pwc-archive/papers-with-abstracts huggingface dataset:
Multiple Intelligent Reflecting Surface aided Multi-user Weighted Sum-Rate Maximization using Manifold Optimization
Authors: Liyue Zhang, Qing Wang, Haozhi Wang
Date: 2021-09-29 00:00:00

Intelligent reflecting surface (IRS) are able to amend radio propagation condition tasks on account of its functional properties in phase shift optimizing. In fact, there exists geometry manifold in the base-station (BS) beamforming matrix and IRS reflection vector. Therefore, we propose a novel double manifold alternating optimization (DMAO) algorithm which makes use of differential geometry theory to improve optimization performance. First, we develop a multi-IRS multi-user system model to maximize the weighted sum-rate, which may lead to the non-convexity in our optimization procedure. Then in order to allocate an optimized coefficients to each BS antenna and IRS reflecting element, we present the beamforming matrix and reflection vector using complex sphere manifold and complex oblique manifold, respectively, which integrates the inner geometry structure and the constrains. By an innovative alternative iteration method, the system gradually converges an optimized stable state, which is associating with the maximized sum-rate. Furthermore, we quantize the IRS reflection coefficient considering the practical constrains. Experimental results demonstrated that our approach significantly outperforms the conventional methods in terms of weighted sum-rate.
Computing the average inter-sample time of event-triggered control using quantitative automata
Authors: Gabriel de Albuquerque Gleizer, Manuel Mazo Jr
Date: 2021-09-29 00:00:00

Event-triggered control (ETC) is a major recent development in cyber-physical systems due to its capability of reducing resource utilization in networked devices. However, while most of the ETC literature reports simulations indicating massive reductions in the sampling required for control, no method so far has been capable of quantifying these results. In this work, we propose an approach through finite-state abstractions to do formal quantification of the traffic generated by ETC of linear systems, in particular aiming at computing its smallest average inter-sample time (SAIST). The method involves abstracting the traffic model through $l$-complete abstractions, finding the cycle of minimum average length in the graph associated to it, and verifying whether this cycle is an infinitely recurring traffic pattern. The method is proven to be robust to sufficiently small model uncertainties, which allows its application to compute the SAIST of ETC of nonlinear systems.
Sample Complexity of the Robust LQG Regulator with Coprime Factors Uncertainty
Authors: Yifei Zhang, Sourav Kumar Ukil, Ephraim Neimand, Serban Sabau, Myron E. Hohil
Date: 2021-09-29 00:00:00
Tasks: Time Series, Time Series Analysis

This paper addresses the end-to-end sample complexity bound for learning the H2 optimal controller (the Linear Quadratic Gaussian (LQG) problem) with unknown dynamics, for potentially unstable Linear Time Invariant (LTI) systems. The robust LQG synthesis procedure is performed by considering bounded additive model uncertainty on the coprime factors of the plant. The closed-loop identification of the nominal model of the true plant is performed by constructing a Hankel-like matrix from a single time-series of noisy finite length input-output data, using the ordinary least squares algorithm from Sarkar et al. (2020). Next, an H-infinity bound on the estimated model error is provided and the robust controller is designed via convex optimization, much in the spirit of Boczar et al. (2018) and Zheng et al. (2020a), while allowing for bounded additive uncertainty on the coprime factors of the model. Our conclusions are consistent with previous results on learning the LQG and LQR controllers.
Peak Infection Time for a Networked SIR Epidemic with Opinion Dynamics
Authors: Baike She, Humphrey C. H. Leung, Shreyas Sundaram, Philip E. Paré
Date: 2021-09-29 00:00:00

We propose an SIR epidemic model coupled with opinion dynamics to study an epidemic and opinions spreading in a network of communities. Our model couples networked SIR epidemic dynamics with opinions towards the severity of the epidemic, and vice versa. We develop an epidemic-opinion based threshold condition to capture the moment when a weighted average of the epidemic states starts to decrease exponentially fast over the network, namely the peak infection time. We define an effective reproduction number to characterize the behavior of the model through the peak infection time. We use both analytical and simulation-based results to illustrate that the opinions reflect the recovered levels within the communities after the epidemic dies out.
Human Reliability Analysis for Oil and Gas Operations: Analysis of Existing Methods
Authors: Marilia Ramos, Camille Major, Nsimah Ekanem, Cesar Malpica, Ali Mosleh
Date: 2021-09-28 00:00:00
Tasks: Management

In the petroleum industry, Quantitative Risk Analysis (QRA) has been one of the main tools for risk management. To date, QRA has mostly focused on technical barriers, despite many accidents having human failure as a primary cause or a contributing factor. Human Reliability Analysis (HRA) allows for the assessment of the human contribution to risk to be assessed both qualitatively and quantitatively. Most credible and highly advanced HRA methods have largely been developed and applied in support of nuclear power plants control room operations and in context of probabilistic risk analysis. Moreover, many of the HRA methods have issues that have led to inconsistencies, insufficient traceability and reproducibility in both the qualitative and quantitative phases. Given the need to assess human error in the context of the oil industry, it is necessary to evaluate available HRA methodologies and assess its applicability to petroleum operations. Furthermore, it is fundamental to assess these methods against good practices of HRA and the requirements for advanced HRA methods. The present paper accomplishes this by analyzing seven HRA methods. The evaluation of the methods was performed in three stages. The first stage consisted of an evaluation of the degree of adaptability of the method for the Oil and Gas industry. In the second stage the methods were evaluated against desirable items in an HRA method. The higher-ranked methods were evaluated, in the third stage, against requirements for advanced HRA methods. In addition to the methods' evaluation, this paper presents an overview of state-of-the-art discussions on HRA, led by the Nuclear industry community. It remarks that these discussions must be seriously considered in defining a technical roadmap to a credible HRA method for the Oil and Gas industry.
Privacy-Preserving Stealthy Attack Detection in Multi-Agent Control Systems
Authors: Mohammad Bahrami, Hamidreza Jafarnejadsani
Date: 2021-09-28 00:00:00
Tasks: Privacy Preserving

This paper develops a glocal (global-local) attack detection framework to detect stealthy cyber-physical attacks, namely covert attack and zero-dynamics attack, against a class of multi-agent control systems seeking average consensus. The detection structure consists of a global (central) observer and local observers for the multi-agent system partitioned into clusters. The proposed structure addresses the scalability of the approach and the privacy preservation of the multi-agent system's state information. The former is addressed by using decentralized local observers, and the latter is achieved by imposing unobservability conditions at the global level. Also, the communication graph model is subject to topology switching, triggered by local observers, allowing for the detection of stealthy attacks by the global observer. Theoretical conditions are derived for detectability of the stealthy attacks using the proposed detection framework. Finally, a numerical simulation is provided to validate the theoretical findings.
Orbital Angular Momentum (OAM) Carrying Vortex Wave generation in Dielectric Filled Circular Waveguide
Authors: Md Khadimul Islam, Arjuna Madanayake, Shubhendu Bhardwaj
Date: 2021-09-28 00:00:00

In this paper, we propose a method to generate Orbital Angular Momentum (OAM) carrying vortex waves inside a metallic circular waveguide (CW). These waves feature ability to carry multiple orthogonal modes at the same frequency, by the virtue of their unique spatial structure. In essence high data rate channels can be developed using such waves. In free space, OAM carrying vortex waves has beam divergence issues and a central NULL, which makes the waves unfavourable for free space communication. But, OAM modes in guided structures do not suffer from these drawbacks. This prospect of enhancement of communication spectrum provides the background for the study of vortex wave in the circular waveguides. In this work, a radial array of monopoles is designed to generate the vortex wave inside the waveguide. Further, we introduced the dielectric materials inside the waveguide in order to manipulate the operating frequency of the OAM modes. Simulation results shows that the various dielectric materials allow us to tune the working frequency of the OAM beam to a desired frequency.
Design of Maximum-Gain Dielectric Lens Antenna via Phase Center Analysis
Authors: Md Khadimul Islam, Arjuna Madanayake, Shubhendu Bhardwaj
Date: 2021-09-28 00:00:00

In this work, a method is presented to maximize the obtained gain from millimeter-wave (mm-wave) lens antennas using phase center analysis. Commonly, for designing a lens antenna, the lens is positioned just on top of the antenna element which is not capable of providing the maximum gain/aperture efficiency. A novel solution method is proposed where the lens will be placed at a distance calculated using phase center analysis to produce the maximum gain from the system. A mm-wave microstrip antenna array is designed and the proposed method is applied for the gain enhancement. Simulation results suggest that the propose scheme obtains around 25% gain enhancement compared to the traditional method.
Longitudinal Deep Truck: Deep learning and deep reinforcement learning for modeling and control of longitudinal dynamics of heavy duty trucks
Authors: Saleh Albeaik, Trevor Wu, Ganeshnikhil Vurimi, Xiao-Yun Lu, Alexandre M. Bayen
Date: 2021-09-28 00:00:00
Tasks: Deep Reinforcement Learning, Reinforcement Learning (RL)

Heavy duty truck mechanical configuration is often tailor designed and built for specific truck mission requirements. This renders the precise derivation of analytical dynamical models and controls for these trucks from first principles challenging, tedious, and often requires several theoretical and applied areas of expertise to carry through. This article investigates deep learning and deep reinforcement learning as truck-configuration-agnostic longitudinal modeling and control approaches for heavy duty trucks. The article outlines a process to develop and validate such models and controllers and highlights relevant practical considerations. The process is applied to simulation and real-full size trucks for validation and experimental performance evaluation. The results presented demonstrate applicability of this approach to trucks of multiple configurations; models generated were accurate for control development purposes both in simulation and the field.
The Impact of Vaccine Hesitancy on Epidemic Spreading
Authors: C. H. Leung, María E. Gibbs, Philip E. Paré
Date: 2021-09-28 00:00:00

The COVID-19 pandemic has devastated the world in an unprecedented way, causing enormous loss of life. Time and again, public health authorities have urged people to become vaccinated to protect themselves and mitigate the spread of the disease. However, vaccine hesitancy has stalled vaccination levels in the United States. This study explores the effect of vaccine hesitancy on the spread of disease by introducing an SIRS-V$_\kappa$ model, with compartments of susceptible (S), infected (I), recovered (R), and vaccinated (V). We leverage the concept of carrying capacity to account for vaccine hesitancy by defining a vaccine confidence level $\kappa$, which is the maximum number of people that will become vaccinated during the course of a disease. The inverse of vaccine confidence is vaccine hesitance, $(\frac{1}{\kappa})$. We explore the equilibria of the SIRS-V$_\kappa$ model and their stability, and illustrate the impact of vaccine hesitance on epidemic spread analytically and via simulations.
Guided Probabilistic Simulation of Complex Systems Toward Rare and Extreme Events
Authors: Parhizkar Tarannom, Mosleh Ali
Date: 2021-09-24 00:00:00

Simulation based or dynamic probabilistic risk assessment methodologies were primarily developed for proving a more realistic and complete representation of complex systems accident response. Such simulation based methodologies have proven to be particularly powerful for systems with control loops and complex interactions between its elements, be they hardware, software, or human, as they provide a natural probabilistic environment to include physical models of system behavior (e.g., coupled neutronics and thermal hydraulic codes for nuclear power plants), mechanistic models of materials or hardware systems to predict failure, and those of natural hazards. Despite the advancements in simulation based methodologies, the fundamental challenge still persists as the space of possible probabilistic system trajectories is nearly infinite in size in simulating even systems of relatively low complexity. In this paper, a framework is developed to identify rare and extreme events and enabling the use of reverse trajectories to trace failures (or other system states) to causes for potential mitigation actions. This framework consists of an Intelligent Guidance module, Trajectory Generation module and Physical Simulation module. The Intelligent Guidance module provides planning information to the Trajectory Generation module that creates scenarios by interacting with the Physical Simulation in its environment. In turn, system trajectories or scenarios are created and post processed to provide updating information to the Intelligent Guidance module or aggregate the results when stopping criteria are met. The objective of guided simulation is to control the growth of the scenario tree and to efficiently identify important scenarios that meet single or multiple criteria. We present several solution strategies, both qualitative and data driven for each module.
Radical pairs may play a role in microtubule reorganization
Authors: Hadi Zadeh-Haghighi, Christoph Simon
Date: 2021-09-28 00:00:00
Tasks: Open-Ended Question Answering

The exact mechanism behind general anesthesia remains an open question in neuroscience. It has been proposed that anesthetics selectively prevent consciousness and memory via acting on microtubules (MTs). It is known that the magnetic field modulates MT organization. A recent study shows that a radical pair model can explain the isotope effect in xenon-induced anesthesia and predicts magnetic field effects on anesthetic potency. Further, reactive oxygen species are also implicated in MT stability and anesthesia. Based on a simple radical pair mechanism model and a simple mathematical model of MT organization, we show that magnetic fields can modulate spin dynamics of naturally occurring radical pairs in MT. We show that the spin dynamics influence a rate in the reaction cycle, which translates into a change in the MT density. We can reproduce magnetic field effects on the MT concentration that have been observed. Our model also predicts additional effects at slightly higher fields. Our model further predicts that the effect of zinc on the MT density exhibits isotopic dependence. The findings of this work make a connection between microtubule-based and radical pair-based quantum theories of consciousness.
A noise reduction method for force measurements in water entry experiments based on the Ensemble Empirical Mode Decomposition
Authors: Emanuele Spinosa, Alessandro Iafrati
Date: 2021-09-29 00:00:00
Tasks: Denoising

In this paper a denoising strategy based on the EEMD (Ensemble Empirical Mode Decomposition) is used to reduce the background noise in non-stationary signals, which represent the forces measured in scaled model testing of the emergency water landing of aircraft, generally referred to as ditching. Ditching tests are performed at a constant horizontal speed of 12 m/s and a vertical velocity at the beginning of the impact of 0.45 m/s. The measured data are affected by a large amplitude broadband noise, which has both mechanical and electronic origin. Noise sources cannot be easily avoided or removed, since they are associated with the vibrations of the structure of the towing carriage and to the interaction between the measurement chain and the electromagnetic fields. The EEMD noise reduction method is based on the decomposition of the signal into modes and on its partial reconstruction using the residue, the signal-dominant modes and some further modes treated with a thresholding technique, which helps to retain some of the sharp features of the signal. The strategy is developed and tested first on a synthetic signal with a superimposed and known background noise. The method is then verified on the measurement of the inertial force acting on the fuselage when it is moving in air, as in this case the added mass is negligible and the denoised force should equal the product of the mass by the acceleration, both of them being known. Finally, the procedure is applied to denoise the forces measured during the actual ditching experiments. The results are superior to those obtained by other classical filtering methods, such as a moving average filter and a low-pass FIR filter, particularly due to the enhanced capabilities of the EEMD-denoising strategy here developed to preserve the sharp features of the signals and to reduce the residual low-frequency oscillations of spurious origin.
Systemic risk in interbank networks: disentangling balance sheets and network effects
Authors: Alessandro Ferracci, Giulio Cimini
Date: 2021-09-29 00:00:00

We study the difference between the level of systemic risk that is empirically measured on an interbank network and the risk that can be deduced from the balance sheets composition of the participating banks. Using generalised DebtRank dynamics, we measure observed systemic risk on e-MID network data (augmented by BankFocus information) and compare it with the expected systemic risk of a null model network, obtained through an appropriate maximum-entropy approach constraining relevant balance sheet variables. We show that the aggregate levels of observed and expected systemic risks are usually compatible but differ significantly during turbulent times (in our case, after the default of Lehman Brothers and the VLTRO implementation by the ECB). At the individual level instead, banks are typically more or less risky than what their balance sheet prescribes due to their position in the network. Our results confirm on one hand that balance sheet information used within a proper maximum-entropy network model provides good aggregate estimates of systemic risk, and on the other hand the importance of knowing the empirical details of the network for conducting precise stress tests on individual banks, especially after systemic events.
Unstable diffusion in social networks
Authors: Teruyoshi Kobayashi, Yoshitaka Ogisu, Tomokatsu Onaga
Date: 2021-09-28 00:00:00

How and to what extent will new activities spread through social ties? Here, we develop a more sophisticated framework than the standard mean-field approach to describe the diffusion dynamics of multiple activities on complex networks. We show that the diffusion of multiple activities follows a saddle path and can be highly unstable. In particular, when the two activities are sufficiently substitutable, either of them would dominate the other by chance even if they are equally attractive ex ante. When such symmetry-breaking occurs, any average-based approach cannot correctly calculate the Nash equilibrium - the steady state of an actual diffusion process.
Testing the Presence of Implicit Hiring Quotas with Application to German Universities
Authors: Lena Janys
Date: 2021-09-29 00:00:00

It is widely accepted that women are underrepresented in academia in general and economics in particular. This paper introduces a test to detect an under-researched form of hiring bias: implicit quotas. I derive a test under the Null of random hiring that requires no information about individual hires under some assumptions. I derive the asymptotic distribution of this test statistic and, as an alternative, propose a parametric bootstrap procedure that samples from the exact distribution. This test can be used to analyze a variety of other hiring settings. I analyze the distribution of female professors at German universities across 50 different disciplines. I show that the distribution of women, given the average number of women in the respective field, is highly unlikely to result from a random allocation of women across departments and more likely to stem from an implicit quota of one or two women on the department level. I also show that a large part of the variation in the share of women across STEM and non-STEM disciplines could be explained by a two-women quota on the department level. These findings have important implications for the potential effectiveness of policies aimed at reducing underrepresentation and providing evidence of how stakeholders perceive and evaluate diversity.
Lobbying Influence -- The Role of Money, Strategies and Measurements
Authors: Fintan Oeri, Adrian Rinscheid, Aya Kachi
Date: 2021-09-28 00:00:00

Comparing the results for preference attainment, self-perceived influence and reputational influence, this paper analyzes the relationship between financial resources and lobbying influence. The empirical analysis builds on data from an original survey with 312 Swiss energy policy stakeholders combined with document data from multiple policy consultation submission processes. The results show that the distribution of influence varies substantially depending on the measure. While financial resources for political purposes predict influence across all measures, the relationship is positive only for some. An analysis of indirect effects sheds light on the potential mechanisms that translate financial resources into influence.
New Solution based on Hodge Decomposition for Abstract Games
Authors: Yihao Luo, Jinhui Pang, Weibin Han, Huafei Sun
Date: 2021-09-29 00:00:00

This paper proposes Hodge Potential Choice (HPC), a new solution for abstract games with irreflexive dominance relations. This solution is formulated by involving geometric tools like differential forms and Hodge decomposition onto abstract games. We provide a workable algorithm for the proposed solution with a new data structure of abstract games. From the view of gaming, HPC overcomes several weaknesses of conventional solutions. HPC coincides with Copeland Choice in complete cases and can be extended to slove games with marginal strengths. It will be proven that the Hodge potential choice possesses three prevalent axiomatic properties: neutrality, strong monotonicity, dominance cycle s reversing independence, and sensitivity to mutual dominance. To compare the HPC with Copeland Choice in large samples of games, we design digital experiments with randomly generated abstract games with different sizes and completeness. The experimental results present the advantage of HPC in the statistical sense.