pwc-archive/papers-with-abstracts huggingface dataset:
Switch as a Verifier: Toward Scalable Data Plane Checking via Distributed, On-Device Verification
Authors: Qiao Xiang, Ridi Wen, Chenyang Huang, Yuxin Wang, Franck Le
Date: 2022-05-16 00:00:00

Data plane verification (DPV) is important for finding network errors. Current DPV tools employ a centralized architecture, where a server collects the data planes of all devices and verifies them. Despite substantial efforts on accelerating DPV, this centralized architecture is inherently unscalable. In this paper, to tackle the scalability challenge of DPV, we circumvent the scalability bottleneck of centralized design and design Coral, a distributed, on-device DPV framework. The key insight of Coral is that DPV can be transformed into a counting problem on a directed acyclic graph, which can be naturally decomposed into lightweight tasks executed at network devices, enabling scalability. Coral consists of (1) a declarative requirement specification language, (2) a planner that employs a novel data structure DVNet to systematically decompose global verification into on-device counting tasks, and (3) a distributed verification (DV) protocol that specifies how on-device verifiers communicate task results efficiently to collaboratively verify the requirements. We implement a prototype of Coral. Extensive experiments with real-world datasets (WAN/LAN/DC) show that Coral consistently achieves scalable DPV under various networks and DPV scenarios, i.e., up to 1250 times speed up in the scenario of burst update, and up to 202 times speed up on 80% quantile of incremental verification, than state-of-the-art DPV tools, with little overhead on commodity network devices.
A Nonlinear Lateral Controller Design for Vehicle Path-following with an Arbitrary Sensor Location
Authors: Wubing B. Qin, Zhaojian Li
Date: 2022-05-16 00:00:00

This paper investigates the lateral control problem in vehicular path-following when the feedback sensor(s) are mounted at an arbitrary location in the longitudinal symmetric axis. We point out that some existing literature has abused the kinematic bicycle model describing the motion of rear axle center for other locations, which may lead to poor performance in practical implementations. A new nonlinear controller with low-complexity and high-maneuverability is then designed that takes into account senor mounting location, driving comfort and transient response with large initial errors. Design insights and intuitions are also provided in detail. Furthermore, analysis on stability and tracking performance for the closed-loop system are studied, and conditions and guidelines are provided on the selection of control parameters. Comprehensive simulations are performed to demonstrate the efficacy of the proposed nonlinear controller for arbitrary sensor locations. Meanwhile, we also show that designing controllers ignoring the sensor location may lead to unexpected vehicular sway motion in non-straight paths.
State of Health Estimation of Lithium-Ion Batteries in Vehicle-to-Grid Applications Using Recurrent Neural Networks for Learning the Impact of Degradation Stress Factors
Authors: Kotub Uddin, James Schofield, W. Dhammika Widanage
Date: 2022-05-16 00:00:00

This work presents an effective state of health indicator to indicate lithium-ion battery degradation based on a long short-term memory (LSTM) recurrent neural network (RNN) coupled with a sliding-window. The developed LSTM RNN is able to capture the underlying long-term dependencies of degraded cell capacity on battery degradation stress factors. The learning performance was robust when there was sufficient training data, with an error of < 5% if more than 1.15 years worth of data was supplied for training.
Statistical Modeling and Forecasting of Automatic Generation Control Signals
Authors: Sarnaduti Brahma, Hamid R. Ossareh, Mads R. Almassalkhi
Date: 2022-05-15 00:00:00
Tasks: Model Predictive Control, Time Series, Time Series Analysis

The performance of frequency regulating units for automatic generation control (AGC) of power systems depends on their ability to track the AGC signal accurately. In addition, representative models and advanced analysis and analytics can yield forecasts of the AGC signal that aids in controller design. In this paper, time-series analyses are conducted on an AGC signal, specifically the PJM Reg-D, and using the results, a statistical model is derived that fairly accurately captures its second moments and saturated nature, as well as a time-series-based predictive model to provide forecasts. As an application, the predictive model is used in a model predictive control framework to ensure optimal tracking performance of a down ramp-limited distributed energy resource coordination scheme. The results provide valuable insight into the properties of the AGC signal and indicate the effectiveness of these models in replicating its behavior.
Chetaev Instability Framework for Kinetostatic Compliance-Based Protein Unfolding
Authors: Alireza Mohammadi, Mark W. Spong
Date: 2022-05-15 00:00:00
Tasks: Protein Folding

Understanding the process of protein unfolding plays a crucial role in various applications such as design of folding-based protein engines. Using the well-established kinetostatic compliance (KCM)-based method for modeling of protein conformation dynamics and a recent nonlinear control theoretic approach to KCM-based protein folding, this paper formulates protein unfolding as a destabilizing control analysis/synthesis problem. In light of this formulation, it is shown that the Chetaev instability framework can be used to investigate the KCM-based unfolding dynamics. In particular, a Chetaev function for analysis of unfolding dynamics under the effect of optical tweezers and a class of control Chetaev functions for synthesizing control inputs that elongate protein strands from their folded conformations are presented. Based on the presented control Chetaev function, an unfolding input is derived from the Artstein-Sontag universal formula and the results are compared against optical tweezer-based unfolding.
Improved Multi-step FCS-MPCC with Disturbance Compensation for PMSM Drives -- Methods and Experimental Validation
Authors: Hai Yang, Yibin Liu, Junxiao Wang, Jun Yang
Date: 2022-05-15 00:00:00
Tasks: Prediction

In this paper, an improved multi-step finite control set model predictive current control (FCS-MPCC) strategy with speed loop disturbance compensation is proposed for permanent magnet synchronous machine (PMSM) drives system. A multi-step prediction mechanism is beneficial to significantly improve the steady-state performance of the motor system. While the conventional multi-step prediction has the defect of heavy computational burden, an improved multi-step finite control set model predictive current control (IM MPCC) strategy is proposed by developing a new multi-step prediction mechanism. Furthermore, in order to improve the dynamic response of the system, a disturbance compensation (DC) mechanism based on an extended state observer (ESO) is proposed to estimate and compensate the total disturbance in the speed loop of the PMSM system. Both simulation and experimental results validate the effectiveness of the proposed control strategy.
SVR-based Observer Design for Unknown Linear Systems: Complexity and Performance
Authors: Xuda Ding, Han Wang, Jianping He, Cailian Chen, Xinping Guan
Date: 2022-05-14 00:00:00

In this paper we consider estimating the system parameters and designing stable observer for unknown noisy linear time-invariant (LTI) systems. We propose a Support Vector Regression (SVR) based estimator to provide adjustable asymmetric error interval for estimations. This estimator is capable to trade-off bias-variance of the estimation error by tuning parameter $\gamma > 0$ in the loss function. This method enjoys the same sample complexity of $\mathcal{O}(1/\sqrt{N})$ as the Ordinary Least Square (OLS) based methods but achieves a $\mathcal{O}(1/(\gamma+1))$ smaller variance. Then, a stable observer gain design procedure based on the estimations is proposed. The observation performance bound based on the estimations is evaluated by the mean square observation error, which is shown to be adjustable by tuning the parameter $\gamma$, thus achieving higher scalability than the OLS methods. The advantages of the estimation error bias-variance trade-off for observer design are also demonstrated through matrix spectrum and observation performance optimality analysis. Extensive simulation validations are conducted to verify the computed estimation error and performance optimality with different $\gamma$ and noise settings. The variances of the estimation error and the fluctuations in performance are smaller with a properly-designed parameter $\gamma$ compared with the OLS methods.
A rigorous multi-population multi-lane hybrid traffic model and its mean-field limit for dissipation of waves via autonomous vehicles
Authors: Nicolas Kardous, Amaury Hayat, Sean T. McQuade, Xiaoqian Gong, Sydney Truong, Tinhinane Mezair, Paige Arnold, Ryan Delorenzo, Alexandre Bayen, Benedetto Piccoli
Date: 2022-05-13 00:00:00
Tasks: Autonomous Vehicles

In this paper, a multi-lane multi-population microscopic model, which presents stop and go waves, is proposed to simulate traffic on a ring-road. Vehicles are divided between human-driven and autonomous vehicles (AV). Control strategies are designed with the ultimate goal of using a small number of AVs (less than 5\% penetration rate) to represent Lagrangian control actuators that can smooth the multilane traffic flow and dissipate the stop-and-go waves. This in turn may reduce fuel consumption and emissions. The lane-changing mechanism is based on three components that we treat as parameters in the model: safety, incentive and cool-down time. The choice of these parameters in the lane-change mechanism is critical to modeling traffic accurately, because different parameter values can lead to drastically different traffic behaviors. In particular, the number of lane-changes and the speed variance are highly affected by the choice of parameters. Despite this modeling issue, when using sufficiently simple and robust controllers for AVs, the stabilization of uniform flow steady-state is effective for any realistic value of the parameters, and ultimately bypasses the observed modeling issue. Our approach is based on accurate and rigorous mathematical models, which allows a limit procedure that is termed, in gas dynamic terminology, mean-field. In simple words, from increasing the human-driven population to infinity, a system of coupled ordinary and partial differential equations are obtained. Moreover, control problems also pass to the limit, allowing the design to be tackled at different scales.
Multi-ship cooperative air defense model based on queuing theory
Authors: Zhongyao Ma, Keyu Wu, Zhong Liu
Date: 2022-05-13 00:00:00

The study of the multi-ship air defense model is of great significance in the simulation and evaluation of the actual combat process, the demonstration of air defense tactics, and the improvement of the security of important targets. The traditional multi-ship air defense model does not consider the coordination between ships, and the model assumptions are often too simple to effectively describe the capabilities of the multi-ship cooperative air defense system in realistic combat scenarios. In response to the above problems, this paper proposes a multi-ship cooperative air defense model, which effectively integrates the attack and defense parameters of both sides such as missile launch rate, missile flight speed, missile launch direction, ship interception rate, ship interception range, and the number of ship interception fire units. Then, the cooperative interception capability among ships is modeled by the method of task assignment. Based on the queuing theory, this paper strictly deduces the penetration probability of the cooperative air defense system, and provides an analytical calculation model for the analysis and design of the cooperative air defense system. Finally, through simulation experiments in typical scenarios, this paper studies and compares the air defense capabilities of the system in two different modes with and without coordination, and verifies the superiority of the multi-ship cooperative air defense model in reducing the probability of missile penetration; Further, the ability changes of the defense system under different parameters such as missile speed, speed, angle, ship interception rate, range, and number of fire units are studied, and the weak points of the defense formation, defense range settings, and interception settings are obtained.
Multi-tasking via baseline control in recurrent neural networks
Authors: Shun Ogawa, Francesco Fumarola, Luca Mazzucato
Date: 2022-05-13 00:00:00

Changes in an animal's behavioral state, such as arousal and movements, induce {complex modulations of the baseline input currents to sensory areas, eliciting sensory modality-specific effects. A simple computational principle explaining the effects of baseline modulations to recurrent cortical circuits is lacking. We investigate the benefits of baseline modulations using a reservoir computing approach in recurrent neural networks with random couplings. Baseline modulations unlock a set of new network phases and phenomena, including chaos enhancement, neural hysteresis and ergodicity breaking. Strikingly, baseline modulations enable reservoir networks to perform multiple tasks, without any optimization of the network couplings.} Baseline control of network dynamics opens new directions for brain-inspired artificial intelligence and sheds new light on behavioral modulations of cortical activity.
2SLS with Multiple Treatments
Authors: Manudeep Bhuller, Henrik Sigstad
Date: 2022-05-16 00:00:00
Tasks: Decision Making

We study what two-stage least squares (2SLS) identifies in models with multiple treatments under treatment effect heterogeneity. Two conditions are shown to be necessary and sufficient for the 2SLS to identify positively weighted sums of agent-specific effects of each treatment: average conditional monotonicity and no cross effects. Our identification analysis allows for any number of treatments, any number of continuous or discrete instruments, and the inclusion of covariates. We provide testable implications and present characterizations of choice behavior implied by our identification conditions.
Inference with Imputed Data: The Allure of Making Stuff Up
Authors: Charles F. Manski
Date: 2022-05-15 00:00:00
Tasks: Imputation, Missing Values

Incomplete observability of data generates an identification problem. There is no panacea for missing data. What one can learn about a population parameter depends on the assumptions one finds credible to maintain. The credibility of assumptions varies with the empirical setting. No specific assumptions can provide a realistic general solution to the problem of inference with missing data. Yet Rubin has promoted random multiple imputation (RMI) as a general way to deal with missing values in public-use data. This recommendation has been influential to empirical researchers who seek a simple fix to the nuisance of missing data. This paper adds to my earlier critiques of imputation. It provides a transparent assessment of the mix of Bayesian and frequentist thinking used by Rubin to argue for RMI. It evaluates random imputation to replace missing outcome or covariate data when the objective is to learn a conditional expectation. It considers steps that might help combat the allure of making stuff up.
Nowcasting Growth using Google Trends Data: A Bayesian Structural Time Series Model
Authors: David Kohns, Arnab Bhattacharjee
Date: 2020-11-02 00:00:00
Tasks: Time Series, Time Series Analysis, Variable Selection

This paper investigates the benefits of internet search data in the form of Google Trends for nowcasting real U.S. GDP growth in real time through the lens of mixed frequency Bayesian Structural Time Series (BSTS) models. We augment and enhance both model and methodology to make these better amenable to nowcasting with large number of potential covariates. Specifically, we allow shrinking state variances towards zero to avoid overfitting, extend the SSVS (spike and slab variable selection) prior to the more flexible normal-inverse-gamma prior which stays agnostic about the underlying model size, as well as adapt the horseshoe prior to the BSTS. The application to nowcasting GDP growth as well as a simulation study demonstrate that the horseshoe prior BSTS improves markedly upon the SSVS and the original BSTS model with the largest gains in dense data-generating-processes. Our application also shows that a large dimensional set of search terms is able to improve nowcasts early in a specific quarter before other macroeconomic data become available. Search terms with high inclusion probability have good economic interpretation, reflecting leading signals of economic anxiety and wealth effects.
How do Bounce Rates vary according to product sold?
Authors: Himanshu Sharma
Date: 2022-05-13 00:00:00

Bounce Rate of different E-commerce websites depends on the different factors based upon the different devices through which traffic share is observed. This research paper focuses on how the type of products sold by different E-commerce websites affects the bounce rate obtained through Mobile/Desktop. It tries to explain the observations which counter the general trend of positive relation between Mobile traffic share and bounce rate and how this is different for the Desktop. To estimate the differences created by the types of products sold by E-commerce websites on the bounce rate according to the data observed for different time, fixed effect model (within group method) is used to determine the difference created by the factors. Along with the effect of the type of products sold by the E-commerce website on bounce rate, the effect of individual website is also compared to verify the results obtained for type of products.
Predicting Emotional Volatility Using 41,000 Participants in the United Kingdom
Authors: George MacKerron, Nattavudh Powdthavee
Date: 2022-05-16 00:00:00

Emotional volatility is a human universal. Yet there has been no large-scale scientific study of predictors of that phenomenon. Building from previous works, which had been ad hoc and based on tiny samples, this paper reports the first large-scale estimation of volatility in human emotional experiences. Our study draws from a large sample of intrapersonal variation in moment-to-moment happiness from over three million observations by 41,023 UK individuals. Holding other things constant, we show that emotional volatility is highest among women with children, the separated, the poor, and the young. Women without children report substantially greater emotional volatility than men with and without children. For any given rate of volatility, women with children also experience more frequent extreme emotional lows than any other socio-demographic group. Our results, which are robust to different specification tests, enable researchers and policymakers to quantify and prioritise different determinants of intrapersonal variability in human emotions.
Network embeddedness indicates the innovation potential of firms
Authors: Giacomo Vaccario, Luca Verginer, Antonios Garas, Mario V. Tomasello, Frank Schweitzer
Date: 2022-05-16 00:00:00
Tasks: Position, regression

Firms' innovation potential depends on their position in the R&D network. But details on this relation remain unclear because measures to quantify network embeddedness have been controversially discussed. We propose and validate a new measure, coreness, obtained from the weighted k-core decomposition of the R&D network. Using data on R&D alliances, we analyse the change of coreness for 14,000 firms over 25 years and patenting activity. A regression analysis demonstrates that coreness explains firms' R&D output by predicting future patenting.
Influencing a Polarized and Connected Legislature
Authors: Ratul Das Chaudhury, C. Matthew Leister, Birendra Rai
Date: 2022-05-16 00:00:00

When can an interest group exploit polarization between political parties to its advantage? Building upon Battaglini and Patacchini (2018), we study a model where an interest group credibly promises payments to legislators conditional on voting for its preferred policy. A legislator can be directly susceptible to other legislators and value voting like them. The overall pattern of inter-legislator susceptibility determines the relative influence of individual legislators, and therefore the relative influence of the parties. We show that high levels of ideological or affective polarization are more likely to benefit the interest group when the party ideologically aligned with the interest group is relatively more influential. However, ideological and affective polarization operate in different ways. The influence of legislators is independent of ideological polarization. In contrast, affective polarization effectively creates negative links between legislators across parties, and thus modifies the relative influence of individual legislators and parties.
Market-Based Asset Price Probability
Authors: Victor Olkhov
Date: 2022-05-15 00:00:00
Tasks: Time Series, Time Series Analysis

We consider volume weighted average price (VWAP) as the 1st market-based statistical moment and derive the dependence of higher statistical moments of price on statistical moments and correlations of the values and volumes of market trades. If all trade volumes are constant during the averaging interval, then the market-based statistical moments equal the frequency-based. We approximate market-based probability of price by a finite number of statistical moments. The use of VWAP results in zero price-volume correlations. We derive the expressions of market-based correlations between prices and squares of trade volumes and between squares of prices and volumes. To forecast market-based averages and volatility of asset prices, one should predict two statistical moments and the correlation of their trade values and volumes. We explain how that limits the number of predicted statistical moments of prices by the first two and limits the accuracy of the forecasts of the probability of asset prices by the accuracy of the Gaussian approximations. To improve the accuracy and reliability of large macroeconomic and market models like those developed by BlackRock's Aladdin, JP Morgan, and the U.S. Fed., the developers should use market-based statistical moments of asset prices.
Risk Sharing and the Adoption of the Euro
Authors: Alessandro Ferrari, Anna Rogantini Picco
Date: 2022-05-14 00:00:00
Tasks: counterfactual

This paper empirically evaluates whether adopting a common currency has changed the level of consumption smoothing of euro area member states. We construct a counterfactual dataset of macroeconomic variables through the synthetic control method. We then use the output variance decomposition of Asdrubali, Sorensen and Yosha (1996) on both the actual and the synthetic data to study if there has been a change in risk sharing and through which channels. We find that the euro adoption has reduced risk sharing and consumption smoothing. We further show that this reduction is mainly driven by the periphery countries of the euro area who have experienced a decrease in risk sharing through private credit.
Reformulating the Value Restriction and the Not-Strict Value Restriction in Terms of Possibility Preference Map
Authors: Fujun Hou
Date: 2022-05-15 00:00:00

In social choice theory, Sen's value restriction and Pattanaik's not-strict value restriction are both attractive conditions for testing social preference transitivity and/or non-empty social choice set existence. This article introduces a novel mathematical representation tool, called possibility preference map (PPM), for weak orderings, and then reformulates the value restriction and the not-strict value restriction in terms of PPM. The reformulations all appear elegant since they take the form of minmax.