pwc-archive/papers-with-abstracts huggingface dataset:
Distributed Optimal Control with Recovered Robustness for Uncertain Network Systems: A Complementary Design Approach
Authors: Zhongkui Li, Junjie Jiao, Xiang Chen
Date: 2022-04-14 00:00:00

This paper considers the distributed robust suboptimal consensus control problem of linear multi-agent systems, with both H2 and H_infty performance requirements. A novel two-step complementary design approach is proposed. In the first step, a distributed control law is designed for the nominal multi-agent system to achieve consensus with a prescribed H2 performance. In the second step, an extra control input, depending on some carefully chosen residual signals indicating the modeling mismatch, is designed to complement the H2 performance by providing robustness guarantee in terms of H_infty requirement with respect to disturbances or uncertainties. The proposed complementary design approach provides an additional degree of freedom for design, having two separate controls to deal with the H2 performance and the robustness of consensus, respectively. Thereby, it does not need to make much trade-off, and can be expected to be much less conservative than the trade-off design such as the mixed H2/H_infty control method. Besides, this complementary approach will recover the achievable H2 performance when external disturbances or uncertainties do not exist. The effectiveness of the theoretical results and the advantages of the complementary approach are validated via numerical simulations.
Formal Development of Safe Automated Driving using Differential Dynamic Logic
Authors: Yuvaraj Selvaraj, Wolfgang Ahrendt, Martin Fabian
Date: 2022-04-14 00:00:00
Tasks: Mathematical Proofs

The challenges in providing convincing arguments for safe and correct behavior of automated driving (AD) systems have so far hindered their widespread commercial deployment. Conventional development approaches such as testing and simulation are limited by non-exhaustive analysis, and can thus not guarantee correctness in all possible scenarios. Formal methods is an approach to provide mathematical proofs of correctness, using a model of a system, that could be used to give the necessary arguments. This paper investigates the use of differential dynamic logic and the deductive verification tool KeYmaera X in the development of an AD feature. Specifically, formal models and safety proofs of different design variants of a Decision & Control module for an in-lane AD feature are presented. In doing so, the assumptions and invariant conditions necessary to guarantee safety are identified, and the paper shows how such an analysis helps during the development process in requirement refinement and formulation of the operational design domain. Furthermore, it is shown how the performance of the different models is formally analyzed exhaustively, in all their allowed behaviors.
A Generalized Switched-Capacitor Modular Multilevel Inverter Topology for Multiphase Electrical Machines with Capacitor-Voltage Self-Balancing Capability
Authors: Farzad Iraji, Nima Tashakor, Stefan Goetz
Date: 2022-04-14 00:00:00

Recent research on multilevel inverters shows exciting properties, including the potential to generate multiple output voltages and integrated voltage boosting. However, most presented inverter topologies have a restricted number of output voltage levels and limited output voltage boosting ratio. In addition, balancing the voltage of capacitors in multilevel converters is very important and should be considered in the topology or control method. This paper describes a generalized switched-capacitor circuit topology for multilevel dc-to-ac inverters that can be developed for the desired output voltage levels. Also, the maximum ac output voltage can vary from values much lower than the dc input voltage to several times of it depending on the design requirement. High-voltage dc input and ac output could be handled using low-voltage capacitors, which substantially decreases overall cost and volume. The proposed topology further allows for easy expansion through stackable circuits for multiple load phases. It has an inherent capacity for balancing the voltage of capacitors. To validate the feasibility and practicality of this concept, we provide circuit descriptions, control strategies, design recommendations, and pertinent simulation findings for the suggested inverter topology.
Upside Risk Effect on Reliability of Microgrids Considering Demand Response Program and COVID-19: An Investigation on Health System and Power System Interactions
Authors: Tohid Khalili, Seyed Iman Habibi, Seyyed Ali Ghorashi Khalil Abadi, Sadaf Ahmadi, Ali Bidram
Date: 2022-04-14 00:00:00
Tasks: Scheduling

COVID-19 has a vast impact on the power systems considering the customers demand and human resources. During this situation, the utilization of microgrids (MGs) may help the power systems balance the generation and consumption of power, which leads to customer satisfaction. In this paper, the optimal power scheduling of energy sources in an islanded MG by considering the upside risk (UR) is proposed for the very first time. The intended islanded MG consists of various sources such as wind turbine (WT), photovoltaic (PV), diesel generator (DGR), and battery. The goals of this work are minimizing the energy not supplied (ENS) in islanded mode considering the COVID-19s effect and implementing the demand response program (DRP). The difference between target ENS and actual ENS when actual ENS is less than the target is defined as UR. The results indicate that the UR related to the ENS of the islanded MG decreases significantly by slightly increasing the ENS. Moreover, COVID-19 decreases the ENS considerably and has a bigger effect than the DRP.
Exploring the Impacts of Power Grid Signals on Data Center Operations using a Receding-Horizon Scheduling Model
Authors: Weiqi Zhang, Line A. Roald, Victor M. Zavala
Date: 2022-04-13 00:00:00
Tasks: Management, Scheduling

Data centers (DCs) can help decarbonize the power grid by helping absorb renewable power (e.g., wind and solar) due to their ability to shift power loads across space and time. However, to harness such load-shifting flexibility, it is necessary to understand how grid signals (carbon signals and market price/load allocations) affect DC operations. An obstacle that arises here is the lack of computationally-tractable DC operation models that can capture objectives, constraints, and information flows that arise at the interface of DCs and the power grid. To address this gap, we present a receding-horizon resource management model (a mixed-integer programming model) that captures the resource management layer between the DC scheduler and the grid while accounting for logical constraints, different types of objectives, and forecasts of incoming job profiles and of available computing capacity. We use our model to conduct extensive case studies based on public data from Microsoft Azure and MISO. Our studies show that DCs can provide significant temporal load-shifting flexibility that results in reduced carbon emissions and peak demand charges. Models and case studies are shared as easy-to-use Julia code.
A Bandwagon Bias Based Model for Opinion Dynamics: Intertwining between Homophily and Influence Mechanisms
Authors: Giulia De Pasquale, Maria Elena Valcher
Date: 2022-04-13 00:00:00

Recently a model for the interplay between homophily-based appraisal dynamics and influence-based opinion dynamics has been proposed. The model explores for the first time how the opinions of a group of agents on a certain number of issues/topics is influenced by the agents' mutual appraisal and, conversely, the agents' mutual appraisal is updated based on the agents' opinions on the various issues, according to a homophily model. In this paper we show that a simplified (and, in some situations, more feasible) version of the model, that accounts only for the signs of the agents' appraisals rather than for their numerical values, provides an equally accurate and effective model of the opinion dynamics in small networks. The equilibria reached by this model correspond, almost surely, to situations in which the agents' network is complete and structurally balanced. On the other hand, we ensure that such equlibria can always be reached in a finite number of steps, and, differently from the original model, we rule out other types of equilibria that correspond to disconnected social networks.
A self-consistent analytical theory for rotator networks under stochastic forcing: effects of intrinsic noise and common input
Authors: Jonas Ranft, Benjamin Lindner
Date: 2022-04-13 00:00:00

Despite the incredible complexity of our brains' neural networks, theoretical descriptions of neural dynamics have led to profound insights into possible network states and dynamics. It remains challenging to develop theories that apply to spiking networks and thus allow one to characterize the dynamic properties of biologically more realistic networks. Here, we build on recent work by van Meegen & Lindner who have shown that "rotator networks," while considerably simpler than real spiking networks and therefore more amenable to mathematical analysis, still allow to capture dynamical properties of networks of spiking neurons. This framework can be easily extended to the case where individual units receive uncorrelated stochastic input which can be interpreted as intrinsic noise. However, the assumptions of the theory do not apply anymore when the input received by the single rotators is strongly correlated among units. As we show, in this case the network fluctuations become significantly non-Gaussian, which calls for a reworking of the theory. Using a cumulant expansion, we develop a self-consistent analytical theory that accounts for the observed non-Gaussian statistics. Our theory provides a starting point for further studies of more general network setups and information transmission properties of these networks.
Prediction and Control of Focal Seizure Spread: Random Walk with Restart on Heterogeneous Brain Networks
Authors: Chen Wang, Sida Chen, Liang Huang, Lianchun Yu
Date: 2022-04-14 00:00:00

Whole-brain models offer a promising method of predicting seizure spread, which is critical for successful surgery treatment of focal epilepsy. Existing methods are largely based on structural connectome, which ignores the effects of heterogeneity in regional excitability of brains. In this study, we used a whole-brain model to show that heterogeneity in nodal excitability had a significant impact on seizure propagation in the networks, and compromised the prediction accuracy with structural connections. We then addressed this problem with an algorithm based on random walk with restart on graphs. We demonstrated that by establishing a relationship between the restarting probability and the excitability for each node, this algorithm could significantly improve the seizure spread prediction accuracy in heterogeneous networks, and was more robust against the extent of heterogeneity. We also strategized surgical seizure control as a process to identify and remove the key nodes (connections) responsible for the early spread of seizures from the focal region. Compared to strategies based on structural connections, virtual surgery with a strategy based on mRWER generated outcomes with a high success rate while maintaining low damage to the brain by removing fewer anatomical connections. These findings may have potential applications in developing personalized surgery strategies for epilepsy.
AlphaFold2 can predict single-mutation effects
Authors: John M. McBride, Konstantin Polev, Amirbek Abdirasulov, Vladimir Reinharz, Bartosz A. Grzybowski, Tsvi Tlusty
Date: 2022-04-14 00:00:00

AlphaFold2 (AF) is a promising tool, but is it accurate enough to predict single mutation effects? Here, we report that the localized structural deformation between protein pairs differing by only 1-3 mutations -- as measured by the effective strain -- is correlated across \num{3901} experimental and AF-predicted structures. Furthermore, analysis of ${\sim} 11000$ proteins shows that the local structural change correlates with various phenotypic changes. These findings suggest that AF can predict the range and magnitude of single-mutation effects on average, and we propose a method to improve precision of AF predictions and to indicate when predictions are unreliable.
Signaling oscillations: molecular mechanisms and functional roles
Authors: Pablo Casani-Galdon, Jordi Garcia-Ojalvo
Date: 2022-04-14 00:00:00

Mounting evidence shows that oscillatory activity is widespread in cell signaling. Here we review some of this recent evidence, focusing on both the molecular mechanisms that potentially underlie such dynamical behavior, and the potential advantages that signaling oscillations might have in cell function. The biological processes considered include tissue maintenance, embryonic development and wound healing. With the aid of mathematical modeling, we show that a common principle, namely delayed negative feedback, underpins this wide variety of phenomena.
Digital Pre-Distorted One-Step Phase Retrieval Algorithm for Real-Time Hologram Generation for Holographic Displays
Authors: Jinze Sha, Adam Goldney, Andrew Kadis, Jana Skirnewskaja, Timothy D. Wilkinson
Date: 2022-04-14 00:00:00
Tasks: Retrieval

In a computer-generated holographic projection system, the image is reconstructed via the diffraction of light from a spatial light modulator. In this process, several factors could contribute to non-linearities between the reconstruction and the target image. This paper evaluates the non-linearity of the overall holographic projection system experimentally, using binary phase holograms computed using the one-step phase retrieval (OSPR) algorithm, and then applies a digital pre-distortion (DPD) method to correct for the non-linearity. Both a notable increase in reconstruction quality and a significant reduction in mean squared error were observed, proving the effectiveness of the proposed DPD-OSPR algorithm.
Option Pricing with Time-Varying Volatility Risk Aversion
Authors: Peter Reinhard Hansen, Chen Tong
Date: 2022-04-14 00:00:00

We introduce a pricing kernel with time-varying volatility risk aversion to explain observed time variations in the shape of the pricing kernel. When combined with the Heston-Nandi GARCH model, this framework yields a tractable option pricing model in which the variance risk ratio (VRR) emerges as a key variable. We show that the VRR is closely linked to economic fundamentals, as well as sentiment and uncertainty measures. A novel approximation method provides analytical option pricing formulas, and we demonstrate substantial reductions in pricing errors through an empirical application to the S&P 500 index, the CBOE VIX, and option prices.
Nonparametric Identification of Differentiated Products Demand Using Micro Data
Authors: Steven T. Berry, Philip A. Haile
Date: 2022-04-13 00:00:00

We examine identification of differentiated products demand when one has "micro data" linking individual consumers' characteristics and choices. Our model nests standard specifications featuring rich observed and unobserved consumer heterogeneity as well as product/market-level unobservables that introduce the problem of econometric endogeneity. Previous work establishes identification of such models using market-level data and instruments for all prices and quantities. Micro data provides a panel structure that facilitates richer demand specifications and reduces requirements on both the number and types of instrumental variables. We address identification of demand in the standard case in which non-price product characteristics are assumed exogenous, but also cover identification of demand elasticities and other key features when product characteristics are endogenous. We discuss implications of these results for applied work.
JUE Insight: The (Non-)Effect of Opportunity Zones on Housing Prices
Authors: Jiafeng Chen, Edward Glaeser, David Wessel
Date: 2022-04-14 00:00:00

Will the Opportunity Zones (OZ) program, America's largest new place-based policy in decades, generate neighborhood change? We compare single-family housing price growth in OZs with price growth in areas that were eligible but not included in the program. We also compare OZs to their nearest geographic neighbors. Our most credible estimates rule out price impacts greater than 0.5 percentage points with 95% confidence, suggesting that, so far, home buyers don't believe that this subsidy will generate major neighborhood change. OZ status reduces prices in areas with little employment, perhaps because buyers think that subsidizing new investment will increase housing supply. Mixed evidence suggests that OZs may have increased residential permitting.
Learning Probability Distributions in Macroeconomics and Finance
Authors: Jozef Barunik, Lubos Hanus
Date: 2022-04-14 00:00:00
Tasks: Decision Making, Time Series, Time Series Analysis

We propose a deep learning approach to probabilistic forecasting of macroeconomic and financial time series. Being able to learn complex patterns from a data rich environment, our approach is useful for a decision making that depends on uncertainty of large number of economic outcomes. Specifically, it is informative to agents facing asymmetric dependence of their loss on outcomes from possibly non-Gaussian and non-linear variables. We show the usefulness of the proposed approach on the two distinct datasets where a machine learns the pattern from data. First, we construct macroeconomic fan charts that reflect information from high-dimensional data set. Second, we illustrate gains in prediction of stock return distributions which are heavy tailed, asymmetric and suffer from low signal-to-noise ratio.
Environmental injustice in America: Racial disparities in exposure to air pollution health damages from freight trucking
Authors: Priyank Lathwal, Parth Vaishnav, M. Granger Morgan
Date: 2022-04-13 00:00:00

PM2.5 produced by freight trucks has adverse impacts on human health. However, it is unknown to what extent freight trucking affects communities of color and the total public health burden arising from the sector. Based on spatially resolved US federal government data, we explore the geographic distribution of freight trucking emissions and demonstrate that Black and Hispanic populations are more likely to be exposed to elevated emissions from freight trucks. Our results indicate that freight trucks contribute ~10% of NOx and ~12% of CO2 emissions from all sources in the continental US. The annual costs to human health and the environment due to NOx, PM2.5, SO2, and CO2 from freight trucking in the US are estimated respectively to be $11B, $5.5B, $110M, and $30B. Overall, the sector is responsible for nearly two-fifths (~$47B out of $120B) of all transportation-related public health damages.
A crowdsourcing approach to video quality assessment
Authors: Babak Naderi, Ross Cutler
Date: 2022-04-14 00:00:00
Tasks: Video Quality Assessment

The gold standard for measuring video quality is the subjective test, and the most prevalent is the ITU-T Rec. P.910, a lab-based subjective standard in use for the past two decades. However, in practice using ITU-T Rec. P.910 is slow, expensive, and requires a lab, which all create barriers to usage. As a result, most research papers that need to measure video quality don't use ITU-T Rec. P.910 but rather metrics that are not well correlated to subjective opinion. We provide an open-source extension of ITU-T Rec. P.910 based on crowdsourcing principles which address the speed, usage cost, and barrier to usage issues. We implement Absolute Category Rating (ACR), ACR with hidden reference (ACR-HR), Degradation Category Rating (DCR), and Comparison Category Rating (CCR). It includes rater, environment, hardware, and network qualifications, as well as gold and trapping questions to ensure quality. We have validated that the implementation is both accurate and highly reproducible compared to existing ITU-T Rec. P.910 lab studies.
A Machine Learning Approach to Automatic Classification of Eight Sleep Disorders
Authors: Dylan Zhuang, Ivey Rao, Ali K Ibrahim
Date: 2022-04-14 00:00:00
Tasks: BIG-bench Machine Learning, EEG, Electroencephalogram (EEG), Specificity

In this research, we attempt to answer the following basic research questions: Is a machine learning model able to classify all types of sleep disorders with high accuracy? Among the different modalities of sleep disorder signals, are some more important than others? Do raw signals improve the performance of a deep learning model when they are used as inputs? Prior research showed that most sleep disorders belong to eight categories. To study the performance of machine learning models in classifying polysomnography recordings into the eight categories of sleep pathologies, we selected the Cyclic Alternating Pattern Sleep Database. We developed a multi-channel Deep Learning model where a set of Convolutional Neural Networks were applied to six channels of raw signals of different modalities, including three channels of EEG signals and one channel each of EMG, ECG , and EOG signals. To compare the performance of the DL model with other models, we designed a model that took spectral features, instead of raw signals, as its inputs. We first studied the "importance" issue of signal modalities using the RF algorithm. We found that ECG contributed most to the important features and EMG second, among the four signal modalities. We then studied the accuracy performance of the proposed machine learning models. We verified that the multi-channel DL-R model, which took raw signals as its inputs, outperformed all other models, with its sensitivity and specificity scores both being above 95 %. This accuracy performance is on a par with those published results which dealt with fewer types of sleep disorders. We adopted two popular heatmap-generating techniques, with which we confirmed that the DL model's superior performance was owing to the CNN network's ability to extract potent features from raw signals.
A Novel Approach for Cancellation of Non-Aligned Inter Spreading Factor Interference in LoRa Systems
Authors: Qiaohan Zhang, Ivo Bizon, Atul Kumar, Ana Belen Martinez, Marwa Chafii, Gerhard Fettweis
Date: 2022-04-14 00:00:00

Long Range (LoRa) has become a key enabler technology for low power wide area networks. However, due to its ALOHA-based medium access scheme, LoRa has to cope with collisions that limit the capacity and network scalability. Collisions between randomly overlapped signals modulated with different spreading factors (SFs) result in inter-SF interference, which increases the packet loss likelihood when signal-to-interference ratio (SIR) is low. This issue cannot be resolved by channel coding since the probability of error distance is not concentrated around the adjacent symbol. In this paper, we analytically model this interference, and propose an interference cancellation method based on the idea of segmentation of the received signal. This scheme has three steps. First, the SF of the interference signal is identified, then the equivalent data symbol and complex amplitude of the interference are estimated. Finally, the estimated interference signal is subtracted from the received signal before demodulation. Unlike conventional serial interference cancellation (SIC), this scheme can directly estimate and reconstruct the non-aligned inter-SF interference without synchronization. Simulation results show that the proposed method can significantly reduce the symbol error rate (SER) under low SIR compared with the conventional demodulation. Moreover, it also shows high robustness to fractional sample timing offset (STO) and carrier frequency offset (CFO) of interference. The presented results clearly show the effectiveness of the proposed method in terms of the SER performance.