-grained biological data. We observe consistent periods are detected by all three methods. We then model inner cycles within each period through identifying change points to observe fluctuations in biological data that may inform the impact of external events on human rhythms. The results provide initial insights into the design of a computational framework for discovering and modeling human rhythms.
On the Correlation between the Noise and a Priori Error Vectors in Affine Projection Algorithms
Authors: Andres Altieri
Date: 2021-09-13 00:00:00
Tasks: valid

This paper analyzes the correlation matrix between the a priori error and measurement noise vectors for affine projection algorithms (APA). This correlation stems from the dependence between the filter tap estimates and the noise samples, and has a strong influence on the mean square behavior of the algorithm. We show that the correlation matrix is upper triangular, and compute the diagonal elements in closed form, showing that they are independent of the input process statistics. Also, for white inputs we show that the matrix is fully diagonal. These results are valid in the transient and steady states of the algorithm considering a possibly variable step-size. Our only assumption is that the filter order is large compared to the projection order of APA and we make no assumptions on the input signal except for stationarity. Using these results, we perform a steady-state analysis of the algorithm for small step size and provide a new simple closed-form expression for mean-square error, which has comparable or better accuracy to many preexisting expressions, and is much simpler to compute. Finally, we also obtain expressions for the steady-state energy of the other components of the error vector.
Safe Nonlinear Control Using Robust Neural Lyapunov-Barrier Functions
Authors: Charles Dawson, Zengyi Qin, Sicun Gao, Chuchu Fan
Date: 2021-09-14 00:00:00

Safety and stability are common requirements for robotic control systems; however, designing safe, stable controllers remains difficult for nonlinear and uncertain models. We develop a model-based learning approach to synthesize robust feedback controllers with safety and stability guarantees. We take inspiration from robust convex optimization and Lyapunov theory to define robust control Lyapunov barrier functions that generalize despite model uncertainty. We demonstrate our approach in simulation on problems including car trajectory tracking, nonlinear control with obstacle avoidance, satellite rendezvous with safety constraints, and flight control with a learned ground effect model. Simulation results show that our approach yields controllers that match or exceed the capabilities of robust MPC while reducing computational costs by an order of magnitude.
Stochastic Observer for SLAM on the Lie Group
Authors: Marium Tawhid, Ajay Singh Ludher, Hashim A. Hashim
Date: 2021-09-13 00:00:00
Tasks: Simultaneous Localization and Mapping

A robust nonlinear stochastic observer for simultaneous localization and mapping (SLAM) is proposed using the available uncertain measurements of angular velocity, translational velocity, and features. The proposed observer is posed on the Lie Group of $\mathbb{SLAM}_{n}\left(3\right)$ to mimic the true stochastic SLAM dynamics. The proposed approach considers the velocity measurements to be attached with an unknown bias and an unknown Gaussian noise. The proposed SLAM observer ensures that the closed loop error signals are semi-globally uniformly ultimately bounded. Simulation results demonstrates the efficiency and robustness of the proposed approach, revealing its ability to localize the unknown vehicle, as well as mapping the unknown environment given measurements obtained from low-cost units.
Self-Supervised Metric Learning With Graph Clustering For Speaker Diarization
Authors: Prachi Singh, Sriram Ganapathy
Date: 2021-09-14 00:00:00
Tasks: Clustering, Graph Clustering, Metric Learning, Representation Learning, Self-Supervised Learning, speaker-diarization, Speaker Diarization

In this paper, we propose a novel algorithm for speaker diarization using metric learning for graph based clustering. The graph clustering algorithms use an adjacency matrix consisting of similarity scores. These scores are computed between speaker embeddings extracted from pairs of audio segments within the given recording. In this paper, we propose an approach that jointly learns the speaker embeddings and the similarity metric using principles of self-supervised learning. The metric learning network implements a neural model of the probabilistic linear discriminant analysis (PLDA). The self-supervision is derived from the pseudo labels obtained from a previous iteration of clustering. The entire model of representation learning and metric learning is trained with a binary cross entropy loss. By combining the self-supervision based metric learning along with the graph-based clustering algorithm, we achieve significant relative improvements of 60% and 7% over the x-vector PLDA agglomerative hierarchical clustering (AHC) approach on AMI and the DIHARD datasets respectively in terms of diarization error rates (DER).
Overlap-aware low-latency online speaker diarization based on end-to-end local segmentation
Authors: Juan M. Coria, Hervé Bredin, Sahar Ghannay, Sophie Rosset
Date: 2021-09-14 00:00:00
Tasks: Clustering, Segmentation, speaker-diarization, Speaker Diarization

We propose to address online speaker diarization as a combination of incremental clustering and local diarization applied to a rolling buffer updated every 500ms. Every single step of the proposed pipeline is designed to take full advantage of the strong ability of a recently proposed end-to-end overlap-aware segmentation to detect and separate overlapping speakers. In particular, we propose a modified version of the statistics pooling layer (initially introduced in the x-vector architecture) to give less weight to frames where the segmentation model predicts simultaneous speakers. Furthermore, we derive cannot-link constraints from the initial segmentation step to prevent two local speakers from being wrongfully merged during the incremental clustering step. Finally, we show how the latency of the proposed approach can be adjusted between 500ms and 5s to match the requirements of a particular use case, and we provide a systematic analysis of the influence of latency on the overall performance (on AMI, DIHARD and VoxConverse).
Dynamic self-triggered control for nonlinear systems based on hybrid Lyapunov functions
Authors: Michael Hertneck, Frank Allgöwer
Date: 2021-09-14 00:00:00

Self-triggered control (STC) is a well-established technique to reduce the amount of samples for sampled-data systems, and is hence particularly useful for Networked Control Systems. At each sampling instant, an STC mechanism determines not only an updated control input but also when the next sample should be taken. In this paper, a dynamic STC mechanism for nonlinear systems is proposed. The mechanism incorporates a dynamic variable for determining the next sampling instant. Such a dynamic variable for the trigger decision has been proven to be a powerful tool for increasing sampling intervals in the closely related concept of event-triggered control, but was so far not exploited for STC. This gap is closed in this paper. For the proposed mechanism, the dynamic variable is chosen to be the filtered values of the Lyapunov function at past sampling instants. The next sampling instant is, based on the dynamic variable and on hybrid Lyapunov function techniques, chosen such that an average decrease of the Lyapunov function is ensured. The proposed mechanism is illustrated with a numerical example from the literature. For this example, the obtained sampling intervals are significantly larger than for existing static STC mechanisms. This paper is the accepted version of [1], containing also proofs of the main results.
Comparison of power sector models by analyzing the impact of modeling features on optimal capacity expansion
Authors: Jonas van Ouwerkerk, Hans Christian Gils, Hedda Gardian, Martin Kittel, Wolf-Peter Schill, Alexander Zerrahn, Alexander Murmann, Jann Launer, Laura Torralba-Díaz, Christian Bußar
Date: 2021-09-14 00:00:00

The transition towards decarbonized energy systems requires the expansion of renewable and flexibility technologies in power sectors. In a model comparison, we examine the optimal expansion of such technologies with six capacity expansion power system models. The technologies under investigation include base- and peak-load power plants, electricity storage, and transmission. We define four highly simplified and harmonized use cases that focus on the expansion of only one or two specific technologies to isolate their effects on model results. We find that deviating assumptions on limited availability factors of technologies cause technology-specific deviations between optimal capacity expansion in models in almost all use cases. Fixed energy-to-power-ratios of storage can entirely change model optimal expansion outcomes, especially shifting the ratio between short- and long-duration storage. Fixed initial and end storage levels can impact the seasonal use of long-duration storage. Models with a pre-ordered dispatch structure significantly deviate from linear optimization models, as limited foresight and flexibility can lead to higher capacity investments. A simplified net transfer capacity approach underestimates the need for grid infrastructure compared to a more detailed direct current load flow approach. We further find deviations in model results of optimal storage and transmission capacity expansion between regions and link them to variable renewable energy generation and demand characteristics. We expect that the general effects identified in our stylized setting also hold in more detailed model applications, although they may be less visible there.
Nonlinear Robust Periodic Output Regulation of Minimum Phase Systems
Authors: Daniele Astolfi, Laurent Praly, Lorenzo Marconi, Mines Paristech
Date: 2021-09-14 00:00:00
Tasks: Blocking

In linear systems theory it's a well known fact that a regulator given by the cascade of an oscillatory dynamics, driven by some regulated variables, and of a stabiliser stabilising the cascade of the plant and of the oscillators has the ability of blocking on the steady state of the regulated variables any harmonics matched with the ones of the oscillators. This is the well-celebrated internal model principle. In this paper we are interested to follow the same design route for a controlled plant that is a nonlinear and periodic system with period T : we add a bunch of linear oscillators, embedding n o harmonics that are multiple of 2$\pi$/T , driven by a "regulated variable" of the nonlinear system, we look for a stabiliser for the nonlinear cascade of the plant and the oscillators, and we study the asymptotic properties of the resulting closedloop regulated variable. In this framework the contributions of the paper are multiple: for specific class of minimum-phase systems we present a systematic way of designing a stabiliser, which is uniform with respect to n o , by using a mix of high-gain and forwarding techniques; we prove that the resulting closed-loop system has a periodic steady state with period T with a domain of attraction not shrinking with n o ; similarly to the linear case, we also show that the spectrum of the steady state closed-loop regulated variable does not contain the n harmonics embedded in the bunch of oscillators and that the L 2 norm of the regulated variable is a monotonically decreasing function of n o. The results are robust, namely the asymptotic properties on the regulated variable hold also in presence of any uncertainties in the controlled plant not destroying closed-loop stability.
Functional Observers with Linear Error Dynamics for Nonlinear Systems
Authors: Costas Kravaris, Sunjeev Venkateswaran
Date: 2021-01-27 00:00:00

In this work, the problem of designing observers for estimating a single nonlinear functional of the state is formulated for general nonlinear systems. Notions of functional observer linearization are also formulated, in terms achieving exactly linear error dynamics in transformed coordinates and with prescribed rate of decay of the error. Necessary and sufficient conditions for the existence of a lower-order functional observer with linear dynamics are derived. The results provide a direct generalization of Luenberger's linear theory of functional observers to nonlinear systems.
An Iterative Approach to Improving Solution Quality for AC Optimal Power Flow Problems
Authors: Ling Zhang, Baosen Zhang
Date: 2021-09-13 00:00:00

The existence of multiple solutions to AC optimal power flow (ACOPF) problems has been noted for decades. Existing solvers are generally successful in finding local solutions, which satisfy first and second order optimality conditions, but may not be globally optimal. In this paper, we propose a simple iterative approach to improve the quality of solutions to ACOPF problems. First, we call an existing solver for the ACOPF problem. From the solution and the associated dual variables, we form a partial Lagrangian. Then we optimize this partial Lagrangian and use its solution as a warm start to call the solver again for the ACOPF problem. By repeating this process, we can iteratively improve the solution quality, moving from local solutions to global ones. We show the effectiveness of our algorithm on standard IEEE networks. The simulation results show that our algorithm can escape from local solutions to achieve global optimums within a few iterations.
On frequency- and time-limited H2-optimal model order reduction
Authors: Umair Zulfiqar, Victor Sreeram, Xin Du
Date: 2021-02-06 00:00:00

In this paper, the problems of frequency-limited and time-limited H2-optimal model order reduction of linear time-invariant systems are considered within the oblique projection framework. It is shown that it is inherently not possible to satisfy all the necessary conditions for the local minimizer in the oblique projection framework. The conditions for exact satisfaction of the optimality conditions are also discussed. Further, the equivalence between the tangential interpolation conditions and the gramians-based necessary condition for the local optimum is established. Based on this equivalence, iterative algorithms that nearly satisfy these interpolation-based necessary conditions are proposed. The deviation in satisfaction of the optimality conditions decay as the order of the reduced-model is increased in the proposed algorithms. Moreover, stationary point iteration algorithms that satisfy two out of three necessary conditions for the local minimizer are also proposed. There also, the deviation in satisfaction of the third optimality conditions decay as the order of the reduced-model is increased in the proposed algorithms. The efficacy of the proposed algorithms is validated by considering one illustrative and three high-order models that are considered a benchmark for testing model order reduction algorithms.
Unsupervised learning approaches to characterize heterogeneous samples using X-ray single particle imaging
Authors: Yulong Zhuang, Salah Awel, Anton Barty, Richard Bean, Johan Bielecki, Martin Bergemann, Benedikt J. Daurer, Tomas Ekeberg, Armando D. Estillore, Hans Fangohr, Klaus Giewekemeyer, Mark S. Hunter, Mikhail Karnevskiy, Richard A. Kirian, Henry Kirkwood, Yoonhee Kim, Jayanath Koliyadu, Holger Lange, Romain Letrun, Jannik Lübke, Abhishek Mall, Thomas Michelat, Andrew J. Morgan, Nils Roth, Amit K. Samanta, Tokushi Sato, Zhou Shen, Marcin Sikorski, Florian Schulz, John C. H. Spence, Patrik Vagovic, Tamme Wollweber, Lena Worbs, P. Lourdu Xavier, Oleksandr Yefanov, Filipe R. N. C. Maia, Daniel A. Horke, Jochen Küpper, N. Duane Loh, Adrian P. Mancuso, Henry N. Chapman, Kartik Ayyer
Date: 2021-09-13 00:00:00

One of the outstanding analytical problems in X-ray single particle imaging (SPI) is the classification of structural heterogeneity, which is especially difficult given the low signal-to-noise ratios of individual patterns and that even identical objects can yield patterns that vary greatly when orientation is taken into consideration. We propose two methods which explicitly account for this orientation-induced variation and can robustly determine the structural landscape of a sample ensemble. The first, termed common-line principal component analysis (PCA) provides a rough classification which is essentially parameter-free and can be run automatically on any SPI dataset. The second method, utilizing variation auto-encoders (VAEs) can generate 3D structures of the objects at any point in the structural landscape. We implement both these methods in combination with the noise-tolerant expand-maximize-compress (EMC) algorithm and demonstrate its utility by applying it to an experimental dataset from gold nanoparticles with only a few thousand photons per pattern and recover both discrete structural classes as well as continuous deformations. These developments diverge from previous approaches of extracting reproducible subsets of patterns from a dataset and open up the possibility to move beyond studying homogeneous sample sets and study open questions on topics such as nanocrystal growth and dynamics as well as phase transitions which have not been externally triggered.
Effective Mathematical Modelling of Health Passes during a Pandemic
Authors: Giacomo Cacciapaglia, Stefan Hohenegger, Francesco Sannino
Date: 2021-09-14 00:00:00

We study the impact on the epidemiological dynamics of a class of restrictive measures that are aimed at reducing the number of contacts of individuals who have a higher risk of being infected with a transmittable disease. Such measures are currently either implemented or at least discussed in numerous countries worldwide to ward off a potential new wave of COVID-19 across Europe. They come in the form of Health Passes (HP), which grant full access to public life only to individuals with a certificate that proves that they have either been fully vaccinated, have recovered from a previous infection or have recently tested negative to SARS-Cov-19 . We develop both a compartmental model as well as an epidemic Renormalisation Group approach, which is capable of describing the dynamics over a longer period of time, notably an entire epidemiological wave. Introducing different versions of HPs in this model, we are capable of providing quantitative estimates on the effectiveness of the underlying measures as a function of the fraction of the population that is vaccinated and the vaccination rate. We apply our models to the latest COVID-19 wave in several European countries, notably Germany and Austria, which validate our theoretical findings.
Vaccination strategies and transmission of COVID-19: evidence across advanced countries
Authors: Dongwoo Kim, Young Jun Lee
Date: 2021-09-14 00:00:00
Tasks: Time Series, Time Series Analysis

Given limited supply of approved vaccines and constrained medical resources, design of a vaccination strategy to control a pandemic is an economic problem. We use time-series and panel methods with real-world country-level data to estimate effects on COVID-19 cases and deaths of two key elements of mass vaccination - time between doses and vaccine type. We find that new infections and deaths are both significantly negatively associated with the fraction of the population vaccinated with at least one dose. Conditional on first-dose coverage, an increased fraction with two doses appears to offer no further reductions in new cases and deaths. For vaccines from China, however, we find significant effects on both health outcomes only after two doses. Our results support a policy of extending the interval between first and second doses of vaccines developed in Europe and the US. As vaccination progresses, population mobility increases, which partially offsets the direct effects of vaccination. This suggests that non-pharmaceutical interventions remain important to contain transmission as vaccination is rolled out.
A General Framework to Forecast the Adoption of Novel Products: A Case of Autonomous Vehicles
Authors: Subodh Dubey, Ishant Sharma, Sabyasachee Mishra, Oded Cats, Prateek Bansal
Date: 2021-09-08 00:00:00
Tasks: Autonomous Vehicles

Due to the unavailability of prototypes, the early adopters of novel products actively seek information from multiple sources (e.g., media and social networks) to minimize the potential risk. The existing behavior models not only fail to capture the information propagation within the individual's social network, but also they do not incorporate the impact of such word-of-mouth (WOM) dissemination on the consumer's risk preferences. Moreover, even cutting-edge forecasting models rely on crude/synthetic consumer behavior models. We propose a general framework to forecast the adoption of novel products by developing a new consumer behavior model and integrating it into a population-level agent-based model. Specifically, we extend the hybrid choice model to estimate consumer behavior, which incorporates social network effects and interplay between WOM and risk aversion. The calibrated consumer behavior model and synthetic population are passed through the agent-based model for forecasting the product market share. We apply the proposed framework to forecast the adoption of autonomous vehicles (AVs) in Nashville, USA. The consumer behavior model is calibrated with a stated preference survey data of 1,495 Nashville residents. The output of the agent-based model provides the effect of the purchase price, post-purchase satisfaction, and safety measures/regulations on the forecasted AV market share. With an annual AV price reduction of 5% at the initial purchase price of $40,000 and 90% of satisfied adopters, AVs are forecasted to attain around 85% market share in thirty years. These findings are crucial for policymakers to develop infrastructure plans and manufacturers to conduct an after-sales cost-benefit analysis.
On the meaning of the Critical Cost Efficiency Index
Authors: Federico Echenique
Date: 2021-09-13 00:00:00

This note provides a critical discussion of the \textit{Critical Cost-Efficiency Index} (CCEI) as used to assess deviations from utility-maximizing behavior. I argue that the CCEI is hard to interpret, and that it can disagree with other plausible measures of "irrational" behavior. The common interpretation of CCEI as wasted income is questionable. Moreover, I show that one agent may have more unstable preferences than another, but seem more rational according to the CCEI. This calls into question the (now common) use of CCEI as an ordinal and cardinal measure of degrees of rationality.