pwc-archive/papers-with-abstracts huggingface dataset:
Parallel Multi-Extended State Observers based {ADRC} with Application to High-Speed Precision Motion Stage
Authors: Guojie Tang, Wenchao Xue, Hao Peng, Yanlong Zhao, Zhijun Yang
Date: 2023-01-18 00:00:00
Tasks: Friction

In this paper, the parallel multi-extended state observers (ESOs) based active disturbance rejection control approach is proposed to achieve desired tracking performance by automatically selecting the estimation values leading to the least tracking error. First, the relationship between the estimation error of ESO and the tracking error of output is quantitatively studied for single ESO with general order. In particular, the algorithm for calculating the tracking error caused by single ESO's estimation error is constructed. Moreover, by timely evaluating the least tracking error caused by different ESOs, a novel switching ADRC approach with parallel multi-ESOs is proposed. In addition, the stability of the algorithm is rigorously proved. Furthermore, the proposed ADRC is applied to the high-speed precision motion stage which has large nonlinear uncertainties and elastic deformation disturbances near the dead zone of friction. The experimental results show that the parallel multi-ESOs based ADRC has higher tracking performance than the traditional single ESO based ADRC.
Evolutionary adaptation is facilitated by the presence of lethal genotypes
Authors: Viktoria Blavatska, Bartlomiej Waclaw
Date: 2023-01-18 00:00:00

The adaptation rate in theoretical models of biological evolution increases with the mutation rate but only to a point when mutations into lethal states cause extinction. One would expect that removing such states should be beneficial for evolution. We show here that, counter-intuitively, lethal mutations speed up adaptation on rugged fitness landscapes with many fitness maxima and minima, if strong competition for resources exist. We consider a modified stochastic version of the quasispecies model with two types of genotypes, viable and lethal, and show that increasing the rate of lethal mutations decreases the time to evolve the best-fit genotype. This can be explained by an increased rate of crossing fitness valleys, facilitated by reduced selection against less-fit variants.
Generative Adversarial Networks to infer velocity components in rotating turbulent flows
Authors: Tianyi Li, Michele Buzzicotti, Luca Biferale, Fabio Bonaccorso
Date: 2023-01-18 00:00:00
Tasks: Generative Adversarial Network

Inference problems for two-dimensional snapshots of rotating turbulent flows are studied. We perform a systematic quantitative benchmark of point-wise and statistical reconstruction capabilities of the linear Extended Proper Orthogonal Decomposition (EPOD) method, a non-linear Convolutional Neural Network (CNN) and a Generative Adversarial Network (GAN). We attack the important task of inferring one velocity component out of the measurement of a second one, and two cases are studied: (I) both components lay in the plane orthogonal to the rotation axis and (II) one of the two is parallel to the rotation axis. We show that EPOD method works well only for the former case where both components are strongly correlated, while CNN and GAN always outperform EPOD both concerning point-wise and statistical reconstructions. For case (II), when the input and output data are weakly correlated, all methods fail to reconstruct faithfully the point-wise information. In this case, only GAN is able to reconstruct the field in a statistical sense. The analysis is performed using both standard validation tools based on $L_2$ spatial distance between the prediction and the ground truth and more sophisticated multi-scale analysis using wavelet decomposition. Statistical validation is based on standard Jensen-Shannon divergence between the probability density functions, spectral properties and multi-scale flatness.
Impact of the Euro 2020 championship on the spread of COVID-19
Authors: Jonas Dehning, Sebastian B. Mohr, Sebastian Contreras, Philipp Dönges, Emil N. Iftekhar, Oliver Schulz, Philip Bechtle, Viola Priesemann
Date: 2023-01-18 00:00:00
Tasks: Attribute

Large-scale events like the UEFA Euro~2020 football (soccer) championship offer a unique opportunity to quantify the impact of gatherings on the spread of COVID-19, as the number and dates of matches played by participating countries resembles a randomized study. Using Bayesian modeling and the gender imbalance in COVID-19 data, we attribute 840,000 (95% CI: [0.39M, 1.26M]) COVID-19 cases across 12 countries to the championship. The impact depends non-linearly on the initial incidence, the reproduction number $R$, and the number of matches played. The strongest effects are seen in Scotland and England, where as much as 10,000 primary cases per million inhabitants occur from championship-related gatherings. The average match-induced increase in $R$ was 0.46 [0.18, 0.75] on match days, but important matches caused an increase as large as +3. Altogether, our results provide quantitative insights that help judge and mitigate the impact of large-scale events on pandemic spread.
Identifying Network Ties from Panel Data: Theory and an Application to Tax Competition
Authors: Aureo de Paula, Imran Rasul, Pedro Souza
Date: 2019-10-16 00:00:00

Social interactions determine many economic behaviors, but information on social ties does not exist in most publicly available and widely used datasets. We present results on the identification of social networks from observational panel data that contains no information on social ties between agents. In the context of a canonical social interactions model, we provide sufficient conditions under which the social interactions matrix, endogenous and exogenous social effect parameters are all globally identified. While this result is relevant across different estimation strategies, we then describe how high-dimensional estimation techniques can be used to estimate the interactions model based on the Adaptive Elastic Net GMM method. We employ the method to study tax competition across US states. We find the identified social interactions matrix implies tax competition differs markedly from the common assumption of competition between geographically neighboring states, providing further insights for the long-standing debate on the relative roles of factor mobility and yardstick competition in driving tax setting behavior across states. Most broadly, our identification and application show the analysis of social interactions can be extended to economic realms where no network data exists.
Unconditional Quantile Partial Effects via Conditional Quantile Regression
Authors: Javier Alejo, Antonio F. Galvao, Julian Martinez-Iriarte, Gabriel Montes-Rojas
Date: 2023-01-18 00:00:00
Tasks: quantile regression, regression

This paper develops a semi-parametric procedure for estimation of unconditional quantile partial effects using quantile regression coefficients. The estimator is based on an identification result showing that, for continuous covariates, unconditional quantile effects are a weighted average of conditional ones at particular quantile levels that depend on the covariates. We propose a two-step estimator for the unconditional effects where in the first step one estimates a structural quantile regression model, and in the second step a nonparametric regression is applied to the first step coefficients. We establish the asymptotic properties of the estimator, say consistency and asymptotic normality. Monte Carlo simulations show numerical evidence that the estimator has very good finite sample performance and is robust to the selection of bandwidth and kernel. To illustrate the proposed method, we study the canonical application of the Engel's curve, i.e. food expenditures as a share of income.
Noisy, Non-Smooth, Non-Convex Estimation of Moment Condition Models
Authors: Jean-Jacques Forneron
Date: 2023-01-17 00:00:00

A practical challenge for structural estimation is the requirement to accurately minimize a sample objective function which is often non-smooth, non-convex, or both. This paper proposes a simple algorithm designed to find accurate solutions without performing an exhaustive search. It augments each iteration from a new Gauss-Newton algorithm with a grid search step. A finite sample analysis derives its optimization and statistical properties simultaneously using only econometric assumptions. After a finite number of iterations, the algorithm automatically transitions from global to fast local convergence, producing accurate estimates with high probability. Simulated examples and an empirical application illustrate the results.
Navigating the energy trilemma during geopolitical and environmental crises
Authors: Richard S. J. Tol
Date: 2023-01-18 00:00:00

There are many indicators of energy security. Few measure what really matters -- affordable and reliable energy supply -- and the trade-offs between the two. Reliability is physical, affordability is economic. Russia's latest invasion of Ukraine highlights some of the problems with energy security, from long-term contracts being broken to supposedly secure supplies being diverted to retired power plants being recommissioned to spillovers to other markets. The transition to carbon-free energy poses new challenges for energy security, from a shift in dependence from some resources (coal, oil, gas) to others (rare earths, wind, sunshine) to substantial redundancies in the energy capital stock to undercapitalized energy companies, while regulatory uncertainty deters investment. Renewables improve energy security in one dimension, but worsen it in others, particularly long spells of little wind. Security problems with rare earths and borrowed capital are less pronounced, as stock rather than flow.
Large Language Models as Simulated Economic Agents: What Can We Learn from Homo Silicus?
Authors: John J. Horton
Date: 2023-01-18 00:00:00

Newly-developed large language models (LLM) -- because of how they are trained and designed -- are implicit computational models of humans -- a homo silicus. These models can be used the same way economists use homo economicus: they can be given endowments, information, preferences, and so on and then their behavior can be explored in scenarios via simulation. I demonstrate this approach using OpenAI's GPT3 with experiments derived from Charness and Rabin (2002), Kahneman, Knetsch and Thaler (1986) and Samuelson and Zeckhauser (1988). The findings are qualitatively similar to the original results, but it is also trivially easy to try variations that offer fresh insights. Departing from the traditional laboratory paradigm, I also create a hiring scenario where an employer faces applicants that differ in experience and wage ask and then analyze how a minimum wage affects realized wages and the extent of labor-labor substitution.
Dereverberation in Acoustic Sensor Networks Using Weighted Prediction Error With Microphone-dependent Prediction Delays
Authors: Anselm Lohmann, Toon van Waterschoot, Joerg Bitzer, Simon Doclo
Date: 2023-01-18 00:00:00
Tasks: Prediction, Speech Dereverberation

In the last decades several multi-microphone speech dereverberation algorithms have been proposed, among which the weighted prediction error (WPE) algorithm. In the WPE algorithm, a prediction delay is required to reduce the correlation between the prediction signals and the direct component in the reference microphone signal. In compact arrays with closely-spaced microphones, the prediction delay is often chosen microphone-independent. In acoustic sensor networks with spatially distributed microphones, large time-differences-of-arrival (TDOAs) of the speech source between the reference microphone and other microphones typically occur. Hence, when using a microphone-independent prediction delay the reference and prediction signals may still be significantly correlated, leading to distortion in the dereverberated output signal. In order to decorrelate the signals, in this paper we propose to apply TDOA compensation with respect to the reference microphone, resulting in microphone-dependent prediction delays for the WPE algorithm. We consider both optimal TDOA compensation using crossband filtering in the short-time Fourier transform domain as well as band-to-band and integer delay approximations. Simulation results for different reverberation times using oracle as well as estimated TDOAs clearly show the benefit of using microphone-dependent prediction delays.
Synthetic Hyperspectral Array Video Database with Applications to Cross-Spectral Reconstruction and Hyperspectral Video Coding
Authors: Frank Sippel, Jürgen Seiler, André Kaup
Date: 2023-01-18 00:00:00
Tasks: Diversity, Image Reconstruction, Spectral Reconstruction

In this paper, a synthetic hyperspectral video database is introduced. Since it is impossible to record ground truth hyperspectral videos, this database offers the possibility to leverage the evaluation of algorithms in diverse applications. For all scenes, depth maps are provided as well to yield the position of a pixel in all spatial dimensions as well as the reflectance in spectral dimension. Two novel algorithms for two different applications are proposed to prove the diversity of applications that can be addressed by this novel database. First, a cross-spectral image reconstruction algorithm is extended to exploit the temporal correlation between two consecutive frames. The evaluation using this hyperspectral database shows an increase in PSNR of up to 5.6 dB dependent on the scene. Second, a hyperspectral video coder is introduced which extends an existing hyperspectral image coder by exploiting temporal correlation. The evaluation shows rate savings of up to 10% depending on the scene. The novel hyperspectral video database and source code is available at https:// github.com/ FAU-LMS/ HyViD for use by the research community.
The Dependence of Parallel Imaging with Linear Predictability on the Undersampling Direction
Authors: Alex McManus, Stephen Becker, Nicholas Dwork
Date: 2023-01-18 00:00:00
Tasks: Image Reconstruction

Parallel imaging with linear predictability takes advantage of information present in multiple receive coils to accurately reconstruct the image with fewer samples. Commonly used algorithms based on linear predictability include GRAPPA and SPIRiT. We present a sufficient condition for reconstruction based on the direction of undersampling and the arrangement of the sensing coils. This condition is justified theoretically and examples are shown using real data. We also propose a metric based on the fully-sampled auto-calibration region which can show which direction(s) of undersampling will allow for a good quality image reconstruction.
Cell-Free Massive MIMO in the O-RAN Architecture: Cluster and Handover Strategies
Authors: Robbert Beerten, Vida Ranjbar, Andrea P. Guevara, Sofie Pollin
Date: 2023-01-18 00:00:00
Tasks: Clustering

Cell-free Massive MIMO has emerged as a promising solution for next-generation radio networks. Particularly the user-centric variant where users are served by a limited subset of access points, a so-called cluster, has garnered significant attention within the research community. Despite numerous proposed physical layer solutions, managing AP clusters in case of user mobility remains challenging. In this study, we first present a realistic method for modeling the temporal evolution of the channel in cell-free Massive MIMO. Next, we develop two clustering and handover strategies: 1) a fixed clustering strategy that computes the ideal cluster when a cluster handover threshold is exceeded and 2) an opportunistic strategy that opportunistically adds serving APs as the user moves. Moreover, we establish a connection between our findings and O-RAN, an emerging network architecture that offers open interfaces and network-wide control capabilities, thus, facilitating practical implementation of our research.
Extended FastSLAM Using Cellular Multipath Component Delays and Angular Information
Authors: Junshi Chen, Russ Whiton, Fredrik Tufvesson
Date: 2023-01-18 00:00:00
Tasks: Position, Simultaneous Localization and Mapping

Opportunistic navigation using cellular signals is appealing for scenarios where other navigation technologies face challenges. In this paper, long-term evolution (LTE) downlink signals from two neighboring commercial base stations (BS) are received by a massive antenna array mounted on a passenger vehicle. Multipath component (MPC) delays and angle-of-arrival (AOA) extracted from the received signals are used to jointly estimate the positions of the vehicle, transmitters, and virtual transmitters (VT) with an extended fast simultaneous localization and mapping (FastSLAM) algorithm. The results show that the algorithm can accurately estimate the positions of the vehicle and the transmitters (and virtual transmitters). The vehicle's horizontal position error of SLAM fused with proprioception is less than 6 meters after a traversed distance of 530 meters, whereas un-aided proprioception results in a horizontal error of 15 meters.
SEN2DWATER: A Novel Multispectral and Multitemporal Dataset and Deep Learning Benchmark for Water Resources Analysis
Authors: Francesco Mauro, Benjamin Rich, Veronica Wairimu Muriga, Alessandro Sebastianelli, Silvia Liberata Ullo
Date: 2023-01-18 00:00:00

Climate change has caused disruption in certain weather patterns, leading to extreme weather events like flooding and drought in different parts of the world. In this paper, we propose machine learning methods for analyzing changes in water resources over a time period of six years, by focusing on lakes and rivers in Italy and Spain. Additionally, we release open-access code to enable the expansion of the study to any region of the world. We create a novel multispectral and multitemporal dataset, SEN2DWATER, which is freely accessible on GitHub. We introduce suitable indices to monitor changes in water resources, and benchmark the new dataset on three different deep learning frameworks: Convolutional Long Short Term Memory (ConvLSTM), Bidirectional ConvLSTM, and Time Distributed Convolutional Neural Networks (TD-CNNs). Future work exploring the many potential applications of this research is also discussed.
Scaffold-Based Multi-Objective Drug Candidate Optimization
Authors: Agustin Kruel, Andrew D. McNaughton, Neeraj Kumar
Date: 2022-12-15 00:00:00
Tasks: Diversity

In therapeutic design, balancing various physiochemical properties is crucial for molecule development, similar to how Multiparameter Optimization (MPO) evaluates multiple variables to meet a primary goal. While many molecular features can now be predicted using \textit{in silico} methods, aiding early drug development, the vast data generated from high throughput virtual screening challenges the practicality of traditional MPO approaches. Addressing this, we introduce a scaffold focused graph-based Markov chain Monte Carlo framework (ScaMARS) built to generate molecules with optimal properties. This innovative framework is capable of self-training and handling a wider array of properties, sampling different chemical spaces according to the starting scaffold. The benchmark analysis on several properties shows that ScaMARS has a diversity score of 84.6\% and has a much higher success rate of 99.5\% compared to conditional models. The integration of new features into MPO significantly enhances its adaptability and effectiveness in therapeutic design, facilitating the discovery of candidates that efficiently optimize multiple properties.
Beyond the exome: what's next in diagnostic testing for Mendelian conditions
Authors: Monica H. Wojcik, Chloe M. Reuter, Shruti Marwaha, Medhat Mahmoud, Michael H. Duyzend, Hayk Barseghyan, Bo Yuan, Philip M. Boone, Emily E. Groopman, Emmanuèle C. Délot, Deepti Jain, Alba Sanchis-Juan, Genomics Research to Elucidate the Genetics of Rare Diseases, Consortium, Lea M. Starita, Michael Talkowski, Stephen B. Montgomery, Michael J. Bamshad, Jessica X. Chong, Matthew T. Wheeler, Seth I. Berger, Anne O'Donnell-Luria, Fritz J. Sedlazeck, Danny E. Miller
Date: 2023-01-18 00:00:00
Tasks: Diagnostic

Despite advances in clinical genetic testing, including the introduction of exome sequencing (ES), more than 50% of individuals with a suspected Mendelian condition lack a precise molecular diagnosis. Clinical evaluation is increasingly undertaken by specialists outside of clinical genetics, often occurring in a tiered fashion and typically ending after ES. The current diagnostic rate reflects multiple factors, including technical limitations, incomplete understanding of variant pathogenicity, missing genotype-phenotype associations, complex gene-environment interactions, and reporting differences between clinical labs. Maintaining a clear understanding of the rapidly evolving landscape of diagnostic tests beyond ES, and their limitations, presents a challenge for non-genetics professionals. Newer tests, such as short-read genome or RNA sequencing, can be challenging to order and emerging technologies, such as optical genome mapping and long-read DNA or RNA sequencing, are not available clinically. Furthermore, there is no clear guidance on the next best steps after inconclusive evaluation. Here, we review why a clinical genetic evaluation may be negative, discuss questions to be asked in this setting, and provide a framework for further investigation, including the advantages and disadvantages of new approaches that are nascent in the clinical sphere. We present a guide for the next best steps after inconclusive molecular testing based upon phenotype and prior evaluation, including when to consider referral to a consortium such as GREGoR, which is focused on elucidating the underlying cause of rare unsolved genetic disorders.
Inconsistent illusory motion in predictive coding deep neural networks
Authors: O. R. Kirubeswaran, Katherine R. Storrs
Date: 2023-01-18 00:00:00

Why do we perceive illusory motion in some static images? Several accounts have been proposed based on eye movements, response latencies to different image elements, or interactions between image patterns and motion energy detectors. Recently, PredNet, a recurrent deep neural network (DNN) based on predictive coding principles, was reported to reproduce the "Rotating Snakes" illusion, suggesting a role for predictive coding in illusory motion. We replicate this finding and then use a series of "in silico psychophysics" experiments to examine whether PredNet behaves consistently with human observers for simplified variants of the illusory stimuli. We also measure response latencies to individual elements of the Rotating Snakes pattern by probing internal units in the network. A pretrained PredNet model predicted illusory motion for all subcomponents of the Rotating Snakes stimulus, consistent with human observers. However, we found no simple response delays in internal units, as found in physiological data. The PredNet model's detection of motion in gradients was based on contrast, not luminance, as it is in human perception. Finally, we tested the robustness of the illusion on 10 identical PredNets trained on the same video data; we found a large variation in the ability of the network to reproduce the illusion and predict motion for simplified variants of the illusion. Also, unlike human observers, none of the networks predicted illusory motion for greyscale variants of the pattern. Even when a DNN successfully reproduces some idiosyncrasy of human vision, a more detailed investigation can reveal inconsistencies between humans and the network and between different instances of the same network. The inconsistency of the Rotating Snakes illusion in PredNets trained from different initializations suggests that predictive coding does not reliably lead to human-like illusory motion.